From 1c3719ff08a4da994e624e62a0b03b1685b7344a Mon Sep 17 00:00:00 2001 From: kf-cuanschutz <127144640+kf-cuanschutz@users.noreply.github.com> Date: Thu, 25 Apr 2024 12:24:50 -0600 Subject: [PATCH 001/134] Metrics that I started modifying --- libs/ccc/sklearn/metrics_gpu.py | 194 ++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 libs/ccc/sklearn/metrics_gpu.py diff --git a/libs/ccc/sklearn/metrics_gpu.py b/libs/ccc/sklearn/metrics_gpu.py new file mode 100644 index 00000000..86812677 --- /dev/null +++ b/libs/ccc/sklearn/metrics_gpu.py @@ -0,0 +1,194 @@ +import numpy as np +import pandas as pd +from numba import cuda + +""" +Contains implementations of different metrics in sklearn but optimized for numba. + +Some code (indicated in each function) is based on scikit-learn's code base +(https://github.com/scikit-learn), for which the copyright notice and license +are shown below. + +BSD 3-Clause License + +Copyright (c) 2007-2021 The scikit-learn developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" +import numpy as np +from numba import njit +from numba import cuda + +@cuda.jit +def get_contingency_matrix(random_feature1_device , random_feature2_device, part0_unique_device, part1_unique_device, cont_mat_device, part1_k_device, part1_j_device, part0_i_device): + """ + Given two clustering partitions with k0 and k1 number of clusters each, it + returns a contingency matrix with k0 rows and k1 columns. It's an implementation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, + but the code is not based on their implementation. + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + Returns: + A contingency matrix with k0 (number of clusters in part0) rows and k1 + (number of clusters in part1) columns. Each cell ij represents the + number of objects grouped in cluster i (in part0) and cluster j (in + part1). + """ + + #Creating the grid + #x, y = cuda.grid(2) + tx = cuda.threadIdx.x + ty = cuda.threadIdx.y + bx = cuda.blockIdx.x + by = cuda.blockIdx.y + bw = cuda.blockDim.x + bh = cuda.blockDim.y + i = tx + bx * bw + j = ty + by * bh + + + + + #part0_unique = np.unique(array1) + #part1_unique = np.unique(array2) + #cont_mat = np.zeros((len(part0_unique), len(part1_unique))) + + if i < M: + part0_k_device = part0_unique_device[i] + if j < N: + part1_k_device = part1_unique_device[j] + #cuda.atomic.compare_and_swap_element(part0_i_device , + part0_i_device = random_feature1_device == part0_k_device + part1_j_device = random_feature2_device == part1_k_device + cont_mat_device[i, j] = np.sum(part0_i_device & part1_j_device) + + return cont_mat_device + +@njit(cache=True, nogil=True) +def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + """ + Returns the pair confusion matrix from two clustering partitions. It is an + implemenetation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html + The code is based on the sklearn implementation. See copyright notice at the + top of this file. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A pair confusion matrix with 2 rows and 2 columns. From sklearn's + pair_confusion_matrix docstring: considering a pair of objects that is + clustered together a positive pair, then as in binary classification the + count of true negatives is in position 00, false negatives in 10, true + positives in 11, and false positives in 01. + """ + n_samples = np.int64(part0.shape[0]) + + # Computation using the contingency data + contingency = get_contingency_matrix(part0, part1) + n_c = np.ravel(contingency.sum(axis=1)) + n_k = np.ravel(contingency.sum(axis=0)) + sum_squares = (contingency**2).sum() + C = np.empty((2, 2), dtype=np.int64) + C[1, 1] = sum_squares - n_samples + C[0, 1] = contingency.dot(n_k).sum() - sum_squares + C[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares + C[0, 0] = n_samples**2 - C[0, 1] - C[1, 0] - sum_squares + return C + + +def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: + """ + Computes the adjusted Rand index (ARI) between two clustering partitions. + The code is based on the sklearn implementation here: + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html + See copyright notice at the top of this file. + + This function should not be compiled with numba, since it depends on + arbitrarily large interger variable (supported by Python) to correctly + compute the ARI in large partitions. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A number representing the adjusted Rand index between two clustering + partitions. This number is between something around 0 (partitions do not + match; it could be negative in some cases) and 1.0 (perfect match). + """ + (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) + # convert to Python integer types, to avoid overflow or underflow + tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) + + # Special cases: empty data or full agreement + if fn == 0 and fp == 0: + return 1.0 + + + return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + + +if __name__ == '__main__': + + # Arrays + random_feature1 = np.random.rand(1000).astype('f') + random_feature2 = np.random.rand(1000).astype('f') + + # Processing the unique arrays: + part0_unique = np.unique(random_feature1) + part1_unique = np.unique(random_feature2) + cont_mat = np.zeros((len(part0_unique), len(part1_unique))) + part1_k = np.ones(1, dtype=np.float64) + part1_j = np.ones(1, dtype=np.float64) + part0_i = np.ones(1, dtype=np.float64) + # Getting other important parts of for the GPU setting: + threadsperblock = (128, 128) + M = part0_unique.shape[0] + N = part1_unique.shape[0] + blockspergrid_x = M + (threadsperblock[0] - 1) // threadsperblock[0] + blockspergrid_y = N + (threadsperblock[1] - 1) // threadsperblock[1] + blockspergrid = (blockspergrid_x, blockspergrid_y) + + #Senign them to the GPU: + random_feature1_device = cuda.to_device(random_feature1) + random_feature2_device = cuda.to_device(random_feature2) + part0_unique_device = cuda.to_device(part0_unique) + part1_unique_device = cuda.to_device(part1_unique) + cont_mat_device = cuda.to_device(cont_mat) + part1_k_device = cuda.to_device(part1_k) + part1_j_device = cuda.to_device(part1_j) + part0_i_device = cuda.to_device(part0_i) + print("checkpoint") + # Calling the get_contingency + out_device = get_contingency_matrix[blockspergrid, threadsperblock](random_feature1_device , random_feature2_device, part0_unique_device, part1_unique_device, cont_mat_device, part1_k_device, part1_j_device, part0_i_device) + print(out_device) + + From e2db3a38b043e2d14c334c99edb0838e86eb394d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 20 May 2024 13:38:58 -0600 Subject: [PATCH 002/134] [benchmark]: Add simple benchmarks --- .gitignore | 6 ++++++ benchmark/cprofile.py | 10 ++++++++++ benchmark/pycallgraph.png | Bin 0 -> 47362 bytes benchmark/trace.py | 24 ++++++++++++++++++++++++ scripts/setup_dev.sh | 2 ++ 5 files changed, 42 insertions(+) create mode 100644 benchmark/cprofile.py create mode 100644 benchmark/pycallgraph.png create mode 100644 benchmark/trace.py create mode 100644 scripts/setup_dev.sh diff --git a/.gitignore b/.gitignore index b6e47617..0b78489a 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,9 @@ dmypy.json # Pyre type checker .pyre/ + +# Pycharm +.idea/* + +# Development directory +__dev \ No newline at end of file diff --git a/benchmark/cprofile.py b/benchmark/cprofile.py new file mode 100644 index 00000000..bfefa1b5 --- /dev/null +++ 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random_feature2) + print(res) + + +if __name__ == "__main__": + main() + + + + diff --git a/scripts/setup_dev.sh b/scripts/setup_dev.sh new file mode 100644 index 00000000..2f09e03d --- /dev/null +++ b/scripts/setup_dev.sh @@ -0,0 +1,2 @@ +export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH +eval `python ./libs/ccc/conf.py` From cf21b48d3e5dd6462657ea83f7e7621dcbaeb292 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 30 May 2024 13:21:21 -0600 Subject: [PATCH 003/134] [test]: Create a new jupyter testing folder --- libs/ccc/sklearn/metrics_gpu2.py | 210 ++ ...-compare_cuda_get_contingency_matrix.ipynb | 1686 +++++++++++++++++ .../01-n_samples_small_50.txt | 26 + .../10-n_samples_large_100000.txt | 26 + .../10-n_samples_large_50000.txt | 26 + .../10-n_samples_small_100.txt | 26 + .../10-n_samples_small_1000.txt | 26 + .../10-n_samples_small_500.txt | 26 + 8 files changed, 2052 insertions(+) create mode 100644 libs/ccc/sklearn/metrics_gpu2.py create mode 100644 nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt create mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt create mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py new file mode 100644 index 00000000..92776bda --- /dev/null +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -0,0 +1,210 @@ +""" +Contains implementations of different metrics in sklearn but optimized for numba. + +Some code (indicated in each function) is based on scikit-learn's code base +(https://github.com/scikit-learn), for which the copyright notice and license +are shown below. + +BSD 3-Clause License + +Copyright (c) 2007-2021 The scikit-learn developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" +import numpy as np +from numba import njit +from numba import cuda + +@njit(cache=True, nogil=True) +def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + """ + Given two clustering partitions with k0 and k1 number of clusters each, it + returns a contingency matrix with k0 rows and k1 columns. It's an implementation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, + but the code is not based on their implementation. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A contingency matrix with k0 (number of clusters in part0) rows and k1 + (number of clusters in part1) columns. Each cell ij represents the + number of objects grouped in cluster i (in part0) and cluster j (in + part1). + """ + part0_unique = np.unique(part0) + part1_unique = np.unique(part1) + + cont_mat = np.zeros((len(part0_unique), len(part1_unique))) + + for i in range(len(part0_unique)): + part0_k = part0_unique[i] + + for j in range(len(part1_unique)): + part1_k = part1_unique[j] + + part0_i = part0 == part0_k + part1_j = part1 == part1_k + + cont_mat[i, j] = np.sum(part0_i & part1_j) + + return cont_mat + + +@njit(cache=True, nogil=True) +def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + """ + Returns the pair confusion matrix from two clustering partitions. It is an + implemenetation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html + The code is based on the sklearn implementation. See copyright notice at the + top of this file. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A pair confusion matrix with 2 rows and 2 columns. From sklearn's + pair_confusion_matrix docstring: considering a pair of objects that is + clustered together a positive pair, then as in binary classification the + count of true negatives is in position 00, false negatives in 10, true + positives in 11, and false positives in 01. + """ + n_samples = np.int64(part0.shape[0]) + + # Computation using the contingency data + contingency = get_contingency_matrix(part0, part1) + n_c = np.ravel(contingency.sum(axis=1)) + n_k = np.ravel(contingency.sum(axis=0)) + sum_squares = (contingency**2).sum() + C = np.empty((2, 2), dtype=np.int64) + C[1, 1] = sum_squares - n_samples + C[0, 1] = contingency.dot(n_k).sum() - sum_squares + C[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares + C[0, 0] = n_samples**2 - C[0, 1] - C[1, 0] - sum_squares + return C + + +def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: + """ + Computes the adjusted Rand index (ARI) between two clustering partitions. + The code is based on the sklearn implementation here: + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html + See copyright notice at the top of this file. + + This function should not be compiled with numba, since it depends on + arbitrarily large interger variable (supported by Python) to correctly + compute the ARI in large partitions. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A number representing the adjusted Rand index between two clustering + partitions. This number is between something around 0 (partitions do not + match; it could be negative in some cases) and 1.0 (perfect match). + """ + (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) + # convert to Python integer types, to avoid overflow or underflow + tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) + + # Special cases: empty data or full agreement + if fn == 0 and fp == 0: + return 1.0 + + return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + + +@cuda.jit +def increment_by_one(an_array): + # Thread id in a 1D block + tx = cuda.threadIdx.x + # Block id in a 1D grid + ty = cuda.blockIdx.x + # Block width, i.e. number of threads per block + bw = cuda.blockDim.x + # Compute flattened index inside the array + pos = tx + ty * bw + if pos < an_array.size: # Check array boundaries + an_array[pos] += 1 + +def _test_cuda1(): + # Initialize the array + data = np.ones(64) + print(f"Data before kernel call: {data}") + # Set the number of threads in a block + threads_per_block = 32 + # Calculate the number of thread blocks in the grid + blocks_per_grid = (data.size + (threads_per_block - 1)) // threads_per_block + # Call the kernel + increment_by_one[blocks_per_grid, threads_per_block](data) + print(f"Data after kernel call: {data}") + return + + +def _test_ari(): + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([0, 0, 1, 1, 2, 2]) + print(adjusted_rand_index(part0, part1)) # 1.0 + + part0 = np.array([0, 0, 1, 1]) + part1 = np.array([0, 0, 1, 2]) + print(adjusted_rand_index(part0, part1)) # 0.57 + + part0 = np.array([0, 0, 1, 1]) + part1 = np.array([0, 1, 0, 1]) + print(adjusted_rand_index(part0, part1)) # -0.5 + + +def print_device_info(): + # Get the current device + device = cuda.get_current_device() + print(dir(device)) + # Print device information + print("Device Information:") + print(f"Device ID: {device.id}") + print(f"Name: {device.name}") + # print(f"Total Memory: {device.total_memory / (1024 ** 3):.2f} GB") + print(f"Multiprocessor Count: {device.MULTIPROCESSOR_COUNT}") + print(f"Max Threads per Block: {device.MAX_THREADS_PER_BLOCK}") + # print(f"Max Threads per Multiprocessor: {device.MAX_THREADS_PER_MULTIPROCESSOR}") + print(f"Max Block Dim X: {device.MAX_BLOCK_DIM_X}") + print(f"Max Block Dim Y: {device.MAX_BLOCK_DIM_Y}") + print(f"Max Block Dim Z: {device.MAX_BLOCK_DIM_Z}") + print(f"Max Grid Dim X: {device.MAX_GRID_DIM_X}") + print(f"Max Grid Dim Y: {device.MAX_GRID_DIM_Y}") + print(f"Max Grid Dim Z: {device.MAX_GRID_DIM_Z}") + print(f"Warp Size: {device.WARP_SIZE}") + print(f"Compute Capability: {device.compute_capability}") + print(f"Concurrent Kernels: {device.CONCURRENT_KERNELS}") + print(f"PCI Bus ID: {device.PCI_BUS_ID}") + print(f"PCI Device ID: {device.PCI_DEVICE_ID}") + print(f"PCI Domain ID: {device.PCI_DOMAIN_ID}") + diff --git a/nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb b/nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb new file mode 100644 index 00000000..2308d212 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb @@ -0,0 +1,1686 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Description", + "id": "392e118bbc62f138" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Compares two different ccc implementations: one using the fully optimized CPU version of ccc, and the other one using new cuda-implemented `get_contingency_matrix`", + "id": "337633a8-d03e-4509-b89d-f8daee598958" + }, + { + "cell_type": "markdown", + "id": "94028e4a-a49a-47b1-94c1-9eddd4e4a488", + "metadata": { + "papermill": { + "duration": 0.095296, + "end_time": "2021-12-02T04:36:58.107054", + "exception": false, + "start_time": "2021-12-02T04:36:58.011758", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:58.301605Z", + "iopub.status.busy": "2021-12-02T04:36:58.301130Z", + "iopub.status.idle": "2021-12-02T04:36:58.896724Z", + "shell.execute_reply": "2021-12-02T04:36:58.898251Z" + }, + "papermill": { + "duration": 0.695866, + "end_time": "2021-12-02T04:36:58.898699", + "exception": false, + "start_time": "2021-12-02T04:36:58.202833", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.119048Z", + "start_time": "2024-05-30T17:14:06.816596Z" + } + }, + "source": [ + "!echo ${CODE_DIR}" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n" + ] + } + ], + "execution_count": 21 + }, + { + "cell_type": "code", + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:59.126956Z", + "iopub.status.busy": "2021-12-02T04:36:59.126507Z", + "iopub.status.idle": "2021-12-02T04:36:59.738873Z", + "shell.execute_reply": "2021-12-02T04:36:59.737339Z" + }, + "papermill": { + "duration": 0.711841, + "end_time": "2021-12-02T04:36:59.739258", + "exception": false, + "start_time": "2021-12-02T04:36:59.027417", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.411709Z", + "start_time": "2024-05-30T17:14:07.120184Z" + } + }, + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ], + "outputs": [], + "execution_count": 22 + }, + { + "cell_type": "code", + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:59.968702Z", + "iopub.status.busy": "2021-12-02T04:36:59.968236Z", + "iopub.status.idle": "2021-12-02T04:37:00.578610Z", + "shell.execute_reply": "2021-12-02T04:37:00.576770Z" + }, + "papermill": { + "duration": 0.710822, + "end_time": "2021-12-02T04:37:00.578986", + "exception": false, + "start_time": "2021-12-02T04:36:59.868164", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.693673Z", + "start_time": "2024-05-30T17:14:07.412618Z" + } + }, + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ], + "outputs": [], + "execution_count": 23 + }, + { + "cell_type": "code", + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:00.786314Z", + "iopub.status.busy": "2021-12-02T04:37:00.785859Z", + "iopub.status.idle": "2021-12-02T04:37:01.385162Z", + "shell.execute_reply": "2021-12-02T04:37:01.383549Z" + }, + "papermill": { + "duration": 0.699623, + "end_time": "2021-12-02T04:37:01.385635", + "exception": false, + "start_time": "2021-12-02T04:37:00.686012", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.974400Z", + "start_time": "2024-05-30T17:14:07.695008Z" + } + }, + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ], + "outputs": [], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.100188, + "end_time": "2021-12-02T04:37:01.613793", + "exception": false, + "start_time": "2021-12-02T04:37:01.513605", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:01.816619Z", + "iopub.status.busy": "2021-12-02T04:37:01.816158Z", + "iopub.status.idle": "2021-12-02T04:37:02.101993Z", + "shell.execute_reply": "2021-12-02T04:37:02.102363Z" + }, + "papermill": { + "duration": 0.386175, + "end_time": "2021-12-02T04:37:02.102492", + "exception": false, + "start_time": "2021-12-02T04:37:01.716317", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.977083Z", + "start_time": "2024-05-30T17:14:07.975252Z" + } + }, + "source": [ + "import numpy as np\n", + "\n", + "from ccc.coef import ccc" + ], + "outputs": [], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.096349, + "end_time": "2021-12-02T04:37:02.297213", + "exception": false, + "start_time": "2021-12-02T04:37:02.200864", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:02.493291Z", + "iopub.status.busy": "2021-12-02T04:37:02.492829Z", + "iopub.status.idle": "2021-12-02T04:37:02.494753Z", + "shell.execute_reply": "2021-12-02T04:37:02.494311Z" + }, + "papermill": { + "duration": 0.101239, + "end_time": "2021-12-02T04:37:02.494848", + "exception": false, + "start_time": "2021-12-02T04:37:02.393609", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.981434Z", + "start_time": "2024-05-30T17:14:07.977494Z" + } + }, + "source": [ + "N_REPS = 10" + ], + "outputs": [], + "execution_count": 26 + }, + { + "cell_type": "code", + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:02.702329Z", + "iopub.status.busy": "2021-12-02T04:37:02.701871Z", + "iopub.status.idle": "2021-12-02T04:37:02.703366Z", + "shell.execute_reply": "2021-12-02T04:37:02.703704Z" + }, + "papermill": { + "duration": 0.1113, + "end_time": "2021-12-02T04:37:02.703820", + "exception": false, + "start_time": "2021-12-02T04:37:02.592520", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.984622Z", + "start_time": "2024-05-30T17:14:07.981817Z" + } + }, + "source": [ + "np.random.seed(0)" + ], + "outputs": [], + "execution_count": 27 + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.096196, + "end_time": "2021-12-02T04:37:02.897029", + "exception": false, + "start_time": "2021-12-02T04:37:02.800833", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:03.093628Z", + "iopub.status.busy": "2021-12-02T04:37:03.093157Z", + "iopub.status.idle": "2021-12-02T04:37:03.105727Z", + "shell.execute_reply": "2021-12-02T04:37:03.105340Z" + }, + "papermill": { + "duration": 0.112075, + "end_time": "2021-12-02T04:37:03.105822", + "exception": false, + "start_time": "2021-12-02T04:37:02.993747", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.989121Z", + "start_time": "2024-05-30T17:14:07.985047Z" + } + }, + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.096529, + "end_time": "2021-12-02T04:37:03.300110", + "exception": false, + "start_time": "2021-12-02T04:37:03.203581", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.096047, + "end_time": "2021-12-02T04:37:03.492931", + "exception": false, + "start_time": "2021-12-02T04:37:03.396884", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:03.698806Z", + "iopub.status.busy": "2021-12-02T04:37:03.698362Z", + "iopub.status.idle": "2021-12-02T04:37:03.700293Z", + "shell.execute_reply": "2021-12-02T04:37:03.699905Z" + }, + "papermill": { + "duration": 0.111014, + "end_time": "2021-12-02T04:37:03.700387", + "exception": false, + "start_time": "2021-12-02T04:37:03.589373", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.990754Z", + "start_time": "2024-05-30T17:14:07.989508Z" + } + }, + "source": [ + "N_SAMPLES = 50" + ], + "outputs": [], + "execution_count": 29 + }, + { + "cell_type": "code", + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:03.899082Z", + "iopub.status.busy": "2021-12-02T04:37:03.898619Z", + "iopub.status.idle": "2021-12-02T04:37:03.900173Z", + "shell.execute_reply": "2021-12-02T04:37:03.900511Z" + }, + "papermill": { + "duration": 0.102818, + "end_time": "2021-12-02T04:37:03.900627", + "exception": false, + "start_time": "2021-12-02T04:37:03.797809", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.993697Z", + "start_time": "2024-05-30T17:14:07.991779Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 30 + }, + { + "cell_type": "code", + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:04.100786Z", + "iopub.status.busy": "2021-12-02T04:37:04.100336Z", + "iopub.status.idle": "2021-12-02T04:37:04.102671Z", + "shell.execute_reply": "2021-12-02T04:37:04.102239Z" + }, + "papermill": { + "duration": 0.104055, + "end_time": "2021-12-02T04:37:04.102764", + "exception": false, + "start_time": "2021-12-02T04:37:03.998709", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:07.996579Z", + "start_time": "2024-05-30T17:14:07.994049Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 31 + }, + { + "cell_type": "code", + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:04.301684Z", + "iopub.status.busy": "2021-12-02T04:37:04.301217Z", + "iopub.status.idle": "2021-12-02T04:37:16.833925Z", + "shell.execute_reply": "2021-12-02T04:37:16.833506Z" + }, + "papermill": { + "duration": 12.634709, + "end_time": "2021-12-02T04:37:16.834035", + "exception": false, + "start_time": "2021-12-02T04:37:04.199326", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:14.727550Z", + "start_time": "2024-05-30T17:14:07.996952Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.2 ms ± 262 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "execution_count": 32 + }, + { + "cell_type": "code", + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:17.040287Z", + "iopub.status.busy": "2021-12-02T04:37:17.039804Z", + "iopub.status.idle": "2021-12-02T04:37:17.163025Z", + "shell.execute_reply": "2021-12-02T04:37:17.162557Z" + }, + "papermill": { + "duration": 0.22787, + "end_time": "2021-12-02T04:37:17.163123", + "exception": false, + "start_time": "2021-12-02T04:37:16.935253", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:14.741654Z", + "start_time": "2024-05-30T17:14:14.728385Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 01-n_samples_small_50.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '01-n_samples_small_50.txt'. \n" + ] + } + ], + "execution_count": 33 + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.100385, + "end_time": "2021-12-02T04:37:17.364961", + "exception": false, + "start_time": "2021-12-02T04:37:17.264576", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:17.599561Z", + "iopub.status.busy": "2021-12-02T04:37:17.599055Z", + "iopub.status.idle": "2021-12-02T04:37:17.600565Z", + "shell.execute_reply": "2021-12-02T04:37:17.600913Z" + }, + "papermill": { + "duration": 0.111244, + "end_time": "2021-12-02T04:37:17.601022", + "exception": false, + "start_time": "2021-12-02T04:37:17.489778", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:14.743833Z", + "start_time": "2024-05-30T17:14:14.742364Z" + } + }, + "source": [ + "N_SAMPLES = 100" + ], + "outputs": [], + "execution_count": 34 + }, + { + "cell_type": "code", + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:17.809636Z", + "iopub.status.busy": "2021-12-02T04:37:17.809179Z", + "iopub.status.idle": "2021-12-02T04:37:17.811012Z", + "shell.execute_reply": "2021-12-02T04:37:17.810630Z" + }, + "papermill": { + "duration": 0.105356, + "end_time": "2021-12-02T04:37:17.811111", + "exception": false, + "start_time": "2021-12-02T04:37:17.705755", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:14.746887Z", + "start_time": "2024-05-30T17:14:14.744414Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 35 + }, + { + "cell_type": "code", + "id": "ede7a328-bad3-40a2-a179-1148a3229620", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:18.013003Z", + "iopub.status.busy": "2021-12-02T04:37:18.012549Z", + "iopub.status.idle": "2021-12-02T04:37:18.014398Z", + "shell.execute_reply": "2021-12-02T04:37:18.014016Z" + }, + "papermill": { + "duration": 0.104201, + "end_time": "2021-12-02T04:37:18.014491", + "exception": false, + "start_time": "2021-12-02T04:37:17.910290", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:14.749823Z", + "start_time": "2024-05-30T17:14:14.747505Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 36 + }, + { + "cell_type": "code", + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:18.220053Z", + "iopub.status.busy": "2021-12-02T04:37:18.219419Z", + "iopub.status.idle": "2021-12-02T04:37:24.963506Z", + "shell.execute_reply": "2021-12-02T04:37:24.962993Z" + }, + "papermill": { + "duration": 6.84888, + "end_time": "2021-12-02T04:37:24.963614", + "exception": false, + "start_time": "2021-12-02T04:37:18.114734", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:29.786993Z", + "start_time": "2024-05-30T17:14:14.750356Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.4 ms ± 405 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "execution_count": 37 + }, + { + "cell_type": "code", + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:25.171582Z", + "iopub.status.busy": "2021-12-02T04:37:25.170725Z", + "iopub.status.idle": "2021-12-02T04:37:25.532258Z", + "shell.execute_reply": "2021-12-02T04:37:25.531874Z" + }, + "papermill": { + "duration": 0.465279, + "end_time": "2021-12-02T04:37:25.532358", + "exception": false, + "start_time": "2021-12-02T04:37:25.067079", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:29.810995Z", + "start_time": "2024-05-30T17:14:29.787791Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_100.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '10-n_samples_small_100.txt'. \n" + ] + } + ], + "execution_count": 38 + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.10645, + "end_time": "2021-12-02T04:37:25.742045", + "exception": false, + "start_time": "2021-12-02T04:37:25.635595", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:25.946981Z", + "iopub.status.busy": "2021-12-02T04:37:25.946503Z", + "iopub.status.idle": "2021-12-02T04:37:25.947981Z", + "shell.execute_reply": "2021-12-02T04:37:25.948333Z" + }, + "papermill": { + "duration": 0.105745, + "end_time": "2021-12-02T04:37:25.948446", + "exception": false, + "start_time": "2021-12-02T04:37:25.842701", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:29.813107Z", + "start_time": "2024-05-30T17:14:29.811679Z" + } + }, + "source": [ + "N_SAMPLES = 500" + ], + "outputs": [], + "execution_count": 39 + }, + { + "cell_type": "code", + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:26.152110Z", + "iopub.status.busy": "2021-12-02T04:37:26.151591Z", + "iopub.status.idle": "2021-12-02T04:37:26.153920Z", + "shell.execute_reply": "2021-12-02T04:37:26.153472Z" + }, + "papermill": { + "duration": 0.105277, + "end_time": "2021-12-02T04:37:26.154017", + "exception": false, + "start_time": "2021-12-02T04:37:26.048740", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:29.816055Z", + "start_time": "2024-05-30T17:14:29.813653Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 40 + }, + { + "cell_type": "code", + "id": "24c352bd", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:26.357429Z", + "iopub.status.busy": "2021-12-02T04:37:26.356978Z", + "iopub.status.idle": "2021-12-02T04:37:26.358844Z", + "shell.execute_reply": "2021-12-02T04:37:26.358481Z" + }, + "papermill": { + "duration": 0.104548, + "end_time": "2021-12-02T04:37:26.358940", + "exception": false, + "start_time": "2021-12-02T04:37:26.254392", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:29.819054Z", + "start_time": "2024-05-30T17:14:29.816583Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 41 + }, + { + "cell_type": "code", + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:26.563098Z", + "iopub.status.busy": "2021-12-02T04:37:26.562627Z", + "iopub.status.idle": "2021-12-02T04:37:33.801602Z", + "shell.execute_reply": "2021-12-02T04:37:33.801037Z" + }, + "papermill": { + "duration": 7.342849, + "end_time": "2021-12-02T04:37:33.801721", + "exception": false, + "start_time": "2021-12-02T04:37:26.458872", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:32.496070Z", + "start_time": "2024-05-30T17:14:29.819690Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.7 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "execution_count": 42 + }, + { + "cell_type": "code", + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:34.015314Z", + "iopub.status.busy": "2021-12-02T04:37:34.014832Z", + "iopub.status.idle": "2021-12-02T04:37:34.410262Z", + "shell.execute_reply": "2021-12-02T04:37:34.410651Z" + }, + "papermill": { + "duration": 0.501944, + "end_time": "2021-12-02T04:37:34.410769", + "exception": false, + "start_time": "2021-12-02T04:37:33.908825", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:32.532594Z", + "start_time": "2024-05-30T17:14:32.496856Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_500.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '10-n_samples_small_500.txt'. \n" + ] + } + ], + "execution_count": 43 + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.103261, + "end_time": "2021-12-02T04:37:34.618220", + "exception": false, + "start_time": "2021-12-02T04:37:34.514959", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:34.832465Z", + "iopub.status.busy": "2021-12-02T04:37:34.831999Z", + "iopub.status.idle": "2021-12-02T04:37:34.833958Z", + "shell.execute_reply": "2021-12-02T04:37:34.833511Z" + }, + "papermill": { + "duration": 0.114715, + "end_time": "2021-12-02T04:37:34.834052", + "exception": false, + "start_time": "2021-12-02T04:37:34.719337", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:32.534788Z", + "start_time": "2024-05-30T17:14:32.533307Z" + } + }, + "source": [ + "N_SAMPLES = 1000" + ], + "outputs": [], + "execution_count": 44 + }, + { + "cell_type": "code", + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:35.043108Z", + "iopub.status.busy": "2021-12-02T04:37:35.042636Z", + "iopub.status.idle": "2021-12-02T04:37:35.044239Z", + "shell.execute_reply": "2021-12-02T04:37:35.044579Z" + }, + "papermill": { + "duration": 0.107686, + "end_time": "2021-12-02T04:37:35.044696", + "exception": false, + "start_time": "2021-12-02T04:37:34.937010", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:32.537720Z", + "start_time": "2024-05-30T17:14:32.535344Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 45 + }, + { + "cell_type": "code", + "id": "d907f1d7", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:35.253125Z", + "iopub.status.busy": "2021-12-02T04:37:35.252650Z", + "iopub.status.idle": "2021-12-02T04:37:35.254497Z", + "shell.execute_reply": "2021-12-02T04:37:35.254118Z" + }, + "papermill": { + "duration": 0.106501, + "end_time": "2021-12-02T04:37:35.254591", + "exception": false, + "start_time": "2021-12-02T04:37:35.148090", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:32.540516Z", + "start_time": "2024-05-30T17:14:32.538243Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 46 + }, + { + "cell_type": "code", + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:35.461330Z", + "iopub.status.busy": "2021-12-02T04:37:35.460856Z", + "iopub.status.idle": "2021-12-02T04:37:49.189938Z", + "shell.execute_reply": "2021-12-02T04:37:49.190314Z" + }, + "papermill": { + "duration": 13.834783, + "end_time": "2021-12-02T04:37:49.190434", + "exception": false, + "start_time": "2021-12-02T04:37:35.355651", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:36.449349Z", + "start_time": "2024-05-30T17:14:32.541057Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43.3 ms ± 1.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "execution_count": 47 + }, + { + "cell_type": "code", + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:49.400379Z", + "iopub.status.busy": "2021-12-02T04:37:49.399912Z", + "iopub.status.idle": "2021-12-02T04:37:50.269813Z", + "shell.execute_reply": "2021-12-02T04:37:50.270191Z" + }, + "papermill": { + "duration": 0.97614, + "end_time": "2021-12-02T04:37:50.270311", + "exception": false, + "start_time": "2021-12-02T04:37:49.294171", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:36.499487Z", + "start_time": "2024-05-30T17:14:36.451149Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_1000.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '10-n_samples_small_1000.txt'. \n" + ] + } + ], + "execution_count": 48 + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.103785, + "end_time": "2021-12-02T04:37:50.684395", + "exception": false, + "start_time": "2021-12-02T04:37:50.580610", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.109125, + "end_time": "2021-12-02T04:37:50.896687", + "exception": false, + "start_time": "2021-12-02T04:37:50.787562", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:51.105200Z", + "iopub.status.busy": "2021-12-02T04:37:51.104752Z", + "iopub.status.idle": "2021-12-02T04:37:51.106157Z", + "shell.execute_reply": "2021-12-02T04:37:51.106510Z" + }, + "papermill": { + "duration": 0.107277, + "end_time": "2021-12-02T04:37:51.106621", + "exception": false, + "start_time": "2021-12-02T04:37:50.999344", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:36.501898Z", + "start_time": "2024-05-30T17:14:36.500200Z" + } + }, + "source": [ + "N_SAMPLES = 50000" + ], + "outputs": [], + "execution_count": 49 + }, + { + "cell_type": "code", + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:51.315976Z", + "iopub.status.busy": "2021-12-02T04:37:51.315458Z", + "iopub.status.idle": "2021-12-02T04:37:51.318127Z", + "shell.execute_reply": "2021-12-02T04:37:51.317763Z" + }, + "papermill": { + "duration": 0.108638, + "end_time": "2021-12-02T04:37:51.318226", + "exception": false, + "start_time": "2021-12-02T04:37:51.209588", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:36.506666Z", + "start_time": "2024-05-30T17:14:36.502604Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 50 + }, + { + "cell_type": "code", + "id": "15cb532e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:51.529025Z", + "iopub.status.busy": "2021-12-02T04:37:51.528568Z", + "iopub.status.idle": "2021-12-02T04:37:51.530041Z", + "shell.execute_reply": "2021-12-02T04:37:51.530382Z" + }, + "papermill": { + "duration": 0.107088, + "end_time": "2021-12-02T04:37:51.530499", + "exception": false, + "start_time": "2021-12-02T04:37:51.423411", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:14:36.509196Z", + "start_time": "2024-05-30T17:14:36.507373Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 51 + }, + { + "cell_type": "code", + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:37:51.741002Z", + "iopub.status.busy": "2021-12-02T04:37:51.740365Z", + "iopub.status.idle": "2021-12-02T04:38:27.435619Z", + "shell.execute_reply": "2021-12-02T04:38:27.436034Z" + }, + "papermill": { + "duration": 35.80273, + "end_time": "2021-12-02T04:38:27.436145", + "exception": false, + "start_time": "2021-12-02T04:37:51.633415", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:15:14.984038Z", + "start_time": "2024-05-30T17:14:36.509908Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.4 s ± 15.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "execution_count": 52 + }, + { + "cell_type": "code", + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:38:27.648543Z", + "iopub.status.busy": "2021-12-02T04:38:27.647940Z", + "iopub.status.idle": "2021-12-02T04:38:29.880143Z", + "shell.execute_reply": "2021-12-02T04:38:29.879649Z" + }, + "papermill": { + "duration": 2.340044, + "end_time": "2021-12-02T04:38:29.880241", + "exception": false, + "start_time": "2021-12-02T04:38:27.540197", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:15:17.381615Z", + "start_time": "2024-05-30T17:15:14.984839Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_large_50000.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '10-n_samples_large_50000.txt'. \n" + ] + } + ], + "execution_count": 53 + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.105829, + "end_time": "2021-12-02T04:38:30.098264", + "exception": false, + "start_time": "2021-12-02T04:38:29.992435", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:38:30.310087Z", + "iopub.status.busy": "2021-12-02T04:38:30.309651Z", + "iopub.status.idle": "2021-12-02T04:38:30.311339Z", + "shell.execute_reply": "2021-12-02T04:38:30.311682Z" + }, + "papermill": { + "duration": 0.109597, + "end_time": "2021-12-02T04:38:30.311799", + "exception": false, + "start_time": "2021-12-02T04:38:30.202202", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:15:17.384055Z", + "start_time": "2024-05-30T17:15:17.382405Z" + } + }, + "source": [ + "N_SAMPLES = 100000" + ], + "outputs": [], + "execution_count": 54 + }, + { + "cell_type": "code", + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:38:30.525019Z", + "iopub.status.busy": "2021-12-02T04:38:30.524568Z", + "iopub.status.idle": "2021-12-02T04:38:30.528283Z", + "shell.execute_reply": "2021-12-02T04:38:30.527854Z" + }, + "papermill": { + "duration": 0.111647, + "end_time": "2021-12-02T04:38:30.528379", + "exception": false, + "start_time": "2021-12-02T04:38:30.416732", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:15:17.390846Z", + "start_time": "2024-05-30T17:15:17.384582Z" + } + }, + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ], + "outputs": [], + "execution_count": 55 + }, + { + "cell_type": "code", + "id": "d408b318", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:38:30.742827Z", + "iopub.status.busy": "2021-12-02T04:38:30.742345Z", + "iopub.status.idle": "2021-12-02T04:38:30.744280Z", + "shell.execute_reply": "2021-12-02T04:38:30.743848Z" + }, + "papermill": { + "duration": 0.110384, + "end_time": "2021-12-02T04:38:30.744375", + "exception": false, + "start_time": "2021-12-02T04:38:30.633991", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:15:17.392961Z", + "start_time": "2024-05-30T17:15:17.391523Z" + } + }, + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ], + "outputs": [], + "execution_count": 56 + }, + { + "cell_type": "code", + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:38:30.958831Z", + "iopub.status.busy": "2021-12-02T04:38:30.958256Z", + "iopub.status.idle": "2021-12-02T04:39:38.969951Z", + "shell.execute_reply": "2021-12-02T04:39:38.970301Z" + }, + "papermill": { + "duration": 68.120109, + "end_time": "2021-12-02T04:39:38.970413", + "exception": false, + "start_time": "2021-12-02T04:38:30.850304", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:16:36.328760Z", + "start_time": "2024-05-30T17:15:17.393479Z" + } + }, + "source": [ + "%%timeit func()\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.92 s ± 31.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "execution_count": 57 + }, + { + "cell_type": "code", + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:39:39.183942Z", + "iopub.status.busy": "2021-12-02T04:39:39.183247Z", + "iopub.status.idle": "2021-12-02T04:39:43.450962Z", + "shell.execute_reply": "2021-12-02T04:39:43.451362Z" + }, + "papermill": { + "duration": 4.37642, + "end_time": "2021-12-02T04:39:43.451480", + "exception": false, + "start_time": "2021-12-02T04:39:39.075060", + "status": "completed" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2024-05-30T17:16:41.225685Z", + "start_time": "2024-05-30T17:16:36.329527Z" + } + }, + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_large_100000.txt\n", + "func()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '10-n_samples_large_100000.txt'. \n" + ] + } + ], + "execution_count": 58 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Profile with CProfile", + "id": "aa9311addc760854" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-05-30T17:26:08.113910Z", + "start_time": "2024-05-30T17:26:03.072637Z" + } + }, + "cell_type": "code", + "source": [ + "from cProfile import Profile\n", + "from pstats import SortKey, Stats\n", + "\n", + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)\n", + "\n", + "with Profile() as profile:\n", + " func()\n", + " (\n", + " Stats(profile)\n", + " .strip_dirs()\n", + " .sort_stats(SortKey.CUMULATIVE)\n", + " .print_stats()\n", + " )" + ], + "id": "e4950c169d3bbf40", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8339 function calls in 5.036 seconds\n", + "\n", + " Ordered by: cumulative time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.014 0.014 5.036 5.036 2445792793.py:4(func)\n", + " 10 0.008 0.001 5.022 0.502 impl.py:307(ccc)\n", + " 200 0.001 0.000 4.991 0.025 threading.py:280(wait)\n", + " 790 4.990 0.006 4.990 0.006 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 3.150 0.315 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 3.149 0.315 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 3.149 0.315 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 3.141 0.031 _base.py:201(as_completed)\n", + " 100 0.000 0.000 3.140 0.031 threading.py:563(wait)\n", + " 100 0.000 0.000 1.851 0.019 _base.py:418(result)\n", + " 20 0.000 0.000 1.851 0.093 _base.py:602(result_iterator)\n", + " 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros}\n", + " 10 0.004 0.000 0.005 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 190 0.000 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.001 0.000 thread.py:216(shutdown)\n", + " 10 0.000 0.000 0.001 0.000 threading.py:1028(join)\n", + " 100 0.000 0.000 0.001 0.000 threading.py:411(acquire)\n", + " 10 0.000 0.000 0.001 0.000 threading.py:1066(_wait_for_tstate_lock)\n", + " 40 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n", + " 20 0.000 0.000 0.000 0.000 impl.py:242(get_chunks)\n", + " 480 0.000 0.000 0.000 0.000 threading.py:256(__enter__)\n", + " 480 0.000 0.000 0.000 0.000 threading.py:259(__exit__)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method _thread.start_new_thread}\n", + " 10 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(amax)\n", + " 100 0.000 0.000 0.000 0.000 _base.py:318(__init__)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2638(amax)\n", + " 130 0.000 0.000 0.000 0.000 threading.py:228(__init__)\n", + " 90 0.000 0.000 0.000 0.000 threading.py:553(clear)\n", + " 30 0.000 0.000 0.000 0.000 numeric.py:289(full)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:69(_wrapreduction)\n", + " 190 0.000 0.000 0.000 0.000 threading.py:268(_acquire_restore)\n", + " 30 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto)\n", + " 190 0.000 0.000 0.000 0.000 threading.py:271(_is_owned)\n", + " 10 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}\n", + " 10 0.000 0.000 0.000 0.000 ipkernel.py:763(init_closure)\n", + " 10 0.000 0.000 0.000 0.000 thread.py:123(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:992(_stop)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:157(_create_and_install_waiters)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:802(__init__)\n", + " 110 0.000 0.000 0.000 0.000 {method 'put' of '_queue.SimpleQueue' objects}\n", + " 30 0.000 0.000 0.000 0.000 {built-in method numpy.arange}\n", + " 600 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.lock' objects}\n", + " 250 0.000 0.000 0.000 0.000 {built-in method _thread.allocate_lock}\n", + " 190 0.000 0.000 0.000 0.000 threading.py:265(_release_save)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:79(__init__)\n", + " 140 0.000 0.000 0.000 0.000 utility_functions.py:117()\n", + " 190 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.RLock' objects}\n", + " 100 0.000 0.000 0.000 0.000 {method 'pop' of 'list' objects}\n", + " 30 0.000 0.000 0.000 0.000 utility_functions.py:109(chunker)\n", + " 180 0.000 0.000 0.000 0.000 {method 'remove' of 'set' objects}\n", + " 20 0.000 0.000 0.000 0.000 threading.py:528(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:775(_maintain_shutdown_locks)\n", + " 30 0.000 0.000 0.000 0.000 {built-in method numpy.empty}\n", + " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'list' objects}\n", + " 100 0.000 0.000 0.000 0.000 _base.py:388(__get_result)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:63(__init__)\n", + " 100 0.000 0.000 0.000 0.000 thread.py:47(__init__)\n", + " 10 0.000 0.000 0.000 0.000 {method 'argmax' of 'numpy.ndarray' objects}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:146(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:405(__init__)\n", + " 290 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.lock' objects}\n", + " 100 0.000 0.000 0.000 0.000 threading.py:82(RLock)\n", + " 10 0.000 0.000 0.000 0.000 core.py:85(unravel_index_2d)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method builtins.sorted}\n", + " 200 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects}\n", + " 200 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.RLock' objects}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:149(__enter__)\n", + " 20 0.000 0.000 0.000 0.000 threading.py:1358(current_thread)\n", + " 10 0.000 0.000 0.000 0.000 impl.py:74(get_range_n_clusters)\n", + " 100 0.000 0.000 0.000 0.000 {method 'reverse' of 'list' objects}\n", + " 10 0.000 0.000 0.000 0.000 weakref.py:370(remove)\n", + " 200 0.000 0.000 0.000 0.000 {method 'release' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.000 0.000 threading.py:785()\n", + " 180 0.000 0.000 0.000 0.000 {built-in method time.monotonic}\n", + " 10 0.000 0.000 0.000 0.000 {method '_acquire_restore' of '_thread.RLock' objects}\n", + " 181 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:153(__exit__)\n", + " 20 0.000 0.000 0.000 0.000 impl.py:284(get_feature_type_and_encode)\n", + " 10 0.000 0.000 0.000 0.000 _weakrefset.py:39(_remove)\n", + " 10 0.000 0.000 0.000 0.000 _weakrefset.py:86(add)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:70()\n", + " 1 0.000 0.000 0.000 0.000 pstats.py:107(__init__)\n", + " 10 0.000 0.000 0.000 0.000 impl.py:218(get_coords_from_index)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:225()\n", + " 1 0.000 0.000 0.000 0.000 pstats.py:117(init)\n", + " 10 0.000 0.000 0.000 0.000 {method 'difference_update' of 'set' objects}\n", + " 10 0.000 0.000 0.000 0.000 threading.py:1229(_make_invoke_excepthook)\n", + " 20 0.000 0.000 0.000 0.000 threading.py:1147(daemon)\n", + " 90 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.RLock' objects}\n", + " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'collections.deque' objects}\n", + " 10 0.000 0.000 0.000 0.000 weakref.py:428(__setitem__)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method numpy.asarray}\n", + " 30 0.000 0.000 0.000 0.000 multiarray.py:1071(copyto)\n", + " 1 0.000 0.000 0.000 0.000 pstats.py:136(load_stats)\n", + " 90 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", + " 30 0.000 0.000 0.000 0.000 {method 'locked' of '_thread.lock' objects}\n", + " 21 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", + " 20 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}\n", + " 90 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects}\n", + " 20 0.000 0.000 0.000 0.000 threading.py:536(is_set)\n", + " 30 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}\n", + " 10 0.000 0.000 0.000 0.000 {method '_release_save' of '_thread.RLock' objects}\n", + " 10 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2633(_amax_dispatcher)\n", + " 1 0.000 0.000 0.000 0.000 cProfile.py:51(create_stats)\n", + " 10 0.000 0.000 0.000 0.000 {method 'discard' of 'set' objects}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:633(__enter__)\n", + " 10 0.000 0.000 0.000 0.000 {method '_is_owned' of '_thread.RLock' objects}\n", + " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", + " 1 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", + "\n", + "\n" + ] + } + ], + "execution_count": 62 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "454593d6e622293" + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "papermill": { + "default_parameters": {}, + "duration": 168.306551, + "end_time": "2021-12-02T04:39:44.188549", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/10-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/10-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:36:55.881998", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt new file mode 100644 index 00000000..a903ab9e --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt @@ -0,0 +1,26 @@ + 6144 function calls in 0.010 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.010 0.010 {built-in method builtins.exec} + 1 0.000 0.000 0.010 0.010 :1() + 1 0.000 0.000 0.010 0.010 837709190.py:1(func) + 10 0.000 0.000 0.010 0.001 impl.py:307(ccc) + 10 0.000 0.000 0.007 0.001 impl.py:492(compute_coef) + 10 0.000 0.000 0.007 0.001 impl.py:485(cdist_func) + 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel) + 140 0.000 0.000 0.007 0.000 threading.py:280(wait) + 550 0.006 0.000 0.006 0.000 {method 'acquire' of '_thread.lock' objects} + 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed) + 70 0.000 0.000 0.006 0.000 threading.py:563(wait) + 70 0.000 0.000 0.002 0.000 thread.py:161(submit) + 70 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 70 0.000 0.000 0.001 0.000 _base.py:418(result) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 70 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt new file mode 100644 index 00000000..1e9ed38a --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 4.893 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 4.893 4.893 {built-in method builtins.exec} + 1 0.000 0.000 4.893 4.893 :1() + 1 0.015 0.015 4.893 4.893 837709190.py:1(func) + 10 0.008 0.001 4.877 0.488 impl.py:307(ccc) + 200 0.001 0.000 4.844 0.024 threading.py:280(wait) + 790 4.843 0.006 4.843 0.006 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 3.061 0.306 impl.py:492(compute_coef) + 10 0.000 0.000 3.060 0.306 impl.py:485(cdist_func) + 10 0.001 0.000 3.060 0.306 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 3.052 0.031 _base.py:201(as_completed) + 100 0.000 0.000 3.051 0.031 threading.py:563(wait) + 100 0.000 0.000 1.794 0.018 _base.py:418(result) + 20 0.000 0.000 1.794 0.090 _base.py:602(result_iterator) + 50 0.011 0.000 0.011 0.000 {built-in method numpy.zeros} + 10 0.005 0.001 0.006 0.001 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt new file mode 100644 index 00000000..7b9d2199 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 2.394 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 2.394 2.394 {built-in method builtins.exec} + 1 0.000 0.000 2.394 2.394 :1() + 1 0.013 0.013 2.394 2.394 837709190.py:1(func) + 10 0.004 0.000 2.381 0.238 impl.py:307(ccc) + 200 0.001 0.000 2.359 0.012 threading.py:280(wait) + 790 2.358 0.003 2.358 0.003 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 1.505 0.151 impl.py:492(compute_coef) + 10 0.000 0.000 1.504 0.150 impl.py:485(cdist_func) + 10 0.001 0.000 1.504 0.150 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 1.498 0.015 _base.py:201(as_completed) + 100 0.000 0.000 1.497 0.015 threading.py:563(wait) + 100 0.000 0.000 0.863 0.009 _base.py:418(result) + 20 0.000 0.000 0.863 0.043 _base.py:602(result_iterator) + 50 0.006 0.000 0.006 0.000 {built-in method numpy.zeros} + 10 0.003 0.000 0.005 0.000 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt new file mode 100644 index 00000000..7fc11aae --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.020 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.020 0.020 {built-in method builtins.exec} + 1 0.000 0.000 0.020 0.020 :1() + 1 0.000 0.000 0.020 0.020 837709190.py:1(func) + 10 0.000 0.000 0.020 0.002 impl.py:307(ccc) + 10 0.000 0.000 0.016 0.002 impl.py:492(compute_coef) + 200 0.000 0.000 0.015 0.000 threading.py:280(wait) + 10 0.000 0.000 0.015 0.002 impl.py:485(cdist_func) + 10 0.000 0.000 0.015 0.002 impl.py:192(cdist_parts_parallel) + 790 0.015 0.000 0.015 0.000 {method 'acquire' of '_thread.lock' objects} + 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed) + 100 0.000 0.000 0.013 0.000 threading.py:563(wait) + 100 0.000 0.000 0.002 0.000 _base.py:418(result) + 20 0.000 0.000 0.002 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.002 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt new file mode 100644 index 00000000..07cee266 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt @@ -0,0 +1,26 @@ + 8310 function calls in 0.046 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.046 0.046 {built-in method builtins.exec} + 1 0.000 0.000 0.046 0.046 :1() + 1 0.000 0.000 0.046 0.046 837709190.py:1(func) + 10 0.001 0.000 0.045 0.005 impl.py:307(ccc) + 199 0.000 0.000 0.041 0.000 threading.py:280(wait) + 786 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.033 0.003 impl.py:492(compute_coef) + 10 0.000 0.000 0.033 0.003 impl.py:485(cdist_func) + 10 0.000 0.000 0.033 0.003 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.031 0.000 _base.py:201(as_completed) + 99 0.000 0.000 0.031 0.000 threading.py:563(wait) + 100 0.000 0.000 0.010 0.000 _base.py:418(result) + 20 0.000 0.000 0.010 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.002 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt new file mode 100644 index 00000000..30127f8b --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.033 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.033 0.033 {built-in method builtins.exec} + 1 0.000 0.000 0.033 0.033 :1() + 1 0.000 0.000 0.033 0.033 837709190.py:1(func) + 10 0.000 0.000 0.033 0.003 impl.py:307(ccc) + 200 0.000 0.000 0.028 0.000 threading.py:280(wait) + 790 0.028 0.000 0.028 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.023 0.002 impl.py:492(compute_coef) + 10 0.000 0.000 0.022 0.002 impl.py:485(cdist_func) + 10 0.000 0.000 0.022 0.002 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.021 0.000 threading.py:563(wait) + 100 0.000 0.000 0.021 0.000 _base.py:201(as_completed) + 100 0.000 0.000 0.007 0.000 _base.py:418(result) + 20 0.000 0.000 0.007 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.002 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file From a7e54c7bccfda700deebd9a0118f232b6200d535 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 30 May 2024 14:31:22 -0600 Subject: [PATCH 004/134] [metrics]: Complete working version of get_contingency_matrix --- libs/ccc/sklearn/metrics_gpu2.py | 101 ++++++++++++++--------- tests/gpu/test_sklearn_metrics.py | 129 ++++++++++++++++++++++++++++++ 2 files changed, 194 insertions(+), 36 deletions(-) create mode 100644 tests/gpu/test_sklearn_metrics.py diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 92776bda..9ea554ef 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -39,42 +39,6 @@ from numba import njit from numba import cuda -@njit(cache=True, nogil=True) -def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: - """ - Given two clustering partitions with k0 and k1 number of clusters each, it - returns a contingency matrix with k0 rows and k1 columns. It's an implementation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, - but the code is not based on their implementation. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A contingency matrix with k0 (number of clusters in part0) rows and k1 - (number of clusters in part1) columns. Each cell ij represents the - number of objects grouped in cluster i (in part0) and cluster j (in - part1). - """ - part0_unique = np.unique(part0) - part1_unique = np.unique(part1) - - cont_mat = np.zeros((len(part0_unique), len(part1_unique))) - - for i in range(len(part0_unique)): - part0_k = part0_unique[i] - - for j in range(len(part1_unique)): - part1_k = part1_unique[j] - - part0_i = part0 == part0_k - part1_j = part1 == part1_k - - cont_mat[i, j] = np.sum(part0_i & part1_j) - - return cont_mat - @njit(cache=True, nogil=True) def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: @@ -142,6 +106,65 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) +@cuda.jit +def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): + """ + CUDA kernel to compute the contingency matrix. + + Args: + part0: 1D array with cluster assignments for n objects. + part1: 1D array with cluster assignments for n objects. + part0_unique: Unique cluster labels in part0. + part1_unique: Unique cluster labels in part1. + cont_mat: The output contingency matrix. + + Each thread computes a single element of the contingency matrix. + """ + i, j = cuda.grid(2) # Get the thread indices in the grid + + # Check if the thread indices are within the bounds of the unique clusters + if i < len(part0_unique) and j < len(part1_unique): + part0_k = part0_unique[i] # Cluster label in part0 + part1_k = part1_unique[j] # Cluster label in part1 + + count = 0 # Initialize the count for this element + for idx in range(len(part0)): + # Count the number of objects in both clusters i and j + if part0[idx] == part0_k and part1[idx] == part1_k: + count += 1 + cont_mat[i, j] = count # Store the result in the contingency matrix + + +def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + """ + Compute the contingency matrix for two clustering partitions using CUDA. + + Args: + part0: 1D array with cluster assignments for n objects. + part1: 1D array with cluster assignments for n objects. + + Returns: + A contingency matrix with k0 rows and k1 columns, where k0 is the number + of clusters in part0 and k1 is the number of clusters in part1. Each cell + (i, j) represents the number of objects in cluster i (part0) and cluster j (part1). + """ + part0_unique = np.unique(part0) # Find unique clusters in part0 + part1_unique = np.unique(part1) # Find unique clusters in part1 + + cont_mat = np.zeros((len(part0_unique), len(part1_unique)), dtype=np.int32) # Initialize the contingency matrix + + # Define the number of threads per block and the number of blocks per grid + threadsperblock = (16, 16) + blockspergrid_x = int(np.ceil(len(part0_unique) / threadsperblock[0])) + blockspergrid_y = int(np.ceil(len(part1_unique) / threadsperblock[1])) + blockspergrid = (blockspergrid_x, blockspergrid_y) + + # Launch the CUDA kernel to compute the contingency matrix + compute_contingency_matrix[blockspergrid, threadsperblock](part0, part1, part0_unique, part1_unique, cont_mat) + + return cont_mat + + @cuda.jit def increment_by_one(an_array): # Thread id in a 1D block @@ -208,3 +231,9 @@ def print_device_info(): print(f"PCI Device ID: {device.PCI_DEVICE_ID}") print(f"PCI Domain ID: {device.PCI_DOMAIN_ID}") + +if __name__ == '__main__': + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([1, 0, 2, 1, 0, 2]) + cont_matrix = get_contingency_matrix(part0, part1) + print(cont_matrix) \ No newline at end of file diff --git a/tests/gpu/test_sklearn_metrics.py b/tests/gpu/test_sklearn_metrics.py new file mode 100644 index 00000000..d81ed124 --- /dev/null +++ b/tests/gpu/test_sklearn_metrics.py @@ -0,0 +1,129 @@ +import numpy as np +from sklearn.metrics import adjusted_rand_score as sklearn_ari + +from ccc.sklearn.metrics_gpu2 import ( + adjusted_rand_index, + get_contingency_matrix, + get_pair_confusion_matrix, +) + + +def test_get_contingency_matrix_k0_equal_k1(): + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([0, 1, 0, 2, 1, 2]) + + expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]]) + + observed_mat = get_contingency_matrix(part0, part1) + + np.testing.assert_array_equal(observed_mat, expected_mat) + + +def test_get_contingency_matrix_k0_greater_k1(): + part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) + part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) + + expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1], [0, 0, 3]]) + + observed_mat = get_contingency_matrix(part0, part1) + + np.testing.assert_array_equal(observed_mat, expected_mat) + + +def test_get_contingency_matrix_k0_lesser_k1(): + part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3, 2, 2, 2, 1]) + part1 = np.array([0, 1, 0, 2, 1, 2, 3, 3, 3, 4, 4, 5, 5]) + + expected_mat = np.array( + [[1, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 1], [0, 1, 1, 0, 2, 1], [0, 0, 0, 3, 0, 0]] + ) + + observed_mat = get_contingency_matrix(part0, part1) + + np.testing.assert_array_equal(observed_mat, expected_mat) + +# +# def test_get_pair_confusion_matrix_k0_equal_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([0, 1, 0, 2, 1, 2]) +# +# expected_mat = np.array([[18, 6], [6, 0]]) +# +# observed_mat = get_pair_confusion_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_get_pair_confusion_matrix_k0_greater_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) +# part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) +# +# expected_mat = np.array([[42, 18], [6, 6]]) +# +# observed_mat = get_pair_confusion_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_adjusted_rand_index_manual_random_partitions_same_k(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([0, 1, 0, 2, 1, 2]) +# +# expected_ari = -0.25 +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_manual_perfect_match(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([2, 2, 3, 3, 4, 4]) +# +# expected_ari = 1.0 +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_random_partitions_same_k(): +# maxk0 = 2 +# maxk1 = maxk0 +# n = 100 +# +# part0 = np.random.randint(0, maxk0 + 1, n) +# part1 = np.random.randint(0, maxk1 + 1, n) +# +# # warning: the sklearn's ari implementation can overflow in older versions +# # when n is large +# expected_ari = sklearn_ari(part0, part1) +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_random_partitions_k0_greater_k1(): +# maxk0 = 5 +# maxk1 = 3 +# n = 100 +# +# part0 = np.random.randint(0, maxk0 + 1, n) +# part1 = np.random.randint(0, maxk1 + 1, n) +# +# # warning: the sklearn's ari implementation can overflow in older versions +# # when n is large +# expected_ari = sklearn_ari(part0, part1) +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari From 52d22f305ad5efd5d1e47a7c11e9af9ea1e9d164 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 30 May 2024 15:08:19 -0600 Subject: [PATCH 005/134] [metrics]: Complete working version of get_contingency_matrix --- libs/ccc/sklearn/metrics_gpu2.py | 62 +++++++---- tests/gpu/test_sklearn_metrics.py | 170 +++++++++++++++--------------- 2 files changed, 128 insertions(+), 104 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 9ea554ef..af31a6a0 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -40,38 +40,60 @@ from numba import cuda -@njit(cache=True, nogil=True) +@cuda.jit +def compute_sum_squares(contingency, result): + """ + CUDA kernel to compute the sum of squares of the contingency matrix elements. + + Args: + contingency: The contingency matrix. + result: The output array to store the sum of squares. + """ + i, j = cuda.grid(2) + + if i < contingency.shape[0] and j < contingency.shape[1]: + cuda.atomic.add(result, 0, contingency[i, j] ** 2) + + def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: """ - Returns the pair confusion matrix from two clustering partitions. It is an - implemenetation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html - The code is based on the sklearn implementation. See copyright notice at the - top of this file. + Returns the pair confusion matrix from two clustering partitions using CUDA. Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. + part0: A 1D array with cluster assignments for n objects. + part1: A 1D array with cluster assignments for n objects. Returns: - A pair confusion matrix with 2 rows and 2 columns. From sklearn's - pair_confusion_matrix docstring: considering a pair of objects that is - clustered together a positive pair, then as in binary classification the - count of true negatives is in position 00, false negatives in 10, true - positives in 11, and false positives in 01. + A pair confusion matrix with 2 rows and 2 columns. """ n_samples = np.int64(part0.shape[0]) - # Computation using the contingency data + # Compute the contingency matrix contingency = get_contingency_matrix(part0, part1) + n_c = np.ravel(contingency.sum(axis=1)) n_k = np.ravel(contingency.sum(axis=0)) - sum_squares = (contingency**2).sum() + + # Allocate space for the sum of squares result + sum_squares = np.zeros(1, dtype=np.int64) + + # Define the number of threads per block and the number of blocks per grid + threadsperblock = (16, 16) + blockspergrid_x = int(np.ceil(contingency.shape[0] / threadsperblock[0])) + blockspergrid_y = int(np.ceil(contingency.shape[1] / threadsperblock[1])) + blockspergrid = (blockspergrid_x, blockspergrid_y) + + # Launch the CUDA kernel to compute the sum of squares + compute_sum_squares[blockspergrid, threadsperblock](contingency, sum_squares) + + sum_squares = sum_squares[0] + C = np.empty((2, 2), dtype=np.int64) C[1, 1] = sum_squares - n_samples - C[0, 1] = contingency.dot(n_k).sum() - sum_squares - C[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares - C[0, 0] = n_samples**2 - C[0, 1] - C[1, 0] - sum_squares + C[0, 1] = np.dot(contingency, n_k).sum() - sum_squares + C[1, 0] = np.dot(contingency.T, n_c).sum() - sum_squares + C[0, 0] = n_samples ** 2 - C[0, 1] - C[1, 0] - sum_squares + return C @@ -236,4 +258,6 @@ def print_device_info(): part0 = np.array([0, 0, 1, 1, 2, 2]) part1 = np.array([1, 0, 2, 1, 0, 2]) cont_matrix = get_contingency_matrix(part0, part1) - print(cont_matrix) \ No newline at end of file + print(cont_matrix) + + _test_ari() diff --git a/tests/gpu/test_sklearn_metrics.py b/tests/gpu/test_sklearn_metrics.py index d81ed124..0a59b65c 100644 --- a/tests/gpu/test_sklearn_metrics.py +++ b/tests/gpu/test_sklearn_metrics.py @@ -42,88 +42,88 @@ def test_get_contingency_matrix_k0_lesser_k1(): np.testing.assert_array_equal(observed_mat, expected_mat) -# -# def test_get_pair_confusion_matrix_k0_equal_k1(): -# part0 = np.array([0, 0, 1, 1, 2, 2]) -# part1 = np.array([0, 1, 0, 2, 1, 2]) -# -# expected_mat = np.array([[18, 6], [6, 0]]) -# -# observed_mat = get_pair_confusion_matrix(part0, part1) -# -# np.testing.assert_array_equal(observed_mat, expected_mat) -# -# -# def test_get_pair_confusion_matrix_k0_greater_k1(): -# part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) -# part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) -# -# expected_mat = np.array([[42, 18], [6, 6]]) -# -# observed_mat = get_pair_confusion_matrix(part0, part1) -# -# np.testing.assert_array_equal(observed_mat, expected_mat) -# -# -# def test_adjusted_rand_index_manual_random_partitions_same_k(): -# part0 = np.array([0, 0, 1, 1, 2, 2]) -# part1 = np.array([0, 1, 0, 2, 1, 2]) -# -# expected_ari = -0.25 -# -# observed_ari = adjusted_rand_index(part0, part1) -# observed_ari_symm = adjusted_rand_index(part1, part0) -# -# assert observed_ari == observed_ari_symm -# assert expected_ari == observed_ari -# -# -# def test_adjusted_rand_index_manual_perfect_match(): -# part0 = np.array([0, 0, 1, 1, 2, 2]) -# part1 = np.array([2, 2, 3, 3, 4, 4]) -# -# expected_ari = 1.0 -# -# observed_ari = adjusted_rand_index(part0, part1) -# observed_ari_symm = adjusted_rand_index(part1, part0) -# -# assert observed_ari == observed_ari_symm -# assert expected_ari == observed_ari -# -# -# def test_adjusted_rand_index_random_partitions_same_k(): -# maxk0 = 2 -# maxk1 = maxk0 -# n = 100 -# -# part0 = np.random.randint(0, maxk0 + 1, n) -# part1 = np.random.randint(0, maxk1 + 1, n) -# -# # warning: the sklearn's ari implementation can overflow in older versions -# # when n is large -# expected_ari = sklearn_ari(part0, part1) -# -# observed_ari = adjusted_rand_index(part0, part1) -# observed_ari_symm = adjusted_rand_index(part1, part0) -# -# assert observed_ari == observed_ari_symm -# assert expected_ari == observed_ari -# -# -# def test_adjusted_rand_index_random_partitions_k0_greater_k1(): -# maxk0 = 5 -# maxk1 = 3 -# n = 100 -# -# part0 = np.random.randint(0, maxk0 + 1, n) -# part1 = np.random.randint(0, maxk1 + 1, n) -# -# # warning: the sklearn's ari implementation can overflow in older versions -# # when n is large -# expected_ari = sklearn_ari(part0, part1) -# -# observed_ari = adjusted_rand_index(part0, part1) -# observed_ari_symm = adjusted_rand_index(part1, part0) -# -# assert observed_ari == observed_ari_symm -# assert expected_ari == observed_ari + +def test_get_pair_confusion_matrix_k0_equal_k1(): + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([0, 1, 0, 2, 1, 2]) + + expected_mat = np.array([[18, 6], [6, 0]]) + + observed_mat = get_pair_confusion_matrix(part0, part1) + + np.testing.assert_array_equal(observed_mat, expected_mat) + + +def test_get_pair_confusion_matrix_k0_greater_k1(): + part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) + part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) + + expected_mat = np.array([[42, 18], [6, 6]]) + + observed_mat = get_pair_confusion_matrix(part0, part1) + + np.testing.assert_array_equal(observed_mat, expected_mat) + + +def test_adjusted_rand_index_manual_random_partitions_same_k(): + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([0, 1, 0, 2, 1, 2]) + + expected_ari = -0.25 + + observed_ari = adjusted_rand_index(part0, part1) + observed_ari_symm = adjusted_rand_index(part1, part0) + + assert observed_ari == observed_ari_symm + assert expected_ari == observed_ari + + +def test_adjusted_rand_index_manual_perfect_match(): + part0 = np.array([0, 0, 1, 1, 2, 2]) + part1 = np.array([2, 2, 3, 3, 4, 4]) + + expected_ari = 1.0 + + observed_ari = adjusted_rand_index(part0, part1) + observed_ari_symm = adjusted_rand_index(part1, part0) + + assert observed_ari == observed_ari_symm + assert expected_ari == observed_ari + + +def test_adjusted_rand_index_random_partitions_same_k(): + maxk0 = 2 + maxk1 = maxk0 + n = 100 + + part0 = np.random.randint(0, maxk0 + 1, n) + part1 = np.random.randint(0, maxk1 + 1, n) + + # warning: the sklearn's ari implementation can overflow in older versions + # when n is large + expected_ari = sklearn_ari(part0, part1) + + observed_ari = adjusted_rand_index(part0, part1) + observed_ari_symm = adjusted_rand_index(part1, part0) + + assert observed_ari == observed_ari_symm + assert expected_ari == observed_ari + + +def test_adjusted_rand_index_random_partitions_k0_greater_k1(): + maxk0 = 5 + maxk1 = 3 + n = 100 + + part0 = np.random.randint(0, maxk0 + 1, n) + part1 = np.random.randint(0, maxk1 + 1, n) + + # warning: the sklearn's ari implementation can overflow in older versions + # when n is large + expected_ari = sklearn_ari(part0, part1) + + observed_ari = adjusted_rand_index(part0, part1) + observed_ari_symm = adjusted_rand_index(part1, part0) + + assert observed_ari == observed_ari_symm + assert expected_ari == observed_ari From b8b50a2110ca4cecb95f299dfd61a03d03f3819a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 3 Jun 2024 20:53:46 -0600 Subject: [PATCH 006/134] Clean up code --- libs/ccc/coef/impl.py | 2 +- libs/ccc/sklearn/metrics_gpu.py | 217 ++++++++------- libs/ccc/sklearn/metrics_gpu2.py | 263 ------------------ ...metrics.py => test_sklearn_metrics_gpu.py} | 2 +- 4 files changed, 122 insertions(+), 362 deletions(-) delete mode 100644 libs/ccc/sklearn/metrics_gpu2.py rename tests/gpu/{test_sklearn_metrics.py => test_sklearn_metrics_gpu.py} (98%) diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index 18532990..abfc74a8 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -13,7 +13,7 @@ from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.sklearn.metrics import adjusted_rand_index as ari +from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor diff --git a/libs/ccc/sklearn/metrics_gpu.py b/libs/ccc/sklearn/metrics_gpu.py index 86812677..ad2671cd 100644 --- a/libs/ccc/sklearn/metrics_gpu.py +++ b/libs/ccc/sklearn/metrics_gpu.py @@ -1,7 +1,3 @@ -import numpy as np -import pandas as pd -from numba import cuda - """ Contains implementations of different metrics in sklearn but optimized for numba. @@ -43,84 +39,61 @@ from numba import njit from numba import cuda + @cuda.jit -def get_contingency_matrix(random_feature1_device , random_feature2_device, part0_unique_device, part1_unique_device, cont_mat_device, part1_k_device, part1_j_device, part0_i_device): +def compute_sum_squares(contingency, result): """ - Given two clustering partitions with k0 and k1 number of clusters each, it - returns a contingency matrix with k0 rows and k1 columns. It's an implementation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, - but the code is not based on their implementation. + CUDA kernel to compute the sum of squares of the contingency matrix elements. + Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - Returns: - A contingency matrix with k0 (number of clusters in part0) rows and k1 - (number of clusters in part1) columns. Each cell ij represents the - number of objects grouped in cluster i (in part0) and cluster j (in - part1). + contingency: The contingency matrix. + result: The output array to store the sum of squares. """ - - #Creating the grid - #x, y = cuda.grid(2) - tx = cuda.threadIdx.x - ty = cuda.threadIdx.y - bx = cuda.blockIdx.x - by = cuda.blockIdx.y - bw = cuda.blockDim.x - bh = cuda.blockDim.y - i = tx + bx * bw - j = ty + by * bh - - - - - #part0_unique = np.unique(array1) - #part1_unique = np.unique(array2) - #cont_mat = np.zeros((len(part0_unique), len(part1_unique))) - - if i < M: - part0_k_device = part0_unique_device[i] - if j < N: - part1_k_device = part1_unique_device[j] - #cuda.atomic.compare_and_swap_element(part0_i_device , - part0_i_device = random_feature1_device == part0_k_device - part1_j_device = random_feature2_device == part1_k_device - cont_mat_device[i, j] = np.sum(part0_i_device & part1_j_device) - - return cont_mat_device - -@njit(cache=True, nogil=True) + i, j = cuda.grid(2) + + if i < contingency.shape[0] and j < contingency.shape[1]: + cuda.atomic.add(result, 0, contingency[i, j] ** 2) + + def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: """ - Returns the pair confusion matrix from two clustering partitions. It is an - implemenetation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html - The code is based on the sklearn implementation. See copyright notice at the - top of this file. + Returns the pair confusion matrix from two clustering partitions using CUDA. Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. + part0: A 1D array with cluster assignments for n objects. + part1: A 1D array with cluster assignments for n objects. Returns: - A pair confusion matrix with 2 rows and 2 columns. From sklearn's - pair_confusion_matrix docstring: considering a pair of objects that is - clustered together a positive pair, then as in binary classification the - count of true negatives is in position 00, false negatives in 10, true - positives in 11, and false positives in 01. + A pair confusion matrix with 2 rows and 2 columns. """ n_samples = np.int64(part0.shape[0]) - # Computation using the contingency data + # Compute the contingency matrix contingency = get_contingency_matrix(part0, part1) + n_c = np.ravel(contingency.sum(axis=1)) n_k = np.ravel(contingency.sum(axis=0)) - sum_squares = (contingency**2).sum() + + # Allocate space for the sum of squares result + sum_squares = np.zeros(1, dtype=np.int64) + + # Define the number of threads per block and the number of blocks per grid + threadsperblock = (16, 16) + blockspergrid_x = int(np.ceil(contingency.shape[0] / threadsperblock[0])) + blockspergrid_y = int(np.ceil(contingency.shape[1] / threadsperblock[1])) + blockspergrid = (blockspergrid_x, blockspergrid_y) + + # Launch the CUDA kernel to compute the sum of squares + compute_sum_squares[blockspergrid, threadsperblock](contingency, sum_squares) + + sum_squares = sum_squares[0] + C = np.empty((2, 2), dtype=np.int64) C[1, 1] = sum_squares - n_samples - C[0, 1] = contingency.dot(n_k).sum() - sum_squares - C[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares - C[0, 0] = n_samples**2 - C[0, 1] - C[1, 0] - sum_squares + C[0, 1] = np.dot(contingency, n_k).sum() - sum_squares + C[1, 0] = np.dot(contingency.T, n_c).sum() - sum_squares + C[0, 0] = n_samples ** 2 - C[0, 1] - C[1, 0] - sum_squares + return C @@ -152,43 +125,93 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: if fn == 0 and fp == 0: return 1.0 - return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) -if __name__ == '__main__': - - # Arrays - random_feature1 = np.random.rand(1000).astype('f') - random_feature2 = np.random.rand(1000).astype('f') - - # Processing the unique arrays: - part0_unique = np.unique(random_feature1) - part1_unique = np.unique(random_feature2) - cont_mat = np.zeros((len(part0_unique), len(part1_unique))) - part1_k = np.ones(1, dtype=np.float64) - part1_j = np.ones(1, dtype=np.float64) - part0_i = np.ones(1, dtype=np.float64) - # Getting other important parts of for the GPU setting: - threadsperblock = (128, 128) - M = part0_unique.shape[0] - N = part1_unique.shape[0] - blockspergrid_x = M + (threadsperblock[0] - 1) // threadsperblock[0] - blockspergrid_y = N + (threadsperblock[1] - 1) // threadsperblock[1] +@cuda.jit +def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): + """ + CUDA kernel to compute the contingency matrix. + + Args: + part0: 1D array with cluster assignments for n objects. + part1: 1D array with cluster assignments for n objects. + part0_unique: Unique cluster labels in part0. + part1_unique: Unique cluster labels in part1. + cont_mat: The output contingency matrix. + + Each thread computes a single element of the contingency matrix. + """ + i, j = cuda.grid(2) # Get the thread indices in the grid + + # Check if the thread indices are within the bounds of the unique clusters + if i < len(part0_unique) and j < len(part1_unique): + part0_k = part0_unique[i] # Cluster label in part0 + part1_k = part1_unique[j] # Cluster label in part1 + + count = 0 # Initialize the count for this element + for idx in range(len(part0)): + # Count the number of objects in both clusters i and j + if part0[idx] == part0_k and part1[idx] == part1_k: + count += 1 + cont_mat[i, j] = count # Store the result in the contingency matrix + + +def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + """ + Compute the contingency matrix for two clustering partitions using CUDA. + + Args: + part0: 1D array with cluster assignments for n objects. + part1: 1D array with cluster assignments for n objects. + + Returns: + A contingency matrix with k0 rows and k1 columns, where k0 is the number + of clusters in part0 and k1 is the number of clusters in part1. Each cell + (i, j) represents the number of objects in cluster i (part0) and cluster j (part1). + """ + part0_unique = np.unique(part0) # Find unique clusters in part0 + part1_unique = np.unique(part1) # Find unique clusters in part1 + + cont_mat = np.zeros((len(part0_unique), len(part1_unique)), dtype=np.int32) # Initialize the contingency matrix + + # Define the number of threads per block and the number of blocks per grid + threadsperblock = (16, 16) + blockspergrid_x = int(np.ceil(len(part0_unique) / threadsperblock[0])) + blockspergrid_y = int(np.ceil(len(part1_unique) / threadsperblock[1])) blockspergrid = (blockspergrid_x, blockspergrid_y) - #Senign them to the GPU: - random_feature1_device = cuda.to_device(random_feature1) - random_feature2_device = cuda.to_device(random_feature2) - part0_unique_device = cuda.to_device(part0_unique) - part1_unique_device = cuda.to_device(part1_unique) - cont_mat_device = cuda.to_device(cont_mat) - part1_k_device = cuda.to_device(part1_k) - part1_j_device = cuda.to_device(part1_j) - part0_i_device = cuda.to_device(part0_i) - print("checkpoint") - # Calling the get_contingency - out_device = get_contingency_matrix[blockspergrid, threadsperblock](random_feature1_device , random_feature2_device, part0_unique_device, part1_unique_device, cont_mat_device, part1_k_device, part1_j_device, part0_i_device) - print(out_device) + # Launch the CUDA kernel to compute the contingency matrix + compute_contingency_matrix[blockspergrid, threadsperblock](part0, part1, part0_unique, part1_unique, cont_mat) + + return cont_mat + + +def print_device_info(): + # Get the current device + device = cuda.get_current_device() + print(dir(device)) + # Print device information + print("Device Information:") + print(f"Device ID: {device.id}") + print(f"Name: {device.name}") + # print(f"Total Memory: {device.total_memory / (1024 ** 3):.2f} GB") + print(f"Multiprocessor Count: {device.MULTIPROCESSOR_COUNT}") + print(f"Max Threads per Block: {device.MAX_THREADS_PER_BLOCK}") + # print(f"Max Threads per Multiprocessor: {device.MAX_THREADS_PER_MULTIPROCESSOR}") + print(f"Max Block Dim X: {device.MAX_BLOCK_DIM_X}") + print(f"Max Block Dim Y: {device.MAX_BLOCK_DIM_Y}") + print(f"Max Block Dim Z: {device.MAX_BLOCK_DIM_Z}") + print(f"Max Grid Dim X: {device.MAX_GRID_DIM_X}") + print(f"Max Grid Dim Y: {device.MAX_GRID_DIM_Y}") + print(f"Max Grid Dim Z: {device.MAX_GRID_DIM_Z}") + print(f"Warp Size: {device.WARP_SIZE}") + print(f"Compute Capability: {device.compute_capability}") + print(f"Concurrent Kernels: {device.CONCURRENT_KERNELS}") + print(f"PCI Bus ID: {device.PCI_BUS_ID}") + print(f"PCI Device ID: {device.PCI_DEVICE_ID}") + print(f"PCI Domain ID: {device.PCI_DOMAIN_ID}") +if __name__ == '__main__': + print_device_info() diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py deleted file mode 100644 index af31a6a0..00000000 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ /dev/null @@ -1,263 +0,0 @@ -""" -Contains implementations of different metrics in sklearn but optimized for numba. - -Some code (indicated in each function) is based on scikit-learn's code base -(https://github.com/scikit-learn), for which the copyright notice and license -are shown below. - -BSD 3-Clause License - -Copyright (c) 2007-2021 The scikit-learn developers. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" -import numpy as np -from numba import njit -from numba import cuda - - -@cuda.jit -def compute_sum_squares(contingency, result): - """ - CUDA kernel to compute the sum of squares of the contingency matrix elements. - - Args: - contingency: The contingency matrix. - result: The output array to store the sum of squares. - """ - i, j = cuda.grid(2) - - if i < contingency.shape[0] and j < contingency.shape[1]: - cuda.atomic.add(result, 0, contingency[i, j] ** 2) - - -def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: - """ - Returns the pair confusion matrix from two clustering partitions using CUDA. - - Args: - part0: A 1D array with cluster assignments for n objects. - part1: A 1D array with cluster assignments for n objects. - - Returns: - A pair confusion matrix with 2 rows and 2 columns. - """ - n_samples = np.int64(part0.shape[0]) - - # Compute the contingency matrix - contingency = get_contingency_matrix(part0, part1) - - n_c = np.ravel(contingency.sum(axis=1)) - n_k = np.ravel(contingency.sum(axis=0)) - - # Allocate space for the sum of squares result - sum_squares = np.zeros(1, dtype=np.int64) - - # Define the number of threads per block and the number of blocks per grid - threadsperblock = (16, 16) - blockspergrid_x = int(np.ceil(contingency.shape[0] / threadsperblock[0])) - blockspergrid_y = int(np.ceil(contingency.shape[1] / threadsperblock[1])) - blockspergrid = (blockspergrid_x, blockspergrid_y) - - # Launch the CUDA kernel to compute the sum of squares - compute_sum_squares[blockspergrid, threadsperblock](contingency, sum_squares) - - sum_squares = sum_squares[0] - - C = np.empty((2, 2), dtype=np.int64) - C[1, 1] = sum_squares - n_samples - C[0, 1] = np.dot(contingency, n_k).sum() - sum_squares - C[1, 0] = np.dot(contingency.T, n_c).sum() - sum_squares - C[0, 0] = n_samples ** 2 - C[0, 1] - C[1, 0] - sum_squares - - return C - - -def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: - """ - Computes the adjusted Rand index (ARI) between two clustering partitions. - The code is based on the sklearn implementation here: - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html - See copyright notice at the top of this file. - - This function should not be compiled with numba, since it depends on - arbitrarily large interger variable (supported by Python) to correctly - compute the ARI in large partitions. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A number representing the adjusted Rand index between two clustering - partitions. This number is between something around 0 (partitions do not - match; it could be negative in some cases) and 1.0 (perfect match). - """ - (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) - # convert to Python integer types, to avoid overflow or underflow - tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) - - # Special cases: empty data or full agreement - if fn == 0 and fp == 0: - return 1.0 - - return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) - - -@cuda.jit -def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): - """ - CUDA kernel to compute the contingency matrix. - - Args: - part0: 1D array with cluster assignments for n objects. - part1: 1D array with cluster assignments for n objects. - part0_unique: Unique cluster labels in part0. - part1_unique: Unique cluster labels in part1. - cont_mat: The output contingency matrix. - - Each thread computes a single element of the contingency matrix. - """ - i, j = cuda.grid(2) # Get the thread indices in the grid - - # Check if the thread indices are within the bounds of the unique clusters - if i < len(part0_unique) and j < len(part1_unique): - part0_k = part0_unique[i] # Cluster label in part0 - part1_k = part1_unique[j] # Cluster label in part1 - - count = 0 # Initialize the count for this element - for idx in range(len(part0)): - # Count the number of objects in both clusters i and j - if part0[idx] == part0_k and part1[idx] == part1_k: - count += 1 - cont_mat[i, j] = count # Store the result in the contingency matrix - - -def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: - """ - Compute the contingency matrix for two clustering partitions using CUDA. - - Args: - part0: 1D array with cluster assignments for n objects. - part1: 1D array with cluster assignments for n objects. - - Returns: - A contingency matrix with k0 rows and k1 columns, where k0 is the number - of clusters in part0 and k1 is the number of clusters in part1. Each cell - (i, j) represents the number of objects in cluster i (part0) and cluster j (part1). - """ - part0_unique = np.unique(part0) # Find unique clusters in part0 - part1_unique = np.unique(part1) # Find unique clusters in part1 - - cont_mat = np.zeros((len(part0_unique), len(part1_unique)), dtype=np.int32) # Initialize the contingency matrix - - # Define the number of threads per block and the number of blocks per grid - threadsperblock = (16, 16) - blockspergrid_x = int(np.ceil(len(part0_unique) / threadsperblock[0])) - blockspergrid_y = int(np.ceil(len(part1_unique) / threadsperblock[1])) - blockspergrid = (blockspergrid_x, blockspergrid_y) - - # Launch the CUDA kernel to compute the contingency matrix - compute_contingency_matrix[blockspergrid, threadsperblock](part0, part1, part0_unique, part1_unique, cont_mat) - - return cont_mat - - -@cuda.jit -def increment_by_one(an_array): - # Thread id in a 1D block - tx = cuda.threadIdx.x - # Block id in a 1D grid - ty = cuda.blockIdx.x - # Block width, i.e. number of threads per block - bw = cuda.blockDim.x - # Compute flattened index inside the array - pos = tx + ty * bw - if pos < an_array.size: # Check array boundaries - an_array[pos] += 1 - -def _test_cuda1(): - # Initialize the array - data = np.ones(64) - print(f"Data before kernel call: {data}") - # Set the number of threads in a block - threads_per_block = 32 - # Calculate the number of thread blocks in the grid - blocks_per_grid = (data.size + (threads_per_block - 1)) // threads_per_block - # Call the kernel - increment_by_one[blocks_per_grid, threads_per_block](data) - print(f"Data after kernel call: {data}") - return - - -def _test_ari(): - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([0, 0, 1, 1, 2, 2]) - print(adjusted_rand_index(part0, part1)) # 1.0 - - part0 = np.array([0, 0, 1, 1]) - part1 = np.array([0, 0, 1, 2]) - print(adjusted_rand_index(part0, part1)) # 0.57 - - part0 = np.array([0, 0, 1, 1]) - part1 = np.array([0, 1, 0, 1]) - print(adjusted_rand_index(part0, part1)) # -0.5 - - -def print_device_info(): - # Get the current device - device = cuda.get_current_device() - print(dir(device)) - # Print device information - print("Device Information:") - print(f"Device ID: {device.id}") - print(f"Name: {device.name}") - # print(f"Total Memory: {device.total_memory / (1024 ** 3):.2f} GB") - print(f"Multiprocessor Count: {device.MULTIPROCESSOR_COUNT}") - print(f"Max Threads per Block: {device.MAX_THREADS_PER_BLOCK}") - # print(f"Max Threads per Multiprocessor: {device.MAX_THREADS_PER_MULTIPROCESSOR}") - print(f"Max Block Dim X: {device.MAX_BLOCK_DIM_X}") - print(f"Max Block Dim Y: {device.MAX_BLOCK_DIM_Y}") - print(f"Max Block Dim Z: {device.MAX_BLOCK_DIM_Z}") - print(f"Max Grid Dim X: {device.MAX_GRID_DIM_X}") - print(f"Max Grid Dim Y: {device.MAX_GRID_DIM_Y}") - print(f"Max Grid Dim Z: {device.MAX_GRID_DIM_Z}") - print(f"Warp Size: {device.WARP_SIZE}") - print(f"Compute Capability: {device.compute_capability}") - print(f"Concurrent Kernels: {device.CONCURRENT_KERNELS}") - print(f"PCI Bus ID: {device.PCI_BUS_ID}") - print(f"PCI Device ID: {device.PCI_DEVICE_ID}") - print(f"PCI Domain ID: {device.PCI_DOMAIN_ID}") - - -if __name__ == '__main__': - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([1, 0, 2, 1, 0, 2]) - cont_matrix = get_contingency_matrix(part0, part1) - print(cont_matrix) - - _test_ari() diff --git a/tests/gpu/test_sklearn_metrics.py b/tests/gpu/test_sklearn_metrics_gpu.py similarity index 98% rename from tests/gpu/test_sklearn_metrics.py rename to tests/gpu/test_sklearn_metrics_gpu.py index 0a59b65c..8d81c19f 100644 --- a/tests/gpu/test_sklearn_metrics.py +++ b/tests/gpu/test_sklearn_metrics_gpu.py @@ -1,7 +1,7 @@ import numpy as np from sklearn.metrics import adjusted_rand_score as sklearn_ari -from ccc.sklearn.metrics_gpu2 import ( +from ccc.sklearn.metrics_gpu import ( adjusted_rand_index, get_contingency_matrix, get_pair_confusion_matrix, From ef1d075f5136c11a6aaeb542b4cf71668bd7d933 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 3 Jun 2024 21:54:31 -0600 Subject: [PATCH 007/134] [bench]: Add reference CPU benchmarks --- libs/ccc/coef/impl.py | 2 +- .../09-cdist_parts_v04.ipynb | 1604 +++++++++++++++++ .../09-n_samples_large_100000.txt | 26 + .../09-n_samples_large_50000.txt | 26 + .../09-n_samples_small_100.txt} | 18 +- .../09-n_samples_small_1000.txt | 26 + .../09-n_samples_small_50.txt} | 20 +- .../09-n_samples_small_500.txt | 26 + .../10-cdist_parts_v04.ipynb} | 996 +++++----- .../10-n_samples_large_100000.txt | 26 + .../10-n_samples_large_50000.txt | 26 + .../10-n_samples_small_100.txt | 26 + .../10-n_samples_small_1000.txt | 26 + .../10-n_samples_small_50.txt | 26 + .../10-n_samples_small_500.txt | 26 + .../10-n_samples_large_100000.txt | 26 - .../10-n_samples_large_50000.txt | 26 - .../10-n_samples_small_1000.txt | 26 - .../10-n_samples_small_500.txt | 26 - nbs/others/10_gpu_ari_profiling/README.md | 23 + 20 files changed, 2398 insertions(+), 629 deletions(-) create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt rename nbs/others/10_gpu_ari_profiling/{10-n_samples_small_100.txt => 00_cpu_version_ref/09-n_samples_small_100.txt} (66%) create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt rename nbs/others/10_gpu_ari_profiling/{01-n_samples_small_50.txt => 00_cpu_version_ref/09-n_samples_small_50.txt} (77%) create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt rename nbs/others/10_gpu_ari_profiling/{01-compare_cuda_get_contingency_matrix.ipynb => 00_cpu_version_ref/10-cdist_parts_v04.ipynb} (64%) create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt create mode 100644 nbs/others/10_gpu_ari_profiling/README.md diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index abfc74a8..18532990 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -13,7 +13,7 @@ from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari +from ccc.sklearn.metrics import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb new file mode 100644 index 00000000..6b6fe417 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb @@ -0,0 +1,1604 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Exactly the same code as in `08`, but here I run the notebook in a different machine (desktop)." + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./libs/ccc/__pycache__\n", + "./libs/ccc/sklearn/__pycache__\n", + "./libs/ccc/scipy/__pycache__\n", + "./libs/ccc/coef/__pycache__\n", + "./libs/ccc/utils/__pycache__\n", + "./libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from ccc.coef import ccc" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.416790Z", + "iopub.status.busy": "2021-12-02T04:34:11.416353Z", + "iopub.status.idle": "2021-12-02T04:34:11.418488Z", + "shell.execute_reply": "2021-12-02T04:34:11.418030Z" + }, + "papermill": { + "duration": 0.098372, + "end_time": "2021-12-02T04:34:11.418578", + "exception": false, + "start_time": "2021-12-02T04:34:11.320206", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.418916Z", + "iopub.status.busy": "2021-12-02T04:34:21.418477Z", + "iopub.status.idle": "2021-12-02T04:34:21.420269Z", + "shell.execute_reply": "2021-12-02T04:34:21.420631Z" + }, + "papermill": { + "duration": 0.098786, + "end_time": "2021-12-02T04:34:21.420745", + "exception": false, + "start_time": "2021-12-02T04:34:21.321959", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.32 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6144 function calls in 0.011 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.011 0.011 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.011 0.011 :1()\n", + " 1 0.000 0.000 0.011 0.011 454136789.py:1(func)\n", + " 10 0.000 0.000 0.011 0.001 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.007 0.001 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.007 0.001 impl.py:485(cdist_func)\n", + " 140 0.000 0.000 0.007 0.000 threading.py:280(wait)\n", + " 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel)\n", + " 550 0.007 0.000 0.007 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 70 0.000 0.000 0.006 0.000 threading.py:563(wait)\n", + " 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed)\n", + " 70 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", + " 70 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", + " 70 0.000 0.000 0.001 0.000 _base.py:418(result)\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 70 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_50.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "ede7a328-bad3-40a2-a179-1148a3229620", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.917755Z", + "iopub.status.busy": "2021-12-02T04:34:34.917311Z", + "iopub.status.idle": "2021-12-02T04:34:34.919529Z", + "shell.execute_reply": "2021-12-02T04:34:34.919083Z" + }, + "papermill": { + "duration": 0.099235, + "end_time": "2021-12-02T04:34:34.919621", + "exception": false, + "start_time": "2021-12-02T04:34:34.820386", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.4 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_100.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.019 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.019 0.019 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.019 0.019 :1()\n", + " 1 0.000 0.000 0.019 0.019 454136789.py:1(func)\n", + " 10 0.000 0.000 0.019 0.002 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.015 0.000 threading.py:280(wait)\n", + " 10 0.000 0.000 0.015 0.001 impl.py:492(compute_coef)\n", + " 790 0.015 0.000 0.015 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.015 0.001 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.015 0.001 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.013 0.000 threading.py:563(wait)\n", + " 100 0.000 0.000 0.002 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.002 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_100.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "24c352bd", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.010047Z", + "iopub.status.busy": "2021-12-02T04:34:39.009506Z", + "iopub.status.idle": "2021-12-02T04:34:39.011025Z", + "shell.execute_reply": "2021-12-02T04:34:39.011367Z" + }, + "papermill": { + "duration": 0.100056, + "end_time": "2021-12-02T04:34:39.011481", + "exception": false, + "start_time": "2021-12-02T04:34:38.911425", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33 ms ± 349 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_500.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.034 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.034 0.034 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.034 0.034 :1()\n", + " 1 0.000 0.000 0.034 0.034 454136789.py:1(func)\n", + " 10 0.001 0.000 0.034 0.003 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.030 0.000 threading.py:280(wait)\n", + " 790 0.030 0.000 0.030 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.026 0.003 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.025 0.003 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.025 0.003 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.024 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.024 0.000 threading.py:563(wait)\n", + " 100 0.000 0.000 0.006 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.006 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_500.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "d907f1d7", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.311356Z", + "iopub.status.busy": "2021-12-02T04:34:44.310862Z", + "iopub.status.idle": "2021-12-02T04:34:44.313206Z", + "shell.execute_reply": "2021-12-02T04:34:44.312818Z" + }, + "papermill": { + "duration": 0.103411, + "end_time": "2021-12-02T04:34:44.313301", + "exception": false, + "start_time": "2021-12-02T04:34:44.209890", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "54 ms ± 514 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_1000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.055 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.055 0.055 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.055 0.055 :1()\n", + " 1 0.000 0.000 0.055 0.055 454136789.py:1(func)\n", + " 10 0.000 0.000 0.055 0.005 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.050 0.000 threading.py:280(wait)\n", + " 790 0.050 0.000 0.050 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.042 0.004 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.042 0.004 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.042 0.004 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.041 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.040 0.000 threading.py:563(wait)\n", + " 100 0.000 0.000 0.010 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.010 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "15cb532e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.206165Z", + "iopub.status.busy": "2021-12-02T04:34:52.205716Z", + "iopub.status.idle": "2021-12-02T04:34:52.207991Z", + "shell.execute_reply": "2021-12-02T04:34:52.207535Z" + }, + "papermill": { + "duration": 0.10765, + "end_time": "2021-12-02T04:34:52.208087", + "exception": false, + "start_time": "2021-12-02T04:34:52.100437", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.99 s ± 6.77 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_50000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 2.990 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 2.990 2.990 {built-in method builtins.exec}\n", + " 1 0.000 0.000 2.990 2.990 :1()\n", + " 1 0.000 0.000 2.990 2.990 454136789.py:1(func)\n", + " 10 0.005 0.000 2.989 0.299 impl.py:307(ccc)\n", + " 200 0.001 0.000 2.965 0.015 threading.py:280(wait)\n", + " 790 2.964 0.004 2.964 0.004 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 2.122 0.212 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 2.121 0.212 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 2.121 0.212 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 2.114 0.021 _base.py:201(as_completed)\n", + " 100 0.000 0.000 2.113 0.021 threading.py:563(wait)\n", + " 100 0.000 0.000 0.853 0.009 _base.py:418(result)\n", + " 20 0.000 0.000 0.852 0.043 _base.py:602(result_iterator)\n", + " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", + " 10 0.003 0.000 0.005 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_50000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "d408b318", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.682735Z", + "iopub.status.busy": "2021-12-02T04:35:31.682239Z", + "iopub.status.idle": "2021-12-02T04:35:31.684357Z", + "shell.execute_reply": "2021-12-02T04:35:31.683794Z" + }, + "papermill": { + "duration": 0.10675, + "end_time": "2021-12-02T04:35:31.684477", + "exception": false, + "start_time": "2021-12-02T04:35:31.577727", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func():\n", + " for i in range(N_REPS):\n", + " ccc(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.12 s ± 70.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_100000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 6.054 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 6.054 6.054 {built-in method builtins.exec}\n", + " 1 0.000 0.000 6.054 6.054 :1()\n", + " 1 0.017 0.017 6.054 6.054 454136789.py:1(func)\n", + " 10 0.008 0.001 6.037 0.604 impl.py:307(ccc)\n", + " 200 0.001 0.000 6.003 0.030 threading.py:280(wait)\n", + " 790 6.002 0.008 6.002 0.008 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 4.230 0.423 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 4.229 0.423 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 4.228 0.423 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 4.220 0.042 _base.py:201(as_completed)\n", + " 100 0.000 0.000 4.219 0.042 threading.py:563(wait)\n", + " 100 0.000 0.000 1.784 0.018 _base.py:418(result)\n", + " 20 0.000 0.000 1.784 0.089 _base.py:602(result_iterator)\n", + " 50 0.012 0.000 0.012 0.000 {built-in method numpy.zeros}\n", + " 10 0.005 0.000 0.006 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt new file mode 100644 index 00000000..3bff5d32 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 6.054 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 6.054 6.054 {built-in method builtins.exec} + 1 0.000 0.000 6.054 6.054 :1() + 1 0.017 0.017 6.054 6.054 454136789.py:1(func) + 10 0.008 0.001 6.037 0.604 impl.py:307(ccc) + 200 0.001 0.000 6.003 0.030 threading.py:280(wait) + 790 6.002 0.008 6.002 0.008 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 4.230 0.423 impl.py:492(compute_coef) + 10 0.000 0.000 4.229 0.423 impl.py:485(cdist_func) + 10 0.001 0.000 4.228 0.423 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 4.220 0.042 _base.py:201(as_completed) + 100 0.000 0.000 4.219 0.042 threading.py:563(wait) + 100 0.000 0.000 1.784 0.018 _base.py:418(result) + 20 0.000 0.000 1.784 0.089 _base.py:602(result_iterator) + 50 0.012 0.000 0.012 0.000 {built-in method numpy.zeros} + 10 0.005 0.000 0.006 0.001 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt new file mode 100644 index 00000000..85084572 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 2.990 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 2.990 2.990 {built-in method builtins.exec} + 1 0.000 0.000 2.990 2.990 :1() + 1 0.000 0.000 2.990 2.990 454136789.py:1(func) + 10 0.005 0.000 2.989 0.299 impl.py:307(ccc) + 200 0.001 0.000 2.965 0.015 threading.py:280(wait) + 790 2.964 0.004 2.964 0.004 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 2.122 0.212 impl.py:492(compute_coef) + 10 0.000 0.000 2.121 0.212 impl.py:485(cdist_func) + 10 0.001 0.000 2.121 0.212 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 2.114 0.021 _base.py:201(as_completed) + 100 0.000 0.000 2.113 0.021 threading.py:563(wait) + 100 0.000 0.000 0.853 0.009 _base.py:418(result) + 20 0.000 0.000 0.852 0.043 _base.py:602(result_iterator) + 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros} + 10 0.003 0.000 0.005 0.000 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt similarity index 66% rename from nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt rename to nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt index 7fc11aae..49d3b98d 100644 --- a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_100.txt +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt @@ -1,24 +1,24 @@ - 8334 function calls in 0.020 seconds + 8334 function calls in 0.019 seconds Ordered by: cumulative time List reduced from 114 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.020 0.020 {built-in method builtins.exec} - 1 0.000 0.000 0.020 0.020 :1() - 1 0.000 0.000 0.020 0.020 837709190.py:1(func) - 10 0.000 0.000 0.020 0.002 impl.py:307(ccc) - 10 0.000 0.000 0.016 0.002 impl.py:492(compute_coef) + 1 0.000 0.000 0.019 0.019 {built-in method builtins.exec} + 1 0.000 0.000 0.019 0.019 :1() + 1 0.000 0.000 0.019 0.019 454136789.py:1(func) + 10 0.000 0.000 0.019 0.002 impl.py:307(ccc) 200 0.000 0.000 0.015 0.000 threading.py:280(wait) - 10 0.000 0.000 0.015 0.002 impl.py:485(cdist_func) - 10 0.000 0.000 0.015 0.002 impl.py:192(cdist_parts_parallel) + 10 0.000 0.000 0.015 0.001 impl.py:492(compute_coef) 790 0.015 0.000 0.015 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.015 0.001 impl.py:485(cdist_func) + 10 0.000 0.000 0.015 0.001 impl.py:192(cdist_parts_parallel) 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed) 100 0.000 0.000 0.013 0.000 threading.py:563(wait) 100 0.000 0.000 0.002 0.000 _base.py:418(result) 20 0.000 0.000 0.002 0.000 _base.py:602(result_iterator) 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) 10 0.000 0.000 0.001 0.000 _base.py:573(map) 10 0.000 0.000 0.001 0.000 _base.py:598() 10 0.000 0.000 0.001 0.000 impl.py:210() diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt new file mode 100644 index 00000000..ec0ba908 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.055 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.055 0.055 {built-in method builtins.exec} + 1 0.000 0.000 0.055 0.055 :1() + 1 0.000 0.000 0.055 0.055 454136789.py:1(func) + 10 0.000 0.000 0.055 0.005 impl.py:307(ccc) + 200 0.000 0.000 0.050 0.000 threading.py:280(wait) + 790 0.050 0.000 0.050 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.042 0.004 impl.py:492(compute_coef) + 10 0.000 0.000 0.042 0.004 impl.py:485(cdist_func) + 10 0.000 0.000 0.042 0.004 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.041 0.000 _base.py:201(as_completed) + 100 0.000 0.000 0.040 0.000 threading.py:563(wait) + 100 0.000 0.000 0.010 0.000 _base.py:418(result) + 20 0.000 0.000 0.010 0.001 _base.py:602(result_iterator) + 100 0.000 0.000 0.002 0.000 thread.py:161(submit) + 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt similarity index 77% rename from nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt rename to nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt index a903ab9e..12a3589f 100644 --- a/nbs/others/10_gpu_ari_profiling/01-n_samples_small_50.txt +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt @@ -1,26 +1,26 @@ - 6144 function calls in 0.010 seconds + 6144 function calls in 0.011 seconds Ordered by: cumulative time List reduced from 114 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.010 0.010 {built-in method builtins.exec} - 1 0.000 0.000 0.010 0.010 :1() - 1 0.000 0.000 0.010 0.010 837709190.py:1(func) - 10 0.000 0.000 0.010 0.001 impl.py:307(ccc) + 1 0.000 0.000 0.011 0.011 {built-in method builtins.exec} + 1 0.000 0.000 0.011 0.011 :1() + 1 0.000 0.000 0.011 0.011 454136789.py:1(func) + 10 0.000 0.000 0.011 0.001 impl.py:307(ccc) 10 0.000 0.000 0.007 0.001 impl.py:492(compute_coef) 10 0.000 0.000 0.007 0.001 impl.py:485(cdist_func) - 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel) 140 0.000 0.000 0.007 0.000 threading.py:280(wait) - 550 0.006 0.000 0.006 0.000 {method 'acquire' of '_thread.lock' objects} - 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed) + 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel) + 550 0.007 0.000 0.007 0.000 {method 'acquire' of '_thread.lock' objects} 70 0.000 0.000 0.006 0.000 threading.py:563(wait) + 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed) 70 0.000 0.000 0.002 0.000 thread.py:161(submit) 70 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 70 0.000 0.000 0.001 0.000 _base.py:418(result) 10 0.000 0.000 0.001 0.000 _base.py:598() + 70 0.000 0.000 0.001 0.000 _base.py:418(result) + 10 0.000 0.000 0.001 0.000 threading.py:880(start) 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator) 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) 70 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt new file mode 100644 index 00000000..7ef6cc27 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.034 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.034 0.034 {built-in method builtins.exec} + 1 0.000 0.000 0.034 0.034 :1() + 1 0.000 0.000 0.034 0.034 454136789.py:1(func) + 10 0.001 0.000 0.034 0.003 impl.py:307(ccc) + 200 0.000 0.000 0.030 0.000 threading.py:280(wait) + 790 0.030 0.000 0.030 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.026 0.003 impl.py:492(compute_coef) + 10 0.000 0.000 0.025 0.003 impl.py:485(cdist_func) + 10 0.000 0.000 0.025 0.003 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.024 0.000 _base.py:201(as_completed) + 100 0.000 0.000 0.024 0.000 threading.py:563(wait) + 100 0.000 0.000 0.006 0.000 _base.py:418(result) + 20 0.000 0.000 0.006 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.002 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.001 0.000 _base.py:573(map) + 10 0.000 0.000 0.001 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb similarity index 64% rename from nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb rename to nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb index 2308d212..3a0d30d4 100644 --- a/nbs/others/10_gpu_ari_profiling/01-compare_cuda_get_contingency_matrix.ipynb +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb @@ -1,16 +1,94 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Description", - "id": "392e118bbc62f138" + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.101131, + "end_time": "2021-12-02T04:36:57.333310", + "exception": false, + "start_time": "2021-12-02T04:36:57.232179", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] }, { + "cell_type": "markdown", + "id": "db88788d", "metadata": {}, + "source": [] + }, + { "cell_type": "markdown", - "source": "Compares two different ccc implementations: one using the fully optimized CPU version of ccc, and the other one using new cuda-implemented `get_contingency_matrix`", - "id": "337633a8-d03e-4509-b89d-f8daee598958" + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.093397, + "end_time": "2021-12-02T04:36:57.520462", + "exception": false, + "start_time": "2021-12-02T04:36:57.427065", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Exactly the same code as in `09`, but here I disable numba." + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.095211, + "end_time": "2021-12-02T04:36:57.716055", + "exception": false, + "start_time": "2021-12-02T04:36:57.620844", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Disable numba" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "20cbd5fd-aeb9-448f-91d9-9ff2d12c9c22", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:57.911147Z", + "iopub.status.busy": "2021-12-02T04:36:57.910632Z", + "iopub.status.idle": "2021-12-02T04:36:57.914417Z", + "shell.execute_reply": "2021-12-02T04:36:57.913918Z" + }, + "papermill": { + "duration": 0.105032, + "end_time": "2021-12-02T04:36:57.914518", + "exception": false, + "start_time": "2021-12-02T04:36:57.809486", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: NUMBA_DISABLE_JIT=1\n" + ] + } + ], + "source": [ + "%env NUMBA_DISABLE_JIT=1" + ] }, { "cell_type": "markdown", @@ -31,6 +109,7 @@ }, { "cell_type": "code", + "execution_count": 2, "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", "metadata": { "execution": { @@ -46,28 +125,24 @@ "start_time": "2021-12-02T04:36:58.202833", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.119048Z", - "start_time": "2024-05-30T17:14:06.816596Z" - } + "tags": [] }, - "source": [ - "!echo ${CODE_DIR}" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\r\n" + "\n" ] } ], - "execution_count": 21 + "source": [ + "!echo ${CODE_DIR}" + ] }, { "cell_type": "code", + "execution_count": 3, "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", "metadata": { "execution": { @@ -83,20 +158,16 @@ "start_time": "2021-12-02T04:36:59.027417", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.411709Z", - "start_time": "2024-05-30T17:14:07.120184Z" - } + "tags": [] }, + "outputs": [], "source": [ "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" - ], - "outputs": [], - "execution_count": 22 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", "metadata": { "execution": { @@ -112,20 +183,16 @@ "start_time": "2021-12-02T04:36:59.868164", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.693673Z", - "start_time": "2024-05-30T17:14:07.412618Z" - } + "tags": [] }, + "outputs": [], "source": [ "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" - ], - "outputs": [], - "execution_count": 23 + ] }, { "cell_type": "code", + "execution_count": 5, "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", "metadata": { "execution": { @@ -141,17 +208,12 @@ "start_time": "2021-12-02T04:37:00.686012", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.974400Z", - "start_time": "2024-05-30T17:14:07.695008Z" - } + "tags": [] }, + "outputs": [], "source": [ "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" - ], - "outputs": [], - "execution_count": 24 + ] }, { "cell_type": "markdown", @@ -172,6 +234,7 @@ }, { "cell_type": "code", + "execution_count": 6, "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", "metadata": { "execution": { @@ -187,19 +250,14 @@ "start_time": "2021-12-02T04:37:01.716317", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.977083Z", - "start_time": "2024-05-30T17:14:07.975252Z" - } + "tags": [] }, + "outputs": [], "source": [ "import numpy as np\n", "\n", "from ccc.coef import ccc" - ], - "outputs": [], - "execution_count": 25 + ] }, { "cell_type": "markdown", @@ -220,6 +278,7 @@ }, { "cell_type": "code", + "execution_count": 7, "id": "c609cefa-f513-4cf8-9573-367744e31c5f", "metadata": { "execution": { @@ -235,20 +294,16 @@ "start_time": "2021-12-02T04:37:02.393609", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.981434Z", - "start_time": "2024-05-30T17:14:07.977494Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_REPS = 10" - ], - "outputs": [], - "execution_count": 26 + ] }, { "cell_type": "code", + "execution_count": 8, "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", "metadata": { "execution": { @@ -264,17 +319,12 @@ "start_time": "2021-12-02T04:37:02.592520", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.984622Z", - "start_time": "2024-05-30T17:14:07.981817Z" - } + "tags": [] }, + "outputs": [], "source": [ "np.random.seed(0)" - ], - "outputs": [], - "execution_count": 27 + ] }, { "cell_type": "markdown", @@ -295,6 +345,7 @@ }, { "cell_type": "code", + "execution_count": 9, "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", "metadata": { "execution": { @@ -310,16 +361,8 @@ "start_time": "2021-12-02T04:37:02.993747", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.989121Z", - "start_time": "2024-05-30T17:14:07.985047Z" - } + "tags": [] }, - "source": [ - "# let numba compile all the code before profiling\n", - "ccc(np.random.rand(10), np.random.rand(10))" - ], "outputs": [ { "data": { @@ -327,12 +370,15 @@ "0.15625" ] }, - "execution_count": 28, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 28 + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] }, { "cell_type": "markdown", @@ -370,6 +416,7 @@ }, { "cell_type": "code", + "execution_count": 10, "id": "68064f0b", "metadata": { "execution": { @@ -385,20 +432,16 @@ "start_time": "2021-12-02T04:37:03.589373", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.990754Z", - "start_time": "2024-05-30T17:14:07.989508Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 50" - ], - "outputs": [], - "execution_count": 29 + ] }, { "cell_type": "code", + "execution_count": 11, "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", "metadata": { "execution": { @@ -414,21 +457,17 @@ "start_time": "2021-12-02T04:37:03.797809", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.993697Z", - "start_time": "2024-05-30T17:14:07.991779Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 30 + ] }, { "cell_type": "code", + "execution_count": 12, "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", "metadata": { "execution": { @@ -444,22 +483,18 @@ "start_time": "2021-12-02T04:37:03.998709", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:07.996579Z", - "start_time": "2024-05-30T17:14:07.994049Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 31 + ] }, { "cell_type": "code", + "execution_count": 13, "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", "metadata": { "execution": { @@ -475,29 +510,25 @@ "start_time": "2021-12-02T04:37:04.199326", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:14.727550Z", - "start_time": "2024-05-30T17:14:07.996952Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "8.2 ms ± 262 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "40.2 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], - "execution_count": 32 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 14, "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", "metadata": { "execution": { @@ -513,27 +544,54 @@ "start_time": "2021-12-02T04:37:16.935253", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:14.741654Z", - "start_time": "2024-05-30T17:14:14.728385Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 01-n_samples_small_50.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", - "*** Profile printout saved to text file '01-n_samples_small_50.txt'. \n" + "*** Profile printout saved to text file '10-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6320 function calls (6310 primitive calls) in 0.044 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.044 0.044 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.044 0.044 :1()\n", + " 1 0.000 0.000 0.044 0.044 454136789.py:1(func)\n", + " 10 0.000 0.000 0.044 0.004 impl.py:307(ccc)\n", + " 139 0.000 0.000 0.040 0.000 threading.py:280(wait)\n", + " 546 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.036 0.004 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.035 0.004 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.035 0.004 impl.py:192(cdist_parts_parallel)\n", + " 69 0.000 0.000 0.035 0.001 threading.py:563(wait)\n", + " 70 0.000 0.000 0.034 0.000 _base.py:201(as_completed)\n", + " 70 0.000 0.000 0.005 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.005 0.000 _base.py:602(result_iterator)\n", + " 70 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" ] } ], - "execution_count": 33 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_50.txt\n", + "func()" + ] }, { "cell_type": "markdown", @@ -554,6 +612,7 @@ }, { "cell_type": "code", + "execution_count": 15, "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", "metadata": { "execution": { @@ -569,20 +628,16 @@ "start_time": "2021-12-02T04:37:17.489778", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:14.743833Z", - "start_time": "2024-05-30T17:14:14.742364Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 100" - ], - "outputs": [], - "execution_count": 34 + ] }, { "cell_type": "code", + "execution_count": 16, "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", "metadata": { "execution": { @@ -598,21 +653,17 @@ "start_time": "2021-12-02T04:37:17.705755", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:14.746887Z", - "start_time": "2024-05-30T17:14:14.744414Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 35 + ] }, { "cell_type": "code", + "execution_count": 17, "id": "ede7a328-bad3-40a2-a179-1148a3229620", "metadata": { "execution": { @@ -628,22 +679,18 @@ "start_time": "2021-12-02T04:37:17.910290", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:14.749823Z", - "start_time": "2024-05-30T17:14:14.747505Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 36 + ] }, { "cell_type": "code", + "execution_count": 18, "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", "metadata": { "execution": { @@ -659,29 +706,25 @@ "start_time": "2021-12-02T04:37:18.114734", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:29.786993Z", - "start_time": "2024-05-30T17:14:14.750356Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "18.4 ms ± 405 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "121 ms ± 566 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], - "execution_count": 37 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 19, "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", "metadata": { "execution": { @@ -697,16 +740,8 @@ "start_time": "2021-12-02T04:37:25.067079", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:29.810995Z", - "start_time": "2024-05-30T17:14:29.787791Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_100.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", @@ -715,9 +750,44 @@ " \n", "*** Profile printout saved to text file '10-n_samples_small_100.txt'. \n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8447 function calls (8437 primitive calls) in 0.124 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.124 0.124 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.124 0.124 :1()\n", + " 1 0.000 0.000 0.124 0.124 454136789.py:1(func)\n", + " 10 0.000 0.000 0.124 0.012 impl.py:307(ccc)\n", + " 196 0.000 0.000 0.118 0.001 threading.py:280(wait)\n", + " 774 0.118 0.000 0.118 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.113 0.011 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.112 0.011 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.112 0.011 impl.py:192(cdist_parts_parallel)\n", + " 97 0.000 0.000 0.110 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.110 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.008 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.008 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.001 0.000 0.002 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] } ], - "execution_count": 38 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_100.txt\n", + "func()" + ] }, { "cell_type": "markdown", @@ -738,6 +808,7 @@ }, { "cell_type": "code", + "execution_count": 20, "id": "4bcf4b42", "metadata": { "execution": { @@ -753,20 +824,16 @@ "start_time": "2021-12-02T04:37:25.842701", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:29.813107Z", - "start_time": "2024-05-30T17:14:29.811679Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 500" - ], - "outputs": [], - "execution_count": 39 + ] }, { "cell_type": "code", + "execution_count": 21, "id": "0bf2f21e", "metadata": { "execution": { @@ -782,21 +849,17 @@ "start_time": "2021-12-02T04:37:26.048740", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:29.816055Z", - "start_time": "2024-05-30T17:14:29.813653Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 40 + ] }, { "cell_type": "code", + "execution_count": 22, "id": "24c352bd", "metadata": { "execution": { @@ -812,22 +875,18 @@ "start_time": "2021-12-02T04:37:26.254392", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:29.819054Z", - "start_time": "2024-05-30T17:14:29.816583Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 41 + ] }, { "cell_type": "code", + "execution_count": 23, "id": "cbde4ce6", "metadata": { "execution": { @@ -843,29 +902,25 @@ "start_time": "2021-12-02T04:37:26.458872", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:32.496070Z", - "start_time": "2024-05-30T17:14:29.819690Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "29.7 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "134 ms ± 444 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], - "execution_count": 42 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 24, "id": "1250547e", "metadata": { "execution": { @@ -881,16 +936,8 @@ "start_time": "2021-12-02T04:37:33.908825", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:32.532594Z", - "start_time": "2024-05-30T17:14:32.496856Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_500.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", @@ -899,9 +946,44 @@ " \n", "*** Profile printout saved to text file '10-n_samples_small_500.txt'. \n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 0.137 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.137 0.137 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.137 0.137 :1()\n", + " 1 0.000 0.000 0.137 0.137 454136789.py:1(func)\n", + " 10 0.001 0.000 0.137 0.014 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.130 0.001 threading.py:280(wait)\n", + " 790 0.130 0.000 0.130 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.121 0.012 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.120 0.012 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.120 0.012 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.119 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.119 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.012 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" + ] } ], - "execution_count": 43 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_500.txt\n", + "func()" + ] }, { "cell_type": "markdown", @@ -922,6 +1004,7 @@ }, { "cell_type": "code", + "execution_count": 25, "id": "f77e8490", "metadata": { "execution": { @@ -937,20 +1020,16 @@ "start_time": "2021-12-02T04:37:34.719337", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:32.534788Z", - "start_time": "2024-05-30T17:14:32.533307Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 1000" - ], - "outputs": [], - "execution_count": 44 + ] }, { "cell_type": "code", + "execution_count": 26, "id": "c99f544a", "metadata": { "execution": { @@ -966,21 +1045,17 @@ "start_time": "2021-12-02T04:37:34.937010", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:32.537720Z", - "start_time": "2024-05-30T17:14:32.535344Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 45 + ] }, { "cell_type": "code", + "execution_count": 27, "id": "d907f1d7", "metadata": { "execution": { @@ -996,22 +1071,18 @@ "start_time": "2021-12-02T04:37:35.148090", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:32.540516Z", - "start_time": "2024-05-30T17:14:32.538243Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 46 + ] }, { "cell_type": "code", + "execution_count": 28, "id": "9721b048", "metadata": { "execution": { @@ -1027,29 +1098,25 @@ "start_time": "2021-12-02T04:37:35.355651", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:36.449349Z", - "start_time": "2024-05-30T17:14:32.541057Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "43.3 ms ± 1.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "154 ms ± 893 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], - "execution_count": 47 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 29, "id": "fd0f4dd6", "metadata": { "execution": { @@ -1065,16 +1132,8 @@ "start_time": "2021-12-02T04:37:49.294171", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:36.499487Z", - "start_time": "2024-05-30T17:14:36.451149Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_1000.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", @@ -1083,9 +1142,61 @@ " \n", "*** Profile printout saved to text file '10-n_samples_small_1000.txt'. \n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 0.156 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.156 0.156 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.156 0.156 :1()\n", + " 1 0.000 0.000 0.156 0.156 454136789.py:1(func)\n", + " 10 0.001 0.000 0.156 0.016 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.148 0.001 threading.py:280(wait)\n", + " 790 0.148 0.000 0.148 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.138 0.014 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.137 0.014 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.137 0.014 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.135 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.135 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.013 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.013 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)" + ] } ], - "execution_count": 48 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "fb1a50ab-a34a-4705-bb3d-e9d6278a30c5", + "metadata": { + "papermill": { + "duration": 0.103807, + "end_time": "2021-12-02T04:37:50.477116", + "exception": false, + "start_time": "2021-12-02T04:37:50.373309", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**CONCLUSION:** as expected, with relatively small samples, the numba-compiled version (`09-cdist_parts_v04`) performs much better than the non-compiled one." + ] }, { "cell_type": "markdown", @@ -1123,6 +1234,7 @@ }, { "cell_type": "code", + "execution_count": 30, "id": "c522396e", "metadata": { "execution": { @@ -1138,20 +1250,16 @@ "start_time": "2021-12-02T04:37:50.999344", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:36.501898Z", - "start_time": "2024-05-30T17:14:36.500200Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 50000" - ], - "outputs": [], - "execution_count": 49 + ] }, { "cell_type": "code", + "execution_count": 31, "id": "a5e536cc", "metadata": { "execution": { @@ -1167,21 +1275,17 @@ "start_time": "2021-12-02T04:37:51.209588", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:36.506666Z", - "start_time": "2024-05-30T17:14:36.502604Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 50 + ] }, { "cell_type": "code", + "execution_count": 32, "id": "15cb532e", "metadata": { "execution": { @@ -1197,22 +1301,18 @@ "start_time": "2021-12-02T04:37:51.423411", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:14:36.509196Z", - "start_time": "2024-05-30T17:14:36.507373Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 51 + ] }, { "cell_type": "code", + "execution_count": 33, "id": "91470f64", "metadata": { "execution": { @@ -1228,29 +1328,25 @@ "start_time": "2021-12-02T04:37:51.633415", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:15:14.984038Z", - "start_time": "2024-05-30T17:14:36.509908Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.4 s ± 15.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "2.35 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], - "execution_count": 52 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 34, "id": "4de4e0b0", "metadata": { "execution": { @@ -1266,16 +1362,8 @@ "start_time": "2021-12-02T04:38:27.540197", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:15:17.381615Z", - "start_time": "2024-05-30T17:15:14.984839Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_large_50000.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", @@ -1284,9 +1372,44 @@ " \n", "*** Profile printout saved to text file '10-n_samples_large_50000.txt'. \n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 2.349 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 2.349 2.349 {built-in method builtins.exec}\n", + " 1 0.000 0.000 2.349 2.349 :1()\n", + " 1 0.000 0.000 2.349 2.349 454136789.py:1(func)\n", + " 10 0.002 0.000 2.349 0.235 impl.py:307(ccc)\n", + " 200 0.001 0.000 2.326 0.012 threading.py:280(wait)\n", + " 790 2.325 0.003 2.325 0.003 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 1.487 0.149 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 1.486 0.149 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 1.486 0.149 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 1.479 0.015 _base.py:201(as_completed)\n", + " 100 0.000 0.000 1.478 0.015 threading.py:563(wait)\n", + " 100 0.000 0.000 0.849 0.008 _base.py:418(result)\n", + " 20 0.000 0.000 0.849 0.042 _base.py:602(result_iterator)\n", + " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", + " 10 0.004 0.000 0.005 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + ] } ], - "execution_count": 53 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_large_50000.txt\n", + "func()" + ] }, { "cell_type": "markdown", @@ -1307,6 +1430,7 @@ }, { "cell_type": "code", + "execution_count": 35, "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", "metadata": { "execution": { @@ -1322,20 +1446,16 @@ "start_time": "2021-12-02T04:38:30.202202", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:15:17.384055Z", - "start_time": "2024-05-30T17:15:17.382405Z" - } + "tags": [] }, + "outputs": [], "source": [ "N_SAMPLES = 100000" - ], - "outputs": [], - "execution_count": 54 + ] }, { "cell_type": "code", + "execution_count": 36, "id": "81765e91", "metadata": { "execution": { @@ -1351,21 +1471,17 @@ "start_time": "2021-12-02T04:38:30.416732", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:15:17.390846Z", - "start_time": "2024-05-30T17:15:17.384582Z" - } + "tags": [] }, + "outputs": [], "source": [ "x = np.random.rand(N_SAMPLES)\n", "y = np.random.rand(N_SAMPLES)" - ], - "outputs": [], - "execution_count": 55 + ] }, { "cell_type": "code", + "execution_count": 37, "id": "d408b318", "metadata": { "execution": { @@ -1381,22 +1497,18 @@ "start_time": "2021-12-02T04:38:30.633991", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:15:17.392961Z", - "start_time": "2024-05-30T17:15:17.391523Z" - } + "tags": [] }, + "outputs": [], "source": [ "def func():\n", " for i in range(N_REPS):\n", " ccc(x, y)" - ], - "outputs": [], - "execution_count": 56 + ] }, { "cell_type": "code", + "execution_count": 38, "id": "aca57100", "metadata": { "execution": { @@ -1412,29 +1524,25 @@ "start_time": "2021-12-02T04:38:30.850304", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:16:36.328760Z", - "start_time": "2024-05-30T17:15:17.393479Z" - } + "tags": [] }, - "source": [ - "%%timeit func()\n", - "func()" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4.92 s ± 31.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "4.7 s ± 21.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], - "execution_count": 57 + "source": [ + "%%timeit func()\n", + "func()" + ] }, { "cell_type": "code", + "execution_count": 39, "id": "b9c25f30", "metadata": { "execution": { @@ -1450,16 +1558,8 @@ "start_time": "2021-12-02T04:39:39.075060", "status": "completed" }, - "tags": [], - "ExecuteTime": { - "end_time": "2024-05-30T17:16:41.225685Z", - "start_time": "2024-05-30T17:16:36.329527Z" - } + "tags": [] }, - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_large_100000.txt\n", - "func()" - ], "outputs": [ { "name": "stdout", @@ -1468,183 +1568,69 @@ " \n", "*** Profile printout saved to text file '10-n_samples_large_100000.txt'. \n" ] - } - ], - "execution_count": 58 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "# Profile with CProfile", - "id": "aa9311addc760854" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-05-30T17:26:08.113910Z", - "start_time": "2024-05-30T17:26:03.072637Z" - } - }, - "cell_type": "code", - "source": [ - "from cProfile import Profile\n", - "from pstats import SortKey, Stats\n", - "\n", - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)\n", - "\n", - "with Profile() as profile:\n", - " func()\n", - " (\n", - " Stats(profile)\n", - " .strip_dirs()\n", - " .sort_stats(SortKey.CUMULATIVE)\n", - " .print_stats()\n", - " )" - ], - "id": "e4950c169d3bbf40", - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - " 8339 function calls in 5.036 seconds\n", + " 8534 function calls (8524 primitive calls) in 4.763 seconds\n", "\n", " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.014 0.014 5.036 5.036 2445792793.py:4(func)\n", - " 10 0.008 0.001 5.022 0.502 impl.py:307(ccc)\n", - " 200 0.001 0.000 4.991 0.025 threading.py:280(wait)\n", - " 790 4.990 0.006 4.990 0.006 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.001 0.000 3.150 0.315 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 3.149 0.315 impl.py:485(cdist_func)\n", - " 10 0.001 0.000 3.149 0.315 impl.py:192(cdist_parts_parallel)\n", - " 100 0.001 0.000 3.141 0.031 _base.py:201(as_completed)\n", - " 100 0.000 0.000 3.140 0.031 threading.py:563(wait)\n", - " 100 0.000 0.000 1.851 0.019 _base.py:418(result)\n", - " 20 0.000 0.000 1.851 0.093 _base.py:602(result_iterator)\n", + " 1 0.000 0.000 4.763 4.763 {built-in method builtins.exec}\n", + " 1 0.000 0.000 4.763 4.763 :1()\n", + " 1 0.007 0.007 4.763 4.763 454136789.py:1(func)\n", + " 10 0.004 0.000 4.756 0.476 impl.py:307(ccc)\n", + " 200 0.001 0.000 4.727 0.024 threading.py:280(wait)\n", + " 790 4.726 0.006 4.726 0.006 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 2.934 0.293 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 2.932 0.293 impl.py:485(cdist_func)\n", + " 10 0.002 0.000 2.932 0.293 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 2.923 0.029 _base.py:201(as_completed)\n", + " 100 0.000 0.000 2.922 0.029 threading.py:563(wait)\n", + " 100 0.000 0.000 1.805 0.018 _base.py:418(result)\n", + " 20 0.000 0.000 1.805 0.090 _base.py:602(result_iterator)\n", " 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros}\n", - " 10 0.004 0.000 0.005 0.001 impl.py:210()\n", - " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.005 0.001 0.007 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", - " 190 0.000 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)\n", - " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)\n", - " 10 0.000 0.000 0.001 0.000 thread.py:216(shutdown)\n", - " 10 0.000 0.000 0.001 0.000 threading.py:1028(join)\n", - " 100 0.000 0.000 0.001 0.000 threading.py:411(acquire)\n", - " 10 0.000 0.000 0.001 0.000 threading.py:1066(_wait_for_tstate_lock)\n", - " 40 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n", - " 20 0.000 0.000 0.000 0.000 impl.py:242(get_chunks)\n", - " 480 0.000 0.000 0.000 0.000 threading.py:256(__enter__)\n", - " 480 0.000 0.000 0.000 0.000 threading.py:259(__exit__)\n", - " 10 0.000 0.000 0.000 0.000 {built-in method _thread.start_new_thread}\n", - " 10 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(amax)\n", - " 100 0.000 0.000 0.000 0.000 _base.py:318(__init__)\n", - " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2638(amax)\n", - " 130 0.000 0.000 0.000 0.000 threading.py:228(__init__)\n", - " 90 0.000 0.000 0.000 0.000 threading.py:553(clear)\n", - " 30 0.000 0.000 0.000 0.000 numeric.py:289(full)\n", - " 10 0.000 0.000 0.000 0.000 fromnumeric.py:69(_wrapreduction)\n", - " 190 0.000 0.000 0.000 0.000 threading.py:268(_acquire_restore)\n", - " 30 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto)\n", - " 190 0.000 0.000 0.000 0.000 threading.py:271(_is_owned)\n", - " 10 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}\n", - " 10 0.000 0.000 0.000 0.000 ipkernel.py:763(init_closure)\n", - " 10 0.000 0.000 0.000 0.000 thread.py:123(__init__)\n", - " 10 0.000 0.000 0.000 0.000 threading.py:992(_stop)\n", - " 10 0.000 0.000 0.000 0.000 _base.py:157(_create_and_install_waiters)\n", - " 10 0.000 0.000 0.000 0.000 threading.py:802(__init__)\n", - " 110 0.000 0.000 0.000 0.000 {method 'put' of '_queue.SimpleQueue' objects}\n", - " 30 0.000 0.000 0.000 0.000 {built-in method numpy.arange}\n", - " 600 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.lock' objects}\n", - " 250 0.000 0.000 0.000 0.000 {built-in method _thread.allocate_lock}\n", - " 190 0.000 0.000 0.000 0.000 threading.py:265(_release_save)\n", - " 10 0.000 0.000 0.000 0.000 _base.py:79(__init__)\n", - " 140 0.000 0.000 0.000 0.000 utility_functions.py:117()\n", - " 190 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.RLock' objects}\n", - " 100 0.000 0.000 0.000 0.000 {method 'pop' of 'list' objects}\n", - " 30 0.000 0.000 0.000 0.000 utility_functions.py:109(chunker)\n", - " 180 0.000 0.000 0.000 0.000 {method 'remove' of 'set' objects}\n", - " 20 0.000 0.000 0.000 0.000 threading.py:528(__init__)\n", - " 10 0.000 0.000 0.000 0.000 threading.py:775(_maintain_shutdown_locks)\n", - " 30 0.000 0.000 0.000 0.000 {built-in method numpy.empty}\n", - " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'list' objects}\n", - " 100 0.000 0.000 0.000 0.000 _base.py:388(__get_result)\n", - " 10 0.000 0.000 0.000 0.000 _base.py:63(__init__)\n", - " 100 0.000 0.000 0.000 0.000 thread.py:47(__init__)\n", - " 10 0.000 0.000 0.000 0.000 {method 'argmax' of 'numpy.ndarray' objects}\n", - " 10 0.000 0.000 0.000 0.000 _base.py:146(__init__)\n", - " 10 0.000 0.000 0.000 0.000 threading.py:405(__init__)\n", - " 290 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.lock' objects}\n", - " 100 0.000 0.000 0.000 0.000 threading.py:82(RLock)\n", - " 10 0.000 0.000 0.000 0.000 core.py:85(unravel_index_2d)\n", - " 10 0.000 0.000 0.000 0.000 {built-in method builtins.sorted}\n", - " 200 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects}\n", - " 200 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.RLock' objects}\n", - " 10 0.000 0.000 0.000 0.000 _base.py:149(__enter__)\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1358(current_thread)\n", - " 10 0.000 0.000 0.000 0.000 impl.py:74(get_range_n_clusters)\n", - " 100 0.000 0.000 0.000 0.000 {method 'reverse' of 'list' objects}\n", - " 10 0.000 0.000 0.000 0.000 weakref.py:370(remove)\n", - " 200 0.000 0.000 0.000 0.000 {method 'release' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.000 0.000 threading.py:785()\n", - " 180 0.000 0.000 0.000 0.000 {built-in method time.monotonic}\n", - " 10 0.000 0.000 0.000 0.000 {method '_acquire_restore' of '_thread.RLock' objects}\n", - " 181 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", - " 10 0.000 0.000 0.000 0.000 _base.py:153(__exit__)\n", - " 20 0.000 0.000 0.000 0.000 impl.py:284(get_feature_type_and_encode)\n", - " 10 0.000 0.000 0.000 0.000 _weakrefset.py:39(_remove)\n", - " 10 0.000 0.000 0.000 0.000 _weakrefset.py:86(add)\n", - " 10 0.000 0.000 0.000 0.000 fromnumeric.py:70()\n", - " 1 0.000 0.000 0.000 0.000 pstats.py:107(__init__)\n", - " 10 0.000 0.000 0.000 0.000 impl.py:218(get_coords_from_index)\n", - " 10 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}\n", - " 10 0.000 0.000 0.000 0.000 _base.py:225()\n", - " 1 0.000 0.000 0.000 0.000 pstats.py:117(init)\n", - " 10 0.000 0.000 0.000 0.000 {method 'difference_update' of 'set' objects}\n", - " 10 0.000 0.000 0.000 0.000 threading.py:1229(_make_invoke_excepthook)\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1147(daemon)\n", - " 90 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.RLock' objects}\n", - " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'collections.deque' objects}\n", - " 10 0.000 0.000 0.000 0.000 weakref.py:428(__setitem__)\n", - " 10 0.000 0.000 0.000 0.000 {built-in method numpy.asarray}\n", - " 30 0.000 0.000 0.000 0.000 multiarray.py:1071(copyto)\n", - " 1 0.000 0.000 0.000 0.000 pstats.py:136(load_stats)\n", - " 90 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", - " 30 0.000 0.000 0.000 0.000 {method 'locked' of '_thread.lock' objects}\n", - " 21 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", - " 20 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}\n", - " 90 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects}\n", - " 20 0.000 0.000 0.000 0.000 threading.py:536(is_set)\n", - " 30 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}\n", - " 10 0.000 0.000 0.000 0.000 {method '_release_save' of '_thread.RLock' objects}\n", - " 10 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2633(_amax_dispatcher)\n", - " 1 0.000 0.000 0.000 0.000 cProfile.py:51(create_stats)\n", - " 10 0.000 0.000 0.000 0.000 {method 'discard' of 'set' objects}\n", - " 10 0.000 0.000 0.000 0.000 _base.py:633(__enter__)\n", - " 10 0.000 0.000 0.000 0.000 {method '_is_owned' of '_thread.RLock' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 1 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - "\n", - "\n" + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)" ] } ], - "execution_count": 62 + "source": [ + "%%prun -s cumulative -l 20 -T 10-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "3719e9b9-c895-4492-924b-b10388d52ce4", + "metadata": { + "papermill": { + "duration": 0.104171, + "end_time": "2021-12-02T04:39:43.662835", + "exception": false, + "start_time": "2021-12-02T04:39:43.558664", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**CONCLUSION:** this is unexpected. With very large samples, the python version performs better! Something to look at in the future. The profiling file for 100,000 samples () shows that the `cdist_parts_parallel` is taking more time in the numba-compiled version than in the python version. Maybe the compiled ARI implementation could be improved in these cases with large samples." + ] }, { + "cell_type": "markdown", + "id": "ebfbcd54", "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "454593d6e622293" + "source": [ + "Haoyu: On my machine, however, the JITed version is slower than the non-JITed version with small sample size, and faster with large sample size. This makes more sense given the overhead of JIT compilation outweighs the benefit of JIT compilation with small sample size, and with large sample size, the JITed version can take advantage of the runtime-compiled code." + ] } ], "metadata": { @@ -1666,7 +1652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.18" }, "papermill": { "default_parameters": {}, diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt new file mode 100644 index 00000000..088dca57 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt @@ -0,0 +1,26 @@ + 8534 function calls (8524 primitive calls) in 4.763 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 4.763 4.763 {built-in method builtins.exec} + 1 0.000 0.000 4.763 4.763 :1() + 1 0.007 0.007 4.763 4.763 454136789.py:1(func) + 10 0.004 0.000 4.756 0.476 impl.py:307(ccc) + 200 0.001 0.000 4.727 0.024 threading.py:280(wait) + 790 4.726 0.006 4.726 0.006 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 2.934 0.293 impl.py:492(compute_coef) + 10 0.000 0.000 2.932 0.293 impl.py:485(cdist_func) + 10 0.002 0.000 2.932 0.293 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 2.923 0.029 _base.py:201(as_completed) + 100 0.000 0.000 2.922 0.029 threading.py:563(wait) + 100 0.000 0.000 1.805 0.018 _base.py:418(result) + 20 0.000 0.000 1.805 0.090 _base.py:602(result_iterator) + 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros} + 10 0.005 0.001 0.007 0.001 impl.py:210() + 100 0.000 0.000 0.004 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt new file mode 100644 index 00000000..8876b8fc --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt @@ -0,0 +1,26 @@ + 8534 function calls (8524 primitive calls) in 2.349 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 2.349 2.349 {built-in method builtins.exec} + 1 0.000 0.000 2.349 2.349 :1() + 1 0.000 0.000 2.349 2.349 454136789.py:1(func) + 10 0.002 0.000 2.349 0.235 impl.py:307(ccc) + 200 0.001 0.000 2.326 0.012 threading.py:280(wait) + 790 2.325 0.003 2.325 0.003 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 1.487 0.149 impl.py:492(compute_coef) + 10 0.000 0.000 1.486 0.149 impl.py:485(cdist_func) + 10 0.001 0.000 1.486 0.149 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 1.479 0.015 _base.py:201(as_completed) + 100 0.000 0.000 1.478 0.015 threading.py:563(wait) + 100 0.000 0.000 0.849 0.008 _base.py:418(result) + 20 0.000 0.000 0.849 0.042 _base.py:602(result_iterator) + 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros} + 10 0.004 0.000 0.005 0.001 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt new file mode 100644 index 00000000..e57621a2 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt @@ -0,0 +1,26 @@ + 8447 function calls (8437 primitive calls) in 0.124 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.124 0.124 {built-in method builtins.exec} + 1 0.000 0.000 0.124 0.124 :1() + 1 0.000 0.000 0.124 0.124 454136789.py:1(func) + 10 0.000 0.000 0.124 0.012 impl.py:307(ccc) + 196 0.000 0.000 0.118 0.001 threading.py:280(wait) + 774 0.118 0.000 0.118 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.113 0.011 impl.py:492(compute_coef) + 10 0.000 0.000 0.112 0.011 impl.py:485(cdist_func) + 10 0.000 0.000 0.112 0.011 impl.py:192(cdist_parts_parallel) + 97 0.000 0.000 0.110 0.001 threading.py:563(wait) + 100 0.000 0.000 0.110 0.001 _base.py:201(as_completed) + 100 0.000 0.000 0.008 0.000 _base.py:418(result) + 20 0.000 0.000 0.008 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.001 0.000 0.002 0.000 impl.py:210() + 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt new file mode 100644 index 00000000..86a44ba7 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt @@ -0,0 +1,26 @@ + 8534 function calls (8524 primitive calls) in 0.156 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.156 0.156 {built-in method builtins.exec} + 1 0.000 0.000 0.156 0.156 :1() + 1 0.000 0.000 0.156 0.156 454136789.py:1(func) + 10 0.001 0.000 0.156 0.016 impl.py:307(ccc) + 200 0.000 0.000 0.148 0.001 threading.py:280(wait) + 790 0.148 0.000 0.148 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.138 0.014 impl.py:492(compute_coef) + 10 0.000 0.000 0.137 0.014 impl.py:485(cdist_func) + 10 0.001 0.000 0.137 0.014 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.135 0.001 threading.py:563(wait) + 100 0.000 0.000 0.135 0.001 _base.py:201(as_completed) + 100 0.000 0.000 0.013 0.000 _base.py:418(result) + 20 0.000 0.000 0.013 0.001 _base.py:602(result_iterator) + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt new file mode 100644 index 00000000..d6426a8c --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt @@ -0,0 +1,26 @@ + 6320 function calls (6310 primitive calls) in 0.044 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.044 0.044 {built-in method builtins.exec} + 1 0.000 0.000 0.044 0.044 :1() + 1 0.000 0.000 0.044 0.044 454136789.py:1(func) + 10 0.000 0.000 0.044 0.004 impl.py:307(ccc) + 139 0.000 0.000 0.040 0.000 threading.py:280(wait) + 546 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.036 0.004 impl.py:492(compute_coef) + 10 0.000 0.000 0.035 0.004 impl.py:485(cdist_func) + 10 0.000 0.000 0.035 0.004 impl.py:192(cdist_parts_parallel) + 69 0.000 0.000 0.035 0.001 threading.py:563(wait) + 70 0.000 0.000 0.034 0.000 _base.py:201(as_completed) + 70 0.000 0.000 0.005 0.000 _base.py:418(result) + 20 0.000 0.000 0.005 0.000 _base.py:602(result_iterator) + 70 0.000 0.000 0.003 0.000 thread.py:161(submit) + 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt new file mode 100644 index 00000000..71b034dd --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt @@ -0,0 +1,26 @@ + 8534 function calls (8524 primitive calls) in 0.137 seconds + + Ordered by: cumulative time + List reduced from 125 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.137 0.137 {built-in method builtins.exec} + 1 0.000 0.000 0.137 0.137 :1() + 1 0.000 0.000 0.137 0.137 454136789.py:1(func) + 10 0.001 0.000 0.137 0.014 impl.py:307(ccc) + 200 0.000 0.000 0.130 0.001 threading.py:280(wait) + 790 0.130 0.000 0.130 0.000 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.121 0.012 impl.py:492(compute_coef) + 10 0.000 0.000 0.120 0.012 impl.py:485(cdist_func) + 10 0.000 0.000 0.120 0.012 impl.py:192(cdist_parts_parallel) + 100 0.000 0.000 0.119 0.001 threading.py:563(wait) + 100 0.000 0.000 0.119 0.001 _base.py:201(as_completed) + 100 0.000 0.000 0.012 0.000 _base.py:418(result) + 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator) + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt deleted file mode 100644 index 1e9ed38a..00000000 --- a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_100000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 4.893 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 4.893 4.893 {built-in method builtins.exec} - 1 0.000 0.000 4.893 4.893 :1() - 1 0.015 0.015 4.893 4.893 837709190.py:1(func) - 10 0.008 0.001 4.877 0.488 impl.py:307(ccc) - 200 0.001 0.000 4.844 0.024 threading.py:280(wait) - 790 4.843 0.006 4.843 0.006 {method 'acquire' of '_thread.lock' objects} - 10 0.001 0.000 3.061 0.306 impl.py:492(compute_coef) - 10 0.000 0.000 3.060 0.306 impl.py:485(cdist_func) - 10 0.001 0.000 3.060 0.306 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 3.052 0.031 _base.py:201(as_completed) - 100 0.000 0.000 3.051 0.031 threading.py:563(wait) - 100 0.000 0.000 1.794 0.018 _base.py:418(result) - 20 0.000 0.000 1.794 0.090 _base.py:602(result_iterator) - 50 0.011 0.000 0.011 0.000 {built-in method numpy.zeros} - 10 0.005 0.001 0.006 0.001 impl.py:210() - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt deleted file mode 100644 index 7b9d2199..00000000 --- a/nbs/others/10_gpu_ari_profiling/10-n_samples_large_50000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 2.394 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 2.394 2.394 {built-in method builtins.exec} - 1 0.000 0.000 2.394 2.394 :1() - 1 0.013 0.013 2.394 2.394 837709190.py:1(func) - 10 0.004 0.000 2.381 0.238 impl.py:307(ccc) - 200 0.001 0.000 2.359 0.012 threading.py:280(wait) - 790 2.358 0.003 2.358 0.003 {method 'acquire' of '_thread.lock' objects} - 10 0.001 0.000 1.505 0.151 impl.py:492(compute_coef) - 10 0.000 0.000 1.504 0.150 impl.py:485(cdist_func) - 10 0.001 0.000 1.504 0.150 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 1.498 0.015 _base.py:201(as_completed) - 100 0.000 0.000 1.497 0.015 threading.py:563(wait) - 100 0.000 0.000 0.863 0.009 _base.py:418(result) - 20 0.000 0.000 0.863 0.043 _base.py:602(result_iterator) - 50 0.006 0.000 0.006 0.000 {built-in method numpy.zeros} - 10 0.003 0.000 0.005 0.000 impl.py:210() - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt deleted file mode 100644 index 07cee266..00000000 --- a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_1000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8310 function calls in 0.046 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.046 0.046 {built-in method builtins.exec} - 1 0.000 0.000 0.046 0.046 :1() - 1 0.000 0.000 0.046 0.046 837709190.py:1(func) - 10 0.001 0.000 0.045 0.005 impl.py:307(ccc) - 199 0.000 0.000 0.041 0.000 threading.py:280(wait) - 786 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.033 0.003 impl.py:492(compute_coef) - 10 0.000 0.000 0.033 0.003 impl.py:485(cdist_func) - 10 0.000 0.000 0.033 0.003 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.031 0.000 _base.py:201(as_completed) - 99 0.000 0.000 0.031 0.000 threading.py:563(wait) - 100 0.000 0.000 0.010 0.000 _base.py:418(result) - 20 0.000 0.000 0.010 0.000 _base.py:602(result_iterator) - 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt deleted file mode 100644 index 30127f8b..00000000 --- a/nbs/others/10_gpu_ari_profiling/10-n_samples_small_500.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 0.033 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.033 0.033 {built-in method builtins.exec} - 1 0.000 0.000 0.033 0.033 :1() - 1 0.000 0.000 0.033 0.033 837709190.py:1(func) - 10 0.000 0.000 0.033 0.003 impl.py:307(ccc) - 200 0.000 0.000 0.028 0.000 threading.py:280(wait) - 790 0.028 0.000 0.028 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.023 0.002 impl.py:492(compute_coef) - 10 0.000 0.000 0.022 0.002 impl.py:485(cdist_func) - 10 0.000 0.000 0.022 0.002 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.021 0.000 threading.py:563(wait) - 100 0.000 0.000 0.021 0.000 _base.py:201(as_completed) - 100 0.000 0.000 0.007 0.000 _base.py:418(result) - 20 0.000 0.000 0.007 0.000 _base.py:602(result_iterator) - 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 10 0.000 0.000 0.001 0.000 impl.py:210() - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/README.md b/nbs/others/10_gpu_ari_profiling/README.md new file mode 100644 index 00000000..ad92427b --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/README.md @@ -0,0 +1,23 @@ +# CCC-GPU profiling + +This folder contains profiling results (with cProfile) of different +optimizations of the clustermatch code. A brief description of each subfolder is +below. + +- `00_cpu_version_ref`: + - Contains benchmarks of the CPU version of CCC (nbs/others/05_clustermatch_profiling/10_cm_optimized): + 1. Numba-enabled, multi-threaded + 2. Numba-disabled, multi-threaded + - Newly added: + 3. Numba-enabled, single-threaded + 4. Numba-disabled, single-threaded + + +* `01_ari_cuda_v0`: + - Contains benchmarks of the CUDA version of CCC, functions rewritten in CUDA: + - `ari` + +The tests were run on a System76 Thelio machine with the following specifications: +- 5.3 GHz Threadripper 7960X (24 Cores - 48 Threads) +- 256 GB ECC DDR5 4800 MHz (4x64) +- 24 GB NVIDIA GeForce RTX 4090 \ No newline at end of file From a91ff20e5df1cf47b3b5bf0a91b58c0a7998404b Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 4 Jun 2024 00:25:32 -0600 Subject: [PATCH 008/134] [bench]: Run single-threaded and multi-threading configs --- ..._parts_v04.ipynb => 00_cpu_1_thread.ipynb} | 404 ++-- .../00_cpu_1_thread_no_jit.ipynb | 1495 +++++++++++++++ .../00_cpu_version_ref/01_cpu_8_threads.ipynb | 1469 +++++++++++++++ .../01_cpu_8_threads_no_jit.ipynb | 1482 +++++++++++++++ .../09-n_samples_large_100000.txt | 26 - .../09-n_samples_large_50000.txt | 26 - .../09-n_samples_small_100.txt | 26 - .../09-n_samples_small_1000.txt | 26 - .../09-n_samples_small_50.txt | 26 - .../09-n_samples_small_500.txt | 26 - .../10-cdist_parts_v04.ipynb | 1672 ----------------- .../10-n_samples_large_100000.txt | 26 - .../10-n_samples_large_50000.txt | 26 - .../10-n_samples_small_100.txt | 26 - .../10-n_samples_small_1000.txt | 26 - .../10-n_samples_small_50.txt | 26 - .../10-n_samples_small_500.txt | 26 - 17 files changed, 4574 insertions(+), 2260 deletions(-) rename nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/{09-cdist_parts_v04.ipynb => 00_cpu_1_thread.ipynb} (78%) create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt delete mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb similarity index 78% rename from nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb rename to nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb index 6b6fe417..071c676e 100644 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-cdist_parts_v04.ipynb +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb @@ -31,7 +31,7 @@ "tags": [] }, "source": [ - "Exactly the same code as in `08`, but here I run the notebook in a different machine (desktop)." + "Multi-threading version of code in `09`" ] }, { @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 67, "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", "metadata": { "execution": { @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 68, "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", "metadata": { "execution": { @@ -104,27 +104,14 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./libs/ccc/__pycache__\n", - "./libs/ccc/sklearn/__pycache__\n", - "./libs/ccc/scipy/__pycache__\n", - "./libs/ccc/coef/__pycache__\n", - "./libs/ccc/utils/__pycache__\n", - "./libs/ccc/pytorch/__pycache__\n" - ] - } - ], + "outputs": [], "source": [ "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 69, "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", "metadata": { "execution": { @@ -149,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 70, "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", "metadata": { "execution": { @@ -191,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 71, "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", "metadata": { "execution": { @@ -235,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 72, "id": "c609cefa-f513-4cf8-9573-367744e31c5f", "metadata": { "execution": { @@ -260,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 73, "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", "metadata": { "execution": { @@ -302,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 74, "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", "metadata": { "execution": { @@ -327,7 +314,7 @@ "0.15625" ] }, - "execution_count": 46, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 75, "id": "68064f0b", "metadata": { "execution": { @@ -398,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 76, "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", "metadata": { "execution": { @@ -424,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 77, "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", "metadata": { "execution": { @@ -444,14 +431,14 @@ }, "outputs": [], "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 78, "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", "metadata": { "execution": { @@ -474,7 +461,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "8.32 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "8.49 ms ± 94.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -485,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 79, "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", "metadata": { "execution": { @@ -516,32 +503,32 @@ "name": "stdout", "output_type": "stream", "text": [ - " 6144 function calls in 0.011 seconds\n", + " 6144 function calls in 0.012 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.011 0.011 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.011 0.011 :1()\n", - " 1 0.000 0.000 0.011 0.011 454136789.py:1(func)\n", - " 10 0.000 0.000 0.011 0.001 impl.py:307(ccc)\n", - " 10 0.000 0.000 0.007 0.001 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 0.007 0.001 impl.py:485(cdist_func)\n", - " 140 0.000 0.000 0.007 0.000 threading.py:280(wait)\n", + " 1 0.000 0.000 0.012 0.012 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.012 0.012 :1()\n", + " 1 0.000 0.000 0.012 0.012 2661685993.py:1(func)\n", + " 10 0.000 0.000 0.012 0.001 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.008 0.001 impl.py:492(compute_coef)\n", + " 140 0.000 0.000 0.008 0.000 threading.py:280(wait)\n", + " 550 0.008 0.000 0.008 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.008 0.001 impl.py:485(cdist_func)\n", " 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel)\n", - " 550 0.007 0.000 0.007 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 70 0.000 0.000 0.006 0.000 threading.py:563(wait)\n", + " 70 0.000 0.000 0.007 0.000 threading.py:563(wait)\n", " 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed)\n", " 70 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", - " 70 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", - " 70 0.000 0.000 0.001 0.000 _base.py:418(result)\n", + " 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 70 0.000 0.000 0.001 0.000 _base.py:418(result)\n", " 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator)\n", " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", - " 70 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" ] } ], @@ -569,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 80, "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", "metadata": { "execution": { @@ -594,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 81, "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", "metadata": { "execution": { @@ -620,34 +607,7 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "ede7a328-bad3-40a2-a179-1148a3229620", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:34:34.917755Z", - "iopub.status.busy": "2021-12-02T04:34:34.917311Z", - "iopub.status.idle": "2021-12-02T04:34:34.919529Z", - "shell.execute_reply": "2021-12-02T04:34:34.919083Z" - }, - "papermill": { - "duration": 0.099235, - "end_time": "2021-12-02T04:34:34.919621", - "exception": false, - "start_time": "2021-12-02T04:34:34.820386", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, + "execution_count": 82, "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", "metadata": { "execution": { @@ -670,7 +630,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "18.4 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "18.7 ms ± 133 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -681,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 83, "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", "metadata": { "execution": { @@ -712,27 +672,27 @@ "name": "stdout", "output_type": "stream", "text": [ - " 8334 function calls in 0.019 seconds\n", + " 8334 function calls in 0.020 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.019 0.019 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.019 0.019 :1()\n", - " 1 0.000 0.000 0.019 0.019 454136789.py:1(func)\n", - " 10 0.000 0.000 0.019 0.002 impl.py:307(ccc)\n", - " 200 0.000 0.000 0.015 0.000 threading.py:280(wait)\n", - " 10 0.000 0.000 0.015 0.001 impl.py:492(compute_coef)\n", - " 790 0.015 0.000 0.015 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.015 0.001 impl.py:485(cdist_func)\n", - " 10 0.000 0.000 0.015 0.001 impl.py:192(cdist_parts_parallel)\n", - " 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed)\n", - " 100 0.000 0.000 0.013 0.000 threading.py:563(wait)\n", + " 1 0.000 0.000 0.020 0.020 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.020 0.020 :1()\n", + " 1 0.000 0.000 0.020 0.020 2661685993.py:1(func)\n", + " 10 0.000 0.000 0.020 0.002 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.016 0.000 threading.py:280(wait)\n", + " 10 0.000 0.000 0.016 0.002 impl.py:492(compute_coef)\n", + " 790 0.016 0.000 0.016 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.015 0.002 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.015 0.002 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.014 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.014 0.000 threading.py:563(wait)\n", " 100 0.000 0.000 0.002 0.000 _base.py:418(result)\n", " 20 0.000 0.000 0.002 0.000 _base.py:602(result_iterator)\n", " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", @@ -765,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 84, "id": "4bcf4b42", "metadata": { "execution": { @@ -790,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 85, "id": "0bf2f21e", "metadata": { "execution": { @@ -816,34 +776,7 @@ }, { "cell_type": "code", - "execution_count": 59, - "id": "24c352bd", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:34:39.010047Z", - "iopub.status.busy": "2021-12-02T04:34:39.009506Z", - "iopub.status.idle": "2021-12-02T04:34:39.011025Z", - "shell.execute_reply": "2021-12-02T04:34:39.011367Z" - }, - "papermill": { - "duration": 0.100056, - "end_time": "2021-12-02T04:34:39.011481", - "exception": false, - "start_time": "2021-12-02T04:34:38.911425", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, + "execution_count": 86, "id": "cbde4ce6", "metadata": { "execution": { @@ -866,7 +799,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "33 ms ± 349 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "33 ms ± 219 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -877,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 87, "id": "1250547e", "metadata": { "execution": { @@ -908,31 +841,31 @@ "name": "stdout", "output_type": "stream", "text": [ - " 8334 function calls in 0.034 seconds\n", + " 8334 function calls in 0.037 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.034 0.034 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.034 0.034 :1()\n", - " 1 0.000 0.000 0.034 0.034 454136789.py:1(func)\n", - " 10 0.001 0.000 0.034 0.003 impl.py:307(ccc)\n", - " 200 0.000 0.000 0.030 0.000 threading.py:280(wait)\n", - " 790 0.030 0.000 0.030 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 1 0.000 0.000 0.037 0.037 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.037 0.037 :1()\n", + " 1 0.000 0.000 0.037 0.037 2661685993.py:1(func)\n", + " 10 0.000 0.000 0.037 0.004 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.032 0.000 threading.py:280(wait)\n", + " 790 0.032 0.000 0.032 0.000 {method 'acquire' of '_thread.lock' objects}\n", " 10 0.000 0.000 0.026 0.003 impl.py:492(compute_coef)\n", " 10 0.000 0.000 0.025 0.003 impl.py:485(cdist_func)\n", " 10 0.000 0.000 0.025 0.003 impl.py:192(cdist_parts_parallel)\n", - " 100 0.000 0.000 0.024 0.000 _base.py:201(as_completed)\n", " 100 0.000 0.000 0.024 0.000 threading.py:563(wait)\n", - " 100 0.000 0.000 0.006 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.006 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.024 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.008 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.008 0.000 _base.py:602(result_iterator)\n", " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" ] } @@ -961,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 88, "id": "f77e8490", "metadata": { "execution": { @@ -986,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 89, "id": "c99f544a", "metadata": { "execution": { @@ -1012,34 +945,7 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "d907f1d7", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:34:44.311356Z", - "iopub.status.busy": "2021-12-02T04:34:44.310862Z", - "iopub.status.idle": "2021-12-02T04:34:44.313206Z", - "shell.execute_reply": "2021-12-02T04:34:44.312818Z" - }, - "papermill": { - "duration": 0.103411, - "end_time": "2021-12-02T04:34:44.313301", - "exception": false, - "start_time": "2021-12-02T04:34:44.209890", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, + "execution_count": 90, "id": "9721b048", "metadata": { "execution": { @@ -1062,7 +968,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "54 ms ± 514 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "53.9 ms ± 347 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -1073,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 91, "id": "fd0f4dd6", "metadata": { "execution": { @@ -1104,27 +1010,27 @@ "name": "stdout", "output_type": "stream", "text": [ - " 8334 function calls in 0.055 seconds\n", + " 8334 function calls in 0.057 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.055 0.055 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.055 0.055 :1()\n", - " 1 0.000 0.000 0.055 0.055 454136789.py:1(func)\n", - " 10 0.000 0.000 0.055 0.005 impl.py:307(ccc)\n", - " 200 0.000 0.000 0.050 0.000 threading.py:280(wait)\n", - " 790 0.050 0.000 0.050 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 1 0.000 0.000 0.057 0.057 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.057 0.057 :1()\n", + " 1 0.000 0.000 0.057 0.057 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.057 0.006 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.052 0.000 threading.py:280(wait)\n", + " 790 0.052 0.000 0.052 0.000 {method 'acquire' of '_thread.lock' objects}\n", " 10 0.000 0.000 0.042 0.004 impl.py:492(compute_coef)\n", " 10 0.000 0.000 0.042 0.004 impl.py:485(cdist_func)\n", " 10 0.000 0.000 0.042 0.004 impl.py:192(cdist_parts_parallel)\n", - " 100 0.000 0.000 0.041 0.000 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.040 0.000 _base.py:201(as_completed)\n", " 100 0.000 0.000 0.040 0.000 threading.py:563(wait)\n", - " 100 0.000 0.000 0.010 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.010 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.012 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.012 0.001 _base.py:602(result_iterator)\n", " 100 0.000 0.000 0.002 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", " 10 0.000 0.000 0.001 0.000 _base.py:573(map)\n", " 10 0.000 0.000 0.001 0.000 _base.py:598()\n", " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", @@ -1174,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 92, "id": "c522396e", "metadata": { "execution": { @@ -1199,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 93, "id": "a5e536cc", "metadata": { "execution": { @@ -1225,34 +1131,7 @@ }, { "cell_type": "code", - "execution_count": 69, - "id": "15cb532e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:34:52.206165Z", - "iopub.status.busy": "2021-12-02T04:34:52.205716Z", - "iopub.status.idle": "2021-12-02T04:34:52.207991Z", - "shell.execute_reply": "2021-12-02T04:34:52.207535Z" - }, - "papermill": { - "duration": 0.10765, - "end_time": "2021-12-02T04:34:52.208087", - "exception": false, - "start_time": "2021-12-02T04:34:52.100437", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, + "execution_count": 94, "id": "91470f64", "metadata": { "execution": { @@ -1275,7 +1154,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.99 s ± 6.77 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "2.96 s ± 15.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -1286,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 95, "id": "4de4e0b0", "metadata": { "execution": { @@ -1317,32 +1196,32 @@ "name": "stdout", "output_type": "stream", "text": [ - " 8334 function calls in 2.990 seconds\n", + " 8334 function calls in 2.979 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 2.990 2.990 {built-in method builtins.exec}\n", - " 1 0.000 0.000 2.990 2.990 :1()\n", - " 1 0.000 0.000 2.990 2.990 454136789.py:1(func)\n", - " 10 0.005 0.000 2.989 0.299 impl.py:307(ccc)\n", - " 200 0.001 0.000 2.965 0.015 threading.py:280(wait)\n", - " 790 2.964 0.004 2.964 0.004 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.001 0.000 2.122 0.212 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 2.121 0.212 impl.py:485(cdist_func)\n", - " 10 0.001 0.000 2.121 0.212 impl.py:192(cdist_parts_parallel)\n", - " 100 0.001 0.000 2.114 0.021 _base.py:201(as_completed)\n", - " 100 0.000 0.000 2.113 0.021 threading.py:563(wait)\n", - " 100 0.000 0.000 0.853 0.009 _base.py:418(result)\n", - " 20 0.000 0.000 0.852 0.043 _base.py:602(result_iterator)\n", + " 1 0.000 0.000 2.979 2.979 {built-in method builtins.exec}\n", + " 1 0.000 0.000 2.979 2.979 :1()\n", + " 1 0.000 0.000 2.979 2.979 2661685993.py:1(func)\n", + " 10 0.005 0.000 2.979 0.298 impl.py:307(ccc)\n", + " 200 0.001 0.000 2.954 0.015 threading.py:280(wait)\n", + " 790 2.953 0.004 2.953 0.004 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 2.109 0.211 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 2.107 0.211 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 2.107 0.211 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 2.101 0.021 _base.py:201(as_completed)\n", + " 100 0.000 0.000 2.100 0.021 threading.py:563(wait)\n", + " 100 0.000 0.000 0.855 0.009 _base.py:418(result)\n", + " 20 0.000 0.000 0.855 0.043 _base.py:602(result_iterator)\n", " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", " 10 0.003 0.000 0.005 0.000 impl.py:210()\n", " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)" ] } ], @@ -1370,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 96, "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", "metadata": { "execution": { @@ -1395,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 97, "id": "81765e91", "metadata": { "execution": { @@ -1421,34 +1300,7 @@ }, { "cell_type": "code", - "execution_count": 74, - "id": "d408b318", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:35:31.682735Z", - "iopub.status.busy": "2021-12-02T04:35:31.682239Z", - "iopub.status.idle": "2021-12-02T04:35:31.684357Z", - "shell.execute_reply": "2021-12-02T04:35:31.683794Z" - }, - "papermill": { - "duration": 0.10675, - "end_time": "2021-12-02T04:35:31.684477", - "exception": false, - "start_time": "2021-12-02T04:35:31.577727", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, + "execution_count": 98, "id": "aca57100", "metadata": { "execution": { @@ -1471,7 +1323,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.12 s ± 70.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "6.28 s ± 143 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -1482,7 +1334,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 99, "id": "b9c25f30", "metadata": { "execution": { @@ -1513,27 +1365,27 @@ "name": "stdout", "output_type": "stream", "text": [ - " 8334 function calls in 6.054 seconds\n", + " 8334 function calls in 6.423 seconds\n", "\n", " Ordered by: cumulative time\n", " List reduced from 114 to 20 due to restriction <20>\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 6.054 6.054 {built-in method builtins.exec}\n", - " 1 0.000 0.000 6.054 6.054 :1()\n", - " 1 0.017 0.017 6.054 6.054 454136789.py:1(func)\n", - " 10 0.008 0.001 6.037 0.604 impl.py:307(ccc)\n", - " 200 0.001 0.000 6.003 0.030 threading.py:280(wait)\n", - " 790 6.002 0.008 6.002 0.008 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.001 0.000 4.230 0.423 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 4.229 0.423 impl.py:485(cdist_func)\n", - " 10 0.001 0.000 4.228 0.423 impl.py:192(cdist_parts_parallel)\n", - " 100 0.001 0.000 4.220 0.042 _base.py:201(as_completed)\n", - " 100 0.000 0.000 4.219 0.042 threading.py:563(wait)\n", - " 100 0.000 0.000 1.784 0.018 _base.py:418(result)\n", - " 20 0.000 0.000 1.784 0.089 _base.py:602(result_iterator)\n", - " 50 0.012 0.000 0.012 0.000 {built-in method numpy.zeros}\n", - " 10 0.005 0.000 0.006 0.001 impl.py:210()\n", + " 1 0.000 0.000 6.423 6.423 {built-in method builtins.exec}\n", + " 1 0.000 0.000 6.423 6.423 :1()\n", + " 1 0.011 0.011 6.423 6.423 2661685993.py:1(func)\n", + " 10 0.007 0.001 6.412 0.641 impl.py:307(ccc)\n", + " 200 0.001 0.000 6.385 0.032 threading.py:280(wait)\n", + " 790 6.384 0.008 6.384 0.008 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 4.487 0.449 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 4.486 0.449 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 4.486 0.449 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 4.480 0.045 _base.py:201(as_completed)\n", + " 100 0.000 0.000 4.479 0.045 threading.py:563(wait)\n", + " 100 0.000 0.000 1.907 0.019 _base.py:418(result)\n", + " 20 0.000 0.000 1.907 0.095 _base.py:602(result_iterator)\n", + " 50 0.008 0.000 0.008 0.000 {built-in method numpy.zeros}\n", + " 10 0.004 0.000 0.005 0.000 impl.py:210()\n", " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb new file mode 100644 index 00000000..bc1edb3d --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb @@ -0,0 +1,1495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Multi-threading version of code in `09`" + ] + }, + { + "cell_type": "markdown", + "id": "84e7fec7", + "metadata": {}, + "source": [ + "## Disable Numba JIT" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ff9b34c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: NUMBA_DISABLE_JIT=1\n" + ] + } + ], + "source": [ + "%env NUMBA_DISABLE_JIT=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./libs/ccc/__pycache__\n", + "./libs/ccc/sklearn/__pycache__\n", + "./libs/ccc/scipy/__pycache__\n", + "./libs/ccc/coef/__pycache__\n", + "./libs/ccc/utils/__pycache__\n", + "./libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from ccc.coef import ccc" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.416790Z", + "iopub.status.busy": "2021-12-02T04:34:11.416353Z", + "iopub.status.idle": "2021-12-02T04:34:11.418488Z", + "shell.execute_reply": "2021-12-02T04:34:11.418030Z" + }, + "papermill": { + "duration": 0.098372, + "end_time": "2021-12-02T04:34:11.418578", + "exception": false, + "start_time": "2021-12-02T04:34:11.320206", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.418916Z", + "iopub.status.busy": "2021-12-02T04:34:21.418477Z", + "iopub.status.idle": "2021-12-02T04:34:21.420269Z", + "shell.execute_reply": "2021-12-02T04:34:21.420631Z" + }, + "papermill": { + "duration": 0.098786, + "end_time": "2021-12-02T04:34:21.420745", + "exception": false, + "start_time": "2021-12-02T04:34:21.321959", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.1 ms ± 146 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6344 function calls (6334 primitive calls) in 0.043 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.043 0.043 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.043 0.043 :1()\n", + " 1 0.000 0.000 0.043 0.043 2661685993.py:1(func)\n", + " 10 0.000 0.000 0.043 0.004 impl.py:307(ccc)\n", + " 140 0.000 0.000 0.039 0.000 threading.py:280(wait)\n", + " 550 0.039 0.000 0.039 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.034 0.003 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.034 0.003 impl.py:485(cdist_func)\n", + " 70 0.000 0.000 0.034 0.000 threading.py:563(wait)\n", + " 10 0.000 0.000 0.034 0.003 impl.py:192(cdist_parts_parallel)\n", + " 70 0.000 0.000 0.033 0.000 _base.py:201(as_completed)\n", + " 70 0.000 0.000 0.006 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.006 0.000 _base.py:602(result_iterator)\n", + " 70 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_50.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "121 ms ± 593 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_100.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8510 function calls (8500 primitive calls) in 0.126 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.126 0.126 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.126 0.126 :1()\n", + " 1 0.000 0.000 0.126 0.126 2661685993.py:1(func)\n", + " 10 0.000 0.000 0.126 0.013 impl.py:307(ccc)\n", + " 199 0.000 0.000 0.120 0.001 threading.py:280(wait)\n", + " 786 0.120 0.000 0.120 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.113 0.011 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.113 0.011 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.112 0.011 impl.py:192(cdist_parts_parallel)\n", + " 99 0.000 0.000 0.111 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.110 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.010 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.010 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)\n", + " 10 0.001 0.000 0.001 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_100.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "135 ms ± 532 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_500.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 0.137 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.137 0.137 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.137 0.137 :1()\n", + " 1 0.000 0.000 0.137 0.137 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.137 0.014 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.131 0.001 threading.py:280(wait)\n", + " 790 0.131 0.000 0.131 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.122 0.012 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.122 0.012 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.122 0.012 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.120 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.120 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.011 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_500.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "154 ms ± 936 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_1000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 0.155 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.155 0.155 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.155 0.155 :1()\n", + " 1 0.000 0.000 0.155 0.155 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.154 0.015 impl.py:307(ccc)\n", + " 200 0.000 0.000 0.148 0.001 threading.py:280(wait)\n", + " 790 0.148 0.000 0.148 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.138 0.014 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.137 0.014 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.137 0.014 impl.py:192(cdist_parts_parallel)\n", + " 100 0.000 0.000 0.135 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.135 0.001 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.013 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.013 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.17 s ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_50000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 2.164 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 2.164 2.164 {built-in method builtins.exec}\n", + " 1 0.000 0.000 2.164 2.164 :1()\n", + " 1 0.000 0.000 2.164 2.164 2661685993.py:1(func)\n", + " 10 0.003 0.000 2.163 0.216 impl.py:307(ccc)\n", + " 200 0.001 0.000 2.139 0.011 threading.py:280(wait)\n", + " 790 2.138 0.003 2.138 0.003 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 1.479 0.148 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 1.477 0.148 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 1.477 0.148 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 1.470 0.015 _base.py:201(as_completed)\n", + " 100 0.000 0.000 1.468 0.015 threading.py:563(wait)\n", + " 100 0.000 0.000 0.672 0.007 _base.py:418(result)\n", + " 20 0.000 0.000 0.671 0.034 _base.py:602(result_iterator)\n", + " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", + " 10 0.004 0.000 0.006 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_50000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.64 s ± 33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_100000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8534 function calls (8524 primitive calls) in 4.658 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 4.658 4.658 {built-in method builtins.exec}\n", + " 1 0.000 0.000 4.658 4.658 :1()\n", + " 1 0.006 0.006 4.658 4.658 2661685993.py:1(func)\n", + " 10 0.004 0.000 4.652 0.465 impl.py:307(ccc)\n", + " 200 0.001 0.000 4.621 0.023 threading.py:280(wait)\n", + " 790 4.620 0.006 4.620 0.006 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 2.880 0.288 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 2.879 0.288 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 2.879 0.288 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 2.869 0.029 _base.py:201(as_completed)\n", + " 100 0.000 0.000 2.868 0.029 threading.py:563(wait)\n", + " 100 0.000 0.000 1.754 0.018 _base.py:418(result)\n", + " 20 0.000 0.000 1.753 0.088 _base.py:602(result_iterator)\n", + " 50 0.011 0.000 0.011 0.000 {built-in method numpy.zeros}\n", + " 10 0.006 0.001 0.007 0.001 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads.ipynb new file mode 100644 index 00000000..12650b86 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads.ipynb @@ -0,0 +1,1469 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Multi-threading version of code in `09`" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./libs/ccc/__pycache__\n", + "./libs/ccc/sklearn/__pycache__\n", + "./libs/ccc/scipy/__pycache__\n", + "./libs/ccc/coef/__pycache__\n", + "./libs/ccc/utils/__pycache__\n", + "./libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from ccc.coef import ccc" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.416790Z", + "iopub.status.busy": "2021-12-02T04:34:11.416353Z", + "iopub.status.idle": "2021-12-02T04:34:11.418488Z", + "shell.execute_reply": "2021-12-02T04:34:11.418030Z" + }, + "papermill": { + "duration": 0.098372, + "end_time": "2021-12-02T04:34:11.418578", + "exception": false, + "start_time": "2021-12-02T04:34:11.320206", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.418916Z", + "iopub.status.busy": "2021-12-02T04:34:21.418477Z", + "iopub.status.idle": "2021-12-02T04:34:21.420269Z", + "shell.execute_reply": "2021-12-02T04:34:21.420631Z" + }, + "papermill": { + "duration": 0.098786, + "end_time": "2021-12-02T04:34:21.420745", + "exception": false, + "start_time": "2021-12-02T04:34:21.321959", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14.6 ms ± 40 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8195 function calls in 0.018 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.018 0.018 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.018 0.018 :1()\n", + " 1 0.000 0.000 0.018 0.018 158102722.py:1(func)\n", + " 10 0.000 0.000 0.018 0.002 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.013 0.001 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.012 0.001 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.012 0.001 impl.py:192(cdist_parts_parallel)\n", + " 659 0.011 0.000 0.011 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 154 0.000 0.000 0.011 0.000 threading.py:280(wait)\n", + " 90 0.000 0.000 0.010 0.000 threading.py:563(wait)\n", + " 80 0.000 0.000 0.010 0.000 thread.py:161(submit)\n", + " 80 0.000 0.000 0.009 0.000 thread.py:180(_adjust_thread_count)\n", + " 57 0.000 0.000 0.008 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.008 0.001 impl.py:210()\n", + " 70 0.000 0.000 0.004 0.000 _base.py:201(as_completed)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.001 0.000 thread.py:216(shutdown)\n", + " 57 0.001 0.000 0.001 0.000 {built-in method _thread.start_new_thread}" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_50.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24.9 ms ± 190 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_100.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 10901 function calls in 0.029 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 120 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.029 0.029 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.029 0.029 :1()\n", + " 1 0.000 0.000 0.029 0.029 158102722.py:1(func)\n", + " 10 0.000 0.000 0.028 0.003 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.023 0.002 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.023 0.002 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.022 0.002 impl.py:192(cdist_parts_parallel)\n", + " 887 0.019 0.000 0.019 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 208 0.000 0.000 0.019 0.000 threading.py:280(wait)\n", + " 124 0.000 0.000 0.019 0.000 threading.py:563(wait)\n", + " 110 0.000 0.000 0.015 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.015 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.013 0.001 impl.py:210()\n", + " 75 0.000 0.000 0.013 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.008 0.000 _base.py:201(as_completed)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.002 0.000 thread.py:216(shutdown)\n", + " 75 0.002 0.000 0.002 0.000 {built-in method _thread.start_new_thread}" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_100.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.3 ms ± 233 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_500.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 11112 function calls in 0.032 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.032 0.032 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.032 0.032 :1()\n", + " 1 0.000 0.000 0.032 0.032 158102722.py:1(func)\n", + " 10 0.000 0.000 0.031 0.003 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.025 0.002 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.024 0.002 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.024 0.002 impl.py:192(cdist_parts_parallel)\n", + " 935 0.022 0.000 0.022 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 224 0.000 0.000 0.022 0.000 threading.py:280(wait)\n", + " 132 0.000 0.000 0.020 0.000 threading.py:563(wait)\n", + " 110 0.000 0.000 0.015 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.014 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.001 0.000 0.013 0.001 impl.py:210()\n", + " 75 0.000 0.000 0.012 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.010 0.000 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.002 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.002 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 75 0.002 0.000 0.002 0.000 {built-in method _thread.start_new_thread}" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_500.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "34.7 ms ± 164 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_1000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 11853 function calls in 0.038 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.038 0.038 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.038 0.038 :1()\n", + " 1 0.000 0.000 0.038 0.038 158102722.py:1(func)\n", + " 10 0.001 0.000 0.038 0.004 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.028 0.003 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.028 0.003 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.028 0.003 impl.py:192(cdist_parts_parallel)\n", + " 1051 0.028 0.000 0.028 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 253 0.000 0.000 0.027 0.000 threading.py:280(wait)\n", + " 150 0.000 0.000 0.023 0.000 threading.py:563(wait)\n", + " 110 0.000 0.000 0.016 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.015 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.001 0.000 0.014 0.001 impl.py:210()\n", + " 79 0.000 0.000 0.013 0.000 threading.py:880(start)\n", + " 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.005 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.005 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 79 0.002 0.000 0.002 0.000 {built-in method _thread.start_new_thread}" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "967 ms ± 5.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_50000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12363 function calls in 0.957 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.957 0.957 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.957 0.957 :1()\n", + " 1 0.009 0.009 0.957 0.957 158102722.py:1(func)\n", + " 10 0.013 0.001 0.949 0.095 impl.py:307(ccc)\n", + " 1148 0.917 0.001 0.917 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 274 0.001 0.000 0.914 0.003 threading.py:280(wait)\n", + " 10 0.000 0.000 0.473 0.047 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.472 0.047 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.472 0.047 impl.py:192(cdist_parts_parallel)\n", + " 170 0.000 0.000 0.464 0.003 threading.py:563(wait)\n", + " 100 0.001 0.000 0.455 0.005 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.450 0.004 _base.py:418(result)\n", + " 30 0.000 0.000 0.450 0.015 _base.py:602(result_iterator)\n", + " 110 0.000 0.000 0.016 0.000 thread.py:161(submit)\n", + " 10 0.002 0.000 0.015 0.002 impl.py:210()\n", + " 110 0.000 0.000 0.015 0.000 thread.py:180(_adjust_thread_count)\n", + " 80 0.000 0.000 0.013 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.006 0.001 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.006 0.001 thread.py:216(shutdown)\n", + " 80 0.000 0.000 0.005 0.000 threading.py:1028(join)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_50000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.96 s ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_100000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12320 function calls in 1.962 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 1.962 1.962 {built-in method builtins.exec}\n", + " 1 0.000 0.000 1.962 1.962 :1()\n", + " 1 0.014 0.014 1.962 1.962 158102722.py:1(func)\n", + " 10 0.021 0.002 1.948 0.195 impl.py:307(ccc)\n", + " 1142 1.898 0.002 1.898 0.002 {method 'acquire' of '_thread.lock' objects}\n", + " 271 0.001 0.000 1.896 0.007 threading.py:280(wait)\n", + " 10 0.000 0.000 0.962 0.096 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.962 0.096 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.961 0.096 impl.py:192(cdist_parts_parallel)\n", + " 170 0.000 0.000 0.952 0.006 threading.py:563(wait)\n", + " 110 0.000 0.000 0.945 0.009 _base.py:418(result)\n", + " 30 0.000 0.000 0.945 0.031 _base.py:602(result_iterator)\n", + " 100 0.001 0.000 0.941 0.009 _base.py:201(as_completed)\n", + " 10 0.004 0.000 0.018 0.002 impl.py:210()\n", + " 110 0.000 0.000 0.018 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.017 0.000 thread.py:180(_adjust_thread_count)\n", + " 80 0.000 0.000 0.015 0.000 threading.py:880(start)\n", + " 50 0.012 0.000 0.012 0.000 {built-in method numpy.zeros}\n", + " 10 0.000 0.000 0.004 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.004 0.000 thread.py:216(shutdown)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb new file mode 100644 index 00000000..7eec9749 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb @@ -0,0 +1,1482 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Multi-threading version of code in `09`" + ] + }, + { + "cell_type": "markdown", + "id": "56d47188", + "metadata": {}, + "source": [ + "## Disable Numba JIT" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af00ffad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: NUMBA_DISABLE_JIT=1\n" + ] + } + ], + "source": [ + "%env NUMBA_DISABLE_JIT=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from ccc.coef import ccc\n" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.416790Z", + "iopub.status.busy": "2021-12-02T04:34:11.416353Z", + "iopub.status.idle": "2021-12-02T04:34:11.418488Z", + "shell.execute_reply": "2021-12-02T04:34:11.418030Z" + }, + "papermill": { + "duration": 0.098372, + "end_time": "2021-12-02T04:34:11.418578", + "exception": false, + "start_time": "2021-12-02T04:34:11.320206", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.418916Z", + "iopub.status.busy": "2021-12-02T04:34:21.418477Z", + "iopub.status.idle": "2021-12-02T04:34:21.420269Z", + "shell.execute_reply": "2021-12-02T04:34:21.420631Z" + }, + "papermill": { + "duration": 0.098786, + "end_time": "2021-12-02T04:34:21.420745", + "exception": false, + "start_time": "2021-12-02T04:34:21.321959", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60.5 ms ± 529 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8093 function calls (8083 primitive calls) in 0.066 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 136 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.066 0.066 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.066 0.066 :1()\n", + " 1 0.000 0.000 0.066 0.066 158102722.py:1(func)\n", + " 10 0.000 0.000 0.066 0.007 impl.py:307(ccc)\n", + " 614 0.059 0.000 0.059 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 146 0.000 0.000 0.059 0.000 threading.py:280(wait)\n", + " 80 0.000 0.000 0.053 0.001 threading.py:563(wait)\n", + " 10 0.000 0.000 0.053 0.005 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.052 0.005 impl.py:485(cdist_func)\n", + " 10 0.000 0.000 0.052 0.005 impl.py:192(cdist_parts_parallel)\n", + " 80 0.000 0.000 0.034 0.000 thread.py:161(submit)\n", + " 80 0.000 0.000 0.033 0.000 thread.py:180(_adjust_thread_count)\n", + " 54 0.000 0.000 0.032 0.001 threading.py:880(start)\n", + " 10 0.000 0.000 0.029 0.003 impl.py:210()\n", + " 70 0.000 0.000 0.023 0.000 _base.py:201(as_completed)\n", + " 80 0.000 0.000 0.006 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.006 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.005 0.001 _base.py:573(map)\n", + " 10 0.000 0.000 0.005 0.001 _base.py:598()\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_50.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "167 ms ± 773 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_100.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 11003 function calls (10993 primitive calls) in 0.172 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.172 0.172 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.172 0.172 :1()\n", + " 1 0.000 0.000 0.172 0.172 158102722.py:1(func)\n", + " 10 0.000 0.000 0.172 0.017 impl.py:307(ccc)\n", + " 879 0.162 0.000 0.162 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 207 0.000 0.000 0.161 0.001 threading.py:280(wait)\n", + " 10 0.000 0.000 0.154 0.015 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.153 0.015 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.153 0.015 impl.py:192(cdist_parts_parallel)\n", + " 120 0.000 0.000 0.151 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.084 0.001 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.073 0.001 thread.py:161(submit)\n", + " 110 0.000 0.000 0.072 0.001 thread.py:180(_adjust_thread_count)\n", + " 75 0.000 0.000 0.070 0.001 threading.py:880(start)\n", + " 10 0.000 0.000 0.069 0.007 impl.py:210()\n", + " 110 0.000 0.000 0.010 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.010 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.004 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.004 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.003 0.000 _base.py:636(__exit__)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_100.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "184 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_500.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12028 function calls (12018 primitive calls) in 0.187 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.187 0.187 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.187 0.187 :1()\n", + " 1 0.000 0.000 0.187 0.187 158102722.py:1(func)\n", + " 10 0.001 0.000 0.187 0.019 impl.py:307(ccc)\n", + " 1054 0.174 0.000 0.174 0.000 {method 'acquire' of '_thread.lock' objects}\n", + " 252 0.000 0.000 0.173 0.001 threading.py:280(wait)\n", + " 10 0.000 0.000 0.166 0.017 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.166 0.017 impl.py:485(cdist_func)\n", + " 10 0.003 0.000 0.165 0.017 impl.py:192(cdist_parts_parallel)\n", + " 146 0.000 0.000 0.161 0.001 threading.py:563(wait)\n", + " 100 0.000 0.000 0.122 0.001 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.045 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.044 0.000 thread.py:180(_adjust_thread_count)\n", + " 80 0.000 0.000 0.042 0.001 threading.py:880(start)\n", + " 10 0.000 0.000 0.041 0.004 impl.py:210()\n", + " 110 0.000 0.000 0.013 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.013 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.005 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.005 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 _base.py:636(__exit__)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_500.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "581 ms ± 12.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_1000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12578 function calls (12568 primitive calls) in 0.596 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.596 0.596 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.596 0.596 :1()\n", + " 1 0.000 0.000 0.596 0.596 158102722.py:1(func)\n", + " 10 0.001 0.000 0.596 0.060 impl.py:307(ccc)\n", + " 1150 0.581 0.001 0.581 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 275 0.001 0.000 0.579 0.002 threading.py:280(wait)\n", + " 10 0.000 0.000 0.570 0.057 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.570 0.057 impl.py:485(cdist_func)\n", + " 10 0.004 0.000 0.570 0.057 impl.py:192(cdist_parts_parallel)\n", + " 170 0.000 0.000 0.562 0.003 threading.py:563(wait)\n", + " 100 0.001 0.000 0.540 0.005 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.028 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.027 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.026 0.003 impl.py:210()\n", + " 80 0.000 0.000 0.025 0.000 threading.py:880(start)\n", + " 110 0.000 0.000 0.018 0.000 _base.py:418(result)\n", + " 30 0.000 0.000 0.018 0.001 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.003 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.003 0.000 thread.py:216(shutdown)\n", + " 80 0.000 0.000 0.003 0.000 threading.py:1028(join)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.33 s ± 6.31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_50000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12538 function calls (12528 primitive calls) in 1.339 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 1.339 1.339 {built-in method builtins.exec}\n", + " 1 0.000 0.000 1.339 1.339 :1()\n", + " 1 0.000 0.000 1.339 1.339 158102722.py:1(func)\n", + " 10 0.002 0.000 1.338 0.134 impl.py:307(ccc)\n", + " 1144 1.307 0.001 1.307 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 273 0.001 0.000 1.305 0.005 threading.py:280(wait)\n", + " 10 0.000 0.000 0.869 0.087 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.868 0.087 impl.py:485(cdist_func)\n", + " 10 0.005 0.000 0.868 0.087 impl.py:192(cdist_parts_parallel)\n", + " 170 0.000 0.000 0.854 0.005 threading.py:563(wait)\n", + " 100 0.001 0.000 0.843 0.008 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.452 0.004 _base.py:418(result)\n", + " 30 0.000 0.000 0.452 0.015 _base.py:602(result_iterator)\n", + " 10 0.005 0.000 0.020 0.002 impl.py:210()\n", + " 110 0.000 0.000 0.017 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.016 0.000 thread.py:180(_adjust_thread_count)\n", + " 80 0.000 0.000 0.014 0.000 threading.py:880(start)\n", + " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", + " 10 0.000 0.000 0.005 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.005 0.000 thread.py:216(shutdown)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_50000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.06 s ± 6.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_100000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12526 function calls (12516 primitive calls) in 2.065 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 125 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 2.065 2.065 {built-in method builtins.exec}\n", + " 1 0.000 0.000 2.064 2.064 :1()\n", + " 1 0.000 0.000 2.064 2.064 158102722.py:1(func)\n", + " 10 0.004 0.000 2.064 0.206 impl.py:307(ccc)\n", + " 1142 2.024 0.002 2.024 0.002 {method 'acquire' of '_thread.lock' objects}\n", + " 271 0.001 0.000 2.022 0.007 threading.py:280(wait)\n", + " 10 0.000 0.000 1.111 0.111 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 1.110 0.111 impl.py:485(cdist_func)\n", + " 10 0.003 0.000 1.110 0.111 impl.py:192(cdist_parts_parallel)\n", + " 170 0.000 0.000 1.095 0.006 threading.py:563(wait)\n", + " 100 0.001 0.000 1.085 0.011 _base.py:201(as_completed)\n", + " 110 0.000 0.000 0.928 0.008 _base.py:418(result)\n", + " 30 0.000 0.000 0.927 0.031 _base.py:602(result_iterator)\n", + " 10 0.006 0.001 0.021 0.002 impl.py:210()\n", + " 110 0.000 0.000 0.017 0.000 thread.py:161(submit)\n", + " 110 0.000 0.000 0.016 0.000 thread.py:180(_adjust_thread_count)\n", + " 80 0.000 0.000 0.014 0.000 threading.py:880(start)\n", + " 50 0.013 0.000 0.013 0.000 {built-in method numpy.zeros}\n", + " 10 0.000 0.000 0.005 0.000 _base.py:636(__exit__)\n", + " 10 0.000 0.000 0.005 0.000 thread.py:216(shutdown)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt deleted file mode 100644 index 3bff5d32..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_100000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 6.054 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 6.054 6.054 {built-in method builtins.exec} - 1 0.000 0.000 6.054 6.054 :1() - 1 0.017 0.017 6.054 6.054 454136789.py:1(func) - 10 0.008 0.001 6.037 0.604 impl.py:307(ccc) - 200 0.001 0.000 6.003 0.030 threading.py:280(wait) - 790 6.002 0.008 6.002 0.008 {method 'acquire' of '_thread.lock' objects} - 10 0.001 0.000 4.230 0.423 impl.py:492(compute_coef) - 10 0.000 0.000 4.229 0.423 impl.py:485(cdist_func) - 10 0.001 0.000 4.228 0.423 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 4.220 0.042 _base.py:201(as_completed) - 100 0.000 0.000 4.219 0.042 threading.py:563(wait) - 100 0.000 0.000 1.784 0.018 _base.py:418(result) - 20 0.000 0.000 1.784 0.089 _base.py:602(result_iterator) - 50 0.012 0.000 0.012 0.000 {built-in method numpy.zeros} - 10 0.005 0.000 0.006 0.001 impl.py:210() - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt deleted file mode 100644 index 85084572..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_large_50000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 2.990 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 2.990 2.990 {built-in method builtins.exec} - 1 0.000 0.000 2.990 2.990 :1() - 1 0.000 0.000 2.990 2.990 454136789.py:1(func) - 10 0.005 0.000 2.989 0.299 impl.py:307(ccc) - 200 0.001 0.000 2.965 0.015 threading.py:280(wait) - 790 2.964 0.004 2.964 0.004 {method 'acquire' of '_thread.lock' objects} - 10 0.001 0.000 2.122 0.212 impl.py:492(compute_coef) - 10 0.000 0.000 2.121 0.212 impl.py:485(cdist_func) - 10 0.001 0.000 2.121 0.212 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 2.114 0.021 _base.py:201(as_completed) - 100 0.000 0.000 2.113 0.021 threading.py:563(wait) - 100 0.000 0.000 0.853 0.009 _base.py:418(result) - 20 0.000 0.000 0.852 0.043 _base.py:602(result_iterator) - 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros} - 10 0.003 0.000 0.005 0.000 impl.py:210() - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt deleted file mode 100644 index 49d3b98d..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_100.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 0.019 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.019 0.019 {built-in method builtins.exec} - 1 0.000 0.000 0.019 0.019 :1() - 1 0.000 0.000 0.019 0.019 454136789.py:1(func) - 10 0.000 0.000 0.019 0.002 impl.py:307(ccc) - 200 0.000 0.000 0.015 0.000 threading.py:280(wait) - 10 0.000 0.000 0.015 0.001 impl.py:492(compute_coef) - 790 0.015 0.000 0.015 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.015 0.001 impl.py:485(cdist_func) - 10 0.000 0.000 0.015 0.001 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.013 0.000 _base.py:201(as_completed) - 100 0.000 0.000 0.013 0.000 threading.py:563(wait) - 100 0.000 0.000 0.002 0.000 _base.py:418(result) - 20 0.000 0.000 0.002 0.000 _base.py:602(result_iterator) - 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt deleted file mode 100644 index ec0ba908..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_1000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 0.055 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.055 0.055 {built-in method builtins.exec} - 1 0.000 0.000 0.055 0.055 :1() - 1 0.000 0.000 0.055 0.055 454136789.py:1(func) - 10 0.000 0.000 0.055 0.005 impl.py:307(ccc) - 200 0.000 0.000 0.050 0.000 threading.py:280(wait) - 790 0.050 0.000 0.050 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.042 0.004 impl.py:492(compute_coef) - 10 0.000 0.000 0.042 0.004 impl.py:485(cdist_func) - 10 0.000 0.000 0.042 0.004 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.041 0.000 _base.py:201(as_completed) - 100 0.000 0.000 0.040 0.000 threading.py:563(wait) - 100 0.000 0.000 0.010 0.000 _base.py:418(result) - 20 0.000 0.000 0.010 0.001 _base.py:602(result_iterator) - 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt deleted file mode 100644 index 12a3589f..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_50.txt +++ /dev/null @@ -1,26 +0,0 @@ - 6144 function calls in 0.011 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.011 0.011 {built-in method builtins.exec} - 1 0.000 0.000 0.011 0.011 :1() - 1 0.000 0.000 0.011 0.011 454136789.py:1(func) - 10 0.000 0.000 0.011 0.001 impl.py:307(ccc) - 10 0.000 0.000 0.007 0.001 impl.py:492(compute_coef) - 10 0.000 0.000 0.007 0.001 impl.py:485(cdist_func) - 140 0.000 0.000 0.007 0.000 threading.py:280(wait) - 10 0.000 0.000 0.007 0.001 impl.py:192(cdist_parts_parallel) - 550 0.007 0.000 0.007 0.000 {method 'acquire' of '_thread.lock' objects} - 70 0.000 0.000 0.006 0.000 threading.py:563(wait) - 70 0.000 0.000 0.006 0.000 _base.py:201(as_completed) - 70 0.000 0.000 0.002 0.000 thread.py:161(submit) - 70 0.000 0.000 0.001 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 70 0.000 0.000 0.001 0.000 _base.py:418(result) - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator) - 10 0.000 0.000 0.001 0.000 impl.py:210() - 70 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt deleted file mode 100644 index 7ef6cc27..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/09-n_samples_small_500.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8334 function calls in 0.034 seconds - - Ordered by: cumulative time - List reduced from 114 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.034 0.034 {built-in method builtins.exec} - 1 0.000 0.000 0.034 0.034 :1() - 1 0.000 0.000 0.034 0.034 454136789.py:1(func) - 10 0.001 0.000 0.034 0.003 impl.py:307(ccc) - 200 0.000 0.000 0.030 0.000 threading.py:280(wait) - 790 0.030 0.000 0.030 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.026 0.003 impl.py:492(compute_coef) - 10 0.000 0.000 0.025 0.003 impl.py:485(cdist_func) - 10 0.000 0.000 0.025 0.003 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.024 0.000 _base.py:201(as_completed) - 100 0.000 0.000 0.024 0.000 threading.py:563(wait) - 100 0.000 0.000 0.006 0.000 _base.py:418(result) - 20 0.000 0.000 0.006 0.000 _base.py:602(result_iterator) - 100 0.000 0.000 0.002 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.001 0.000 _base.py:573(map) - 10 0.000 0.000 0.001 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb deleted file mode 100644 index 3a0d30d4..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-cdist_parts_v04.ipynb +++ /dev/null @@ -1,1672 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", - "metadata": { - "papermill": { - "duration": 0.101131, - "end_time": "2021-12-02T04:36:57.333310", - "exception": false, - "start_time": "2021-12-02T04:36:57.232179", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Description" - ] - }, - { - "cell_type": "markdown", - "id": "db88788d", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "337633a8-d03e-4509-b89d-f8daee598958", - "metadata": { - "papermill": { - "duration": 0.093397, - "end_time": "2021-12-02T04:36:57.520462", - "exception": false, - "start_time": "2021-12-02T04:36:57.427065", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "Exactly the same code as in `09`, but here I disable numba." - ] - }, - { - "cell_type": "markdown", - "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", - "metadata": { - "papermill": { - "duration": 0.095211, - "end_time": "2021-12-02T04:36:57.716055", - "exception": false, - "start_time": "2021-12-02T04:36:57.620844", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Disable numba" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "20cbd5fd-aeb9-448f-91d9-9ff2d12c9c22", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:36:57.911147Z", - "iopub.status.busy": "2021-12-02T04:36:57.910632Z", - "iopub.status.idle": "2021-12-02T04:36:57.914417Z", - "shell.execute_reply": "2021-12-02T04:36:57.913918Z" - }, - "papermill": { - "duration": 0.105032, - "end_time": "2021-12-02T04:36:57.914518", - "exception": false, - "start_time": "2021-12-02T04:36:57.809486", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: NUMBA_DISABLE_JIT=1\n" - ] - } - ], - "source": [ - "%env NUMBA_DISABLE_JIT=1" - ] - }, - { - "cell_type": "markdown", - "id": "94028e4a-a49a-47b1-94c1-9eddd4e4a488", - "metadata": { - "papermill": { - "duration": 0.095296, - "end_time": "2021-12-02T04:36:58.107054", - "exception": false, - "start_time": "2021-12-02T04:36:58.011758", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Remove pycache dir" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:36:58.301605Z", - "iopub.status.busy": "2021-12-02T04:36:58.301130Z", - "iopub.status.idle": "2021-12-02T04:36:58.896724Z", - "shell.execute_reply": "2021-12-02T04:36:58.898251Z" - }, - "papermill": { - "duration": 0.695866, - "end_time": "2021-12-02T04:36:58.898699", - "exception": false, - "start_time": "2021-12-02T04:36:58.202833", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "!echo ${CODE_DIR}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:36:59.126956Z", - "iopub.status.busy": "2021-12-02T04:36:59.126507Z", - "iopub.status.idle": "2021-12-02T04:36:59.738873Z", - "shell.execute_reply": "2021-12-02T04:36:59.737339Z" - }, - "papermill": { - "duration": 0.711841, - "end_time": "2021-12-02T04:36:59.739258", - "exception": false, - "start_time": "2021-12-02T04:36:59.027417", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:36:59.968702Z", - "iopub.status.busy": "2021-12-02T04:36:59.968236Z", - "iopub.status.idle": "2021-12-02T04:37:00.578610Z", - "shell.execute_reply": "2021-12-02T04:37:00.576770Z" - }, - "papermill": { - "duration": 0.710822, - "end_time": "2021-12-02T04:37:00.578986", - "exception": false, - "start_time": "2021-12-02T04:36:59.868164", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:00.786314Z", - "iopub.status.busy": "2021-12-02T04:37:00.785859Z", - "iopub.status.idle": "2021-12-02T04:37:01.385162Z", - "shell.execute_reply": "2021-12-02T04:37:01.383549Z" - }, - "papermill": { - "duration": 0.699623, - "end_time": "2021-12-02T04:37:01.385635", - "exception": false, - "start_time": "2021-12-02T04:37:00.686012", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" - ] - }, - { - "cell_type": "markdown", - "id": "c2251313-41ac-46fd-a845-0f209689ecf6", - "metadata": { - "papermill": { - "duration": 0.100188, - "end_time": "2021-12-02T04:37:01.613793", - "exception": false, - "start_time": "2021-12-02T04:37:01.513605", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Modules" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:01.816619Z", - "iopub.status.busy": "2021-12-02T04:37:01.816158Z", - "iopub.status.idle": "2021-12-02T04:37:02.101993Z", - "shell.execute_reply": "2021-12-02T04:37:02.102363Z" - }, - "papermill": { - "duration": 0.386175, - "end_time": "2021-12-02T04:37:02.102492", - "exception": false, - "start_time": "2021-12-02T04:37:01.716317", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from ccc.coef import ccc" - ] - }, - { - "cell_type": "markdown", - "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", - "metadata": { - "papermill": { - "duration": 0.096349, - "end_time": "2021-12-02T04:37:02.297213", - "exception": false, - "start_time": "2021-12-02T04:37:02.200864", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Settings" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c609cefa-f513-4cf8-9573-367744e31c5f", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:02.493291Z", - "iopub.status.busy": "2021-12-02T04:37:02.492829Z", - "iopub.status.idle": "2021-12-02T04:37:02.494753Z", - "shell.execute_reply": "2021-12-02T04:37:02.494311Z" - }, - "papermill": { - "duration": 0.101239, - "end_time": "2021-12-02T04:37:02.494848", - "exception": false, - "start_time": "2021-12-02T04:37:02.393609", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_REPS = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:02.702329Z", - "iopub.status.busy": "2021-12-02T04:37:02.701871Z", - "iopub.status.idle": "2021-12-02T04:37:02.703366Z", - "shell.execute_reply": "2021-12-02T04:37:02.703704Z" - }, - "papermill": { - "duration": 0.1113, - "end_time": "2021-12-02T04:37:02.703820", - "exception": false, - "start_time": "2021-12-02T04:37:02.592520", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "np.random.seed(0)" - ] - }, - { - "cell_type": "markdown", - "id": "6fd3067b-a4f7-475e-9575-20246934537d", - "metadata": { - "papermill": { - "duration": 0.096196, - "end_time": "2021-12-02T04:37:02.897029", - "exception": false, - "start_time": "2021-12-02T04:37:02.800833", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:03.093628Z", - "iopub.status.busy": "2021-12-02T04:37:03.093157Z", - "iopub.status.idle": "2021-12-02T04:37:03.105727Z", - "shell.execute_reply": "2021-12-02T04:37:03.105340Z" - }, - "papermill": { - "duration": 0.112075, - "end_time": "2021-12-02T04:37:03.105822", - "exception": false, - "start_time": "2021-12-02T04:37:02.993747", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.15625" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# let numba compile all the code before profiling\n", - "ccc(np.random.rand(10), np.random.rand(10))" - ] - }, - { - "cell_type": "markdown", - "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", - "metadata": { - "papermill": { - "duration": 0.096529, - "end_time": "2021-12-02T04:37:03.300110", - "exception": false, - "start_time": "2021-12-02T04:37:03.203581", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Run with `n_samples` small" - ] - }, - { - "cell_type": "markdown", - "id": "13ba811b", - "metadata": { - "papermill": { - "duration": 0.096047, - "end_time": "2021-12-02T04:37:03.492931", - "exception": false, - "start_time": "2021-12-02T04:37:03.396884", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=50`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "68064f0b", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:03.698806Z", - "iopub.status.busy": "2021-12-02T04:37:03.698362Z", - "iopub.status.idle": "2021-12-02T04:37:03.700293Z", - "shell.execute_reply": "2021-12-02T04:37:03.699905Z" - }, - "papermill": { - "duration": 0.111014, - "end_time": "2021-12-02T04:37:03.700387", - "exception": false, - "start_time": "2021-12-02T04:37:03.589373", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:03.899082Z", - "iopub.status.busy": "2021-12-02T04:37:03.898619Z", - "iopub.status.idle": "2021-12-02T04:37:03.900173Z", - "shell.execute_reply": "2021-12-02T04:37:03.900511Z" - }, - "papermill": { - "duration": 0.102818, - "end_time": "2021-12-02T04:37:03.900627", - "exception": false, - "start_time": "2021-12-02T04:37:03.797809", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:04.100786Z", - "iopub.status.busy": "2021-12-02T04:37:04.100336Z", - "iopub.status.idle": "2021-12-02T04:37:04.102671Z", - "shell.execute_reply": "2021-12-02T04:37:04.102239Z" - }, - "papermill": { - "duration": 0.104055, - "end_time": "2021-12-02T04:37:04.102764", - "exception": false, - "start_time": "2021-12-02T04:37:03.998709", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:04.301684Z", - "iopub.status.busy": "2021-12-02T04:37:04.301217Z", - "iopub.status.idle": "2021-12-02T04:37:16.833925Z", - "shell.execute_reply": "2021-12-02T04:37:16.833506Z" - }, - "papermill": { - "duration": 12.634709, - "end_time": "2021-12-02T04:37:16.834035", - "exception": false, - "start_time": "2021-12-02T04:37:04.199326", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "40.2 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:17.040287Z", - "iopub.status.busy": "2021-12-02T04:37:17.039804Z", - "iopub.status.idle": "2021-12-02T04:37:17.163025Z", - "shell.execute_reply": "2021-12-02T04:37:17.162557Z" - }, - "papermill": { - "duration": 0.22787, - "end_time": "2021-12-02T04:37:17.163123", - "exception": false, - "start_time": "2021-12-02T04:37:16.935253", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_small_50.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 6320 function calls (6310 primitive calls) in 0.044 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.044 0.044 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.044 0.044 :1()\n", - " 1 0.000 0.000 0.044 0.044 454136789.py:1(func)\n", - " 10 0.000 0.000 0.044 0.004 impl.py:307(ccc)\n", - " 139 0.000 0.000 0.040 0.000 threading.py:280(wait)\n", - " 546 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.036 0.004 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 0.035 0.004 impl.py:485(cdist_func)\n", - " 10 0.000 0.000 0.035 0.004 impl.py:192(cdist_parts_parallel)\n", - " 69 0.000 0.000 0.035 0.001 threading.py:563(wait)\n", - " 70 0.000 0.000 0.034 0.000 _base.py:201(as_completed)\n", - " 70 0.000 0.000 0.005 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.005 0.000 _base.py:602(result_iterator)\n", - " 70 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", - " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", - " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_50.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "2548440c", - "metadata": { - "papermill": { - "duration": 0.100385, - "end_time": "2021-12-02T04:37:17.364961", - "exception": false, - "start_time": "2021-12-02T04:37:17.264576", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=100`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:17.599561Z", - "iopub.status.busy": "2021-12-02T04:37:17.599055Z", - "iopub.status.idle": "2021-12-02T04:37:17.600565Z", - "shell.execute_reply": "2021-12-02T04:37:17.600913Z" - }, - "papermill": { - "duration": 0.111244, - "end_time": "2021-12-02T04:37:17.601022", - "exception": false, - "start_time": "2021-12-02T04:37:17.489778", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:17.809636Z", - "iopub.status.busy": "2021-12-02T04:37:17.809179Z", - "iopub.status.idle": "2021-12-02T04:37:17.811012Z", - "shell.execute_reply": "2021-12-02T04:37:17.810630Z" - }, - "papermill": { - "duration": 0.105356, - "end_time": "2021-12-02T04:37:17.811111", - "exception": false, - "start_time": "2021-12-02T04:37:17.705755", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ede7a328-bad3-40a2-a179-1148a3229620", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:18.013003Z", - "iopub.status.busy": "2021-12-02T04:37:18.012549Z", - "iopub.status.idle": "2021-12-02T04:37:18.014398Z", - "shell.execute_reply": "2021-12-02T04:37:18.014016Z" - }, - "papermill": { - "duration": 0.104201, - "end_time": "2021-12-02T04:37:18.014491", - "exception": false, - "start_time": "2021-12-02T04:37:17.910290", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:18.220053Z", - "iopub.status.busy": "2021-12-02T04:37:18.219419Z", - "iopub.status.idle": "2021-12-02T04:37:24.963506Z", - "shell.execute_reply": "2021-12-02T04:37:24.962993Z" - }, - "papermill": { - "duration": 6.84888, - "end_time": "2021-12-02T04:37:24.963614", - "exception": false, - "start_time": "2021-12-02T04:37:18.114734", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "121 ms ± 566 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:25.171582Z", - "iopub.status.busy": "2021-12-02T04:37:25.170725Z", - "iopub.status.idle": "2021-12-02T04:37:25.532258Z", - "shell.execute_reply": "2021-12-02T04:37:25.531874Z" - }, - "papermill": { - "duration": 0.465279, - "end_time": "2021-12-02T04:37:25.532358", - "exception": false, - "start_time": "2021-12-02T04:37:25.067079", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_small_100.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8447 function calls (8437 primitive calls) in 0.124 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.124 0.124 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.124 0.124 :1()\n", - " 1 0.000 0.000 0.124 0.124 454136789.py:1(func)\n", - " 10 0.000 0.000 0.124 0.012 impl.py:307(ccc)\n", - " 196 0.000 0.000 0.118 0.001 threading.py:280(wait)\n", - " 774 0.118 0.000 0.118 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.113 0.011 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 0.112 0.011 impl.py:485(cdist_func)\n", - " 10 0.000 0.000 0.112 0.011 impl.py:192(cdist_parts_parallel)\n", - " 97 0.000 0.000 0.110 0.001 threading.py:563(wait)\n", - " 100 0.000 0.000 0.110 0.001 _base.py:201(as_completed)\n", - " 100 0.000 0.000 0.008 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.008 0.000 _base.py:602(result_iterator)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", - " 10 0.001 0.000 0.002 0.000 impl.py:210()\n", - " 100 0.000 0.000 0.000 0.000 threading.py:411(acquire)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_100.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "611ff8e1", - "metadata": { - "papermill": { - "duration": 0.10645, - "end_time": "2021-12-02T04:37:25.742045", - "exception": false, - "start_time": "2021-12-02T04:37:25.635595", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=500`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4bcf4b42", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:25.946981Z", - "iopub.status.busy": "2021-12-02T04:37:25.946503Z", - "iopub.status.idle": "2021-12-02T04:37:25.947981Z", - "shell.execute_reply": "2021-12-02T04:37:25.948333Z" - }, - "papermill": { - "duration": 0.105745, - "end_time": "2021-12-02T04:37:25.948446", - "exception": false, - "start_time": "2021-12-02T04:37:25.842701", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 500" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0bf2f21e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:26.152110Z", - "iopub.status.busy": "2021-12-02T04:37:26.151591Z", - "iopub.status.idle": "2021-12-02T04:37:26.153920Z", - "shell.execute_reply": "2021-12-02T04:37:26.153472Z" - }, - "papermill": { - "duration": 0.105277, - "end_time": "2021-12-02T04:37:26.154017", - "exception": false, - "start_time": "2021-12-02T04:37:26.048740", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "24c352bd", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:26.357429Z", - "iopub.status.busy": "2021-12-02T04:37:26.356978Z", - "iopub.status.idle": "2021-12-02T04:37:26.358844Z", - "shell.execute_reply": "2021-12-02T04:37:26.358481Z" - }, - "papermill": { - "duration": 0.104548, - "end_time": "2021-12-02T04:37:26.358940", - "exception": false, - "start_time": "2021-12-02T04:37:26.254392", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "cbde4ce6", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:26.563098Z", - "iopub.status.busy": "2021-12-02T04:37:26.562627Z", - "iopub.status.idle": "2021-12-02T04:37:33.801602Z", - "shell.execute_reply": "2021-12-02T04:37:33.801037Z" - }, - "papermill": { - "duration": 7.342849, - "end_time": "2021-12-02T04:37:33.801721", - "exception": false, - "start_time": "2021-12-02T04:37:26.458872", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "134 ms ± 444 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "1250547e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:34.015314Z", - "iopub.status.busy": "2021-12-02T04:37:34.014832Z", - "iopub.status.idle": "2021-12-02T04:37:34.410262Z", - "shell.execute_reply": "2021-12-02T04:37:34.410651Z" - }, - "papermill": { - "duration": 0.501944, - "end_time": "2021-12-02T04:37:34.410769", - "exception": false, - "start_time": "2021-12-02T04:37:33.908825", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_small_500.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8534 function calls (8524 primitive calls) in 0.137 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.137 0.137 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.137 0.137 :1()\n", - " 1 0.000 0.000 0.137 0.137 454136789.py:1(func)\n", - " 10 0.001 0.000 0.137 0.014 impl.py:307(ccc)\n", - " 200 0.000 0.000 0.130 0.001 threading.py:280(wait)\n", - " 790 0.130 0.000 0.130 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.121 0.012 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 0.120 0.012 impl.py:485(cdist_func)\n", - " 10 0.000 0.000 0.120 0.012 impl.py:192(cdist_parts_parallel)\n", - " 100 0.000 0.000 0.119 0.001 threading.py:563(wait)\n", - " 100 0.000 0.000 0.119 0.001 _base.py:201(as_completed)\n", - " 100 0.000 0.000 0.012 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", - " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", - " 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_500.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "6853c300", - "metadata": { - "papermill": { - "duration": 0.103261, - "end_time": "2021-12-02T04:37:34.618220", - "exception": false, - "start_time": "2021-12-02T04:37:34.514959", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=1000`" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f77e8490", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:34.832465Z", - "iopub.status.busy": "2021-12-02T04:37:34.831999Z", - "iopub.status.idle": "2021-12-02T04:37:34.833958Z", - "shell.execute_reply": "2021-12-02T04:37:34.833511Z" - }, - "papermill": { - "duration": 0.114715, - "end_time": "2021-12-02T04:37:34.834052", - "exception": false, - "start_time": "2021-12-02T04:37:34.719337", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 1000" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c99f544a", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:35.043108Z", - "iopub.status.busy": "2021-12-02T04:37:35.042636Z", - "iopub.status.idle": "2021-12-02T04:37:35.044239Z", - "shell.execute_reply": "2021-12-02T04:37:35.044579Z" - }, - "papermill": { - "duration": 0.107686, - "end_time": "2021-12-02T04:37:35.044696", - "exception": false, - "start_time": "2021-12-02T04:37:34.937010", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "d907f1d7", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:35.253125Z", - "iopub.status.busy": "2021-12-02T04:37:35.252650Z", - "iopub.status.idle": "2021-12-02T04:37:35.254497Z", - "shell.execute_reply": "2021-12-02T04:37:35.254118Z" - }, - "papermill": { - "duration": 0.106501, - "end_time": "2021-12-02T04:37:35.254591", - "exception": false, - "start_time": "2021-12-02T04:37:35.148090", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "9721b048", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:35.461330Z", - "iopub.status.busy": "2021-12-02T04:37:35.460856Z", - "iopub.status.idle": "2021-12-02T04:37:49.189938Z", - "shell.execute_reply": "2021-12-02T04:37:49.190314Z" - }, - "papermill": { - "duration": 13.834783, - "end_time": "2021-12-02T04:37:49.190434", - "exception": false, - "start_time": "2021-12-02T04:37:35.355651", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "154 ms ± 893 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "fd0f4dd6", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:49.400379Z", - "iopub.status.busy": "2021-12-02T04:37:49.399912Z", - "iopub.status.idle": "2021-12-02T04:37:50.269813Z", - "shell.execute_reply": "2021-12-02T04:37:50.270191Z" - }, - "papermill": { - "duration": 0.97614, - "end_time": "2021-12-02T04:37:50.270311", - "exception": false, - "start_time": "2021-12-02T04:37:49.294171", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_small_1000.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8534 function calls (8524 primitive calls) in 0.156 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.156 0.156 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.156 0.156 :1()\n", - " 1 0.000 0.000 0.156 0.156 454136789.py:1(func)\n", - " 10 0.001 0.000 0.156 0.016 impl.py:307(ccc)\n", - " 200 0.000 0.000 0.148 0.001 threading.py:280(wait)\n", - " 790 0.148 0.000 0.148 0.000 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 0.138 0.014 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 0.137 0.014 impl.py:485(cdist_func)\n", - " 10 0.001 0.000 0.137 0.014 impl.py:192(cdist_parts_parallel)\n", - " 100 0.000 0.000 0.135 0.001 threading.py:563(wait)\n", - " 100 0.000 0.000 0.135 0.001 _base.py:201(as_completed)\n", - " 100 0.000 0.000 0.013 0.000 _base.py:418(result)\n", - " 20 0.000 0.000 0.013 0.001 _base.py:602(result_iterator)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", - " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", - " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_small_1000.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "fb1a50ab-a34a-4705-bb3d-e9d6278a30c5", - "metadata": { - "papermill": { - "duration": 0.103807, - "end_time": "2021-12-02T04:37:50.477116", - "exception": false, - "start_time": "2021-12-02T04:37:50.373309", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "**CONCLUSION:** as expected, with relatively small samples, the numba-compiled version (`09-cdist_parts_v04`) performs much better than the non-compiled one." - ] - }, - { - "cell_type": "markdown", - "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", - "metadata": { - "papermill": { - "duration": 0.103785, - "end_time": "2021-12-02T04:37:50.684395", - "exception": false, - "start_time": "2021-12-02T04:37:50.580610", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# Run with `n_samples` large" - ] - }, - { - "cell_type": "markdown", - "id": "8f2e407c", - "metadata": { - "papermill": { - "duration": 0.109125, - "end_time": "2021-12-02T04:37:50.896687", - "exception": false, - "start_time": "2021-12-02T04:37:50.787562", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=50000`" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "c522396e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:51.105200Z", - "iopub.status.busy": "2021-12-02T04:37:51.104752Z", - "iopub.status.idle": "2021-12-02T04:37:51.106157Z", - "shell.execute_reply": "2021-12-02T04:37:51.106510Z" - }, - "papermill": { - "duration": 0.107277, - "end_time": "2021-12-02T04:37:51.106621", - "exception": false, - "start_time": "2021-12-02T04:37:50.999344", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 50000" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a5e536cc", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:51.315976Z", - "iopub.status.busy": "2021-12-02T04:37:51.315458Z", - "iopub.status.idle": "2021-12-02T04:37:51.318127Z", - "shell.execute_reply": "2021-12-02T04:37:51.317763Z" - }, - "papermill": { - "duration": 0.108638, - "end_time": "2021-12-02T04:37:51.318226", - "exception": false, - "start_time": "2021-12-02T04:37:51.209588", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "15cb532e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:51.529025Z", - "iopub.status.busy": "2021-12-02T04:37:51.528568Z", - "iopub.status.idle": "2021-12-02T04:37:51.530041Z", - "shell.execute_reply": "2021-12-02T04:37:51.530382Z" - }, - "papermill": { - "duration": 0.107088, - "end_time": "2021-12-02T04:37:51.530499", - "exception": false, - "start_time": "2021-12-02T04:37:51.423411", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "91470f64", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:37:51.741002Z", - "iopub.status.busy": "2021-12-02T04:37:51.740365Z", - "iopub.status.idle": "2021-12-02T04:38:27.435619Z", - "shell.execute_reply": "2021-12-02T04:38:27.436034Z" - }, - "papermill": { - "duration": 35.80273, - "end_time": "2021-12-02T04:38:27.436145", - "exception": false, - "start_time": "2021-12-02T04:37:51.633415", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.35 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "4de4e0b0", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:38:27.648543Z", - "iopub.status.busy": "2021-12-02T04:38:27.647940Z", - "iopub.status.idle": "2021-12-02T04:38:29.880143Z", - "shell.execute_reply": "2021-12-02T04:38:29.879649Z" - }, - "papermill": { - "duration": 2.340044, - "end_time": "2021-12-02T04:38:29.880241", - "exception": false, - "start_time": "2021-12-02T04:38:27.540197", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_large_50000.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8534 function calls (8524 primitive calls) in 2.349 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 2.349 2.349 {built-in method builtins.exec}\n", - " 1 0.000 0.000 2.349 2.349 :1()\n", - " 1 0.000 0.000 2.349 2.349 454136789.py:1(func)\n", - " 10 0.002 0.000 2.349 0.235 impl.py:307(ccc)\n", - " 200 0.001 0.000 2.326 0.012 threading.py:280(wait)\n", - " 790 2.325 0.003 2.325 0.003 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 1.487 0.149 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 1.486 0.149 impl.py:485(cdist_func)\n", - " 10 0.001 0.000 1.486 0.149 impl.py:192(cdist_parts_parallel)\n", - " 100 0.001 0.000 1.479 0.015 _base.py:201(as_completed)\n", - " 100 0.000 0.000 1.478 0.015 threading.py:563(wait)\n", - " 100 0.000 0.000 0.849 0.008 _base.py:418(result)\n", - " 20 0.000 0.000 0.849 0.042 _base.py:602(result_iterator)\n", - " 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}\n", - " 10 0.004 0.000 0.005 0.001 impl.py:210()\n", - " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.001 0.000 threading.py:880(start)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_large_50000.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "b0c07894", - "metadata": { - "papermill": { - "duration": 0.105829, - "end_time": "2021-12-02T04:38:30.098264", - "exception": false, - "start_time": "2021-12-02T04:38:29.992435", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## `n_samples=100000`" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:38:30.310087Z", - "iopub.status.busy": "2021-12-02T04:38:30.309651Z", - "iopub.status.idle": "2021-12-02T04:38:30.311339Z", - "shell.execute_reply": "2021-12-02T04:38:30.311682Z" - }, - "papermill": { - "duration": 0.109597, - "end_time": "2021-12-02T04:38:30.311799", - "exception": false, - "start_time": "2021-12-02T04:38:30.202202", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "N_SAMPLES = 100000" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "81765e91", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:38:30.525019Z", - "iopub.status.busy": "2021-12-02T04:38:30.524568Z", - "iopub.status.idle": "2021-12-02T04:38:30.528283Z", - "shell.execute_reply": "2021-12-02T04:38:30.527854Z" - }, - "papermill": { - "duration": 0.111647, - "end_time": "2021-12-02T04:38:30.528379", - "exception": false, - "start_time": "2021-12-02T04:38:30.416732", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "x = np.random.rand(N_SAMPLES)\n", - "y = np.random.rand(N_SAMPLES)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "d408b318", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:38:30.742827Z", - "iopub.status.busy": "2021-12-02T04:38:30.742345Z", - "iopub.status.idle": "2021-12-02T04:38:30.744280Z", - "shell.execute_reply": "2021-12-02T04:38:30.743848Z" - }, - "papermill": { - "duration": 0.110384, - "end_time": "2021-12-02T04:38:30.744375", - "exception": false, - "start_time": "2021-12-02T04:38:30.633991", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "def func():\n", - " for i in range(N_REPS):\n", - " ccc(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "aca57100", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:38:30.958831Z", - "iopub.status.busy": "2021-12-02T04:38:30.958256Z", - "iopub.status.idle": "2021-12-02T04:39:38.969951Z", - "shell.execute_reply": "2021-12-02T04:39:38.970301Z" - }, - "papermill": { - "duration": 68.120109, - "end_time": "2021-12-02T04:39:38.970413", - "exception": false, - "start_time": "2021-12-02T04:38:30.850304", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.7 s ± 21.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%%timeit func()\n", - "func()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b9c25f30", - "metadata": { - "execution": { - "iopub.execute_input": "2021-12-02T04:39:39.183942Z", - "iopub.status.busy": "2021-12-02T04:39:39.183247Z", - "iopub.status.idle": "2021-12-02T04:39:43.450962Z", - "shell.execute_reply": "2021-12-02T04:39:43.451362Z" - }, - "papermill": { - "duration": 4.37642, - "end_time": "2021-12-02T04:39:43.451480", - "exception": false, - "start_time": "2021-12-02T04:39:39.075060", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "*** Profile printout saved to text file '10-n_samples_large_100000.txt'. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 8534 function calls (8524 primitive calls) in 4.763 seconds\n", - "\n", - " Ordered by: cumulative time\n", - " List reduced from 125 to 20 due to restriction <20>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 4.763 4.763 {built-in method builtins.exec}\n", - " 1 0.000 0.000 4.763 4.763 :1()\n", - " 1 0.007 0.007 4.763 4.763 454136789.py:1(func)\n", - " 10 0.004 0.000 4.756 0.476 impl.py:307(ccc)\n", - " 200 0.001 0.000 4.727 0.024 threading.py:280(wait)\n", - " 790 4.726 0.006 4.726 0.006 {method 'acquire' of '_thread.lock' objects}\n", - " 10 0.000 0.000 2.934 0.293 impl.py:492(compute_coef)\n", - " 10 0.000 0.000 2.932 0.293 impl.py:485(cdist_func)\n", - " 10 0.002 0.000 2.932 0.293 impl.py:192(cdist_parts_parallel)\n", - " 100 0.001 0.000 2.923 0.029 _base.py:201(as_completed)\n", - " 100 0.000 0.000 2.922 0.029 threading.py:563(wait)\n", - " 100 0.000 0.000 1.805 0.018 _base.py:418(result)\n", - " 20 0.000 0.000 1.805 0.090 _base.py:602(result_iterator)\n", - " 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros}\n", - " 10 0.005 0.001 0.007 0.001 impl.py:210()\n", - " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", - " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", - " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", - " 10 0.000 0.000 0.002 0.000 threading.py:880(start)" - ] - } - ], - "source": [ - "%%prun -s cumulative -l 20 -T 10-n_samples_large_100000.txt\n", - "func()" - ] - }, - { - "cell_type": "markdown", - "id": "3719e9b9-c895-4492-924b-b10388d52ce4", - "metadata": { - "papermill": { - "duration": 0.104171, - "end_time": "2021-12-02T04:39:43.662835", - "exception": false, - "start_time": "2021-12-02T04:39:43.558664", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "**CONCLUSION:** this is unexpected. With very large samples, the python version performs better! Something to look at in the future. The profiling file for 100,000 samples () shows that the `cdist_parts_parallel` is taking more time in the numba-compiled version than in the python version. Maybe the compiled ARI implementation could be improved in these cases with large samples." - ] - }, - { - "cell_type": "markdown", - "id": "ebfbcd54", - "metadata": {}, - "source": [ - "Haoyu: On my machine, however, the JITed version is slower than the non-JITed version with small sample size, and faster with large sample size. This makes more sense given the overhead of JIT compilation outweighs the benefit of JIT compilation with small sample size, and with large sample size, the JITed version can take advantage of the runtime-compiled code." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "all,-execution,-papermill,-trusted" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "papermill": { - "default_parameters": {}, - "duration": 168.306551, - "end_time": "2021-12-02T04:39:44.188549", - "environment_variables": {}, - "exception": null, - "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/10-cdist_parts_v04.ipynb", - "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/10-cdist_parts_v04.run.ipynb", - "parameters": {}, - "start_time": "2021-12-02T04:36:55.881998", - "version": "2.3.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt deleted file mode 100644 index 088dca57..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_100000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8534 function calls (8524 primitive calls) in 4.763 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 4.763 4.763 {built-in method builtins.exec} - 1 0.000 0.000 4.763 4.763 :1() - 1 0.007 0.007 4.763 4.763 454136789.py:1(func) - 10 0.004 0.000 4.756 0.476 impl.py:307(ccc) - 200 0.001 0.000 4.727 0.024 threading.py:280(wait) - 790 4.726 0.006 4.726 0.006 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 2.934 0.293 impl.py:492(compute_coef) - 10 0.000 0.000 2.932 0.293 impl.py:485(cdist_func) - 10 0.002 0.000 2.932 0.293 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 2.923 0.029 _base.py:201(as_completed) - 100 0.000 0.000 2.922 0.029 threading.py:563(wait) - 100 0.000 0.000 1.805 0.018 _base.py:418(result) - 20 0.000 0.000 1.805 0.090 _base.py:602(result_iterator) - 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros} - 10 0.005 0.001 0.007 0.001 impl.py:210() - 100 0.000 0.000 0.004 0.000 thread.py:161(submit) - 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt deleted file mode 100644 index 8876b8fc..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_large_50000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8534 function calls (8524 primitive calls) in 2.349 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 2.349 2.349 {built-in method builtins.exec} - 1 0.000 0.000 2.349 2.349 :1() - 1 0.000 0.000 2.349 2.349 454136789.py:1(func) - 10 0.002 0.000 2.349 0.235 impl.py:307(ccc) - 200 0.001 0.000 2.326 0.012 threading.py:280(wait) - 790 2.325 0.003 2.325 0.003 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 1.487 0.149 impl.py:492(compute_coef) - 10 0.000 0.000 1.486 0.149 impl.py:485(cdist_func) - 10 0.001 0.000 1.486 0.149 impl.py:192(cdist_parts_parallel) - 100 0.001 0.000 1.479 0.015 _base.py:201(as_completed) - 100 0.000 0.000 1.478 0.015 threading.py:563(wait) - 100 0.000 0.000 0.849 0.008 _base.py:418(result) - 20 0.000 0.000 0.849 0.042 _base.py:602(result_iterator) - 50 0.007 0.000 0.007 0.000 {built-in method numpy.zeros} - 10 0.004 0.000 0.005 0.001 impl.py:210() - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.001 0.000 threading.py:880(start) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt deleted file mode 100644 index e57621a2..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_100.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8447 function calls (8437 primitive calls) in 0.124 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.124 0.124 {built-in method builtins.exec} - 1 0.000 0.000 0.124 0.124 :1() - 1 0.000 0.000 0.124 0.124 454136789.py:1(func) - 10 0.000 0.000 0.124 0.012 impl.py:307(ccc) - 196 0.000 0.000 0.118 0.001 threading.py:280(wait) - 774 0.118 0.000 0.118 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.113 0.011 impl.py:492(compute_coef) - 10 0.000 0.000 0.112 0.011 impl.py:485(cdist_func) - 10 0.000 0.000 0.112 0.011 impl.py:192(cdist_parts_parallel) - 97 0.000 0.000 0.110 0.001 threading.py:563(wait) - 100 0.000 0.000 0.110 0.001 _base.py:201(as_completed) - 100 0.000 0.000 0.008 0.000 _base.py:418(result) - 20 0.000 0.000 0.008 0.000 _base.py:602(result_iterator) - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) - 10 0.001 0.000 0.002 0.000 impl.py:210() - 100 0.000 0.000 0.000 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt deleted file mode 100644 index 86a44ba7..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_1000.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8534 function calls (8524 primitive calls) in 0.156 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.156 0.156 {built-in method builtins.exec} - 1 0.000 0.000 0.156 0.156 :1() - 1 0.000 0.000 0.156 0.156 454136789.py:1(func) - 10 0.001 0.000 0.156 0.016 impl.py:307(ccc) - 200 0.000 0.000 0.148 0.001 threading.py:280(wait) - 790 0.148 0.000 0.148 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.138 0.014 impl.py:492(compute_coef) - 10 0.000 0.000 0.137 0.014 impl.py:485(cdist_func) - 10 0.001 0.000 0.137 0.014 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.135 0.001 threading.py:563(wait) - 100 0.000 0.000 0.135 0.001 _base.py:201(as_completed) - 100 0.000 0.000 0.013 0.000 _base.py:418(result) - 20 0.000 0.000 0.013 0.001 _base.py:602(result_iterator) - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt deleted file mode 100644 index d6426a8c..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_50.txt +++ /dev/null @@ -1,26 +0,0 @@ - 6320 function calls (6310 primitive calls) in 0.044 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.044 0.044 {built-in method builtins.exec} - 1 0.000 0.000 0.044 0.044 :1() - 1 0.000 0.000 0.044 0.044 454136789.py:1(func) - 10 0.000 0.000 0.044 0.004 impl.py:307(ccc) - 139 0.000 0.000 0.040 0.000 threading.py:280(wait) - 546 0.040 0.000 0.040 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.036 0.004 impl.py:492(compute_coef) - 10 0.000 0.000 0.035 0.004 impl.py:485(cdist_func) - 10 0.000 0.000 0.035 0.004 impl.py:192(cdist_parts_parallel) - 69 0.000 0.000 0.035 0.001 threading.py:563(wait) - 70 0.000 0.000 0.034 0.000 _base.py:201(as_completed) - 70 0.000 0.000 0.005 0.000 _base.py:418(result) - 20 0.000 0.000 0.005 0.000 _base.py:602(result_iterator) - 70 0.000 0.000 0.003 0.000 thread.py:161(submit) - 70 0.000 0.000 0.002 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt deleted file mode 100644 index 71b034dd..00000000 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/10-n_samples_small_500.txt +++ /dev/null @@ -1,26 +0,0 @@ - 8534 function calls (8524 primitive calls) in 0.137 seconds - - Ordered by: cumulative time - List reduced from 125 to 20 due to restriction <20> - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 0.137 0.137 {built-in method builtins.exec} - 1 0.000 0.000 0.137 0.137 :1() - 1 0.000 0.000 0.137 0.137 454136789.py:1(func) - 10 0.001 0.000 0.137 0.014 impl.py:307(ccc) - 200 0.000 0.000 0.130 0.001 threading.py:280(wait) - 790 0.130 0.000 0.130 0.000 {method 'acquire' of '_thread.lock' objects} - 10 0.000 0.000 0.121 0.012 impl.py:492(compute_coef) - 10 0.000 0.000 0.120 0.012 impl.py:485(cdist_func) - 10 0.000 0.000 0.120 0.012 impl.py:192(cdist_parts_parallel) - 100 0.000 0.000 0.119 0.001 threading.py:563(wait) - 100 0.000 0.000 0.119 0.001 _base.py:201(as_completed) - 100 0.000 0.000 0.012 0.000 _base.py:418(result) - 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator) - 100 0.000 0.000 0.003 0.000 thread.py:161(submit) - 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) - 10 0.000 0.000 0.002 0.000 _base.py:573(map) - 10 0.000 0.000 0.002 0.000 _base.py:598() - 10 0.000 0.000 0.002 0.000 threading.py:880(start) - 10 0.000 0.000 0.001 0.000 impl.py:210() - 10 0.000 0.000 0.000 0.000 _base.py:636(__exit__) \ No newline at end of file From fec6aa6c178415a1878dab2385239ce6e14f6d95 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 11 Jun 2024 14:52:10 -0600 Subject: [PATCH 009/134] [bench]: Test ccc-gpu under different configs --- 09-n_samples_large_100000.txt | 26 + 09-n_samples_large_50000.txt | 26 + 09-n_samples_small_100.txt | 26 + 09-n_samples_small_1000.txt | 26 + 09-n_samples_small_50.txt | 26 + 09-n_samples_small_500.txt | 26 + libs/ccc/coef/impl.py | 2 +- .../00_cpu_version_ref/00_cpu_1_thread.ipynb | 18 +- .../00_cpu_1_thread_no_jit.ipynb | 12 - .../01_cpu_8_threads_no_jit.ipynb | 12 - .../00_cpu_version_ref/02_cuda_ari.ipynb | 1595 +++++++++++++++++ 11 files changed, 1758 insertions(+), 37 deletions(-) create mode 100644 09-n_samples_large_100000.txt create mode 100644 09-n_samples_large_50000.txt create mode 100644 09-n_samples_small_100.txt create mode 100644 09-n_samples_small_1000.txt create mode 100644 09-n_samples_small_50.txt create mode 100644 09-n_samples_small_500.txt create mode 100644 nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb diff --git a/09-n_samples_large_100000.txt b/09-n_samples_large_100000.txt new file mode 100644 index 00000000..a522db21 --- /dev/null +++ b/09-n_samples_large_100000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 7.515 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 7.515 7.515 {built-in method builtins.exec} + 1 0.000 0.000 7.515 7.515 :1() + 1 0.000 0.000 7.515 7.515 2661685993.py:1(func) + 10 0.008 0.001 7.515 0.751 impl.py:307(ccc) + 200 0.001 0.000 7.490 0.037 threading.py:280(wait) + 790 7.489 0.009 7.489 0.009 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 5.731 0.573 impl.py:492(compute_coef) + 10 0.000 0.000 5.730 0.573 impl.py:485(cdist_func) + 10 0.001 0.000 5.729 0.573 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 5.724 0.057 _base.py:201(as_completed) + 100 0.000 0.000 5.723 0.057 threading.py:563(wait) + 100 0.000 0.000 1.767 0.018 _base.py:418(result) + 20 0.000 0.000 1.767 0.088 _base.py:602(result_iterator) + 50 0.005 0.000 0.005 0.000 {built-in method numpy.zeros} + 100 0.000 0.000 0.004 0.000 thread.py:161(submit) + 10 0.002 0.000 0.003 0.000 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/09-n_samples_large_50000.txt b/09-n_samples_large_50000.txt new file mode 100644 index 00000000..e5ba8949 --- /dev/null +++ b/09-n_samples_large_50000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 4.119 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 4.119 4.119 {built-in method builtins.exec} + 1 0.000 0.000 4.119 4.119 :1() + 1 0.012 0.012 4.119 4.119 2661685993.py:1(func) + 10 0.005 0.000 4.107 0.411 impl.py:307(ccc) + 200 0.001 0.000 4.088 0.020 threading.py:280(wait) + 790 4.087 0.005 4.087 0.005 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 3.239 0.324 impl.py:492(compute_coef) + 10 0.000 0.000 3.238 0.324 impl.py:485(cdist_func) + 10 0.002 0.000 3.237 0.324 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 3.232 0.032 _base.py:201(as_completed) + 100 0.000 0.000 3.231 0.032 threading.py:563(wait) + 100 0.000 0.000 0.857 0.009 _base.py:418(result) + 20 0.000 0.000 0.857 0.043 _base.py:602(result_iterator) + 100 0.000 0.000 0.004 0.000 thread.py:161(submit) + 10 0.002 0.000 0.003 0.000 impl.py:210() + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 50 0.003 0.000 0.003 0.000 {built-in method numpy.zeros} + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) \ No newline at end of file diff --git a/09-n_samples_small_100.txt b/09-n_samples_small_100.txt new file mode 100644 index 00000000..a1d578c2 --- /dev/null +++ b/09-n_samples_small_100.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.759 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.759 0.759 {built-in method builtins.exec} + 1 0.000 0.000 0.759 0.759 :1() + 1 0.000 0.000 0.759 0.759 2661685993.py:1(func) + 10 0.001 0.000 0.759 0.076 impl.py:307(ccc) + 10 0.000 0.000 0.751 0.075 impl.py:492(compute_coef) + 10 0.000 0.000 0.750 0.075 impl.py:485(cdist_func) + 10 0.002 0.000 0.750 0.075 impl.py:192(cdist_parts_parallel) + 200 0.001 0.000 0.747 0.004 threading.py:280(wait) + 100 0.001 0.000 0.746 0.007 _base.py:201(as_completed) + 790 0.746 0.001 0.746 0.001 {method 'acquire' of '_thread.lock' objects} + 100 0.000 0.000 0.745 0.007 threading.py:563(wait) + 100 0.000 0.000 0.004 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 _base.py:418(result) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 20 0.000 0.000 0.003 0.000 _base.py:602(result_iterator) + 10 0.000 0.000 0.003 0.000 _base.py:573(map) + 10 0.000 0.000 0.003 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.001 0.000 0.002 0.000 impl.py:210() + 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures) \ No newline at end of file diff --git a/09-n_samples_small_1000.txt b/09-n_samples_small_1000.txt new file mode 100644 index 00000000..7c3d8e70 --- /dev/null +++ b/09-n_samples_small_1000.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.812 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.812 0.812 {built-in method builtins.exec} + 1 0.000 0.000 0.812 0.812 :1() + 1 0.000 0.000 0.812 0.812 2661685993.py:1(func) + 10 0.001 0.000 0.812 0.081 impl.py:307(ccc) + 200 0.001 0.000 0.801 0.004 threading.py:280(wait) + 790 0.801 0.001 0.801 0.001 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.796 0.080 impl.py:492(compute_coef) + 10 0.000 0.000 0.796 0.080 impl.py:485(cdist_func) + 10 0.002 0.000 0.795 0.080 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 0.792 0.008 _base.py:201(as_completed) + 100 0.000 0.000 0.791 0.008 threading.py:563(wait) + 100 0.000 0.000 0.011 0.000 _base.py:418(result) + 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator) + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures) \ No newline at end of file diff --git a/09-n_samples_small_50.txt b/09-n_samples_small_50.txt new file mode 100644 index 00000000..7f895ad5 --- /dev/null +++ b/09-n_samples_small_50.txt @@ -0,0 +1,26 @@ + 6144 function calls in 0.346 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.346 0.346 {built-in method builtins.exec} + 1 0.000 0.000 0.346 0.346 :1() + 1 0.000 0.000 0.346 0.346 2661685993.py:1(func) + 10 0.001 0.000 0.345 0.035 impl.py:307(ccc) + 10 0.000 0.000 0.340 0.034 impl.py:492(compute_coef) + 10 0.000 0.000 0.339 0.034 impl.py:485(cdist_func) + 10 0.001 0.000 0.339 0.034 impl.py:192(cdist_parts_parallel) + 140 0.001 0.000 0.337 0.002 threading.py:280(wait) + 550 0.336 0.001 0.336 0.001 {method 'acquire' of '_thread.lock' objects} + 70 0.001 0.000 0.336 0.005 _base.py:201(as_completed) + 70 0.000 0.000 0.336 0.005 threading.py:563(wait) + 70 0.000 0.000 0.003 0.000 thread.py:161(submit) + 70 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.003 0.000 _base.py:573(map) + 10 0.000 0.000 0.003 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 70 0.000 0.000 0.001 0.000 _base.py:418(result) + 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__) \ No newline at end of file diff --git a/09-n_samples_small_500.txt b/09-n_samples_small_500.txt new file mode 100644 index 00000000..b7ab5d10 --- /dev/null +++ b/09-n_samples_small_500.txt @@ -0,0 +1,26 @@ + 8334 function calls in 0.786 seconds + + Ordered by: cumulative time + List reduced from 114 to 20 due to restriction <20> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 0.786 0.786 {built-in method builtins.exec} + 1 0.000 0.000 0.786 0.786 :1() + 1 0.000 0.000 0.786 0.786 2661685993.py:1(func) + 10 0.001 0.000 0.786 0.079 impl.py:307(ccc) + 200 0.001 0.000 0.775 0.004 threading.py:280(wait) + 790 0.774 0.001 0.774 0.001 {method 'acquire' of '_thread.lock' objects} + 10 0.000 0.000 0.774 0.077 impl.py:492(compute_coef) + 10 0.000 0.000 0.773 0.077 impl.py:485(cdist_func) + 10 0.002 0.000 0.773 0.077 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 0.770 0.008 _base.py:201(as_completed) + 100 0.000 0.000 0.769 0.008 threading.py:563(wait) + 100 0.000 0.000 0.008 0.000 _base.py:418(result) + 20 0.000 0.000 0.007 0.000 _base.py:602(result_iterator) + 100 0.000 0.000 0.003 0.000 thread.py:161(submit) + 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.002 0.000 _base.py:573(map) + 10 0.000 0.000 0.002 0.000 _base.py:598() + 10 0.000 0.000 0.002 0.000 threading.py:880(start) + 10 0.000 0.000 0.001 0.000 impl.py:210() + 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures) \ No newline at end of file diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index 18532990..abfc74a8 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -13,7 +13,7 @@ from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.sklearn.metrics import adjusted_rand_index as ari +from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb index 071c676e..d8d54875 100644 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread.ipynb @@ -1399,6 +1399,12 @@ "func()" ] }, + { + "cell_type": "markdown", + "id": "8044128e", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -1426,18 +1432,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, "papermill": { "default_parameters": {}, "duration": 167.355469, diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb index bc1edb3d..81335340 100644 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/00_cpu_1_thread_no_jit.ipynb @@ -1465,18 +1465,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, "papermill": { "default_parameters": {}, "duration": 167.355469, diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb index 7eec9749..ac0ebe48 100644 --- a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/01_cpu_8_threads_no_jit.ipynb @@ -1452,18 +1452,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, "papermill": { "default_parameters": {}, "duration": 167.355469, diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb new file mode 100644 index 00000000..2c1769ed --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb @@ -0,0 +1,1595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Multi-threading version of code in `09`" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-06-04 09:14:42,188 - numba.cuda.cudadrv.driver] INFO: init\n", + "[2024-06-04 09:14:42,300 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-04 09:14:42,301 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-04 09:14:42,301 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,301 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,302 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 16 bytes\n", + "[2024-06-04 09:14:42,374 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 16 bytes\n", + "[2024-06-04 09:14:42,374 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 8 bytes\n", + 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"c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c0341a42-b8de-419f-ab37-1e4fee9dde75", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.416790Z", + "iopub.status.busy": "2021-12-02T04:34:11.416353Z", + "iopub.status.idle": "2021-12-02T04:34:11.418488Z", + "shell.execute_reply": "2021-12-02T04:34:11.418030Z" + }, + "papermill": { + "duration": 0.098372, + "end_time": "2021-12-02T04:34:11.418578", + "exception": false, + "start_time": "2021-12-02T04:34:11.320206", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-06-04 09:14:42,636 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-04 09:14:42,637 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-04 09:14:42,637 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,637 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,638 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 16 bytes\n", + "[2024-06-04 09:14:42,638 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-04 09:14:42,638 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,638 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + 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numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,654 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-04 09:14:42,654 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-04 09:14:42,654 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-04 09:14:42,654 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 8 bytes\n" + ] + }, + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dfbd22fe", + "metadata": {}, + "outputs": [], + "source": [ + "# Disable numba logging\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d63012be-4fc7-4fba-bccd-ad155905d1d6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.418916Z", + "iopub.status.busy": "2021-12-02T04:34:21.418477Z", + "iopub.status.idle": "2021-12-02T04:34:21.420269Z", + "shell.execute_reply": "2021-12-02T04:34:21.420631Z" + }, + "papermill": { + "duration": 0.098786, + "end_time": "2021-12-02T04:34:21.420745", + "exception": false, + "start_time": "2021-12-02T04:34:21.321959", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "341 ms ± 1.26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_50.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 6144 function calls in 0.346 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.346 0.346 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.346 0.346 :1()\n", + " 1 0.000 0.000 0.346 0.346 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.345 0.035 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.340 0.034 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.339 0.034 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 0.339 0.034 impl.py:192(cdist_parts_parallel)\n", + " 140 0.001 0.000 0.337 0.002 threading.py:280(wait)\n", + " 550 0.336 0.001 0.336 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 70 0.001 0.000 0.336 0.005 _base.py:201(as_completed)\n", + " 70 0.000 0.000 0.336 0.005 threading.py:563(wait)\n", + " 70 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 70 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.003 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.003 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 70 0.000 0.000 0.001 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.001 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_50.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "754 ms ± 4.44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_100.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.759 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.759 0.759 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.759 0.759 :1()\n", + " 1 0.000 0.000 0.759 0.759 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.759 0.076 impl.py:307(ccc)\n", + " 10 0.000 0.000 0.751 0.075 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.750 0.075 impl.py:485(cdist_func)\n", + " 10 0.002 0.000 0.750 0.075 impl.py:192(cdist_parts_parallel)\n", + " 200 0.001 0.000 0.747 0.004 threading.py:280(wait)\n", + " 100 0.001 0.000 0.746 0.007 _base.py:201(as_completed)\n", + " 790 0.746 0.001 0.746 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 100 0.000 0.000 0.745 0.007 threading.py:563(wait)\n", + " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 _base.py:418(result)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 20 0.000 0.000 0.003 0.000 _base.py:602(result_iterator)\n", + " 10 0.000 0.000 0.003 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.003 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.001 0.000 0.002 0.000 impl.py:210()\n", + " 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_100.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "770 ms ± 3.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_500.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.786 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.786 0.786 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.786 0.786 :1()\n", + " 1 0.000 0.000 0.786 0.786 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.786 0.079 impl.py:307(ccc)\n", + " 200 0.001 0.000 0.775 0.004 threading.py:280(wait)\n", + " 790 0.774 0.001 0.774 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.774 0.077 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.773 0.077 impl.py:485(cdist_func)\n", + " 10 0.002 0.000 0.773 0.077 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 0.770 0.008 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.769 0.008 threading.py:563(wait)\n", + " 100 0.000 0.000 0.008 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.007 0.000 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_500.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "802 ms ± 2.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_small_1000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 0.812 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 0.812 0.812 {built-in method builtins.exec}\n", + " 1 0.000 0.000 0.812 0.812 :1()\n", + " 1 0.000 0.000 0.812 0.812 2661685993.py:1(func)\n", + " 10 0.001 0.000 0.812 0.081 impl.py:307(ccc)\n", + " 200 0.001 0.000 0.801 0.004 threading.py:280(wait)\n", + " 790 0.801 0.001 0.801 0.001 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.796 0.080 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 0.796 0.080 impl.py:485(cdist_func)\n", + " 10 0.002 0.000 0.795 0.080 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 0.792 0.008 _base.py:201(as_completed)\n", + " 100 0.000 0.000 0.791 0.008 threading.py:563(wait)\n", + " 100 0.000 0.000 0.011 0.000 _base.py:418(result)\n", + " 20 0.000 0.000 0.011 0.001 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.001 0.000 impl.py:210()\n", + " 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_small_1000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.13 s ± 13.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_50000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 4.119 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 4.119 4.119 {built-in method builtins.exec}\n", + " 1 0.000 0.000 4.119 4.119 :1()\n", + " 1 0.012 0.012 4.119 4.119 2661685993.py:1(func)\n", + " 10 0.005 0.000 4.107 0.411 impl.py:307(ccc)\n", + " 200 0.001 0.000 4.088 0.020 threading.py:280(wait)\n", + " 790 4.087 0.005 4.087 0.005 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 3.239 0.324 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 3.238 0.324 impl.py:485(cdist_func)\n", + " 10 0.002 0.000 3.237 0.324 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 3.232 0.032 _base.py:201(as_completed)\n", + " 100 0.000 0.000 3.231 0.032 threading.py:563(wait)\n", + " 100 0.000 0.000 0.857 0.009 _base.py:418(result)\n", + " 20 0.000 0.000 0.857 0.043 _base.py:602(result_iterator)\n", + " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", + " 10 0.002 0.000 0.003 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 50 0.003 0.000 0.003 0.000 {built-in method numpy.zeros}\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_50000.txt\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.51 s ± 11.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '09-n_samples_large_100000.txt'. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8334 function calls in 7.515 seconds\n", + "\n", + " Ordered by: cumulative time\n", + " List reduced from 114 to 20 due to restriction <20>\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 7.515 7.515 {built-in method builtins.exec}\n", + " 1 0.000 0.000 7.515 7.515 :1()\n", + " 1 0.000 0.000 7.515 7.515 2661685993.py:1(func)\n", + " 10 0.008 0.001 7.515 0.751 impl.py:307(ccc)\n", + " 200 0.001 0.000 7.490 0.037 threading.py:280(wait)\n", + " 790 7.489 0.009 7.489 0.009 {method 'acquire' of '_thread.lock' objects}\n", + " 10 0.001 0.000 5.731 0.573 impl.py:492(compute_coef)\n", + " 10 0.000 0.000 5.730 0.573 impl.py:485(cdist_func)\n", + " 10 0.001 0.000 5.729 0.573 impl.py:192(cdist_parts_parallel)\n", + " 100 0.001 0.000 5.724 0.057 _base.py:201(as_completed)\n", + " 100 0.000 0.000 5.723 0.057 threading.py:563(wait)\n", + " 100 0.000 0.000 1.767 0.018 _base.py:418(result)\n", + " 20 0.000 0.000 1.767 0.088 _base.py:602(result_iterator)\n", + " 50 0.005 0.000 0.005 0.000 {built-in method numpy.zeros}\n", + " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", + " 10 0.002 0.000 0.003 0.000 impl.py:210()\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20 -T 09-n_samples_large_100000.txt\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 8fcfefd2a026bf3b359ecf603cd892a039a4d48a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 20 Jun 2024 13:36:48 -0600 Subject: [PATCH 010/134] bench]: Move test results --- .../01_ari_cuda_v0/00-n_samples_large_100000.txt | 0 .../01_ari_cuda_v0/00-n_samples_large_50000.txt | 0 .../01_ari_cuda_v0/00-n_samples_small_100.txt | 0 .../01_ari_cuda_v0/00-n_samples_small_1000.txt | 0 .../10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_50.txt | 0 .../01_ari_cuda_v0/00-n_samples_small_500.txt | 0 .../02_cuda_ari.ipynb => 01_ari_cuda_v0/00_cuda_ari.ipynb} | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename 09-n_samples_large_100000.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_100000.txt (100%) rename 09-n_samples_large_50000.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_50000.txt (100%) rename 09-n_samples_small_100.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_100.txt (100%) rename 09-n_samples_small_1000.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_1000.txt (100%) rename 09-n_samples_small_50.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_50.txt (100%) rename 09-n_samples_small_500.txt => nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_500.txt (100%) rename nbs/others/10_gpu_ari_profiling/{00_cpu_version_ref/02_cuda_ari.ipynb => 01_ari_cuda_v0/00_cuda_ari.ipynb} (100%) diff --git a/09-n_samples_large_100000.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_100000.txt similarity index 100% rename from 09-n_samples_large_100000.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_100000.txt diff --git a/09-n_samples_large_50000.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_50000.txt similarity index 100% rename from 09-n_samples_large_50000.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_large_50000.txt diff --git a/09-n_samples_small_100.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_100.txt similarity index 100% rename from 09-n_samples_small_100.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_100.txt diff --git a/09-n_samples_small_1000.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_1000.txt similarity index 100% rename from 09-n_samples_small_1000.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_1000.txt diff --git a/09-n_samples_small_50.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_50.txt similarity index 100% rename from 09-n_samples_small_50.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_50.txt diff --git a/09-n_samples_small_500.txt b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_500.txt similarity index 100% rename from 09-n_samples_small_500.txt rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00-n_samples_small_500.txt diff --git a/nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb b/nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00_cuda_ari.ipynb similarity index 100% rename from nbs/others/10_gpu_ari_profiling/00_cpu_version_ref/02_cuda_ari.ipynb rename to nbs/others/10_gpu_ari_profiling/01_ari_cuda_v0/00_cuda_ari.ipynb From c25080870ef89fcf127f83bf9510b9b6c7cb3609 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 20 Jun 2024 16:29:15 -0600 Subject: [PATCH 011/134] [bench]: Add template notebook --- .../99_scratch/profile0.ipynb | 238 ++++++++++++++++++ .../99_scratch/profile0_n_samples_500000.txt | 31 +++ .../10_gpu_ari_profiling/common/__init__.py | 0 .../10_gpu_ari_profiling/common/utils.py | 2 + setup_dev.sh | 3 + 5 files changed, 274 insertions(+) create mode 100644 nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/99_scratch/profile0_n_samples_500000.txt create mode 100644 nbs/others/10_gpu_ari_profiling/common/__init__.py create mode 100644 nbs/others/10_gpu_ari_profiling/common/utils.py create mode 100644 setup_dev.sh diff --git a/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb new file mode 100644 index 00000000..9593ea16 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "from pathlib import Path\n", + "from IPython import get_ipython\n", + "\n", + "root_dir = Path(os.path.abspath('')) / 'nbs/others/10_gpu_ari_profiling'\n", + "common_dir = root_dir / 'common'\n", + "sys.path.append(str(common_dir))\n", + "\n", + "this_name = \"profile0\"\n", + "this_path = root_dir / '99_scratch' / this_name" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from utils import generate_bench_filename\n", + "from ccc.coef import ccc" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# Disable Numba cuda info\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(0)\n", + "\n", + "N_REPS = 10\n", + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)\n", + " \n", + "N_SAMPLES = 500000\n", + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "outfile = generate_bench_filename(this_path, \"n_samples\", N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "*** Profile printout saved to text file '/home/haoyu/_database/projs/ccc-gpu/nbs/others/10_gpu_ari_profiling/99_scratch/profile0_n_samples_500000.txt'. \n", + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 665 function calls in 39.215 seconds\n", + "\n", + " Ordered by: internal time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 39.215 39.215 39.215 39.215 {method 'enable' of '_lsprof.Profiler' objects}\n", + " 7 0.000 0.000 0.000 0.000 shlex.py:133(read_token)\n", + " 1 0.000 0.000 39.215 39.215 {built-in method builtins.exec}\n", + " 6 0.000 0.000 0.000 0.000 tokenize.py:429(_tokenize)\n", + " 22 0.000 0.000 0.000 0.000 traitlets.py:676(__get__)\n", + " 22 0.000 0.000 0.000 0.000 traitlets.py:629(get)\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:2909(_bind)\n", + " 1 0.000 0.000 0.000 0.000 magic.py:621(parse_options)\n", + " 149 0.000 0.000 0.000 0.000 shlex.py:68(punctuation_chars)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:462(make_tokens_by_line)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:255(find_handler)\n", + " 1 0.000 0.000 0.000 0.000 getopt.py:56(getopt)\n", + " 1 0.000 0.000 0.000 0.000 ipstruct.py:273(merge)\n", + " 1 0.000 0.000 0.000 0.000 interactiveshell.py:3275(transform_cell)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:579(transform_cell)\n", + " 134 0.000 0.000 0.000 0.000 {method 'read' of '_io.StringIO' objects}\n", + " 7 0.000 0.000 0.000 0.000 shlex.py:101(get_token)\n", + " 8 0.000 0.000 0.000 0.000 {method 'match' of 're.Pattern' objects}\n", + " 3 0.000 0.000 0.000 0.000 getopt.py:192(do_shorts)\n", + " 1 0.000 0.000 39.215 39.215 interactiveshell.py:2430(run_cell_magic)\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:2648(args)\n", + " 15 0.000 0.000 0.000 0.000 {built-in method builtins.next}\n", + " 7 0.000 0.000 0.000 0.000 shlex.py:299(__next__)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:538(do_one_token_transform)\n", + " 1 0.000 0.000 39.215 39.215 execution.py:195(prun)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:271(prefilter_line)\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:2701(apply_defaults)\n", + " 1 0.000 0.000 39.215 39.215 execution.py:319(_run_with_profiler)\n", + " 1 0.000 0.000 0.000 0.000 splitinput.py:53(split_user_input)\n", + " 3 0.000 0.000 0.000 0.000 getopt.py:207(short_has_arg)\n", + " 1 0.000 0.000 0.000 0.000 _process_common.py:177(arg_split)\n", + " 4 0.000 0.000 0.000 0.000 re.py:289(_compile)\n", + " 1 0.000 0.000 0.000 0.000 decorator.py:199(fix)\n", + " 5 0.000 0.000 0.000 0.000 :1()\n", + " 4 0.000 0.000 0.000 0.000 inputtransformer2.py:108(_find_assign_op)\n", + " 1 0.000 0.000 0.000 0.000 shlex.py:21(__init__)\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:2671(kwargs)\n", + " 1 0.000 0.000 0.000 0.000 getipython.py:17(get_ipython)\n", + " 4 0.000 0.000 0.000 0.000 types.py:171(__get__)\n", + " 44 0.000 0.000 0.000 0.000 typing.py:1375(cast)\n", + " 4 0.000 0.000 0.000 0.000 tokenize.py:98(_compile)\n", + " 1 0.000 0.000 39.215 39.215 :1()\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:314(prefilter_lines)\n", + " 4 0.000 0.000 0.000 0.000 re.py:250(compile)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:458(check)\n", + " 1 0.000 0.000 0.000 0.000 interactiveshell.py:2481(find_cell_magic)\n", + " 2 0.000 0.000 0.000 0.000 ipstruct.py:41(__init__)\n", + " 1 0.000 0.000 39.215 39.215 decorator.py:229(fun)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:23(leading_empty_lines)\n", + " 13 0.000 0.000 0.000 0.000 {method 'startswith' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 splitinput.py:110(__init__)\n", + " 21 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:36(leading_indent)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:216(find)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:570(do_token_transforms)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:368(find)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:414(check)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:482(check)\n", + " 4 0.000 0.000 0.000 0.000 ipstruct.py:66(__setitem__)\n", + " 15 0.000 0.000 0.000 0.000 inspect.py:2560(kind)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:426(check)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:246(prefilter_line_info)\n", + " 2 0.000 0.000 0.000 0.000 inputtransformer2.py:81(__call__)\n", + " 15 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", + " 1 0.000 0.000 0.000 0.000 interactiveshell.py:2344(_find_with_lazy_load)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:248(find)\n", + " 1 0.000 0.000 0.000 0.000 interactiveshell.py:2487(find_magic)\n", + " 1 0.000 0.000 0.000 0.000 configurable.py:597(initialized)\n", + " 11 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:3040(bind)\n", + " 9 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", + " 1 0.000 0.000 39.215 39.215 magic.py:187()\n", + " 1 0.000 0.000 0.000 0.000 configurable.py:553(instance)\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:264(transform_line)\n", + " 1 0.000 0.000 39.215 39.215 cProfile.py:98(runctx)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:428(find)\n", + " 2 0.000 0.000 0.000 0.000 builtin_trap.py:39(__enter__)\n", + " 2 0.000 0.000 0.000 0.000 {method 'split' of 'str' objects}\n", + " 5 0.000 0.000 0.000 0.000 {built-in method __new__ of type object at 0x5d1ba0e40380}\n", + " 2 0.000 0.000 0.000 0.000 prefilter.py:234(get_handler_by_name)\n", + " 3 0.000 0.000 0.000 0.000 {method 'splitlines' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 {built-in method fromkeys}\n", + " 6 0.000 0.000 0.000 0.000 inspect.py:2548(name)\n", + " 1 0.000 0.000 0.000 0.000 encoding.py:21(get_stream_enc)\n", + " 1 0.000 0.000 0.000 0.000 inputtransformer2.py:96(cell_magic)\n", + " 4 0.000 0.000 0.000 0.000 enum.py:792(value)\n", + " 2 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}\n", + " 3 0.000 0.000 0.000 0.000 ipstruct.py:364()\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:183(checkers)\n", + " 4 0.000 0.000 0.000 0.000 inspect.py:2865(parameters)\n", + " 4 0.000 0.000 0.000 0.000 {method 'span' of 're.Match' objects}\n", + " 1 0.000 0.000 0.000 0.000 builtin_trap.py:46(__exit__)\n", + " 2 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", + " 3 0.000 0.000 0.000 0.000 {method 'items' of 'mappingproxy' objects}\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:549(handle)\n", + " 3 0.000 0.000 0.000 0.000 {built-in method builtins.iter}\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:440(check)\n", + " 1 0.000 0.000 0.000 0.000 {method 'groups' of 're.Match' objects}\n", + " 3 0.000 0.000 0.000 0.000 {method 'isidentifier' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 py3compat.py:26(cast_unicode)\n", + " 1 0.000 0.000 0.000 0.000 inspect.py:2640(__init__)\n", + " 3 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 tokenize.py:612(generate_tokens)\n", + " 2 0.000 0.000 0.000 0.000 {method 'isspace' of 'str' objects}\n", + " 3 0.000 0.000 0.000 0.000 {method 'strip' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 {method 'values' of 'mappingproxy' objects}\n", + " 1 0.000 0.000 0.000 0.000 {method 'endswith' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 prefilter.py:147(transformers)\n", + " 1 0.000 0.000 0.000 0.000 {method 'rstrip' of 'str' objects}\n", + " 1 0.000 0.000 0.000 0.000 {built-in method sys._getframe}\n", + " 1 0.000 0.000 0.000 0.000 {method 'lstrip' of 'str' objects}" + ] + } + ], + "source": [ + "prun_cmd = f\"%%prun -s cumulative -l 25 -T {outfile}\"\n", + "get_ipython().run_cell_magic('prun', prun_cmd, 'func()')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nbs/others/10_gpu_ari_profiling/99_scratch/profile0_n_samples_500000.txt b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0_n_samples_500000.txt new file mode 100644 index 00000000..aa28c801 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0_n_samples_500000.txt @@ -0,0 +1,31 @@ + 8334 function calls in 39.213 seconds + + Ordered by: cumulative time + List reduced from 113 to 25 due to restriction <25> + + ncalls tottime percall cumtime percall filename:lineno(function) + 1 0.000 0.000 39.213 39.213 {built-in method builtins.exec} + 1 0.000 0.000 39.213 39.213 :1() + 1 0.007 0.007 39.213 39.213 1858089180.py:4(func) + 10 0.047 0.005 39.206 3.921 impl.py:307(ccc) + 200 0.002 0.000 39.130 0.196 threading.py:280(wait) + 790 39.128 0.050 39.128 0.050 {method 'acquire' of '_thread.lock' objects} + 10 0.001 0.000 28.402 2.840 impl.py:492(compute_coef) + 10 0.000 0.000 28.401 2.840 impl.py:485(cdist_func) + 10 0.002 0.000 28.400 2.840 impl.py:192(cdist_parts_parallel) + 100 0.001 0.000 28.390 0.284 threading.py:563(wait) + 100 0.002 0.000 28.388 0.284 _base.py:201(as_completed) + 100 0.001 0.000 10.742 0.107 _base.py:418(result) + 20 0.000 0.000 10.742 0.537 _base.py:602(result_iterator) + 10 0.007 0.001 0.009 0.001 impl.py:210() + 100 0.000 0.000 0.008 0.000 thread.py:161(submit) + 100 0.000 0.000 0.007 0.000 thread.py:180(_adjust_thread_count) + 10 0.000 0.000 0.007 0.001 _base.py:573(map) + 10 0.000 0.000 0.007 0.001 _base.py:598() + 10 0.000 0.000 0.006 0.001 threading.py:880(start) + 50 0.005 0.000 0.005 0.000 {built-in method numpy.zeros} + 190 0.001 0.000 0.001 0.000 _base.py:179(_yield_finished_futures) + 20 0.001 0.000 0.001 0.000 impl.py:242(get_chunks) + 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__) + 10 0.000 0.000 0.001 0.000 thread.py:216(shutdown) + 100 0.000 0.000 0.001 0.000 threading.py:411(acquire) \ No newline at end of file diff --git a/nbs/others/10_gpu_ari_profiling/common/__init__.py b/nbs/others/10_gpu_ari_profiling/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/nbs/others/10_gpu_ari_profiling/common/utils.py b/nbs/others/10_gpu_ari_profiling/common/utils.py new file mode 100644 index 00000000..5702c3e1 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/common/utils.py @@ -0,0 +1,2 @@ +def generate_bench_filename(filename: str, var_name: str, var_value: int): + return f"{filename}_{var_name}_{var_value}.txt" diff --git a/setup_dev.sh b/setup_dev.sh new file mode 100644 index 00000000..98f1e77c --- /dev/null +++ b/setup_dev.sh @@ -0,0 +1,3 @@ +conda activate ccc +export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH +eval `python ./libs/ccc/conf.py` From 1c4ddf1f66c7b127ca5d86e57c6be2ffc28d259c Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 28 Jun 2024 09:43:52 -0600 Subject: [PATCH 012/134] [bench]: Add pyinstrument metrics --- benchmark/trace.py | 14 +- libs/ccc/coef/impl.py | 4 +- .../00_1_core_pyinstrument.ipynb | 1437 +++++++++++++++++ .../99_scratch/profile0.ipynb | 5 +- setup_dev.sh | 1 + 5 files changed, 1448 insertions(+), 13 deletions(-) create mode 100644 nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_pyinstrument.ipynb mode change 100644 => 100755 setup_dev.sh diff --git a/benchmark/trace.py b/benchmark/trace.py index 7d3a709c..e2ff2583 100644 --- a/benchmark/trace.py +++ b/benchmark/trace.py @@ -1,19 +1,13 @@ from ccc.coef import ccc import numpy as np -from pycallgraph import PyCallGraph -from pycallgraph.output import GraphvizOutput -from pycallgraph import Config - def main(): - random_feature1 = np.random.rand(1000) - random_feature2 = np.random.rand(1000) + random_feature1 = np.random.rand(100) + random_feature2 = np.random.rand(100) - config = Config(max_depth=10) - with PyCallGraph(output=GraphvizOutput(), config=config): - res = ccc(random_feature1, random_feature2) - print(res) + res = ccc(random_feature1, random_feature2, n_jobs=2) + print(res) if __name__ == "__main__": diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index abfc74a8..4758f769 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -13,7 +13,8 @@ from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari +from ccc.sklearn.metrics import adjusted_rand_index as ari +# from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor @@ -670,6 +671,7 @@ def ccc( # get number of cores to use n_workers = get_n_workers(n_jobs) + # Converts internal_n_clusters to a list of integers if it's provided. if internal_n_clusters is not None: _tmp_list = List() diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_pyinstrument.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_pyinstrument.ipynb new file mode 100644 index 00000000..347d1eba --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_pyinstrument.ipynb @@ -0,0 +1,1437 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CM_N_JOBS=1\n" + ] + } + ], + "source": [ + "# Set core count to 1\n", + "%env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.63 ms ± 147 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:30:54 Samples: 10\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.011 CPU time: 0.012\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "0.010 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 0.010 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 0.010 ccc ccc/coef/impl.py:308\n", + " |- 0.008 compute_coef ccc/coef/impl.py:494\n", + " | |- 0.006 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.006 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.005 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.005 lock.acquire \n", + " | | `- 0.001 [self] ccc/coef/impl.py\n", + " | |- 0.001 [self] ccc/coef/impl.py\n", + " | `- 0.001 amax <__array_function__ internals>:2\n", + " | [4 frames hidden] <__array_function__ internals>, numpy...\n", + " `- 0.002 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.002 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.9 ms ± 289 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:31:10 Samples: 20\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.021 CPU time: 0.023\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "0.021 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 0.021 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 0.021 ccc ccc/coef/impl.py:308\n", + " |- 0.016 compute_coef ccc/coef/impl.py:494\n", + " | |- 0.014 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.014 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | `- 0.014 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.014 lock.acquire \n", + " | |- 0.001 amax <__array_function__ internals>:2\n", + " | | [4 frames hidden] <__array_function__ internals>, numpy\n", + " | `- 0.001 [self] ccc/coef/impl.py\n", + " |- 0.003 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " | [5 frames hidden] concurrent, threading, \n", + " |- 0.001 [self] ccc/coef/impl.py\n", + " `- 0.001 function.map concurrent/futures/_base.py:573\n", + " [8 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "34 ms ± 344 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:31:13 Samples: 30\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.036 CPU time: 0.039\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "0.036 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 0.036 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 0.036 ccc ccc/coef/impl.py:308\n", + " |- 0.025 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.025 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.025 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.023 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.023 lock.acquire \n", + " | `- 0.002 ccc/coef/impl.py:211\n", + " `- 0.011 result_iterator concurrent/futures/_base.py:602\n", + " [5 frames hidden] concurrent, threading, \n", + " 0.010 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.8 ms ± 342 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:31:18 Samples: 42\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.061 CPU time: 0.064\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "0.060 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 0.060 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 0.060 ccc ccc/coef/impl.py:308\n", + " |- 0.039 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.039 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.039 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 0.039 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.039 lock.acquire \n", + " `- 0.021 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.021 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.03 s ± 14.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:32:07 Samples: 117\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 3.032 CPU time: 3.049\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "3.032 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 3.032 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 3.026 ccc ccc/coef/impl.py:308\n", + " |- 2.140 compute_coef ccc/coef/impl.py:494\n", + " | `- 2.140 cdist_func ccc/coef/impl.py:487\n", + " | `- 2.140 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 2.135 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 2.134 lock.acquire \n", + " `- 0.879 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.879 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.21 s ± 25.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:33:49 Samples: 130\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 6.094 CPU time: 6.112\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-8902ea9b-48cc-4415-91ca-e3b7a7a3f867.json\n", + "\n", + "6.094 ../../../tmp/ipykernel_1934644/2991494264.py:2\n", + "`- 6.094 func ../../../tmp/ipykernel_1934644/2661685993.py:1\n", + " `- 6.080 ccc ccc/coef/impl.py:308\n", + " |- 4.257 compute_coef ccc/coef/impl.py:494\n", + " | `- 4.257 cdist_func ccc/coef/impl.py:487\n", + " | `- 4.257 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 4.247 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 4.247 lock.acquire \n", + " `- 1.810 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 1.810 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb index 9593ea16..b067b02b 100644 --- a/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb +++ b/nbs/others/10_gpu_ari_profiling/99_scratch/profile0.ipynb @@ -209,8 +209,9 @@ } ], "source": [ - "prun_cmd = f\"%%prun -s cumulative -l 25 -T {outfile}\"\n", - "get_ipython().run_cell_magic('prun', prun_cmd, 'func()')" + "# prun_cmd = f\"%%prun -s cumulative -l 25 -T {outfile}\"\n", + "# get_ipython().run_cell_magic('prun', prun_cmd, 'func()')\n", + "# Use cProfile instead of prun magic" ] } ], diff --git a/setup_dev.sh b/setup_dev.sh old mode 100644 new mode 100755 index 98f1e77c..46ab76fc --- a/setup_dev.sh +++ b/setup_dev.sh @@ -1,3 +1,4 @@ conda activate ccc +export CODE_DIR=/home/haoyu/_database/projs/ccc-gpu export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH eval `python ./libs/ccc/conf.py` From 1e02f13c00f326eaf0336e4da5079a9f0d19d4a6 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 28 Jun 2024 10:32:57 -0600 Subject: [PATCH 013/134] [bench]: Add pyinstrument metrics for different cores --- .../00_12_core_pyinstrument.ipynb | 1472 ++++++++++++++++ .../00_1_core_cprofile.ipynb | 1568 +++++++++++++++++ .../00_24_core_pyinstrument.ipynb | 1477 ++++++++++++++++ .../00_6_core_pyinstrument.ipynb | 1465 +++++++++++++++ 4 files changed, 5982 insertions(+) create mode 100644 nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_12_core_pyinstrument.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_24_core_pyinstrument.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_12_core_pyinstrument.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_12_core_pyinstrument.ipynb new file mode 100644 index 00000000..105762a9 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_12_core_pyinstrument.ipynb @@ -0,0 +1,1472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CM_N_JOBS=1\n" + ] + } + ], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=12)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14.7 ms ± 35.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:38:47 Samples: 16\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.017 CPU time: 0.023\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "0.017 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "`- 0.017 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + " `- 0.017 ccc ccc/coef/impl.py:308\n", + " |- 0.013 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.013 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.013 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.009 ccc/coef/impl.py:211\n", + " | | `- 0.009 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [10 frames hidden] concurrent, threading, , ip...\n", + " | | 0.008 lock.acquire \n", + " | |- 0.003 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | `- 0.001 [self] ccc/coef/impl.py\n", + " |- 0.002 result_iterator concurrent/futures/_base.py:602\n", + " | [5 frames hidden] concurrent, threading, \n", + " |- 0.001 [self] ccc/coef/impl.py\n", + " `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " [5 frames hidden] concurrent, threading\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:38:49 Samples: 26\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.029 CPU time: 0.051\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "0.028 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "`- 0.028 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + " `- 0.028 ccc ccc/coef/impl.py:308\n", + " |- 0.024 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.024 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.024 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.013 ccc/coef/impl.py:211\n", + " | | |- 0.012 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | | [9 frames hidden] concurrent, threading, , ip...\n", + " | | | 0.010 lock.acquire \n", + " | | `- 0.001 [self] ccc/coef/impl.py\n", + " | `- 0.011 as_completed concurrent/futures/_base.py:201\n", + " | [5 frames hidden] concurrent, threading, \n", + " | 0.010 lock.acquire \n", + " |- 0.003 function.map concurrent/futures/_base.py:573\n", + " | [9 frames hidden] concurrent, threading, \n", + " `- 0.001 [self] ccc/coef/impl.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.4 ms ± 123 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:38:52 Samples: 30\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.032 CPU time: 0.075\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "0.032 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "|- 0.031 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + "| `- 0.031 ccc ccc/coef/impl.py:308\n", + "| |- 0.023 compute_coef ccc/coef/impl.py:494\n", + "| | `- 0.023 cdist_func ccc/coef/impl.py:487\n", + "| | `- 0.023 cdist_parts_parallel ccc/coef/impl.py:193\n", + "| | |- 0.013 ccc/coef/impl.py:211\n", + "| | | `- 0.013 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + "| | | [8 frames hidden] concurrent, threading, \n", + "| | | 0.010 lock.acquire \n", + "| | |- 0.010 as_completed concurrent/futures/_base.py:201\n", + "| | | [4 frames hidden] concurrent, threading, \n", + "| | | 0.010 lock.acquire \n", + "| | `- 0.001 [self] ccc/coef/impl.py\n", + "| |- 0.003 result_iterator concurrent/futures/_base.py:602\n", + "| | [4 frames hidden] concurrent, threading, \n", + "| |- 0.003 function.map concurrent/futures/_base.py:573\n", + "| | [9 frames hidden] concurrent, threading, , ip...\n", + "| `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + "| [5 frames hidden] concurrent, threading, \n", + "`- 0.001 Profiler.stop pyinstrument/profiler.py:138\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "34.9 ms ± 197 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:38:55 Samples: 34\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.038 CPU time: 0.101\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "0.037 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "`- 0.037 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + " |- 0.035 ccc ccc/coef/impl.py:308\n", + " | |- 0.029 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.029 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.029 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.016 ccc/coef/impl.py:211\n", + " | | | |- 0.014 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | | | [10 frames hidden] concurrent, threading, , ip...\n", + " | | | | 0.011 lock.acquire \n", + " | | | `- 0.002 [self] ccc/coef/impl.py\n", + " | | `- 0.012 as_completed concurrent/futures/_base.py:201\n", + " | | [6 frames hidden] concurrent, threading, \n", + " | | 0.010 lock.acquire \n", + " | |- 0.005 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | |- 0.001 full numpy/core/numeric.py:289\n", + " | | [3 frames hidden] numpy, <__array_function__ internals>...\n", + " | `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " | [4 frames hidden] concurrent, threading\n", + " `- 0.002 [self] ../../../tmp/ipykernel_1959742/2380024278.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "827 ms ± 1.54 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:39:09 Samples: 130\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.825 CPU time: 3.196\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "0.824 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "`- 0.824 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + " `- 0.817 ccc ccc/coef/impl.py:308\n", + " |- 0.461 result_iterator concurrent/futures/_base.py:602\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.461 lock.acquire \n", + " `- 0.344 compute_coef ccc/coef/impl.py:494\n", + " `- 0.343 cdist_func ccc/coef/impl.py:487\n", + " `- 0.343 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.328 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.328 lock.acquire \n", + " `- 0.014 ccc/coef/impl.py:211\n", + " `- 0.009 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [2 frames hidden] concurrent\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.67 s ± 6.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:39:37 Samples: 147\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.672 CPU time: 6.485\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-a72911ea-456f-4e5b-8129-d7dd4e04f39f.json\n", + "\n", + "1.671 ../../../tmp/ipykernel_1959742/2991494264.py:2\n", + "`- 1.671 func ../../../tmp/ipykernel_1959742/2380024278.py:1\n", + " |- 1.654 ccc ccc/coef/impl.py:308\n", + " | |- 0.948 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.948 lock.acquire \n", + " | |- 0.671 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.671 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.671 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.654 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.654 lock.acquire \n", + " | | `- 0.017 ccc/coef/impl.py:211\n", + " | `- 0.020 [self] ccc/coef/impl.py\n", + " `- 0.018 [self] ../../../tmp/ipykernel_1959742/2380024278.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb new file mode 100644 index 00000000..ff9b31d6 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb @@ -0,0 +1,1568 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CM_N_JOBS=1\n" + ] + } + ], + "source": [ + "# Set core count to 1\n", + "%env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "import cProfile" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.51 ms ± 83.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:50:58 Samples: 10\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.011 CPU time: 0.013\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-76f1e206-b6a7-40f2-8d7f-4e421d5c241e.json\n", + "\n", + "0.010 ../../../tmp/ipykernel_359614/2991494264.py:2\n", + "`- 0.010 func ../../../tmp/ipykernel_359614/2661685993.py:1\n", + " `- 0.010 ccc ccc/coef/impl.py:308\n", + " `- 0.010 compute_coef ccc/coef/impl.py:493\n", + " `- 0.010 cdist_func ccc/coef/impl.py:486\n", + " `- 0.010 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.009 as_completed concurrent/futures/_base.py:201\n", + " | [5 frames hidden] concurrent, threading, \n", + " | 0.008 lock.acquire \n", + " `- 0.001 [self] ccc/coef/impl.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.4 ms ± 219 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:51:13 Samples: 18\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.020 CPU time: 0.022\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-76f1e206-b6a7-40f2-8d7f-4e421d5c241e.json\n", + "\n", + "0.019 ../../../tmp/ipykernel_359614/2991494264.py:2\n", + "`- 0.019 func ../../../tmp/ipykernel_359614/2661685993.py:1\n", + " |- 0.018 ccc ccc/coef/impl.py:308\n", + " | |- 0.015 compute_coef ccc/coef/impl.py:493\n", + " | | `- 0.015 cdist_func ccc/coef/impl.py:486\n", + " | | `- 0.015 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.013 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.013 lock.acquire \n", + " | | |- 0.001 ccc/utils/utility_functions.py:117\n", + " | | `- 0.001 ccc/coef/impl.py:211\n", + " | | `- 0.001 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [5 frames hidden] concurrent, threading\n", + " | |- 0.002 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " | [5 frames hidden] concurrent, threading, \n", + " `- 0.001 [self] ../../../tmp/ipykernel_359614/2661685993.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33.2 ms ± 178 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:51:16 Samples: 30\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.035 CPU time: 0.037\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-76f1e206-b6a7-40f2-8d7f-4e421d5c241e.json\n", + "\n", + "0.034 ../../../tmp/ipykernel_359614/2991494264.py:2\n", + "`- 0.034 func ../../../tmp/ipykernel_359614/2661685993.py:1\n", + " `- 0.034 ccc ccc/coef/impl.py:308\n", + " |- 0.025 compute_coef ccc/coef/impl.py:493\n", + " | `- 0.025 cdist_func ccc/coef/impl.py:486\n", + " | `- 0.025 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.022 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.022 lock.acquire \n", + " | |- 0.002 ccc/coef/impl.py:211\n", + " | | `- 0.002 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [4 frames hidden] concurrent, threading\n", + " | `- 0.001 Future.result concurrent/futures/_base.py:418\n", + " |- 0.009 result_iterator concurrent/futures/_base.py:602\n", + " | [5 frames hidden] concurrent, threading, \n", + " | 0.008 lock.acquire \n", + " `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " [3 frames hidden] concurrent, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55.5 ms ± 477 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:51:21 Samples: 41\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.057 CPU time: 0.061\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-76f1e206-b6a7-40f2-8d7f-4e421d5c241e.json\n", + "\n", + "0.057 ../../../tmp/ipykernel_359614/2991494264.py:2\n", + "`- 0.057 func ../../../tmp/ipykernel_359614/2661685993.py:1\n", + " `- 0.057 ccc ccc/coef/impl.py:308\n", + " |- 0.040 compute_coef ccc/coef/impl.py:493\n", + " | `- 0.040 cdist_func ccc/coef/impl.py:486\n", + " | `- 0.040 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 0.040 as_completed concurrent/futures/_base.py:201\n", + " | [5 frames hidden] concurrent, threading, \n", + " | 0.039 lock.acquire \n", + " `- 0.017 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.017 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.99 s ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:52:09 Samples: 118\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.978 CPU time: 2.996\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-76f1e206-b6a7-40f2-8d7f-4e421d5c241e.json\n", + "\n", + "2.977 ../../../tmp/ipykernel_359614/2991494264.py:2\n", + "`- 2.977 func ../../../tmp/ipykernel_359614/2661685993.py:1\n", + " `- 2.966 ccc ccc/coef/impl.py:308\n", + " |- 2.100 compute_coef ccc/coef/impl.py:493\n", + " | `- 2.100 cdist_func ccc/coef/impl.py:486\n", + " | `- 2.100 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 2.093 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 2.093 lock.acquire \n", + " `- 0.864 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.864 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.17 s ± 40 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 8394 function calls in 6.163 seconds\n", + "\n", + " Ordered by: internal time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 790 6.112 0.008 6.112 0.008 {method 'acquire' of '_thread.lock' objects}\n", + " 1 0.017 0.017 6.163 6.163 2661685993.py:1(func)\n", + " 50 0.010 0.000 0.010 0.000 {built-in method numpy.zeros}\n", + " 10 0.008 0.001 6.145 0.615 impl.py:308(ccc)\n", + " 10 0.005 0.000 0.006 0.001 impl.py:211()\n", + " 10 0.001 0.000 4.302 0.430 impl.py:193(cdist_parts_parallel)\n", + " 100 0.001 0.000 4.294 0.043 _base.py:201(as_completed)\n", + " 200 0.001 0.000 6.113 0.031 threading.py:280(wait)\n", + " 10 0.001 0.000 4.303 0.430 impl.py:493(compute_coef)\n", + " 190 0.000 0.000 0.001 0.000 _base.py:179(_yield_finished_futures)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method _thread.start_new_thread}\n", + " 100 0.000 0.000 0.004 0.000 thread.py:161(submit)\n", + " 100 0.000 0.000 4.293 0.043 threading.py:563(wait)\n", + " 100 0.000 0.000 1.821 0.018 _base.py:418(result)\n", + " 480 0.000 0.000 0.000 0.000 threading.py:259(__exit__)\n", + " 130 0.000 0.000 0.000 0.000 threading.py:228(__init__)\n", + " 20 0.000 0.000 0.000 0.000 impl.py:243(get_chunks)\n", + " 480 0.000 0.000 0.000 0.000 threading.py:256(__enter__)\n", + " 100 0.000 0.000 0.001 0.000 threading.py:411(acquire)\n", + " 10 0.000 0.000 4.302 0.430 impl.py:486(cdist_func)\n", + " 100 0.000 0.000 0.003 0.000 thread.py:180(_adjust_thread_count)\n", + " 10 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}\n", + " 190 0.000 0.000 0.000 0.000 threading.py:268(_acquire_restore)\n", + " 90 0.000 0.000 0.000 0.000 threading.py:553(clear)\n", + " 100 0.000 0.000 0.000 0.000 _base.py:318(__init__)\n", + " 40 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}\n", + " 110 0.000 0.000 0.000 0.000 {method 'put' of '_queue.SimpleQueue' objects}\n", + " 30 0.000 0.000 0.000 0.000 {built-in method numpy.arange}\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:69(_wrapreduction)\n", + " 600 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.lock' objects}\n", + " 140 0.000 0.000 0.000 0.000 utility_functions.py:117()\n", + " 10 0.000 0.000 0.000 0.000 thread.py:123(__init__)\n", + " 190 0.000 0.000 0.000 0.000 threading.py:271(_is_owned)\n", + " 190 0.000 0.000 0.000 0.000 threading.py:265(_release_save)\n", + " 30 0.000 0.000 0.000 0.000 numeric.py:289(full)\n", + " 250 0.000 0.000 0.000 0.000 {built-in method _thread.allocate_lock}\n", + " 20 0.000 0.000 1.821 0.091 _base.py:602(result_iterator)\n", + " 190 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.RLock' objects}\n", + " 180 0.000 0.000 0.000 0.000 {method 'remove' of 'set' objects}\n", + " 10 0.000 0.000 0.000 0.000 threading.py:802(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:992(_stop)\n", + " 30 0.000 0.000 0.000 0.000 utility_functions.py:109(chunker)\n", + " 10 0.000 0.000 0.001 0.000 thread.py:216(shutdown)\n", + " 30 0.000 0.000 0.000 0.000 {built-in method numpy.empty}\n", + " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'list' objects}\n", + " 100 0.000 0.000 0.000 0.000 thread.py:47(__init__)\n", + " 100 0.000 0.000 0.000 0.000 _base.py:388(__get_result)\n", + " 100 0.000 0.000 0.000 0.000 threading.py:82(RLock)\n", + " 10 0.000 0.000 0.000 0.000 {method 'argmax' of 'numpy.ndarray' objects}\n", + " 100 0.000 0.000 0.000 0.000 {method 'pop' of 'list' objects}\n", + " 290 0.000 0.000 0.000 0.000 {method '__enter__' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.000 0.000 core.py:85(unravel_index_2d)\n", + " 30 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(copyto)\n", + " 10 0.000 0.000 0.001 0.000 threading.py:1028(join)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method builtins.sorted}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:157(_create_and_install_waiters)\n", + " 10 0.000 0.000 0.001 0.000 threading.py:1066(_wait_for_tstate_lock)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2638(amax)\n", + " 10 0.000 0.000 0.000 0.000 <__array_function__ internals>:2(amax)\n", + " 10 0.000 0.000 0.000 0.000 impl.py:75(get_range_n_clusters)\n", + " 200 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.RLock' objects}\n", + " 20 0.000 0.000 0.000 0.000 threading.py:1358(current_thread)\n", + " 10 0.000 0.000 0.001 0.000 _base.py:636(__exit__)\n", + " 2 0.000 0.000 0.000 0.000 {built-in method builtins.compile}\n", + " 10 0.000 0.000 0.002 0.000 threading.py:880(start)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:79(__init__)\n", + " 200 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects}\n", + " 10 0.000 0.000 0.000 0.000 threading.py:775(_maintain_shutdown_locks)\n", + " 10 0.000 0.000 0.000 0.000 weakref.py:370(remove)\n", + " 10 0.000 0.000 0.000 0.000 {method '_acquire_restore' of '_thread.RLock' objects}\n", + " 100 0.000 0.000 0.000 0.000 {method 'reverse' of 'list' objects}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:149(__enter__)\n", + " 180 0.000 0.000 0.000 0.000 {built-in method time.monotonic}\n", + " 200 0.000 0.000 0.000 0.000 {method 'release' of '_thread.lock' objects}\n", + " 10 0.000 0.000 0.000 0.000 ipkernel.py:763(init_closure)\n", + " 20 0.000 0.000 0.000 0.000 threading.py:528(__init__)\n", + " 10 0.000 0.000 0.002 0.000 _base.py:573(map)\n", + " 182 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", + " 10 0.000 0.000 0.002 0.000 _base.py:598()\n", + " 20 0.000 0.000 0.000 0.000 impl.py:285(get_feature_type_and_encode)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:63(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:405(__init__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:785()\n", + " 10 0.000 0.000 0.000 0.000 _weakrefset.py:39(_remove)\n", + " 14 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}\n", + " 10 0.000 0.000 0.000 0.000 impl.py:219(get_coords_from_index)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:146(__init__)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:153(__exit__)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:70()\n", + " 2 0.000 0.000 6.163 3.081 interactiveshell.py:3511(run_code)\n", + " 10 0.000 0.000 0.000 0.000 _base.py:225()\n", + " 10 0.000 0.000 0.000 0.000 _weakrefset.py:86(add)\n", + " 20 0.000 0.000 0.000 0.000 threading.py:1147(daemon)\n", + " 10 0.000 0.000 0.000 0.000 {method 'difference_update' of 'set' objects}\n", + " 10 0.000 0.000 0.000 0.000 weakref.py:428(__setitem__)\n", + " 10 0.000 0.000 0.000 0.000 threading.py:1229(_make_invoke_excepthook)\n", + " 90 0.000 0.000 0.000 0.000 {method 'remove' of 'collections.deque' objects}\n", + " 90 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.RLock' objects}\n", + " 2 0.000 0.000 0.000 0.000 interactiveshell.py:3336(_update_code_co_name)\n", + " 10 0.000 0.000 0.000 0.000 {built-in method numpy.asarray}\n", + " 30 0.000 0.000 0.000 0.000 multiarray.py:1071(copyto)\n", + " 90 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", + " 30 0.000 0.000 0.000 0.000 {method 'locked' of '_thread.lock' objects}\n", + " 20 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", + " 20 0.000 0.000 0.000 0.000 threading.py:536(is_set)\n", + " 20 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}\n", + " 2 0.000 0.000 0.000 0.000 codeop.py:142(__call__)\n", + " 4 0.000 0.000 0.000 0.000 dis.py:449(findlinestarts)\n", + " 30 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}\n", + " 10 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", + " 90 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects}\n", + " 10 0.000 0.000 0.000 0.000 {method '_release_save' of '_thread.RLock' objects}\n", + " 2 0.000 0.000 0.000 0.000 traitlets.py:676(__get__)\n", + " 2 0.000 0.000 0.000 0.000 contextlib.py:86(__init__)\n", + " 10 0.000 0.000 0.000 0.000 fromnumeric.py:2633(_amax_dispatcher)\n", + " 2 0.000 0.000 0.000 0.000 hooks.py:103(__call__)\n", + " 10 0.000 0.000 0.000 0.000 {method 'discard' of 'set' objects}\n", + " 2 0.000 0.000 0.000 0.000 traitlets.py:629(get)\n", + " 4 0.000 0.000 0.000 0.000 compilerop.py:166(extra_flags)\n", + " 1 0.000 0.000 0.000 0.000 689822237.py:4()\n", + " 2 0.000 0.000 0.000 0.000 contextlib.py:261(helper)\n", + " 6 0.000 0.000 0.000 0.000 {built-in method builtins.next}\n", + " 2 0.000 0.000 6.163 3.081 {built-in method builtins.exec}\n", + " 2 0.000 0.000 0.000 0.000 contextlib.py:114(__enter__)\n", + " 10 0.000 0.000 0.000 0.000 {method '_is_owned' of '_thread.RLock' objects}\n", + " 10 0.000 0.000 0.000 0.000 _base.py:633(__enter__)\n", + " 2 0.000 0.000 0.000 0.000 contextlib.py:123(__exit__)\n", + " 2 0.000 0.000 0.000 0.000 ipstruct.py:125(__getattr__)\n", + " 2 0.000 0.000 0.000 0.000 interactiveshell.py:3448(compare)\n", + " 2 0.000 0.000 0.000 0.000 {method 'replace' of 'code' objects}\n", + " 2 0.000 0.000 0.000 0.000 interactiveshell.py:1301(user_global_ns)\n", + " 1 0.000 0.000 6.163 6.163 689822237.py:3()\n", + " 2 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", + " 2 0.000 0.000 0.000 0.000 hooks.py:168(pre_run_code_hook)\n", + " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", + " 4 0.000 0.000 0.000 0.000 typing.py:1375(cast)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "%%prun -s cumulative -l 20\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_24_core_pyinstrument.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_24_core_pyinstrument.ipynb new file mode 100644 index 00000000..de15fd46 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_24_core_pyinstrument.ipynb @@ -0,0 +1,1477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CM_N_JOBS=1\n" + ] + } + ], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=24)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14.8 ms ± 83.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:45:50 Samples: 16\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.017 CPU time: 0.023\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "0.017 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 0.017 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " `- 0.017 ccc ccc/coef/impl.py:308\n", + " |- 0.013 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.013 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.013 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.009 ccc/coef/impl.py:211\n", + " | | `- 0.009 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [7 frames hidden] concurrent, threading, , ip...\n", + " | | 0.008 lock.acquire \n", + " | `- 0.004 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.004 lock.acquire \n", + " |- 0.002 function.map concurrent/futures/_base.py:573\n", + " | [8 frames hidden] concurrent, threading, \n", + " |- 0.001 result_iterator concurrent/futures/_base.py:602\n", + " | [3 frames hidden] concurrent, threading\n", + " `- 0.001 zeros \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25.1 ms ± 189 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:45:52 Samples: 25\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.028 CPU time: 0.048\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "0.027 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 0.027 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " `- 0.027 ccc ccc/coef/impl.py:308\n", + " |- 0.021 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.021 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.021 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.012 ccc/coef/impl.py:211\n", + " | | `- 0.012 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [8 frames hidden] concurrent, threading, \n", + " | | 0.009 lock.acquire \n", + " | |- 0.008 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.008 lock.acquire \n", + " | `- 0.001 [self] ccc/coef/impl.py\n", + " |- 0.005 function.map concurrent/futures/_base.py:573\n", + " | [10 frames hidden] concurrent, threading, \n", + " `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " [5 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.4 ms ± 264 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:45:55 Samples: 31\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.034 CPU time: 0.076\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "0.033 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 0.033 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " |- 0.032 ccc ccc/coef/impl.py:308\n", + " | |- 0.025 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.025 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.025 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.015 ccc/coef/impl.py:211\n", + " | | | `- 0.015 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | | [9 frames hidden] concurrent, threading, , we...\n", + " | | | 0.010 lock.acquire \n", + " | | |- 0.008 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.008 lock.acquire \n", + " | | `- 0.002 [self] ccc/coef/impl.py\n", + " | |- 0.003 function.map concurrent/futures/_base.py:573\n", + " | | [10 frames hidden] concurrent, threading, \n", + " | |- 0.002 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | |- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " | | [5 frames hidden] concurrent, threading, \n", + " | `- 0.001 zeros \n", + " `- 0.001 _remove _weakrefset.py:39\n", + " [2 frames hidden] _weakrefset, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "34.9 ms ± 177 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:45:58 Samples: 34\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.038 CPU time: 0.105\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "0.037 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 0.037 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " `- 0.037 ccc ccc/coef/impl.py:308\n", + " |- 0.030 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.030 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.030 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.015 ccc/coef/impl.py:211\n", + " | | |- 0.014 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | | [9 frames hidden] concurrent, threading, \n", + " | | | 0.011 lock.acquire \n", + " | | `- 0.001 [self] ccc/coef/impl.py\n", + " | |- 0.011 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.011 lock.acquire \n", + " | |- 0.002 [self] ccc/coef/impl.py\n", + " | `- 0.001 ccc/utils/utility_functions.py:117\n", + " |- 0.004 result_iterator concurrent/futures/_base.py:602\n", + " | [4 frames hidden] concurrent, threading, \n", + " |- 0.003 function.map concurrent/futures/_base.py:573\n", + " | [9 frames hidden] concurrent, threading, \n", + " `- 0.001 [self] ccc/coef/impl.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "827 ms ± 4.45 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:46:12 Samples: 136\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.838 CPU time: 3.210\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "0.838 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 0.838 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " |- 0.827 ccc ccc/coef/impl.py:308\n", + " | |- 0.464 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.464 lock.acquire \n", + " | |- 0.344 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.344 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.344 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.330 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.329 lock.acquire \n", + " | | `- 0.014 ccc/coef/impl.py:211\n", + " | | `- 0.012 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [3 frames hidden] concurrent, threading\n", + " | `- 0.014 [self] ccc/coef/impl.py\n", + " `- 0.011 [self] ../../../tmp/ipykernel_2035782/2287242631.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.67 s ± 7.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 20:46:40 Samples: 148\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.674 CPU time: 6.470\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-63ceb30c-8d12-4cd8-83db-a05c2fd442ee.json\n", + "\n", + "1.673 ../../../tmp/ipykernel_2035782/2991494264.py:2\n", + "`- 1.673 func ../../../tmp/ipykernel_2035782/2287242631.py:1\n", + " |- 1.654 ccc ccc/coef/impl.py:308\n", + " | |- 0.952 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.952 lock.acquire \n", + " | |- 0.668 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.668 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.668 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.650 as_completed concurrent/futures/_base.py:201\n", + " | | | [4 frames hidden] concurrent, threading, \n", + " | | | 0.650 lock.acquire \n", + " | | `- 0.018 ccc/coef/impl.py:211\n", + " | `- 0.020 [self] ccc/coef/impl.py\n", + " `- 0.019 [self] ../../../tmp/ipykernel_2035782/2287242631.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb new file mode 100644 index 00000000..c1cba991 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb @@ -0,0 +1,1465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=6)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14.7 ms ± 36.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:15:03 Samples: 17\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.018 CPU time: 0.024\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "0.017 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 0.017 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " `- 0.017 ccc ccc/coef/impl.py:308\n", + " |- 0.014 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.014 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.014 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.010 ccc/coef/impl.py:211\n", + " | | `- 0.010 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [9 frames hidden] concurrent, threading, , ip...\n", + " | | 0.008 lock.acquire \n", + " | `- 0.004 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.004 lock.acquire \n", + " |- 0.002 function.map concurrent/futures/_base.py:573\n", + " | [11 frames hidden] concurrent, threading, , ip...\n", + " `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " [5 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "23.6 ms ± 224 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:15:05 Samples: 24\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.027 CPU time: 0.045\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "0.026 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 0.026 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " `- 0.026 ccc ccc/coef/impl.py:308\n", + " |- 0.022 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.022 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.022 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.011 ccc/coef/impl.py:211\n", + " | | `- 0.011 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [8 frames hidden] concurrent, threading, \n", + " | | 0.009 lock.acquire \n", + " | |- 0.010 as_completed concurrent/futures/_base.py:201\n", + " | | [7 frames hidden] concurrent, threading, \n", + " | | 0.009 lock.acquire \n", + " | `- 0.001 [self] ccc/coef/impl.py\n", + " |- 0.003 function.map concurrent/futures/_base.py:573\n", + " | [9 frames hidden] concurrent, threading, \n", + " `- 0.001 [self] ccc/coef/impl.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27.6 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:15:08 Samples: 29\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.031 CPU time: 0.073\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "0.031 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 0.031 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " |- 0.030 ccc ccc/coef/impl.py:308\n", + " | |- 0.023 compute_coef ccc/coef/impl.py:494\n", + " | | `- 0.023 cdist_func ccc/coef/impl.py:487\n", + " | | `- 0.023 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | | |- 0.015 as_completed concurrent/futures/_base.py:201\n", + " | | | [8 frames hidden] concurrent, threading, \n", + " | | | 0.014 lock.acquire \n", + " | | `- 0.008 ccc/coef/impl.py:211\n", + " | | `- 0.008 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [13 frames hidden] concurrent, threading, , ip...\n", + " | |- 0.002 result_iterator concurrent/futures/_base.py:602\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | |- 0.002 [self] ccc/coef/impl.py\n", + " | |- 0.001 function.map concurrent/futures/_base.py:573\n", + " | | [8 frames hidden] concurrent, threading, \n", + " | `- 0.001 ThreadPoolExecutor.__exit__ concurrent/futures/_base.py:636\n", + " | [3 frames hidden] concurrent, threading\n", + " `- 0.001 [self] ../../../tmp/ipykernel_2706800/1687822962.py\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32.4 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:15:11 Samples: 31\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.036 CPU time: 0.098\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "0.035 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 0.035 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " `- 0.035 ccc ccc/coef/impl.py:308\n", + " |- 0.023 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.023 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.023 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | |- 0.018 as_completed concurrent/futures/_base.py:201\n", + " | | [4 frames hidden] concurrent, threading, \n", + " | | 0.018 lock.acquire \n", + " | `- 0.005 ccc/coef/impl.py:211\n", + " | |- 0.004 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " | | [10 frames hidden] concurrent, threading, \n", + " | `- 0.001 [self] ccc/coef/impl.py\n", + " |- 0.011 result_iterator concurrent/futures/_base.py:602\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.011 lock.acquire \n", + " `- 0.001 function.map concurrent/futures/_base.py:573\n", + " [8 frames hidden] concurrent, ipykernel, threading\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.02 s ± 2.82 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:15:27 Samples: 125\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.034 CPU time: 3.193\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "1.033 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 1.033 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " `- 1.022 ccc ccc/coef/impl.py:308\n", + " |- 0.539 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.539 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.539 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 0.533 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.531 lock.acquire \n", + " `- 0.471 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.470 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.07 s ± 8.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 10:16:02 Samples: 141\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.064 CPU time: 6.393\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-725a4d21-ead1-453d-ad0d-e5e7411475f1.json\n", + "\n", + "2.063 ../../../tmp/ipykernel_2706800/2991494264.py:2\n", + "`- 2.063 func ../../../tmp/ipykernel_2706800/1687822962.py:1\n", + " `- 2.046 ccc ccc/coef/impl.py:308\n", + " |- 1.058 compute_coef ccc/coef/impl.py:494\n", + " | `- 1.058 cdist_func ccc/coef/impl.py:487\n", + " | `- 1.058 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 1.048 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.048 lock.acquire \n", + " `- 0.953 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.953 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c55ca5afb2a28cb7b9222e877a428b8c9dd6ac33 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 2 Jul 2024 09:57:44 -0600 Subject: [PATCH 014/134] [bench]: Use pyinstrument to profile ari_gpu --- libs/ccc/coef/impl.py | 4 +- .../00_1_core_cprofile.ipynb | 12 - .../00_6_core_pyinstrument.ipynb | 12 - .../00_cuda_ari_12_cpu_Core copy 3.ipynb | 1503 +++++++++++++++++ .../00_cuda_ari_1_cpu_Core.ipynb | 1424 ++++++++++++++++ .../00_cuda_ari_24_cpu_Core copy 2.ipynb | 1503 +++++++++++++++++ .../00_cuda_ari_6_cpu_Core copy.ipynb | 1503 +++++++++++++++++ 7 files changed, 5935 insertions(+), 26 deletions(-) create mode 100644 nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_12_cpu_Core copy 3.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_1_cpu_Core.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_24_cpu_Core copy 2.ipynb create mode 100644 nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_6_cpu_Core copy.ipynb diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index 4758f769..9b966cce 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -13,8 +13,8 @@ from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.sklearn.metrics import adjusted_rand_index as ari -# from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari +# from ccc.sklearn.metrics import adjusted_rand_index as ari +from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb index ff9b31d6..1681c539 100644 --- a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_1_core_cprofile.ipynb @@ -1538,18 +1538,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, "papermill": { "default_parameters": {}, "duration": 167.355469, diff --git a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb index c1cba991..4fc26b4f 100644 --- a/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb +++ b/nbs/others/10_gpu_ari_profiling/02_cpu_version_more_profilers/00_6_core_pyinstrument.ipynb @@ -1435,18 +1435,6 @@ "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - }, "papermill": { "default_parameters": {}, "duration": 167.355469, diff --git a/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_12_cpu_Core copy 3.ipynb b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_12_cpu_Core copy 3.ipynb new file mode 100644 index 00000000..6f28ec6f --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_12_cpu_Core copy 3.ipynb @@ -0,0 +1,1503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + 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- numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:41:36,903 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:41:36,903 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:41:36,903 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 24 bytes\n", + "[2024-06-28 11:41:36,904 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 24 bytes\n", + "[2024-06-28 11:41:36,904 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 8 bytes\n", + "[2024-06-28 11:41:36,904 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:41:36,904 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:41:36,904 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:41:36,905 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:41:36,905 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:41:36,905 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:41:36,906 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 8 bytes\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)\n", + "\n", + "# Disable numba logging\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=12)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "851 ms ± 23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:41:50 Samples: 50\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.877 CPU time: 1.054\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "0.877 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 0.877 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 0.876 ccc ccc/coef/impl.py:308\n", + " `- 0.871 compute_coef ccc/coef/impl.py:494\n", + " `- 0.871 cdist_func ccc/coef/impl.py:487\n", + " `- 0.870 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.844 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.844 lock.acquire \n", + " `- 0.026 ccc/coef/impl.py:211\n", + " `- 0.026 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.96 s ± 20.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:42:23 Samples: 81\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.064 CPU time: 2.494\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "2.064 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 2.064 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 2.064 ccc ccc/coef/impl.py:308\n", + " `- 2.058 compute_coef ccc/coef/impl.py:494\n", + " `- 2.058 cdist_func ccc/coef/impl.py:487\n", + " `- 2.058 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 2.009 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 2.009 lock.acquire \n", + " `- 0.048 ccc/coef/impl.py:211\n", + " `- 0.048 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.96 s ± 46.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:42:57 Samples: 83\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.112 CPU time: 2.552\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "2.112 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 2.112 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 2.112 ccc ccc/coef/impl.py:308\n", + " `- 2.102 compute_coef ccc/coef/impl.py:494\n", + " `- 2.101 cdist_func ccc/coef/impl.py:487\n", + " `- 2.101 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 2.057 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 2.056 lock.acquire \n", + " `- 0.043 ccc/coef/impl.py:211\n", + " `- 0.043 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.99 s ± 18.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:43:31 Samples: 84\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.073 CPU time: 2.539\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "2.073 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 2.073 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 2.073 ccc ccc/coef/impl.py:308\n", + " `- 2.062 compute_coef ccc/coef/impl.py:494\n", + " `- 2.062 cdist_func ccc/coef/impl.py:487\n", + " `- 2.062 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 2.019 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 2.019 lock.acquire \n", + " `- 0.042 ccc/coef/impl.py:211\n", + " `- 0.042 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.88 s ± 15.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:44:36 Samples: 100\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 3.886 CPU time: 6.037\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "3.885 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 3.885 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 3.876 ccc ccc/coef/impl.py:308\n", + " |- 3.402 compute_coef ccc/coef/impl.py:494\n", + " | `- 3.401 cdist_func ccc/coef/impl.py:487\n", + " | `- 3.400 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 3.372 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 3.371 lock.acquire \n", + " `- 0.464 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.42 s ± 51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:46:22 Samples: 113\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 6.543 CPU time: 10.126\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-be61aca2-fe7c-48f0-8dd2-214e4d957c13.json\n", + "\n", + "6.543 ../../../tmp/ipykernel_2854891/2991494264.py:2\n", + "`- 6.543 func ../../../tmp/ipykernel_2854891/2380024278.py:1\n", + " `- 6.532 ccc ccc/coef/impl.py:308\n", + " |- 5.548 compute_coef ccc/coef/impl.py:494\n", + " | `- 5.547 cdist_func ccc/coef/impl.py:487\n", + " | `- 5.547 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 5.530 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 5.527 lock.acquire \n", + " `- 0.960 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_1_cpu_Core.ipynb b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_1_cpu_Core.ipynb new file mode 100644 index 00000000..05d983a5 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_1_cpu_Core.ipynb @@ -0,0 +1,1424 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "5c010e62", + "metadata": {}, + "outputs": [], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The pyinstrument extension is already loaded. To reload it, use:\n", + " %reload_ext pyinstrument\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)\n", + "\n", + "# Disable numba logging\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "347 ms ± 4.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:08:29 Samples: 64\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.357 CPU time: 0.361\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "0.357 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 0.357 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 0.357 ccc ccc/coef/impl.py:308\n", + " `- 0.357 compute_coef ccc/coef/impl.py:494\n", + " `- 0.357 cdist_func ccc/coef/impl.py:487\n", + " `- 0.357 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.353 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.353 lock.acquire \n", + " `- 0.004 ccc/coef/impl.py:211\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "767 ms ± 7.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:08:42 Samples: 96\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.783 CPU time: 0.788\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "0.783 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 0.783 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 0.783 ccc ccc/coef/impl.py:308\n", + " `- 0.779 compute_coef ccc/coef/impl.py:494\n", + " `- 0.779 cdist_func ccc/coef/impl.py:487\n", + " `- 0.779 cdist_parts_parallel ccc/coef/impl.py:193\n", + " `- 0.778 as_completed concurrent/futures/_base.py:201\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.777 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "786 ms ± 9.29 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:08:55 Samples: 100\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.805 CPU time: 0.810\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "0.805 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 0.805 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 0.805 ccc ccc/coef/impl.py:308\n", + " |- 0.793 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.793 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.793 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 0.791 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.791 lock.acquire \n", + " `- 0.012 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "809 ms ± 3.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:09:09 Samples: 100\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.825 CPU time: 0.828\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "0.824 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 0.824 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 0.824 ccc ccc/coef/impl.py:308\n", + " |- 0.809 compute_coef ccc/coef/impl.py:494\n", + " | `- 0.809 cdist_func ccc/coef/impl.py:487\n", + " | `- 0.809 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 0.809 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.809 lock.acquire \n", + " `- 0.016 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.16 s ± 9.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:10:17 Samples: 106\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 4.176 CPU time: 4.194\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "4.176 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 4.176 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 4.176 ccc ccc/coef/impl.py:308\n", + " |- 3.320 compute_coef ccc/coef/impl.py:494\n", + " | `- 3.320 cdist_func ccc/coef/impl.py:487\n", + " | `- 3.320 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 3.320 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 3.320 lock.acquire \n", + " `- 0.849 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 0.849 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.73 s ± 16.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:12:25 Samples: 118\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 7.721 CPU time: 7.748\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-7232d267-3e1d-4c9d-aeeb-5254ca0449dc.json\n", + "\n", + "7.721 ../../../tmp/ipykernel_2806375/2991494264.py:2\n", + "`- 7.721 func ../../../tmp/ipykernel_2806375/2661685993.py:1\n", + " `- 7.705 ccc ccc/coef/impl.py:308\n", + " |- 5.907 compute_coef ccc/coef/impl.py:494\n", + " | `- 5.906 cdist_func ccc/coef/impl.py:487\n", + " | `- 5.906 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 5.901 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 5.901 lock.acquire \n", + " `- 1.796 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + " 1.796 lock.acquire \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_24_cpu_Core copy 2.ipynb b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_24_cpu_Core copy 2.ipynb new file mode 100644 index 00000000..5e43c37b --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_24_cpu_Core copy 2.ipynb @@ -0,0 +1,1503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-06-28 11:47:39,657 - numba.cuda.cudadrv.driver] INFO: init\n", + "[2024-06-28 11:47:39,755 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 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"[2024-06-28 11:47:39,840 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:47:39,840 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 24 bytes\n", + "[2024-06-28 11:47:39,840 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 24 bytes\n", + "[2024-06-28 11:47:39,840 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 8 bytes\n", + "[2024-06-28 11:47:39,840 - numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:47:39,841 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:47:39,841 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:47:39,841 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:47:39,841 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:47:39,842 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:47:39,842 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 8 bytes\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)\n", + "\n", + "# Disable numba logging\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=24)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "865 ms ± 13.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:47:54 Samples: 57\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.855 CPU time: 1.033\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "0.854 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 0.854 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 0.854 ccc ccc/coef/impl.py:308\n", + " `- 0.852 compute_coef ccc/coef/impl.py:494\n", + " `- 0.851 cdist_func ccc/coef/impl.py:487\n", + " `- 0.851 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.817 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.814 lock.acquire \n", + " `- 0.034 ccc/coef/impl.py:211\n", + " `- 0.032 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.96 s ± 20.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:48:26 Samples: 83\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.042 CPU time: 2.476\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "2.042 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 2.042 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 2.042 ccc ccc/coef/impl.py:308\n", + " `- 2.034 compute_coef ccc/coef/impl.py:494\n", + " `- 2.032 cdist_func ccc/coef/impl.py:487\n", + " `- 2.032 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.986 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.986 lock.acquire \n", + " `- 0.046 ccc/coef/impl.py:211\n", + " `- 0.046 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 s ± 17.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:49:00 Samples: 85\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 2.008 CPU time: 2.430\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "2.008 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 2.008 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 2.007 ccc ccc/coef/impl.py:308\n", + " `- 1.996 compute_coef ccc/coef/impl.py:494\n", + " `- 1.996 cdist_func ccc/coef/impl.py:487\n", + " `- 1.996 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.959 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.958 lock.acquire \n", + " `- 0.038 ccc/coef/impl.py:211\n", + " `- 0.037 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.01 s ± 13.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:49:34 Samples: 89\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.982 CPU time: 2.440\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "1.981 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 1.981 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 1.980 ccc ccc/coef/impl.py:308\n", + " `- 1.965 compute_coef ccc/coef/impl.py:494\n", + " `- 1.965 cdist_func ccc/coef/impl.py:487\n", + " `- 1.965 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.921 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.920 lock.acquire \n", + " `- 0.043 ccc/coef/impl.py:211\n", + " `- 0.043 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.98 s ± 30.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:50:40 Samples: 97\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 4.035 CPU time: 6.229\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "4.035 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 4.035 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 4.024 ccc ccc/coef/impl.py:308\n", + " |- 3.542 compute_coef ccc/coef/impl.py:494\n", + " | `- 3.541 cdist_func ccc/coef/impl.py:487\n", + " | `- 3.541 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 3.524 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 3.524 lock.acquire \n", + " `- 0.467 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.61 s ± 50.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:52:30 Samples: 97\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 6.613 CPU time: 10.182\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-cd684189-5184-4139-b799-41e3f2597cfb.json\n", + "\n", + "6.613 ../../../tmp/ipykernel_2868492/2991494264.py:2\n", + "`- 6.613 func ../../../tmp/ipykernel_2868492/2287242631.py:1\n", + " `- 6.597 ccc ccc/coef/impl.py:308\n", + " |- 5.609 compute_coef ccc/coef/impl.py:494\n", + " | `- 5.609 cdist_func ccc/coef/impl.py:487\n", + " | `- 5.609 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 5.593 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 5.592 lock.acquire \n", + " `- 0.969 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_6_cpu_Core copy.ipynb b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_6_cpu_Core copy.ipynb new file mode 100644 index 00000000..e1e05403 --- /dev/null +++ b/nbs/others/10_gpu_ari_profiling/03_gpu_version_more_profilers/00_cuda_ari_6_cpu_Core copy.ipynb @@ -0,0 +1,1503 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36c65ddb-8246-4b0c-a231-68a373acc2cf", + "metadata": { + "papermill": { + "duration": 0.095646, + "end_time": "2021-12-02T04:34:06.384775", + "exception": false, + "start_time": "2021-12-02T04:34:06.289129", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "337633a8-d03e-4509-b89d-f8daee598958", + "metadata": { + "papermill": { + "duration": 0.091407, + "end_time": "2021-12-02T04:34:06.566258", + "exception": false, + "start_time": "2021-12-02T04:34:06.474851", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Single-threaded version, force to use `cdist_parts_basic` to see the overhead of `air` " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c010e62", + "metadata": {}, + "outputs": [], + "source": [ + "# Set core count to 1\n", + "# %env CM_N_JOBS=1" + ] + }, + { + "cell_type": "markdown", + "id": "63c2b099-cc44-4fe2-93d1-40336e0a8466", + "metadata": { + "papermill": { + "duration": 0.09056, + "end_time": "2021-12-02T04:34:06.747753", + "exception": false, + "start_time": "2021-12-02T04:34:06.657193", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Remove pycache dir" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "960c4ff0-2a73-4eaa-97d6-3269102233eb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:06.942330Z", + "iopub.status.busy": "2021-12-02T04:34:06.941822Z", + "iopub.status.idle": "2021-12-02T04:34:07.534005Z", + "shell.execute_reply": "2021-12-02T04:34:07.531916Z" + }, + "papermill": { + "duration": 0.696141, + "end_time": "2021-12-02T04:34:07.534420", + "exception": false, + "start_time": "2021-12-02T04:34:06.838279", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu\n" + ] + } + ], + "source": [ + "!echo ${CODE_DIR}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e18db58a-316a-445b-a376-8b2ec18e08d8", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:07.733067Z", + "iopub.status.busy": "2021-12-02T04:34:07.732593Z", + "iopub.status.idle": "2021-12-02T04:34:08.445221Z", + "shell.execute_reply": "2021-12-02T04:34:08.443302Z" + }, + "papermill": { + "duration": 0.817887, + "end_time": "2021-12-02T04:34:08.445598", + "exception": false, + "start_time": "2021-12-02T04:34:07.627711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/sklearn/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/scipy/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/coef/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/utils/__pycache__\n", + "/home/haoyu/_database/projs/ccc-gpu/libs/ccc/pytorch/__pycache__\n" + ] + } + ], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b54099b0-a990-4bbd-bcbd-e206eb0f0f0e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:08.668700Z", + "iopub.status.busy": "2021-12-02T04:34:08.668226Z", + "iopub.status.idle": "2021-12-02T04:34:09.290588Z", + "shell.execute_reply": "2021-12-02T04:34:09.288752Z" + }, + "papermill": { + "duration": 0.719545, + "end_time": "2021-12-02T04:34:09.290958", + "exception": false, + "start_time": "2021-12-02T04:34:08.571413", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -prune -exec rm -rf {} \\;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a9a8098-8160-46bf-8d83-bc398cbe2382", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:09.513928Z", + "iopub.status.busy": "2021-12-02T04:34:09.513465Z", + "iopub.status.idle": "2021-12-02T04:34:10.120602Z", + "shell.execute_reply": "2021-12-02T04:34:10.118684Z" + }, + "papermill": { + "duration": 0.704384, + "end_time": "2021-12-02T04:34:10.120972", + "exception": false, + "start_time": "2021-12-02T04:34:09.416588", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "!find ${CODE_DIR} -regex '^.*\\(__pycache__\\)$' -print" + ] + }, + { + "cell_type": "markdown", + "id": "c2251313-41ac-46fd-a845-0f209689ecf6", + "metadata": { + "papermill": { + "duration": 0.093051, + "end_time": "2021-12-02T04:34:10.338738", + "exception": false, + "start_time": "2021-12-02T04:34:10.245687", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "987ef5f1-be49-4a6c-a4f4-b24a0a2094cb", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:10.528319Z", + "iopub.status.busy": "2021-12-02T04:34:10.527833Z", + "iopub.status.idle": "2021-12-02T04:34:10.845421Z", + "shell.execute_reply": "2021-12-02T04:34:10.845020Z" + }, + "papermill": { + "duration": 0.414249, + "end_time": "2021-12-02T04:34:10.845518", + "exception": false, + "start_time": "2021-12-02T04:34:10.431269", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-06-28 11:32:00,811 - numba.cuda.cudadrv.driver] INFO: init\n", + "[2024-06-28 11:32:00,930 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-28 11:32:00,931 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 4 bytes\n", + "[2024-06-28 11:32:00,931 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:32:00,931 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:32:00,931 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 16 bytes\n", + 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numba.cuda.cudadrv.driver] INFO: dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:32:01,017 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 6 bytes\n", + "[2024-06-28 11:32:01,017 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:32:01,018 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 20 bytes\n", + "[2024-06-28 11:32:01,018 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:32:01,019 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 36 bytes\n", + "[2024-06-28 11:32:01,019 - numba.cuda.cudadrv.driver] INFO: add pending dealloc: cuMemFree_v2 8 bytes\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from ccc.coef import ccc\n", + "%load_ext pyinstrument" + ] + }, + { + "cell_type": "markdown", + "id": "24399ccb-d33d-4bad-9baf-638c9c56feb2", + "metadata": { + "papermill": { + "duration": 0.095359, + "end_time": "2021-12-02T04:34:11.037941", + "exception": false, + "start_time": "2021-12-02T04:34:10.942582", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c609cefa-f513-4cf8-9573-367744e31c5f", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.224902Z", + "iopub.status.busy": "2021-12-02T04:34:11.224429Z", + "iopub.status.idle": "2021-12-02T04:34:11.226352Z", + "shell.execute_reply": "2021-12-02T04:34:11.226694Z" + }, + "papermill": { + "duration": 0.097459, + "end_time": "2021-12-02T04:34:11.226809", + "exception": false, + "start_time": "2021-12-02T04:34:11.129350", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_REPS = 10\n", + "np.random.seed(0)\n", + "\n", + "# Disable numba logging\n", + "import logging\n", + "\n", + "numba_logger = logging.getLogger('numba')\n", + "numba_logger.setLevel(logging.WARNING)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd3067b-a4f7-475e-9575-20246934537d", + "metadata": { + "papermill": { + "duration": 0.092004, + "end_time": "2021-12-02T04:34:11.602824", + "exception": false, + "start_time": "2021-12-02T04:34:11.510820", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02e0507c-43ff-4693-8a3b-8ccd8f23168c", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:11.790398Z", + "iopub.status.busy": "2021-12-02T04:34:11.789933Z", + "iopub.status.idle": "2021-12-02T04:34:20.454299Z", + "shell.execute_reply": "2021-12-02T04:34:20.453925Z" + }, + "papermill": { + "duration": 8.760307, + "end_time": "2021-12-02T04:34:20.454396", + "exception": false, + "start_time": "2021-12-02T04:34:11.694089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15625" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let numba compile all the code before profiling\n", + "ccc(np.random.rand(10), np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc5236a7", + "metadata": {}, + "outputs": [], + "source": [ + "def func(n_reps=N_REPS):\n", + " for i in range(n_reps):\n", + " ccc(x, y, n_jobs=6)" + ] + }, + { + "cell_type": "markdown", + "id": "8549179d-1517-4a40-9a51-b95dc02d0fcc", + "metadata": { + "papermill": { + "duration": 0.092955, + "end_time": "2021-12-02T04:34:20.640996", + "exception": false, + "start_time": "2021-12-02T04:34:20.548041", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` small" + ] + }, + { + "cell_type": "markdown", + "id": "13ba811b", + "metadata": { + "papermill": { + "duration": 0.092926, + "end_time": "2021-12-02T04:34:20.826776", + "exception": false, + "start_time": "2021-12-02T04:34:20.733850", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68064f0b", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.026855Z", + "iopub.status.busy": "2021-12-02T04:34:21.026243Z", + "iopub.status.idle": "2021-12-02T04:34:21.028562Z", + "shell.execute_reply": "2021-12-02T04:34:21.027971Z" + }, + "papermill": { + "duration": 0.107158, + "end_time": "2021-12-02T04:34:21.028689", + "exception": false, + "start_time": "2021-12-02T04:34:20.921531", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f2ff213-d6b7-458f-acbf-8c73dd497a2a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.225799Z", + "iopub.status.busy": "2021-12-02T04:34:21.225350Z", + "iopub.status.idle": "2021-12-02T04:34:21.226800Z", + "shell.execute_reply": "2021-12-02T04:34:21.227141Z" + }, + "papermill": { + "duration": 0.09836, + "end_time": "2021-12-02T04:34:21.227254", + "exception": false, + "start_time": "2021-12-02T04:34:21.128894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c0abc65a-2f3c-4476-9c2a-f8d7753b75e6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:21.614745Z", + "iopub.status.busy": "2021-12-02T04:34:21.614223Z", + "iopub.status.idle": "2021-12-02T04:34:33.925655Z", + "shell.execute_reply": "2021-12-02T04:34:33.925209Z" + }, + "papermill": { + "duration": 12.410211, + "end_time": "2021-12-02T04:34:33.925764", + "exception": false, + "start_time": "2021-12-02T04:34:21.515553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "847 ms ± 24.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e80a7b02-310f-4bd9-90d5-2a41186db39e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.125850Z", + "iopub.status.busy": "2021-12-02T04:34:34.125263Z", + "iopub.status.idle": "2021-12-02T04:34:34.148529Z", + "shell.execute_reply": "2021-12-02T04:34:34.148886Z" + }, + "papermill": { + "duration": 0.124845, + "end_time": "2021-12-02T04:34:34.149006", + "exception": false, + "start_time": "2021-12-02T04:34:34.024161", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:32:14 Samples: 68\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 0.863 CPU time: 1.045\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "0.862 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 0.862 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 0.862 ccc ccc/coef/impl.py:308\n", + " `- 0.860 compute_coef ccc/coef/impl.py:494\n", + " `- 0.859 cdist_func ccc/coef/impl.py:487\n", + " `- 0.859 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 0.808 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 0.807 lock.acquire \n", + " `- 0.051 ccc/coef/impl.py:211\n", + " `- 0.051 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "2548440c", + "metadata": { + "papermill": { + "duration": 0.094866, + "end_time": "2021-12-02T04:34:34.338010", + "exception": false, + "start_time": "2021-12-02T04:34:34.243144", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ddb768d7-9b74-424c-bdb9-9e52b9e5b181", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.530550Z", + "iopub.status.busy": "2021-12-02T04:34:34.530085Z", + "iopub.status.idle": "2021-12-02T04:34:34.531559Z", + "shell.execute_reply": "2021-12-02T04:34:34.531910Z" + }, + "papermill": { + "duration": 0.099465, + "end_time": "2021-12-02T04:34:34.532025", + "exception": false, + "start_time": "2021-12-02T04:34:34.432560", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "161cf922-41f7-4ced-af1f-e3d25b36b200", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:34.723894Z", + "iopub.status.busy": "2021-12-02T04:34:34.723405Z", + "iopub.status.idle": "2021-12-02T04:34:34.725017Z", + "shell.execute_reply": "2021-12-02T04:34:34.725362Z" + }, + "papermill": { + "duration": 0.099541, + "end_time": "2021-12-02T04:34:34.725479", + "exception": false, + "start_time": "2021-12-02T04:34:34.625938", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0a7f2b1f-4b87-4dde-a46b-2acec5ac93ba", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:35.120950Z", + "iopub.status.busy": "2021-12-02T04:34:35.120480Z", + "iopub.status.idle": "2021-12-02T04:34:37.984893Z", + "shell.execute_reply": "2021-12-02T04:34:37.985354Z" + }, + "papermill": { + "duration": 2.971389, + "end_time": "2021-12-02T04:34:37.985494", + "exception": false, + "start_time": "2021-12-02T04:34:35.014105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.71 s ± 28.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "74acbe27-9807-4f26-8b7e-b74d0f745b63", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.190471Z", + "iopub.status.busy": "2021-12-02T04:34:38.189361Z", + "iopub.status.idle": "2021-12-02T04:34:38.227298Z", + "shell.execute_reply": "2021-12-02T04:34:38.227692Z" + }, + "papermill": { + "duration": 0.13744, + "end_time": "2021-12-02T04:34:38.227812", + "exception": false, + "start_time": "2021-12-02T04:34:38.090372", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:32:43 Samples: 103\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.793 CPU time: 2.167\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "1.792 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 1.792 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 1.792 ccc ccc/coef/impl.py:308\n", + " `- 1.787 compute_coef ccc/coef/impl.py:494\n", + " `- 1.785 cdist_func ccc/coef/impl.py:487\n", + " `- 1.785 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.744 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.743 lock.acquire \n", + " `- 0.041 ccc/coef/impl.py:211\n", + " `- 0.040 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "611ff8e1", + "metadata": { + "papermill": { + "duration": 0.09562, + "end_time": "2021-12-02T04:34:38.420643", + "exception": false, + "start_time": "2021-12-02T04:34:38.325023", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=500`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bcf4b42", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.617318Z", + "iopub.status.busy": "2021-12-02T04:34:38.616836Z", + "iopub.status.idle": "2021-12-02T04:34:38.618469Z", + "shell.execute_reply": "2021-12-02T04:34:38.618817Z" + }, + "papermill": { + "duration": 0.101998, + "end_time": "2021-12-02T04:34:38.618933", + "exception": false, + "start_time": "2021-12-02T04:34:38.516935", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0bf2f21e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:38.813589Z", + "iopub.status.busy": "2021-12-02T04:34:38.813117Z", + "iopub.status.idle": "2021-12-02T04:34:38.815386Z", + "shell.execute_reply": "2021-12-02T04:34:38.815020Z" + }, + "papermill": { + "duration": 0.100616, + "end_time": "2021-12-02T04:34:38.815484", + "exception": false, + "start_time": "2021-12-02T04:34:38.714868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cbde4ce6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:39.216775Z", + "iopub.status.busy": "2021-12-02T04:34:39.216014Z", + "iopub.status.idle": "2021-12-02T04:34:43.223179Z", + "shell.execute_reply": "2021-12-02T04:34:43.223640Z" + }, + "papermill": { + "duration": 4.108094, + "end_time": "2021-12-02T04:34:43.223780", + "exception": false, + "start_time": "2021-12-02T04:34:39.115686", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.73 s ± 25.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1250547e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.427169Z", + "iopub.status.busy": "2021-12-02T04:34:43.426487Z", + "iopub.status.idle": "2021-12-02T04:34:43.474288Z", + "shell.execute_reply": "2021-12-02T04:34:43.473832Z" + }, + "papermill": { + "duration": 0.148908, + "end_time": "2021-12-02T04:34:43.474385", + "exception": false, + "start_time": "2021-12-02T04:34:43.325477", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:33:13 Samples: 102\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.743 CPU time: 2.118\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "1.742 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 1.742 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 1.742 ccc ccc/coef/impl.py:308\n", + " `- 1.731 compute_coef ccc/coef/impl.py:494\n", + " `- 1.730 cdist_func ccc/coef/impl.py:487\n", + " `- 1.730 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.696 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.696 lock.acquire \n", + " `- 0.030 ccc/coef/impl.py:211\n", + " `- 0.030 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "6853c300", + "metadata": { + "papermill": { + "duration": 0.09707, + "end_time": "2021-12-02T04:34:43.669776", + "exception": false, + "start_time": "2021-12-02T04:34:43.572706", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=1000`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f77e8490", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:43.871037Z", + "iopub.status.busy": "2021-12-02T04:34:43.870573Z", + "iopub.status.idle": "2021-12-02T04:34:43.872099Z", + "shell.execute_reply": "2021-12-02T04:34:43.872442Z" + }, + "papermill": { + "duration": 0.103066, + "end_time": "2021-12-02T04:34:43.872558", + "exception": false, + "start_time": "2021-12-02T04:34:43.769492", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c99f544a", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.102128Z", + "iopub.status.busy": "2021-12-02T04:34:44.101540Z", + "iopub.status.idle": "2021-12-02T04:34:44.103376Z", + "shell.execute_reply": "2021-12-02T04:34:44.103817Z" + }, + "papermill": { + "duration": 0.13265, + "end_time": "2021-12-02T04:34:44.103967", + "exception": false, + "start_time": "2021-12-02T04:34:43.971317", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9721b048", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:44.515965Z", + "iopub.status.busy": "2021-12-02T04:34:44.515462Z", + "iopub.status.idle": "2021-12-02T04:34:50.907897Z", + "shell.execute_reply": "2021-12-02T04:34:50.908362Z" + }, + "papermill": { + "duration": 6.496377, + "end_time": "2021-12-02T04:34:50.908503", + "exception": false, + "start_time": "2021-12-02T04:34:44.412126", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.76 s ± 18.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fd0f4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.122723Z", + "iopub.status.busy": "2021-12-02T04:34:51.122244Z", + "iopub.status.idle": "2021-12-02T04:34:51.194219Z", + "shell.execute_reply": "2021-12-02T04:34:51.193813Z" + }, + "papermill": { + "duration": 0.179898, + "end_time": "2021-12-02T04:34:51.194319", + "exception": false, + "start_time": "2021-12-02T04:34:51.014421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:33:42 Samples: 99\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 1.772 CPU time: 2.171\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "1.771 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 1.771 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 1.771 ccc ccc/coef/impl.py:308\n", + " `- 1.759 compute_coef ccc/coef/impl.py:494\n", + " `- 1.759 cdist_func ccc/coef/impl.py:487\n", + " `- 1.759 cdist_parts_parallel ccc/coef/impl.py:193\n", + " |- 1.738 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 1.736 lock.acquire \n", + " `- 0.021 ccc/coef/impl.py:211\n", + " `- 0.021 ThreadPoolExecutor.submit concurrent/futures/thread.py:161\n", + " [6 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "f6b9adcf-1da4-4496-b05f-47952fd80d7f", + "metadata": { + "papermill": { + "duration": 0.099861, + "end_time": "2021-12-02T04:34:51.394504", + "exception": false, + "start_time": "2021-12-02T04:34:51.294643", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Run with `n_samples` large" + ] + }, + { + "cell_type": "markdown", + "id": "8f2e407c", + "metadata": { + "papermill": { + "duration": 0.098767, + "end_time": "2021-12-02T04:34:51.593072", + "exception": false, + "start_time": "2021-12-02T04:34:51.494305", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=50000`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c522396e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.793258Z", + "iopub.status.busy": "2021-12-02T04:34:51.792385Z", + "iopub.status.idle": "2021-12-02T04:34:51.794710Z", + "shell.execute_reply": "2021-12-02T04:34:51.795054Z" + }, + "papermill": { + "duration": 0.103749, + "end_time": "2021-12-02T04:34:51.795172", + "exception": false, + "start_time": "2021-12-02T04:34:51.691423", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 50000" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a5e536cc", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:51.996666Z", + "iopub.status.busy": "2021-12-02T04:34:51.996120Z", + "iopub.status.idle": "2021-12-02T04:34:51.999329Z", + "shell.execute_reply": "2021-12-02T04:34:51.998896Z" + }, + "papermill": { + "duration": 0.104554, + "end_time": "2021-12-02T04:34:51.999423", + "exception": false, + "start_time": "2021-12-02T04:34:51.894869", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91470f64", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:34:52.408864Z", + "iopub.status.busy": "2021-12-02T04:34:52.408310Z", + "iopub.status.idle": "2021-12-02T04:35:28.383597Z", + "shell.execute_reply": "2021-12-02T04:35:28.384010Z" + }, + "papermill": { + "duration": 36.078113, + "end_time": "2021-12-02T04:35:28.384125", + "exception": false, + "start_time": "2021-12-02T04:34:52.306012", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.9 s ± 19.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4de4e0b0", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:28.594907Z", + "iopub.status.busy": "2021-12-02T04:35:28.594395Z", + "iopub.status.idle": "2021-12-02T04:35:30.850079Z", + "shell.execute_reply": "2021-12-02T04:35:30.850466Z" + }, + "papermill": { + "duration": 2.36055, + "end_time": "2021-12-02T04:35:30.850581", + "exception": false, + "start_time": "2021-12-02T04:35:28.490031", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:34:47 Samples: 115\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 3.939 CPU time: 5.797\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "3.938 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 3.938 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 3.929 ccc ccc/coef/impl.py:308\n", + " |- 3.458 compute_coef ccc/coef/impl.py:494\n", + " | `- 3.457 cdist_func ccc/coef/impl.py:487\n", + " | `- 3.457 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 3.445 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 3.442 lock.acquire \n", + " `- 0.459 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "markdown", + "id": "b0c07894", + "metadata": { + "papermill": { + "duration": 0.100749, + "end_time": "2021-12-02T04:35:31.053465", + "exception": false, + "start_time": "2021-12-02T04:35:30.952716", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## `n_samples=100000`" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7fb2ab2e-a6de-412b-9540-d00f8641290e", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.262806Z", + "iopub.status.busy": "2021-12-02T04:35:31.262280Z", + "iopub.status.idle": "2021-12-02T04:35:31.264634Z", + "shell.execute_reply": "2021-12-02T04:35:31.264124Z" + }, + "papermill": { + "duration": 0.110468, + "end_time": "2021-12-02T04:35:31.264738", + "exception": false, + "start_time": "2021-12-02T04:35:31.154270", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "N_SAMPLES = 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "81765e91", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.472530Z", + "iopub.status.busy": "2021-12-02T04:35:31.472083Z", + "iopub.status.idle": "2021-12-02T04:35:31.475862Z", + "shell.execute_reply": "2021-12-02T04:35:31.475424Z" + }, + "papermill": { + "duration": 0.107444, + "end_time": "2021-12-02T04:35:31.476016", + "exception": false, + "start_time": "2021-12-02T04:35:31.368572", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x = np.random.rand(N_SAMPLES)\n", + "y = np.random.rand(N_SAMPLES)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "aca57100", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:35:31.892045Z", + "iopub.status.busy": "2021-12-02T04:35:31.891417Z", + "iopub.status.idle": "2021-12-02T04:36:46.681345Z", + "shell.execute_reply": "2021-12-02T04:36:46.681695Z" + }, + "papermill": { + "duration": 74.896315, + "end_time": "2021-12-02T04:36:46.681806", + "exception": false, + "start_time": "2021-12-02T04:35:31.785491", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.43 s ± 33.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit func()\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b9c25f30", + "metadata": { + "execution": { + "iopub.execute_input": "2021-12-02T04:36:46.889124Z", + "iopub.status.busy": "2021-12-02T04:36:46.888441Z", + "iopub.status.idle": "2021-12-02T04:36:51.544747Z", + "shell.execute_reply": "2021-12-02T04:36:51.544315Z" + }, + "papermill": { + "duration": 4.761869, + "end_time": "2021-12-02T04:36:51.544839", + "exception": false, + "start_time": "2021-12-02T04:36:46.782970", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 11:36:34 Samples: 111\n", + " /_//_/// /_\\ / //_// / //_'/ // Duration: 6.452 CPU time: 9.550\n", + "/ _/ v4.6.2\n", + "\n", + "Program: /home/haoyu/.conda/envs/ccc/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/haoyu/.local/share/jupyter/runtime/kernel-03331e06-fc00-41d5-a7eb-285b28b98172.json\n", + "\n", + "6.451 ../../../tmp/ipykernel_2841216/2991494264.py:2\n", + "`- 6.451 func ../../../tmp/ipykernel_2841216/1687822962.py:1\n", + " `- 6.437 ccc ccc/coef/impl.py:308\n", + " |- 5.452 compute_coef ccc/coef/impl.py:494\n", + " | `- 5.452 cdist_func ccc/coef/impl.py:487\n", + " | `- 5.452 cdist_parts_parallel ccc/coef/impl.py:193\n", + " | `- 5.444 as_completed concurrent/futures/_base.py:201\n", + " | [4 frames hidden] concurrent, threading, \n", + " | 5.443 lock.acquire \n", + " `- 0.965 result_iterator concurrent/futures/_base.py:602\n", + " [4 frames hidden] concurrent, threading, \n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%pyinstrument\n", + "func()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2aa76596-f126-4ba8-8bba-4d31225e0e5d", + "metadata": { + "papermill": { + "duration": 0.102208, + "end_time": "2021-12-02T04:36:51.764154", + "exception": false, + "start_time": "2021-12-02T04:36:51.661946", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "all,-execution,-papermill,-trusted" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 167.355469, + "end_time": "2021-12-02T04:36:52.275357", + "environment_variables": {}, + "exception": null, + "input_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.ipynb", + "output_path": "nbs/others/05_clustermatch_profiling/10_cm_optimized/09-cdist_parts_v04.run.ipynb", + "parameters": {}, + "start_time": "2021-12-02T04:34:04.919888", + "version": "2.3.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5a95636d1b1166b82dd510ef2eaa39a4938e1b04 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 6 Aug 2024 15:05:15 -0600 Subject: [PATCH 015/134] [cuda/coef]: Refactor functions for future use --- benchmark/trace.py | 16 +- libs/ccc/coef/impl_gpu.py | 507 ++++++++++++++++++++++++++++++++ libs/ccc/sklearn/metrics_gpu.py | 1 + tests/gpu/test_coef.py | 70 +++++ tests/gpu/test_cupy.py | 13 + tests/gpu/test_impl_gpu.py | 20 ++ 6 files changed, 621 insertions(+), 6 deletions(-) create mode 100644 libs/ccc/coef/impl_gpu.py create mode 100644 tests/gpu/test_coef.py create mode 100644 tests/gpu/test_cupy.py create mode 100644 tests/gpu/test_impl_gpu.py diff --git a/benchmark/trace.py b/benchmark/trace.py index e2ff2583..ab32aeed 100644 --- a/benchmark/trace.py +++ b/benchmark/trace.py @@ -1,13 +1,17 @@ -from ccc.coef import ccc +from ccc.coef.impl_gpu import ccc import numpy as np def main(): - random_feature1 = np.random.rand(100) - random_feature2 = np.random.rand(100) - - res = ccc(random_feature1, random_feature2, n_jobs=2) - print(res) + # random_feature1 = np.random.rand(100) + # random_feature2 = np.random.rand(100) + # + # res = ccc(random_feature1, random_feature2, n_jobs=2) + # print(res) + + data = np.random.rand(10, 100) + c = ccc(data, n_jobs=5) + print(c) if __name__ == "__main__": diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py new file mode 100644 index 00000000..0731b814 --- /dev/null +++ b/libs/ccc/coef/impl_gpu.py @@ -0,0 +1,507 @@ +""" +This module contains the CUDA implementation of the CCC +""" +import os +import math +from typing import Iterable, Union, List, Tuple + +import numpy as np +import cupy +from numpy.typing import NDArray +from numba import njit +from numba import cuda + +from ccc.pytorch.core import unravel_index_2d +from ccc.scipy.stats import rank + + +@njit(cache=True, nogil=True) +def get_perc_from_k(k: int) -> np.ndarray: + """ + It returns the percentiles (from 0.0 to 1.0) that separate the data into k + clusters. For example, if k=2, it returns [0.5]; if k=4, it returns [0.25, + 0.50, 0.75]. + + Args: + k: number of clusters. If less than 2, the function returns an empty + list. + + Returns: + A numpy array of percentiles (from 0.0 to 1.0). + """ + if k < 2: + return np.empty(0, dtype=np.float32) + return np.array([(1.0 / k) * i for i in range(1, k)], dtype=np.float32) + + +# @njit(cache=True, nogil=True) +def get_range_n_percs(ks: List[int]) -> List[List[float]]: + """ + It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters + + Args: + ks: list of numbers of clusters. + + Returns: + A list of lists of percentiles (from 0.0 to 1.0). + """ + # Todo: research on if numba can optimize this + percentiles: List[List[float]] = [] + for k in ks: + perc = get_perc_from_k(k) + percentiles.append(perc) + return percentiles + + +def get_feature_type_and_encode(feature_data: NDArray) -> tuple[NDArray, bool]: + """ + Given the data of one feature as a 1d numpy array (it could also be a pandas.Series), + it returns the same data if it is numerical (float, signed or unsigned integer) or an + encoded version if it is categorical (each category value has a unique integer starting from + zero).` f + + Args: + feature_data: a 1d array with data. + + Returns: + A tuple with two elements: + 1. the feature data: same as input if numerical, encoded version if not numerical. + 2. A boolean indicating whether the feature data is numerical or not. + """ + data_type_is_numerical = feature_data.dtype.kind in ("f", "i", "u") + if data_type_is_numerical: + return feature_data, data_type_is_numerical + + # here np.unique with return_inverse encodes categorical values into numerical ones + return np.unique(feature_data, return_inverse=True)[1], data_type_is_numerical + + +@njit(cache=True, nogil=True) +def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: + """ + Performs a simple quantile clustering on one dimensional data (1d). Quantile + clustering is defined as the procedure that forms clusters in 1d data by + separating objects using quantiles (for instance, if the median is used, two + clusters are generated with objects separated by the median). In the case + data contains all the same values (zero variance), this implementation can + return less clusters than specified with k. + + Args: + data: a 1d numpy array with numerical values. + k: the number of clusters to split the data into. + + Returns: + A 1d array with the data partition. + """ + data_sorted = np.argsort(data, kind="quicksort") + data_rank = rank(data, data_sorted) + data_perc = data_rank / len(data) + + percentiles = [0.0] + get_perc_from_k(k) + [1.0] + + cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") + + current_cluster = 0 + part = np.zeros(data.shape, dtype=np.int16) - 1 + + for i in range(len(cut_points) - 1): + lim1 = cut_points[i] + lim2 = cut_points[i + 1] + + part[data_sorted[lim1:lim2]] = current_cluster + current_cluster += 1 + + return part + + +@njit(cache=True, nogil=True) +def get_range_n_clusters( + n_items: int, internal_n_clusters: Iterable[int] = None +) -> NDArray[np.uint8]: + """ + Given the number of features it returns a tuple of k values to cluster those + features into. By default, it generates a tuple of k values from 2 to + int(np.round(np.sqrt(n_items))) (inclusive). For example, for 25 features, + it will generate this tuple: (2, 3, 4, 5). + + Args: + n_items: a positive number representing the number of features that + will be clustered into different groups/clusters. + internal_n_clusters: it allows to force a different list of clusters. It + must be a list of integers. Repeated or invalid values will be dropped, + such as values lesser than 2 (a singleton partition is not allowed). + + Returns: + A numpy array with integer values representing numbers of clusters. + """ + + if internal_n_clusters is not None: + # remove k values that are invalid + clusters_range_list = list( + set([int(x) for x in internal_n_clusters if 1 < x < n_items]) + ) + else: + # default behavior if no internal_n_clusters is given: return range from + # 2 to sqrt(n_items) + n_sqrt = int(np.round(np.sqrt(n_items))) + n_sqrt = min((n_sqrt, 10)) + clusters_range_list = list(range(2, n_sqrt + 1)) + + return np.array(clusters_range_list, dtype=np.uint16) + + +@njit(cache=True, nogil=True) +def get_parts( + data: NDArray, range_n_clusters: tuple[int], data_is_numerical: bool = True +) -> NDArray[np.int16]: + """ + Given a 1d data array, it computes a partition for each k value in the given + range of clusters. This function only supports numerical data, and it + always runs run_run_quantile_clustering with the different k values. + If partitions with only one cluster are returned (singletons), then the + returned array will have negative values. + + Args: + data: a 1d data vector. It is assumed that there are no nans. + range_n_clusters: a tuple with the number of clusters. + data_is_numerical: indicates whether data is numerical (True) or categorical (False) + + Returns: + A numpy array with shape (number of clusters, data rows) with + partitions of data. + + Partitions could have negative values in some scenarios, with different + meanings: -1 is used for categorical data, where only one partition is generated + and the rest (-1) are marked as "empty". -2 is used when singletons have been + detected (partitions with one cluster), usually because of problems with the + input data (it has all the same values, for example). + """ + # parts[i] represents the partition for cluster i + # parts[i][j] represents the cluster assignment for element j, using i-th cluster's configuration + parts = np.zeros((len(range_n_clusters), data.shape[0]), dtype=np.int16) - 1 + + # can use cupy.digitize here + if data_is_numerical: + for idx in range(len(range_n_clusters)): + k = range_n_clusters[idx] + parts[idx] = run_quantile_clustering(data, k) + + # remove singletons by putting a -2 as values + partitions_ks = np.array([len(np.unique(p)) for p in parts]) + parts[partitions_ks == 1, :] = -2 + else: + # if the data is categorical, then the encoded feature is already the partition + # only the first partition is filled, the rest will be -1 (missing) + parts[0] = data.astype(np.int16) + + return parts + + +@njit(cache=True, nogil=True) +def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: + """ + Given the number of objects and and index, it returns the row/column + position of the pairwise matrix. For example, if there are n_obj objects + (such as genes), a condensed 1d array can be created with pairwise + comparisons between genes, as well as a squared symmetric matrix. This + function receives the number of objects and the index of the condensed + array, and returns the coordiates of the squared symmetric matrix. + + Args: + n_obj: the number of objects. + idx: the index of the condensed pairwise array across all n_obj objects. + + Returns + A tuple (i, j) with the coordinates of the squared symmetric matrix + equivalent to the condensed array. + """ + b = 1 - 2 * n_obj + x = np.floor((-b - np.sqrt(b ** 2 - 8 * idx)) / 2) + y = idx + x * (b + x + 2) / 2 + 1 + return int(x), int(y) + + +@cuda.jit(device=True) +def get_parts( + data: NDArray, res: NDArray[np.int16], ange_n_clusters: tuple[int], data_is_numerical: bool = True): + return + + +# store result to device global memory +@cuda.jit +def compute_parts(X: np.ndarray, parts: np.ndarray, n_range_cluster: NDArray[np.uint8]): + x, y, z = cuda.grid(3) + if x < parts.shape[0] and y < parts.shape[1] and z < parts.shape[2]: + parts[x, y, z] += 1 + return + + +@cuda.jit +def compute_parts2(parts: np.ndarray): + x, y = cuda.grid(2) + if x < parts.shape[0] and y < parts.shape[1]: + parts[x, y] += 1 + return + + +# Opt: may lower uint16 to reduce memory consumption and data movement +def bin_objects(objs: NDArray[np.uint16], n_clusters: int) -> NDArray[np.uint16]: + """ + This function is a CUDA kernel for binning (digitizing) objects according to the percentiles provided + """ + raise NotImplementedError + + +def ccc( + x: NDArray, + y: NDArray = None, + internal_n_clusters: Union[int, Iterable[int]] = None, + return_parts: bool = False, + n_chunks_threads_ratio: int = 1, + n_jobs: int = 1, +) -> tuple[NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: + """ + This is the main function that computes the Clustermatch Correlation + Coefficient (CCC) between two arrays. The implementation supports numerical + and categorical data. + + Args: + x: an 1d or 2d numerical array with the data. NaN are not supported. + If it is 2d, then the coefficient is computed for each pair of rows + (in case x is a numpy.array) or each pair of columns (pandas.DataFrame). + y: an optional 1d numerical array. If x is 1d and y is given, it computes + the coefficient between x and y. + internal_n_clusters: this parameter can be an integer (the maximum number + of clusters used to split x and y, starting from k=2) or a list of + integer values (a custom list of k values). + return_parts: if True, for each object pair, it returns the partitions + that maximized the coefficient. + n_chunks_threads_ratio: allows to modify how pairwise comparisons are + split across different threads. It's given as the ratio parameter of + function get_chunks. + n_jobs: number of CPU cores to use for parallelization. The value + None will use all available cores (`os.cpu_count()`), and negative + values will use `os.cpu_count() - n_jobs`. Default is 1. + + Returns: + If return_parts is False, only CCC values are returned. + In that case, if x is 2d, a np.ndarray of size n x n is + returned with the coefficient values, where n is the number of rows in x. + If only a single coefficient was computed (for example, x and y were + given as 1d arrays each), then a single scalar is returned. + + If returns_parts is True, then it returns a tuple with three values: + 1) the + coefficients, 2) the partitions indexes that maximized the coefficient + for each object pair, and 3) the partitions for all objects. + + cm_values: if x is 2d, then it is a 1d condensed array of pairwise + coefficients. It has size (n * (n - 1)) / 2, where n is the number + of rows in x. If x and y are given, and they are 1d, then this is a + scalar. The CCC is always between 0 and 1 + (inclusive). If any of the two variables being compared has no + variation (all values are the same), the coefficient is not defined + (np.nan). + + max_parts: an array with n * (n - 1)) / 2 rows (one for each object + pair) and two columns. It has the indexes pointing to each object's + partition (parts, see below) that maximized the ARI. If + cm_values[idx] is nan, then max_parts[idx] will be meaningless. + + parts: a 3d array that contains all the internal partitions generated + for each object in data. parts[i] has the partitions for object i, + whereas parts[i,j] has the partition j generated for object i. The + third dimension is the number of columns in x (if 2d) or elements in + x/y (if 1d). For example, if you want to access the pair of + partitions that maximized the CCC given x and y + (a pair of objects), then max_parts[0] and max_parts[1] have the + partition indexes in parts, respectively: parts[0][max_parts[0]] + points to the partition for x, and parts[1][max_parts[1]] points to + the partition for y. Values could be negative in case + singleton cases were found (-1; usually because input data has all the same + value) or for categorical features (-2). + """ + n_objects = None + n_features = None + # this is a boolean array of size n_features with True if the feature is numerical and False otherwise + X_numerical_type = None + if x.ndim == 1 and (y is not None and y.ndim == 1): + # both x and y are 1d arrays + if not x.shape == y.shape: + raise ValueError("x and y need to be of the same size") + n_objects = x.shape[0] + n_features = 2 + # Create a matrix to store both x and y + X = np.zeros((n_features, n_objects)) + X_numerical_type = np.full((n_features,), True, dtype=bool) + + X[0, :], X_numerical_type[0] = get_feature_type_and_encode(x) + X[1, :], X_numerical_type[1] = get_feature_type_and_encode(y) + elif x.ndim == 2 and y is None: + # x is a 2d array; two things could happen: 1) this is an numpy array, + # in that case, features are in rows, objects are in columns; 2) or this is a + # pandas dataframe, which is the opposite (features in columns and objects in rows), + # plus we have the features data type (numerical, categorical, etc) + + if isinstance(x, np.ndarray): + assert get_feature_type_and_encode(x[0, :])[1], ( + "If data is a 2d numpy array, it has to be numerical. Use pandas.DataFrame if " + "you need to mix features with different data types" + ) + n_objects = x.shape[1] + n_features = x.shape[0] + + X = x + X_numerical_type = np.full((n_features,), True, dtype=bool) + elif hasattr(x, "to_numpy"): + # Here I assume that if x has the attribute "to_numpy" is of type pandas.DataFrame + # Using isinstance(x, pandas.DataFrame) would be more appropriate, but I dont want to + # have pandas as a dependency just for that + n_objects = x.shape[0] + n_features = x.shape[1] + + X = np.zeros((n_features, n_objects)) + X_numerical_type = np.full((n_features,), True, dtype=bool) + + for idx in range(n_features): + X[idx, :], X_numerical_type[idx] = get_feature_type_and_encode( + x.iloc[:, idx] + ) + else: + raise ValueError("Wrong combination of parameters x and y") + + # Converts internal_n_clusters to a list of integers if it's provided. + if internal_n_clusters is not None: + _tmp_list = List[int] + + if isinstance(internal_n_clusters, int): + # this interprets internal_n_clusters as the maximum k + internal_n_clusters = range(2, internal_n_clusters + 1) + + for x in internal_n_clusters: + _tmp_list.append(x) + internal_n_clusters = _tmp_list + + # Get matrix of partitions for each object pair + range_n_clusters = get_range_n_clusters(n_objects, internal_n_clusters) + + if range_n_clusters.shape[0] == 0: + raise ValueError(f"Data has too few objects: {n_objects}") + + # Store a set of partitions per row (object) in X as a multidimensional array, where the second dimension is the + # number of partitions per object. + # The value at parts[i, j, k] will represent the cluster assignment for the k-th object, using the j-th cluster + # configuration, for the i-th feature. + # Allocate this directly on the GPU + parts = ( + np.zeros((n_features, range_n_clusters.shape[0], n_objects), dtype=np.int16) - 1 + ) + + # cm_values stores the CCC coefficients + n_features_comp = (n_features * (n_features - 1)) // 2 + cm_values = np.full(n_features_comp, np.nan) + + # for each object pair being compared, max_parts has the indexes of the + # partitions that maximimized the ARI + max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) + + # X here (and following) is a numpy array features are in rows, objects are in columns + + # Notes for CUDA dim: for genetic data, number of features is usually small, so we can use a 1D grid? + # but number of objects is usually large. + + # cuda.synchronize() + # For this iteration, we use 1D block and 2D grid + # grid[i] stands for partitions for feature i + # grid[i][j] stands for partitions for feature i with k=j clusters + + threads_per_block = (32, 16, 16) + nx = n_features + ny = range_n_clusters.shape[0] + nz = n_objects + # equivalent to blocks_per_grid_x = math.ceil(nx / threads_per_block[0]) + blocks_per_grid_x = (nx + threads_per_block[0] - 1) // threads_per_block[0] + blocks_per_grid_y = (ny + threads_per_block[1] - 1) // threads_per_block[1] + blocks_per_grid_z = (nz + threads_per_block[2] - 1) // threads_per_block[2] + blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y, blocks_per_grid_z) + + # Transfer data to device + h_X = X + d_X = cuda.to_device(h_X) + d_parts = cuda.to_device(parts) + + # For this iteration, use CPU multi-threading to compute quantile lists using range_n_clusters + # Refer to https://docs.cupy.dev/en/stable/reference/generated/cupy.quantile.html for the GPU implementation + + # Call the compute_parts kernel, results are stored in d_parts Passing an array that resides in host memory will + # implicitly cause a copy back to the host, which will be synchronous. + # compute_parts[blocks_per_grid, threads_per_block](d_X, d_parts, range_n_clusters) + # # Wait for all previous kernels + # cuda.synchronize() + # print(parts) + + # can also try compute_parts.forall() + an_array = np.zeros((n_features, n_objects), dtype=np.int16) + print(f"prev array: {an_array}") + threadsperblock = (16, 16) + blockspergrid_x = math.ceil(an_array.shape[0] / threadsperblock[0]) + blockspergrid_y = math.ceil(an_array.shape[1] / threadsperblock[1]) + blockspergrid = (blockspergrid_x, blockspergrid_y) + compute_parts2[blockspergrid, threadsperblock](an_array) + cuda.synchronize() + print(f"after array: {an_array}") + + # compute coefficients + # def compute_coef(idx_list: List[int]) -> Tuple[np.ndarray, np.ndarray]: + # """ + # Given a list of indexes representing each a pair of + # objects/rows/genes, it computes the CCC coefficient for + # each of them. This function is supposed to be used to parallelize + # processing. + # + # Args: + # idx_list: a list of indexes (integers), each of them + # representing a pair of objects. + # + # Returns: + # Returns a tuple with two arrays. These two arrays are the same + # arrays returned by the main cm function (cm_values and + # max_parts) but for a subset of the data. + # """ + # n_idxs = len(idx_list) + # max_ari_list = np.full(n_idxs, np.nan, dtype=float) + # max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + # + # for idx, data_idx in enumerate(idx_list): + # i, j = get_coords_from_index(n_features, data_idx) + # + # # obji_parts and objj_parts are the partitions for the objects i and j. + # obji_parts, objj_parts = parts[i], parts[j] + # + # # compute ari only if partitions are not marked as "missing" + # # (negative values), which is assigned when partitions have + # # one cluster (usually when all data in the feature has the same + # # value). + # if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + # continue + # + # # compare all partitions of one object to the all the partitions + # # of the other object, and get the maximium ARI + # comp_values = cdist_func( + # obji_parts, + # objj_parts, + # ) + # max_flat_idx = comp_values.argmax() + # + # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + # max_part_idx_list[idx] = max_idx + # max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + # + # return max_ari_list, max_part_idx_list + + +# Dev notes +# 1. parallelize get_parst +# 1.1 gpu percentile computation +# 1.1 gpu data points binning +# can be a kernel for-loop to compute parts on different percentile diff --git a/libs/ccc/sklearn/metrics_gpu.py b/libs/ccc/sklearn/metrics_gpu.py index ad2671cd..a430d1a1 100644 --- a/libs/ccc/sklearn/metrics_gpu.py +++ b/libs/ccc/sklearn/metrics_gpu.py @@ -128,6 +128,7 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) +# Todo: __device__ ? @cuda.jit def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): """ diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py new file mode 100644 index 00000000..d5d9f41d --- /dev/null +++ b/tests/gpu/test_coef.py @@ -0,0 +1,70 @@ +import pytest +from typing import List + +import numpy as np + + +from ccc.coef.impl_gpu import get_perc_from_k, get_range_n_percs + + +def test_get_perc_from_k_with_k_less_than_two(): + empty_array = np.empty(0) + np.testing.assert_array_equal(get_perc_from_k(1), empty_array) + np.testing.assert_array_equal(get_perc_from_k(0), empty_array) + np.testing.assert_array_equal(get_perc_from_k(-1), empty_array) + + +def test_get_perc_from_k(): + assert get_perc_from_k(2) == [0.5] + assert np.round(get_perc_from_k(3), 3).tolist() == [0.333, 0.667] + assert get_perc_from_k(4) == [0.25, 0.50, 0.75] + + +def test_get_range_n_percs_basic(): + ks = [2, 3, 4] + expected: List[List[float]] = [ + [0.5], + [0.3333333333333333, 0.6666666666666666], + [0.25, 0.5, 0.75], + [] + ] + result = get_range_n_percs(ks) + assert np.allclose(result, expected) + + +def test_get_range_n_percs_empty(): + ks: List[int] = [] + expected: List[List[float]] = [] + result = get_range_n_percs(ks) + assert result == expected + + +def test_get_range_n_percs_single(): + ks = [1, 0, -1] + expected = [[], [], []] + result = get_range_n_percs(ks) + assert result == expected + + +def test_get_range_n_percs_large(): + ks = [10, 5, 2] + expected = [ + [0.1 * i for i in range(1, 10)], + [0.2, 0.4, 0.6, 0.8], + [0.5] + ] + result = get_range_n_percs(ks) + assert result == expected + + +def test_get_range_n_percs_mixed(): + ks = [4, 3, 0, 1, 5] + expected = [ + [0.25, 0.5, 0.75], + [0.3333333333333333, 0.6666666666666666], + [], + [], + [0.2, 0.4, 0.6, 0.8] + ] + result = get_range_n_percs(ks) + assert result == expected diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py new file mode 100644 index 00000000..dadb83d8 --- /dev/null +++ b/tests/gpu/test_cupy.py @@ -0,0 +1,13 @@ +import cupy as cp +import numpy as np + + +def test_percentile(): + # random_feature1 = np.random.rand(100) + # random_feature2 = np.random.rand(100) + # + # res = ccc(random_feature1, random_feature2, n_jobs=2) + # print(res) + + data = np.random.rand(10, 100) + diff --git a/tests/gpu/test_impl_gpu.py b/tests/gpu/test_impl_gpu.py new file mode 100644 index 00000000..fe868823 --- /dev/null +++ b/tests/gpu/test_impl_gpu.py @@ -0,0 +1,20 @@ +from ccc.coef.impl_gpu import ccc +import numpy as np + + +def test_compute_parts(): + # random_feature1 = np.random.rand(100) + # random_feature2 = np.random.rand(100) + # + # res = ccc(random_feature1, random_feature2, n_jobs=2) + # print(res) + + data = np.random.rand(10, 100) + c = ccc(data) + print(c) + + + + + + From 855534fdff48adc30e786bd6141f0ee46024e58f Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 8 Aug 2024 10:38:08 -0600 Subject: [PATCH 016/134] [cuda/coef]: Factor out more functions --- libs/ccc/coef/impl_gpu.py | 46 +++++++------ tests/gpu/test_coef.py | 136 ++++++++++++++++++++++---------------- 2 files changed, 105 insertions(+), 77 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 0731b814..3d00fd59 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -3,7 +3,7 @@ """ import os import math -from typing import Iterable, Union, List, Tuple +from typing import Optional, Iterable, Union, List, Tuple import numpy as np import cupy @@ -16,7 +16,7 @@ @njit(cache=True, nogil=True) -def get_perc_from_k(k: int) -> np.ndarray: +def get_perc_from_k(k: int) -> NDArray[np.float32]: """ It returns the percentiles (from 0.0 to 1.0) that separate the data into k clusters. For example, if k=2, it returns [0.5]; if k=4, it returns [0.25, @@ -34,22 +34,29 @@ def get_perc_from_k(k: int) -> np.ndarray: return np.array([(1.0 / k) * i for i in range(1, k)], dtype=np.float32) -# @njit(cache=True, nogil=True) -def get_range_n_percs(ks: List[int]) -> List[List[float]]: +@njit(cache=True, nogil=True) +def get_range_n_percs(ks: NDArray[np.int8]) -> NDArray[np.float32]: """ It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters Args: - ks: list of numbers of clusters. + ks: an array of numbers of clusters. Returns: - A list of lists of percentiles (from 0.0 to 1.0). + A 2D sparse matrix of percentiles (from 0.0 to 1.0). """ # Todo: research on if numba can optimize this - percentiles: List[List[float]] = [] - for k in ks: + # Emtpy & null check + if ks.size == 0: + return np.empty((0, 0), dtype=np.float32) + # Number of rows of the returning matrix + n_rows = len(ks) + # Number of columns of the returning matrix, dominated by the largest k, which specifies the # of clusters + n_cols = np.max(ks) - 1 + percentiles = np.full((n_rows, n_cols), np.nan, dtype=np.float32) + for idx, k in enumerate(ks): perc = get_perc_from_k(k) - percentiles.append(perc) + percentiles[idx, :len(perc)] = perc return percentiles @@ -252,6 +259,16 @@ def bin_objects(objs: NDArray[np.uint16], n_clusters: int) -> NDArray[np.uint16] raise NotImplementedError +def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> List[int]: + if internal_n_clusters is None: + return [] + + if isinstance(internal_n_clusters, int): + return list(range(2, internal_n_clusters + 1)) + + return list(internal_n_clusters) + + def ccc( x: NDArray, y: NDArray = None, @@ -371,16 +388,7 @@ def ccc( raise ValueError("Wrong combination of parameters x and y") # Converts internal_n_clusters to a list of integers if it's provided. - if internal_n_clusters is not None: - _tmp_list = List[int] - - if isinstance(internal_n_clusters, int): - # this interprets internal_n_clusters as the maximum k - internal_n_clusters = range(2, internal_n_clusters + 1) - - for x in internal_n_clusters: - _tmp_list.append(x) - internal_n_clusters = _tmp_list + internal_n_clusters = convert_n_clusters(internal_n_clusters) # Get matrix of partitions for each object pair range_n_clusters = get_range_n_clusters(n_objects, internal_n_clusters) diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index d5d9f41d..f16cb548 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -2,69 +2,89 @@ from typing import List import numpy as np +from numpy.testing import assert_array_equal, assert_allclose +from numpy.typing import NDArray - -from ccc.coef.impl_gpu import get_perc_from_k, get_range_n_percs +from ccc.coef.impl_gpu import ( + get_perc_from_k, + get_range_n_percs, + convert_n_clusters, +) def test_get_perc_from_k_with_k_less_than_two(): empty_array = np.empty(0) - np.testing.assert_array_equal(get_perc_from_k(1), empty_array) - np.testing.assert_array_equal(get_perc_from_k(0), empty_array) - np.testing.assert_array_equal(get_perc_from_k(-1), empty_array) - - -def test_get_perc_from_k(): - assert get_perc_from_k(2) == [0.5] - assert np.round(get_perc_from_k(3), 3).tolist() == [0.333, 0.667] - assert get_perc_from_k(4) == [0.25, 0.50, 0.75] - - -def test_get_range_n_percs_basic(): - ks = [2, 3, 4] - expected: List[List[float]] = [ - [0.5], - [0.3333333333333333, 0.6666666666666666], - [0.25, 0.5, 0.75], - [] - ] - result = get_range_n_percs(ks) - assert np.allclose(result, expected) - - -def test_get_range_n_percs_empty(): - ks: List[int] = [] - expected: List[List[float]] = [] - result = get_range_n_percs(ks) - assert result == expected - - -def test_get_range_n_percs_single(): - ks = [1, 0, -1] - expected = [[], [], []] - result = get_range_n_percs(ks) - assert result == expected - - -def test_get_range_n_percs_large(): - ks = [10, 5, 2] - expected = [ - [0.1 * i for i in range(1, 10)], - [0.2, 0.4, 0.6, 0.8], - [0.5] + assert_array_equal(get_perc_from_k(1), empty_array) + assert_array_equal(get_perc_from_k(0), empty_array) + assert_array_equal(get_perc_from_k(-1), empty_array) + + +@pytest.mark.parametrize("k, expected", [ + (2, [0.5]), + (3, [0.333, 0.667]), + (4, [0.25, 0.50, 0.75]) +]) +def test_get_perc_from_k(k, expected): + assert_allclose(np.ndarray.round(get_perc_from_k(k), 3), expected) + + +@pytest.mark.parametrize( + "ks, expected", + [ + ( + np.array([], dtype=np.int8), + np.empty((0, 0), dtype=np.float32) + ), + ( + np.array([2, 3, 4], dtype=np.int8), + np.array([ + [0.5, np.nan, np.nan], + [0.33333334, 0.6666667, np.nan], + [0.25, 0.5, 0.75] + ], dtype=np.float32) + ), + ( + np.array([2], dtype=np.int8), + np.array([[0.5]], dtype=np.float32) + ), + ( + np.array([10], dtype=np.int8), + np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32) + ), + ( + np.array([2, 4, 6, 8], dtype=np.int8), + np.array([ + [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], + [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], + [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] + ], dtype=np.float32) + ), + ( + np.array([2, 3, 4], dtype=np.int8), + np.array([ + [0.5, np.nan, np.nan], + [0.33333334, 0.6666667, np.nan], + [0.25, 0.5, 0.75], + ], dtype=np.float32) + ), ] +) +def test_get_range_n_percs(ks, expected): result = get_range_n_percs(ks) - assert result == expected - - -def test_get_range_n_percs_mixed(): - ks = [4, 3, 0, 1, 5] - expected = [ - [0.25, 0.5, 0.75], - [0.3333333333333333, 0.6666666666666666], - [], - [], - [0.2, 0.4, 0.6, 0.8] + np.testing.assert_array_almost_equal(result, expected) + + +@pytest.mark.parametrize( + "input_value, expected_output", + [ + (None, []), + (2, [2]), + (5, [2, 3, 4, 5]), + ([1, 3, 5], [1, 3, 5]), + ([], []), + ((7, 8, 9), [7, 8, 9]), ] - result = get_range_n_percs(ks) - assert result == expected +) +def test_convert_n_clusters(input_value, expected_output): + assert convert_n_clusters(input_value) == expected_output \ No newline at end of file From d424a402b71720c94547d322a1f4da5bee9c946d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 8 Aug 2024 11:25:52 -0600 Subject: [PATCH 017/134] [coef]: Add tests for functions --- libs/ccc/coef/impl_gpu.py | 4 +- tests/gpu/test_coef.py | 157 +++++++++++++++++++++++++++++++++++++- 2 files changed, 158 insertions(+), 3 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 3d00fd59..90f0e231 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -121,7 +121,7 @@ def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: return part -@njit(cache=True, nogil=True) +# @njit(cache=True, nogil=True) def get_range_n_clusters( n_items: int, internal_n_clusters: Iterable[int] = None ) -> NDArray[np.uint8]: @@ -129,7 +129,7 @@ def get_range_n_clusters( Given the number of features it returns a tuple of k values to cluster those features into. By default, it generates a tuple of k values from 2 to int(np.round(np.sqrt(n_items))) (inclusive). For example, for 25 features, - it will generate this tuple: (2, 3, 4, 5). + it will generate this array: (2, 3, 4, 5). Args: n_items: a positive number representing the number of features that diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index f16cb548..3ac1b4f2 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -9,6 +9,7 @@ get_perc_from_k, get_range_n_percs, convert_n_clusters, + get_range_n_clusters, ) @@ -87,4 +88,158 @@ def test_get_range_n_percs(ks, expected): ] ) def test_convert_n_clusters(input_value, expected_output): - assert convert_n_clusters(input_value) == expected_output \ No newline at end of file + assert convert_n_clusters(input_value) == expected_output + + +def test_get_range_n_clusters_without_internal_n_clusters(): + # 100 features + range_n_clusters = get_range_n_clusters(100) + assert range_n_clusters is not None + np.testing.assert_array_equal( + range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) + ) + + # 25 features + range_n_clusters = get_range_n_clusters(25) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) + + +def test_get_range_n_clusters_with_internal_n_clusters_is_list(): + # 100 features + range_n_clusters = get_range_n_clusters( + 100, + internal_n_clusters=[2], + ) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2])) + + # 25 features + range_n_clusters = get_range_n_clusters( + 25, + internal_n_clusters=[2], + ) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2])) + + # 25 features + range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[2, 3, 4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) + + +def test_get_range_n_clusters_with_internal_n_clusters_none(): + # 100 features + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=None) + assert range_n_clusters is not None + np.testing.assert_array_equal( + range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) + ) + + # 25 features + range_n_clusters = get_range_n_clusters(25, internal_n_clusters=None) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) + + +def test_get_range_n_clusters_with_internal_n_clusters_has_single_int(): + # 100 features + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2])) + + # 25 features + range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[3]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([3])) + + # 5 features + range_n_clusters = get_range_n_clusters(5, internal_n_clusters=[4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([4])) + + # 25 features but invalid number of clusters + range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[1]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + # 25 features but invalid number of clusters + range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[25]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + +def test_get_range_n_clusters_with_internal_n_clusters_are_less_than_two(): + # 100 features + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) + + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, 4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) + + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 1]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3])) + + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 0, 4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) + + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, -4, 6]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 6])) + + +def test_get_range_n_clusters_with_internal_n_clusters_are_repeated(): + # 100 features + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 3, 2, 4]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) + + range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 2, 2]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2])) + + +def test_get_range_n_clusters_with_very_few_features(): + # 3 features + range_n_clusters = get_range_n_clusters(3) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([2])) + + # 2 features + range_n_clusters = get_range_n_clusters(2) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + # 1 features + range_n_clusters = get_range_n_clusters(1) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + # 0 features + range_n_clusters = get_range_n_clusters(0) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + +def test_get_range_n_clusters_with_larger_k_than_features(): + # 10 features + range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[10]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + # 10 features + range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[11]) + assert range_n_clusters is not None + np.testing.assert_array_equal(range_n_clusters, np.array([])) + + +def test_get_range_n_clusters_with_default_max_k(): + range_n_clusters = get_range_n_clusters(200) + assert range_n_clusters is not None + np.testing.assert_array_equal( + range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) + ) \ No newline at end of file From 0eeefadb30d3820f0baf349798a39bbd504cbba8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 11 Aug 2024 14:22:37 -0600 Subject: [PATCH 018/134] [coef]: Apply grid-stride loops --- libs/ccc/coef/impl_gpu.py | 62 ++++++++++++++++++++++----------------- 1 file changed, 35 insertions(+), 27 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 90f0e231..97f9804b 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -142,7 +142,7 @@ def get_range_n_clusters( A numpy array with integer values representing numbers of clusters. """ - if internal_n_clusters is not None: + if internal_n_clusters: # remove k values that are invalid clusters_range_list = list( set([int(x) for x in internal_n_clusters if 1 < x < n_items]) @@ -236,10 +236,16 @@ def get_parts( # store result to device global memory @cuda.jit -def compute_parts(X: np.ndarray, parts: np.ndarray, n_range_cluster: NDArray[np.uint8]): - x, y, z = cuda.grid(3) - if x < parts.shape[0] and y < parts.shape[1] and z < parts.shape[2]: - parts[x, y, z] += 1 +def compute_parts(parts: np.ndarray, X: np.ndarray, cluster_id: np.int8, feature_id: np.int64): + feature_row = X[feature_id, :] + size = feature_row.shape[0] + # Use 1D Grid-Stride Loops Pattern to handle large # of features that can't be processed using all threads + i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x + # i = cuda.grid(1) + while i < size: + parts[cluster_id, feature_id, i] = -1 + i += cuda.gridDim.x * cuda.blockDim.x + return @@ -400,10 +406,7 @@ def ccc( # number of partitions per object. # The value at parts[i, j, k] will represent the cluster assignment for the k-th object, using the j-th cluster # configuration, for the i-th feature. - # Allocate this directly on the GPU - parts = ( - np.zeros((n_features, range_n_clusters.shape[0], n_objects), dtype=np.int16) - 1 - ) + # cm_values stores the CCC coefficients n_features_comp = (n_features * (n_features - 1)) // 2 @@ -418,25 +421,39 @@ def ccc( # Notes for CUDA dim: for genetic data, number of features is usually small, so we can use a 1D grid? # but number of objects is usually large. + # cuda.synchronize() # For this iteration, we use 1D block and 2D grid # grid[i] stands for partitions for feature i # grid[i][j] stands for partitions for feature i with k=j clusters - threads_per_block = (32, 16, 16) - nx = n_features - ny = range_n_clusters.shape[0] + # 1D kernel + threads_per_block = 36 + nx = range_n_clusters.shape[0] + ny = n_features nz = n_objects # equivalent to blocks_per_grid_x = math.ceil(nx / threads_per_block[0]) - blocks_per_grid_x = (nx + threads_per_block[0] - 1) // threads_per_block[0] - blocks_per_grid_y = (ny + threads_per_block[1] - 1) // threads_per_block[1] - blocks_per_grid_z = (nz + threads_per_block[2] - 1) // threads_per_block[2] - blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y, blocks_per_grid_z) - + blocks_per_grid_x = (nx + threads_per_block - 1) // threads_per_block + # blocks_per_grid_y = (ny + threads_per_block[1] - 1) // threads_per_block[1] + # blocks_per_grid_z = (nz + threads_per_block[2] - 1) // threads_per_block[2] + blocks_per_grid = blocks_per_grid_x + + # Allocate arrays on device global memory + # parts' shape is different from the original implementation + d_parts = cuda.device_array((nx, ny, nz), dtype=np.int16) # Transfer data to device h_X = X d_X = cuda.to_device(h_X) - d_parts = cuda.to_device(parts) + + # Debug + print(f"prev parts: {d_parts}") + for i in range(nz): + for j in range(ny): + compute_parts[blocks_per_grid, threads_per_block](d_parts, d_X, i, j) + # Move data back to host + h_parts = d_parts.copy_to_host() + print(f"after parts: {h_parts}") + return(2) # For this iteration, use CPU multi-threading to compute quantile lists using range_n_clusters # Refer to https://docs.cupy.dev/en/stable/reference/generated/cupy.quantile.html for the GPU implementation @@ -449,15 +466,6 @@ def ccc( # print(parts) # can also try compute_parts.forall() - an_array = np.zeros((n_features, n_objects), dtype=np.int16) - print(f"prev array: {an_array}") - threadsperblock = (16, 16) - blockspergrid_x = math.ceil(an_array.shape[0] / threadsperblock[0]) - blockspergrid_y = math.ceil(an_array.shape[1] / threadsperblock[1]) - blockspergrid = (blockspergrid_x, blockspergrid_y) - compute_parts2[blockspergrid, threadsperblock](an_array) - cuda.synchronize() - print(f"after array: {an_array}") # compute coefficients # def compute_coef(idx_list: List[int]) -> Tuple[np.ndarray, np.ndarray]: From 42faf9a9a6ef6f3faf8d34f165c779c27d93512a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 11 Aug 2024 18:41:53 -0600 Subject: [PATCH 019/134] [build]: Update dependencies --- environment/environment.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/environment/environment.yml b/environment/environment.yml index 7c3738a4..560cb740 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -3,14 +3,16 @@ channels: - conda-forge - defaults dependencies: + - cudatoolkit=11.2.* + - cupy=13.2.* - ipython=7.* - ipywidgets - jupyterlab=3.3.* - jupytext=1.11.* - matplotlib=3.4.* - minepy=1.2.* - - numba=0.53.* - - numpy=1.21.* + - numba=0.60.* + - numpy=1.26.* - openpyxl=3.0.* - pandas=1.3.* - papermill=2.3.* From ef4132bc5987e41a2a43fc75ebddd3cc5307b96b Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 11 Aug 2024 19:12:24 -0600 Subject: [PATCH 020/134] [coef]: Test cupy routines --- libs/ccc/coef/impl_gpu.py | 12 ++------ tests/gpu/test_coef.py | 58 +++++++++++++++++++++++++++++++++++ tests/gpu/test_cupy.py | 57 ++++++++++++++++++++++++++++++++-- tests/gpu/test_impl_gpu.py | 5 +-- tests/gpu/tmp_regress_test.py | 0 5 files changed, 119 insertions(+), 13 deletions(-) create mode 100644 tests/gpu/tmp_regress_test.py diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 97f9804b..223a63e2 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -35,7 +35,7 @@ def get_perc_from_k(k: int) -> NDArray[np.float32]: @njit(cache=True, nogil=True) -def get_range_n_percs(ks: NDArray[np.int8]) -> NDArray[np.float32]: +def get_range_n_percs(ks: NDArray[np.int8], as_percentage: bool = False) -> NDArray[np.float32]: """ It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters @@ -56,6 +56,8 @@ def get_range_n_percs(ks: NDArray[np.int8]) -> NDArray[np.float32]: percentiles = np.full((n_rows, n_cols), np.nan, dtype=np.float32) for idx, k in enumerate(ks): perc = get_perc_from_k(k) + if as_percentage: + perc = np.round(perc * 100).astype(np.float32) # Convert to percentage and round percentiles[idx, :len(perc)] = perc return percentiles @@ -249,14 +251,6 @@ def compute_parts(parts: np.ndarray, X: np.ndarray, cluster_id: np.int8, feature return -@cuda.jit -def compute_parts2(parts: np.ndarray): - x, y = cuda.grid(2) - if x < parts.shape[0] and y < parts.shape[1]: - parts[x, y] += 1 - return - - # Opt: may lower uint16 to reduce memory consumption and data movement def bin_objects(objs: NDArray[np.uint16], n_clusters: int) -> NDArray[np.uint16]: """ diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index 3ac1b4f2..972bb9b4 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -76,6 +76,64 @@ def test_get_range_n_percs(ks, expected): np.testing.assert_array_almost_equal(result, expected) +@pytest.mark.parametrize( + "ks, expected_frac, expected_perc", + [ + ( + np.array([], dtype=np.int8), + np.empty((0, 0), dtype=np.float32), + np.empty((0, 0), dtype=np.float32) + ), + ( + np.array([2, 3, 4], dtype=np.int8), + np.array([ + [0.5, np.nan, np.nan], + [0.33333334, 0.6666667, np.nan], + [0.25, 0.5, 0.75] + ], dtype=np.float32), + np.array([ + [50, np.nan, np.nan], + [33, 67, np.nan], + [25, 50, 75] + ], dtype=np.float32) + ), + ( + np.array([2], dtype=np.int8), + np.array([[0.5]], dtype=np.float32), + np.array([[50]], dtype=np.float32) + ), + ( + np.array([10], dtype=np.int8), + np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32), + np.array([[10, 20, 30, 40, 50, 60, 70, 80, 90]], dtype=np.float32) + ), + ( + np.array([2, 4, 6, 8], dtype=np.int8), + np.array([ + [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], + [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], + [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] + ], dtype=np.float32), + np.array([ + [50, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [25, 50, 75, np.nan, np.nan, np.nan, np.nan], + [17, 33, 50, 67, 83, np.nan, np.nan], + [12, 25, 38, 50, 62, 75, 88] + ], dtype=np.float32) + ), + ] +) +def test_get_range_n_percs_as_percentage(ks, expected_frac, expected_perc): + # Test fractional percentiles (original behavior) + result_frac = get_range_n_percs(ks, as_percentage=False) + np.testing.assert_array_almost_equal(result_frac, expected_frac) + + # Test percentage numbers + result_perc = get_range_n_percs(ks, as_percentage=True) + np.testing.assert_array_almost_equal(result_perc, expected_perc) + + @pytest.mark.parametrize( "input_value, expected_output", [ diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index dadb83d8..4f2ec140 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -1,13 +1,66 @@ import cupy as cp import numpy as np +import matplotlib.pyplot as plt -def test_percentile(): +def test_digitize(): # random_feature1 = np.random.rand(100) # random_feature2 = np.random.rand(100) # # res = ccc(random_feature1, random_feature2, n_jobs=2) # print(res) - data = np.random.rand(10, 100) + # Create a sample CuPy array + x = cp.array([1.2, 3.0, 4.5, 6.7, 8.9, 10.1, 12.3, 14.5, 16.7, 18.9]) + + # Create bins + bins = cp.array([0, 5, 10, 15, 20]) + + # Use digitize to find which bin each value in x belongs to + indices = cp.digitize(x, bins) + + print("Input array x:", x) + print("Bins:", bins) + print("Bin indices:", indices) + + # Demonstrate the effect of the 'right' parameter + indices_right = cp.digitize(x, bins, right=True) + print("Bin indices (right=True):", indices_right) + + # Use digitize with decreasing bins + decreasing_bins = cp.array([20, 15, 10, 5, 0]) + indices_decreasing = cp.digitize(x, decreasing_bins) + print("Bin indices (decreasing bins):", indices_decreasing) + + # Create a larger random dataset + large_x = cp.random.uniform(0, 100, 1000000) + large_bins = cp.linspace(0, 100, 11) # 10 bins + + # Digitize the large dataset + large_indices = cp.digitize(large_x, large_bins) + + # Compute histogram + hist, _ = cp.histogram(large_x, bins=large_bins) + + print("Histogram of large dataset:", hist) + + # Plot the histogram (using CPU arrays for matplotlib) + plt.figure(figsize=(10, 6)) + plt.hist(cp.asnumpy(large_x), bins=cp.asnumpy(large_bins)) + plt.title("Histogram of Large Dataset") + plt.xlabel("Value") + plt.ylabel("Frequency") + plt.savefig('histogram.png') # Saves as PNG + + # Compare with NumPy results + np_x = cp.asnumpy(x) + np_bins = cp.asnumpy(bins) + np_indices = np.digitize(np_x, np_bins) + + print("CuPy indices:", indices) + print("NumPy indices:", np_indices) + print("Results match:", cp.allclose(indices, cp.asarray(np_indices))) + + + diff --git a/tests/gpu/test_impl_gpu.py b/tests/gpu/test_impl_gpu.py index fe868823..56bfa61d 100644 --- a/tests/gpu/test_impl_gpu.py +++ b/tests/gpu/test_impl_gpu.py @@ -8,8 +8,9 @@ def test_compute_parts(): # # res = ccc(random_feature1, random_feature2, n_jobs=2) # print(res) - - data = np.random.rand(10, 100) + import os + print(os.environ['CUDA_HOME']) + data = np.random.rand(5, 10) c = ccc(data) print(c) diff --git a/tests/gpu/tmp_regress_test.py b/tests/gpu/tmp_regress_test.py new file mode 100644 index 00000000..e69de29b From 29f0c8884c96bcdd7b10367c87723fd84a42526e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 11 Aug 2024 22:07:20 -0600 Subject: [PATCH 021/134] [coef]: Finish get_parts cuda version --- libs/ccc/coef/impl_gpu.py | 38 ++++++++++++++++---------------- tests/gpu/test_coef.py | 8 +++---- tests/gpu/test_cupy.py | 44 ++++++++++++++++++++++++++++++++++++-- tests/gpu/test_impl_gpu.py | 5 ++--- 4 files changed, 68 insertions(+), 27 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 223a63e2..e125adf4 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -1,12 +1,10 @@ """ This module contains the CUDA implementation of the CCC """ -import os -import math from typing import Optional, Iterable, Union, List, Tuple import numpy as np -import cupy +import cupy as cp from numpy.typing import NDArray from numba import njit from numba import cuda @@ -35,7 +33,7 @@ def get_perc_from_k(k: int) -> NDArray[np.float32]: @njit(cache=True, nogil=True) -def get_range_n_percs(ks: NDArray[np.int8], as_percentage: bool = False) -> NDArray[np.float32]: +def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[np.float32]: """ It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters @@ -396,6 +394,10 @@ def ccc( if range_n_clusters.shape[0] == 0: raise ValueError(f"Data has too few objects: {n_objects}") + # Get cutting percentages for each cluster + range_n_percentages = get_range_n_percentages(range_n_clusters) + + # Store a set of partitions per row (object) in X as a multidimensional array, where the second dimension is the # number of partitions per object. # The value at parts[i, j, k] will represent the cluster assignment for the k-th object, using the j-th cluster @@ -412,15 +414,6 @@ def ccc( # X here (and following) is a numpy array features are in rows, objects are in columns - # Notes for CUDA dim: for genetic data, number of features is usually small, so we can use a 1D grid? - # but number of objects is usually large. - - - # cuda.synchronize() - # For this iteration, we use 1D block and 2D grid - # grid[i] stands for partitions for feature i - # grid[i][j] stands for partitions for feature i with k=j clusters - # 1D kernel threads_per_block = 36 nx = range_n_clusters.shape[0] @@ -434,18 +427,27 @@ def ccc( # Allocate arrays on device global memory # parts' shape is different from the original implementation - d_parts = cuda.device_array((nx, ny, nz), dtype=np.int16) + d_parts = cp.empty((nx, ny, nz), dtype=np.int16) + # Transfer data to device + # Original dataframe h_X = X - d_X = cuda.to_device(h_X) + d_X = cp.asarray(h_X) + # Percentages + h_range_n_percentages = range_n_percentages + d_range_n_percentages = cp.asarray(h_range_n_percentages) # Debug print(f"prev parts: {d_parts}") - for i in range(nz): + for i in range(nx): for j in range(ny): - compute_parts[blocks_per_grid, threads_per_block](d_parts, d_X, i, j) + feature_row = d_X[j, :] + percentages = d_range_n_percentages[i, :] + bins = cp.quantile(feature_row, percentages) + partition = cp.digitize(feature_row, bins) + d_parts[i, j, :] = partition # Move data back to host - h_parts = d_parts.copy_to_host() + h_parts = cp.asnumpy(d_parts) print(f"after parts: {h_parts}") return(2) diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index 972bb9b4..930e5744 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -7,7 +7,7 @@ from ccc.coef.impl_gpu import ( get_perc_from_k, - get_range_n_percs, + get_range_n_percentages, convert_n_clusters, get_range_n_clusters, ) @@ -72,7 +72,7 @@ def test_get_perc_from_k(k, expected): ] ) def test_get_range_n_percs(ks, expected): - result = get_range_n_percs(ks) + result = get_range_n_percentages(ks) np.testing.assert_array_almost_equal(result, expected) @@ -126,11 +126,11 @@ def test_get_range_n_percs(ks, expected): ) def test_get_range_n_percs_as_percentage(ks, expected_frac, expected_perc): # Test fractional percentiles (original behavior) - result_frac = get_range_n_percs(ks, as_percentage=False) + result_frac = get_range_n_percentages(ks, as_percentage=False) np.testing.assert_array_almost_equal(result_frac, expected_frac) # Test percentage numbers - result_perc = get_range_n_percs(ks, as_percentage=True) + result_perc = get_range_n_percentages(ks, as_percentage=True) np.testing.assert_array_almost_equal(result_perc, expected_perc) diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 4f2ec140..e2700f64 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -62,5 +62,45 @@ def test_digitize(): print("Results match:", cp.allclose(indices, cp.asarray(np_indices))) - - +def test_quantile(): + # Create a sample CuPy array + a = cp.array([[10, 7, 4], [3, 2, 1]]) + + # Simple usage: compute the median (50th percentile) of the entire array + median = cp.quantile(a, 0.5) + print("Median of the entire array:", median) + + # Compute multiple quantiles + quantiles = cp.quantile(a, [0.25, 0.5, 0.75]) + print("25th, 50th, and 75th percentiles:", quantiles) + + # Compute quantiles along a specific axis + axis_quantiles = cp.quantile(a, 0.5, axis=0) + print("Median along axis 0:", axis_quantiles) + + # Compute quantiles for a larger array + large_array = cp.random.randn(1000000) + large_quantiles = cp.quantile(large_array, [0.1, 0.5, 0.9]) + print("Quantiles of large array:", large_quantiles) + + # Use an output array + out_array = cp.zeros(3) + cp.quantile(large_array, [0.1, 0.5, 0.9], out=out_array) + print("Output array:", out_array) + + # Compare with NumPy (CPU) results + np_array = cp.asnumpy(large_array) + np_quantiles = np.quantile(np_array, [0.1, 0.5, 0.9]) + print("NumPy quantiles:", np_quantiles) + print("CuPy and NumPy results are close:", cp.allclose(large_quantiles, np_quantiles)) + + # NANs in array + nan_array = cp.array([1, 2, cp.nan, 4, 5]) + nan_quantiles = cp.quantile(nan_array, 0.5) + print("Quantile with NaNs:", nan_quantiles) + + # NANs in q + array_with_q = cp.array([1, 2, 3, 4, 5]) + q_with_nan = cp.array([0.5, cp.nan]) + quantiles_with_nan = cp.quantile(array_with_q, q_with_nan) + print("Quantiles with NaN in q:", quantiles_with_nan) \ No newline at end of file diff --git a/tests/gpu/test_impl_gpu.py b/tests/gpu/test_impl_gpu.py index 56bfa61d..1f62aa67 100644 --- a/tests/gpu/test_impl_gpu.py +++ b/tests/gpu/test_impl_gpu.py @@ -8,9 +8,8 @@ def test_compute_parts(): # # res = ccc(random_feature1, random_feature2, n_jobs=2) # print(res) - import os - print(os.environ['CUDA_HOME']) - data = np.random.rand(5, 10) + data = np.random.rand(5, 30) * 100 + print(f"data: {data}") c = ccc(data) print(c) From 9bb2af8769ff9c690dd5868820e0ca03c3dcef9f Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 11 Aug 2024 23:44:26 -0600 Subject: [PATCH 022/134] [test/coef]: Add tests for get_parts --- libs/ccc/coef/impl_gpu.py | 98 +++++++++++++++++++++------------------ tests/gpu/test_coef.py | 64 ++++++++++++++++++++++++- 2 files changed, 116 insertions(+), 46 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index e125adf4..853fcd4c 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -228,12 +228,6 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: return int(x), int(y) -@cuda.jit(device=True) -def get_parts( - data: NDArray, res: NDArray[np.int16], ange_n_clusters: tuple[int], data_is_numerical: bool = True): - return - - # store result to device global memory @cuda.jit def compute_parts(parts: np.ndarray, X: np.ndarray, cluster_id: np.int8, feature_id: np.int64): @@ -267,6 +261,58 @@ def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> return list(internal_n_clusters) +def get_parts(X: NDArray, + range_n_clusters: NDArray[np.uint8], + data_is_numerical: bool = True + ) -> NDArray: + """ + Compute parts using CuPy for GPU acceleration. + + Parameters: + X: Input data array of shape (n_features, n_objects) + range_n_clusters: Array of cluster numbers + range_n_percentages: Array of percentages for each cluster number + + Returns: + Computed parts array + """ + nx = range_n_clusters.shape[0] + ny, nz = X.shape # n_features, n_objects + + # Allocate arrays on device global memory + d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 + print(f"prev parts: {d_parts}") + + if data_is_numerical: + # Transfer data to device + d_X = cp.asarray(X) + # Get cutting percentages for each cluster + range_n_percentages = get_range_n_percentages(range_n_clusters) + d_range_n_percentages = cp.asarray(range_n_percentages) + + for i in range(nx): + for j in range(ny): + feature_row = d_X[j, :] + percentages = d_range_n_percentages[i, :] + bins = cp.quantile(feature_row, percentages) + partition = cp.digitize(feature_row, bins) + d_parts[i, j, :] = partition + + # Remove singletons by putting -2 as values + partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, ny) + d_parts[partitions_ks == 1] = -2 + else: + # If the data is categorical, then the encoded feature is already the partition + # Only the first partition is filled, the rest will be -1 (missing) + d_parts[0] = cp.asarray(X.astype(np.int16)) + + # Move data back to host + h_parts = cp.asnumpy(d_parts) + print(f"after parts: {h_parts}") + + return h_parts + + def ccc( x: NDArray, y: NDArray = None, @@ -394,9 +440,6 @@ def ccc( if range_n_clusters.shape[0] == 0: raise ValueError(f"Data has too few objects: {n_objects}") - # Get cutting percentages for each cluster - range_n_percentages = get_range_n_percentages(range_n_clusters) - # Store a set of partitions per row (object) in X as a multidimensional array, where the second dimension is the # number of partitions per object. @@ -414,42 +457,7 @@ def ccc( # X here (and following) is a numpy array features are in rows, objects are in columns - # 1D kernel - threads_per_block = 36 - nx = range_n_clusters.shape[0] - ny = n_features - nz = n_objects - # equivalent to blocks_per_grid_x = math.ceil(nx / threads_per_block[0]) - blocks_per_grid_x = (nx + threads_per_block - 1) // threads_per_block - # blocks_per_grid_y = (ny + threads_per_block[1] - 1) // threads_per_block[1] - # blocks_per_grid_z = (nz + threads_per_block[2] - 1) // threads_per_block[2] - blocks_per_grid = blocks_per_grid_x - - # Allocate arrays on device global memory - # parts' shape is different from the original implementation - d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - - # Transfer data to device - # Original dataframe - h_X = X - d_X = cp.asarray(h_X) - # Percentages - h_range_n_percentages = range_n_percentages - d_range_n_percentages = cp.asarray(h_range_n_percentages) - - # Debug - print(f"prev parts: {d_parts}") - for i in range(nx): - for j in range(ny): - feature_row = d_X[j, :] - percentages = d_range_n_percentages[i, :] - bins = cp.quantile(feature_row, percentages) - partition = cp.digitize(feature_row, bins) - d_parts[i, j, :] = partition - # Move data back to host - h_parts = cp.asnumpy(d_parts) - print(f"after parts: {h_parts}") - return(2) + parts = get_parts(X, range_n_clusters) # For this iteration, use CPU multi-threading to compute quantile lists using range_n_clusters # Refer to https://docs.cupy.dev/en/stable/reference/generated/cupy.quantile.html for the GPU implementation diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index 930e5744..739d57a2 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -10,6 +10,7 @@ get_range_n_percentages, convert_n_clusters, get_range_n_clusters, + get_parts, ) @@ -300,4 +301,65 @@ def test_get_range_n_clusters_with_default_max_k(): assert range_n_clusters is not None np.testing.assert_array_equal( range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) - ) \ No newline at end of file + ) + +# get_parts +def test_get_parts_simple(): + np.random.seed(0) + + # Test with 2 clusters + features_2 = np.random.rand(2, 100) * 100 + parts = get_parts(features_2, np.array([2], dtype=np.uint8)) + assert parts is not None + # only one cluster configuration + assert len(parts) == 1 + # only two clusters should be created + assert len(np.unique(parts[0])) == 2 + + # Test with [2, 3] clusters + parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)) + assert parts is not None + assert len(parts) == 2 + assert len(np.unique(parts[0])) == 2 + assert len(np.unique(parts[1])) == 3 + + +def test_get_parts_with_singletons(): + np.random.seed(0) + + features_2 = np.array([[1.3] * 10, [2.1] * 10]) + + # run + parts = get_parts(features_2, np.array([2], dtype=np.uint8)) + assert parts is not None + assert len(parts) == 1 + # all the elements (2D) should be -2 + np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) + + parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)) + assert parts is not None + assert len(parts) == 2 + np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) + np.testing.assert_array_equal(np.unique(parts[1]), np.array([-2])) + + +def test_get_parts_with_categorical_feature(): + np.random.seed(0) + + features_2 = np.array([[4] * 10, [4] * 10]) + + # run + # only one partition is requested + parts = get_parts(features_2, np.array([2], dtype=np.uint8), data_is_numerical=False) + assert parts is not None + assert len(parts) == 1 + np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) + + # Todo: think about whether this is a valid test + # more partitions are requested; only the first two has valid information + # parts = get_parts(features_2, np.array([2, 3, 4], dtype=np.uint8), data_is_numerical=False) + # assert parts is not None + # assert len(parts) == 3 + # np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) + # np.testing.assert_array_equal(np.unique(parts[1]), np.array([-1])) + # np.testing.assert_array_equal(np.unique(parts[2]), np.array([-1])) From 88e3fc9425e3a58cae39a68cf01c68b9f376b50c Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 12 Aug 2024 00:53:35 -0600 Subject: [PATCH 023/134] [coef]: Return gpu reference for get_parts --- libs/ccc/coef/impl_gpu.py | 10 +++++----- tests/gpu/test_coef.py | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 853fcd4c..8cb0ee12 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -264,7 +264,7 @@ def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> def get_parts(X: NDArray, range_n_clusters: NDArray[np.uint8], data_is_numerical: bool = True - ) -> NDArray: + ) -> cp.ndarray: """ Compute parts using CuPy for GPU acceleration. @@ -274,7 +274,7 @@ def get_parts(X: NDArray, range_n_percentages: Array of percentages for each cluster number Returns: - Computed parts array + Reference to the computed partitions on the device global memory """ nx = range_n_clusters.shape[0] ny, nz = X.shape # n_features, n_objects @@ -307,10 +307,10 @@ def get_parts(X: NDArray, d_parts[0] = cp.asarray(X.astype(np.int16)) # Move data back to host - h_parts = cp.asnumpy(d_parts) - print(f"after parts: {h_parts}") + # h_parts = cp.asnumpy(d_parts) + print(f"after parts: {d_parts}") - return h_parts + return d_parts def ccc( diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index 739d57a2..c8636cde 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -309,7 +309,7 @@ def test_get_parts_simple(): # Test with 2 clusters features_2 = np.random.rand(2, 100) * 100 - parts = get_parts(features_2, np.array([2], dtype=np.uint8)) + parts = get_parts(features_2, np.array([2], dtype=np.uint8)).get() assert parts is not None # only one cluster configuration assert len(parts) == 1 @@ -317,7 +317,7 @@ def test_get_parts_simple(): assert len(np.unique(parts[0])) == 2 # Test with [2, 3] clusters - parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)) + parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)).get() assert parts is not None assert len(parts) == 2 assert len(np.unique(parts[0])) == 2 @@ -330,13 +330,13 @@ def test_get_parts_with_singletons(): features_2 = np.array([[1.3] * 10, [2.1] * 10]) # run - parts = get_parts(features_2, np.array([2], dtype=np.uint8)) + parts = get_parts(features_2, np.array([2], dtype=np.uint8)).get() assert parts is not None assert len(parts) == 1 # all the elements (2D) should be -2 np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) - parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)) + parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)).get() assert parts is not None assert len(parts) == 2 np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) @@ -350,7 +350,7 @@ def test_get_parts_with_categorical_feature(): # run # only one partition is requested - parts = get_parts(features_2, np.array([2], dtype=np.uint8), data_is_numerical=False) + parts = get_parts(features_2, np.array([2], dtype=np.uint8), data_is_numerical=False).get() assert parts is not None assert len(parts) == 1 np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) From 550c663d07da421914abb5b6eb598869995f8f48 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 12 Aug 2024 19:29:12 -0600 Subject: [PATCH 024/134] [coef]: Prepare to do matrix transformation --- libs/ccc/coef/impl_gpu.py | 243 ++++++++++++++++---------------------- 1 file changed, 99 insertions(+), 144 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 8cb0ee12..c2b04931 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -11,7 +11,7 @@ from ccc.pytorch.core import unravel_index_2d from ccc.scipy.stats import rank - +from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari @njit(cache=True, nogil=True) def get_perc_from_k(k: int) -> NDArray[np.float32]: @@ -83,44 +83,6 @@ def get_feature_type_and_encode(feature_data: NDArray) -> tuple[NDArray, bool]: return np.unique(feature_data, return_inverse=True)[1], data_type_is_numerical -@njit(cache=True, nogil=True) -def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: - """ - Performs a simple quantile clustering on one dimensional data (1d). Quantile - clustering is defined as the procedure that forms clusters in 1d data by - separating objects using quantiles (for instance, if the median is used, two - clusters are generated with objects separated by the median). In the case - data contains all the same values (zero variance), this implementation can - return less clusters than specified with k. - - Args: - data: a 1d numpy array with numerical values. - k: the number of clusters to split the data into. - - Returns: - A 1d array with the data partition. - """ - data_sorted = np.argsort(data, kind="quicksort") - data_rank = rank(data, data_sorted) - data_perc = data_rank / len(data) - - percentiles = [0.0] + get_perc_from_k(k) + [1.0] - - cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") - - current_cluster = 0 - part = np.zeros(data.shape, dtype=np.int16) - 1 - - for i in range(len(cut_points) - 1): - lim1 = cut_points[i] - lim2 = cut_points[i + 1] - - part[data_sorted[lim1:lim2]] = current_cluster - current_cluster += 1 - - return part - - # @njit(cache=True, nogil=True) def get_range_n_clusters( n_items: int, internal_n_clusters: Iterable[int] = None @@ -157,53 +119,6 @@ def get_range_n_clusters( return np.array(clusters_range_list, dtype=np.uint16) -@njit(cache=True, nogil=True) -def get_parts( - data: NDArray, range_n_clusters: tuple[int], data_is_numerical: bool = True -) -> NDArray[np.int16]: - """ - Given a 1d data array, it computes a partition for each k value in the given - range of clusters. This function only supports numerical data, and it - always runs run_run_quantile_clustering with the different k values. - If partitions with only one cluster are returned (singletons), then the - returned array will have negative values. - - Args: - data: a 1d data vector. It is assumed that there are no nans. - range_n_clusters: a tuple with the number of clusters. - data_is_numerical: indicates whether data is numerical (True) or categorical (False) - - Returns: - A numpy array with shape (number of clusters, data rows) with - partitions of data. - - Partitions could have negative values in some scenarios, with different - meanings: -1 is used for categorical data, where only one partition is generated - and the rest (-1) are marked as "empty". -2 is used when singletons have been - detected (partitions with one cluster), usually because of problems with the - input data (it has all the same values, for example). - """ - # parts[i] represents the partition for cluster i - # parts[i][j] represents the cluster assignment for element j, using i-th cluster's configuration - parts = np.zeros((len(range_n_clusters), data.shape[0]), dtype=np.int16) - 1 - - # can use cupy.digitize here - if data_is_numerical: - for idx in range(len(range_n_clusters)): - k = range_n_clusters[idx] - parts[idx] = run_quantile_clustering(data, k) - - # remove singletons by putting a -2 as values - partitions_ks = np.array([len(np.unique(p)) for p in parts]) - parts[partitions_ks == 1, :] = -2 - else: - # if the data is categorical, then the encoded feature is already the partition - # only the first partition is filled, the rest will be -1 (missing) - parts[0] = data.astype(np.int16) - - return parts - - @njit(cache=True, nogil=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ @@ -313,6 +228,95 @@ def get_parts(X: NDArray, return d_parts +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: + """ + It implements the same functionality in scipy.spatial.distance.cdist but + for clustering partitions, and instead of a distance it returns the adjusted + Rand index (ARI). In other words, it mimics this function call: + + cdist(x, y, metric=ari) + + Only partitions with positive labels (> 0) are compared. This means that + partitions marked as "singleton" or "empty" (categorical data) are not + compared. This has the effect of leaving an ARI of 0.0 (zero). + + Args: + x: a 2d array with m_x clustering partitions in rows and n objects in + columns. + y: a 2d array with m_y clustering partitions in rows and n objects in + columns. + + Returns: + A 2d array with m_x rows and m_y columns and the ARI between each + partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i + and j. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + for i in range(res.shape[0]): + if x[i, 0] < 0: + continue + + for j in range(res.shape[1]): + if y[j, 0] < 0: + continue + + res[i, j] = ari(x[i], y[j]) + + return res + + +@cuda.jit(device=True) +def compute_coef( + parts: cuda.cudadrv.devicearray, + compare_pair_id: int, + n_features: Optional[int], +): + """ + Given an index representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. + + Args: + compare_pair_id: An id representing a pair of partitions to be compared. + + Returns: + Returns a tuple with two arrays. These two arrays are the same + arrays returned by the main cm function (cm_values and + max_parts) but for a subset of the data. + """ + n_idxs = len(compare_pair_id) + max_ari_list = np.full(n_idxs, np.nan, dtype=float) + max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + + # for idx, data_idx in enumerate(compare_pair_id): + i, j = get_coords_from_index(n_features, compare_pair_id) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + return + + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + # comp_values = cdist_func( + # obji_parts, + # objj_parts, + # ) + # max_flat_idx = comp_values.argmax() + # + # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + # max_part_idx_list[idx] = max_idx + # max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + # + # return max_ari_list, max_part_idx_list + + def ccc( x: NDArray, y: NDArray = None, @@ -457,67 +461,18 @@ def ccc( # X here (and following) is a numpy array features are in rows, objects are in columns - parts = get_parts(X, range_n_clusters) + # Compute partitions for each feature using CuPy + d_parts = get_parts(X, range_n_clusters) + # Directly pass CuPy arrays to kernels JITed with Numba + threads_per_block = 1 + blocks_per_grid = n_features_comp + for i in range(n_features_comp): + compute_coef[blocks_per_grid, threads_per_block](d_parts, i, n_features) + # Wait for all comparisons to finish + cuda.synchronize() - # For this iteration, use CPU multi-threading to compute quantile lists using range_n_clusters - # Refer to https://docs.cupy.dev/en/stable/reference/generated/cupy.quantile.html for the GPU implementation - # Call the compute_parts kernel, results are stored in d_parts Passing an array that resides in host memory will - # implicitly cause a copy back to the host, which will be synchronous. - # compute_parts[blocks_per_grid, threads_per_block](d_X, d_parts, range_n_clusters) - # # Wait for all previous kernels - # cuda.synchronize() - # print(parts) - # can also try compute_parts.forall() - - # compute coefficients - # def compute_coef(idx_list: List[int]) -> Tuple[np.ndarray, np.ndarray]: - # """ - # Given a list of indexes representing each a pair of - # objects/rows/genes, it computes the CCC coefficient for - # each of them. This function is supposed to be used to parallelize - # processing. - # - # Args: - # idx_list: a list of indexes (integers), each of them - # representing a pair of objects. - # - # Returns: - # Returns a tuple with two arrays. These two arrays are the same - # arrays returned by the main cm function (cm_values and - # max_parts) but for a subset of the data. - # """ - # n_idxs = len(idx_list) - # max_ari_list = np.full(n_idxs, np.nan, dtype=float) - # max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) - # - # for idx, data_idx in enumerate(idx_list): - # i, j = get_coords_from_index(n_features, data_idx) - # - # # obji_parts and objj_parts are the partitions for the objects i and j. - # obji_parts, objj_parts = parts[i], parts[j] - # - # # compute ari only if partitions are not marked as "missing" - # # (negative values), which is assigned when partitions have - # # one cluster (usually when all data in the feature has the same - # # value). - # if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: - # continue - # - # # compare all partitions of one object to the all the partitions - # # of the other object, and get the maximium ARI - # comp_values = cdist_func( - # obji_parts, - # objj_parts, - # ) - # max_flat_idx = comp_values.argmax() - # - # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - # max_part_idx_list[idx] = max_idx - # max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) - # - # return max_ari_list, max_part_idx_list # Dev notes From 074a6a675b94bc1cc757bff29ce0b3f549798f9a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 12 Aug 2024 21:10:29 -0600 Subject: [PATCH 025/134] [coef]: Finish matrix transformation for d_parts --- libs/ccc/coef/impl_gpu.py | 28 +++++++++++++-------- tests/gpu/test_coef.py | 52 ++++++++++++++++++++------------------- 2 files changed, 45 insertions(+), 35 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index c2b04931..4229cccf 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -191,8 +191,16 @@ def get_parts(X: NDArray, Returns: Reference to the computed partitions on the device global memory """ - nx = range_n_clusters.shape[0] - ny, nz = X.shape # n_features, n_objects + + # Handle case when X is a 1D array + if X.ndim == 1: + nx = 1 # n_features + ny = range_n_clusters.shape[0] + nz = X.shape[0] # n_objects + else: + nx = X.shape[0] # n_features + ny = range_n_clusters.shape[0] + nz = X.shape[1] # n_objects # Allocate arrays on device global memory d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 @@ -205,13 +213,13 @@ def get_parts(X: NDArray, range_n_percentages = get_range_n_percentages(range_n_clusters) d_range_n_percentages = cp.asarray(range_n_percentages) - for i in range(nx): - for j in range(ny): - feature_row = d_X[j, :] - percentages = d_range_n_percentages[i, :] - bins = cp.quantile(feature_row, percentages) - partition = cp.digitize(feature_row, bins) - d_parts[i, j, :] = partition + for x in range(nx): + for y in range(ny): + objects = d_X if X.ndim == 1 else d_X[y, :] # feature row + percentages = d_range_n_percentages[y, :] + bins = cp.quantile(objects, percentages) + partition = cp.digitize(objects, bins) + d_parts[x, y, :] = partition # Remove singletons by putting -2 as values partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, ny) @@ -219,7 +227,7 @@ def get_parts(X: NDArray, else: # If the data is categorical, then the encoded feature is already the partition # Only the first partition is filled, the rest will be -1 (missing) - d_parts[0] = cp.asarray(X.astype(np.int16)) + d_parts[:, 0] = cp.asarray(X.astype(np.int16)) # Move data back to host # h_parts = cp.asnumpy(d_parts) diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index c8636cde..dc9bf30e 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -308,58 +308,60 @@ def test_get_parts_simple(): np.random.seed(0) # Test with 2 clusters - features_2 = np.random.rand(2, 100) * 100 - parts = get_parts(features_2, np.array([2], dtype=np.uint8)).get() + features0 = np.random.rand(100) + parts = get_parts(features0, np.array([2], dtype=np.uint8)).get() assert parts is not None - # only one cluster configuration - assert len(parts) == 1 - # only two clusters should be created - assert len(np.unique(parts[0])) == 2 + assert len(parts) == 1, "should have only one feature" + assert len(parts[0]) == 1, "should have only one partition" + assert len(np.unique(parts[0])) == 2, "should have 2 cluster indexes" # Test with [2, 3] clusters - parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)).get() + parts = get_parts(features0, np.array([2, 3], dtype=np.uint8)).get() assert parts is not None - assert len(parts) == 2 - assert len(np.unique(parts[0])) == 2 - assert len(np.unique(parts[1])) == 3 + assert len(parts) == 1 + assert len(parts[0]) == 2, "feature should have 2 clusters" + assert len(np.unique(parts[0][0])) == 2 + assert len(np.unique(parts[0][1])) == 3 def test_get_parts_with_singletons(): np.random.seed(0) - features_2 = np.array([[1.3] * 10, [2.1] * 10]) + feature0 = np.array([1.3] * 10) # run - parts = get_parts(features_2, np.array([2], dtype=np.uint8)).get() + parts = get_parts(feature0, np.array([2], dtype=np.uint8)).get() assert parts is not None assert len(parts) == 1 + assert len(parts[0]) == 1 # all the elements (2D) should be -2 np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) - parts = get_parts(features_2, np.array([2, 3], dtype=np.uint8)).get() + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8)).get() assert parts is not None - assert len(parts) == 2 - np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) - np.testing.assert_array_equal(np.unique(parts[1]), np.array([-2])) + assert len(parts) == 1 + assert len(parts[0]) == 2, "feature should have 2 clusters" + np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) + np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) def test_get_parts_with_categorical_feature(): np.random.seed(0) - features_2 = np.array([[4] * 10, [4] * 10]) + feature0 = np.array([4] * 10) # run # only one partition is requested - parts = get_parts(features_2, np.array([2], dtype=np.uint8), data_is_numerical=False).get() + parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False).get() assert parts is not None assert len(parts) == 1 + assert len(parts[0]) == 1 np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) - # Todo: think about whether this is a valid test # more partitions are requested; only the first two has valid information - # parts = get_parts(features_2, np.array([2, 3, 4], dtype=np.uint8), data_is_numerical=False) - # assert parts is not None - # assert len(parts) == 3 - # np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) - # np.testing.assert_array_equal(np.unique(parts[1]), np.array([-1])) - # np.testing.assert_array_equal(np.unique(parts[2]), np.array([-1])) + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False).get() + assert parts is not None + assert len(parts) == 1 + assert len(parts[0]) == 2 + np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) + np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) From 15b3531733ab5ad805334c9dec47ddd85460c310 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 12 Aug 2024 23:38:50 -0600 Subject: [PATCH 026/134] [coef]: Pass compilation for compute_coef --- libs/ccc/coef/impl_gpu.py | 121 +++++++++++++++++++++----------------- 1 file changed, 66 insertions(+), 55 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 4229cccf..4b9bd8dd 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -119,51 +119,44 @@ def get_range_n_clusters( return np.array(clusters_range_list, dtype=np.uint16) -@njit(cache=True, nogil=True) +@cuda.jit(device=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ - Given the number of objects and and index, it returns the row/column + Given the number of objects and an index, it returns the row/column position of the pairwise matrix. For example, if there are n_obj objects (such as genes), a condensed 1d array can be created with pairwise comparisons between genes, as well as a squared symmetric matrix. This function receives the number of objects and the index of the condensed - array, and returns the coordiates of the squared symmetric matrix. - + array, and returns the coordinates of the squared symmetric matrix. Args: n_obj: the number of objects. idx: the index of the condensed pairwise array across all n_obj objects. - Returns A tuple (i, j) with the coordinates of the squared symmetric matrix equivalent to the condensed array. """ b = 1 - 2 * n_obj - x = np.floor((-b - np.sqrt(b ** 2 - 8 * idx)) / 2) - y = idx + x * (b + x + 2) / 2 + 1 - return int(x), int(y) + # Manual square root calculation using the Newton-Raphson method + def sqrt(value: float, epsilon: float = 1e-6) -> float: + x = value + while True: + root = 0.5 * (x + (value / x)) + if abs(root - x) < epsilon: + return root + x = root -# store result to device global memory -@cuda.jit -def compute_parts(parts: np.ndarray, X: np.ndarray, cluster_id: np.int8, feature_id: np.int64): - feature_row = X[feature_id, :] - size = feature_row.shape[0] - # Use 1D Grid-Stride Loops Pattern to handle large # of features that can't be processed using all threads - i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x - # i = cuda.grid(1) - while i < size: - parts[cluster_id, feature_id, i] = -1 - i += cuda.gridDim.x * cuda.blockDim.x + # Compute x using the manually computed square root + discriminant = b ** 2 - 8 * idx + sqrt_discriminant = sqrt(discriminant) + x_float = (-b - sqrt_discriminant) / 2 - return + # Manual floor calculation + x = int(x_float) if x_float >= 0 else int(x_float) - 1 + y = idx + x * (b + x + 2) // 2 + 1 -# Opt: may lower uint16 to reduce memory consumption and data movement -def bin_objects(objs: NDArray[np.uint16], n_clusters: int) -> NDArray[np.uint16]: - """ - This function is a CUDA kernel for binning (digitizing) objects according to the percentiles provided - """ - raise NotImplementedError + return int(x), int(y) def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> List[int]: @@ -236,7 +229,8 @@ def get_parts(X: NDArray, return d_parts -def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: +@cuda.jit(device=True) +def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: """ It implements the same functionality in scipy.spatial.distance.cdist but for clustering partitions, and instead of a distance it returns the adjusted @@ -259,26 +253,28 @@ def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i and j. """ - res = np.zeros((x.shape[0], y.shape[0])) - for i in range(res.shape[0]): + for i in range(out.shape[0]): if x[i, 0] < 0: continue - for j in range(res.shape[1]): + for j in range(out.shape[1]): if y[j, 0] < 0: continue - res[i, j] = ari(x[i], y[j]) + # res[i, j] = ari(x[i], y[j]) + out[compare_pair_id, i, j] = 1.0 - return res + return -@cuda.jit(device=True) +@cuda.jit def compute_coef( - parts: cuda.cudadrv.devicearray, - compare_pair_id: int, - n_features: Optional[int], + parts: cuda.cudadrv.devicearray, + max_ari_list: cuda.cudadrv.devicearray, + max_part_idx_list: cuda.cudadrv.devicearray, + temp_outs: cuda.cudadrv.devicearray, + compare_pair_id: int, ): """ Given an index representing each a pair of @@ -286,6 +282,9 @@ def compute_coef( each of them. Args: + parts: A reference to the 3d GPU partitions array. + max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. + max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. compare_pair_id: An id representing a pair of partitions to be compared. Returns: @@ -293,9 +292,7 @@ def compute_coef( arrays returned by the main cm function (cm_values and max_parts) but for a subset of the data. """ - n_idxs = len(compare_pair_id) - max_ari_list = np.full(n_idxs, np.nan, dtype=float) - max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + n_features = parts.shape[0] # for idx, data_idx in enumerate(compare_pair_id): i, j = get_coords_from_index(n_features, compare_pair_id) @@ -312,17 +309,21 @@ def compute_coef( # compare all partitions of one object to the all the partitions # of the other object, and get the maximium ARI - # comp_values = cdist_func( - # obji_parts, - # objj_parts, - # ) + + cdist_parts_basic( + obji_parts, + objj_parts, + temp_outs, + compare_pair_id, + ) # max_flat_idx = comp_values.argmax() - # + # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - # max_part_idx_list[idx] = max_idx - # max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + # max_part_idx_list[compare_pair_id] = max_idx + # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) # # return max_ari_list, max_part_idx_list + return def ccc( @@ -443,6 +444,8 @@ def ccc( else: raise ValueError("Wrong combination of parameters x and y") + # 1. Partitions Computation + # Converts internal_n_clusters to a list of integers if it's provided. internal_n_clusters = convert_n_clusters(internal_n_clusters) @@ -452,13 +455,6 @@ def ccc( if range_n_clusters.shape[0] == 0: raise ValueError(f"Data has too few objects: {n_objects}") - - # Store a set of partitions per row (object) in X as a multidimensional array, where the second dimension is the - # number of partitions per object. - # The value at parts[i, j, k] will represent the cluster assignment for the k-th object, using the j-th cluster - # configuration, for the i-th feature. - - # cm_values stores the CCC coefficients n_features_comp = (n_features * (n_features - 1)) // 2 cm_values = np.full(n_features_comp, np.nan) @@ -471,14 +467,29 @@ def ccc( # Compute partitions for each feature using CuPy d_parts = get_parts(X, range_n_clusters) - # Directly pass CuPy arrays to kernels JITed with Numba + + # 2. CCC coefficient computation + + # allocate result arrays on device global memory + d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) + d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) + # allocate temporary arrays on device global memory + d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + # use 1D gird to parallelize the computation of CCC coefficients + # Todo: optimize this using updated c_dist function that only compare one partition at a time threads_per_block = 1 blocks_per_grid = n_features_comp for i in range(n_features_comp): - compute_coef[blocks_per_grid, threads_per_block](d_parts, i, n_features) + # Directly pass CuPy arrays to kernels JITed with Numba + compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) # Wait for all comparisons to finish cuda.synchronize() + # Transfer data back to host + max_ari_list = cp.asnumpy(d_max_ari_list) + max_part_idx_list = cp.asnumpy(d_max_part_idx_list) + print(max_ari_list) + print(max_part_idx_list) From 8c8486d710a0f891e828f23c5bdf3805ff5a936a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 13 Aug 2024 00:18:09 -0600 Subject: [PATCH 027/134] [coef]: Need to adapt the ari function --- libs/ccc/coef/impl_gpu.py | 5 +++-- libs/ccc/sklearn/metrics_gpu.py | 15 ++++++++------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 4b9bd8dd..22a29e1c 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -263,7 +263,7 @@ def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int continue # res[i, j] = ari(x[i], y[j]) - out[compare_pair_id, i, j] = 1.0 + ari(x[i], y[j], out, compare_pair_id, i, j) return @@ -475,6 +475,7 @@ def ccc( d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) # allocate temporary arrays on device global memory d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + print(f"before d_outs: {d_outs}") # use 1D gird to parallelize the computation of CCC coefficients # Todo: optimize this using updated c_dist function that only compare one partition at a time threads_per_block = 1 @@ -484,7 +485,7 @@ def ccc( compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) # Wait for all comparisons to finish cuda.synchronize() - + print(f"after d_outs: {d_outs}") # Transfer data back to host max_ari_list = cp.asnumpy(d_max_ari_list) max_part_idx_list = cp.asnumpy(d_max_part_idx_list) diff --git a/libs/ccc/sklearn/metrics_gpu.py b/libs/ccc/sklearn/metrics_gpu.py index a430d1a1..5ba6da78 100644 --- a/libs/ccc/sklearn/metrics_gpu.py +++ b/libs/ccc/sklearn/metrics_gpu.py @@ -55,6 +55,7 @@ def compute_sum_squares(contingency, result): cuda.atomic.add(result, 0, contingency[i, j] ** 2) +@cuda.jit(device=True) def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: """ Returns the pair confusion matrix from two clustering partitions using CUDA. @@ -96,8 +97,8 @@ def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarra return C - -def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: +@cuda.jit(device=True) +def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, compare_pair_id: int, i: int, j: int) -> float: """ Computes the adjusted Rand index (ARI) between two clustering partitions. The code is based on the sklearn implementation here: @@ -123,13 +124,12 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: # Special cases: empty data or full agreement if fn == 0 and fp == 0: - return 1.0 - - return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + res = 1.0 + res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + out[compare_pair_id, i, j] = res -# Todo: __device__ ? -@cuda.jit +@cuda.jit(device=True) def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): """ CUDA kernel to compute the contingency matrix. @@ -158,6 +158,7 @@ def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_ma cont_mat[i, j] = count # Store the result in the contingency matrix +@cuda.jit(device=True) def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: """ Compute the contingency matrix for two clustering partitions using CUDA. From 6fc442b6d892e65a70fdbb34820fa2f0040d655e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 15 Aug 2024 11:04:56 -0600 Subject: [PATCH 028/134] [build]: Upgrade scipy --- environment/environment.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/environment/environment.yml b/environment/environment.yml index 560cb740..4ad6ea0e 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -12,7 +12,7 @@ dependencies: - matplotlib=3.4.* - minepy=1.2.* - numba=0.60.* - - numpy=1.26.* + - numpy=1.25.* - openpyxl=3.0.* - pandas=1.3.* - papermill=2.3.* @@ -29,7 +29,7 @@ dependencies: - r-svglite=2.* - rpy2=3.4.* - scikit-learn=0.24.* - - scipy=1.7.* + - scipy=1.9.* - seaborn=0.11.* - svgutils=0.3.* - tabulate=0.8.* From b53802043489c12736f3851763a03e84a5052e67 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 15 Aug 2024 22:52:35 -0600 Subject: [PATCH 029/134] [test]: Fail on previous test --- libs/ccc/coef/impl_gpu.py | 417 ++++++++++++++++++++++------- libs/ccc/sklearn/metrics_device.py | 149 +++++++++++ 2 files changed, 464 insertions(+), 102 deletions(-) create mode 100644 libs/ccc/sklearn/metrics_device.py diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 22a29e1c..bf87f08c 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -1,6 +1,9 @@ """ This module contains the CUDA implementation of the CCC """ +import math +import os +from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Optional, Iterable, Union, List, Tuple import numpy as np @@ -11,7 +14,8 @@ from ccc.pytorch.core import unravel_index_2d from ccc.scipy.stats import rank -from ccc.sklearn.metrics_gpu import adjusted_rand_index as ari +from ccc.sklearn.metrics import adjusted_rand_index as ari +from ccc.utils import chunker @njit(cache=True, nogil=True) def get_perc_from_k(k: int) -> NDArray[np.float32]: @@ -119,43 +123,50 @@ def get_range_n_clusters( return np.array(clusters_range_list, dtype=np.uint16) -@cuda.jit(device=True) +# # Todo: restore the original implementation +# @cuda.jit(device=True) +# def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: +# """ +# Given the number of objects and an index, it returns the row/column +# position of the pairwise matrix. For example, if there are n_obj objects +# (such as genes), a condensed 1d array can be created with pairwise +# comparisons between genes, as well as a squared symmetric matrix. This +# function receives the number of objects and the index of the condensed +# array, and returns the coordinates of the squared symmetric matrix. +# Args: +# n_obj: the number of objects. +# idx: the index of the condensed pairwise array across all n_obj objects. +# Returns +# A tuple (i, j) with the coordinates of the squared symmetric matrix +# equivalent to the condensed array. +# """ +# b = 1 - 2 * n_obj +# x = math.floor((-b - math.sqrt(b**2 - 8 * idx)) / 2) +# y = idx + x * (b + x + 2) / 2 + 1 +# return int(x), int(y) + + +@njit(cache=True, nogil=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ - Given the number of objects and an index, it returns the row/column + Given the number of objects and and index, it returns the row/column position of the pairwise matrix. For example, if there are n_obj objects (such as genes), a condensed 1d array can be created with pairwise comparisons between genes, as well as a squared symmetric matrix. This function receives the number of objects and the index of the condensed - array, and returns the coordinates of the squared symmetric matrix. + array, and returns the coordiates of the squared symmetric matrix. + Args: n_obj: the number of objects. idx: the index of the condensed pairwise array across all n_obj objects. + Returns A tuple (i, j) with the coordinates of the squared symmetric matrix equivalent to the condensed array. """ b = 1 - 2 * n_obj - - # Manual square root calculation using the Newton-Raphson method - def sqrt(value: float, epsilon: float = 1e-6) -> float: - x = value - while True: - root = 0.5 * (x + (value / x)) - if abs(root - x) < epsilon: - return root - x = root - - # Compute x using the manually computed square root - discriminant = b ** 2 - 8 * idx - sqrt_discriminant = sqrt(discriminant) - x_float = (-b - sqrt_discriminant) / 2 - - # Manual floor calculation - x = int(x_float) if x_float >= 0 else int(x_float) - 1 - - y = idx + x * (b + x + 2) // 2 + 1 - + x = np.floor((-b - np.sqrt(b**2 - 8 * idx)) / 2) + y = idx + x * (b + x + 2) / 2 + 1 return int(x), int(y) @@ -197,7 +208,7 @@ def get_parts(X: NDArray, # Allocate arrays on device global memory d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 - print(f"prev parts: {d_parts}") + # print(f"prev parts: {d_parts}") if data_is_numerical: # Transfer data to device @@ -224,13 +235,154 @@ def get_parts(X: NDArray, # Move data back to host # h_parts = cp.asnumpy(d_parts) - print(f"after parts: {d_parts}") + # print(f"after parts: {d_parts}") return d_parts -@cuda.jit(device=True) -def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: +# # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) +# @cuda.jit(device=True) +# def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: +# """ +# It implements the same functionality in scipy.spatial.distance.cdist but +# for clustering partitions, and instead of a distance it returns the adjusted +# Rand index (ARI). In other words, it mimics this function call: +# +# cdist(x, y, metric=ari) +# +# Only partitions with positive labels (> 0) are compared. This means that +# partitions marked as "singleton" or "empty" (categorical data) are not +# compared. This has the effect of leaving an ARI of 0.0 (zero). +# +# Args: +# x: a 2d array with m_x clustering partitions in rows and n objects in +# columns. +# y: a 2d array with m_y clustering partitions in rows and n objects in +# columns. +# +# Returns: +# A 2d array with m_x rows and m_y columns and the ARI between each +# partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i +# and j. +# """ +# +# for i in range(out.shape[0]): +# if x[i, 0] < 0: +# continue +# +# for j in range(out.shape[1]): +# if y[j, 0] < 0: +# continue +# +# # res[i, j] = ari(x[i], y[j]) +# # ari(x[i], y[j], out, compare_pair_id, i, j) +# res = ari(x[i], y[j]) +# print(res) +# +# return +# +# +# @cuda.jit +# def compute_coef( +# parts: cuda.cudadrv.devicearray, +# max_ari_list: cuda.cudadrv.devicearray, +# max_part_idx_list: cuda.cudadrv.devicearray, +# temp_outs: cuda.cudadrv.devicearray, +# compare_pair_id: int, +# ): +# """ +# Given an index representing each a pair of +# objects/rows/genes, it computes the CCC coefficient for +# each of them. +# +# Args: +# parts: A reference to the 3d GPU partitions array. +# max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. +# max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. +# compare_pair_id: An id representing a pair of partitions to be compared. +# +# Returns: +# Returns a tuple with two arrays. These two arrays are the same +# arrays returned by the main cm function (cm_values and +# max_parts) but for a subset of the data. +# """ +# n_features = parts.shape[0] +# +# # for idx, data_idx in enumerate(compare_pair_id): +# i, j = get_coords_from_index(n_features, compare_pair_id) +# +# # get partitions for the pair of objects +# obji_parts, objj_parts = parts[i], parts[j] +# +# # compute ari only if partitions are not marked as "missing" +# # (negative values), which is assigned when partitions have +# # one cluster (usually when all data in the feature has the same +# # value). +# if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: +# return +# +# # compare all partitions of one object to the all the partitions +# # of the other object, and get the maximium ARI +# +# cdist_parts_basic( +# obji_parts, +# objj_parts, +# temp_outs, +# compare_pair_id, +# ) +# # max_flat_idx = comp_values.argmax() +# +# # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) +# # max_part_idx_list[compare_pair_id] = max_idx +# # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) +# # +# # return max_ari_list, max_part_idx_list +# return + +def get_chunks( + iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 +) -> Iterable[Iterable[int]]: + """ + It splits elements in an iterable in chunks according to the number of + CPU cores available for parallel processing. + + Args: + iterable: an iterable to be split in chunks. If it is an integer, it + will split the iterable given by np.arange(iterable). + n_threads: number of threads available for parallelization. + ratio: a ratio that allows to increase the number of splits given + n_threads. For example, with ratio=1, the function will just split + the iterable in n_threads chunks. If ratio is larger than 1, then + it will split in n_threads * ratio chunks. + + Results: + Another iterable with chunks according to the arguments given. For + example, if iterable is [0, 1, 2, 3, 4, 5] and n_threads is 2, it will + return [[0, 1, 2], [3, 4, 5]]. + """ + if isinstance(iterable, int): + iterable = np.arange(iterable) + + n = len(iterable) + expected_n_chunks = n_threads * ratio + + res = list(chunker(iterable, int(np.ceil(n / expected_n_chunks)))) + + while len(res) < expected_n_chunks <= n: + # look for an element in res that can be split in two + idx = 0 + while len(res[idx]) == 1: + idx = idx + 1 + # Got two chunks + new_chunk = get_chunks(res[idx], 2) + res[idx] = new_chunk[0] + # Insert the second chunk in the next position + res.insert(idx + 1, new_chunk[1]) + + return res + + +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: """ It implements the same functionality in scipy.spatial.distance.cdist but for clustering partitions, and instead of a distance it returns the adjusted @@ -253,77 +405,45 @@ def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i and j. """ + res = np.zeros((x.shape[0], y.shape[0])) - for i in range(out.shape[0]): + for i in range(res.shape[0]): if x[i, 0] < 0: continue - for j in range(out.shape[1]): + for j in range(res.shape[1]): if y[j, 0] < 0: continue - # res[i, j] = ari(x[i], y[j]) - ari(x[i], y[j], out, compare_pair_id, i, j) + res[i, j] = ari(x[i], y[j]) - return + return res -@cuda.jit -def compute_coef( - parts: cuda.cudadrv.devicearray, - max_ari_list: cuda.cudadrv.devicearray, - max_part_idx_list: cuda.cudadrv.devicearray, - temp_outs: cuda.cudadrv.devicearray, - compare_pair_id: int, -): +def cdist_parts_parallel( + x: NDArray, y: NDArray, executor: ThreadPoolExecutor +) -> NDArray[float]: """ - Given an index representing each a pair of - objects/rows/genes, it computes the CCC coefficient for - each of them. + It parallelizes cdist_parts_basic function. Args: - parts: A reference to the 3d GPU partitions array. - max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. - max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. - compare_pair_id: An id representing a pair of partitions to be compared. + x: same as in cdist_parts_basic + y: same as in cdist_parts_basic + executor: an pool executor where jobs will be submitted. - Returns: - Returns a tuple with two arrays. These two arrays are the same - arrays returned by the main cm function (cm_values and - max_parts) but for a subset of the data. + Results: + Same as in cdist_parts_basic. """ - n_features = parts.shape[0] - - # for idx, data_idx in enumerate(compare_pair_id): - i, j = get_coords_from_index(n_features, compare_pair_id) + res = np.zeros((x.shape[0], y.shape[0])) - # get partitions for the pair of objects - obji_parts, objj_parts = parts[i], parts[j] + inputs = list(chunker(np.arange(res.shape[0]), 1)) - # compute ari only if partitions are not marked as "missing" - # (negative values), which is assigned when partitions have - # one cluster (usually when all data in the feature has the same - # value). - if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: - return + tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} + for t in as_completed(tasks): + idx = tasks[t] + res[idx, :] = t.result() - # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI - - cdist_parts_basic( - obji_parts, - objj_parts, - temp_outs, - compare_pair_id, - ) - # max_flat_idx = comp_values.argmax() - - # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - # max_part_idx_list[compare_pair_id] = max_idx - # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) - # - # return max_ari_list, max_part_idx_list - return + return res def ccc( @@ -470,30 +590,123 @@ def ccc( # 2. CCC coefficient computation - # allocate result arrays on device global memory - d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) - d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) - # allocate temporary arrays on device global memory - d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) - print(f"before d_outs: {d_outs}") - # use 1D gird to parallelize the computation of CCC coefficients - # Todo: optimize this using updated c_dist function that only compare one partition at a time - threads_per_block = 1 - blocks_per_grid = n_features_comp - for i in range(n_features_comp): - # Directly pass CuPy arrays to kernels JITed with Numba - compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) - # Wait for all comparisons to finish - cuda.synchronize() - print(f"after d_outs: {d_outs}") - # Transfer data back to host - max_ari_list = cp.asnumpy(d_max_ari_list) - max_part_idx_list = cp.asnumpy(d_max_part_idx_list) - print(max_ari_list) - print(max_part_idx_list) + # # allocate result arrays on device global memory + # d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) + # d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) + # # allocate temporary arrays on device global memory + # d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + # print(f"before d_outs: {d_outs}") + # # use 1D gird to parallelize the computation of CCC coefficients + # # Todo: optimize this using updated c_dist function that only compare one partition at a time + # threads_per_block = 1 + # blocks_per_grid = n_features_comp + # for i in range(n_features_comp): + # # Directly pass CuPy arrays to kernels JITed with Numba + # compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) + # # Wait for all comparisons to finish + # cuda.synchronize() + # print(f"after d_outs: {d_outs}") + # # Transfer data back to host + # max_ari_list = cp.asnumpy(d_max_ari_list) + # max_part_idx_list = cp.asnumpy(d_max_part_idx_list) + # print(max_ari_list) + # print(max_part_idx_list) + + # Use CPU multi-threading for baseline + parts = cp.asnumpy(d_parts) + + default_n_threads = os.cpu_count() + + with ThreadPoolExecutor(max_workers=default_n_threads) as executor: + + # Below, there are two layers of parallelism: 1) parallel execution + # across feature pairs and 2) the cdist_parts_parallel function, which + # also runs several threads to compare partitions using ari. In 2) we + # need to disable parallelization in case len(cm_values) > 1 (that is, + # we have several feature pairs to compare), because parallelization is + # already performed at this level. Otherwise, more threads than + # specified by the user are started. + cdist_parts_enable_threading = True if n_features_comp == 1 else False + + cdist_func = None + map_func = executor.map + if cdist_parts_enable_threading: + map_func = map + + def cdist_func(x, y): + return cdist_parts_parallel(x, y, executor) + + else: + cdist_func = cdist_parts_basic + + # compute coefficients + def compute_coef(idx_list): + """ + Given a list of indexes representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. This function is supposed to be used to parallelize + processing. + + Args: + idx_list: a list of indexes (integers), each of them + representing a pair of objects. + + Returns: + Returns a tuple with two arrays. These two arrays are the same + arrays returned by the main cm function (cm_values and + max_parts) but for a subset of the data. + """ + n_idxs = len(idx_list) + max_ari_list = np.full(n_idxs, np.nan, dtype=float) + max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + + for idx, data_idx in enumerate(idx_list): + i, j = get_coords_from_index(n_features, data_idx) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + continue + + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + comp_values = cdist_func( + obji_parts, + objj_parts, + ) + max_flat_idx = comp_values.argmax() + + max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + max_part_idx_list[idx] = max_idx + max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + return max_ari_list, max_part_idx_list + # iterate over all chunks of object pairs and compute the coefficient + inputs = get_chunks(n_features_comp, default_n_threads, n_chunks_threads_ratio) + for idx, (max_ari_list, max_part_idx_list) in zip( + inputs, map_func(compute_coef, inputs) + ): + cm_values[idx] = max_ari_list + max_parts[idx, :] = max_part_idx_list + + # return an array of values or a single scalar, depending on the input data + if cm_values.shape[0] == 1: + if return_parts: + return cm_values[0], max_parts[0], parts + else: + return cm_values[0] + + if return_parts: + return cm_values, max_parts, parts + else: + return cm_values # Dev notes # 1. parallelize get_parst diff --git a/libs/ccc/sklearn/metrics_device.py b/libs/ccc/sklearn/metrics_device.py new file mode 100644 index 00000000..5f19dc08 --- /dev/null +++ b/libs/ccc/sklearn/metrics_device.py @@ -0,0 +1,149 @@ +""" +Contains implementations of different metrics in sklearn but optimized for numba. + +Some code (indicated in each function) is based on scikit-learn's code base +(https://github.com/scikit-learn), for which the copyright notice and license +are shown below. + +BSD 3-Clause License + +Copyright (c) 2007-2021 The scikit-learn developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" +import numpy as np +import cupy as cp +from numba import njit, cuda + + +@cuda.jit(device=True) +def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray, out): + """ + Given two clustering partitions with k0 and k1 number of clusters each, it + returns a contingency matrix with k0 rows and k1 columns. It's an implementation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, + but the code is not based on their implementation. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A contingency matrix with k0 (number of clusters in part0) rows and k1 + (number of clusters in part1) columns. Each cell ij represents the + number of objects grouped in cluster i (in part0) and cluster j (in + part1). + """ + part0_unique = 2#np.unique(part0) + part1_unique = 2#np.unique(part1) + + cont_mat = out + + for i in range(2): + # part0_k = part0_unique[i] + + for j in range(2): + # part1_k = part1_unique[j] + + # part0_i = part0 == part0_k + # part1_j = part1 == part1_k + + cont_mat[i, j] = 4 + + return + + +@cuda.jit(device=True) +def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray, out) -> np.ndarray: + """ + Returns the pair confusion matrix from two clustering partitions. It is an + implemenetation of + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html + The code is based on the sklearn implementation. See copyright notice at the + top of this file. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A pair confusion matrix with 2 rows and 2 columns. From sklearn's + pair_confusion_matrix docstring: considering a pair of objects that is + clustered together a positive pair, then as in binary classification the + count of true negatives is in position 00, false negatives in 10, true + positives in 11, and false positives in 01. + """ + n_samples = np.int64(part0.shape[0]) + + # Computation using the contingency data + part0_unique = 2#np.unique(part0) + part1_unique = 2#np.unique(part1) + contingency = cuda.shared.array((part0_unique, part1_unique), np.int64) + get_contingency_matrix(part0, part1, contingency) + + n_c = cp.ravel(contingency.sum(axis=1)) + n_k = cp.ravel(contingency.sum(axis=0)) + sum_squares = (contingency**2).sum() + # C = np.empty((2, 2), dtype=np.int64) + out[1, 1] = sum_squares - n_samples + out[0, 1] = contingency.dot(n_k).sum() - sum_squares + out[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares + out[0, 0] = n_samples**2 - out[0, 1] - out[1, 0] - sum_squares + + +@cuda.jit(device=True) +def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: + """ + Computes the adjusted Rand index (ARI) between two clustering partitions. + The code is based on the sklearn implementation here: + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html + See copyright notice at the top of this file. + + This function should not be compiled with numba, since it depends on + arbitrarily large interger variable (supported by Python) to correctly + compute the ARI in large partitions. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + + Returns: + A number representing the adjusted Rand index between two clustering + partitions. This number is between something around 0 (partitions do not + match; it could be negative in some cases) and 1.0 (perfect match). + """ + shr = cuda.shared.array((2, 2), np.int64) + get_pair_confusion_matrix(part0, part1, shr) + (tn, fp), (fn, tp) = shr + # convert to Python integer types, to avoid overflow or underflow + tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) + + # Special cases: empty data or full agreement + if fn == 0 and fp == 0: + return 1.0 + + return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) From 61ad1cbd6e52918520947fd04ee99249f33a5fca Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 23 Aug 2024 10:26:04 -0600 Subject: [PATCH 030/134] [metrics]: Eliminate in-kernel memory allocations --- libs/ccc/sklearn/metrics_device.py | 181 +++++++++++++++-------------- 1 file changed, 91 insertions(+), 90 deletions(-) diff --git a/libs/ccc/sklearn/metrics_device.py b/libs/ccc/sklearn/metrics_device.py index 5f19dc08..1428b328 100644 --- a/libs/ccc/sklearn/metrics_device.py +++ b/libs/ccc/sklearn/metrics_device.py @@ -36,114 +36,115 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import numpy as np -import cupy as cp -from numba import njit, cuda - +from numba import cuda +import math @cuda.jit(device=True) -def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray, out): - """ - Given two clustering partitions with k0 and k1 number of clusters each, it - returns a contingency matrix with k0 rows and k1 columns. It's an implementation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html, - but the code is not based on their implementation. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A contingency matrix with k0 (number of clusters in part0) rows and k1 - (number of clusters in part1) columns. Each cell ij represents the - number of objects grouped in cluster i (in part0) and cluster j (in - part1). - """ - part0_unique = 2#np.unique(part0) - part1_unique = 2#np.unique(part1) +def find_unique(arr, max_unique): + """Find unique elements in an array using shared memory.""" + unique = cuda.local.array(max_unique, dtype=np.int32) + counts = cuda.local.array(max_unique, dtype=np.int32) + num_unique = 0 + + for i in range(len(arr)): + found = False + for j in range(num_unique): + if arr[i] == unique[j]: + counts[j] += 1 + found = True + break + if not found and num_unique < max_unique: + unique[num_unique] = arr[i] + counts[num_unique] = 1 + num_unique += 1 + + return unique[:num_unique], counts[:num_unique], num_unique - cont_mat = out +@cuda.jit(device=True) +def compute_contingency_matrix(part0, part1, cont_mat, max_clusters): + """Compute the contingency matrix using shared memory.""" + unique0, counts0, num_unique0 = find_unique(part0, max_clusters) + unique1, counts1, num_unique1 = find_unique(part1, max_clusters) - for i in range(2): - # part0_k = part0_unique[i] + for i in range(num_unique0): + for j in range(num_unique1): + count = 0 + for k in range(len(part0)): + if part0[k] == unique0[i] and part1[k] == unique1[j]: + count += 1 + cont_mat[i, j] = count - for j in range(2): - # part1_k = part1_unique[j] + return num_unique0, num_unique1 - # part0_i = part0 == part0_k - # part1_j = part1 == part1_k +@cuda.jit(device=True) +def sum_2d_array(arr, rows, cols): + """Sum elements in a 2D array.""" + total = 0 + for i in range(rows): + for j in range(cols): + total += arr[i, j] + return total - cont_mat[i, j] = 4 +@cuda.jit(device=True) +def sum_squares_2d_array(arr, rows, cols): + """Sum squares of elements in a 2D array.""" + total = 0 + for i in range(rows): + for j in range(cols): + total += arr[i, j] * arr[i, j] + return total - return +@cuda.jit(device=True) +def get_pair_confusion_matrix(part0, part1, max_clusters): + """Compute the pair confusion matrix.""" + cont_mat = cuda.local.array((max_clusters, max_clusters), dtype=np.int32) + num_clusters0, num_clusters1 = compute_contingency_matrix(part0, part1, cont_mat, max_clusters) + n_samples = len(part0) + sum_squares = sum_squares_2d_array(cont_mat, num_clusters0, num_clusters1) -@cuda.jit(device=True) -def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray, out) -> np.ndarray: - """ - Returns the pair confusion matrix from two clustering partitions. It is an - implemenetation of - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html - The code is based on the sklearn implementation. See copyright notice at the - top of this file. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A pair confusion matrix with 2 rows and 2 columns. From sklearn's - pair_confusion_matrix docstring: considering a pair of objects that is - clustered together a positive pair, then as in binary classification the - count of true negatives is in position 00, false negatives in 10, true - positives in 11, and false positives in 01. - """ - n_samples = np.int64(part0.shape[0]) + n_c = cuda.local.array(max_clusters, dtype=np.int32) + n_k = cuda.local.array(max_clusters, dtype=np.int32) - # Computation using the contingency data - part0_unique = 2#np.unique(part0) - part1_unique = 2#np.unique(part1) - contingency = cuda.shared.array((part0_unique, part1_unique), np.int64) - get_contingency_matrix(part0, part1, contingency) + for i in range(num_clusters0): + n_c[i] = sum(cont_mat[i, :num_clusters1]) + for j in range(num_clusters1): + n_k[j] = sum(cont_mat[:num_clusters0, j]) - n_c = cp.ravel(contingency.sum(axis=1)) - n_k = cp.ravel(contingency.sum(axis=0)) - sum_squares = (contingency**2).sum() - # C = np.empty((2, 2), dtype=np.int64) - out[1, 1] = sum_squares - n_samples - out[0, 1] = contingency.dot(n_k).sum() - sum_squares - out[1, 0] = contingency.transpose().dot(n_c).sum() - sum_squares - out[0, 0] = n_samples**2 - out[0, 1] - out[1, 0] - sum_squares + C = cuda.local.array((2, 2), dtype=np.int64) + C[1, 1] = sum_squares - n_samples + C[0, 1] = sum([cont_mat[i, j] * n_k[j] for i in range(num_clusters0) for j in range(num_clusters1)]) - sum_squares + C[1, 0] = sum([cont_mat[i, j] * n_c[i] for i in range(num_clusters0) for j in range(num_clusters1)]) - sum_squares + C[0, 0] = n_samples * n_samples - C[0, 1] - C[1, 0] - sum_squares + return C @cuda.jit(device=True) -def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray) -> float: +def adjusted_rand_index(part0, part1, out, compare_pair_id, i, j, max_clusters): """ - Computes the adjusted Rand index (ARI) between two clustering partitions. - The code is based on the sklearn implementation here: - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html - See copyright notice at the top of this file. - - This function should not be compiled with numba, since it depends on - arbitrarily large interger variable (supported by Python) to correctly - compute the ARI in large partitions. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A number representing the adjusted Rand index between two clustering - partitions. This number is between something around 0 (partitions do not - match; it could be negative in some cases) and 1.0 (perfect match). + Compute the adjusted Rand index (ARI) between two clustering partitions. """ - shr = cuda.shared.array((2, 2), np.int64) - get_pair_confusion_matrix(part0, part1, shr) - (tn, fp), (fn, tp) = shr - # convert to Python integer types, to avoid overflow or underflow - tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) + C = get_pair_confusion_matrix(part0, part1, max_clusters) + tn, fp, fn, tp = C[0, 0], C[0, 1], C[1, 0], C[1, 1] # Special cases: empty data or full agreement if fn == 0 and fp == 0: - return 1.0 + res = 1.0 + else: + res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + + out[compare_pair_id, i, j] = res - return 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + +# Main kernel function +# 1st iteration: try assign parts[i] (2D) to each block +@cuda.jit +def compute_ari(partitions, out, max_clusters): + """ + CUDA kernel to compute ARI for multiple partition pairs. + """ + compare_pair_id, i, j = cuda.grid(3) + if compare_pair_id < partitions.shape[0] and i < partitions.shape[1] and j < partitions.shape[1]: + part0 = partitions[compare_pair_id, i] + part1 = partitions[compare_pair_id, j] + adjusted_rand_index(part0, part1, out, compare_pair_id, i, j, max_clusters) \ No newline at end of file From 1cfc7a51e78f05b3de47e1b5f590f62ced6898ac Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 25 Aug 2024 12:21:33 -0600 Subject: [PATCH 031/134] [get_parts]: GPU version fail on n_cluster = 6 --- libs/ccc/coef/impl.py | 2 + libs/ccc/coef/impl_gpu.py | 13 ++- tests/gpu/test_get_parts.py | 129 ++++++++++++++++++++++ tests/gpu/test_impl_gpu.py | 20 ---- tests/gpu/test_impl_gpu_against_impl.py | 139 ++++++++++++++++++++++++ 5 files changed, 278 insertions(+), 25 deletions(-) create mode 100644 tests/gpu/test_get_parts.py delete mode 100644 tests/gpu/test_impl_gpu.py create mode 100644 tests/gpu/test_impl_gpu_against_impl.py diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index 9b966cce..e4b4cf41 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -796,6 +796,8 @@ def ccc( max_parts[f_idx, :] = max_part_idx_list cm_pvalues[f_idx] = pvalues + print("CPU parts:") + print(parts) # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: if return_parts: diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index bf87f08c..1879f27d 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -199,12 +199,13 @@ def get_parts(X: NDArray, # Handle case when X is a 1D array if X.ndim == 1: nx = 1 # n_features - ny = range_n_clusters.shape[0] + ny = range_n_clusters.shape[0] # n_clusters nz = X.shape[0] # n_objects else: nx = X.shape[0] # n_features - ny = range_n_clusters.shape[0] + ny = range_n_clusters.shape[0] # n_clusters nz = X.shape[1] # n_objects + print(f"{nx}, {ny}, {nz}") # Allocate arrays on device global memory d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 @@ -219,7 +220,7 @@ def get_parts(X: NDArray, for x in range(nx): for y in range(ny): - objects = d_X if X.ndim == 1 else d_X[y, :] # feature row + objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row percentages = d_range_n_percentages[y, :] bins = cp.quantile(objects, percentages) partition = cp.digitize(objects, bins) @@ -231,12 +232,12 @@ def get_parts(X: NDArray, else: # If the data is categorical, then the encoded feature is already the partition # Only the first partition is filled, the rest will be -1 (missing) - d_parts[:, 0] = cp.asarray(X.astype(np.int16)) + d_parts[:, 0] = cp.asarray(X.astype(cp.int16)) # Move data back to host # h_parts = cp.asnumpy(d_parts) # print(f"after parts: {d_parts}") - + cp.cuda.runtime.deviceSynchronize() return d_parts @@ -587,6 +588,8 @@ def ccc( # Compute partitions for each feature using CuPy d_parts = get_parts(X, range_n_clusters) + print("GPU parts:") + print(d_parts) # 2. CCC coefficient computation diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py new file mode 100644 index 00000000..81a22951 --- /dev/null +++ b/tests/gpu/test_get_parts.py @@ -0,0 +1,129 @@ +import pytest +from typing import List + +import numpy as np +from numba import cuda +from numpy.testing import assert_array_equal, assert_allclose +from numpy.typing import NDArray + +from ccc.coef.impl_gpu import ( + get_perc_from_k, + get_range_n_percentages, + convert_n_clusters, + get_range_n_clusters, + get_parts, +) + +from ccc.coef import get_parts as get_parts_cpu + + +@pytest.mark.parametrize("feature_size", [100, 1000, 10000]) +@pytest.mark.parametrize("cluster_settings", [ + # ([2], (2,)), + # ([2, 3], (2, 3)), + # ([2, 3, 4], (2, 3, 4)), + # ([5], (5,)), + ([6], (6,)), + # ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), +]) +def test_get_parts(feature_size, cluster_settings): + np.random.seed(0) + + gpu_clusters, cpu_clusters = cluster_settings + feature = np.random.rand(feature_size) + + # GPU implementation + parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() + + # CPU implementation + parts_cpu = get_parts_cpu(feature, cpu_clusters) + + assert parts_gpu is not None + assert len(parts_gpu) == 1, "should have only one feature" + assert len(parts_gpu[0]) == len(gpu_clusters), f"should have {len(gpu_clusters)} partition(s)" + + for i, n_clusters in enumerate(gpu_clusters): + assert len(np.unique(parts_gpu[0][i])) == n_clusters, f"should have {n_clusters} cluster indexes" + assert np.array_equal(parts_gpu[0][i], parts_cpu[i]), f"GPU and CPU results don't match for {n_clusters} clusters" + + # Additional checks for multi-cluster settings + if len(gpu_clusters) > 1: + for i in range(len(gpu_clusters)): + for j in range(i + 1, len(gpu_clusters)): + assert not np.array_equal(parts_gpu[0][i], parts_cpu[j]), f"Partitions {i} and {j} should not be equal" + + +def test_get_parts_with_singletons(): + np.random.seed(0) + + feature0 = np.array([1.3] * 100) + + # run + parts = get_parts(feature0, np.array([2], dtype=np.uint8)).get() + parts_cpu = get_parts_cpu(feature0, (2,)) + assert parts is not None + assert len(parts) == 1 # 1 feature + assert len(parts[0]) == 1 # 1 partition + # all the elements (2D) should be -2 + np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) + assert np.array_equal(parts[0], parts_cpu) + + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8)).get() + parts_cpu = get_parts_cpu(feature0, (2, 3)) + assert parts is not None + assert len(parts) == 1 + assert len(parts[0]) == 2, "feature should have 2 clusters" + np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) + np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) + assert np.array_equal(parts[0][0], parts_cpu[0]) + assert np.array_equal(parts[0][1], parts_cpu[1]) + + + +def test_get_parts_with_categorical_feature(): + np.random.seed(0) + + feature0 = np.array([4] * 10) + + # run + # only one partition is requested + parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False).get() + parts_cpu = get_parts_cpu(feature0, (2,), data_is_numerical=False) + assert parts is not None + assert len(parts) == 1 + assert len(parts[0]) == 1 + np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) + assert np.array_equal(parts[0], parts_cpu) + + # more partitions are requested; only the first one has valid information + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False).get() + parts_cpu = get_parts_cpu(feature0, (2, 3), data_is_numerical=False) + assert parts is not None + assert len(parts) == 1 + assert len(parts[0]) == 2 + print("parts:") + print(parts) + print("parts_cpu:") + print(parts_cpu) + np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) + np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) + assert (parts == parts_cpu).all() + assert np.array_equal(parts[0][0], parts_cpu[0]) + assert np.array_equal(parts[0][1], parts_cpu[1]) + +def test_get_parts_2d_simple(): + np.random.seed(0) + array = np.random.rand(5, 1000) + print(f"array : \n{array}") + parts = get_parts(array, np.array([3], dtype=np.uint8)).get() + parts_cpu_row0 = get_parts_cpu(array[0], (3, )) + parts_cpu_row1 = get_parts_cpu(array[1], (3, )) + assert parts is not None + assert (parts[0] == parts_cpu_row0).all() + assert (parts[1] == parts_cpu_row1).all() + print("parts:") + print(parts) + print("parts_cpu_row0:") + print(parts_cpu_row0) + print("parts_cpu_row1:") + print(parts_cpu_row1) diff --git a/tests/gpu/test_impl_gpu.py b/tests/gpu/test_impl_gpu.py deleted file mode 100644 index 1f62aa67..00000000 --- a/tests/gpu/test_impl_gpu.py +++ /dev/null @@ -1,20 +0,0 @@ -from ccc.coef.impl_gpu import ccc -import numpy as np - - -def test_compute_parts(): - # random_feature1 = np.random.rand(100) - # random_feature2 = np.random.rand(100) - # - # res = ccc(random_feature1, random_feature2, n_jobs=2) - # print(res) - data = np.random.rand(5, 30) * 100 - print(f"data: {data}") - c = ccc(data) - print(c) - - - - - - diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py new file mode 100644 index 00000000..ca52e706 --- /dev/null +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -0,0 +1,139 @@ +import pytest +import time +from ccc.coef.impl_gpu import ccc as ccc_gpu +from ccc.coef.impl import ccc +import numpy as np + + + +@pytest.mark.parametrize("seed, size, distribution, params", [ + (0, 1000, "rand", {}), # Uniform distribution + (42, 5000, "randn", {}), # Normal distribution + (123, 100, "randint", {"low": 0, "high": 100}), # Integer distribution + (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution + # (789, 100, "binomial", {"n": 10, "p": 0.5}), # Binomial distribution +]) +def test_ccc_gpu_1d(seed, size, distribution, params): + np.random.seed(seed) + + # Generate random features based on the specified distribution + if distribution == "rand": + random_feature1 = np.random.rand(size) + random_feature2 = np.random.rand(size) + elif distribution == "randn": + random_feature1 = np.random.randn(size) + random_feature2 = np.random.randn(size) + elif distribution == "randint": + random_feature1 = np.random.randint(params["low"], params["high"], size) + random_feature2 = np.random.randint(params["low"], params["high"], size) + elif distribution == "exponential": + random_feature1 = np.random.exponential(params["scale"], size) + random_feature2 = np.random.exponential(params["scale"], size) + elif distribution == "binomial": + random_feature1 = np.random.binomial(params["n"], params["p"], size) + random_feature2 = np.random.binomial(params["n"], params["p"], size) + else: + raise ValueError(f"Unsupported distribution: {distribution}") + + c1 = ccc_gpu(random_feature1, random_feature2) + c2 = ccc(random_feature1, random_feature2) + + assert np.isclose(c1, c2, rtol=1e-5, atol=1e-8) + + +# Additional test for edge cases +@pytest.mark.parametrize("case", [ + "identical", + "opposite", + "constant", + "single_value", +]) +def test_ccc_gpu_1d_edge_cases(case): + if case == "identical": + feature = np.random.rand(1000) + c1 = ccc_gpu(feature, feature) + c2 = ccc(feature, feature) + elif case == "opposite": + feature = np.random.rand(1000) + c1 = ccc_gpu(feature, -feature) + c2 = ccc(feature, -feature) + elif case == "constant": + feature1 = np.full(1000, 5) + feature2 = np.full(1000, 3) + c1 = ccc_gpu(feature1, feature2) + c2 = ccc(feature1, feature2) + elif case == "single_value": + # Too few objects + feature = np.array([1]) + with pytest.raises(ValueError) as e: + c1 = ccc_gpu(feature, feature) + assert "Too few objects" in e.value + with pytest.raises(ValueError) as e: + c2 = ccc(feature, feature) + assert "Too few objects" in e.value + return + + +def test_ccc_gpu_2d_simple(): + np.random.seed(0) + shape = (2, 20) # 200 features, 1,000 samples + print(f"Testing with {shape[0]} features and {shape[1]} samples") + df = np.random.rand(*shape) + + # Time GPU version + start_gpu = time.time() + c1 = ccc_gpu(df) + end_gpu = time.time() + gpu_time = end_gpu - start_gpu + + # Time CPU version + start_cpu = time.time() + c2 = ccc(df) + end_cpu = time.time() + cpu_time = end_cpu - start_cpu + + # Calculate speedup + speedup = cpu_time / gpu_time + + print(f"\nGPU time: {gpu_time:.4f} seconds") + print(f"CPU time: {cpu_time:.4f} seconds") + print(f"Speedup: {speedup:.2f}x") + + print(f"GPU coef:\n {c1}") + print(f"CPU coef:\n {c2}") + + assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) + + return gpu_time, cpu_time + + +# Test for very large arrays (may be slow and memory-intensive) +@pytest.mark.slow +def test_ccc_gpu_2d_very_large(): + np.random.seed(0) + shape = (200, 1000) # 200 features, 1,000 samples + print(f"Testing with {shape[0]} features and {shape[1]} samples") + df = np.random.rand(*shape) + + # Time GPU version + start_gpu = time.time() + c1 = ccc_gpu(df) + end_gpu = time.time() + gpu_time = end_gpu - start_gpu + + # Time CPU version + start_cpu = time.time() + c2 = ccc(df) + end_cpu = time.time() + cpu_time = end_cpu - start_cpu + + # Calculate speedup + speedup = cpu_time / gpu_time + + print(f"\nGPU time: {gpu_time:.4f} seconds") + print(f"CPU time: {cpu_time:.4f} seconds") + print(f"Speedup: {speedup:.2f}x") + + assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) + + return gpu_time, cpu_time, speedup \ No newline at end of file From 690f6632a0ffe7e399b3bbe44f7b0073a4bad1f8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 26 Aug 2024 11:30:24 -0600 Subject: [PATCH 032/134] [test/get_parts]: Located the cause of the partitioning issue --- libs/ccc/coef/impl.py | 37 ++++--- libs/ccc/coef/impl_gpu.py | 25 +++-- tests/gpu/feature_array.txt | 100 ++++++++++++++++++ tests/gpu/test_get_parts.py | 132 ++++++++++++++++++++++-- tests/gpu/test_get_percentiles.py | 33 ++++++ tests/gpu/test_impl_gpu_against_impl.py | 2 +- 6 files changed, 291 insertions(+), 38 deletions(-) create mode 100644 tests/gpu/feature_array.txt create mode 100644 tests/gpu/test_get_percentiles.py diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index e4b4cf41..a1f177cf 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -36,7 +36,7 @@ def get_perc_from_k(k: int) -> list[float]: return [(1.0 / k) * i for i in range(1, k)] -@njit(cache=True, nogil=True) +# @njit(cache=True, nogil=True) def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: """ Performs a simple quantile clustering on one dimensional data (1d). Quantile @@ -57,24 +57,27 @@ def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: data_rank = rank(data, data_sorted) data_perc = data_rank / len(data) - percentiles = [0.0] + get_perc_from_k(k) + [1.0] - - cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") - - current_cluster = 0 - part = np.zeros(data.shape, dtype=np.int16) - 1 - - for i in range(len(cut_points) - 1): - lim1 = cut_points[i] - lim2 = cut_points[i + 1] - - part[data_sorted[lim1:lim2]] = current_cluster - current_cluster += 1 - + # percentiles = [0.0] + get_perc_from_k(k) + [1.0] + percentiles = get_perc_from_k(k) + # print(f"CPU percentages: {str(percentiles)}") + + # cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") + # + # current_cluster = 0 + # part = np.zeros(data.shape, dtype=np.int16) - 1 + # + # for i in range(len(cut_points) - 1): + # lim1 = cut_points[i] + # lim2 = cut_points[i + 1] + # + # part[data_sorted[lim1:lim2]] = current_cluster + # current_cluster += 1 + bins = np.quantile(data, percentiles) + part = np.digitize(data, bins) return part -@njit(cache=True, nogil=True) +# @njit(cache=True, nogil=True) def get_range_n_clusters( n_features: int, internal_n_clusters: Iterable[int] = None ) -> NDArray[np.uint8]: @@ -110,7 +113,7 @@ def get_range_n_clusters( return np.array(clusters_range_list, dtype=np.uint16) -@njit(cache=True, nogil=True) +# @njit(cache=True, nogil=True) def get_parts( data: NDArray, range_n_clusters: tuple[int], data_is_numerical: bool = True ) -> NDArray[np.int16]: diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 1879f27d..b3c949f9 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -11,14 +11,15 @@ from numpy.typing import NDArray from numba import njit from numba import cuda +from fractions import Fraction from ccc.pytorch.core import unravel_index_2d from ccc.scipy.stats import rank from ccc.sklearn.metrics import adjusted_rand_index as ari from ccc.utils import chunker -@njit(cache=True, nogil=True) -def get_perc_from_k(k: int) -> NDArray[np.float32]: +# @njit(cache=True, nogil=True) +def get_perc_from_k(k: int) -> NDArray[np.float64]: """ It returns the percentiles (from 0.0 to 1.0) that separate the data into k clusters. For example, if k=2, it returns [0.5]; if k=4, it returns [0.25, @@ -31,13 +32,14 @@ def get_perc_from_k(k: int) -> NDArray[np.float32]: Returns: A numpy array of percentiles (from 0.0 to 1.0). """ + np.set_printoptions(precision=17) if k < 2: - return np.empty(0, dtype=np.float32) - return np.array([(1.0 / k) * i for i in range(1, k)], dtype=np.float32) + return np.array([], dtype='float64') + return np.linspace(1/k, 1-1/k, k-1, dtype='float64') -@njit(cache=True, nogil=True) -def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[np.float32]: +# @njit(cache=True, nogil=True) +def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[float]: """ It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters @@ -50,16 +52,16 @@ def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) # Todo: research on if numba can optimize this # Emtpy & null check if ks.size == 0: - return np.empty((0, 0), dtype=np.float32) + return np.empty((0, 0), dtype=float) # Number of rows of the returning matrix n_rows = len(ks) # Number of columns of the returning matrix, dominated by the largest k, which specifies the # of clusters n_cols = np.max(ks) - 1 - percentiles = np.full((n_rows, n_cols), np.nan, dtype=np.float32) + percentiles = np.full((n_rows, n_cols), np.nan, dtype=float) for idx, k in enumerate(ks): perc = get_perc_from_k(k) if as_percentage: - perc = np.round(perc * 100).astype(np.float32) # Convert to percentage and round + perc = np.round(perc * 100).astype(float) # Convert to percentage and round percentiles[idx, :len(perc)] = perc return percentiles @@ -205,7 +207,7 @@ def get_parts(X: NDArray, nx = X.shape[0] # n_features ny = range_n_clusters.shape[0] # n_clusters nz = X.shape[1] # n_objects - print(f"{nx}, {ny}, {nz}") + # print(f"{nx}, {ny}, {nz}") # Allocate arrays on device global memory d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 @@ -216,12 +218,13 @@ def get_parts(X: NDArray, d_X = cp.asarray(X) # Get cutting percentages for each cluster range_n_percentages = get_range_n_percentages(range_n_clusters) - d_range_n_percentages = cp.asarray(range_n_percentages) + d_range_n_percentages = cp.asarray(range_n_percentages, dtype=float) for x in range(nx): for y in range(ny): objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row percentages = d_range_n_percentages[y, :] + print(f"GPU percentiles: {percentages}") bins = cp.quantile(objects, percentages) partition = cp.digitize(objects, bins) d_parts[x, y, :] = partition diff --git a/tests/gpu/feature_array.txt b/tests/gpu/feature_array.txt new file mode 100644 index 00000000..e1f07384 --- /dev/null +++ b/tests/gpu/feature_array.txt @@ -0,0 +1,100 @@ +5.488135039273247529e-01 +7.151893663724194772e-01 +6.027633760716438749e-01 +5.448831829968968643e-01 +4.236547993389047084e-01 +6.458941130666561170e-01 +4.375872112626925103e-01 +8.917730007820797722e-01 +9.636627605010292807e-01 +3.834415188257777052e-01 +7.917250380826645895e-01 +5.288949197529044799e-01 +5.680445610939323098e-01 +9.255966382926610336e-01 +7.103605819788694209e-02 +8.712929970154070780e-02 +2.021839744032571939e-02 +8.326198455479379978e-01 +7.781567509498504842e-01 +8.700121482468191614e-01 +9.786183422327640047e-01 +7.991585642167235992e-01 +4.614793622529318462e-01 +7.805291762864554617e-01 +1.182744258689332195e-01 +6.399210213275238202e-01 +1.433532874090464038e-01 +9.446689170495838894e-01 +5.218483217500716753e-01 +4.146619399905235870e-01 +2.645556121046269693e-01 +7.742336894342166653e-01 +4.561503322165485486e-01 +5.684339488686485087e-01 +1.878980043635514185e-02 +6.176354970758770602e-01 +6.120957227224214092e-01 +6.169339968747569181e-01 +9.437480785146241669e-01 +6.818202991034834071e-01 +3.595079005737860101e-01 +4.370319537993414549e-01 +6.976311959272648577e-01 +6.022547162926983333e-02 +6.667667154456676792e-01 +6.706378696181594101e-01 +2.103825610738409013e-01 +1.289262976548533057e-01 +3.154283509241838646e-01 +3.637107709426226076e-01 +5.701967704178796392e-01 +4.386015134623203471e-01 +9.883738380592261841e-01 +1.020448107480280697e-01 +2.088767560948346924e-01 +1.613095178849962563e-01 +6.531083254653984316e-01 +2.532916025397821125e-01 +4.663107728563062881e-01 +2.444255920016027428e-01 +1.589695836455197187e-01 +1.103751411643051350e-01 +6.563295894652734219e-01 +1.381829513486138028e-01 +1.965823616800534968e-01 +3.687251706609641078e-01 +8.209932298479351021e-01 +9.710127579306127021e-02 +8.379449074988039037e-01 +9.609840789396306704e-02 +9.764594650133957554e-01 +4.686512016477015763e-01 +9.767610881903371345e-01 +6.048455197450459675e-01 +7.392635793983016734e-01 +3.918779225432067470e-02 +2.828069625764095818e-01 +1.201965612131689065e-01 +2.961401975221449323e-01 +1.187277189542440547e-01 +3.179831793939760232e-01 +4.142629945146699688e-01 +6.414749634878436080e-02 +6.924721193700198452e-01 +5.666014542065751503e-01 +2.653894909394454160e-01 +5.232480534666996697e-01 +9.394051075844167542e-02 +5.759464955561792721e-01 +9.292961975762140669e-01 +3.185689524513236615e-01 +6.674103799636816881e-01 +1.317978624043921743e-01 +7.163272041185655414e-01 +2.894060929472010990e-01 +1.831913620071168314e-01 +5.865129348100831530e-01 +2.010754618749355238e-02 +8.289400292173630946e-01 +4.695476192547065608e-03 diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py index 81a22951..3514cc27 100644 --- a/tests/gpu/test_get_parts.py +++ b/tests/gpu/test_get_parts.py @@ -15,9 +15,46 @@ ) from ccc.coef import get_parts as get_parts_cpu +from ccc.coef import get_perc_from_k as get_perc_from_k_cpu + + +def find_partition(value, quantiles): + for i in range(len(quantiles)): + if value <= quantiles[i]: + return i + return len(quantiles) # If value is greater than all quantiles + +def verify_partition(feature, index, n_clusters): + """ + Verify the partition for a specific element in the feature array. + """ + parts_cpu = get_parts_cpu(feature, (n_clusters,)) + percentages_cpu = get_perc_from_k_cpu(n_clusters) + quantities = np.quantile(feature, percentages_cpu) + + value = feature[index] + partition = find_partition(value, quantities) + + print(f"\nVerifying partition for feature[{index}] = {value}") + print(f"CPU percentages: {percentages_cpu}") + print(f"CPU quantities: {quantities}") + + print("\nAll partition ranges:") + for i in range(n_clusters): + if i == 0: + print(f"Partition {i} range: (-inf, {quantities[i]}]") + elif i == n_clusters - 1: + print(f"Partition {i} range: ({quantities[i-1]}, inf)") + else: + print(f"Partition {i} range: ({quantities[i-1]}, {quantities[i]}]") + + print(f"Data point {value} should fall in partition {partition}") + print(f"Partition computed by CCC_CPU: {parts_cpu[0][index]}") + + assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" + return partition - -@pytest.mark.parametrize("feature_size", [100, 1000, 10000]) +@pytest.mark.parametrize("feature_size", [100])#, 1000, 10000]) @pytest.mark.parametrize("cluster_settings", [ # ([2], (2,)), # ([2, 3], (2, 3)), @@ -31,26 +68,103 @@ def test_get_parts(feature_size, cluster_settings): gpu_clusters, cpu_clusters = cluster_settings feature = np.random.rand(feature_size) + # print(f"\nfeature 77: {feature[77]}") + # print(f"feature 78: {feature[78]}") # GPU implementation parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() # CPU implementation parts_cpu = get_parts_cpu(feature, cpu_clusters) + + print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}") + print(f"GPU output shape: {parts_gpu.shape}") + print(f"CPU output shape: {parts_cpu.shape}") - assert parts_gpu is not None - assert len(parts_gpu) == 1, "should have only one feature" - assert len(parts_gpu[0]) == len(gpu_clusters), f"should have {len(gpu_clusters)} partition(s)" + assert parts_gpu is not None, "GPU output is None" + assert len(parts_gpu) == 1, f"Expected 1 feature, got {len(parts_gpu)}" + assert len(parts_gpu[0]) == len(gpu_clusters), f"Expected {len(gpu_clusters)} partition(s), got {len(parts_gpu[0])}" for i, n_clusters in enumerate(gpu_clusters): - assert len(np.unique(parts_gpu[0][i])) == n_clusters, f"should have {n_clusters} cluster indexes" - assert np.array_equal(parts_gpu[0][i], parts_cpu[i]), f"GPU and CPU results don't match for {n_clusters} clusters" + gpu_unique = np.unique(parts_gpu[0][i]) + cpu_unique = np.unique(parts_cpu[i]) + + print(f"\nPartition {i}:") + print(f" GPU unique values (partitions): {gpu_unique}") + print(f" CPU unique values (partitions): {cpu_unique}") + + assert len(gpu_unique) == n_clusters, f"Expected {n_clusters} cluster indexes, got {len(gpu_unique)}" + + if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): + diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] + print(f"\nDifferences found in partition {i}:") + print(f" Number of differing elements: {len(diff_indices)}") + print(f" First 10 differing indices: {diff_indices[:10]}") + print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") + print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") + print(f" Object values at these indices: {feature[diff_indices[:10]]}") + + # Verify partitions for differing elements + for idx in diff_indices[:10]: + expected_partition = verify_partition(feature, idx, n_clusters) + assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" + + assert False, f"GPU and CPU results don't match for {n_clusters} clusters" # Additional checks for multi-cluster settings if len(gpu_clusters) > 1: for i in range(len(gpu_clusters)): for j in range(i + 1, len(gpu_clusters)): - assert not np.array_equal(parts_gpu[0][i], parts_cpu[j]), f"Partitions {i} and {j} should not be equal" + if np.array_equal(parts_gpu[0][i], parts_cpu[j]): + print(f"\nUnexpected equality between partitions {i} and {j}:") + print(f" Partition {i}: {parts_gpu[0][i]}") + print(f" Partition {j}: {parts_cpu[j]}") + assert False, f"Partitions {i} and {j} should not be equal" + + +def test_specific_elements(): + np.random.seed(0) + feature = np.random.rand(100) + assert feature[77] == 0.1201965612131689 + assert feature[78] == 0.29614019752214493 + + verify_partition(feature, 77, 6) + verify_partition(feature, 78, 6) + + +def test_potential_buggy_cpu_impl(): + np.random.seed(0) + feature = np.random.rand(100) + assert feature[77] == 0.1201965612131689 + assert feature[78] == 0.29614019752214493 + parts_cpu = get_parts_cpu(feature, (6, )) + percentages_cpu = get_perc_from_k_cpu(6) + quantities = np.quantile(feature, percentages_cpu) + print() + print(f"CPU parts: \n{parts_cpu}") + print(f"CPU percentages: \n{percentages_cpu}") + print(f"CPU quantities: \n{quantities}") + + # Find which partitions feature[77] and feature[78] fall into + value_77 = feature[77] + value_78 = feature[78] + partition_77 = find_partition(value_77, quantities) + partition_78 = find_partition(value_78, quantities) + + print(f"feature[77] = {value_77} falls in partition {partition_77}") + print(f"feature[78] = {value_78} falls in partition {partition_78}") + if partition_77 > 0: + print(f"Partition {partition_77} range: ({quantities[partition_77-1]}, {quantities[partition_77]}]") + else: + print(f"Partition {partition_77} range: (-inf, {quantities[partition_77]}]") + if partition_78 > 0: + print(f"Partition {partition_78} range: ({quantities[partition_78-1]}, {quantities[partition_78]}]") + else: + print(f"Partition {partition_78} range: (-inf, {quantities[partition_78]}]") + print(f"Partition computed by CCC_CPU for feature[77]: {parts_cpu[0][77]}") + print(f"Partition computed by CCC_CPU for feature[78]: {parts_cpu[0][78]}") + assert partition_77 == parts_cpu[0][77] + assert partition_78 == parts_cpu[0][78] def test_get_parts_with_singletons(): @@ -126,4 +240,4 @@ def test_get_parts_2d_simple(): print("parts_cpu_row0:") print(parts_cpu_row0) print("parts_cpu_row1:") - print(parts_cpu_row1) + print(parts_cpu_row1) \ No newline at end of file diff --git a/tests/gpu/test_get_percentiles.py b/tests/gpu/test_get_percentiles.py new file mode 100644 index 00000000..e5eb363a --- /dev/null +++ b/tests/gpu/test_get_percentiles.py @@ -0,0 +1,33 @@ +import pytest +from typing import List + +import numpy as np +from numpy.testing import assert_array_equal, assert_allclose +from numpy.typing import NDArray + +from ccc.coef.impl_gpu import ( + get_perc_from_k, + get_range_n_percentages, + convert_n_clusters, + get_range_n_clusters, + get_parts, +) + +from ccc.coef import get_perc_from_k as get_perc_from_k_cpu + +def test_get_perc_from_k_with_k_less_than_two(): + empty_array = np.empty(0) + assert_array_equal(get_perc_from_k(1), empty_array) + assert_array_equal(get_perc_from_k(0), empty_array) + assert_array_equal(get_perc_from_k(-1), empty_array) + + +@pytest.mark.parametrize("k", [ + # 2, 3, 4, 5, 6, 7, 8, 9, 10 + 3 +]) +def test_get_perc_from_k(k): + np.set_printoptions(precision=17) + gpu_result = get_perc_from_k(k) + cpu_result = get_perc_from_k_cpu(k) + assert np.equal(gpu_result, cpu_result).all() diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index ca52e706..e8ea5b37 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -76,7 +76,7 @@ def test_ccc_gpu_1d_edge_cases(case): def test_ccc_gpu_2d_simple(): np.random.seed(0) - shape = (2, 20) # 200 features, 1,000 samples + shape = (20 , 200) # 200 features, 1,000 samples print(f"Testing with {shape[0]} features and {shape[1]} samples") df = np.random.rand(*shape) From e32f03a81692476436a2902d244383b9edf6fe5d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 5 Sep 2024 08:25:36 -0600 Subject: [PATCH 033/134] [test]: Add a debugging scrit --- tests/gpu/test_get_parts_debug.py | 120 ++++++++++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 tests/gpu/test_get_parts_debug.py diff --git a/tests/gpu/test_get_parts_debug.py b/tests/gpu/test_get_parts_debug.py new file mode 100644 index 00000000..96d62d1d --- /dev/null +++ b/tests/gpu/test_get_parts_debug.py @@ -0,0 +1,120 @@ +""" +Code to reproduce the edge cased that may be missed by the CPU version of get_parts +""" +import pytest +from typing import List + +import numpy as np +from numba import cuda +from numpy.testing import assert_array_equal, assert_allclose +from numpy.typing import NDArray + +from ccc.coef.impl_gpu import ( + get_perc_from_k, + get_range_n_percentages, + convert_n_clusters, + get_range_n_clusters, + get_parts, +) + +from ccc.coef import get_parts as get_parts_cpu +from ccc.coef import get_perc_from_k as get_perc_from_k_cpu + + +def find_partition(value, quantiles): + for i in range(len(quantiles)): + if value <= quantiles[i]: + return i + return len(quantiles) # If value is greater than all quantiles + + +def verify_partition(feature, index, n_clusters): + """ + Verify the partition for a specific element in the feature array. + """ + parts_cpu = get_parts_cpu(feature, (n_clusters,)) + percentages_cpu = get_perc_from_k_cpu(n_clusters) + quantities = np.quantile(feature, percentages_cpu) + + value = feature[index] + partition = find_partition(value, quantities) + + print(f"\nVerifying partition for feature[{index}] = {value}") + print(f"CPU percentages: {percentages_cpu}") + print(f"CPU quantities: {quantities}") + + print("\nAll partition ranges:") + for i in range(n_clusters): + if i == 0: + print(f"Partition {i} range: (-inf, {quantities[i]}]") + elif i == n_clusters - 1: + print(f"Partition {i} range: ({quantities[i - 1]}, inf)") + else: + print(f"Partition {i} range: ({quantities[i - 1]}, {quantities[i]}]") + + print(f"Data point {value} should fall in partition {partition}") + print(f"Partition computed by CCC_CPU: {parts_cpu[0][index]}") + + assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" + return partition + + +@pytest.mark.parametrize("feature_size", [100]) # 100 features +@pytest.mark.parametrize("cluster_settings", [ + ([6], (6,)), # 6 internal clusters +]) +def test_get_parts(feature_size, cluster_settings): + np.random.seed(0) + + gpu_clusters, cpu_clusters = cluster_settings + feature = np.random.rand(feature_size) + + # GPU implementation + parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() + + # CPU implementation + parts_cpu = get_parts_cpu(feature, cpu_clusters) + + print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}") + print(f"GPU output shape: {parts_gpu.shape}") + print(f"CPU output shape: {parts_cpu.shape}") + + assert parts_gpu is not None, "GPU output is None" + assert len(parts_gpu) == 1, f"Expected 1 feature, got {len(parts_gpu)}" + assert len(parts_gpu[0]) == len(gpu_clusters), f"Expected {len(gpu_clusters)} partition(s), got {len(parts_gpu[0])}" + + for i, n_clusters in enumerate(gpu_clusters): + gpu_unique = np.unique(parts_gpu[0][i]) + cpu_unique = np.unique(parts_cpu[i]) + + print(f"\nPartition {i}:") + print(f" GPU unique values (partitions): {gpu_unique}") + print(f" CPU unique values (partitions): {cpu_unique}") + + assert len(gpu_unique) == n_clusters, f"Expected {n_clusters} cluster indexes, got {len(gpu_unique)}" + + if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): + diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] + print(f"\nDifferences found in partition {i}:") + print(f" Number of differing elements: {len(diff_indices)}") + print(f" First 10 differing indices: {diff_indices[:10]}") + print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") + print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") + print(f" Object values at these indices: {feature[diff_indices[:10]]}") + + # Verify partitions for differing elements + for idx in diff_indices[:10]: + expected_partition = verify_partition(feature, idx, n_clusters) + assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" + + assert False, f"GPU and CPU results don't match for {n_clusters} clusters" + + # Additional checks for multi-cluster settings + if len(gpu_clusters) > 1: + for i in range(len(gpu_clusters)): + for j in range(i + 1, len(gpu_clusters)): + if np.array_equal(parts_gpu[0][i], parts_cpu[j]): + print(f"\nUnexpected equality between partitions {i} and {j}:") + print(f" Partition {i}: {parts_gpu[0][i]}") + print(f" Partition {j}: {parts_cpu[j]}") + assert False, f"Partitions {i} and {j} should not be equal" \ No newline at end of file From 623272681a635625061e5c598261ac4fc98c3430 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 5 Sep 2024 14:36:58 -0600 Subject: [PATCH 034/134] [test]: Use all-close to take CPU-GPU FP errors into consideration --- tests/gpu/test_get_percentiles.py | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/tests/gpu/test_get_percentiles.py b/tests/gpu/test_get_percentiles.py index e5eb363a..cefacfdb 100644 --- a/tests/gpu/test_get_percentiles.py +++ b/tests/gpu/test_get_percentiles.py @@ -1,16 +1,10 @@ import pytest -from typing import List import numpy as np -from numpy.testing import assert_array_equal, assert_allclose -from numpy.typing import NDArray +from numpy.testing import assert_array_equal from ccc.coef.impl_gpu import ( get_perc_from_k, - get_range_n_percentages, - convert_n_clusters, - get_range_n_clusters, - get_parts, ) from ccc.coef import get_perc_from_k as get_perc_from_k_cpu @@ -23,11 +17,10 @@ def test_get_perc_from_k_with_k_less_than_two(): @pytest.mark.parametrize("k", [ - # 2, 3, 4, 5, 6, 7, 8, 9, 10 - 3 + 2, 3, 4, 5, 6, 7, 8, 9, 10 ]) def test_get_perc_from_k(k): np.set_printoptions(precision=17) gpu_result = get_perc_from_k(k) cpu_result = get_perc_from_k_cpu(k) - assert np.equal(gpu_result, cpu_result).all() + assert np.allclose(gpu_result, cpu_result) From f4ef1653b64d0ba2feaf06ddc868dc5ba2a171d1 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 5 Sep 2024 22:31:16 -0600 Subject: [PATCH 035/134] [get_parts]: Fix many edge cases --- libs/ccc/coef/impl.py | 2 +- libs/ccc/coef/impl_gpu.py | 3 ++- tests/gpu/test_coef.py | 6 +++-- tests/gpu/test_get_parts.py | 35 ++++++++++++++++++------- tests/gpu/test_impl_gpu_against_impl.py | 8 +++--- 5 files changed, 36 insertions(+), 18 deletions(-) diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index a1f177cf..d18d1e05 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -73,7 +73,7 @@ def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: # part[data_sorted[lim1:lim2]] = current_cluster # current_cluster += 1 bins = np.quantile(data, percentiles) - part = np.digitize(data, bins) + part = np.digitize(data, bins, right=True) return part diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index b3c949f9..41862400 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -226,7 +226,8 @@ def get_parts(X: NDArray, percentages = d_range_n_percentages[y, :] print(f"GPU percentiles: {percentages}") bins = cp.quantile(objects, percentages) - partition = cp.digitize(objects, bins) + print(f"GPU quantiles: {bins}") + partition = cp.digitize(objects, bins, right=True) d_parts[x, y, :] = partition # Remove singletons by putting -2 as values diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef.py index dc9bf30e..7e5059ec 100644 --- a/tests/gpu/test_coef.py +++ b/tests/gpu/test_coef.py @@ -1,9 +1,8 @@ import pytest -from typing import List import numpy as np +import cupy as cp from numpy.testing import assert_array_equal, assert_allclose -from numpy.typing import NDArray from ccc.coef.impl_gpu import ( get_perc_from_k, @@ -346,6 +345,9 @@ def test_get_parts_with_singletons(): def test_get_parts_with_categorical_feature(): + mempool = cp.get_default_memory_pool() + mempool.free_all_blocks() + np.random.seed(0) feature0 = np.array([4] * 10) diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py index 3514cc27..a3d0787c 100644 --- a/tests/gpu/test_get_parts.py +++ b/tests/gpu/test_get_parts.py @@ -1,22 +1,25 @@ import pytest -from typing import List import numpy as np -from numba import cuda -from numpy.testing import assert_array_equal, assert_allclose -from numpy.typing import NDArray +import cupy as cp from ccc.coef.impl_gpu import ( - get_perc_from_k, - get_range_n_percentages, - convert_n_clusters, - get_range_n_clusters, get_parts, ) from ccc.coef import get_parts as get_parts_cpu from ccc.coef import get_perc_from_k as get_perc_from_k_cpu +import functools +def clean_gpu_memory(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + finally: + mempool = cp.get_default_memory_pool() + mempool.free_all_blocks() + return wrapper def find_partition(value, quantiles): for i in range(len(quantiles)): @@ -54,6 +57,8 @@ def verify_partition(feature, index, n_clusters): assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" return partition + +@clean_gpu_memory @pytest.mark.parametrize("feature_size", [100])#, 1000, 10000]) @pytest.mark.parametrize("cluster_settings", [ # ([2], (2,)), @@ -64,6 +69,7 @@ def verify_partition(feature, index, n_clusters): # ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), ]) def test_get_parts(feature_size, cluster_settings): + np.random.seed(0) gpu_clusters, cpu_clusters = cluster_settings @@ -123,6 +129,9 @@ def test_get_parts(feature_size, cluster_settings): def test_specific_elements(): + mempool = cp.get_default_memory_pool() + mempool.free_all_blocks() + np.random.seed(0) feature = np.random.rand(100) assert feature[77] == 0.1201965612131689 @@ -132,7 +141,9 @@ def test_specific_elements(): verify_partition(feature, 78, 6) +@clean_gpu_memory def test_potential_buggy_cpu_impl(): + np.random.seed(0) feature = np.random.rand(100) assert feature[77] == 0.1201965612131689 @@ -167,7 +178,9 @@ def test_potential_buggy_cpu_impl(): assert partition_78 == parts_cpu[0][78] +@clean_gpu_memory def test_get_parts_with_singletons(): + np.random.seed(0) feature0 = np.array([1.3] * 100) @@ -193,7 +206,7 @@ def test_get_parts_with_singletons(): assert np.array_equal(parts[0][1], parts_cpu[1]) - +@clean_gpu_memory def test_get_parts_with_categorical_feature(): np.random.seed(0) @@ -225,6 +238,8 @@ def test_get_parts_with_categorical_feature(): assert np.array_equal(parts[0][0], parts_cpu[0]) assert np.array_equal(parts[0][1], parts_cpu[1]) + +@clean_gpu_memory def test_get_parts_2d_simple(): np.random.seed(0) array = np.random.rand(5, 1000) @@ -240,4 +255,4 @@ def test_get_parts_2d_simple(): print("parts_cpu_row0:") print(parts_cpu_row0) print("parts_cpu_row1:") - print(parts_cpu_row1) \ No newline at end of file + print(parts_cpu_row1) diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index e8ea5b37..5c0457ee 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -7,10 +7,10 @@ @pytest.mark.parametrize("seed, size, distribution, params", [ - (0, 1000, "rand", {}), # Uniform distribution - (42, 5000, "randn", {}), # Normal distribution + # (0, 1000, "rand", {}), # Uniform distribution + # (42, 5000, "randn", {}), # Normal distribution (123, 100, "randint", {"low": 0, "high": 100}), # Integer distribution - (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution + # (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution # (789, 100, "binomial", {"n": 10, "p": 0.5}), # Binomial distribution ]) def test_ccc_gpu_1d(seed, size, distribution, params): @@ -76,7 +76,7 @@ def test_ccc_gpu_1d_edge_cases(case): def test_ccc_gpu_2d_simple(): np.random.seed(0) - shape = (20 , 200) # 200 features, 1,000 samples + shape = (20 , 200) # 200 features, 1,000 samples print(f"Testing with {shape[0]} features and {shape[1]} samples") df = np.random.rand(*shape) From 2988cdefc6a32c1b12c078b4cfc49d171010ae73 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 6 Sep 2024 12:17:03 -0600 Subject: [PATCH 036/134] [test/get_parts]: Add different distributions to generate features --- tests/gpu/test_get_parts.py | 68 ++++++++++++++++++------- tests/gpu/test_impl_gpu_against_impl.py | 10 ++-- 2 files changed, 54 insertions(+), 24 deletions(-) diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py index a3d0787c..56577b23 100644 --- a/tests/gpu/test_get_parts.py +++ b/tests/gpu/test_get_parts.py @@ -59,31 +59,53 @@ def verify_partition(feature, index, n_clusters): @clean_gpu_memory -@pytest.mark.parametrize("feature_size", [100])#, 1000, 10000]) +@pytest.mark.parametrize("feature_size", [100, 1000, 10000, 100000]) @pytest.mark.parametrize("cluster_settings", [ - # ([2], (2,)), - # ([2, 3], (2, 3)), - # ([2, 3, 4], (2, 3, 4)), - # ([5], (5,)), + ([2], (2,)), + ([2, 3], (2, 3)), + ([2, 3, 4], (2, 3, 4)), + ([5], (5,)), ([6], (6,)), - # ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), + ([9], (9,)), + ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), ]) -def test_get_parts(feature_size, cluster_settings): +@pytest.mark.parametrize("seed, distribution, params", [ + (0, "rand", {}), # Uniform distribution + (42, "randn", {}), # Normal distribution + (123, "randint", {"low": 0, "high": 100}), # Integer distribution + (456, "exponential", {"scale": 2.0}), # Exponential distribution +]) +def test_get_parts(feature_size, cluster_settings, seed, distribution, params): + # Given FP arithmetic is not associative and the difference between GPU and CPU FP arithmetic, + # we need to allow for some tolerance. This is a tentative value that may need to be adjusted. + # Note that the difference between GPU and CPU results is not expected to be larger than 1. + n_diff_tolerance = int(feature_size * 0.04) - np.random.seed(0) + np.random.seed(seed) gpu_clusters, cpu_clusters = cluster_settings - feature = np.random.rand(feature_size) - # print(f"\nfeature 77: {feature[77]}") - # print(f"feature 78: {feature[78]}") - + + # Generate random features based on the specified distribution + if distribution == "rand": + feature = np.random.rand(feature_size) + elif distribution == "randn": + feature = np.random.randn(feature_size) + elif distribution == "randint": + feature = np.random.randint(params["low"], params["high"], feature_size) + elif distribution == "exponential": + feature = np.random.exponential(params["scale"], feature_size) + elif distribution == "binomial": + feature = np.random.binomial(params["n"], params["p"], feature_size) + else: + raise ValueError(f"Unsupported distribution: {distribution}") + # GPU implementation parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() # CPU implementation parts_cpu = get_parts_cpu(feature, cpu_clusters) - print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}") + print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}, distribution={distribution}") print(f"GPU output shape: {parts_gpu.shape}") print(f"CPU output shape: {parts_cpu.shape}") @@ -103,19 +125,27 @@ def test_get_parts(feature_size, cluster_settings): if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] + diff_values = np.abs(parts_gpu[0][i][diff_indices] - parts_cpu[i][diff_indices]) + max_diff = np.max(diff_values) + print(f"\nDifferences found in partition {i}:") print(f" Number of differing elements: {len(diff_indices)}") + print(f" Maximum difference: {max_diff}") print(f" First 10 differing indices: {diff_indices[:10]}") print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") print(f" Object values at these indices: {feature[diff_indices[:10]]}") - # Verify partitions for differing elements - for idx in diff_indices[:10]: - expected_partition = verify_partition(feature, idx, n_clusters) - assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" - - assert False, f"GPU and CPU results don't match for {n_clusters} clusters" + if len(diff_indices) > n_diff_tolerance or max_diff > 1: + # Verify partitions for differing elements + for idx in diff_indices[:10]: + expected_partition = verify_partition(feature, idx, n_clusters) + assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" + + assert False, f"GPU and CPU results don't match for {n_clusters} clusters: " \ + f"diff count = {len(diff_indices)}, max diff = {max_diff}" + else: + print(f" Differences within tolerance (count <= {n_diff_tolerance} and max diff <= 1)") # Additional checks for multi-cluster settings if len(gpu_clusters) > 1: diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index 5c0457ee..7a1b69e2 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -7,11 +7,11 @@ @pytest.mark.parametrize("seed, size, distribution, params", [ - # (0, 1000, "rand", {}), # Uniform distribution - # (42, 5000, "randn", {}), # Normal distribution - (123, 100, "randint", {"low": 0, "high": 100}), # Integer distribution - # (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution - # (789, 100, "binomial", {"n": 10, "p": 0.5}), # Binomial distribution + (0, 1000, "rand", {}), # Uniform distribution + (42, 5000, "randn", {}), # Normal distribution + (123, 100, "randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference + (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution + (789, 100, "binomial", {"n": 10, "p": 0.5}), # Binomial distribution ]) def test_ccc_gpu_1d(seed, size, distribution, params): np.random.seed(seed) From 6b06a3f0c2d49a1928f47b9e688688e9bd86aa3a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 6 Sep 2024 12:29:01 -0600 Subject: [PATCH 037/134] Clean up code --- tests/gpu/test_impl_gpu_against_impl.py | 2 +- tests/gpu/tmp_regress_test.py | 0 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 tests/gpu/tmp_regress_test.py diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index 7a1b69e2..1735f1c8 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -4,7 +4,7 @@ from ccc.coef.impl import ccc import numpy as np - +# This test needs to be improved @pytest.mark.parametrize("seed, size, distribution, params", [ (0, 1000, "rand", {}), # Uniform distribution diff --git a/tests/gpu/tmp_regress_test.py b/tests/gpu/tmp_regress_test.py deleted file mode 100644 index e69de29b..00000000 From e5cddca06161ec62d5225221f2ec12bf91daded8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 12 Sep 2024 14:29:38 -0600 Subject: [PATCH 038/134] [test/regress]: Add parameters --- tests/gpu/test_impl_gpu_against_impl.py | 21 ++++++++++++--------- tests/gpu/utils.py | 12 ++++++++++++ 2 files changed, 24 insertions(+), 9 deletions(-) create mode 100644 tests/gpu/utils.py diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index 1735f1c8..e15c78a7 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -1,19 +1,22 @@ import pytest import time -from ccc.coef.impl_gpu import ccc as ccc_gpu -from ccc.coef.impl import ccc + import numpy as np +from ccc.coef.impl_gpu import ccc as ccc_gpu +from ccc.coef.impl import ccc +from utils import clean_gpu_memory # This test needs to be improved -@pytest.mark.parametrize("seed, size, distribution, params", [ - (0, 1000, "rand", {}), # Uniform distribution - (42, 5000, "randn", {}), # Normal distribution - (123, 100, "randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference - (456, 10000, "exponential", {"scale": 2.0}), # Exponential distribution - (789, 100, "binomial", {"n": 10, "p": 0.5}), # Binomial distribution +@clean_gpu_memory +@pytest.mark.parametrize("size", [100, 1000, 10000, 100000, 100000]) +@pytest.mark.parametrize("seed, distribution, params", [ + (0, "rand", {}), # Uniform distribution + (42, "randn", {}), # Normal distribution + (123, "randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference due to partition errors + (456, "exponential", {"scale": 2.0}), # Exponential distribution ]) -def test_ccc_gpu_1d(seed, size, distribution, params): +def test_ccc_gpu_1d(size, seed, distribution, params): np.random.seed(seed) # Generate random features based on the specified distribution diff --git a/tests/gpu/utils.py b/tests/gpu/utils.py new file mode 100644 index 00000000..26f0e662 --- /dev/null +++ b/tests/gpu/utils.py @@ -0,0 +1,12 @@ +import functools +import cupy as cp + +def clean_gpu_memory(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + finally: + mempool = cp.get_default_memory_pool() + mempool.free_all_blocks() + return wrapper From d93d15a36f9d810be80c714fb85ba17fb5e96aa7 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 12 Sep 2024 19:42:05 -0600 Subject: [PATCH 039/134] [test/coef]: Rewrite tests to introduce error tolerance --- tests/gpu/test_impl_gpu_against_impl.py | 118 ++++++++++++++++-------- 1 file changed, 77 insertions(+), 41 deletions(-) diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index e15c78a7..997505ef 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -9,15 +9,9 @@ # This test needs to be improved @clean_gpu_memory -@pytest.mark.parametrize("size", [100, 1000, 10000, 100000, 100000]) -@pytest.mark.parametrize("seed, distribution, params", [ - (0, "rand", {}), # Uniform distribution - (42, "randn", {}), # Normal distribution - (123, "randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference due to partition errors - (456, "exponential", {"scale": 2.0}), # Exponential distribution -]) -def test_ccc_gpu_1d(size, seed, distribution, params): +def run_ccc_test(size, seed, distribution, params): np.random.seed(seed) + absolute_tolerance = 1e-3 # allow 0.001 as max coefficient difference # Generate random features based on the specified distribution if distribution == "rand": @@ -32,19 +26,59 @@ def test_ccc_gpu_1d(size, seed, distribution, params): elif distribution == "exponential": random_feature1 = np.random.exponential(params["scale"], size) random_feature2 = np.random.exponential(params["scale"], size) - elif distribution == "binomial": - random_feature1 = np.random.binomial(params["n"], params["p"], size) - random_feature2 = np.random.binomial(params["n"], params["p"], size) else: raise ValueError(f"Unsupported distribution: {distribution}") c1 = ccc_gpu(random_feature1, random_feature2) c2 = ccc(random_feature1, random_feature2) - assert np.isclose(c1, c2, rtol=1e-5, atol=1e-8) + is_close = np.isclose(c1, c2, atol=absolute_tolerance) + return is_close, c1, c2 + +@pytest.mark.parametrize("distribution, params", [ + ("rand", {}), # Uniform distribution + ("randn", {}), # Normal distribution + ("randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference due to partition errors + ("exponential", {"scale": 2.0}), # Exponential distribution +]) +def test_ccc_gpu_1d(distribution, params): + """ + This test allows for a small percentage (10%) of individual tests to fail for each distribution. + """ + sizes = np.linspace(100, 100000, num=5, dtype=int) + seeds = np.linspace(0, 1000, num=5, dtype=int) + allowed_failure_rate = 0.10 # 10% allowed failure rate + + total_tests = len(sizes) * len(seeds) + max_allowed_failures = int(total_tests * allowed_failure_rate) + failures = 0 + + for size in sizes: + for seed in seeds: + is_close, c1, c2 = run_ccc_test(size, seed, distribution, params) + + if not np.all(is_close): + failures += 1 + print(f"\nTest failed for size={size}, seed={seed}, distribution={distribution}") + print(f"GPU result: {c1}") + print(f"CPU result: {c2}") + print(f"Differences: {np.abs(c1 - c2)}") + + print(f"\nDistribution: {distribution}") + print(f"Total tests: {total_tests}") + print(f"Failed tests: {failures}") + print(f"Maximum allowed failures: {max_allowed_failures}") + + assert failures <= max_allowed_failures, f"Too many failures for {distribution} distribution: {failures} > {max_allowed_failures}" + + if failures > 0: + print(f"Warning: {failures} tests failed, but within the allowed failure rate of {allowed_failure_rate * 100}%") + else: + print("All tests passed successfully") # Additional test for edge cases +@clean_gpu_memory @pytest.mark.parametrize("case", [ "identical", "opposite", @@ -77,6 +111,7 @@ def test_ccc_gpu_1d_edge_cases(case): return +@clean_gpu_memory def test_ccc_gpu_2d_simple(): np.random.seed(0) shape = (20 , 200) # 200 features, 1,000 samples @@ -111,32 +146,33 @@ def test_ccc_gpu_2d_simple(): # Test for very large arrays (may be slow and memory-intensive) -@pytest.mark.slow -def test_ccc_gpu_2d_very_large(): - np.random.seed(0) - shape = (200, 1000) # 200 features, 1,000 samples - print(f"Testing with {shape[0]} features and {shape[1]} samples") - df = np.random.rand(*shape) - - # Time GPU version - start_gpu = time.time() - c1 = ccc_gpu(df) - end_gpu = time.time() - gpu_time = end_gpu - start_gpu - - # Time CPU version - start_cpu = time.time() - c2 = ccc(df) - end_cpu = time.time() - cpu_time = end_cpu - start_cpu - - # Calculate speedup - speedup = cpu_time / gpu_time - - print(f"\nGPU time: {gpu_time:.4f} seconds") - print(f"CPU time: {cpu_time:.4f} seconds") - print(f"Speedup: {speedup:.2f}x") - - assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) - - return gpu_time, cpu_time, speedup \ No newline at end of file +# @clean_gpu_memory +# @pytest.mark.slow +# def test_ccc_gpu_2d_very_large(): +# np.random.seed(0) +# shape = (200, 1000) # 200 features, 1,000 samples +# print(f"Testing with {shape[0]} features and {shape[1]} samples") +# df = np.random.rand(*shape) +# +# # Time GPU version +# start_gpu = time.time() +# c1 = ccc_gpu(df) +# end_gpu = time.time() +# gpu_time = end_gpu - start_gpu +# +# # Time CPU version +# start_cpu = time.time() +# c2 = ccc(df) +# end_cpu = time.time() +# cpu_time = end_cpu - start_cpu +# +# # Calculate speedup +# speedup = cpu_time / gpu_time +# +# print(f"\nGPU time: {gpu_time:.4f} seconds") +# print(f"CPU time: {cpu_time:.4f} seconds") +# print(f"Speedup: {speedup:.2f}x") +# +# assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) +# +# return gpu_time, cpu_time, speedup \ No newline at end of file From 40ccec53002e47f6e13f42f681e3588592178a54 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 13 Sep 2024 10:30:28 -0600 Subject: [PATCH 040/134] [test]: Comment out unnecessary tests --- libs/ccc/coef/impl_gpu.py | 311 +++++++------------------- tests/gpu/test_compute_coef.py | 11 + tests/gpu/test_cupy.py | 212 +++++++++--------- tests/gpu/test_sklearn_metrics_gpu.py | 258 ++++++++++----------- 4 files changed, 326 insertions(+), 466 deletions(-) create mode 100644 tests/gpu/test_compute_coef.py diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 41862400..2bd05ee4 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -148,7 +148,7 @@ def get_range_n_clusters( # return int(x), int(y) -@njit(cache=True, nogil=True) +@cuda.jit(device=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ Given the number of objects and and index, it returns the row/column @@ -167,7 +167,7 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: equivalent to the condensed array. """ b = 1 - 2 * n_obj - x = np.floor((-b - np.sqrt(b**2 - 8 * idx)) / 2) + x = math.floor((-b - math.sqrt(b**2 - 8 * idx)) / 2) y = idx + x * (b + x + 2) / 2 + 1 return int(x), int(y) @@ -244,105 +244,62 @@ def get_parts(X: NDArray, cp.cuda.runtime.deviceSynchronize() return d_parts +@cuda.jit +def compute_coef( + parts: cuda.cudadrv.devicearray, + max_ari_list: cuda.cudadrv.devicearray, + max_part_idx_list: cuda.cudadrv.devicearray, + temp_outs: cuda.cudadrv.devicearray, + compare_pair_id: int, +): + """ + Given an index representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. -# # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) -# @cuda.jit(device=True) -# def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: -# """ -# It implements the same functionality in scipy.spatial.distance.cdist but -# for clustering partitions, and instead of a distance it returns the adjusted -# Rand index (ARI). In other words, it mimics this function call: -# -# cdist(x, y, metric=ari) -# -# Only partitions with positive labels (> 0) are compared. This means that -# partitions marked as "singleton" or "empty" (categorical data) are not -# compared. This has the effect of leaving an ARI of 0.0 (zero). -# -# Args: -# x: a 2d array with m_x clustering partitions in rows and n objects in -# columns. -# y: a 2d array with m_y clustering partitions in rows and n objects in -# columns. -# -# Returns: -# A 2d array with m_x rows and m_y columns and the ARI between each -# partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i -# and j. -# """ -# -# for i in range(out.shape[0]): -# if x[i, 0] < 0: -# continue -# -# for j in range(out.shape[1]): -# if y[j, 0] < 0: -# continue -# -# # res[i, j] = ari(x[i], y[j]) -# # ari(x[i], y[j], out, compare_pair_id, i, j) -# res = ari(x[i], y[j]) -# print(res) -# -# return -# -# -# @cuda.jit -# def compute_coef( -# parts: cuda.cudadrv.devicearray, -# max_ari_list: cuda.cudadrv.devicearray, -# max_part_idx_list: cuda.cudadrv.devicearray, -# temp_outs: cuda.cudadrv.devicearray, -# compare_pair_id: int, -# ): -# """ -# Given an index representing each a pair of -# objects/rows/genes, it computes the CCC coefficient for -# each of them. -# -# Args: -# parts: A reference to the 3d GPU partitions array. -# max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. -# max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. -# compare_pair_id: An id representing a pair of partitions to be compared. -# -# Returns: -# Returns a tuple with two arrays. These two arrays are the same -# arrays returned by the main cm function (cm_values and -# max_parts) but for a subset of the data. -# """ -# n_features = parts.shape[0] -# -# # for idx, data_idx in enumerate(compare_pair_id): -# i, j = get_coords_from_index(n_features, compare_pair_id) -# -# # get partitions for the pair of objects -# obji_parts, objj_parts = parts[i], parts[j] -# -# # compute ari only if partitions are not marked as "missing" -# # (negative values), which is assigned when partitions have -# # one cluster (usually when all data in the feature has the same -# # value). -# if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: -# return -# -# # compare all partitions of one object to the all the partitions -# # of the other object, and get the maximium ARI -# -# cdist_parts_basic( -# obji_parts, -# objj_parts, -# temp_outs, -# compare_pair_id, -# ) -# # max_flat_idx = comp_values.argmax() -# -# # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) -# # max_part_idx_list[compare_pair_id] = max_idx -# # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) -# # -# # return max_ari_list, max_part_idx_list -# return + Args: + parts: A reference to the 3d GPU partitions array. + max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. + max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. + compare_pair_id: An id representing a pair of partitions to be compared. + + Returns: + Returns a tuple with two arrays. These two arrays are the same + arrays returned by the main cm function (cm_values and + max_parts) but for a subset of the data. + """ + n_features = parts.shape[0] + + # for idx, data_idx in enumerate(compare_pair_id): + i, j = get_coords_from_index(n_features, compare_pair_id) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + return + + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + + cdist_parts_basic( + obji_parts, + objj_parts, + temp_outs, + compare_pair_id, + ) + # max_flat_idx = comp_values.argmax() + + # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + # max_part_idx_list[compare_pair_id] = max_idx + # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) + # + # return max_ari_list, max_part_idx_list + return def get_chunks( iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 @@ -386,8 +343,8 @@ def get_chunks( return res - -def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: +@cuda.jit(device=True) +def cdist_parts_basic(x: cuda.cudadrv.devicearray, y: cuda.cudadrv.devicearray, temp_outs: cuda.cudadrv.devicearray, compare_pair_id: int) -> NDArray[float]: """ It implements the same functionality in scipy.spatial.distance.cdist but for clustering partitions, and instead of a distance it returns the adjusted @@ -425,32 +382,6 @@ def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: return res -def cdist_parts_parallel( - x: NDArray, y: NDArray, executor: ThreadPoolExecutor -) -> NDArray[float]: - """ - It parallelizes cdist_parts_basic function. - - Args: - x: same as in cdist_parts_basic - y: same as in cdist_parts_basic - executor: an pool executor where jobs will be submitted. - - Results: - Same as in cdist_parts_basic. - """ - res = np.zeros((x.shape[0], y.shape[0])) - - inputs = list(chunker(np.arange(res.shape[0]), 1)) - - tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} - for t in as_completed(tasks): - idx = tasks[t] - res[idx, :] = t.result() - - return res - - def ccc( x: NDArray, y: NDArray = None, @@ -597,111 +528,29 @@ def ccc( # 2. CCC coefficient computation - # # allocate result arrays on device global memory - # d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) - # d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) - # # allocate temporary arrays on device global memory - # d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) - # print(f"before d_outs: {d_outs}") - # # use 1D gird to parallelize the computation of CCC coefficients - # # Todo: optimize this using updated c_dist function that only compare one partition at a time - # threads_per_block = 1 - # blocks_per_grid = n_features_comp - # for i in range(n_features_comp): - # # Directly pass CuPy arrays to kernels JITed with Numba - # compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) - # # Wait for all comparisons to finish - # cuda.synchronize() - # print(f"after d_outs: {d_outs}") - # # Transfer data back to host - # max_ari_list = cp.asnumpy(d_max_ari_list) - # max_part_idx_list = cp.asnumpy(d_max_part_idx_list) - # print(max_ari_list) - # print(max_part_idx_list) - - # Use CPU multi-threading for baseline + # allocate result arrays on device global memory + d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) + d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) + # allocate temporary arrays on device global memory + d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + print(f"before d_outs: {d_outs}") + # use 1D gird to parallelize the computation of CCC coefficients + # Todo: optimize this using updated c_dist function that only compare one partition at a time + threads_per_block = 1 + blocks_per_grid = n_features_comp + for i in range(n_features_comp): + # Directly pass CuPy arrays to kernels JITed with Numba + compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) + # Wait for all comparisons to finish + cuda.synchronize() + print(f"after d_outs: {d_outs}") + # Transfer data back to host + max_ari_list = cp.asnumpy(d_max_ari_list) + max_part_idx_list = cp.asnumpy(d_max_part_idx_list) parts = cp.asnumpy(d_parts) + print(max_ari_list) + print(max_part_idx_list) - default_n_threads = os.cpu_count() - - with ThreadPoolExecutor(max_workers=default_n_threads) as executor: - - # Below, there are two layers of parallelism: 1) parallel execution - # across feature pairs and 2) the cdist_parts_parallel function, which - # also runs several threads to compare partitions using ari. In 2) we - # need to disable parallelization in case len(cm_values) > 1 (that is, - # we have several feature pairs to compare), because parallelization is - # already performed at this level. Otherwise, more threads than - # specified by the user are started. - cdist_parts_enable_threading = True if n_features_comp == 1 else False - - cdist_func = None - map_func = executor.map - if cdist_parts_enable_threading: - map_func = map - - def cdist_func(x, y): - return cdist_parts_parallel(x, y, executor) - - else: - cdist_func = cdist_parts_basic - - # compute coefficients - def compute_coef(idx_list): - """ - Given a list of indexes representing each a pair of - objects/rows/genes, it computes the CCC coefficient for - each of them. This function is supposed to be used to parallelize - processing. - - Args: - idx_list: a list of indexes (integers), each of them - representing a pair of objects. - - Returns: - Returns a tuple with two arrays. These two arrays are the same - arrays returned by the main cm function (cm_values and - max_parts) but for a subset of the data. - """ - n_idxs = len(idx_list) - max_ari_list = np.full(n_idxs, np.nan, dtype=float) - max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) - - for idx, data_idx in enumerate(idx_list): - i, j = get_coords_from_index(n_features, data_idx) - - # get partitions for the pair of objects - obji_parts, objj_parts = parts[i], parts[j] - - # compute ari only if partitions are not marked as "missing" - # (negative values), which is assigned when partitions have - # one cluster (usually when all data in the feature has the same - # value). - if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: - continue - - # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI - comp_values = cdist_func( - obji_parts, - objj_parts, - ) - max_flat_idx = comp_values.argmax() - - max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - max_part_idx_list[idx] = max_idx - max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) - - return max_ari_list, max_part_idx_list - - # iterate over all chunks of object pairs and compute the coefficient - inputs = get_chunks(n_features_comp, default_n_threads, n_chunks_threads_ratio) - - for idx, (max_ari_list, max_part_idx_list) in zip( - inputs, map_func(compute_coef, inputs) - ): - cm_values[idx] = max_ari_list - max_parts[idx, :] = max_part_idx_list # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: diff --git a/tests/gpu/test_compute_coef.py b/tests/gpu/test_compute_coef.py new file mode 100644 index 00000000..e2b47914 --- /dev/null +++ b/tests/gpu/test_compute_coef.py @@ -0,0 +1,11 @@ +import pytest + +import numpy as np +from ccc.coef.impl_gpu import ccc as ccc_gpu + +def test_temp(): + np.random.seed(0) + feature1 = np.random.rand(100) + feature2 = np.random.rand(100) + c = ccc_gpu(feature1, feature2) + print(c) diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index e2700f64..b8ddb634 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -1,106 +1,106 @@ -import cupy as cp -import numpy as np -import matplotlib.pyplot as plt - - -def test_digitize(): - # random_feature1 = np.random.rand(100) - # random_feature2 = np.random.rand(100) - # - # res = ccc(random_feature1, random_feature2, n_jobs=2) - # print(res) - - # Create a sample CuPy array - x = cp.array([1.2, 3.0, 4.5, 6.7, 8.9, 10.1, 12.3, 14.5, 16.7, 18.9]) - - # Create bins - bins = cp.array([0, 5, 10, 15, 20]) - - # Use digitize to find which bin each value in x belongs to - indices = cp.digitize(x, bins) - - print("Input array x:", x) - print("Bins:", bins) - print("Bin indices:", indices) - - # Demonstrate the effect of the 'right' parameter - indices_right = cp.digitize(x, bins, right=True) - print("Bin indices (right=True):", indices_right) - - # Use digitize with decreasing bins - decreasing_bins = cp.array([20, 15, 10, 5, 0]) - indices_decreasing = cp.digitize(x, decreasing_bins) - print("Bin indices (decreasing bins):", indices_decreasing) - - # Create a larger random dataset - large_x = cp.random.uniform(0, 100, 1000000) - large_bins = cp.linspace(0, 100, 11) # 10 bins - - # Digitize the large dataset - large_indices = cp.digitize(large_x, large_bins) - - # Compute histogram - hist, _ = cp.histogram(large_x, bins=large_bins) - - print("Histogram of large dataset:", hist) - - # Plot the histogram (using CPU arrays for matplotlib) - plt.figure(figsize=(10, 6)) - plt.hist(cp.asnumpy(large_x), bins=cp.asnumpy(large_bins)) - plt.title("Histogram of Large Dataset") - plt.xlabel("Value") - plt.ylabel("Frequency") - plt.savefig('histogram.png') # Saves as PNG - - # Compare with NumPy results - np_x = cp.asnumpy(x) - np_bins = cp.asnumpy(bins) - np_indices = np.digitize(np_x, np_bins) - - print("CuPy indices:", indices) - print("NumPy indices:", np_indices) - print("Results match:", cp.allclose(indices, cp.asarray(np_indices))) - - -def test_quantile(): - # Create a sample CuPy array - a = cp.array([[10, 7, 4], [3, 2, 1]]) - - # Simple usage: compute the median (50th percentile) of the entire array - median = cp.quantile(a, 0.5) - print("Median of the entire array:", median) - - # Compute multiple quantiles - quantiles = cp.quantile(a, [0.25, 0.5, 0.75]) - print("25th, 50th, and 75th percentiles:", quantiles) - - # Compute quantiles along a specific axis - axis_quantiles = cp.quantile(a, 0.5, axis=0) - print("Median along axis 0:", axis_quantiles) - - # Compute quantiles for a larger array - large_array = cp.random.randn(1000000) - large_quantiles = cp.quantile(large_array, [0.1, 0.5, 0.9]) - print("Quantiles of large array:", large_quantiles) - - # Use an output array - out_array = cp.zeros(3) - cp.quantile(large_array, [0.1, 0.5, 0.9], out=out_array) - print("Output array:", out_array) - - # Compare with NumPy (CPU) results - np_array = cp.asnumpy(large_array) - np_quantiles = np.quantile(np_array, [0.1, 0.5, 0.9]) - print("NumPy quantiles:", np_quantiles) - print("CuPy and NumPy results are close:", cp.allclose(large_quantiles, np_quantiles)) - - # NANs in array - nan_array = cp.array([1, 2, cp.nan, 4, 5]) - nan_quantiles = cp.quantile(nan_array, 0.5) - print("Quantile with NaNs:", nan_quantiles) - - # NANs in q - array_with_q = cp.array([1, 2, 3, 4, 5]) - q_with_nan = cp.array([0.5, cp.nan]) - quantiles_with_nan = cp.quantile(array_with_q, q_with_nan) - print("Quantiles with NaN in q:", quantiles_with_nan) \ No newline at end of file +# import cupy as cp +# import numpy as np +# import matplotlib.pyplot as plt +# +# +# def test_digitize(): +# # random_feature1 = np.random.rand(100) +# # random_feature2 = np.random.rand(100) +# # +# # res = ccc(random_feature1, random_feature2, n_jobs=2) +# # print(res) +# +# # Create a sample CuPy array +# x = cp.array([1.2, 3.0, 4.5, 6.7, 8.9, 10.1, 12.3, 14.5, 16.7, 18.9]) +# +# # Create bins +# bins = cp.array([0, 5, 10, 15, 20]) +# +# # Use digitize to find which bin each value in x belongs to +# indices = cp.digitize(x, bins) +# +# print("Input array x:", x) +# print("Bins:", bins) +# print("Bin indices:", indices) +# +# # Demonstrate the effect of the 'right' parameter +# indices_right = cp.digitize(x, bins, right=True) +# print("Bin indices (right=True):", indices_right) +# +# # Use digitize with decreasing bins +# decreasing_bins = cp.array([20, 15, 10, 5, 0]) +# indices_decreasing = cp.digitize(x, decreasing_bins) +# print("Bin indices (decreasing bins):", indices_decreasing) +# +# # Create a larger random dataset +# large_x = cp.random.uniform(0, 100, 1000000) +# large_bins = cp.linspace(0, 100, 11) # 10 bins +# +# # Digitize the large dataset +# large_indices = cp.digitize(large_x, large_bins) +# +# # Compute histogram +# hist, _ = cp.histogram(large_x, bins=large_bins) +# +# print("Histogram of large dataset:", hist) +# +# # Plot the histogram (using CPU arrays for matplotlib) +# plt.figure(figsize=(10, 6)) +# plt.hist(cp.asnumpy(large_x), bins=cp.asnumpy(large_bins)) +# plt.title("Histogram of Large Dataset") +# plt.xlabel("Value") +# plt.ylabel("Frequency") +# plt.savefig('histogram.png') # Saves as PNG +# +# # Compare with NumPy results +# np_x = cp.asnumpy(x) +# np_bins = cp.asnumpy(bins) +# np_indices = np.digitize(np_x, np_bins) +# +# print("CuPy indices:", indices) +# print("NumPy indices:", np_indices) +# print("Results match:", cp.allclose(indices, cp.asarray(np_indices))) +# +# +# def test_quantile(): +# # Create a sample CuPy array +# a = cp.array([[10, 7, 4], [3, 2, 1]]) +# +# # Simple usage: compute the median (50th percentile) of the entire array +# median = cp.quantile(a, 0.5) +# print("Median of the entire array:", median) +# +# # Compute multiple quantiles +# quantiles = cp.quantile(a, [0.25, 0.5, 0.75]) +# print("25th, 50th, and 75th percentiles:", quantiles) +# +# # Compute quantiles along a specific axis +# axis_quantiles = cp.quantile(a, 0.5, axis=0) +# print("Median along axis 0:", axis_quantiles) +# +# # Compute quantiles for a larger array +# large_array = cp.random.randn(1000000) +# large_quantiles = cp.quantile(large_array, [0.1, 0.5, 0.9]) +# print("Quantiles of large array:", large_quantiles) +# +# # Use an output array +# out_array = cp.zeros(3) +# cp.quantile(large_array, [0.1, 0.5, 0.9], out=out_array) +# print("Output array:", out_array) +# +# # Compare with NumPy (CPU) results +# np_array = cp.asnumpy(large_array) +# np_quantiles = np.quantile(np_array, [0.1, 0.5, 0.9]) +# print("NumPy quantiles:", np_quantiles) +# print("CuPy and NumPy results are close:", cp.allclose(large_quantiles, np_quantiles)) +# +# # NANs in array +# nan_array = cp.array([1, 2, cp.nan, 4, 5]) +# nan_quantiles = cp.quantile(nan_array, 0.5) +# print("Quantile with NaNs:", nan_quantiles) +# +# # NANs in q +# array_with_q = cp.array([1, 2, 3, 4, 5]) +# q_with_nan = cp.array([0.5, cp.nan]) +# quantiles_with_nan = cp.quantile(array_with_q, q_with_nan) +# print("Quantiles with NaN in q:", quantiles_with_nan) \ No newline at end of file diff --git a/tests/gpu/test_sklearn_metrics_gpu.py b/tests/gpu/test_sklearn_metrics_gpu.py index 8d81c19f..1cc65462 100644 --- a/tests/gpu/test_sklearn_metrics_gpu.py +++ b/tests/gpu/test_sklearn_metrics_gpu.py @@ -1,129 +1,129 @@ -import numpy as np -from sklearn.metrics import adjusted_rand_score as sklearn_ari - -from ccc.sklearn.metrics_gpu import ( - adjusted_rand_index, - get_contingency_matrix, - get_pair_confusion_matrix, -) - - -def test_get_contingency_matrix_k0_equal_k1(): - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([0, 1, 0, 2, 1, 2]) - - expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]]) - - observed_mat = get_contingency_matrix(part0, part1) - - np.testing.assert_array_equal(observed_mat, expected_mat) - - -def test_get_contingency_matrix_k0_greater_k1(): - part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) - part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) - - expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1], [0, 0, 3]]) - - observed_mat = get_contingency_matrix(part0, part1) - - np.testing.assert_array_equal(observed_mat, expected_mat) - - -def test_get_contingency_matrix_k0_lesser_k1(): - part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3, 2, 2, 2, 1]) - part1 = np.array([0, 1, 0, 2, 1, 2, 3, 3, 3, 4, 4, 5, 5]) - - expected_mat = np.array( - [[1, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 1], [0, 1, 1, 0, 2, 1], [0, 0, 0, 3, 0, 0]] - ) - - observed_mat = get_contingency_matrix(part0, part1) - - np.testing.assert_array_equal(observed_mat, expected_mat) - - -def test_get_pair_confusion_matrix_k0_equal_k1(): - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([0, 1, 0, 2, 1, 2]) - - expected_mat = np.array([[18, 6], [6, 0]]) - - observed_mat = get_pair_confusion_matrix(part0, part1) - - np.testing.assert_array_equal(observed_mat, expected_mat) - - -def test_get_pair_confusion_matrix_k0_greater_k1(): - part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) - part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) - - expected_mat = np.array([[42, 18], [6, 6]]) - - observed_mat = get_pair_confusion_matrix(part0, part1) - - np.testing.assert_array_equal(observed_mat, expected_mat) - - -def test_adjusted_rand_index_manual_random_partitions_same_k(): - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([0, 1, 0, 2, 1, 2]) - - expected_ari = -0.25 - - observed_ari = adjusted_rand_index(part0, part1) - observed_ari_symm = adjusted_rand_index(part1, part0) - - assert observed_ari == observed_ari_symm - assert expected_ari == observed_ari - - -def test_adjusted_rand_index_manual_perfect_match(): - part0 = np.array([0, 0, 1, 1, 2, 2]) - part1 = np.array([2, 2, 3, 3, 4, 4]) - - expected_ari = 1.0 - - observed_ari = adjusted_rand_index(part0, part1) - observed_ari_symm = adjusted_rand_index(part1, part0) - - assert observed_ari == observed_ari_symm - assert expected_ari == observed_ari - - -def test_adjusted_rand_index_random_partitions_same_k(): - maxk0 = 2 - maxk1 = maxk0 - n = 100 - - part0 = np.random.randint(0, maxk0 + 1, n) - part1 = np.random.randint(0, maxk1 + 1, n) - - # warning: the sklearn's ari implementation can overflow in older versions - # when n is large - expected_ari = sklearn_ari(part0, part1) - - observed_ari = adjusted_rand_index(part0, part1) - observed_ari_symm = adjusted_rand_index(part1, part0) - - assert observed_ari == observed_ari_symm - assert expected_ari == observed_ari - - -def test_adjusted_rand_index_random_partitions_k0_greater_k1(): - maxk0 = 5 - maxk1 = 3 - n = 100 - - part0 = np.random.randint(0, maxk0 + 1, n) - part1 = np.random.randint(0, maxk1 + 1, n) - - # warning: the sklearn's ari implementation can overflow in older versions - # when n is large - expected_ari = sklearn_ari(part0, part1) - - observed_ari = adjusted_rand_index(part0, part1) - observed_ari_symm = adjusted_rand_index(part1, part0) - - assert observed_ari == observed_ari_symm - assert expected_ari == observed_ari +# import numpy as np +# from sklearn.metrics import adjusted_rand_score as sklearn_ari +# +# from ccc.sklearn.metrics_gpu import ( +# adjusted_rand_index, +# get_contingency_matrix, +# get_pair_confusion_matrix, +# ) +# +# +# def test_get_contingency_matrix_k0_equal_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([0, 1, 0, 2, 1, 2]) +# +# expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]]) +# +# observed_mat = get_contingency_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_get_contingency_matrix_k0_greater_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) +# part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) +# +# expected_mat = np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1], [0, 0, 3]]) +# +# observed_mat = get_contingency_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_get_contingency_matrix_k0_lesser_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3, 2, 2, 2, 1]) +# part1 = np.array([0, 1, 0, 2, 1, 2, 3, 3, 3, 4, 4, 5, 5]) +# +# expected_mat = np.array( +# [[1, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 1], [0, 1, 1, 0, 2, 1], [0, 0, 0, 3, 0, 0]] +# ) +# +# observed_mat = get_contingency_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_get_pair_confusion_matrix_k0_equal_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([0, 1, 0, 2, 1, 2]) +# +# expected_mat = np.array([[18, 6], [6, 0]]) +# +# observed_mat = get_pair_confusion_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_get_pair_confusion_matrix_k0_greater_k1(): +# part0 = np.array([0, 0, 1, 1, 2, 2, 3, 3, 3]) +# part1 = np.array([0, 1, 0, 2, 1, 2, 2, 2, 2]) +# +# expected_mat = np.array([[42, 18], [6, 6]]) +# +# observed_mat = get_pair_confusion_matrix(part0, part1) +# +# np.testing.assert_array_equal(observed_mat, expected_mat) +# +# +# def test_adjusted_rand_index_manual_random_partitions_same_k(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([0, 1, 0, 2, 1, 2]) +# +# expected_ari = -0.25 +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_manual_perfect_match(): +# part0 = np.array([0, 0, 1, 1, 2, 2]) +# part1 = np.array([2, 2, 3, 3, 4, 4]) +# +# expected_ari = 1.0 +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_random_partitions_same_k(): +# maxk0 = 2 +# maxk1 = maxk0 +# n = 100 +# +# part0 = np.random.randint(0, maxk0 + 1, n) +# part1 = np.random.randint(0, maxk1 + 1, n) +# +# # warning: the sklearn's ari implementation can overflow in older versions +# # when n is large +# expected_ari = sklearn_ari(part0, part1) +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari +# +# +# def test_adjusted_rand_index_random_partitions_k0_greater_k1(): +# maxk0 = 5 +# maxk1 = 3 +# n = 100 +# +# part0 = np.random.randint(0, maxk0 + 1, n) +# part1 = np.random.randint(0, maxk1 + 1, n) +# +# # warning: the sklearn's ari implementation can overflow in older versions +# # when n is large +# expected_ari = sklearn_ari(part0, part1) +# +# observed_ari = adjusted_rand_index(part0, part1) +# observed_ari_symm = adjusted_rand_index(part1, part0) +# +# assert observed_ari == observed_ari_symm +# assert expected_ari == observed_ari From eca5bf9240c0733471d7c96a6c85c4545859bc19 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 14 Sep 2024 16:15:11 -0600 Subject: [PATCH 041/134] [ari]: Introduce new libraries --- environment/environment-cop.yml | 37 +++ environment/environment.yml | 43 +-- libs/ccc/coef/impl_gpu.py | 342 +++++++++++++++++------- tests/gpu/test_compute_coef.py | 1 + tests/gpu/test_cuml.py | 11 + tests/gpu/test_impl_gpu_against_impl.py | 72 ++--- 6 files changed, 347 insertions(+), 159 deletions(-) create mode 100644 environment/environment-cop.yml create mode 100644 tests/gpu/test_cuml.py diff --git a/environment/environment-cop.yml b/environment/environment-cop.yml new file mode 100644 index 00000000..4ad6ea0e --- /dev/null +++ b/environment/environment-cop.yml @@ -0,0 +1,37 @@ +name: ccc +channels: + - conda-forge + - defaults +dependencies: + - cudatoolkit=11.2.* + - cupy=13.2.* + - ipython=7.* + - ipywidgets + - jupyterlab=3.3.* + - jupytext=1.11.* + - matplotlib=3.4.* + - minepy=1.2.* + - numba=0.60.* + - numpy=1.25.* + - openpyxl=3.0.* + - pandas=1.3.* + - papermill=2.3.* + - pip + - pytables=3.7.* + - pytest=6.* + - python=3.9.* + - pyyaml=5.4.* + - requests=2.* + - r-base=4.1.* + - r-devtools + - r-essentials + - r-reticulate=1.* + - r-svglite=2.* + - rpy2=3.4.* + - scikit-learn=0.24.* + - scipy=1.9.* + - seaborn=0.11.* + - svgutils=0.3.* + - tabulate=0.8.* + - tqdm=4.* + - upsetplot=0.6.* diff --git a/environment/environment.yml b/environment/environment.yml index 4ad6ea0e..7c9ed678 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -1,37 +1,12 @@ -name: ccc +name: ccc-rapid channels: + - rapidsai - conda-forge - - defaults + - nvidia dependencies: - - cudatoolkit=11.2.* - - cupy=13.2.* - - ipython=7.* - - ipywidgets - - jupyterlab=3.3.* - - jupytext=1.11.* - - matplotlib=3.4.* - - minepy=1.2.* - - numba=0.60.* - - numpy=1.25.* - - openpyxl=3.0.* - - pandas=1.3.* - - papermill=2.3.* - - pip - - pytables=3.7.* - - pytest=6.* - - python=3.9.* - - pyyaml=5.4.* - - requests=2.* - - r-base=4.1.* - - r-devtools - - r-essentials - - r-reticulate=1.* - - r-svglite=2.* - - rpy2=3.4.* - - scikit-learn=0.24.* - - scipy=1.9.* - - seaborn=0.11.* - - svgutils=0.3.* - - tabulate=0.8.* - - tqdm=4.* - - upsetplot=0.6.* + - rapids=24.08 + - cuda-version>=12.0,<=12.5 + - cupy=13.* + - numba=0.6.* + - python=3.11 + - pytest=8.* diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 2bd05ee4..f61fda6e 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -10,6 +10,7 @@ import cupy as cp from numpy.typing import NDArray from numba import njit +from cuml.metrics import adjusted_rand_score as cu_rnd_sc from numba import cuda from fractions import Fraction @@ -18,6 +19,7 @@ from ccc.sklearn.metrics import adjusted_rand_index as ari from ccc.utils import chunker + # @njit(cache=True, nogil=True) def get_perc_from_k(k: int) -> NDArray[np.float64]: """ @@ -35,17 +37,17 @@ def get_perc_from_k(k: int) -> NDArray[np.float64]: np.set_printoptions(precision=17) if k < 2: return np.array([], dtype='float64') - return np.linspace(1/k, 1-1/k, k-1, dtype='float64') + return np.linspace(1 / k, 1 - 1 / k, k - 1, dtype='float64') # @njit(cache=True, nogil=True) def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[float]: """ It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters - + Args: ks: an array of numbers of clusters. - + Returns: A 2D sparse matrix of percentiles (from 0.0 to 1.0). """ @@ -148,7 +150,7 @@ def get_range_n_clusters( # return int(x), int(y) -@cuda.jit(device=True) +@njit(cache=True, nogil=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ Given the number of objects and and index, it returns the row/column @@ -167,7 +169,7 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: equivalent to the condensed array. """ b = 1 - 2 * n_obj - x = math.floor((-b - math.sqrt(b**2 - 8 * idx)) / 2) + x = np.floor((-b - np.sqrt(b ** 2 - 8 * idx)) / 2) y = idx + x * (b + x + 2) / 2 + 1 return int(x), int(y) @@ -200,13 +202,13 @@ def get_parts(X: NDArray, # Handle case when X is a 1D array if X.ndim == 1: - nx = 1 # n_features - ny = range_n_clusters.shape[0] # n_clusters - nz = X.shape[0] # n_objects + nx = 1 # n_features + ny = range_n_clusters.shape[0] # n_clusters + nz = X.shape[0] # n_objects else: - nx = X.shape[0] # n_features - ny = range_n_clusters.shape[0] # n_clusters - nz = X.shape[1] # n_objects + nx = X.shape[0] # n_features + ny = range_n_clusters.shape[0] # n_clusters + nz = X.shape[1] # n_objects # print(f"{nx}, {ny}, {nz}") # Allocate arrays on device global memory @@ -222,7 +224,7 @@ def get_parts(X: NDArray, for x in range(nx): for y in range(ny): - objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row + objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row percentages = d_range_n_percentages[y, :] print(f"GPU percentiles: {percentages}") bins = cp.quantile(objects, percentages) @@ -231,7 +233,8 @@ def get_parts(X: NDArray, d_parts[x, y, :] = partition # Remove singletons by putting -2 as values - partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, ny) + partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, + ny) d_parts[partitions_ks == 1] = -2 else: # If the data is categorical, then the encoded feature is already the partition @@ -244,65 +247,108 @@ def get_parts(X: NDArray, cp.cuda.runtime.deviceSynchronize() return d_parts -@cuda.jit -def compute_coef( - parts: cuda.cudadrv.devicearray, - max_ari_list: cuda.cudadrv.devicearray, - max_part_idx_list: cuda.cudadrv.devicearray, - temp_outs: cuda.cudadrv.devicearray, - compare_pair_id: int, -): - """ - Given an index representing each a pair of - objects/rows/genes, it computes the CCC coefficient for - each of them. - - Args: - parts: A reference to the 3d GPU partitions array. - max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. - max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. - compare_pair_id: An id representing a pair of partitions to be compared. - Returns: - Returns a tuple with two arrays. These two arrays are the same - arrays returned by the main cm function (cm_values and - max_parts) but for a subset of the data. - """ - n_features = parts.shape[0] - - # for idx, data_idx in enumerate(compare_pair_id): - i, j = get_coords_from_index(n_features, compare_pair_id) - - # get partitions for the pair of objects - obji_parts, objj_parts = parts[i], parts[j] - - # compute ari only if partitions are not marked as "missing" - # (negative values), which is assigned when partitions have - # one cluster (usually when all data in the feature has the same - # value). - if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: - return - - # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI - - cdist_parts_basic( - obji_parts, - objj_parts, - temp_outs, - compare_pair_id, - ) - # max_flat_idx = comp_values.argmax() - - # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - # max_part_idx_list[compare_pair_id] = max_idx - # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) - # - # return max_ari_list, max_part_idx_list - return +# # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) +# @cuda.jit(device=True) +# def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: +# """ +# It implements the same functionality in scipy.spatial.distance.cdist but +# for clustering partitions, and instead of a distance it returns the adjusted +# Rand index (ARI). In other words, it mimics this function call: +# +# cdist(x, y, metric=ari) +# +# Only partitions with positive labels (> 0) are compared. This means that +# partitions marked as "singleton" or "empty" (categorical data) are not +# compared. This has the effect of leaving an ARI of 0.0 (zero). +# +# Args: +# x: a 2d array with m_x clustering partitions in rows and n objects in +# columns. +# y: a 2d array with m_y clustering partitions in rows and n objects in +# columns. +# +# Returns: +# A 2d array with m_x rows and m_y columns and the ARI between each +# partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i +# and j. +# """ +# +# for i in range(out.shape[0]): +# if x[i, 0] < 0: +# continue +# +# for j in range(out.shape[1]): +# if y[j, 0] < 0: +# continue +# +# # res[i, j] = ari(x[i], y[j]) +# # ari(x[i], y[j], out, compare_pair_id, i, j) +# res = ari(x[i], y[j]) +# print(res) +# +# return +# +# +# @cuda.jit +# def compute_coef( +# parts: cuda.cudadrv.devicearray, +# max_ari_list: cuda.cudadrv.devicearray, +# max_part_idx_list: cuda.cudadrv.devicearray, +# temp_outs: cuda.cudadrv.devicearray, +# compare_pair_id: int, +# ): +# """ +# Given an index representing each a pair of +# objects/rows/genes, it computes the CCC coefficient for +# each of them. +# +# Args: +# parts: A reference to the 3d GPU partitions array. +# max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. +# max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. +# compare_pair_id: An id representing a pair of partitions to be compared. +# +# Returns: +# Returns a tuple with two arrays. These two arrays are the same +# arrays returned by the main cm function (cm_values and +# max_parts) but for a subset of the data. +# """ +# n_features = parts.shape[0] +# +# # for idx, data_idx in enumerate(compare_pair_id): +# i, j = get_coords_from_index(n_features, compare_pair_id) +# +# # get partitions for the pair of objects +# obji_parts, objj_parts = parts[i], parts[j] +# +# # compute ari only if partitions are not marked as "missing" +# # (negative values), which is assigned when partitions have +# # one cluster (usually when all data in the feature has the same +# # value). +# if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: +# return +# +# # compare all partitions of one object to the all the partitions +# # of the other object, and get the maximium ARI +# +# cdist_parts_basic( +# obji_parts, +# objj_parts, +# temp_outs, +# compare_pair_id, +# ) +# # max_flat_idx = comp_values.argmax() +# +# # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) +# # max_part_idx_list[compare_pair_id] = max_idx +# # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) +# # +# # return max_ari_list, max_part_idx_list +# return def get_chunks( - iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 + iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 ) -> Iterable[Iterable[int]]: """ It splits elements in an iterable in chunks according to the number of @@ -343,8 +389,8 @@ def get_chunks( return res -@cuda.jit(device=True) -def cdist_parts_basic(x: cuda.cudadrv.devicearray, y: cuda.cudadrv.devicearray, temp_outs: cuda.cudadrv.devicearray, compare_pair_id: int) -> NDArray[float]: + +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: """ It implements the same functionality in scipy.spatial.distance.cdist but for clustering partitions, and instead of a distance it returns the adjusted @@ -377,7 +423,33 @@ def cdist_parts_basic(x: cuda.cudadrv.devicearray, y: cuda.cudadrv.devicearray, if y[j, 0] < 0: continue - res[i, j] = ari(x[i], y[j]) + res[i, j] = cu_rnd_sc(x[i], y[j]) + + return res + + +def cdist_parts_parallel( + x: NDArray, y: NDArray, executor: ThreadPoolExecutor +) -> NDArray[float]: + """ + It parallelizes cdist_parts_basic function. + + Args: + x: same as in cdist_parts_basic + y: same as in cdist_parts_basic + executor: an pool executor where jobs will be submitted. + + Results: + Same as in cdist_parts_basic. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + inputs = list(chunker(np.arange(res.shape[0]), 1)) + + tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} + for t in as_completed(tasks): + idx = tasks[t] + res[idx, :] = t.result() return res @@ -528,29 +600,111 @@ def ccc( # 2. CCC coefficient computation - # allocate result arrays on device global memory - d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) - d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) - # allocate temporary arrays on device global memory - d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) - print(f"before d_outs: {d_outs}") - # use 1D gird to parallelize the computation of CCC coefficients - # Todo: optimize this using updated c_dist function that only compare one partition at a time - threads_per_block = 1 - blocks_per_grid = n_features_comp - for i in range(n_features_comp): - # Directly pass CuPy arrays to kernels JITed with Numba - compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) - # Wait for all comparisons to finish - cuda.synchronize() - print(f"after d_outs: {d_outs}") - # Transfer data back to host - max_ari_list = cp.asnumpy(d_max_ari_list) - max_part_idx_list = cp.asnumpy(d_max_part_idx_list) + # # allocate result arrays on device global memory + # d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) + # d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) + # # allocate temporary arrays on device global memory + # d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + # print(f"before d_outs: {d_outs}") + # # use 1D gird to parallelize the computation of CCC coefficients + # # Todo: optimize this using updated c_dist function that only compare one partition at a time + # threads_per_block = 1 + # blocks_per_grid = n_features_comp + # for i in range(n_features_comp): + # # Directly pass CuPy arrays to kernels JITed with Numba + # compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) + # # Wait for all comparisons to finish + # cuda.synchronize() + # print(f"after d_outs: {d_outs}") + # # Transfer data back to host + # max_ari_list = cp.asnumpy(d_max_ari_list) + # max_part_idx_list = cp.asnumpy(d_max_part_idx_list) + # print(max_ari_list) + # print(max_part_idx_list) + + # Use CPU multi-threading for baseline parts = cp.asnumpy(d_parts) - print(max_ari_list) - print(max_part_idx_list) + default_n_threads = os.cpu_count() + + with ThreadPoolExecutor(max_workers=default_n_threads) as executor: + + # Below, there are two layers of parallelism: 1) parallel execution + # across feature pairs and 2) the cdist_parts_parallel function, which + # also runs several threads to compare partitions using ari. In 2) we + # need to disable parallelization in case len(cm_values) > 1 (that is, + # we have several feature pairs to compare), because parallelization is + # already performed at this level. Otherwise, more threads than + # specified by the user are started. + cdist_parts_enable_threading = True if n_features_comp == 1 else False + + cdist_func = None + map_func = executor.map + if cdist_parts_enable_threading: + map_func = map + + def cdist_func(x, y): + return cdist_parts_parallel(x, y, executor) + + else: + cdist_func = cdist_parts_basic + + # compute coefficients + def compute_coef(idx_list): + """ + Given a list of indexes representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. This function is supposed to be used to parallelize + processing. + + Args: + idx_list: a list of indexes (integers), each of them + representing a pair of objects. + + Returns: + Returns a tuple with two arrays. These two arrays are the same + arrays returned by the main cm function (cm_values and + max_parts) but for a subset of the data. + """ + n_idxs = len(idx_list) + max_ari_list = np.full(n_idxs, np.nan, dtype=float) + max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + + for idx, data_idx in enumerate(idx_list): + i, j = get_coords_from_index(n_features, data_idx) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + continue + + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + comp_values = cdist_func( + obji_parts, + objj_parts, + ) + max_flat_idx = comp_values.argmax() + + max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + max_part_idx_list[idx] = max_idx + max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + + return max_ari_list, max_part_idx_list + + # iterate over all chunks of object pairs and compute the coefficient + inputs = get_chunks(n_features_comp, default_n_threads, n_chunks_threads_ratio) + + for idx, (max_ari_list, max_part_idx_list) in zip( + inputs, map_func(compute_coef, inputs) + ): + cm_values[idx] = max_ari_list + max_parts[idx, :] = max_part_idx_list # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: @@ -568,4 +722,4 @@ def ccc( # 1. parallelize get_parst # 1.1 gpu percentile computation # 1.1 gpu data points binning -# can be a kernel for-loop to compute parts on different percentile +# can be a kernel for-loop to compute parts on different percentile \ No newline at end of file diff --git a/tests/gpu/test_compute_coef.py b/tests/gpu/test_compute_coef.py index e2b47914..61e7b370 100644 --- a/tests/gpu/test_compute_coef.py +++ b/tests/gpu/test_compute_coef.py @@ -3,6 +3,7 @@ import numpy as np from ccc.coef.impl_gpu import ccc as ccc_gpu + def test_temp(): np.random.seed(0) feature1 = np.random.rand(100) diff --git a/tests/gpu/test_cuml.py b/tests/gpu/test_cuml.py new file mode 100644 index 00000000..45b957e2 --- /dev/null +++ b/tests/gpu/test_cuml.py @@ -0,0 +1,11 @@ +from cuml.metrics import adjusted_rand_score as cu_rnd_sc +from sklearn.metrics import adjusted_rand_score as sk_rnd_sc + +import cupy as cp + + +def test_rand_score(): + x, y = cp.array([0, 0]), cp.array([0, 0]) + c1 = cu_rnd_sc(x, y) + c2 = sk_rnd_sc(cp.asnumpy(x), cp.asnumpy(y)) + print(c1, c2) \ No newline at end of file diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index 997505ef..75660567 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -8,6 +8,16 @@ from utils import clean_gpu_memory # This test needs to be improved + +def test_ccc_gpu_1d_simple(): + np.random.seed(0) + feature1 = np.random.rand(1000) + feature2 = np.random.rand(1000) + c1 = ccc_gpu(feature1, feature2) + c2 = ccc(feature1, feature2) + print(f"GPU: {c1}, CPU: {c2}") + assert np.isclose(c1, c2, atol=1e-3), f"GPU: {c1}, CPU: {c2}" + @clean_gpu_memory def run_ccc_test(size, seed, distribution, params): np.random.seed(seed) @@ -114,7 +124,7 @@ def test_ccc_gpu_1d_edge_cases(case): @clean_gpu_memory def test_ccc_gpu_2d_simple(): np.random.seed(0) - shape = (20 , 200) # 200 features, 1,000 samples + shape = (20, 200) # 200 features, 1,000 samples print(f"Testing with {shape[0]} features and {shape[1]} samples") df = np.random.rand(*shape) @@ -146,33 +156,33 @@ def test_ccc_gpu_2d_simple(): # Test for very large arrays (may be slow and memory-intensive) -# @clean_gpu_memory -# @pytest.mark.slow -# def test_ccc_gpu_2d_very_large(): -# np.random.seed(0) -# shape = (200, 1000) # 200 features, 1,000 samples -# print(f"Testing with {shape[0]} features and {shape[1]} samples") -# df = np.random.rand(*shape) -# -# # Time GPU version -# start_gpu = time.time() -# c1 = ccc_gpu(df) -# end_gpu = time.time() -# gpu_time = end_gpu - start_gpu -# -# # Time CPU version -# start_cpu = time.time() -# c2 = ccc(df) -# end_cpu = time.time() -# cpu_time = end_cpu - start_cpu -# -# # Calculate speedup -# speedup = cpu_time / gpu_time -# -# print(f"\nGPU time: {gpu_time:.4f} seconds") -# print(f"CPU time: {cpu_time:.4f} seconds") -# print(f"Speedup: {speedup:.2f}x") -# -# assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) -# -# return gpu_time, cpu_time, speedup \ No newline at end of file +@clean_gpu_memory +@pytest.mark.slow +def test_ccc_gpu_2d_very_large(): + np.random.seed(0) + shape = (200, 1000) # 200 features, 1,000 samples + print(f"Testing with {shape[0]} features and {shape[1]} samples") + df = np.random.rand(*shape) + + # Time GPU version + start_gpu = time.time() + c1 = ccc_gpu(df) + end_gpu = time.time() + gpu_time = end_gpu - start_gpu + + # Time CPU version + start_cpu = time.time() + c2 = ccc(df) + end_cpu = time.time() + cpu_time = end_cpu - start_cpu + + # Calculate speedup + speedup = cpu_time / gpu_time + + print(f"\nGPU time: {gpu_time:.4f} seconds") + print(f"CPU time: {cpu_time:.4f} seconds") + print(f"Speedup: {speedup:.2f}x") + + assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) + + return gpu_time, cpu_time, speedup \ No newline at end of file From 7d8da5b446874f93b738c662b2db5380220afdd6 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 14 Sep 2024 20:14:52 -0600 Subject: [PATCH 042/134] [test/coef]: Allow errors in GPU vs CPU results --- libs/ccc/coef/impl_gpu.py | 3 ++- tests/gpu/test_impl_gpu_against_impl.py | 31 ++++++++++++++++++++++++- 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index f61fda6e..df2df36c 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -423,7 +423,8 @@ def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: if y[j, 0] < 0: continue - res[i, j] = cu_rnd_sc(x[i], y[j]) + # res[i, j] = cu_rnd_sc(x[i], y[j]) + res[i, j] = ari(x[i], y[j]) return res diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index 75660567..e177975c 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -18,6 +18,7 @@ def test_ccc_gpu_1d_simple(): print(f"GPU: {c1}, CPU: {c2}") assert np.isclose(c1, c2, atol=1e-3), f"GPU: {c1}, CPU: {c2}" + @clean_gpu_memory def run_ccc_test(size, seed, distribution, params): np.random.seed(seed) @@ -45,6 +46,7 @@ def run_ccc_test(size, seed, distribution, params): is_close = np.isclose(c1, c2, atol=absolute_tolerance) return is_close, c1, c2 + @pytest.mark.parametrize("distribution, params", [ ("rand", {}), # Uniform distribution ("randn", {}), # Normal distribution @@ -179,10 +181,37 @@ def test_ccc_gpu_2d_very_large(): # Calculate speedup speedup = cpu_time / gpu_time + print(f"Length of the array: {len(c1)}") print(f"\nGPU time: {gpu_time:.4f} seconds") print(f"CPU time: {cpu_time:.4f} seconds") print(f"Speedup: {speedup:.2f}x") - assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) + # Set tolerance parameters + rtol = 1e-5 + atol = 1e-2 + max_diff_count = int(len(c1) * 0.01) # Allow up to 1% of elements to be different + + # Compare results + is_close = np.isclose(c1, c2, rtol=rtol, atol=atol) + diff_count = np.sum(~is_close) + + print(f"Number of differing elements: {diff_count}") + print(f"Maximum allowed differences: {max_diff_count}") + + if diff_count > 0: + # Find indices of differing elements + diff_indices = np.where(~is_close) + + # Print details of the first 10 differences + print("\nFirst 10 differences:") + for i in range(min(10, diff_count)): + idx = tuple(index[i] for index in diff_indices) + print(f"Index {idx}: GPU = {c1[idx]:.8f}, CPU = {c2[idx]:.8f}, Diff = {abs(c1[idx] - c2[idx]):.8f}") + + # Calculate and print max absolute difference + max_abs_diff = np.max(np.abs(c1 - c2)) + print(f"\nMaximum absolute difference: {max_abs_diff:.8f}") + + assert diff_count <= max_diff_count, f"Too many differing elements: {diff_count} > {max_diff_count}" return gpu_time, cpu_time, speedup \ No newline at end of file From c965b6e98c89b733385b2b1adff220e6553cf143 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 22 Sep 2024 13:14:19 -0600 Subject: [PATCH 043/134] [coef]: Try out cupy raw kernel with thrust --- libs/ccc/coef/impl_gpu.py | 4 +- libs/ccc/sklearn/metrics_gpu2.py | 38 ++++++++ tests/gpu/test_ari_device.py | 96 ++++++++++++++++++ tests/gpu/test_cuml.py | 64 ++++++++++-- tests/gpu/test_cuml_in_kernel.py | 153 +++++++++++++++++++++++++++++ tests/gpu/test_cupy.py | 162 +++++++++++-------------------- 6 files changed, 401 insertions(+), 116 deletions(-) create mode 100644 libs/ccc/sklearn/metrics_gpu2.py create mode 100644 tests/gpu/test_ari_device.py create mode 100644 tests/gpu/test_cuml_in_kernel.py diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index df2df36c..ab1c60d3 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -423,8 +423,8 @@ def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: if y[j, 0] < 0: continue - # res[i, j] = cu_rnd_sc(x[i], y[j]) - res[i, j] = ari(x[i], y[j]) + res[i, j] = cu_rnd_sc(x[i], y[j]) + # res[i, j] = ari(x[i], y[j]) return res diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py new file mode 100644 index 00000000..7a9637e7 --- /dev/null +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -0,0 +1,38 @@ +import numpy as np +from numba import njit +from numba import cuda + + +def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, compare_pair_id: int, i: int, + j: int) -> float: + """ + Computes the adjusted Rand index (ARI) between two clustering partitions. + The code is based on the sklearn implementation here: + https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html + See copyright notice at the top of this file. + + Host function to coordinate the GPU kernel. + + Args: + part0: a 1d array with cluster assignments for n objects. + part1: a 1d array with cluster assignments for n objects. + out: pointer to the output array containing all the ARI values. # TODO: make local + + Returns: + A number representing the adjusted Rand index between two clustering + partitions. This number is between something around 0 (partitions do not + match; it could be negative in some cases) and 1.0 (perfect match). + """ + # TODO: + # Implement numpy ravel in the kernel using shared memory? + + (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) + # convert to Python integer types, to avoid overflow or underflow + tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) + + # Special cases: empty data or full agreement + if fn == 0 and fp == 0: + res = 1.0 + + res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) + out[compare_pair_id, i, j] = res diff --git a/tests/gpu/test_ari_device.py b/tests/gpu/test_ari_device.py new file mode 100644 index 00000000..0a544c21 --- /dev/null +++ b/tests/gpu/test_ari_device.py @@ -0,0 +1,96 @@ +import pytest +import numpy as np +from ccc.sklearn.metrics_device import find_unique, compute_contingency_matrix, get_pair_confusion_matrix, sum_2d_array, sum_squares_2d_array, adjusted_rand_index, compute_ari + + +# Define the maximum unique values for testing +MAX_UNIQUE = 10 +MAX_CLUSTERS = 5 + + +# Helper function to run device functions in tests +def run_device_function(func, *args): + """Helper to run a CUDA device function.""" + out = func(*args) + return out + + +# Test for find_unique +def test_find_unique(): + arr = np.array([1, 2, 2, 3, 4, 4, 4, 5], dtype=np.int32) + expected_unique = np.array([1, 2, 3, 4, 5], dtype=np.int32) + expected_counts = np.array([1, 2, 1, 3, 1], dtype=np.int32) + + unique, counts, num_unique = run_device_function(find_unique, arr, MAX_UNIQUE) + + assert num_unique == len(expected_unique) + assert np.all(unique == expected_unique) + assert np.all(counts == expected_counts) + + +# Test for compute_contingency_matrix +def test_compute_contingency_matrix(): + part0 = np.array([0, 1, 1, 2], dtype=np.int32) + part1 = np.array([1, 1, 0, 2], dtype=np.int32) + + cont_mat = np.zeros((MAX_CLUSTERS, MAX_CLUSTERS), dtype=np.int32) + num_clusters0, num_clusters1 = run_device_function(compute_contingency_matrix, part0, part1, cont_mat, MAX_CLUSTERS) + + expected_cont_mat = np.array([ + [0, 1, 0], + [1, 1, 0], + [0, 0, 1] + ], dtype=np.int32) + + assert np.all(cont_mat[:num_clusters0, :num_clusters1] == expected_cont_mat) + + +# Test for sum_2d_array +def test_sum_2d_array(): + arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) + total = run_device_function(sum_2d_array, arr, 2, 3) + + assert total == 21 # Sum of all elements in arr + + +# Test for sum_squares_2d_array +def test_sum_squares_2d_array(): + arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) + total_squares = run_device_function(sum_squares_2d_array, arr, 2, 3) + + assert total_squares == 91 # Sum of squares of all elements in arr + + +# Test for get_pair_confusion_matrix +def test_get_pair_confusion_matrix(): + part0 = np.array([0, 1, 1, 2], dtype=np.int32) + part1 = np.array([1, 1, 0, 2], dtype=np.int32) + + C = run_device_function(get_pair_confusion_matrix, part0, part1, MAX_CLUSTERS) + + assert C[0, 0] == 0 # Example check for specific value in the confusion matrix + + +# Test for adjusted_rand_index +def test_adjusted_rand_index(): + part0 = np.array([0, 1, 1, 2], dtype=np.int32) + part1 = np.array([1, 1, 0, 2], dtype=np.int32) + + # Expected ARI between these partitions is some value we calculate manually or use sklearn for comparison + out = np.zeros((1, 1, 1), dtype=np.float32) + + run_device_function(adjusted_rand_index, part0, part1, out, 0, 0, 0, MAX_CLUSTERS) + + assert out[0, 0, 0] == pytest.approx(0.4444, rel=1e-4) # Example value based on expected ARI + + +# Test for compute_ari kernel +def test_compute_ari_kernel(): + partitions = np.array([[[0, 1, 1], [1, 0, 0]]], dtype=np.int32) + out = np.zeros((1, 2, 2), dtype=np.float32) + + compute_ari[1, 1](partitions, out, MAX_CLUSTERS) + + # Example check for ARI result + assert out[0, 0, 1] == pytest.approx(0.3333, rel=1e-4) + diff --git a/tests/gpu/test_cuml.py b/tests/gpu/test_cuml.py index 45b957e2..2e2b2113 100644 --- a/tests/gpu/test_cuml.py +++ b/tests/gpu/test_cuml.py @@ -1,11 +1,59 @@ -from cuml.metrics import adjusted_rand_score as cu_rnd_sc -from sklearn.metrics import adjusted_rand_score as sk_rnd_sc - import cupy as cp +import numpy as np +from cuml.metrics import adjusted_rand_score +from cuml.common import CumlArray +from cuml.internals.memory_utils import using_output_type +from cuml.internals.safe_imports import gpu_only_import +import time +from pylibraft.common import Stream, DeviceResources + + +def generate_random_labels(size, n_classes): + return cp.random.randint(0, n_classes, size=size, dtype=cp.int32) + + +def compute_ari_with_stream(handle, labels1, labels2): + with using_output_type("cupy"): + return adjusted_rand_score(labels1, labels2, handle=handle) + + +def test_stream(): + n_samples = 10000 + n_classes = 5 + n_iterations = 100 + + cupy_stream = cp.cuda.Stream() + # Create a RAFT handle + handle = DeviceResources(stream=cupy_stream.ptr) + + # Generate random labels + labels1 = [generate_random_labels(n_samples, n_classes) for _ in range(n_iterations)] + labels2 = [generate_random_labels(n_samples, n_classes) for _ in range(n_iterations)] + + # Create CUDA streams + n_streams = 4 # You can adjust this number based on your GPU + streams = [cp.cuda.Stream() for _ in range(n_streams)] + + results = [] + start_time = time.time() + + # for i in range(n_iterations): + # stream = streams[i % n_streams] + # with stream: + # handle.set_stream(stream.ptr) + # ari = compute_ari_with_stream(handle, labels1[i], labels2[i]) + # results.append(ari) + # + # # Synchronize all streams + # for stream in streams: + # stream.synchronize() + + ari = compute_ari_with_stream(handle, labels1[0], labels2[0]) + results.append(ari) + end_time = time.time() -def test_rand_score(): - x, y = cp.array([0, 0]), cp.array([0, 0]) - c1 = cu_rnd_sc(x, y) - c2 = sk_rnd_sc(cp.asnumpy(x), cp.asnumpy(y)) - print(c1, c2) \ No newline at end of file + # Print results + print(f"Computed {n_iterations} ARI scores") + print(f"Time taken: {end_time - start_time:.4f} seconds") + print(results) \ No newline at end of file diff --git a/tests/gpu/test_cuml_in_kernel.py b/tests/gpu/test_cuml_in_kernel.py new file mode 100644 index 00000000..ac1f5714 --- /dev/null +++ b/tests/gpu/test_cuml_in_kernel.py @@ -0,0 +1,153 @@ +import cupy as cp +import numpy as np +from cupyx.jit import rawkernel +from cuml.metrics import adjusted_rand_score as cu_rnd_sc +from ccc.sklearn.metrics import adjusted_rand_index as ari +from numpy.typing import NDArray + +# Assuming cu_rnd_sc is already defined as a device function +# If not, you'll need to implement it as a CUDA device function + + +@rawkernel() +def ari_kernel(x, y, res, m_x, m_y, n): + i = jit.blockIdx.x * jit.blockDim.x + jit.threadIdx.x + if i < m_x * m_y: + row_x = i // m_y + row_y = i % m_y + if x[row_x, 0] >= 0 and y[row_y, 0] >= 0: + res[i] = cu_rnd_sc(x[row_x], y[row_y], n) + else: + res[i] = 0.0 + + +def cdist_parts_cuda(x: cp.ndarray, y: cp.ndarray) -> cp.ndarray: + """ + CUDA-accelerated version of cdist_parts_basic using CuPy. + Each CUDA thread compares one row of x with one row of y. + + Args: + x: a 2d array with m_x clustering partitions in rows and n objects in columns. + y: a 2d array with m_y clustering partitions in rows and n objects in columns. + + Returns: + A 2d array with m_x rows and m_y columns and the ARI between each partition pair. + """ + m_x, n = x.shape + m_y, _ = y.shape + res = cp.zeros(m_x * m_y, dtype=cp.float32) + + threads_per_block = 256 + blocks = (m_x * m_y + threads_per_block - 1) // threads_per_block + + ari_kernel[blocks, threads_per_block](x, y, res, m_x, m_y, n) + + return res.reshape(m_x, m_y) + + +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: + """ + It implements the same functionality in scipy.spatial.distance.cdist but + for clustering partitions, and instead of a distance it returns the adjusted + Rand index (ARI). In other words, it mimics this function call: + + cdist(x, y, metric=ari) + + Only partitions with positive labels (> 0) are compared. This means that + partitions marked as "singleton" or "empty" (categorical data) are not + compared. This has the effect of leaving an ARI of 0.0 (zero). + + Args: + x: a 2d array with m_x clustering partitions in rows and n objects in + columns. + y: a 2d array with m_y clustering partitions in rows and n objects in + columns. + + Returns: + A 2d array with m_x rows and m_y columns and the ARI between each + partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i + and j. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + for i in range(res.shape[0]): + if x[i, 0] < 0: + continue + + for j in range(res.shape[1]): + if y[j, 0] < 0: + continue + + res[i, j] = ari(x[i], y[j]) + + return res + + +# Test function +def test_cdist_parts_cuda(): + # Generate sample data + np.random.seed(0) + m_x, m_y, n = 100, 80, 1000 + x = np.random.randint(0, 5, size=(m_x, n)) + y = np.random.randint(0, 5, size=(m_y, n)) + + # Convert to CuPy arrays + x_gpu = cp.asarray(x) + y_gpu = cp.asarray(y) + + # Run CUDA version + res_cuda = cdist_parts_cuda(x_gpu, y_gpu) + + # Run CPU version for comparison + res_cpu = cdist_parts_basic(x, y) + + # Compare results + cp.cuda.Stream.null.synchronize() + res_cuda_np = cp.asnumpy(res_cuda) + + assert np.allclose(res_cuda_np, res_cpu, atol=1e-6), "CUDA and CPU results do not match" + + print("CUDA implementation matches CPU implementation") + + # Performance comparison + import time + + start_time = time.time() + for _ in range(10): + cdist_parts_cuda(x_gpu, y_gpu) + cp.cuda.Stream.null.synchronize() + cuda_time = (time.time() - start_time) / 10 + + start_time = time.time() + for _ in range(10): + cdist_parts_basic(x, y) + cpu_time = (time.time() - start_time) / 10 + + print(f"CUDA time: {cuda_time:.6f} seconds") + print(f"CPU time: {cpu_time:.6f} seconds") + print(f"Speedup: {cpu_time / cuda_time:.2f}x") + + +from cupyx import jit + + +@jit.rawkernel() +def elementwise_copy(x, y, size): + tid = jit.blockIdx.x * jit.blockDim.x + jit.threadIdx.x + ntid = jit.gridDim.x * jit.blockDim.x + for i in range(tid, size, ntid): + y[i] = x[i] + + +def test_elementwise(): + size = cp.uint32(2 ** 22) + x = cp.random.normal(size=(size,), dtype=cp.float32) + y = cp.empty((size,), dtype=cp.float32) + + elementwise_copy((128,), (1024,), (x, y, size)) # RawKernel style + + + assert (x == y).all() + + elementwise_copy[128, 1024](x, y, size) # Numba style + assert (x == y).all() \ No newline at end of file diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index b8ddb634..5d48a751 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -1,106 +1,56 @@ -# import cupy as cp -# import numpy as np -# import matplotlib.pyplot as plt -# -# -# def test_digitize(): -# # random_feature1 = np.random.rand(100) -# # random_feature2 = np.random.rand(100) -# # -# # res = ccc(random_feature1, random_feature2, n_jobs=2) -# # print(res) -# -# # Create a sample CuPy array -# x = cp.array([1.2, 3.0, 4.5, 6.7, 8.9, 10.1, 12.3, 14.5, 16.7, 18.9]) -# -# # Create bins -# bins = cp.array([0, 5, 10, 15, 20]) -# -# # Use digitize to find which bin each value in x belongs to -# indices = cp.digitize(x, bins) -# -# print("Input array x:", x) -# print("Bins:", bins) -# print("Bin indices:", indices) -# -# # Demonstrate the effect of the 'right' parameter -# indices_right = cp.digitize(x, bins, right=True) -# print("Bin indices (right=True):", indices_right) -# -# # Use digitize with decreasing bins -# decreasing_bins = cp.array([20, 15, 10, 5, 0]) -# indices_decreasing = cp.digitize(x, decreasing_bins) -# print("Bin indices (decreasing bins):", indices_decreasing) -# -# # Create a larger random dataset -# large_x = cp.random.uniform(0, 100, 1000000) -# large_bins = cp.linspace(0, 100, 11) # 10 bins -# -# # Digitize the large dataset -# large_indices = cp.digitize(large_x, large_bins) -# -# # Compute histogram -# hist, _ = cp.histogram(large_x, bins=large_bins) -# -# print("Histogram of large dataset:", hist) -# -# # Plot the histogram (using CPU arrays for matplotlib) -# plt.figure(figsize=(10, 6)) -# plt.hist(cp.asnumpy(large_x), bins=cp.asnumpy(large_bins)) -# plt.title("Histogram of Large Dataset") -# plt.xlabel("Value") -# plt.ylabel("Frequency") -# plt.savefig('histogram.png') # Saves as PNG -# -# # Compare with NumPy results -# np_x = cp.asnumpy(x) -# np_bins = cp.asnumpy(bins) -# np_indices = np.digitize(np_x, np_bins) -# -# print("CuPy indices:", indices) -# print("NumPy indices:", np_indices) -# print("Results match:", cp.allclose(indices, cp.asarray(np_indices))) -# -# -# def test_quantile(): -# # Create a sample CuPy array -# a = cp.array([[10, 7, 4], [3, 2, 1]]) -# -# # Simple usage: compute the median (50th percentile) of the entire array -# median = cp.quantile(a, 0.5) -# print("Median of the entire array:", median) -# -# # Compute multiple quantiles -# quantiles = cp.quantile(a, [0.25, 0.5, 0.75]) -# print("25th, 50th, and 75th percentiles:", quantiles) -# -# # Compute quantiles along a specific axis -# axis_quantiles = cp.quantile(a, 0.5, axis=0) -# print("Median along axis 0:", axis_quantiles) -# -# # Compute quantiles for a larger array -# large_array = cp.random.randn(1000000) -# large_quantiles = cp.quantile(large_array, [0.1, 0.5, 0.9]) -# print("Quantiles of large array:", large_quantiles) -# -# # Use an output array -# out_array = cp.zeros(3) -# cp.quantile(large_array, [0.1, 0.5, 0.9], out=out_array) -# print("Output array:", out_array) -# -# # Compare with NumPy (CPU) results -# np_array = cp.asnumpy(large_array) -# np_quantiles = np.quantile(np_array, [0.1, 0.5, 0.9]) -# print("NumPy quantiles:", np_quantiles) -# print("CuPy and NumPy results are close:", cp.allclose(large_quantiles, np_quantiles)) -# -# # NANs in array -# nan_array = cp.array([1, 2, cp.nan, 4, 5]) -# nan_quantiles = cp.quantile(nan_array, 0.5) -# print("Quantile with NaNs:", nan_quantiles) -# -# # NANs in q -# array_with_q = cp.array([1, 2, 3, 4, 5]) -# q_with_nan = cp.array([0.5, cp.nan]) -# quantiles_with_nan = cp.quantile(array_with_q, q_with_nan) -# print("Quantiles with NaN in q:", quantiles_with_nan) \ No newline at end of file +import cupy as cp +import numpy as np +import matplotlib.pyplot as plt + + +def test_raw_kernel(): + # Define a raw kernel + kernel = cp.RawKernel(r''' + extern "C" __global__ + void my_raw_kernel(float* x, float* y, int n) { + int tid = blockDim.x * blockIdx.x + threadIdx.x; + if (tid < n) { + y[tid] = x[tid] * x[tid]; + } + } + ''', 'my_raw_kernel') + + # Prepare input data + n = 10 + x = cp.arange(n, dtype=cp.float32) + + # Allocate output array + y = cp.empty_like(x) + + # Launch the kernel + kernel((n,), (1,), (x, y, n)) + + # Check the result + assert cp.all(y == x * x) + + +def test_raw_kernel_with_thrust(): + N = 100 + code = """ + #include + #include + extern "C" __global__ + void xyzw_frequency_thrust_device(int *count, char *text, int n) + { + const char letters[] { 'x','y','z','w' }; + + *count = thrust::count_if(thrust::device, text, text+n, [=](char c) { + for (const auto x : letters) + if (c == x) return true; + return false; + }); + }""" + kernel=cp.RawModule(code=code,backend='nvcc') + code = kernel.get_function("xyzw_frequency_thrust_device") + + in_str = 'xxxzzzwwax' + count = cp.zeros([1],dtype=cp.int64) + in_arr = cp.array([ord(x) for x in in_str],dtype=cp.int8) + + # count[0] == 9 Define a raw kernel + code(grid=(N,),block=(N,),args=(count,in_arr,len(in_str))) \ No newline at end of file From 1aae2696836890b023c0985d443a7a3fadd39bc8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 22 Sep 2024 20:47:18 -0600 Subject: [PATCH 044/134] [coef]: Precompute number of unique partition values --- libs/ccc/coef/impl_gpu.py | 15 +++++++---- libs/ccc/sklearn/metrics_gpu2.py | 43 ++++++++++++++++++++++++++++++-- tests/gpu/test_cupy.py | 32 +++++++++++++++++++++--- 3 files changed, 79 insertions(+), 11 deletions(-) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index ab1c60d3..8bb37b4f 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -187,7 +187,7 @@ def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> def get_parts(X: NDArray, range_n_clusters: NDArray[np.uint8], data_is_numerical: bool = True - ) -> cp.ndarray: + ) -> tuple[cp.ndarray, cp.ndarray]: """ Compute parts using CuPy for GPU acceleration. @@ -213,6 +213,7 @@ def get_parts(X: NDArray, # Allocate arrays on device global memory d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 + d_unique_elem_counts = cp.empty((nx, ny), dtype=np.int16) - 1 # print(f"prev parts: {d_parts}") if data_is_numerical: @@ -225,12 +226,15 @@ def get_parts(X: NDArray, for x in range(nx): for y in range(ny): objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row + # Todo: use cupy fusion to optimize the two operations below percentages = d_range_n_percentages[y, :] - print(f"GPU percentiles: {percentages}") + # print(f"GPU percentiles: {percentages}") bins = cp.quantile(objects, percentages) - print(f"GPU quantiles: {bins}") + # print(f"GPU quantiles: {bins}") partition = cp.digitize(objects, bins, right=True) d_parts[x, y, :] = partition + # Count number of unique elements in each partition, used in the ARI computation + d_unique_elem_counts[x, y] = len(cp.unique(partition)) # Remove singletons by putting -2 as values partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, @@ -239,13 +243,14 @@ def get_parts(X: NDArray, else: # If the data is categorical, then the encoded feature is already the partition # Only the first partition is filled, the rest will be -1 (missing) + # Todo: fix this to handle categorical data d_parts[:, 0] = cp.asarray(X.astype(cp.int16)) # Move data back to host # h_parts = cp.asnumpy(d_parts) # print(f"after parts: {d_parts}") cp.cuda.runtime.deviceSynchronize() - return d_parts + return d_parts, d_unique_elem_counts # # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) @@ -595,7 +600,7 @@ def ccc( # X here (and following) is a numpy array features are in rows, objects are in columns # Compute partitions for each feature using CuPy - d_parts = get_parts(X, range_n_clusters) + d_parts, d_uniq_ele_counts = get_parts(X, range_n_clusters) print("GPU parts:") print(d_parts) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 7a9637e7..bd54a9ca 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -1,10 +1,19 @@ import numpy as np +import cupy as cp from numba import njit from numba import cuda +import rmm -def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, compare_pair_id: int, i: int, - j: int) -> float: +def adjusted_rand_index( + part0: np.ndarray, + part1: np.ndarray, + size: int, + out: np.ndarray, + compare_pair_id: int, + i: int, + j: int, + stream: cp.cuda.Stream = None): """ Computes the adjusted Rand index (ARI) between two clustering partitions. The code is based on the sklearn implementation here: @@ -16,7 +25,12 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, c Args: part0: a 1d array with cluster assignments for n objects. part1: a 1d array with cluster assignments for n objects. + size: the number of objects in the partitions. out: pointer to the output array containing all the ARI values. # TODO: make local + compare_pair_id: the index of the pair of partitions to compare. + i: the index of the first partition. + j: the index of the second partition. + stream: the CUDA stream to use. Returns: A number representing the adjusted Rand index between two clustering @@ -25,6 +39,12 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, c """ # TODO: # Implement numpy ravel in the kernel using shared memory? + # Use different streams for different pairs? + # Ref api: CUML confusion_matrix + if not size >= 2: + raise ValueError("Need at least two samples to compare.") + + (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) # convert to Python integer types, to avoid overflow or underflow @@ -36,3 +56,22 @@ def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, c res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) out[compare_pair_id, i, j] = res + + +def ari_dim2(parts: cp.ndarray, n_features_comp, out: cp.ndarray): + """ + Function to compute the ARI between partitions on the GPU. This function is responsible for launching the kernel + in different streams for each pair of partitions. + + Args: + parts: 3D device array with cluster assignments for x features, y partitions, and z objects. + Example initialization for this array: d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 + + n_features_comp: Pre-computed number of features to compare. + + out: Pointer to the pre-allocated 1D device output array with length of n_features_comp. + """ + + # Can use non-blocking CPU scheduling or CUDA dynamic parallelism to launch the kernel for each pair of partitions. + + raise NotImplementedError("Not implemented yet") diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 5d48a751..41018aca 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -45,12 +45,36 @@ def test_raw_kernel_with_thrust(): return false; }); }""" - kernel=cp.RawModule(code=code,backend='nvcc') + kernel = cp.RawModule(code=code,backend='nvcc') code = kernel.get_function("xyzw_frequency_thrust_device") in_str = 'xxxzzzwwax' - count = cp.zeros([1],dtype=cp.int64) - in_arr = cp.array([ord(x) for x in in_str],dtype=cp.int8) + count = cp.zeros([1], dtype=cp.int64) + in_arr = cp.array([ord(x) for x in in_str], dtype=cp.int8) # count[0] == 9 Define a raw kernel - code(grid=(N,),block=(N,),args=(count,in_arr,len(in_str))) \ No newline at end of file + code(grid=(N,),block=(N,), args=(count, in_arr, len(in_str))) + print() + print(count) + + +def test_thrust_unique_count(): + N = 100 + code = """ + #include + #include + extern "C" __global__ + void unique_count_thrust_device(int *count, int *data, int n) + { + *count = thrust::unique_count(thrust::device, data, data + n), thrust::equal_to(); + }""" + kernel = cp.RawModule(code=code, backend='nvcc') + code = kernel.get_function("unique_count_thrust_device") + + # in_arr = cp.random.randint(0, 10, N) + in_arr = cp.asarray([1, 3, 3, 3, 2, 2, 1], dtype=cp.int32) + count = cp.zeros([1], dtype=cp.int32) + + # count[0] == 9 Define a raw kernel + code(grid=(1,), block=(1,), args=(count, in_arr, 7)) + print(count) From 2d9a9951a466867edb3c6f49ac997db5b4d5bf43 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 23 Sep 2024 17:38:01 -0600 Subject: [PATCH 045/134] [coef]: Add tests for raw device function --- libs/ccc/sklearn/metrics.py | 8 +- tests/gpu/test_cupy.py | 185 +++++++++++++++++++++++++++++++++++- tests/gpu/test_kernel.py | 50 ++++++++++ 3 files changed, 239 insertions(+), 4 deletions(-) create mode 100644 tests/gpu/test_kernel.py diff --git a/libs/ccc/sklearn/metrics.py b/libs/ccc/sklearn/metrics.py index 387b31bb..2ab12862 100644 --- a/libs/ccc/sklearn/metrics.py +++ b/libs/ccc/sklearn/metrics.py @@ -69,7 +69,7 @@ def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: return cont_mat -@njit(cache=True, nogil=True) +# @njit(cache=True, nogil=True) def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: """ Returns the pair confusion matrix from two clustering partitions. It is an @@ -93,8 +93,10 @@ def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarra # Computation using the contingency data contingency = get_contingency_matrix(part0, part1) - n_c = np.ravel(contingency.sum(axis=1)) - n_k = np.ravel(contingency.sum(axis=0)) + sum1 = contingency.sum(axis=1) + sum0 = contingency.sum(axis=0) + n_c = np.ravel(sum1) + n_k = np.ravel(sum0) sum_squares = (contingency**2).sum() C = np.empty((2, 2), dtype=np.int64) C[1, 1] = sum_squares - n_samples diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 41018aca..4c8dff6b 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -45,7 +45,7 @@ def test_raw_kernel_with_thrust(): return false; }); }""" - kernel = cp.RawModule(code=code,backend='nvcc') + kernel = cp.RawModule(code=code, backend='nvcc') code = kernel.get_function("xyzw_frequency_thrust_device") in_str = 'xxxzzzwwax' @@ -78,3 +78,186 @@ def test_thrust_unique_count(): # count[0] == 9 Define a raw kernel code(grid=(1,), block=(1,), args=(count, in_arr, 7)) print(count) + + +def test_3d_raw_kernel(): + # Define a raw kernel to increment all elements by 1 + kernel = cp.RawKernel(r''' + extern "C" __global__ + void increment_3d(float* array, int x, int y, int z) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + int idy = blockIdx.y * blockDim.y + threadIdx.y; + int idz = blockIdx.z * blockDim.z + threadIdx.z; + + if (idx < x && idy < y && idz < z) { + int index = idz * y * x + idy * x + idx; + array[index] += 1.0f; + } + } + ''', 'increment_3d') + + # Define the shape of the 3D array + shape = (64, 64, 64) + + # Allocate and initialize a 3D array on the device + d_array = cp.zeros(shape, dtype=cp.float32) + + # Define grid and block dimensions + block_dim = (8, 8, 8) + grid_dim = ( + (shape[0] + block_dim[0] - 1) // block_dim[0], + (shape[1] + block_dim[1] - 1) // block_dim[1], + (shape[2] + block_dim[2] - 1) // block_dim[2] + ) + + # Launch the kernel + kernel(grid_dim, block_dim, (d_array, shape[0], shape[1], shape[2])) + + # Copy the result back to CPU for verification + h_result = cp.asnumpy(d_array) + + # Verify the result + expected = np.ones(shape, dtype=np.float32) + np.testing.assert_array_almost_equal(h_result, expected, decimal=6) + + print("Test passed successfully!") + + +def test_3d_raw_kernel_1d_grid(): + # Define a raw kernel to increment all elements by 1 using 1D grid and block + kernel = cp.RawKernel(r''' + extern "C" __global__ + void increment_3d_1d(float* array, int x, int y, int z) { + int tid = blockDim.x * blockIdx.x + threadIdx.x; + int total_size = x * y * z; + + if (tid < total_size) { + int idz = tid / (x * y); + int idy = (tid % (x * y)) / x; + int idx = tid % x; + + array[tid] += 1.0f; + } + } + ''', 'increment_3d_1d') + + # Define the shape of the 3D array + shape = (64, 64, 64) + + # Allocate and initialize a 3D array on the device + d_array = cp.zeros(shape, dtype=cp.float32) + + # Calculate total number of elements + total_elements = np.prod(shape) + + # Define 1D grid and block dimensions + block_dim = (256,) + grid_dim = ((total_elements + block_dim[0] - 1) // block_dim[0],) + + # Launch the kernel + kernel(grid_dim, block_dim, (d_array, shape[0], shape[1], shape[2])) + + # Copy the result back to CPU for verification + h_result = cp.asnumpy(d_array) + + # Verify the result + expected = np.ones(shape, dtype=np.float32) + np.testing.assert_array_almost_equal(h_result, expected, decimal=6) + + print("Test passed successfully!") + + +def test_ravle(): + from sklearn.metrics import confusion_matrix + y_true = [2, 0, 2, 2, 0, 1] + y_pred = [0, 0, 2, 2, 0, 2] + mat = confusion_matrix(y_true, y_pred) + print(mat) + + +def test_3d_raw_kernel_grid_stride(): + # Define a raw kernel to increment all elements by 1 using grid-stride pattern + kernel = cp.RawKernel(r''' + extern "C" __global__ + void increment_3d_grid_stride(float* array, int total_size) { + int tid = blockDim.x * blockIdx.x + threadIdx.x; + + for (int i = tid; i < total_size; i += blockDim.x * gridDim.x) { + // Memory layout: CuPy, like NumPy, stores multi-dimensional arrays in contiguous memory + // in row-major order (C-style). This means that elements are laid out sequentially in memory, + // regardless of the array's shape. + array[i] += 1.0f; + } + } + ''', 'increment_3d_grid_stride') + + # Define the shape of the 3D array + shape = (64, 64, 64) + + # Allocate and initialize a 3D array on the device + d_array = cp.zeros(shape, dtype=cp.float32) + + # Calculate total number of elements + total_elements = np.prod(shape) + + # Define 1D grid and block dimensions + block_dim = (256,) + grid_dim = (min(1024, (total_elements + block_dim[0] - 1) // block_dim[0]),) + + # Launch the kernel + kernel(grid_dim, block_dim, (d_array, total_elements)) + + # Copy the result back to CPU for verification + h_result = cp.asnumpy(d_array) + + # Verify the result + expected = np.ones(shape, dtype=np.float32) + np.testing.assert_array_almost_equal(h_result, expected, decimal=6) + + print("Test passed successfully!") + + +def test_3d_raw_kernel_grid_stride_indexing(): + # Define a raw kernel to increment all elements by 1 using grid-stride pattern + # and explicit 3D indexing + kernel = cp.RawKernel(r''' + extern "C" __global__ + void increment_3d_grid_stride(float* array, int x, int y, int z) { + int tid = blockDim.x * blockIdx.x + threadIdx.x; + int total_size = x * y * z; + + for (int i = tid; i < total_size; i += blockDim.x * gridDim.x) { + int iz = i / (x * y); + int iy = (i % (x * y)) / x; + int ix = i % x; + + // Accessing the 3D array using 3D indices + array[iz * (x * y) + iy * x + ix] += 1.0f; + } + } + ''', 'increment_3d_grid_stride') + + # Define the shape of the 3D array + shape = (64, 64, 64) + + # Allocate and initialize a 3D array on the device + d_array = cp.zeros(shape, dtype=cp.float32) + + # Calculate total number of elements + total_elements = np.prod(shape) + + # Define 1D grid and block dimensions + block_dim = (256,) + grid_dim = (min(1024, (total_elements + block_dim[0] - 1) // block_dim[0]),) + + # Launch the kernel + kernel(grid_dim, block_dim, (d_array, shape[0], shape[1], shape[2])) + + # Copy the result back to CPU for verification + h_result = cp.asnumpy(d_array) + + # Verify the result + expected = np.ones(shape, dtype=np.float32) + np.testing.assert_array_almost_equal(h_result, expected, decimal=6) + + print("Test passed successfully!") \ No newline at end of file diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py new file mode 100644 index 00000000..d72b0aa2 --- /dev/null +++ b/tests/gpu/test_kernel.py @@ -0,0 +1,50 @@ +import cupy as cp +import numpy as np +from ccc.sklearn.metrics_gpu2 import device_func_str +from ccc.coef import get_coords_from_index + + +def test_get_coords_from_index_kernel(): + test_kernel_code = """ + extern "C" __global__ + void test_kernel(int n_obj, int* indices, int* results, int num_indices) { + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < num_indices) { + int x, y; + get_coords_from_index(n_obj, indices[tid], &x, &y); + results[tid * 2] = x; + results[tid * 2 + 1] = y; + } + } + """ + cuda_code = device_func_str + test_kernel_code + module = cp.RawModule(code=cuda_code, backend='nvcc') + kernel = module.get_function("test_kernel") + + # Test parameters + n_obj = 10 + num_indices = 45 # (n_obj * (n_obj - 1)) // 2 + + # Create input indices + indices = cp.arange(num_indices, dtype=cp.int32) + + # Allocate memory for results + d_results = cp.empty(num_indices * 2, dtype=cp.int32) + + # Launch the kernel + threads_per_block = 256 + blocks = (num_indices + threads_per_block - 1) // threads_per_block + kernel((blocks,), (threads_per_block,), (n_obj, indices, d_results, num_indices)) + + # Get results back to host + h_results = cp.asnumpy(d_results) + + # Compare with Python implementation + for i in range(num_indices): + x_cuda, y_cuda = h_results[i * 2], h_results[i * 2 + 1] + x_py, y_py = get_coords_from_index(n_obj, i) + + assert x_cuda == x_py, f"Mismatch in x for index {i}: CUDA={x_cuda}, Python={x_py}" + assert y_cuda == y_py, f"Mismatch in y for index {i}: CUDA={y_cuda}, Python={y_py}" + + print("All tests passed successfully!") From 00ecdc9d47396b9f89f2accfe0af2d7eac544fee Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 24 Sep 2024 00:04:10 -0600 Subject: [PATCH 046/134] [coef]: Add device function to unravel 2D index --- libs/ccc/sklearn/metrics_gpu2.py | 106 ++++++++++++++++++++++++++++++- tests/gpu/test_kernel.py | 57 ++++++++++++++++- 2 files changed, 160 insertions(+), 3 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index bd54a9ca..75f14c19 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -5,10 +5,92 @@ import rmm +d_unravel_index_str = """ +extern "C" __device__ __host__ inline void unravel_index(size_t flat_idx, size_t num_cols, size_t* row, size_t* col) { + *row = flat_idx / num_cols; // Compute row index + *col = flat_idx % num_cols; // Compute column index +} + +""" + +d_get_coords_from_index_str = """ +#include +extern "C" __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x, int* y) { + // Calculate 'b' based on the input n_obj + int b = 1 - 2 * n_obj; + // Calculate 'x' using the quadratic formula part + float discriminant = b * b - 8 * idx; + float x_float = floor((-b - sqrt(discriminant)) / 2); + // Assign the integer part of 'x' + *x = static_cast(x_float); + // Calculate 'y' based on 'x' and the index + *y = static_cast(idx + (*x) * (b + (*x) + 2) / 2 + 1); +} + +""" + +k_ari_str = """ +extern "C" __global__ +void ari(const int* parts, + const int* uniqs, + const int n_aris, + const int n_parts, + const int n_part_mat_elems, + float* out) + ) +{ + // tid corresponds to the ari idx + int tid = blockIdx.x * blockDim.x + threadIdx.x; + int stride = blockDim.x * gridDim.x; + // used for max reduction + // int part_part_elems = n_parts * n_parts; + + // obtain the corresponding parts and unique counts + int feature_comp_idx = tid / n_part_mat_elems; // comparison pair index for two features + int part_pair_idx = tid % part_part_elems; // comparison pair index for two partitions of one feature pair + int i, j; + // unravel the feature indices + get_coords_from_index(n_parts, feature_comp_idx, &i, &j); + // unravel the partition indices + + + // Make pointers to select the parts and unique counts for the feature pair + + // Initialize shared memory + int part_mat_first_tid = tid * part_part_elems; + __syncthreads(); +} + +""" + + +def get_kernel(): + """ + Kernel to compute the air between two partitions indexed from the 3D input array parts. + + The first thread of each logical part vs part ari matrix is responsible to reduce the matrix to the max ari. + See the document for illustrations. + + raw kernel args: + parts: 3D device array with cluster assignments for x features, y partitions, and z objects. + uniqs: 2D device array with the number of unique elements for feature x and partition y. + n_aris: Number of ARI computations to perform. + n_parts: Number of partitions of a feature, i.e., len(n_range_clusters) to compare. + out: Pointer to the pre-allocated 1D device output array with length of number of features to compare. + """ + + cuda_code = d_get_coords_from_index_str + k_ari_str + + kernel = cp.RawKernel(code=cuda_code, backend="nvcc").get_function("ari") + return kernel + + def adjusted_rand_index( part0: np.ndarray, part1: np.ndarray, size: int, + n_uniq0: int, + n_uniq1: int, out: np.ndarray, compare_pair_id: int, i: int, @@ -26,6 +108,8 @@ def adjusted_rand_index( part0: a 1d array with cluster assignments for n objects. part1: a 1d array with cluster assignments for n objects. size: the number of objects in the partitions. + n_uniq0: the number of unique elements in part0. + n_uniq1: the number of unique elements in part1. out: pointer to the output array containing all the ARI values. # TODO: make local compare_pair_id: the index of the pair of partitions to compare. i: the index of the first partition. @@ -58,7 +142,11 @@ def adjusted_rand_index( out[compare_pair_id, i, j] = res -def ari_dim2(parts: cp.ndarray, n_features_comp, out: cp.ndarray): +def ari_dim2(parts: cp.ndarray, + n_partitions: int, + n_features_comp: int, + out: cp.ndarray, + unique_element_counts: cp.ndarray): """ Function to compute the ARI between partitions on the GPU. This function is responsible for launching the kernel in different streams for each pair of partitions. @@ -67,11 +155,27 @@ def ari_dim2(parts: cp.ndarray, n_features_comp, out: cp.ndarray): parts: 3D device array with cluster assignments for x features, y partitions, and z objects. Example initialization for this array: d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 + n_partitions: Number of partitions of a feature to compare. + n_features_comp: Pre-computed number of features to compare. out: Pointer to the pre-allocated 1D device output array with length of n_features_comp. + + unique_element_counts: 2D device array with the number of unique elements for feature x and partition y. """ # Can use non-blocking CPU scheduling or CUDA dynamic parallelism to launch the kernel for each pair of partitions. + # Each kernel launch will be responsible for computing the ARI between two partitions. + n_part_mat_elems = n_partitions * n_partitions + n_ari_pairs = n_partitions * n_part_mat_elems + cm_values = cp.full(n_features_comp, cp.nan) + # Todo: how many ari pairs? n_range_cluster? + threads_per_block = 256 + blocks_per_grid = (n_ari_pairs + threads_per_block - 1) // threads_per_block + ari_kernel = get_kernel() + ari_kernel(grid=(blocks_per_grid,), + block=(threads_per_block,), + args=(parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) + raise NotImplementedError("Not implemented yet") diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index d72b0aa2..17ea61a2 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -1,6 +1,8 @@ +import pytest +import math import cupy as cp import numpy as np -from ccc.sklearn.metrics_gpu2 import device_func_str +from ccc.sklearn.metrics_gpu2 import d_get_coords_from_index_str, d_unravel_index_str from ccc.coef import get_coords_from_index @@ -17,7 +19,7 @@ def test_get_coords_from_index_kernel(): } } """ - cuda_code = device_func_str + test_kernel_code + cuda_code = d_get_coords_from_index_str + test_kernel_code module = cp.RawModule(code=cuda_code, backend='nvcc') kernel = module.get_function("test_kernel") @@ -48,3 +50,54 @@ def test_get_coords_from_index_kernel(): assert y_cuda == y_py, f"Mismatch in y for index {i}: CUDA={y_cuda}, Python={y_py}" print("All tests passed successfully!") + + +@pytest.mark.parametrize("num_cols, num_indices", [ + (10, 45), + (15, 100), + (20, 200) +]) +def test_unravel_index_kernel(num_cols, num_indices): + test_kernel_code = """ + extern "C" __global__ void test_unravel_index_kernel(size_t* flat_indices, size_t* rows, size_t* cols, size_t num_cols, size_t num_indices) { + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < num_indices) { + unravel_index(flat_indices[tid], num_cols, &rows[tid], &cols[tid]); + } + } + """ + + cuda_code = d_unravel_index_str + test_kernel_code + # Compile the CUDA kernel + module = cp.RawModule(code=cuda_code, backend='nvcc') + kernel = module.get_function("test_unravel_index_kernel") + + # Create test inputs + flat_indices = cp.arange(num_indices, dtype=cp.uint64) + + # Allocate memory for results (rows and cols) + d_rows = cp.empty(num_indices, dtype=cp.uint64) + d_cols = cp.empty(num_indices, dtype=cp.uint64) + + # Launch the kernel + threads_per_block = 256 + blocks = (num_indices + threads_per_block - 1) // threads_per_block + kernel((blocks,), (threads_per_block,), (flat_indices, d_rows, d_cols, num_cols, num_indices)) + + # Get results back to host + h_rows = cp.asnumpy(d_rows) + h_cols = cp.asnumpy(d_cols) + + # Compare with NumPy's unravel_index implementation + for i in range(num_indices): + # Use numpy.unravel_index as the reference + # row_py, col_py = divmod(i, num_cols) + row_py, col_py = np.unravel_index(i, (num_cols, num_cols)) + row_cuda, col_cuda = h_rows[i], h_cols[i] + + # Assertions to ensure CUDA and NumPy match + assert row_cuda == row_py, f"Mismatch in row for index {i}: CUDA={row_cuda}, NumPy={row_py}" + assert col_cuda == col_py, f"Mismatch in col for index {i}: CUDA={col_cuda}, NumPy={col_py}" + + print("All tests passed successfully!") + From 9604af403423e8891e5465c732e4ebe111783952 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 24 Sep 2024 12:02:11 -0600 Subject: [PATCH 047/134] [test]: Fix calls to get_parts --- histogram.png | Bin 0 -> 19558 bytes libs/ccc/coef/impl.py | 4 ++-- tests/gpu/test_get_parts.py | 12 ++++++------ 3 files changed, 8 insertions(+), 8 deletions(-) create mode 100644 histogram.png diff --git a/histogram.png b/histogram.png new file mode 100644 index 0000000000000000000000000000000000000000..7d4bb4e6a2deb1525b78302790b8db9bb060f6b9 GIT binary patch literal 19558 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as ari +# from ccc.sklearn.metrics_gpu2 import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py index 56577b23..5d1eabf2 100644 --- a/tests/gpu/test_get_parts.py +++ b/tests/gpu/test_get_parts.py @@ -100,7 +100,7 @@ def test_get_parts(feature_size, cluster_settings, seed, distribution, params): raise ValueError(f"Unsupported distribution: {distribution}") # GPU implementation - parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() + parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8))[0].get() # CPU implementation parts_cpu = get_parts_cpu(feature, cpu_clusters) @@ -216,7 +216,7 @@ def test_get_parts_with_singletons(): feature0 = np.array([1.3] * 100) # run - parts = get_parts(feature0, np.array([2], dtype=np.uint8)).get() + parts = get_parts(feature0, np.array([2], dtype=np.uint8))[0].get() parts_cpu = get_parts_cpu(feature0, (2,)) assert parts is not None assert len(parts) == 1 # 1 feature @@ -225,7 +225,7 @@ def test_get_parts_with_singletons(): np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) assert np.array_equal(parts[0], parts_cpu) - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8)).get() + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8))[0].get() parts_cpu = get_parts_cpu(feature0, (2, 3)) assert parts is not None assert len(parts) == 1 @@ -244,7 +244,7 @@ def test_get_parts_with_categorical_feature(): # run # only one partition is requested - parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False).get() + parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False)[0].get() parts_cpu = get_parts_cpu(feature0, (2,), data_is_numerical=False) assert parts is not None assert len(parts) == 1 @@ -253,7 +253,7 @@ def test_get_parts_with_categorical_feature(): assert np.array_equal(parts[0], parts_cpu) # more partitions are requested; only the first one has valid information - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False).get() + parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False)[0].get() parts_cpu = get_parts_cpu(feature0, (2, 3), data_is_numerical=False) assert parts is not None assert len(parts) == 1 @@ -274,7 +274,7 @@ def test_get_parts_2d_simple(): np.random.seed(0) array = np.random.rand(5, 1000) print(f"array : \n{array}") - parts = get_parts(array, np.array([3], dtype=np.uint8)).get() + parts = get_parts(array, np.array([3], dtype=np.uint8))[0].get() parts_cpu_row0 = get_parts_cpu(array[0], (3, )) parts_cpu_row1 = get_parts_cpu(array[1], (3, )) assert parts is not None From 98e07dad56ce381c72aa9ec985d55cec9aa3220a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 28 Sep 2024 18:37:51 -0600 Subject: [PATCH 048/134] [test]: Add test skeleton --- libs/ccc/sklearn/metrics_gpu2.py | 41 +++++++++++++++++++----- tests/gpu/test_cuml.py | 43 ++++++++++++++++++++++++- tests/gpu/test_cupy.py | 54 +++++++++++++++++++++++++++++++- 3 files changed, 129 insertions(+), 9 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 75f14c19..81306bdf 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -6,6 +6,13 @@ d_unravel_index_str = """ +/** + * @brief Unravel a flat index to the corresponding 2D indicis + * @param[in] flat_idx The flat index to unravel + * @param[in] num_cols Number of columns in the 2D array + * @param[out] row Pointer to the row index + * @param[out] col Pointer to the column index + */ extern "C" __device__ __host__ inline void unravel_index(size_t flat_idx, size_t num_cols, size_t* row, size_t* col) { *row = flat_idx / num_cols; // Compute row index *col = flat_idx % num_cols; // Compute column index @@ -30,11 +37,20 @@ """ k_ari_str = """ +/** + * @brief Main ARI kernel. Now only compare a pair of ARIs + * @param n_parts Number of partitions of each feature + * @param n_objs Number of objects in each partitions + * @param n_part_mat_elems Number of elements in the square partition matrix + * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y + */ extern "C" __global__ -void ari(const int* parts, - const int* uniqs, +void ari(const int4* parts, + const int4* uniqs, const int n_aris, const int n_parts, + const int n_objs, + const uint32 n_elems_per_feat, const int n_part_mat_elems, float* out) ) @@ -46,15 +62,24 @@ // int part_part_elems = n_parts * n_parts; // obtain the corresponding parts and unique counts - int feature_comp_idx = tid / n_part_mat_elems; // comparison pair index for two features - int part_pair_idx = tid % part_part_elems; // comparison pair index for two partitions of one feature pair + int feature_comp_flat_idx = tid / n_part_mat_elems; // comparison pair index for two features + int part_pair_flat_idx = tid % part_part_elems; // comparison pair index for two partitions of one feature pair int i, j; // unravel the feature indices - get_coords_from_index(n_parts, feature_comp_idx, &i, &j); + get_coords_from_index(n_parts, feature_comp_flat_idx, &i, &j); // unravel the partition indices - + int m, n; + unravel_index(part_pair_flat_idx, n_parts, &m, &n); // Make pointers to select the parts and unique counts for the feature pair + int4* t_data_parti = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread + int4* t_data_partj = parts + j * n_elems_per_feat + n * n_objs ; + int4* t_data_uniqi = uniqs + i * n_parts + m; + int4* t_data_uniqj = uniqs + j * n_parts + n; + + // Load gmem data into smem by using different threads + + // Initialize shared memory int part_mat_first_tid = tid * part_part_elems; @@ -168,12 +193,14 @@ def ari_dim2(parts: cp.ndarray, # Each kernel launch will be responsible for computing the ARI between two partitions. n_part_mat_elems = n_partitions * n_partitions + # Each thread n_ari_pairs = n_partitions * n_part_mat_elems cm_values = cp.full(n_features_comp, cp.nan) # Todo: how many ari pairs? n_range_cluster? - threads_per_block = 256 + threads_per_block = 1 blocks_per_grid = (n_ari_pairs + threads_per_block - 1) // threads_per_block ari_kernel = get_kernel() + # Todo: use different streams ari_kernel(grid=(blocks_per_grid,), block=(threads_per_block,), args=(parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) diff --git a/tests/gpu/test_cuml.py b/tests/gpu/test_cuml.py index 2e2b2113..001a1fa2 100644 --- a/tests/gpu/test_cuml.py +++ b/tests/gpu/test_cuml.py @@ -1,6 +1,8 @@ import cupy as cp import numpy as np +import pytest from cuml.metrics import adjusted_rand_score +from sklearn.metrics import adjusted_rand_score from cuml.common import CumlArray from cuml.internals.memory_utils import using_output_type from cuml.internals.safe_imports import gpu_only_import @@ -56,4 +58,43 @@ def test_stream(): # Print results print(f"Computed {n_iterations} ARI scores") print(f"Time taken: {end_time - start_time:.4f} seconds") - print(results) \ No newline at end of file + print(results) + + +def generate_data(size): + np.random.seed(42) + labels_true = np.random.randint(0, 10, size=size) + labels_pred = np.random.randint(0, 10, size=size) + return labels_true, labels_pred + + +def time_function(func, *args): + start_time = time.time() + result = func(*args) + end_time = time.time() + return result, end_time - start_time + + +@pytest.mark.parametrize("size", [1000, 10000, 100000, 1000000]) +def test_adjusted_rand_score_speedup(size): + from cuml.metrics import adjusted_rand_score as cuml_ari + from sklearn.metrics import adjusted_rand_score as sklearn_ari + labels_true, labels_pred = generate_data(size) + + # Sklearn (CPU) implementation + _, sklearn_time = time_function(sklearn_ari, labels_true, labels_pred) + + # cuML (GPU) implementation + labels_true_gpu = cp.asarray(labels_true) + labels_pred_gpu = cp.asarray(labels_pred) + _, cuml_time = time_function(cuml_ari, labels_true_gpu, labels_pred_gpu) + + speedup = sklearn_time / cuml_time + + print(f"\nData size: {size}") + print(f"Sklearn time: {sklearn_time:.6f} seconds") + print(f"cuML time: {cuml_time:.6f} seconds") + print(f"Speedup: {speedup:.2f}x") + + # assert speedup > 1, f"cuML should be faster than sklearn, but speedup was only {speedup:.2f}x" + diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 4c8dff6b..9714f4fd 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -260,4 +260,56 @@ def test_3d_raw_kernel_grid_stride_indexing(): expected = np.ones(shape, dtype=np.float32) np.testing.assert_array_almost_equal(h_result, expected, decimal=6) - print("Test passed successfully!") \ No newline at end of file + print("Test passed successfully!") + + +def test_raft_api(): + code = cp.RawKernel(r''' + extern "C" __global__ + #include + #include + #include + #include + + raft::handle_t handle; + + int n_samples = 5000; + int n_features = 50; + + auto input = raft::make_device_matrix(handle, n_samples, n_features); + auto labels = raft::make_device_vector(handle, n_samples); + auto output = raft::make_device_matrix(handle, n_samples, n_samples); + + raft::random::make_blobs(handle, input.view(), labels.view()); + + auto metric = raft::distance::DistanceType::L2SqrtExpanded; + raft::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric); + ''', 'raft_test') + + +def test_pair_wise_reduction(): + # Define a 3D parts array + h_parts = np.array([ + [ + [1, 2, 3], + [0, 2, 2], + [1, 3, 3], + ], + [ + [1, 1, 1], + [3, 1, 2], + [1, 3, 3], + ], + [ + [0, 0, 3], + [2, 1, 2], + [1, 0, 1], + ], + ]) + # Host loop + n_features = h_parts.shape[0] + n_parts = h_parts.shape[1] + n_objs = h_parts.shape[2] + + n_feat_comp = n_features * (n_features - 1) // 2 + From 1349d993b8a60daf04ad171bebd68bbbf596c1f7 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 29 Sep 2024 13:45:04 -0600 Subject: [PATCH 049/134] [cccl]: Compile cub raw kernel successfully --- libs/ccc/sklearn/metrics_gpu2.py | 2 + tests/gpu/test_cupy.py | 169 +++++++++++++++++++++++++++++++ 2 files changed, 171 insertions(+) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 81306bdf..17c3346c 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -84,6 +84,8 @@ // Initialize shared memory int part_mat_first_tid = tid * part_part_elems; __syncthreads(); + + // Todo: use a for loop to compute the ARI and do the max reduction } """ diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 9714f4fd..95e597c3 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -1,6 +1,9 @@ import cupy as cp import numpy as np import matplotlib.pyplot as plt +import pytest + +from ccc.sklearn.metrics import get_contingency_matrix def test_raw_kernel(): @@ -313,3 +316,169 @@ def test_pair_wise_reduction(): n_feat_comp = n_features * (n_features - 1) // 2 + +def test_cub_block_sort_kernel(): + kernel_code = r''' + #include + + // template + extern "C" __global__ + void BlockSortKernel(int *d_in, int *d_out) + { + using BlockLoadT = cub::BlockLoad< + int, 128, 4, cub::BLOCK_LOAD_TRANSPOSE>; + using BlockStoreT = cub::BlockStore< + int, 128, 4z, cub::BLOCK_STORE_TRANSPOSE>; + using BlockRadixSortT = cub::BlockRadixSort< + int, 128, 4>; + + __shared__ union { + typename BlockLoadT::TempStorage load; + typename BlockStoreT::TempStorage store; + typename BlockRadixSortT::TempStorage sort; + } temp_storage; + + int thread_keys[4]; + int block_offset = blockIdx.x * (128 * 4); + BlockLoadT(temp_storage.load).Load(d_in + block_offset, thread_keys); + + __syncthreads(); + + BlockRadixSortT(temp_storage.sort).Sort(thread_keys); + + __syncthreads(); + + BlockStoreT(temp_storage.store).Store(d_out + block_offset, thread_keys); + } + + /* + extern "C" __global__ + void launch_block_sort_kernel(int *d_in, int *d_out, int num_items) + { + const int BLOCK_THREADS = 128; + const int ITEMS_PER_THREAD = 4; + const int BLOCK_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD; + + int grid_size = (num_items + BLOCK_ITEMS - 1) / BLOCK_ITEMS; + BlockSortKernel<<>>(d_in, d_out); + } + */ + ''' + + # Compile the CUDA kernel + module = cp.RawModule(code=kernel_code, backend='nvcc') + kernel = module.get_function('BlockSortKernel') + + # Set up test parameters + num_items = 1024 # Must be a multiple of BLOCK_ITEMS (128 * 4 = 512 in this case) + + # Generate random input data + np_input = np.random.randint(0, 1000, num_items, dtype=np.int32) + d_input = cp.asarray(np_input) + d_output = cp.empty_like(d_input) + + # Launch the kernel + block_threads = 128 + items_per_thread = 4 + block_items = block_threads * items_per_thread + grid_size = (num_items + block_items - 1) // block_items + kernel((grid_size,), (block_threads,), (d_input, d_output)) + + # Get the results back to host + cp_output = cp.asnumpy(d_output) + + # Verify the results + np_sorted = np.sort(np_input) + + # Check if each block is sorted + block_size = 512 # BLOCK_THREADS * ITEMS_PER_THREAD + for i in range(0, num_items, block_size): + block_end = min(i + block_size, num_items) + assert np.all(np.diff(cp_output[i:block_end]) >= 0), f"Block starting at index {i} is not sorted" + + print("All blocks are correctly sorted!") + + # Optional: Check if the entire array is sorted (it won't be, as we're only sorting within blocks) + # assert np.array_equal(cp_output, np_sorted), "The entire array is not globally sorted" + + +def contingency_matrix_cuda(part0, part1, k0, k1): + # CUDA kernel as a string + cuda_kernel = r""" + extern "C" __global__ void contingency_matrix_kernel( + const int* part0, + const int* part1, + int* cont_mat, + int n, + int k0, + int k1 + ) { + extern __shared__ int shared_mem[]; + int* shared_part0 = shared_mem; + int* shared_part1 = &shared_mem[blockDim.x]; + int tid = threadIdx.x; + int bid = blockIdx.x; + int gid = bid * blockDim.x + tid; + // Load data into shared memory + if (gid < n) { + shared_part0[tid] = part0[gid]; + shared_part1[tid] = part1[gid]; + } + __syncthreads(); + // Compute contingency matrix + for (int i = tid; i < k0 * k1; i += blockDim.x) { + int row = i / k1; + int col = i % k1; + int count = 0; + for (int j = 0; j < blockDim.x && j < n; ++j) { + if (shared_part0[j] == row && shared_part1[j] == col) { + count++; + } + } + atomicAdd(&cont_mat[row * k1 + col], count); + } + } + """ + + # Compile the CUDA kernel + module = cp.RawModule(code=cuda_kernel) + kernel = module.get_function("contingency_matrix_kernel") + + n = len(part0) + d_part0 = cp.asarray(part0) + d_part1 = cp.asarray(part1) + d_cont_mat = cp.zeros((k0, k1), dtype=np.int32) + + block_size = 256 + grid_size = (n + block_size - 1) // block_size + shared_mem_size = 2 * block_size * 4 # 4 bytes per int + + kernel( + grid=(grid_size,), + block=(block_size,), + args=(d_part0, d_part1, d_cont_mat, n, k0, k1), + shared_mem=shared_mem_size + ) + + return cp.asnumpy(d_cont_mat) + + +@pytest.mark.parametrize("n, k0, k1", [ + (1000, 5, 5), + (10000, 10, 8), + (100000, 20, 15), +]) +def test_contingency_matrix(n, k0, k1): + # Generate random input data + rng = np.random.default_rng(42) + part0 = rng.integers(0, k0, size=n) + part1 = rng.integers(0, k1, size=n) + + # Compute contingency matrix using CUDA + cuda_result = contingency_matrix_cuda(part0, part1, k0, k1) + + # Compute contingency matrix using NumPy + numpy_result = get_contingency_matrix(part0, part1) + + # Assert that the results are equal + np.testing.assert_array_equal(cuda_result, numpy_result) \ No newline at end of file From 0ae0ac5aca2941e58cc1359c1abe792f727c8a60 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 29 Sep 2024 15:51:23 -0600 Subject: [PATCH 050/134] [cccl]: Try out cupy shared_mem dynamic allocation --- tests/gpu/test_cupy.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index 95e597c3..dfe9d3dd 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -325,6 +325,8 @@ def test_cub_block_sort_kernel(): extern "C" __global__ void BlockSortKernel(int *d_in, int *d_out) { + // extern __shared__ int tmp[]; + // tmp[threadIdx.x] = 1; using BlockLoadT = cub::BlockLoad< int, 128, 4, cub::BLOCK_LOAD_TRANSPOSE>; using BlockStoreT = cub::BlockStore< @@ -382,7 +384,7 @@ def test_cub_block_sort_kernel(): items_per_thread = 4 block_items = block_threads * items_per_thread grid_size = (num_items + block_items - 1) // block_items - kernel((grid_size,), (block_threads,), (d_input, d_output)) + kernel((grid_size,), (block_threads,), (d_input, d_output, 4), shared_mem=block_threads * 4 * 4) # Get the results back to host cp_output = cp.asnumpy(d_output) From 6a693124357aa4690679996389c99022f9bf6f57 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 29 Sep 2024 17:04:40 -0600 Subject: [PATCH 051/134] [cccl]: Hack non-type template parameters --- tests/gpu/test_cupy.py | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/tests/gpu/test_cupy.py b/tests/gpu/test_cupy.py index dfe9d3dd..b36020d5 100644 --- a/tests/gpu/test_cupy.py +++ b/tests/gpu/test_cupy.py @@ -2,6 +2,7 @@ import numpy as np import matplotlib.pyplot as plt import pytest +import re from ccc.sklearn.metrics import get_contingency_matrix @@ -319,8 +320,17 @@ def test_pair_wise_reduction(): def test_cub_block_sort_kernel(): kernel_code = r''' + /* + These constants can be dynamically manipulated using string formatting, providing a hack to non-type + template parameters in CUDA kernels using cupy + */ + + /* Headers */ #include - + #define BLOCK_THREADS {BLOCK_THREADS} + #define ITERM_PER_THREAD {ITERM_PER_THREAD} + + // Todo: research on how to compile these non-type template parameters using cupy // template extern "C" __global__ void BlockSortKernel(int *d_in, int *d_out) @@ -328,11 +338,11 @@ def test_cub_block_sort_kernel(): // extern __shared__ int tmp[]; // tmp[threadIdx.x] = 1; using BlockLoadT = cub::BlockLoad< - int, 128, 4, cub::BLOCK_LOAD_TRANSPOSE>; + int, BLOCK_THREADS, ITERM_PER_THREAD, cub::BLOCK_LOAD_TRANSPOSE>; using BlockStoreT = cub::BlockStore< - int, 128, 4z, cub::BLOCK_STORE_TRANSPOSE>; + int, BLOCK_THREADS, ITERM_PER_THREAD, cub::BLOCK_STORE_TRANSPOSE>; using BlockRadixSortT = cub::BlockRadixSort< - int, 128, 4>; + int, BLOCK_THREADS, ITERM_PER_THREAD>; __shared__ union { typename BlockLoadT::TempStorage load; @@ -340,8 +350,8 @@ def test_cub_block_sort_kernel(): typename BlockRadixSortT::TempStorage sort; } temp_storage; - int thread_keys[4]; - int block_offset = blockIdx.x * (128 * 4); + int thread_keys[ITERM_PER_THREAD]; + int block_offset = blockIdx.x * (BLOCK_THREADS * ITERM_PER_THREAD); BlockLoadT(temp_storage.load).Load(d_in + block_offset, thread_keys); __syncthreads(); @@ -367,9 +377,6 @@ def test_cub_block_sort_kernel(): */ ''' - # Compile the CUDA kernel - module = cp.RawModule(code=kernel_code, backend='nvcc') - kernel = module.get_function('BlockSortKernel') # Set up test parameters num_items = 1024 # Must be a multiple of BLOCK_ITEMS (128 * 4 = 512 in this case) @@ -379,11 +386,18 @@ def test_cub_block_sort_kernel(): d_input = cp.asarray(np_input) d_output = cp.empty_like(d_input) - # Launch the kernel + # Configure the kernel block_threads = 128 items_per_thread = 4 block_items = block_threads * items_per_thread grid_size = (num_items + block_items - 1) // block_items + # Format the kernel string + kernel_code = re.sub(r'\{BLOCK_THREADS\}', str(block_threads), kernel_code) + kernel_code = re.sub(r'\{ITERM_PER_THREAD\}', str(items_per_thread), kernel_code) + # Compile the CUDA kernel + module = cp.RawModule(code=kernel_code, backend='nvcc') + kernel = module.get_function('BlockSortKernel') + kernel((grid_size,), (block_threads,), (d_input, d_output, 4), shared_mem=block_threads * 4 * 4) # Get the results back to host From 1858f6db15eda0c7552bbf1dea57b04647a2e26d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 30 Sep 2024 18:57:11 -0600 Subject: [PATCH 052/134] [kernel]: Test fail on partition selection --- libs/ccc/sklearn/metrics_gpu2.py | 144 ++++++++++++++----------------- tests/gpu/test_kernel.py | 74 +++++++++++++++- 2 files changed, 139 insertions(+), 79 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 17c3346c..d9f17eea 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -13,7 +13,7 @@ * @param[out] row Pointer to the row index * @param[out] col Pointer to the column index */ -extern "C" __device__ __host__ inline void unravel_index(size_t flat_idx, size_t num_cols, size_t* row, size_t* col) { +extern "C" __device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int* row, int* col) { *row = flat_idx / num_cols; // Compute row index *col = flat_idx % num_cols; // Compute column index } @@ -37,33 +37,44 @@ """ k_ari_str = """ +#define debug 0 + /** * @brief Main ARI kernel. Now only compare a pair of ARIs * @param n_parts Number of partitions of each feature * @param n_objs Number of objects in each partitions * @param n_part_mat_elems Number of elements in the square partition matrix * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @param uniqs Array of unique counts + * @param n_aris Number of ARIs to compute + * @param out Output array of ARIs + * @param part0 Output array of partition 0, for debugging + * @param part1 Output array of partition 1, for debugging */ extern "C" __global__ -void ari(const int4* parts, - const int4* uniqs, +void ari(int* parts, + int* uniqs, const int n_aris, const int n_parts, const int n_objs, - const uint32 n_elems_per_feat, + const int n_elems_per_feat, const int n_part_mat_elems, - float* out) + float* out, + int* part_pairs ) { // tid corresponds to the ari idx - int tid = blockIdx.x * blockDim.x + threadIdx.x; - int stride = blockDim.x * gridDim.x; + int tid = threadIdx.x; + int global_tid = blockIdx.x * blockDim.x + threadIdx.x; + int ari_block_idx = blockIdx.x; + // int stride = blockDim.x * gridDim.x; // used for max reduction // int part_part_elems = n_parts * n_parts; // obtain the corresponding parts and unique counts - int feature_comp_flat_idx = tid / n_part_mat_elems; // comparison pair index for two features - int part_pair_flat_idx = tid % part_part_elems; // comparison pair index for two partitions of one feature pair + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // comparison pair index for two partitions of one feature pair int i, j; // unravel the feature indices get_coords_from_index(n_parts, feature_comp_flat_idx, &i, &j); @@ -72,18 +83,41 @@ unravel_index(part_pair_flat_idx, n_parts, &m, &n); // Make pointers to select the parts and unique counts for the feature pair - int4* t_data_parti = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread - int4* t_data_partj = parts + j * n_elems_per_feat + n * n_objs ; - int4* t_data_uniqi = uniqs + i * n_parts + m; - int4* t_data_uniqj = uniqs + j * n_parts + n; + // Todo: Use int4*? + int* t_data_parti = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread + int* t_data_partj = parts + j * n_elems_per_feat + n * n_objs ; + //int* t_data_uniqi = uniqs + i * n_parts + m; + //int* t_data_uniqj = uniqs + j * n_parts + n; + int* blk_part_pairs = part_pairs + ari_block_idx * (2 * n_objs); // Load gmem data into smem by using different threads + extern __shared__ int shared_mem[]; - - + // Number of chunks of data this block will load + // In case block size is smaller than the partition size + const int num_chunks = (n_objs + blockDim.x - 1) / blockDim.x; + // Loop over the chunks of data + for (int chunk = 0; chunk < num_chunks; ++chunk) { + // idx is the linear global memory index of the element to load + int idx = chunk * blockDim.x + global_tid; + + if (idx < n_objs) { + // Load part_i and part_j into shared memory + shared_mem[tid] = t_data_parti[idx]; + shared_mem[tid + n_objs] = t_data_partj[idx]; + __syncthreads(); // Synchronize to ensure all threads have loaded data into shared memory + + // Each thread writes data back to global memory (for demonstration purposes) + // part0[idx] = shared_mem[tid]; + // part1[idx] = shared_mem[tid + n_objs]; + blk_part_pairs[idx] = shared_mem[tid]; + blk_part_pairs[idx + n_objs] = shared_mem[tid + n_objs]; + __syncthreads(); // Synchronize before moving to the next chunk + } + } + // Initialize shared memory - int part_mat_first_tid = tid * part_part_elems; - __syncthreads(); + // int part_mat_first_tid = tid * part_part_elems; // Todo: use a for loop to compute the ARI and do the max reduction } @@ -112,64 +146,7 @@ def get_kernel(): return kernel -def adjusted_rand_index( - part0: np.ndarray, - part1: np.ndarray, - size: int, - n_uniq0: int, - n_uniq1: int, - out: np.ndarray, - compare_pair_id: int, - i: int, - j: int, - stream: cp.cuda.Stream = None): - """ - Computes the adjusted Rand index (ARI) between two clustering partitions. - The code is based on the sklearn implementation here: - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html - See copyright notice at the top of this file. - - Host function to coordinate the GPU kernel. - - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - size: the number of objects in the partitions. - n_uniq0: the number of unique elements in part0. - n_uniq1: the number of unique elements in part1. - out: pointer to the output array containing all the ARI values. # TODO: make local - compare_pair_id: the index of the pair of partitions to compare. - i: the index of the first partition. - j: the index of the second partition. - stream: the CUDA stream to use. - - Returns: - A number representing the adjusted Rand index between two clustering - partitions. This number is between something around 0 (partitions do not - match; it could be negative in some cases) and 1.0 (perfect match). - """ - # TODO: - # Implement numpy ravel in the kernel using shared memory? - # Use different streams for different pairs? - # Ref api: CUML confusion_matrix - if not size >= 2: - raise ValueError("Need at least two samples to compare.") - - - - (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) - # convert to Python integer types, to avoid overflow or underflow - tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) - - # Special cases: empty data or full agreement - if fn == 0 and fp == 0: - res = 1.0 - - res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) - out[compare_pair_id, i, j] = res - - -def ari_dim2(parts: cp.ndarray, +def ari_dim2(feature_parts: cp.ndarray, n_partitions: int, n_features_comp: int, out: cp.ndarray, @@ -179,7 +156,7 @@ def ari_dim2(parts: cp.ndarray, in different streams for each pair of partitions. Args: - parts: 3D device array with cluster assignments for x features, y partitions, and z objects. + feature_parts: 3D device array with cluster assignments for x features, y partitions, and z objects. Example initialization for this array: d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 n_partitions: Number of partitions of a feature to compare. @@ -193,6 +170,9 @@ def ari_dim2(parts: cp.ndarray, # Can use non-blocking CPU scheduling or CUDA dynamic parallelism to launch the kernel for each pair of partitions. + # Get metadata + n_features, n_parts, n_objs = feature_parts.shape + # Each kernel launch will be responsible for computing the ARI between two partitions. n_part_mat_elems = n_partitions * n_partitions # Each thread @@ -201,10 +181,18 @@ def ari_dim2(parts: cp.ndarray, # Todo: how many ari pairs? n_range_cluster? threads_per_block = 1 blocks_per_grid = (n_ari_pairs + threads_per_block - 1) // threads_per_block + ari_kernel = get_kernel() - # Todo: use different streams + # Todo: use different streams? + # Allocate output arrays for parts (debugging) + out_parts0 = cp.empty(n_objs, dtype=np.int32) + out_parts1 = cp.empty(n_objs, dtype=np.int32) + shared_mem_size = 2 * n_objs + + # Launch the kernel, using one block per ARI ari_kernel(grid=(blocks_per_grid,), block=(threads_per_block,), - args=(parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) + shared_mem=shared_mem_size, + args=(feature_parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) raise NotImplementedError("Not implemented yet") diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 17ea61a2..ac2d6150 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -2,7 +2,7 @@ import math import cupy as cp import numpy as np -from ccc.sklearn.metrics_gpu2 import d_get_coords_from_index_str, d_unravel_index_str +from ccc.sklearn.metrics_gpu2 import d_get_coords_from_index_str, d_unravel_index_str, k_ari_str from ccc.coef import get_coords_from_index @@ -101,3 +101,75 @@ def test_unravel_index_kernel(num_cols, num_indices): print("All tests passed successfully!") + +def generate_pairwise_combinations(arr): + pairs = [] + num_slices = arr.shape[0] # Number of 2D arrays in the 3D array + + for i in range(num_slices): + for j in range(i + 1, num_slices): # Only consider pairs in different slices + for row_i in arr[i]: # Each row in slice i + for row_j in arr[j]: # Pairs with each row in slice j + pairs.append([row_i, row_j]) + + # Convert list of pairs to a NumPy array + return np.array(pairs) + + +@pytest.mark.parametrize("parts", [ + # 3D array + np.array([[[11, 12, 23, 34], + [12, 23, 34, 45], + [13, 34, 45, 56]], + + [[21, 12, 23, 34], + [22, 23, 34, 45], + [23, 34, 45, 56]], + + [[31, 12, 23, 34], + [32, 23, 34, 45], + [33, 34, 45, 56]]]), +]) +def test_art_parts_selection(parts): + pairs = generate_pairwise_combinations(parts) + + kernel_code = d_unravel_index_str + d_get_coords_from_index_str + k_ari_str + # Compile the CUDA kernel + module = cp.RawModule(code=kernel_code, backend='nvcc') + kernel = module.get_function("ari") + + # Create test inputs + n_features, n_parts, n_objs = parts.shape + n_feature_comp = n_features * (n_features - 1) // 2 + n_aris = n_feature_comp * n_parts * n_parts + block_size = 2 + grid_size = (n_aris + block_size - 1) // block_size + s_mem_size = n_objs * 2 * cp.int32().itemsize + + d_out = cp.empty(n_aris, dtype=cp.int32) + d_parts = cp.asarray(parts, dtype=cp.int32) + # Each pair of partitions will be compared, used for debugging purposes + d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) + d_uniqs = cp.empty(n_objs, dtype=cp.int32) + + # Print stats + print(f"Number of ARIs: {n_aris}") + # Print kernel configuration + print(f"Grid size: {grid_size}, Block size: {block_size}, Shared memory: {s_mem_size}") + # Launch the kernel + kernel((grid_size,), (block_size,), (d_parts, + d_uniqs, + n_aris, + n_parts, + n_objs, + n_parts * n_objs, + n_objs * n_objs, + d_out, + d_parts_pairs), + shared_mem=s_mem_size) + cp.cuda.runtime.deviceSynchronize() + # Get results back to host + h_parts_pairs = cp.asnumpy(d_parts_pairs) + print(h_parts_pairs) + # Assert pairs == d_parts_pairs + assert np.all(np.equal(h_parts_pairs, pairs)) From 5b19b2f8e80d77d6801680a3c2fe24e7fe014897 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 1 Oct 2024 16:48:25 -0600 Subject: [PATCH 053/134] [ari/kernel]: Make sure variable "parts" is correctly loaded --- libs/ccc/sklearn/metrics_gpu2.py | 36 +++++++++++++++++++++++++------- tests/gpu/test_kernel.py | 1 + 2 files changed, 29 insertions(+), 8 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index d9f17eea..476f0052 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -37,6 +37,7 @@ """ k_ari_str = """ +#include #define debug 0 /** @@ -56,6 +57,7 @@ void ari(int* parts, int* uniqs, const int n_aris, + const int n_features, const int n_parts, const int n_objs, const int n_elems_per_feat, @@ -64,23 +66,41 @@ int* part_pairs ) { - // tid corresponds to the ari idx + // tid is the block-wide thread index [0, blockDim.x] int tid = threadIdx.x; int global_tid = blockIdx.x * blockDim.x + threadIdx.x; - int ari_block_idx = blockIdx.x; - // int stride = blockDim.x * gridDim.x; - // used for max reduction - // int part_part_elems = n_parts * n_parts; - + // each block is responsible for one ARI computation + // int ari_block_idx = blockIdx.x; + int ari_block_idx = 3; + // obtain the corresponding parts and unique counts - int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // comparison pair index for two features - int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // comparison pair index for two partitions of one feature pair + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; + + // print parts for debugging + if (global_tid == 0) { + for (int i = 0; i < n_features; ++i) { + for (int j = 0; j < n_parts; ++j) { + for (int k = 0; k < n_objs; ++k) { + printf("parts[%d][%d][%d]: %d\\n", i, j, k, parts[i * n_parts * n_objs + j * n_objs + k]); + } + } + printf("\\n"); + } + } + // unravel the feature indices get_coords_from_index(n_parts, feature_comp_flat_idx, &i, &j); + if (global_tid == 0) { + printf("global_tid: %d, i: %d, j: %d\\n", global_tid, i, j); + } // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); + if (global_tid == 0){ + printf("global_tid: %d, m: %d, n: %d\\n", global_tid, m, n); + } // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index ac2d6150..26da5ef9 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -160,6 +160,7 @@ def test_art_parts_selection(parts): kernel((grid_size,), (block_size,), (d_parts, d_uniqs, n_aris, + n_features, n_parts, n_objs, n_parts * n_objs, From a3ce15093223ff593f704f52d2336b65ca31de75 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 4 Oct 2024 22:14:52 -0600 Subject: [PATCH 054/134] [kernel]: Add cuda cppp source code --- libs/ccc/sklearn/Makefile | 11 ++ libs/ccc/sklearn/metrics.cu | 276 ++++++++++++++++++++++++++++++++++++ 2 files changed, 287 insertions(+) create mode 100644 libs/ccc/sklearn/Makefile create mode 100644 libs/ccc/sklearn/metrics.cu diff --git a/libs/ccc/sklearn/Makefile b/libs/ccc/sklearn/Makefile new file mode 100644 index 00000000..60e3e87a --- /dev/null +++ b/libs/ccc/sklearn/Makefile @@ -0,0 +1,11 @@ +CU_APPS=metrics +C_APPS= + +all: ${C_APPS} ${CU_APPS} + +%: %.cu + nvcc -O2 -arch=sm_89 -o $@ $< -lcudadevrt --relocatable-device-code true +%: %.c + gcc -O2 -std=c99 -o $@ $< +clean: + rm -f ${CU_APPS} ${C_APPS} diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu new file mode 100644 index 00000000..1c0a526a --- /dev/null +++ b/libs/ccc/sklearn/metrics.cu @@ -0,0 +1,276 @@ +#include +#include +#include +#include +#include + + +// #define N_OBJS 16 +// #define N_PARTS 1 +// #define N_FEATURES 2 + + +/** + * @brief Unravel a flat index to the corresponding 2D indicis + * @param[in] flat_idx The flat index to unravel + * @param[in] num_cols Number of columns in the 2D array + * @param[out] row Pointer to the row index + * @param[out] col Pointer to the column index + */ +__device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int* row, int* col) { + // change int to uint32_t + *row = flat_idx / num_cols; // Compute row index + *col = flat_idx % num_cols; // Compute column index +} + + +__device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x, int* y) { + // Calculate 'b' based on the input n_obj + int b = 1 - 2 * n_obj; + // Calculate 'x' using the quadratic formula part + float discriminant = b * b - 8 * idx; + float x_float = floor((-b - sqrt(discriminant)) / 2); + // Assign the integer part of 'x' + *x = static_cast(x_float); + // Calculate 'y' based on 'x' and the index + *y = static_cast(idx + (*x) * (b + (*x) + 2) / 2 + 1); +} + + +/** + * @brief Main ARI kernel. Now only compare a pair of ARIs + * @param n_parts Number of partitions of each feature + * @param n_objs Number of objects in each partitions + * @param n_part_mat_elems Number of elements in the square partition matrix + * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @param uniqs Array of unique counts + * @param n_aris Number of ARIs to compute + * @param out Output array of ARIs + * @param part_pairs Output array of part pairs to be compared by ARI + */ +__global__ +void ari(int* parts, + int* uniqs, + const int n_aris, + const int n_features, + const int n_parts, + const int n_objs, + const int n_elems_per_feat, + const int n_part_mat_elems, + float* out, + int* part_pairs = nullptr + ) +{ + int global_tid = blockIdx.x * blockDim.x + threadIdx.x; + // each block is responsible for one ARI computation + int ari_block_idx = blockIdx.x; + + // print parts for debugging + if (global_tid == 0) { + for (int i = 0; i < n_features; ++i) { + for (int j = 0; j < n_parts; ++j) { + for (int k = 0; k < n_objs; ++k) { + printf("parts[%d][%d][%d]: %d\n", i, j, k, parts[i * n_parts * n_objs + j * n_objs + k]); + } + } + printf("\n"); + } + } + + // obtain the corresponding parts and unique counts + printf("n_part_mat_elems: %d\n", n_part_mat_elems); + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair + int i, j; + + if (global_tid == 0) { + printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); + } + + // unravel the feature indices + get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); + assert(i < n_features && j < n_features); + assert(i >= 0 && j >= 0); + if (global_tid == 0) { + printf("global_tid: %d, i: %d, j: %d\n", global_tid, i, j); + } + // unravel the partition indices + int m, n; + unravel_index(part_pair_flat_idx, n_parts, &m, &n); + if (global_tid == 0){ + printf("global_tid: %d, m: %d, n: %d\n", global_tid, m, n); + } + + // Make pointers to select the parts and unique counts for the feature pair + // Todo: Use int4*? + int* t_data_part0 = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread + int* t_data_part1 = parts + j * n_elems_per_feat + n * n_objs ; + //int* t_data_uniqi = uniqs + i * n_parts + m; + //int* t_data_uniqj = uniqs + j * n_parts + n; + + // Load gmem data into smem by using different threads + extern __shared__ int shared_mem[]; + int* s_part0 = shared_mem; + int* s_part1 = shared_mem + n_objs; + + // Loop over the data using the block-stride pattern + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) { + s_part0[i] = t_data_part0[i]; + s_part1[i] = t_data_part1[i]; + } + __syncthreads(); + + // Copy data to global memory + if (part_pairs != nullptr) { + int* out_part0 = part_pairs + ari_block_idx * (2 * n_objs); + int* out_part1 = out_part0 + n_objs; + + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) { + out_part0[i] = s_part0[i]; + out_part1[i] = s_part1[i]; + } + } + + // Todo: use a for loop to compute the ARI and do the max reduction +} + +// Helper function to generate pairwise combinations (implement this according to your needs) +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>>& arr) { + std::vector, std::vector>> pairs; + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) { + for (size_t j = i + 1; j < num_slices; ++j) { // Only consider pairs in different slices + for (const auto& row_i : arr[i]) { // Each row in slice i + for (const auto& row_j : arr[j]) { // Pairs with each row in slice j + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; +} + +void test_ari_parts_selection() { + // Define test input + std::vector>> parts = { + {{11, 12, 23, 34}, + {12, 23, 34, 45}, + {13, 34, 45, 56}}, + {{21, 12, 23, 34}, + {22, 23, 34, 45}, + {23, 34, 45, 56}}, + {{31, 12, 23, 34}, + {32, 23, 34, 45}, + {33, 34, 45, 56}} + }; + + + // Get dimensions + int n_features = parts.size(); + int n_parts = parts[0].size(); + int n_objs = parts[0][0].size(); + int n_feature_comp = n_features * (n_features - 1) / 2; + int n_aris = n_feature_comp * n_parts * n_parts; + std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + + // Allocate host memory for C-style array + int* h_parts = new int[n_features * n_parts * n_objs]; + + // Copy data from vector to C-style array + for (int i = 0; i < n_features; ++i) { + for (int j = 0; j < n_parts; ++j) { + for (int k = 0; k < n_objs; ++k) { + h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; + } + } + } + + // Set up CUDA kernel configuration + int block_size = 2; + // Each block is responsible for one ARI computation + int grid_size = n_aris; + size_t s_mem_size = n_objs * 2 * sizeof(int); + + // Allocate device memory + int *d_parts, *d_uniqs, *d_parts_pairs; + float *d_out; + cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); + cudaMalloc(&d_uniqs, n_objs * sizeof(int)); + cudaMalloc(&d_out, n_aris * sizeof(float)); + cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); + + // Copy data to device + cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); + + // Launch kernel + ari<<>>( + d_parts, + d_uniqs, + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + d_out, + d_parts_pairs + ); + + // Synchronize device + cudaDeviceSynchronize(); + + // Copy results back to host + int* h_parts_pairs = new int[n_aris * 2 * n_objs]; + cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); + + // Print results + std::cout << "Parts pairs: " << std::endl; + for (int i = 0; i < n_aris; ++i) { + std::cout << "Pair:" << i << std::endl; + for (int j = 0; j < 2; ++j) { + for (int k = 0; k < n_objs; ++k) { + std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << " "; + } + std::cout << std::endl; + } + std::cout << std::endl << std::endl; + } + std::cout << std::endl; + + // Assert equality + bool all_equal = true; + auto pairs = generate_pairwise_combinations(parts); + int n_pairs = pairs.size(); + for (int i = 0; i < n_pairs; ++i) { + for (int j = 0; j < 2; ++j) { + const std::vector& current_vector = (j == 0) ? pairs[i].first : pairs[i].second; + for (int k = 0; k < n_objs; ++k) { + int flattened_index = i * 2 * n_objs + j * n_objs + k; + if (h_parts_pairs[flattened_index] != current_vector[k]) { + all_equal = false; + std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; + std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; + } + } + } + } + + if (all_equal) { + std::cout << "Test passed: All elements match." << std::endl; + } else { + std::cout << "Test failed: Mismatches found." << std::endl; + } + + // Clean up + cudaFree(d_parts); + cudaFree(d_uniqs); + cudaFree(d_out); + cudaFree(d_parts_pairs); + delete[] h_parts_pairs; +} + +int main() { + test_ari_parts_selection(); + return 0; +} \ No newline at end of file From 386e7e2727ae3cf8379de0fc9e20c5a9ac448117 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 4 Oct 2024 22:34:53 -0600 Subject: [PATCH 055/134] [kernel/cu]: Clean up code --- libs/ccc/sklearn/metrics.cu | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 1c0a526a..9ba2355f 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -62,6 +62,9 @@ void ari(int* parts, int* part_pairs = nullptr ) { + /* + * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory + */ int global_tid = blockIdx.x * blockDim.x + threadIdx.x; // each block is responsible for one ARI computation int ari_block_idx = blockIdx.x; @@ -121,7 +124,7 @@ void ari(int* parts, } __syncthreads(); - // Copy data to global memory + // Copy data to global memory if part_pairs is specified if (part_pairs != nullptr) { int* out_part0 = part_pairs + ari_block_idx * (2 * n_objs); int* out_part1 = out_part0 + n_objs; @@ -132,7 +135,18 @@ void ari(int* parts, } } - // Todo: use a for loop to compute the ARI and do the max reduction + /* + * Step 2: Compute contingency matrix within the block + */ + + + /* + * Step 3: Construct pair confusion matrix + */ + + /* + * Step 4: Compute ARI and write to global memory + */ } // Helper function to generate pairwise combinations (implement this according to your needs) @@ -238,7 +252,7 @@ void test_ari_parts_selection() { } std::cout << std::endl; - // Assert equality + // Assert equality on the parts pairs bool all_equal = true; auto pairs = generate_pairwise_combinations(parts); int n_pairs = pairs.size(); From bccf7a08e0fb669de7ba2c301941b0cd28583b61 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 4 Oct 2024 23:44:42 -0600 Subject: [PATCH 056/134] [coef]: Precompute the max for each partition --- libs/ccc/coef/impl_gpu.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 8bb37b4f..ba14a75e 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -601,8 +601,13 @@ def ccc( # Compute partitions for each feature using CuPy d_parts, d_uniq_ele_counts = get_parts(X, range_n_clusters) + # used in the ARI computation later + n_parts = range_n_clusters.shape[0] + # d_parts_max_per_part = cp.empty(n_features * n_parts, dtype=np.int8) + d_parts_max_per_part = cp.amax(d_parts, axis=2) print("GPU parts:") print(d_parts) + print(f"Max per part: {d_parts_max_per_part}") # 2. CCC coefficient computation From a8d3d687f9635ef783d5c09a98351bdab0aee76d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 9 Oct 2024 13:41:33 -0600 Subject: [PATCH 057/134] [metrics/kennel]: Add device function to compute the contingency matrix --- libs/ccc/sklearn/metrics.cu | 83 +++++++++++++++++++------ libs/ccc/sklearn/metrics_gpu2.py | 34 ++++++++++ tests/gpu/test_impl_gpu_against_impl.py | 4 +- tests/gpu/test_kernel.py | 62 +++++++++++++++++- 4 files changed, 161 insertions(+), 22 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 9ba2355f..bf439eb1 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -37,6 +37,38 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x } +/** + * @brief Compute the contingency matrix for two partitions using shared memory + * @param[in] part0 Pointer to the first partition array + * @param[in] part1 Pointer to the second partition array + * @param[in] n Number of elements in each partition array + * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix + * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) + */ +__device__ void get_contingency_matrix(int* part0, int* part1, int n, int* shared_cont_mat, int k) { + int tid = threadIdx.x; + int bid = blockIdx.x; + int num_threads = blockDim.x; + int num_blocks = gridDim.x; + + // Initialize shared memory + for (int i = tid; i < k * k; i += num_threads) { + shared_cont_mat[i] = 0; + } + __syncthreads(); + + // Process elements + for (int i = tid; i < n; i += num_threads) { + int row = part0[i]; + int col = part1[i]; + + if (row < k && col < k) { + atomicAdd(&shared_cont_mat[row * k + col], 1); + } + } + __syncthreads(); +} + /** * @brief Main ARI kernel. Now only compare a pair of ARIs * @param n_parts Number of partitions of each feature @@ -44,20 +76,21 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x * @param n_part_mat_elems Number of elements in the square partition matrix * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @param uniqs Array of unique counts + * @param d_part_maxes Array of unique counts * @param n_aris Number of ARIs to compute * @param out Output array of ARIs * @param part_pairs Output array of part pairs to be compared by ARI */ __global__ void ari(int* parts, - int* uniqs, + int* d_part_maxes, const int n_aris, const int n_features, const int n_parts, const int n_objs, const int n_elems_per_feat, const int n_part_mat_elems, + const int k, float* out, int* part_pairs = nullptr ) @@ -109,8 +142,8 @@ void ari(int* parts, // Todo: Use int4*? int* t_data_part0 = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread int* t_data_part1 = parts + j * n_elems_per_feat + n * n_objs ; - //int* t_data_uniqi = uniqs + i * n_parts + m; - //int* t_data_uniqj = uniqs + j * n_parts + n; + //int* t_data_uniqi = d_part_maxes + i * n_parts + m; + //int* t_data_uniqj = d_part_maxes + j * n_parts + n; // Load gmem data into smem by using different threads extern __shared__ int shared_mem[]; @@ -138,8 +171,13 @@ void ari(int* parts, /* * Step 2: Compute contingency matrix within the block */ - - + // start shared mem address for the max values + int* s_contingency = shared_mem + 2 * n_objs; + // initialize the contingency matrix to zero + const int n_contingency_items = k * k; + for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { + s_contingency[i] = 0; + } /* * Step 3: Construct pair confusion matrix */ @@ -168,17 +206,20 @@ std::vector, std::vector>> generate_pairwise_com void test_ari_parts_selection() { // Define test input std::vector>> parts = { - {{11, 12, 23, 34}, - {12, 23, 34, 45}, - {13, 34, 45, 56}}, - {{21, 12, 23, 34}, - {22, 23, 34, 45}, - {23, 34, 45, 56}}, - {{31, 12, 23, 34}, - {32, 23, 34, 45}, - {33, 34, 45, 56}} + {{0, 1, 2, 3}, + {0, 2, 3, 4}, + {0, 3, 4, 5}}, + {{1, 1, 2, 3}, + {1, 2, 3, 4}, + {1, 3, 4, 5}}, + {{2, 1, 2, 3}, + {2, 2, 3, 4}, + {2, 3, 4, 5}} }; + const int k = 6; // specified by the call to ccc , part number from [0...9] + vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; + // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); // Get dimensions int n_features = parts.size(); @@ -204,29 +245,33 @@ void test_ari_parts_selection() { int block_size = 2; // Each block is responsible for one ARI computation int grid_size = n_aris; + // Compute shared memory size size_t s_mem_size = n_objs * 2 * sizeof(int); + s_mem_size += k * sizeof(int); // For the max values // Allocate device memory - int *d_parts, *d_uniqs, *d_parts_pairs; + int *d_parts, *d_parts_pairs, *d_part_maxes; float *d_out; cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); - cudaMalloc(&d_uniqs, n_objs * sizeof(int)); cudaMalloc(&d_out, n_aris * sizeof(float)); cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); + cudaMalloc(&d_part_maxes, n_features * n_parts * sizeof(int)); // Copy data to device cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); + cudaMemcpy(d_part_maxes, part_maxes, n_objs * sizeof(int), cudaMemcpyHostToDevice); // Launch kernel ari<<>>( d_parts, - d_uniqs, + d_part_maxes, n_aris, n_features, n_parts, n_objs, n_parts * n_objs, n_parts * n_parts, + k, d_out, d_parts_pairs ); @@ -278,7 +323,7 @@ void test_ari_parts_selection() { // Clean up cudaFree(d_parts); - cudaFree(d_uniqs); + cudaFree(d_part_maxes); cudaFree(d_out); cudaFree(d_parts_pairs); delete[] h_parts_pairs; diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 476f0052..7d2acd87 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -4,6 +4,40 @@ from numba import cuda import rmm +d_get_contingency_matrix_str = """ +/** + * @brief Compute the contingency matrix for two partitions using shared memory + * @param[in] part0 Pointer to the first partition array + * @param[in] part1 Pointer to the second partition array + * @param[in] n Number of elements in each partition array + * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix + * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) + */ +__device__ void get_contingency_matrix(int* part0, int* part1, int n, int* shared_cont_mat, int k) { + int tid = threadIdx.x; + int bid = blockIdx.x; + int num_threads = blockDim.x; + int num_blocks = gridDim.x; + + // Initialize shared memory + for (int i = tid; i < k * k; i += num_threads) { + shared_cont_mat[i] = 0; + } + __syncthreads(); + + // Process elements + for (int i = tid; i < n; i += num_threads) { + int row = part0[i]; + int col = part1[i]; + + if (row < k && col < k) { + atomicAdd(&shared_cont_mat[row * k + col], 1); + } + } + __syncthreads(); +} + +""" d_unravel_index_str = """ /** diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu_against_impl.py index e177975c..9f2514de 100644 --- a/tests/gpu/test_impl_gpu_against_impl.py +++ b/tests/gpu/test_impl_gpu_against_impl.py @@ -11,8 +11,8 @@ def test_ccc_gpu_1d_simple(): np.random.seed(0) - feature1 = np.random.rand(1000) - feature2 = np.random.rand(1000) + feature1 = np.random.rand(10) + feature2 = np.random.rand(10) c1 = ccc_gpu(feature1, feature2) c2 = ccc(feature1, feature2) print(f"GPU: {c1}, CPU: {c2}") diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 26da5ef9..dbaa6121 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -2,8 +2,14 @@ import math import cupy as cp import numpy as np -from ccc.sklearn.metrics_gpu2 import d_get_coords_from_index_str, d_unravel_index_str, k_ari_str +from ccc.sklearn.metrics_gpu2 import ( + d_get_coords_from_index_str, + d_unravel_index_str, + d_get_contingency_matrix_str, + k_ari_str +) from ccc.coef import get_coords_from_index +from ccc.sklearn.metrics import get_contingency_matrix def test_get_coords_from_index_kernel(): @@ -102,6 +108,60 @@ def test_unravel_index_kernel(num_cols, num_indices): print("All tests passed successfully!") +def test_get_contingency_matrix_kernel(): + test_kernel_code = """ + extern "C" + __global__ void test_kernel(int* part0, int* part1, int n, int* cont_mat, int k) { + extern __shared__ int shared_cont_mat[]; + + // Call the function to compute contingency matrix in shared memory + get_contingency_matrix(part0, part1, n, shared_cont_mat, k); + + // Copy shared memory back to global memory + int tid = threadIdx.x; + int num_threads = blockDim.x; + + for (int i = tid; i < k * k; i += num_threads) { + atomicAdd(&cont_mat[i], shared_cont_mat[i]); + } + } + """ + cuda_code = d_get_contingency_matrix_str + test_kernel_code + # Compile the CUDA kernel + module = cp.RawModule(code=cuda_code, backend='nvcc') + kernel = module.get_function("test_kernel") + + # Test parameters + n = 1000 + k = 5 + + # Generate random partitions + part0 = np.random.randint(0, k, size=n, dtype=np.int32) + part1 = np.random.randint(0, k, size=n, dtype=np.int32) + # Transfer data to GPU + d_part0 = cp.asarray(part0) + d_part1 = cp.asarray(part1) + d_cont_mat = cp.zeros((k, k), dtype=cp.int32) + + # Launch the kernel + threads_per_block = 256 + blocks = (n + threads_per_block - 1) // threads_per_block + shared_mem_size = k * k * 4 # 4 bytes per int + kernel((blocks,), (threads_per_block,), + (d_part0, d_part1, n, d_cont_mat, k), + shared_mem=shared_mem_size) + + # Get results back to host + h_cont_mat = cp.asnumpy(d_cont_mat) + + # Compare with reference implementation + ref_cont_mat = get_contingency_matrix(part0, part1) + + np.testing.assert_array_equal(h_cont_mat, ref_cont_mat, + err_msg="CUDA and reference implementations do not match") + print("Test passed successfully!") + + def generate_pairwise_combinations(arr): pairs = [] num_slices = arr.shape[0] # Number of 2D arrays in the 3D array From fc0252511de5adee38911ec35c922e2b25288e2e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 9 Oct 2024 14:03:59 -0600 Subject: [PATCH 058/134] [metrics/kernel]: Fix test --- tests/gpu/test_kernel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index dbaa6121..755b3047 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -145,7 +145,7 @@ def test_get_contingency_matrix_kernel(): # Launch the kernel threads_per_block = 256 - blocks = (n + threads_per_block - 1) // threads_per_block + blocks = 1 # A pair of partitions is handled by one block shared_mem_size = k * k * 4 # 4 bytes per int kernel((blocks,), (threads_per_block,), (d_part0, d_part1, n, d_cont_mat, k), From e1483204caa374129fa8ec8e35dba5fd615ca223 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 9 Oct 2024 14:14:49 -0600 Subject: [PATCH 059/134] [metrics/kernel]: Parameterize tests --- tests/gpu/test_kernel.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 755b3047..c2052199 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -108,7 +108,10 @@ def test_unravel_index_kernel(num_cols, num_indices): print("All tests passed successfully!") -def test_get_contingency_matrix_kernel(): +@pytest.mark.parametrize("n", [100, 1000, 10000]) +@pytest.mark.parametrize("threads_per_block", [1, 2, 64, 128, 256, 512]) +@pytest.mark.parametrize("k", [2, 5, 10]) +def test_get_contingency_matrix_kernel(n, threads_per_block, k): test_kernel_code = """ extern "C" __global__ void test_kernel(int* part0, int* part1, int n, int* cont_mat, int k) { @@ -131,21 +134,17 @@ def test_get_contingency_matrix_kernel(): module = cp.RawModule(code=cuda_code, backend='nvcc') kernel = module.get_function("test_kernel") - # Test parameters - n = 1000 - k = 5 - # Generate random partitions part0 = np.random.randint(0, k, size=n, dtype=np.int32) part1 = np.random.randint(0, k, size=n, dtype=np.int32) + # Transfer data to GPU d_part0 = cp.asarray(part0) d_part1 = cp.asarray(part1) d_cont_mat = cp.zeros((k, k), dtype=cp.int32) # Launch the kernel - threads_per_block = 256 - blocks = 1 # A pair of partitions is handled by one block + blocks = 1 # Each pair of partitions is handled by only one block (to fully utilize shared memory) shared_mem_size = k * k * 4 # 4 bytes per int kernel((blocks,), (threads_per_block,), (d_part0, d_part1, n, d_cont_mat, k), @@ -158,8 +157,8 @@ def test_get_contingency_matrix_kernel(): ref_cont_mat = get_contingency_matrix(part0, part1) np.testing.assert_array_equal(h_cont_mat, ref_cont_mat, - err_msg="CUDA and reference implementations do not match") - print("Test passed successfully!") + err_msg=f"CUDA and reference implementations do not match for n={n}, threads_per_block={threads_per_block}, k={k}") + print(f"Test passed successfully for n={n}, threads_per_block={threads_per_block}, k={k}") def generate_pairwise_combinations(arr): From 951b004081d4c8ae61202c51ccfcc37ea39a3d40 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 9 Oct 2024 15:02:39 -0600 Subject: [PATCH 060/134] [metrics/kernel]: Update C++ CUDA code --- libs/ccc/sklearn/metrics.cu | 231 +++++++++++++++++++++--------------- 1 file changed, 135 insertions(+), 96 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index bf439eb1..4b5c9a73 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -4,12 +4,10 @@ #include #include - // #define N_OBJS 16 // #define N_PARTS 1 // #define N_FEATURES 2 - /** * @brief Unravel a flat index to the corresponding 2D indicis * @param[in] flat_idx The flat index to unravel @@ -17,14 +15,15 @@ * @param[out] row Pointer to the row index * @param[out] col Pointer to the column index */ -__device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int* row, int* col) { +__device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int *row, int *col) +{ // change int to uint32_t - *row = flat_idx / num_cols; // Compute row index - *col = flat_idx % num_cols; // Compute column index + *row = flat_idx / num_cols; // Compute row index + *col = flat_idx % num_cols; // Compute column index } - -__device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x, int* y) { +__device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int *x, int *y) +{ // Calculate 'b' based on the input n_obj int b = 1 - 2 * n_obj; // Calculate 'x' using the quadratic formula part @@ -36,7 +35,6 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x *y = static_cast(idx + (*x) * (b + (*x) + 2) / 2 + 1); } - /** * @brief Compute the contingency matrix for two partitions using shared memory * @param[in] part0 Pointer to the first partition array @@ -45,24 +43,29 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) */ -__device__ void get_contingency_matrix(int* part0, int* part1, int n, int* shared_cont_mat, int k) { +__device__ void get_contingency_matrix(int *part0, int *part1, int n, int *shared_cont_mat, int k) +{ int tid = threadIdx.x; int bid = blockIdx.x; int num_threads = blockDim.x; int num_blocks = gridDim.x; + int size = k * k; // Initialize shared memory - for (int i = tid; i < k * k; i += num_threads) { + for (int i = tid; i < size; i += num_threads) + { shared_cont_mat[i] = 0; } __syncthreads(); // Process elements - for (int i = tid; i < n; i += num_threads) { + for (int i = tid; i < n; i += num_threads) + { int row = part0[i]; int col = part1[i]; - - if (row < k && col < k) { + + if (row < k && col < k) + { atomicAdd(&shared_cont_mat[row * k + col], 1); } } @@ -76,37 +79,37 @@ __device__ void get_contingency_matrix(int* part0, int* part1, int n, int* share * @param n_part_mat_elems Number of elements in the square partition matrix * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @param d_part_maxes Array of unique counts * @param n_aris Number of ARIs to compute * @param out Output array of ARIs * @param part_pairs Output array of part pairs to be compared by ARI */ -__global__ -void ari(int* parts, - int* d_part_maxes, - const int n_aris, - const int n_features, - const int n_parts, - const int n_objs, - const int n_elems_per_feat, - const int n_part_mat_elems, - const int k, - float* out, - int* part_pairs = nullptr - ) +__global__ void ari(int *parts, + const int n_aris, + const int n_features, + const int n_parts, + const int n_objs, + const int n_elems_per_feat, + const int n_part_mat_elems, + const int k, + float *out, + int *part_pairs = nullptr) { /* - * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory - */ + * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory + */ int global_tid = blockIdx.x * blockDim.x + threadIdx.x; // each block is responsible for one ARI computation int ari_block_idx = blockIdx.x; // print parts for debugging - if (global_tid == 0) { - for (int i = 0; i < n_features; ++i) { - for (int j = 0; j < n_parts; ++j) { - for (int k = 0; k < n_objs; ++k) { + if (global_tid == 0) + { + for (int i = 0; i < n_features; ++i) + { + for (int j = 0; j < n_parts; ++j) + { + for (int k = 0; k < n_objs; ++k) + { printf("parts[%d][%d][%d]: %d\n", i, j, k, parts[i * n_parts * n_objs + j * n_objs + k]); } } @@ -116,11 +119,12 @@ void ari(int* parts, // obtain the corresponding parts and unique counts printf("n_part_mat_elems: %d\n", n_part_mat_elems); - int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features - int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - if (global_tid == 0) { + if (global_tid == 0) + { printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); } @@ -128,73 +132,95 @@ void ari(int* parts, get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); assert(i < n_features && j < n_features); assert(i >= 0 && j >= 0); - if (global_tid == 0) { + if (global_tid == 0) + { printf("global_tid: %d, i: %d, j: %d\n", global_tid, i, j); } // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); - if (global_tid == 0){ + if (global_tid == 0) + { printf("global_tid: %d, m: %d, n: %d\n", global_tid, m, n); } - + // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? - int* t_data_part0 = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread - int* t_data_part1 = parts + j * n_elems_per_feat + n * n_objs ; - //int* t_data_uniqi = d_part_maxes + i * n_parts + m; - //int* t_data_uniqj = d_part_maxes + j * n_parts + n; - + int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread + int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; + // int* t_data_uniqi = d_part_maxes + i * n_parts + m; + // int* t_data_uniqj = d_part_maxes + j * n_parts + n; + // Load gmem data into smem by using different threads extern __shared__ int shared_mem[]; - int* s_part0 = shared_mem; - int* s_part1 = shared_mem + n_objs; - + int *s_part0 = shared_mem; + int *s_part1 = shared_mem + n_objs; + // Loop over the data using the block-stride pattern - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) { + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { s_part0[i] = t_data_part0[i]; s_part1[i] = t_data_part1[i]; } __syncthreads(); // Copy data to global memory if part_pairs is specified - if (part_pairs != nullptr) { - int* out_part0 = part_pairs + ari_block_idx * (2 * n_objs); - int* out_part1 = out_part0 + n_objs; + if (part_pairs != nullptr) + { + int *out_part0 = part_pairs + ari_block_idx * (2 * n_objs); + int *out_part1 = out_part0 + n_objs; - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) { + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { out_part0[i] = s_part0[i]; out_part1[i] = s_part1[i]; } } - + /* - * Step 2: Compute contingency matrix within the block - */ + * Step 2: Compute contingency matrix within the block + */ // start shared mem address for the max values - int* s_contingency = shared_mem + 2 * n_objs; + int *s_contingency = shared_mem + 2 * n_objs; // initialize the contingency matrix to zero - const int n_contingency_items = k * k; - for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { - s_contingency[i] = 0; + // const int n_contingency_items = k * k; + // for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { + // s_contingency[i] = 0; + // } + get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); + if (global_tid == 0) + { + for (int i = 0; i < k; ++i) + { + for (int j = 0; j < k; ++j) + { + printf("s_contingency[%d][%d]: %d\n", i, j, s_contingency[i * k + j]); + } + } } + // /* - * Step 3: Construct pair confusion matrix - */ + * Step 3: Construct pair confusion matrix + */ /* - * Step 4: Compute ARI and write to global memory - */ + * Step 4: Compute ARI and write to global memory + */ } // Helper function to generate pairwise combinations (implement this according to your needs) -std::vector, std::vector>> generate_pairwise_combinations(const std::vector>>& arr) { +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) +{ std::vector, std::vector>> pairs; - size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector - for (size_t i = 0; i < num_slices; ++i) { - for (size_t j = i + 1; j < num_slices; ++j) { // Only consider pairs in different slices - for (const auto& row_i : arr[i]) { // Each row in slice i - for (const auto& row_j : arr[j]) { // Pairs with each row in slice j + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) + { + for (size_t j = i + 1; j < num_slices; ++j) + { // Only consider pairs in different slices + for (const auto &row_i : arr[i]) + { // Each row in slice i + for (const auto &row_j : arr[j]) + { // Pairs with each row in slice j pairs.emplace_back(row_i, row_j); } } @@ -203,7 +229,8 @@ std::vector, std::vector>> generate_pairwise_com return pairs; } -void test_ari_parts_selection() { +void test_ari_parts_selection() +{ // Define test input std::vector>> parts = { {{0, 1, 2, 3}, @@ -214,11 +241,10 @@ void test_ari_parts_selection() { {1, 3, 4, 5}}, {{2, 1, 2, 3}, {2, 2, 3, 4}, - {2, 3, 4, 5}} - }; + {2, 3, 4, 5}}}; const int k = 6; // specified by the call to ccc , part number from [0...9] - vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; + std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); // Get dimensions @@ -227,15 +253,19 @@ void test_ari_parts_selection() { int n_objs = parts[0][0].size(); int n_feature_comp = n_features * (n_features - 1) / 2; int n_aris = n_feature_comp * n_parts * n_parts; - std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl + << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; // Allocate host memory for C-style array - int* h_parts = new int[n_features * n_parts * n_objs]; + int *h_parts = new int[n_features * n_parts * n_objs]; // Copy data from vector to C-style array - for (int i = 0; i < n_features; ++i) { - for (int j = 0; j < n_parts; ++j) { - for (int k = 0; k < n_objs; ++k) { + for (int i = 0; i < n_features; ++i) + { + for (int j = 0; j < n_parts; ++j) + { + for (int k = 0; k < n_objs; ++k) + { h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; } } @@ -247,7 +277,7 @@ void test_ari_parts_selection() { int grid_size = n_aris; // Compute shared memory size size_t s_mem_size = n_objs * 2 * sizeof(int); - s_mem_size += k * sizeof(int); // For the max values + s_mem_size += k * sizeof(int); // For the max values // Allocate device memory int *d_parts, *d_parts_pairs, *d_part_maxes; @@ -259,12 +289,10 @@ void test_ari_parts_selection() { // Copy data to device cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); - cudaMemcpy(d_part_maxes, part_maxes, n_objs * sizeof(int), cudaMemcpyHostToDevice); // Launch kernel ari<<>>( d_parts, - d_part_maxes, n_aris, n_features, n_parts, @@ -273,27 +301,30 @@ void test_ari_parts_selection() { n_parts * n_parts, k, d_out, - d_parts_pairs - ); + d_parts_pairs); // Synchronize device cudaDeviceSynchronize(); // Copy results back to host - int* h_parts_pairs = new int[n_aris * 2 * n_objs]; + int *h_parts_pairs = new int[n_aris * 2 * n_objs]; cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); // Print results std::cout << "Parts pairs: " << std::endl; - for (int i = 0; i < n_aris; ++i) { + for (int i = 0; i < n_aris; ++i) + { std::cout << "Pair:" << i << std::endl; - for (int j = 0; j < 2; ++j) { - for (int k = 0; k < n_objs; ++k) { + for (int j = 0; j < 2; ++j) + { + for (int k = 0; k < n_objs; ++k) + { std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << " "; } std::cout << std::endl; } - std::cout << std::endl << std::endl; + std::cout << std::endl + << std::endl; } std::cout << std::endl; @@ -301,12 +332,16 @@ void test_ari_parts_selection() { bool all_equal = true; auto pairs = generate_pairwise_combinations(parts); int n_pairs = pairs.size(); - for (int i = 0; i < n_pairs; ++i) { - for (int j = 0; j < 2; ++j) { - const std::vector& current_vector = (j == 0) ? pairs[i].first : pairs[i].second; - for (int k = 0; k < n_objs; ++k) { + for (int i = 0; i < n_pairs; ++i) + { + for (int j = 0; j < 2; ++j) + { + const std::vector ¤t_vector = (j == 0) ? pairs[i].first : pairs[i].second; + for (int k = 0; k < n_objs; ++k) + { int flattened_index = i * 2 * n_objs + j * n_objs + k; - if (h_parts_pairs[flattened_index] != current_vector[k]) { + if (h_parts_pairs[flattened_index] != current_vector[k]) + { all_equal = false; std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; @@ -315,9 +350,12 @@ void test_ari_parts_selection() { } } - if (all_equal) { + if (all_equal) + { std::cout << "Test passed: All elements match." << std::endl; - } else { + } + else + { std::cout << "Test failed: Mismatches found." << std::endl; } @@ -329,7 +367,8 @@ void test_ari_parts_selection() { delete[] h_parts_pairs; } -int main() { +int main() +{ test_ari_parts_selection(); return 0; } \ No newline at end of file From 945de797b8e34ae6d7001cf266ed0dd98c5350b9 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 11 Oct 2024 11:38:51 -0600 Subject: [PATCH 061/134] [metrics/kernel]: Add initial version of pair_confusion_matrix --- libs/ccc/sklearn/metrics.cu | 61 +++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 4b5c9a73..8692a902 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -72,6 +72,67 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share __syncthreads(); } + +/** + * @brief CUDA device function to compute the pair confusion matrix + * @param[in] part0 Pointer to the first partition array + * @param[in] part1 Pointer to the second partition array + * @param[in] n_samples Number of samples in each partition + * @param[in] contingency Pointer to the contingency matrix + * @param[in] k Number of clusters (assuming k is the max of clusters in part0 and part1) + * @param[out] C Pointer to the output pair confusion matrix (2x2) + */ +__device__ void get_pair_confusion_matrix_kernel(const int* part0, const int* part1, + int n_samples, const int* contingency, + int k, long long* C) { + // Compute sum1 and sum0 + __shared__ int sum1[32]; // Assume max 32 clusters, adjust if needed + __shared__ int sum0[32]; + + for (int i = threadIdx.x; i < k; i += blockDim.x) { + sum1[i] = 0; + sum0[i] = 0; + for (int j = 0; j < k; ++j) { + sum1[i] += contingency[i * k + j]; + sum0[i] += contingency[j * k + i]; + } + } + __syncthreads(); + + // Compute sum_squares + __shared__ long long sum_squares; + if (threadIdx.x == 0) { + sum_squares = 0; + for (int i = 0; i < k * k; ++i) { + sum_squares += static_cast(contingency[i]) * contingency[i]; + } + } + __syncthreads(); + + // Compute C[1,1], C[0,1], C[1,0], and C[0,0] + if (threadIdx.x == 0) { + C[3] = sum_squares - n_samples; // C[1,1] + + long long temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += static_cast(contingency[i * k + j]) * sum0[j]; + } + } + C[1] = temp - sum_squares; // C[0,1] + + temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += static_cast(contingency[j * k + i]) * sum1[j]; + } + } + C[2] = temp - sum_squares; // C[1,0] + + C[0] = static_cast(n_samples) * n_samples - C[1] - C[2] - sum_squares; // C[0,0] + } +} + /** * @brief Main ARI kernel. Now only compare a pair of ARIs * @param n_parts Number of partitions of each feature From f2bc8cfee764fd51822ed17232c761c1d1b879ca Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 15 Oct 2024 15:24:48 -0600 Subject: [PATCH 062/134] [metric/device]: Add logic to perform matrix sum --- libs/ccc/sklearn/metrics.cu | 67 +++++++++++++++++++++---------------- 1 file changed, 39 insertions(+), 28 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 8692a902..c977cad0 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -75,36 +75,46 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share /** * @brief CUDA device function to compute the pair confusion matrix - * @param[in] part0 Pointer to the first partition array - * @param[in] part1 Pointer to the second partition array - * @param[in] n_samples Number of samples in each partition * @param[in] contingency Pointer to the contingency matrix + * @param[in] sum_rows Pointer to the sum of rows in the contingency matrix + * @param[in] sum_cols Pointer to the sum of columns in the contingency matrix + * @param[in] n_objs Number of objects in each partition * @param[in] k Number of clusters (assuming k is the max of clusters in part0 and part1) * @param[out] C Pointer to the output pair confusion matrix (2x2) */ -__device__ void get_pair_confusion_matrix_kernel(const int* part0, const int* part1, - int n_samples, const int* contingency, - int k, long long* C) { - // Compute sum1 and sum0 - __shared__ int sum1[32]; // Assume max 32 clusters, adjust if needed - __shared__ int sum0[32]; - +__device__ void get_pair_confusion_matrix( + const int* __restrict__ contingency, + int * sum_rows, + int * sum_cols, + const int n_objs, + const int k, + int* C +) { + // Initialize sum_rows and sum_cols for (int i = threadIdx.x; i < k; i += blockDim.x) { - sum1[i] = 0; - sum0[i] = 0; - for (int j = 0; j < k; ++j) { - sum1[i] += contingency[i * k + j]; - sum0[i] += contingency[j * k + i]; + sum_rows[i] = 0; + sum_cols[i] = 0; + } + __syncthreads(); + + // Compute sum_rows and sum_cols + for (int i = threadIdx.x; i < k; i += blockDim.x) { + for (int m = 0; m < k; ++m) { + for (int n = 0; n < k; ++n) { + const int val = contingency[m * k + n]; + atomicAdd(&sum_rows[m], val); + atomicAdd(&sum_cols[n], val); + } } } __syncthreads(); // Compute sum_squares - __shared__ long long sum_squares; + int sum_squares; if (threadIdx.x == 0) { sum_squares = 0; for (int i = 0; i < k * k; ++i) { - sum_squares += static_cast(contingency[i]) * contingency[i]; + sum_squares += (contingency[i]) * contingency[i]; } } __syncthreads(); @@ -209,8 +219,6 @@ __global__ void ari(int *parts, // Todo: Use int4*? int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; - // int* t_data_uniqi = d_part_maxes + i * n_parts + m; - // int* t_data_uniqj = d_part_maxes + j * n_parts + n; // Load gmem data into smem by using different threads extern __shared__ int shared_mem[]; @@ -241,7 +249,7 @@ __global__ void ari(int *parts, /* * Step 2: Compute contingency matrix within the block */ - // start shared mem address for the max values + // shared mem address for the contingency matrix int *s_contingency = shared_mem + 2 * n_objs; // initialize the contingency matrix to zero // const int n_contingency_items = k * k; @@ -259,11 +267,15 @@ __global__ void ari(int *parts, } } } - // + /* * Step 3: Construct pair confusion matrix */ - + // shared mem address for the pair confusion matrix + int *s_sum_rows = s_contingency + k * k; + int *s_sum_cols = s_sum_rows + k; + int *s_pair_confusion_matrix = s_sum_cols + k; + /* * Step 4: Compute ARI and write to global memory */ @@ -305,7 +317,7 @@ void test_ari_parts_selection() {2, 3, 4, 5}}}; const int k = 6; // specified by the call to ccc , part number from [0...9] - std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; + // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); // Get dimensions @@ -337,16 +349,16 @@ void test_ari_parts_selection() // Each block is responsible for one ARI computation int grid_size = n_aris; // Compute shared memory size - size_t s_mem_size = n_objs * 2 * sizeof(int); - s_mem_size += k * sizeof(int); // For the max values + size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared + s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays + s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix // Allocate device memory - int *d_parts, *d_parts_pairs, *d_part_maxes; + int *d_parts, *d_parts_pairs; float *d_out; cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); cudaMalloc(&d_out, n_aris * sizeof(float)); cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); - cudaMalloc(&d_part_maxes, n_features * n_parts * sizeof(int)); // Copy data to device cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); @@ -422,7 +434,6 @@ void test_ari_parts_selection() // Clean up cudaFree(d_parts); - cudaFree(d_part_maxes); cudaFree(d_out); cudaFree(d_parts_pairs); delete[] h_parts_pairs; From 39d06db145af3bb2e2d264cfd21fa7163698b4d9 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 12:46:53 -0600 Subject: [PATCH 063/134] [metrics/device]: Finish first version of get_pair_conf matrix --- libs/ccc/sklearn/metrics.cu | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index c977cad0..10023bd9 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -121,12 +121,12 @@ __device__ void get_pair_confusion_matrix( // Compute C[1,1], C[0,1], C[1,0], and C[0,0] if (threadIdx.x == 0) { - C[3] = sum_squares - n_samples; // C[1,1] + C[3] = sum_squares - n_objs; // C[1,1] - long long temp = 0; + int temp = 0; for (int i = 0; i < k; ++i) { for (int j = 0; j < k; ++j) { - temp += static_cast(contingency[i * k + j]) * sum0[j]; + temp += (contingency[i * k + j]) * sum_cols[j]; } } C[1] = temp - sum_squares; // C[0,1] @@ -134,12 +134,15 @@ __device__ void get_pair_confusion_matrix( temp = 0; for (int i = 0; i < k; ++i) { for (int j = 0; j < k; ++j) { - temp += static_cast(contingency[j * k + i]) * sum1[j]; + temp += (contingency[j * k + i]) * sum_rows[j]; } } C[2] = temp - sum_squares; // C[1,0] - C[0] = static_cast(n_samples) * n_samples - C[1] - C[2] - sum_squares; // C[0,0] + C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] + + // print C + printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\n", C[0], C[1], C[2], C[3]); } } @@ -151,6 +154,7 @@ __device__ void get_pair_confusion_matrix( * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) * @param n_aris Number of ARIs to compute + * @param k The max value of cluster number + 1 * @param out Output array of ARIs * @param part_pairs Output array of part pairs to be compared by ARI */ @@ -189,7 +193,7 @@ __global__ void ari(int *parts, } // obtain the corresponding parts and unique counts - printf("n_part_mat_elems: %d\n", n_part_mat_elems); + // printf("n_part_mat_elems: %d\n", n_part_mat_elems); int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; @@ -275,7 +279,7 @@ __global__ void ari(int *parts, int *s_sum_rows = s_contingency + k * k; int *s_sum_cols = s_sum_rows + k; int *s_pair_confusion_matrix = s_sum_cols + k; - + get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, s_pair_confusion_matrix); /* * Step 4: Compute ARI and write to global memory */ From 3c14345a79f886d708416d271f4dc41a60a3f643 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 13:12:53 -0600 Subject: [PATCH 064/134] [doc]: Add function documentation --- libs/ccc/sklearn/metrics.cu | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 10023bd9..19f855e5 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -22,6 +22,21 @@ __device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int *r *col = flat_idx % num_cols; // Compute column index } +/** + * @brief Given the number of objects and an index, this function calculates + * the coordinates in a symmetric matrix from a flat index. + * For example, if there are n_obj objects (such as genes), a condensed + * 1D array can be created with pairwise comparisons between these + * objects, which corresponds to a symmetric 2D matrix. This function + * calculates the 2D coordinates (x, y) in the symmetric matrix that + * corresponds to the given flat index. + * + * @param[in] n_obj The total number of objects (i.e., the size of one dimension + * of the square symmetric matrix). + * @param[in] idx The flat index from the condensed pairwise array. + * @param[out] x Pointer to the calculated row coordinate in the symmetric matrix. + * @param[out] y Pointer to the calculated column coordinate in the symmetric matrix. + */ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int *x, int *y) { // Calculate 'b' based on the input n_obj From 195e269acace383b29e990a823a7481e6ba9efd1 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 13:27:41 -0600 Subject: [PATCH 065/134] [test/kernel]: Update input matrix --- tests/gpu/test_kernel.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index c2052199..1cb4bdc3 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -177,17 +177,19 @@ def generate_pairwise_combinations(arr): @pytest.mark.parametrize("parts", [ # 3D array - np.array([[[11, 12, 23, 34], - [12, 23, 34, 45], - [13, 34, 45, 56]], - - [[21, 12, 23, 34], - [22, 23, 34, 45], - [23, 34, 45, 56]], - - [[31, 12, 23, 34], - [32, 23, 34, 45], - [33, 34, 45, 56]]]), + np.array([ + [[0, 1, 2, 3], + [0, 2, 3, 4], + [0, 3, 4, 5]], + + [[1, 1, 2, 3], + [1, 2, 3, 4], + [1, 3, 4, 5]], + + [[2, 1, 2, 3], + [2, 2, 3, 4], + [2, 3, 4, 5]] + ]) ]) def test_art_parts_selection(parts): pairs = generate_pairwise_combinations(parts) From d13e94687c300715a0814551aa9f7320cbb489ae Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 13:34:33 -0600 Subject: [PATCH 066/134] [metrics/device]: Add device function string for get_pair_conf matrics --- libs/ccc/sklearn/metrics_gpu2.py | 76 ++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 7d2acd87..46deb6b8 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -4,6 +4,82 @@ from numba import cuda import rmm + +d_get_confusion_matrix_str = """ +/** + * @brief CUDA device function to compute the pair confusion matrix + * @param[in] contingency Pointer to the contingency matrix + * @param[in] sum_rows Pointer to the sum of rows in the contingency matrix + * @param[in] sum_cols Pointer to the sum of columns in the contingency matrix + * @param[in] n_objs Number of objects in each partition + * @param[in] k Number of clusters (assuming k is the max of clusters in part0 and part1) + * @param[out] C Pointer to the output pair confusion matrix (2x2) + */ +__device__ void get_pair_confusion_matrix( + const int* __restrict__ contingency, + int * sum_rows, + int * sum_cols, + const int n_objs, + const int k, + int* C +) { + // Initialize sum_rows and sum_cols + for (int i = threadIdx.x; i < k; i += blockDim.x) { + sum_rows[i] = 0; + sum_cols[i] = 0; + } + __syncthreads(); + + // Compute sum_rows and sum_cols + for (int i = threadIdx.x; i < k; i += blockDim.x) { + for (int m = 0; m < k; ++m) { + for (int n = 0; n < k; ++n) { + const int val = contingency[m * k + n]; + atomicAdd(&sum_rows[m], val); + atomicAdd(&sum_cols[n], val); + } + } + } + __syncthreads(); + + // Compute sum_squares + int sum_squares; + if (threadIdx.x == 0) { + sum_squares = 0; + for (int i = 0; i < k * k; ++i) { + sum_squares += (contingency[i]) * contingency[i]; + } + } + __syncthreads(); + + // Compute C[1,1], C[0,1], C[1,0], and C[0,0] + if (threadIdx.x == 0) { + C[3] = sum_squares - n_objs; // C[1,1] + + int temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += (contingency[i * k + j]) * sum_cols[j]; + } + } + C[1] = temp - sum_squares; // C[0,1] + + temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += (contingency[j * k + i]) * sum_rows[j]; + } + } + C[2] = temp - sum_squares; // C[1,0] + + C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] + + // print C + printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\\n", C[0], C[1], C[2], C[3]); + } +} +""" + d_get_contingency_matrix_str = """ /** * @brief Compute the contingency matrix for two partitions using shared memory From 806dfdc223593038ba347488a6bddd11a13d5e1e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 14:20:50 -0600 Subject: [PATCH 067/134] [test/device]: Fix device memory type --- tests/gpu/test_kernel.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 1cb4bdc3..dc3f53f6 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -63,9 +63,9 @@ def test_get_coords_from_index_kernel(): (15, 100), (20, 200) ]) -def test_unravel_index_kernel(num_cols, num_indices): +def test_unravel_index_device(num_cols, num_indices): test_kernel_code = """ - extern "C" __global__ void test_unravel_index_kernel(size_t* flat_indices, size_t* rows, size_t* cols, size_t num_cols, size_t num_indices) { + extern "C" __global__ void test_unravel_index_kernel(int* flat_indices, int* rows, int* cols, int num_cols, int num_indices) { int tid = blockIdx.x * blockDim.x + threadIdx.x; if (tid < num_indices) { unravel_index(flat_indices[tid], num_cols, &rows[tid], &cols[tid]); @@ -79,11 +79,11 @@ def test_unravel_index_kernel(num_cols, num_indices): kernel = module.get_function("test_unravel_index_kernel") # Create test inputs - flat_indices = cp.arange(num_indices, dtype=cp.uint64) + flat_indices = cp.arange(num_indices, dtype=cp.int32) # Allocate memory for results (rows and cols) - d_rows = cp.empty(num_indices, dtype=cp.uint64) - d_cols = cp.empty(num_indices, dtype=cp.uint64) + d_rows = cp.zeros(num_indices, dtype=cp.int32) + d_cols = cp.zeros(num_indices, dtype=cp.int32) # Launch the kernel threads_per_block = 256 From 124ef0dfd609c08c9ac9903f18bc4ed9accec2eb Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 16:07:14 -0600 Subject: [PATCH 068/134] [test/device]: Compile kernel test successfully --- libs/ccc/sklearn/metrics_gpu2.py | 1 + tests/gpu/test_kernel.py | 50 +++++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 1 deletion(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 46deb6b8..e593a908 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -78,6 +78,7 @@ printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\\n", C[0], C[1], C[2], C[3]); } } + """ d_get_contingency_matrix_str = """ diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index dc3f53f6..98a1e1e3 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -3,6 +3,7 @@ import cupy as cp import numpy as np from ccc.sklearn.metrics_gpu2 import ( + d_get_confusion_matrix_str, d_get_coords_from_index_str, d_unravel_index_str, d_get_contingency_matrix_str, @@ -110,7 +111,7 @@ def test_unravel_index_device(num_cols, num_indices): @pytest.mark.parametrize("n", [100, 1000, 10000]) @pytest.mark.parametrize("threads_per_block", [1, 2, 64, 128, 256, 512]) -@pytest.mark.parametrize("k", [2, 5, 10]) +@pytest.mark.parametrize("k", [3, 5, 10]) # Max value of a cluster number + 1 def test_get_contingency_matrix_kernel(n, threads_per_block, k): test_kernel_code = """ extern "C" @@ -161,6 +162,53 @@ def test_get_contingency_matrix_kernel(n, threads_per_block, k): print(f"Test passed successfully for n={n}, threads_per_block={threads_per_block}, k={k}") +@pytest.mark.parametrize("n_objs", [10]) +@pytest.mark.parametrize("threads_per_block", [2]) +@pytest.mark.parametrize("k", [5]) # Max value of a cluster number + 1 +def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): + test_kernel_code = """ + extern "C" + __global__ void test_kernel(int* part0, int* part1, int n_objs, int k, int* out) { + extern __shared__ int shared_mem[]; + + // Call the function to compute contingency matrix in shared memory + int *s_contingency = shared_mem; + get_contingency_matrix(part0, part1, n_objs, s_contingency, k); + + int *s_sum_rows = s_contingency + k * k; + int *s_sum_cols = s_sum_rows + k; + int *C = s_sum_cols + k; + + get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, C); + } + """ + + cuda_code = d_get_contingency_matrix_str + d_get_confusion_matrix_str + test_kernel_code + # Compile the CUDA kernel + module = cp.RawModule(code=cuda_code, backend='nvcc') + kernel = module.get_function("test_kernel") + + # Generate random partitions + part0 = np.random.randint(0, k, size=n_objs, dtype=np.int32) + part1 = np.random.randint(0, k, size=n_objs, dtype=np.int32) + + # Transfer data to GPU + d_part0 = cp.asarray(part0) + d_part1 = cp.asarray(part1) + d_c = cp.zeros((2, 2), dtype=cp.int32) + + # Launch the kernel + blocks = 1 # Each pair of partitions is handled by only one block (to fully utilize shared memory) + shared_mem_size = k * k * 4 # 4 bytes per int for the cont matrix + shared_mem_size += 2 * k * 4 # For the internal sum arrays + shared_mem_size += 4 * 4 # For the C matrix + kernel((blocks,), (threads_per_block,), + (d_part0, d_part1, n_objs, k, d_c), + shared_mem=shared_mem_size) + + return + + def generate_pairwise_combinations(arr): pairs = [] num_slices = arr.shape[0] # Number of 2D arrays in the 3D array From 4e3cb6aa81c8e861f79b7f4f865457680837ca6d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 16 Oct 2024 16:31:30 -0600 Subject: [PATCH 069/134] [test/device]: Get incorrect conf matrix results --- tests/gpu/test_kernel.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 98a1e1e3..18e86b02 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -10,7 +10,7 @@ k_ari_str ) from ccc.coef import get_coords_from_index -from ccc.sklearn.metrics import get_contingency_matrix +from ccc.sklearn.metrics import get_contingency_matrix, get_pair_confusion_matrix def test_get_coords_from_index_kernel(): @@ -180,6 +180,12 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): int *C = s_sum_cols + k; get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, C); + if (threadIdx.x == 0){ + for (int i = 0; i < 4; ++i){ + out[i] = C[i]; + } + } + __syncthreads(); } """ @@ -189,6 +195,7 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): kernel = module.get_function("test_kernel") # Generate random partitions + np.random.seed(0) part0 = np.random.randint(0, k, size=n_objs, dtype=np.int32) part1 = np.random.randint(0, k, size=n_objs, dtype=np.int32) @@ -206,8 +213,11 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): (d_part0, d_part1, n_objs, k, d_c), shared_mem=shared_mem_size) - return - + h_c = cp.asnumpy(d_c) + py_c = get_pair_confusion_matrix(part0, part1) + print(f"h_c: {h_c}") + print(f"py_c: {py_c}") + np.testing.assert_array_equal(h_c, py_c) def generate_pairwise_combinations(arr): pairs = [] From f666d4dcffa5d0cbd6574f77101028f7783c1f2b Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 17 Oct 2024 14:42:31 -0600 Subject: [PATCH 070/134] [metrics/device]: Fix get_conf_matrix and pass the first test --- libs/ccc/sklearn/metrics.py | 4 ++++ libs/ccc/sklearn/metrics_gpu2.py | 40 +++++++++++++++++++++++++------- 2 files changed, 36 insertions(+), 8 deletions(-) diff --git a/libs/ccc/sklearn/metrics.py b/libs/ccc/sklearn/metrics.py index 2ab12862..c046f6d6 100644 --- a/libs/ccc/sklearn/metrics.py +++ b/libs/ccc/sklearn/metrics.py @@ -93,11 +93,15 @@ def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarra # Computation using the contingency data contingency = get_contingency_matrix(part0, part1) + print(f"py contingency:\n {contingency}") sum1 = contingency.sum(axis=1) sum0 = contingency.sum(axis=0) n_c = np.ravel(sum1) + print(f"py sum_row: {n_c}") n_k = np.ravel(sum0) + print(f"py sum_col: {n_k}") sum_squares = (contingency**2).sum() + print(f"py sum_squares: {sum_squares}") C = np.empty((2, 2), dtype=np.int64) C[1, 1] = sum_squares - n_samples C[0, 1] = contingency.dot(n_k).sum() - sum_squares diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index e593a908..4a1aee3a 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -31,16 +31,25 @@ __syncthreads(); // Compute sum_rows and sum_cols - for (int i = threadIdx.x; i < k; i += blockDim.x) { - for (int m = 0; m < k; ++m) { - for (int n = 0; n < k; ++n) { - const int val = contingency[m * k + n]; - atomicAdd(&sum_rows[m], val); - atomicAdd(&sum_cols[n], val); - } - } + for (int i = threadIdx.x; i < k * k; i += blockDim.x) { + int row = i / k; + int col = i % k; + int val = contingency[i]; + atomicAdd(&sum_cols[col], val); + atomicAdd(&sum_rows[row], val); } __syncthreads(); + // print sum_rows and sum_cols in arrays for debugging + if (threadIdx.x == 0) { + printf("sum_rows:\\n"); + for (int i = 0; i < k; ++i) { + printf("%d ", sum_rows[i]); + } + printf("\\nsum_col:\\n"); + for (int i = 0; i < k; ++i) { + printf("%d ", sum_cols[i]); + } + } // Compute sum_squares int sum_squares; @@ -51,6 +60,9 @@ } } __syncthreads(); + if (threadIdx.x == 0) { + printf("sum_squares: %d\\n", sum_squares); + } // Compute C[1,1], C[0,1], C[1,0], and C[0,0] if (threadIdx.x == 0) { @@ -112,6 +124,18 @@ } } __syncthreads(); + if (tid == 0) + { + for (int i = 0; i < k; ++i) + { + printf("\\n"); + for (int j = 0; j < k; ++j) + { + printf("%d, ", shared_cont_mat[i * k + j]); + } + } + printf("\\n"); + } } """ From 662bfc653b7828a7c4ab6b92e9a90dc052b37fca Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 17 Oct 2024 15:00:16 -0600 Subject: [PATCH 071/134] [test/device]: Add parameterized tests --- tests/gpu/test_kernel.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 18e86b02..f15df734 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -109,17 +109,17 @@ def test_unravel_index_device(num_cols, num_indices): print("All tests passed successfully!") -@pytest.mark.parametrize("n", [100, 1000, 10000]) +@pytest.mark.parametrize("n_objs", [100, 1000, 10000]) @pytest.mark.parametrize("threads_per_block", [1, 2, 64, 128, 256, 512]) @pytest.mark.parametrize("k", [3, 5, 10]) # Max value of a cluster number + 1 -def test_get_contingency_matrix_kernel(n, threads_per_block, k): +def test_get_contingency_matrix_kernel(n_objs, threads_per_block, k): test_kernel_code = """ extern "C" - __global__ void test_kernel(int* part0, int* part1, int n, int* cont_mat, int k) { + __global__ void test_kernel(int* part0, int* part1, int n_objs, int* cont_mat, int k) { extern __shared__ int shared_cont_mat[]; // Call the function to compute contingency matrix in shared memory - get_contingency_matrix(part0, part1, n, shared_cont_mat, k); + get_contingency_matrix(part0, part1, n_objs, shared_cont_mat, k); // Copy shared memory back to global memory int tid = threadIdx.x; @@ -136,8 +136,8 @@ def test_get_contingency_matrix_kernel(n, threads_per_block, k): kernel = module.get_function("test_kernel") # Generate random partitions - part0 = np.random.randint(0, k, size=n, dtype=np.int32) - part1 = np.random.randint(0, k, size=n, dtype=np.int32) + part0 = np.random.randint(0, k, size=n_objs, dtype=np.int32) + part1 = np.random.randint(0, k, size=n_objs, dtype=np.int32) # Transfer data to GPU d_part0 = cp.asarray(part0) @@ -148,7 +148,7 @@ def test_get_contingency_matrix_kernel(n, threads_per_block, k): blocks = 1 # Each pair of partitions is handled by only one block (to fully utilize shared memory) shared_mem_size = k * k * 4 # 4 bytes per int kernel((blocks,), (threads_per_block,), - (d_part0, d_part1, n, d_cont_mat, k), + (d_part0, d_part1, n_objs, d_cont_mat, k), shared_mem=shared_mem_size) # Get results back to host @@ -158,13 +158,13 @@ def test_get_contingency_matrix_kernel(n, threads_per_block, k): ref_cont_mat = get_contingency_matrix(part0, part1) np.testing.assert_array_equal(h_cont_mat, ref_cont_mat, - err_msg=f"CUDA and reference implementations do not match for n={n}, threads_per_block={threads_per_block}, k={k}") - print(f"Test passed successfully for n={n}, threads_per_block={threads_per_block}, k={k}") + err_msg=f"CUDA and reference implementations do not match for n_objs={n_objs}, threads_per_block={threads_per_block}, k={k}") + print(f"Test passed successfully for n_objs={n_objs}, threads_per_block={threads_per_block}, k={k}") -@pytest.mark.parametrize("n_objs", [10]) -@pytest.mark.parametrize("threads_per_block", [2]) -@pytest.mark.parametrize("k", [5]) # Max value of a cluster number + 1 +@pytest.mark.parametrize("n_objs", [100, 1000, 10000]) +@pytest.mark.parametrize("threads_per_block", [1, 2, 64, 128, 256, 512]) +@pytest.mark.parametrize("k", [3, 5, 10]) # Max value of a cluster number + 1 def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): test_kernel_code = """ extern "C" From c28cf9a75395a614cb62cfe0b985b3b0e82a6f6e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 17 Oct 2024 15:12:26 -0600 Subject: [PATCH 072/134] [metrics/device]: Update CUDA C++ code --- libs/ccc/sklearn/metrics.cu | 39 +++++++++++++++++++++++++++++-------- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 19f855e5..3286c179 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -85,6 +85,18 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share } } __syncthreads(); + // print shared_cont_mat for debugging in a 2D way + if (bid == 0) + { + for (int i = 0; i < k; ++i) + { + for (int j = 0; j < k; ++j) + { + printf("shared_cont_mat[%d][%d]: %d\n", i, j, shared_cont_mat[i * k + j]); + } + } + } + } @@ -113,16 +125,26 @@ __device__ void get_pair_confusion_matrix( __syncthreads(); // Compute sum_rows and sum_cols - for (int i = threadIdx.x; i < k; i += blockDim.x) { - for (int m = 0; m < k; ++m) { - for (int n = 0; n < k; ++n) { - const int val = contingency[m * k + n]; - atomicAdd(&sum_rows[m], val); - atomicAdd(&sum_cols[n], val); - } - } + for (int i = threadIdx.x; i < k * k; i += blockDim.x) { + int row = i / k; + int col = i % k; + int val = contingency[i]; + atomicAdd(&sum_cols[col], val); + atomicAdd(&sum_rows[row], val); } __syncthreads(); + // print sum_rows and sum_cols in arrays for debugging + if (threadIdx.x == 0) { + printf("sum_rows:\n"); + for (int i = 0; i < k; ++i) { + printf("%d ", sum_rows[i]); + } + printf("\nsum_col:\n"); + for (int i = 0; i < k; ++i) { + printf("%d ", sum_cols[i]); + } + } + // Compute sum_squares int sum_squares; @@ -133,6 +155,7 @@ __device__ void get_pair_confusion_matrix( } } __syncthreads(); + printf("sum_squares: %d\n", sum_squares); // Compute C[1,1], C[0,1], C[1,0], and C[0,0] if (threadIdx.x == 0) { From 10e2e22fb2d73f6a882dfd7e6b7d3c8a8eb17f5f Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 18 Oct 2024 10:20:05 -0600 Subject: [PATCH 073/134] [test/ari]: Yield correct GPU ari --- libs/ccc/sklearn/metrics.cu | 41 ++++++++++++++++++++++++++++++++ libs/ccc/sklearn/metrics_gpu2.py | 15 ++++++++++++ tests/gpu/test_kernel.py | 17 +++++++++---- 3 files changed, 68 insertions(+), 5 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 3286c179..bf48ff88 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -181,6 +181,20 @@ __device__ void get_pair_confusion_matrix( // print C printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\n", C[0], C[1], C[2], C[3]); + + // compute ARI + int tn = static_cast(C[0]); + int fp = static_cast(C[1]); + int fn = static_cast(C[2]); + int tp = static_cast(C[3]); + printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); + float ari = 0.0; + if (fn == 0 && fp ==0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + printf("ari: %f\n", ari); } } @@ -321,6 +335,23 @@ __global__ void ari(int *parts, /* * Step 4: Compute ARI and write to global memory */ + if (threadIdx.x == 0) { + int tn = static_cast(s_pair_confusion_matrix[0]); + int fp = static_cast(s_pair_confusion_matrix[1]); + int fn = static_cast(s_pair_confusion_matrix[2]); + int tp = static_cast(s_pair_confusion_matrix[3]); + printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); + float ari = 0.0; + if (fn == 0 && fp ==0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + printf("ari: %f\n", ari); + out[ari_block_idx] = ari; + } + __syncthreads(); + } // Helper function to generate pairwise combinations (implement this according to your needs) @@ -474,6 +505,16 @@ void test_ari_parts_selection() std::cout << "Test failed: Mismatches found." << std::endl; } + // Print ARI results + float *h_out = new float[n_aris]; + cudaMemcpy(h_out, d_out, n_aris * sizeof(float), cudaMemcpyDeviceToHost); + std::cout << "ARI results: " << std::endl; + for (int i = 0; i < n_aris; ++i) + { + printf("%f, ", h_out[i]); + } + std::cout << std::endl; + // Clean up cudaFree(d_parts); cudaFree(d_out); diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 4a1aee3a..d97a971a 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -88,7 +88,22 @@ // print C printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\\n", C[0], C[1], C[2], C[3]); + + // compute ARI + int tn = static_cast(C[0]); + int fp = static_cast(C[1]); + int fn = static_cast(C[2]); + int tp = static_cast(C[3]); + printf("tn: %d, fp: %d, fn: %d, tp: %d\\n", tn, fp, fn, tp); + float ari = 0.0; + if (fn == 0 && fp ==0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + printf("ari: %f\\n", ari); } + __syncthreads(); } """ diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index f15df734..e8819ffd 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -7,10 +7,14 @@ d_get_coords_from_index_str, d_unravel_index_str, d_get_contingency_matrix_str, - k_ari_str + k_ari_str, ) from ccc.coef import get_coords_from_index -from ccc.sklearn.metrics import get_contingency_matrix, get_pair_confusion_matrix +from ccc.sklearn.metrics import ( + get_contingency_matrix, + get_pair_confusion_matrix, + adjusted_rand_index, +) def test_get_coords_from_index_kernel(): @@ -162,9 +166,9 @@ def test_get_contingency_matrix_kernel(n_objs, threads_per_block, k): print(f"Test passed successfully for n_objs={n_objs}, threads_per_block={threads_per_block}, k={k}") -@pytest.mark.parametrize("n_objs", [100, 1000, 10000]) -@pytest.mark.parametrize("threads_per_block", [1, 2, 64, 128, 256, 512]) -@pytest.mark.parametrize("k", [3, 5, 10]) # Max value of a cluster number + 1 +@pytest.mark.parametrize("n_objs", [100]) +@pytest.mark.parametrize("threads_per_block", [32]) +@pytest.mark.parametrize("k", [3]) # Max value of a cluster number + 1 def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): test_kernel_code = """ extern "C" @@ -215,10 +219,13 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): h_c = cp.asnumpy(d_c) py_c = get_pair_confusion_matrix(part0, part1) + ari_py = adjusted_rand_index(part0, part1) + print(f"ari_py: {ari_py}") print(f"h_c: {h_c}") print(f"py_c: {py_c}") np.testing.assert_array_equal(h_c, py_c) + def generate_pairwise_combinations(arr): pairs = [] num_slices = arr.shape[0] # Number of 2D arrays in the 3D array From 5176540a249357781f4b29387ba9f3ba5c684e3a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 23 Oct 2024 11:44:00 -0600 Subject: [PATCH 074/134] [test]: Encounter illegal memory access in the ari kernel --- libs/ccc/sklearn/metrics_gpu2.py | 184 +++++++++++++++++++------------ tests/gpu/test_kernel.py | 2 +- 2 files changed, 113 insertions(+), 73 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index d97a971a..4581fac8 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -197,99 +197,139 @@ * @param n_part_mat_elems Number of elements in the square partition matrix * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @param uniqs Array of unique counts * @param n_aris Number of ARIs to compute + * @param k The max value of cluster number + 1 * @param out Output array of ARIs - * @param part0 Output array of partition 0, for debugging - * @param part1 Output array of partition 1, for debugging + * @param part_pairs Output array of part pairs to be compared by ARI */ -extern "C" __global__ -void ari(int* parts, - int* uniqs, - const int n_aris, - const int n_features, - const int n_parts, - const int n_objs, - const int n_elems_per_feat, - const int n_part_mat_elems, - float* out, - int* part_pairs - ) +extern "C" __global__ void ari(int *parts, + const int n_aris, + const int n_features, + const int n_parts, + const int n_objs, + const int n_elems_per_feat, + const int n_part_mat_elems, + const int k, + float *out, + int *part_pairs = nullptr) { - // tid is the block-wide thread index [0, blockDim.x] - int tid = threadIdx.x; + /* + * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory + */ int global_tid = blockIdx.x * blockDim.x + threadIdx.x; // each block is responsible for one ARI computation - // int ari_block_idx = blockIdx.x; - int ari_block_idx = 3; + int ari_block_idx = blockIdx.x; + + // print parts for debugging + // obtain the corresponding parts and unique counts - int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features - int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair + // printf("n_part_mat_elems: %d\\n", n_part_mat_elems); + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - // print parts for debugging - if (global_tid == 0) { - for (int i = 0; i < n_features; ++i) { - for (int j = 0; j < n_parts; ++j) { - for (int k = 0; k < n_objs; ++k) { - printf("parts[%d][%d][%d]: %d\\n", i, j, k, parts[i * n_parts * n_objs + j * n_objs + k]); - } - } - printf("\\n"); - } - } + // if (global_tid == 0) + // { + // printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); + // } // unravel the feature indices - get_coords_from_index(n_parts, feature_comp_flat_idx, &i, &j); - if (global_tid == 0) { - printf("global_tid: %d, i: %d, j: %d\\n", global_tid, i, j); - } + get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); + assert(i < n_features && j < n_features); + assert(i >= 0 && j >= 0); + // if (global_tid == 0) + // { + // printf("global_tid: %d, i: %d, j: %d\\n", global_tid, i, j); + // } // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); - if (global_tid == 0){ - printf("global_tid: %d, m: %d, n: %d\\n", global_tid, m, n); - } - + // if (global_tid == 0) + // { + // printf("global_tid: %d, m: %d, n: %d\\n", global_tid, m, n); + // } + // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? - int* t_data_parti = parts + i * n_elems_per_feat + m * n_objs ; // t_ for thread - int* t_data_partj = parts + j * n_elems_per_feat + n * n_objs ; - //int* t_data_uniqi = uniqs + i * n_parts + m; - //int* t_data_uniqj = uniqs + j * n_parts + n; - int* blk_part_pairs = part_pairs + ari_block_idx * (2 * n_objs); - + int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread + int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; + // Load gmem data into smem by using different threads extern __shared__ int shared_mem[]; - - // Number of chunks of data this block will load - // In case block size is smaller than the partition size - const int num_chunks = (n_objs + blockDim.x - 1) / blockDim.x; - // Loop over the chunks of data - for (int chunk = 0; chunk < num_chunks; ++chunk) { - // idx is the linear global memory index of the element to load - int idx = chunk * blockDim.x + global_tid; - - if (idx < n_objs) { - // Load part_i and part_j into shared memory - shared_mem[tid] = t_data_parti[idx]; - shared_mem[tid + n_objs] = t_data_partj[idx]; - __syncthreads(); // Synchronize to ensure all threads have loaded data into shared memory - - // Each thread writes data back to global memory (for demonstration purposes) - // part0[idx] = shared_mem[tid]; - // part1[idx] = shared_mem[tid + n_objs]; - blk_part_pairs[idx] = shared_mem[tid]; - blk_part_pairs[idx + n_objs] = shared_mem[tid + n_objs]; - __syncthreads(); // Synchronize before moving to the next chunk + int *s_part0 = shared_mem; + int *s_part1 = shared_mem + n_objs; + + // Loop over the data using the block-stride pattern + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { + s_part0[i] = t_data_part0[i]; + s_part1[i] = t_data_part1[i]; + } + __syncthreads(); + + // Copy data to global memory if part_pairs is specified + if (part_pairs != nullptr) + { + int *out_part0 = part_pairs + ari_block_idx * (2 * n_objs); + int *out_part1 = out_part0 + n_objs; + + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { + out_part0[i] = s_part0[i]; + out_part1[i] = s_part1[i]; } } - - // Initialize shared memory - // int part_mat_first_tid = tid * part_part_elems; - - // Todo: use a for loop to compute the ARI and do the max reduction + + /* + * Step 2: Compute contingency matrix within the block + */ + // shared mem address for the contingency matrix + int *s_contingency = shared_mem + 2 * n_objs; + // initialize the contingency matrix to zero + // const int n_contingency_items = k * k; + // for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { + // s_contingency[i] = 0; + // } + get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); + // if (global_tid == 0) + // { + // for (int i = 0; i < k; ++i) + // { + // for (int j = 0; j < k; ++j) + // { + // printf("s_contingency[%d][%d]: %d\\n", i, j, s_contingency[i * k + j]); + // } + // } + // } + + /* + * Step 3: Construct pair confusion matrix + */ + // shared mem address for the pair confusion matrix + int *s_sum_rows = s_contingency + k * k; + int *s_sum_cols = s_sum_rows + k; + int *s_pair_confusion_matrix = s_sum_cols + k; + get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, s_pair_confusion_matrix); + /* + * Step 4: Compute ARI and write to global memory + */ + if (threadIdx.x == 0) { + int tn = static_cast(s_pair_confusion_matrix[0]); + int fp = static_cast(s_pair_confusion_matrix[1]); + int fn = static_cast(s_pair_confusion_matrix[2]); + int tp = static_cast(s_pair_confusion_matrix[3]); + printf("tn: %d, fp: %d, fn: %d, tp: %d\\n", tn, fp, fn, tp); + float ari = 0.0; + if (fn == 0 && fp == 0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + printf("ari: %f\\n", ari); + out[ari_block_idx] = ari; + } + __syncthreads(); } """ diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index e8819ffd..4857a107 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -259,7 +259,7 @@ def generate_pairwise_combinations(arr): def test_art_parts_selection(parts): pairs = generate_pairwise_combinations(parts) - kernel_code = d_unravel_index_str + d_get_coords_from_index_str + k_ari_str + kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str # Compile the CUDA kernel module = cp.RawModule(code=kernel_code, backend='nvcc') kernel = module.get_function("ari") From 864058332ec75baf19aecfc149a54fd80318b8e9 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 23 Oct 2024 13:45:56 -0600 Subject: [PATCH 075/134] [test/kernel]: Fix test_art_parts_selection --- libs/ccc/sklearn/metrics.cu | 126 ++++++++++++++++--------------- libs/ccc/sklearn/metrics_gpu2.py | 4 +- tests/gpu/sctrach.py | 9 +++ tests/gpu/test_kernel.py | 16 ++-- 4 files changed, 85 insertions(+), 70 deletions(-) create mode 100644 tests/gpu/sctrach.py diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index bf48ff88..ed89f7a4 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -86,16 +86,16 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share } __syncthreads(); // print shared_cont_mat for debugging in a 2D way - if (bid == 0) - { - for (int i = 0; i < k; ++i) - { - for (int j = 0; j < k; ++j) - { - printf("shared_cont_mat[%d][%d]: %d\n", i, j, shared_cont_mat[i * k + j]); - } - } - } + // if (bid == 0) + // { + // for (int i = 0; i < k; ++i) + // { + // for (int j = 0; j < k; ++j) + // { + // printf("shared_cont_mat[%d][%d]: %d\n", i, j, shared_cont_mat[i * k + j]); + // } + // } + // } } @@ -134,16 +134,16 @@ __device__ void get_pair_confusion_matrix( } __syncthreads(); // print sum_rows and sum_cols in arrays for debugging - if (threadIdx.x == 0) { - printf("sum_rows:\n"); - for (int i = 0; i < k; ++i) { - printf("%d ", sum_rows[i]); - } - printf("\nsum_col:\n"); - for (int i = 0; i < k; ++i) { - printf("%d ", sum_cols[i]); - } - } + // if (threadIdx.x == 0) { + // printf("sum_rows:\n"); + // for (int i = 0; i < k; ++i) { + // printf("%d ", sum_rows[i]); + // } + // printf("\nsum_col:\n"); + // for (int i = 0; i < k; ++i) { + // printf("%d ", sum_cols[i]); + // } + // } // Compute sum_squares @@ -155,7 +155,7 @@ __device__ void get_pair_confusion_matrix( } } __syncthreads(); - printf("sum_squares: %d\n", sum_squares); + // printf("sum_squares: %d\n", sum_squares); // Compute C[1,1], C[0,1], C[1,0], and C[0,0] if (threadIdx.x == 0) { @@ -229,20 +229,7 @@ __global__ void ari(int *parts, int ari_block_idx = blockIdx.x; // print parts for debugging - if (global_tid == 0) - { - for (int i = 0; i < n_features; ++i) - { - for (int j = 0; j < n_parts; ++j) - { - for (int k = 0; k < n_objs; ++k) - { - printf("parts[%d][%d][%d]: %d\n", i, j, k, parts[i * n_parts * n_objs + j * n_objs + k]); - } - } - printf("\n"); - } - } + // obtain the corresponding parts and unique counts // printf("n_part_mat_elems: %d\n", n_part_mat_elems); @@ -250,26 +237,26 @@ __global__ void ari(int *parts, int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - if (global_tid == 0) - { - printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); - } + // if (global_tid == 0) + // { + // printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); + // } // unravel the feature indices get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); assert(i < n_features && j < n_features); assert(i >= 0 && j >= 0); - if (global_tid == 0) - { - printf("global_tid: %d, i: %d, j: %d\n", global_tid, i, j); - } + // if (global_tid == 0) + // { + // printf("global_tid: %d, i: %d, j: %d\n", global_tid, i, j); + // } // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); - if (global_tid == 0) - { - printf("global_tid: %d, m: %d, n: %d\n", global_tid, m, n); - } + // if (global_tid == 0) + // { + // printf("global_tid: %d, m: %d, n: %d\n", global_tid, m, n); + // } // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? @@ -313,16 +300,16 @@ __global__ void ari(int *parts, // s_contingency[i] = 0; // } get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); - if (global_tid == 0) - { - for (int i = 0; i < k; ++i) - { - for (int j = 0; j < k; ++j) - { - printf("s_contingency[%d][%d]: %d\n", i, j, s_contingency[i * k + j]); - } - } - } + // if (global_tid == 0) + // { + // for (int i = 0; i < k; ++i) + // { + // for (int j = 0; j < k; ++j) + // { + // printf("s_contingency[%d][%d]: %d\n", i, j, s_contingency[i * k + j]); + // } + // } + // } /* * Step 3: Construct pair confusion matrix @@ -342,7 +329,7 @@ __global__ void ari(int *parts, int tp = static_cast(s_pair_confusion_matrix[3]); printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); float ari = 0.0; - if (fn == 0 && fp ==0) { + if (fn == 0 && fp == 0) { ari = 1.0; } else { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); @@ -390,6 +377,24 @@ void test_ari_parts_selection() {2, 3, 4, 5}}}; const int k = 6; // specified by the call to ccc , part number from [0...9] + + // std::vector>> parts = { + // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, + // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, + // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, + + // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, + // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, + // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, + + // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, + // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, + // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} + // }; + + // const int k = 7; // specified by the call to ccc , max(parts) + 1 + + // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); @@ -465,12 +470,11 @@ void test_ari_parts_selection() { for (int k = 0; k < n_objs; ++k) { - std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << " "; + std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << ", "; } std::cout << std::endl; } - std::cout << std::endl - << std::endl; + std::cout << std::endl; } std::cout << std::endl; diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index 4581fac8..e43abaa5 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -187,9 +187,6 @@ """ k_ari_str = """ -#include -#define debug 0 - /** * @brief Main ARI kernel. Now only compare a pair of ARIs * @param n_parts Number of partitions of each feature @@ -330,6 +327,7 @@ out[ari_block_idx] = ari; } __syncthreads(); + } """ diff --git a/tests/gpu/sctrach.py b/tests/gpu/sctrach.py new file mode 100644 index 00000000..22044f72 --- /dev/null +++ b/tests/gpu/sctrach.py @@ -0,0 +1,9 @@ +from ccc.coef import ccc +import numpy as np + + +def test_ccc(): + part0 = np.array([2, 3, 6, 1, 0, 5, 4, 3, 6, 2]) + part1 = np.array([0, 6, 2, 5, 1, 3, 4, 6, 0, 2]) + c = ccc(part0, part1) + print(c) \ No newline at end of file diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index 4857a107..f9a1deab 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -166,7 +166,7 @@ def test_get_contingency_matrix_kernel(n_objs, threads_per_block, k): print(f"Test passed successfully for n_objs={n_objs}, threads_per_block={threads_per_block}, k={k}") -@pytest.mark.parametrize("n_objs", [100]) +@pytest.mark.parametrize("n_objs", [10]) @pytest.mark.parametrize("threads_per_block", [32]) @pytest.mark.parametrize("k", [3]) # Max value of a cluster number + 1 def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): @@ -202,6 +202,8 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): np.random.seed(0) part0 = np.random.randint(0, k, size=n_objs, dtype=np.int32) part1 = np.random.randint(0, k, size=n_objs, dtype=np.int32) + print(f"part0: {part0}") + print(f"part1: {part1}") # Transfer data to GPU d_part0 = cp.asarray(part0) @@ -257,6 +259,7 @@ def generate_pairwise_combinations(arr): ]) ]) def test_art_parts_selection(parts): + k = np.max(parts) + 1 pairs = generate_pairwise_combinations(parts) kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str @@ -269,14 +272,15 @@ def test_art_parts_selection(parts): n_feature_comp = n_features * (n_features - 1) // 2 n_aris = n_feature_comp * n_parts * n_parts block_size = 2 - grid_size = (n_aris + block_size - 1) // block_size - s_mem_size = n_objs * 2 * cp.int32().itemsize + grid_size = n_aris + s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared + s_mem_size += 2 * k * cp.int32().itemsize # For the internal sum arrays + s_mem_size += 4 * cp.int32().itemsize # For the 2 x 2 confusion matrix d_out = cp.empty(n_aris, dtype=cp.int32) d_parts = cp.asarray(parts, dtype=cp.int32) # Each pair of partitions will be compared, used for debugging purposes d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) - d_uniqs = cp.empty(n_objs, dtype=cp.int32) # Print stats print(f"Number of ARIs: {n_aris}") @@ -284,13 +288,13 @@ def test_art_parts_selection(parts): print(f"Grid size: {grid_size}, Block size: {block_size}, Shared memory: {s_mem_size}") # Launch the kernel kernel((grid_size,), (block_size,), (d_parts, - d_uniqs, n_aris, n_features, n_parts, n_objs, n_parts * n_objs, - n_objs * n_objs, + n_parts * n_parts, + k, d_out, d_parts_pairs), shared_mem=s_mem_size) From bf6d6a5740a4e6dfe328319043c4d2b4c8f730c2 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 23 Oct 2024 15:36:02 -0600 Subject: [PATCH 076/134] [test/kernel]: Pass ari tests --- libs/ccc/coef/impl.py | 4 +-- libs/ccc/sklearn/metrics.py | 8 ++--- tests/gpu/test_kernel.py | 66 +++++++++++++++++++++++++++++++++++-- 3 files changed, 70 insertions(+), 8 deletions(-) diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index f9ff4a20..e8a2c25d 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -799,8 +799,8 @@ def ccc( max_parts[f_idx, :] = max_part_idx_list cm_pvalues[f_idx] = pvalues - print("CPU parts:") - print(parts) + # print("CPU parts:") + # print(parts) # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: if return_parts: diff --git a/libs/ccc/sklearn/metrics.py b/libs/ccc/sklearn/metrics.py index c046f6d6..e045f077 100644 --- a/libs/ccc/sklearn/metrics.py +++ b/libs/ccc/sklearn/metrics.py @@ -93,15 +93,15 @@ def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarra # Computation using the contingency data contingency = get_contingency_matrix(part0, part1) - print(f"py contingency:\n {contingency}") + # print(f"py contingency:\n {contingency}") sum1 = contingency.sum(axis=1) sum0 = contingency.sum(axis=0) n_c = np.ravel(sum1) - print(f"py sum_row: {n_c}") + # print(f"py sum_row: {n_c}") n_k = np.ravel(sum0) - print(f"py sum_col: {n_k}") + # print(f"py sum_col: {n_k}") sum_squares = (contingency**2).sum() - print(f"py sum_squares: {sum_squares}") + # print(f"py sum_squares: {sum_squares}") C = np.empty((2, 2), dtype=np.int64) C[1, 1] = sum_squares - n_samples C[0, 1] = contingency.dot(n_k).sum() - sum_squares diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_kernel.py index f9a1deab..9deeb455 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_kernel.py @@ -9,7 +9,9 @@ d_get_contingency_matrix_str, k_ari_str, ) -from ccc.coef import get_coords_from_index +from ccc.coef import ( + get_coords_from_index, +) from ccc.sklearn.metrics import ( get_contingency_matrix, get_pair_confusion_matrix, @@ -166,7 +168,7 @@ def test_get_contingency_matrix_kernel(n_objs, threads_per_block, k): print(f"Test passed successfully for n_objs={n_objs}, threads_per_block={threads_per_block}, k={k}") -@pytest.mark.parametrize("n_objs", [10]) +@pytest.mark.parametrize("n_objs", [100]) @pytest.mark.parametrize("threads_per_block", [32]) @pytest.mark.parametrize("k", [3]) # Max value of a cluster number + 1 def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): @@ -271,6 +273,7 @@ def test_art_parts_selection(parts): n_features, n_parts, n_objs = parts.shape n_feature_comp = n_features * (n_features - 1) // 2 n_aris = n_feature_comp * n_parts * n_parts + # Todo: parameterize this block_size = 2 grid_size = n_aris s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared @@ -304,3 +307,62 @@ def test_art_parts_selection(parts): print(h_parts_pairs) # Assert pairs == d_parts_pairs assert np.all(np.equal(h_parts_pairs, pairs)) + + +@pytest.mark.parametrize("n_features, n_parts, n_objs, k", [ + (2, 2, 100, 10), + (5, 10, 200, 10), +]) +@pytest.mark.parametrize("block_size", [32, 64, 128, 256]) +def test_pairwise_ari(n_features, n_parts, n_objs, k, block_size): + parts = np.random.randint(0, k, size=(n_features, n_parts, n_objs), dtype=np.int32) + # Create test inputs + n_features, n_parts, n_objs = parts.shape + n_feature_comp = n_features * (n_features - 1) // 2 + n_aris = n_feature_comp * n_parts * n_parts + ref_aris = np.zeros(n_aris, dtype=np.float32) + # Get partition pairs + pairs = generate_pairwise_combinations(parts) + # Use map-reduce to compute ARIs for all pairs of partitions + for i, (part0, part1) in enumerate(pairs): + ari = adjusted_rand_index(part0, part1) + ref_aris[i] = ari + + print(ref_aris) + + # Compute ARIs using the CUDA kernel + grid_size = n_aris + s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared + s_mem_size += 2 * k * cp.int32().itemsize # For the internal sum arrays + s_mem_size += 4 * cp.int32().itemsize # For the 2 x 2 confusion matrix + + d_out = cp.empty(n_aris, dtype=cp.float32) + d_parts = cp.asarray(parts, dtype=cp.int32) + d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) + # Each pair of partitions will be compared, used for debugging purposes + + # Print stats + print(f"Number of ARIs: {n_aris}") + # Print kernel configuration + print(f"Grid size: {grid_size}, Block size: {block_size}, Shared memory: {s_mem_size}") + # Compile the CUDA kernel + kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str + module = cp.RawModule(code=kernel_code, backend='nvcc') + kernel = module.get_function("ari") + # Launch the kernel + kernel((grid_size,), (block_size,), (d_parts, + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + k, + d_out, + d_parts_pairs), + shared_mem=s_mem_size) + cp.cuda.runtime.deviceSynchronize() + # Get results back to host + h_out = cp.asnumpy(d_out) + print(h_out) + assert np.allclose(h_out, ref_aris) From 9964cc7c2d683aee85b25d66299bb018b46ed28e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 24 Oct 2024 14:23:09 -0600 Subject: [PATCH 077/134] [kernel]: Clean up debugging code --- libs/ccc/sklearn/metrics.cu | 61 ++----------------------------------- 1 file changed, 2 insertions(+), 59 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index ed89f7a4..21320579 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -4,9 +4,7 @@ #include #include -// #define N_OBJS 16 -// #define N_PARTS 1 -// #define N_FEATURES 2 +// Todo: Add CudaCheckError /** * @brief Unravel a flat index to the corresponding 2D indicis @@ -85,18 +83,6 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share } } __syncthreads(); - // print shared_cont_mat for debugging in a 2D way - // if (bid == 0) - // { - // for (int i = 0; i < k; ++i) - // { - // for (int j = 0; j < k; ++j) - // { - // printf("shared_cont_mat[%d][%d]: %d\n", i, j, shared_cont_mat[i * k + j]); - // } - // } - // } - } @@ -133,19 +119,7 @@ __device__ void get_pair_confusion_matrix( atomicAdd(&sum_rows[row], val); } __syncthreads(); - // print sum_rows and sum_cols in arrays for debugging - // if (threadIdx.x == 0) { - // printf("sum_rows:\n"); - // for (int i = 0; i < k; ++i) { - // printf("%d ", sum_rows[i]); - // } - // printf("\nsum_col:\n"); - // for (int i = 0; i < k; ++i) { - // printf("%d ", sum_cols[i]); - // } - // } - // Compute sum_squares int sum_squares; if (threadIdx.x == 0) { @@ -228,35 +202,19 @@ __global__ void ari(int *parts, // each block is responsible for one ARI computation int ari_block_idx = blockIdx.x; - // print parts for debugging - - // obtain the corresponding parts and unique counts - // printf("n_part_mat_elems: %d\n", n_part_mat_elems); int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - // if (global_tid == 0) - // { - // printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); - // } - // unravel the feature indices get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); assert(i < n_features && j < n_features); assert(i >= 0 && j >= 0); - // if (global_tid == 0) - // { - // printf("global_tid: %d, i: %d, j: %d\n", global_tid, i, j); - // } + // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); - // if (global_tid == 0) - // { - // printf("global_tid: %d, m: %d, n: %d\n", global_tid, m, n); - // } // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? @@ -294,22 +252,7 @@ __global__ void ari(int *parts, */ // shared mem address for the contingency matrix int *s_contingency = shared_mem + 2 * n_objs; - // initialize the contingency matrix to zero - // const int n_contingency_items = k * k; - // for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { - // s_contingency[i] = 0; - // } get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); - // if (global_tid == 0) - // { - // for (int i = 0; i < k; ++i) - // { - // for (int j = 0; j < k; ++j) - // { - // printf("s_contingency[%d][%d]: %d\n", i, j, s_contingency[i * k + j]); - // } - // } - // } /* * Step 3: Construct pair confusion matrix From 33cfb1bf5bf9e0b9ab3309689aa46a1852a652b8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 24 Oct 2024 15:34:20 -0600 Subject: [PATCH 078/134] [kernel]: Add CUDA check macro --- libs/ccc/sklearn/metrics.cu | 105 +++++++++++++++++++++++------------- 1 file changed, 68 insertions(+), 37 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 21320579..2d6c66f1 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -5,6 +5,24 @@ #include // Todo: Add CudaCheckError +#define gpuErrorCheck(ans, abort) +{ + gpuAssert((ans), __FILE__, __LINE__, abort); +} +inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) +{ + if (code != cudaSuccess) + { + fprintf(stderr, "assert: %s %s %d\n", cudaGetErrorString(code), file, line); + if (abort) + { + exit(code); + } + } +} +// call like this +// gpuErrorCheck(cudaMalloc(...)); // if fails, print message and continue +// gpuErrorCheck(cudaMalloc(...), true); // if fails, print message and abort /** * @brief Unravel a flat index to the corresponding 2D indicis @@ -23,13 +41,13 @@ __device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int *r /** * @brief Given the number of objects and an index, this function calculates * the coordinates in a symmetric matrix from a flat index. - * For example, if there are n_obj objects (such as genes), a condensed - * 1D array can be created with pairwise comparisons between these - * objects, which corresponds to a symmetric 2D matrix. This function - * calculates the 2D coordinates (x, y) in the symmetric matrix that + * For example, if there are n_obj objects (such as genes), a condensed + * 1D array can be created with pairwise comparisons between these + * objects, which corresponds to a symmetric 2D matrix. This function + * calculates the 2D coordinates (x, y) in the symmetric matrix that * corresponds to the given flat index. * - * @param[in] n_obj The total number of objects (i.e., the size of one dimension + * @param[in] n_obj The total number of objects (i.e., the size of one dimension * of the square symmetric matrix). * @param[in] idx The flat index from the condensed pairwise array. * @param[out] x Pointer to the calculated row coordinate in the symmetric matrix. @@ -85,7 +103,6 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share __syncthreads(); } - /** * @brief CUDA device function to compute the pair confusion matrix * @param[in] contingency Pointer to the contingency matrix @@ -96,22 +113,24 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share * @param[out] C Pointer to the output pair confusion matrix (2x2) */ __device__ void get_pair_confusion_matrix( - const int* __restrict__ contingency, - int * sum_rows, - int * sum_cols, + const int *__restrict__ contingency, + int *sum_rows, + int *sum_cols, const int n_objs, const int k, - int* C -) { + int *C) +{ // Initialize sum_rows and sum_cols - for (int i = threadIdx.x; i < k; i += blockDim.x) { + for (int i = threadIdx.x; i < k; i += blockDim.x) + { sum_rows[i] = 0; sum_cols[i] = 0; } __syncthreads(); // Compute sum_rows and sum_cols - for (int i = threadIdx.x; i < k * k; i += blockDim.x) { + for (int i = threadIdx.x; i < k * k; i += blockDim.x) + { int row = i / k; int col = i % k; int val = contingency[i]; @@ -119,12 +138,14 @@ __device__ void get_pair_confusion_matrix( atomicAdd(&sum_rows[row], val); } __syncthreads(); - + // Compute sum_squares int sum_squares; - if (threadIdx.x == 0) { + if (threadIdx.x == 0) + { sum_squares = 0; - for (int i = 0; i < k * k; ++i) { + for (int i = 0; i < k * k; ++i) + { sum_squares += (contingency[i]) * contingency[i]; } } @@ -132,26 +153,31 @@ __device__ void get_pair_confusion_matrix( // printf("sum_squares: %d\n", sum_squares); // Compute C[1,1], C[0,1], C[1,0], and C[0,0] - if (threadIdx.x == 0) { - C[3] = sum_squares - n_objs; // C[1,1] + if (threadIdx.x == 0) + { + C[3] = sum_squares - n_objs; // C[1,1] int temp = 0; - for (int i = 0; i < k; ++i) { - for (int j = 0; j < k; ++j) { + for (int i = 0; i < k; ++i) + { + for (int j = 0; j < k; ++j) + { temp += (contingency[i * k + j]) * sum_cols[j]; } } - C[1] = temp - sum_squares; // C[0,1] + C[1] = temp - sum_squares; // C[0,1] temp = 0; - for (int i = 0; i < k; ++i) { - for (int j = 0; j < k; ++j) { + for (int i = 0; i < k; ++i) + { + for (int j = 0; j < k; ++j) + { temp += (contingency[j * k + i]) * sum_rows[j]; } } - C[2] = temp - sum_squares; // C[1,0] + C[2] = temp - sum_squares; // C[1,0] - C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] + C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] // print C printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\n", C[0], C[1], C[2], C[3]); @@ -163,9 +189,12 @@ __device__ void get_pair_confusion_matrix( int tp = static_cast(C[3]); printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); float ari = 0.0; - if (fn == 0 && fp ==0) { + if (fn == 0 && fp == 0) + { ari = 1.0; - } else { + } + else + { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } printf("ari: %f\n", ari); @@ -265,23 +294,26 @@ __global__ void ari(int *parts, /* * Step 4: Compute ARI and write to global memory */ - if (threadIdx.x == 0) { + if (threadIdx.x == 0) + { int tn = static_cast(s_pair_confusion_matrix[0]); int fp = static_cast(s_pair_confusion_matrix[1]); int fn = static_cast(s_pair_confusion_matrix[2]); int tp = static_cast(s_pair_confusion_matrix[3]); printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); float ari = 0.0; - if (fn == 0 && fp == 0) { + if (fn == 0 && fp == 0) + { ari = 1.0; - } else { + } + else + { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } printf("ari: %f\n", ari); out[ari_block_idx] = ari; } __syncthreads(); - } // Helper function to generate pairwise combinations (implement this according to your needs) @@ -320,23 +352,22 @@ void test_ari_parts_selection() {2, 3, 4, 5}}}; const int k = 6; // specified by the call to ccc , part number from [0...9] - + // std::vector>> parts = { // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, - + // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, - + // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} // }; // const int k = 7; // specified by the call to ccc , max(parts) + 1 - // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); @@ -371,8 +402,8 @@ void test_ari_parts_selection() int grid_size = n_aris; // Compute shared memory size size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared - s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays - s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix + s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays + s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix // Allocate device memory int *d_parts, *d_parts_pairs; From 7a3f7d4592de9565e758ed2a5c3a8f01ebf434d8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 25 Oct 2024 12:23:52 -0600 Subject: [PATCH 079/134] [kernel]: Clean up cupy raw strings --- libs/ccc/sklearn/metrics_gpu2.py | 61 +------------------------------- 1 file changed, 1 insertion(+), 60 deletions(-) diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py index e43abaa5..c460ef32 100644 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ b/libs/ccc/sklearn/metrics_gpu2.py @@ -39,17 +39,6 @@ atomicAdd(&sum_rows[row], val); } __syncthreads(); - // print sum_rows and sum_cols in arrays for debugging - if (threadIdx.x == 0) { - printf("sum_rows:\\n"); - for (int i = 0; i < k; ++i) { - printf("%d ", sum_rows[i]); - } - printf("\\nsum_col:\\n"); - for (int i = 0; i < k; ++i) { - printf("%d ", sum_cols[i]); - } - } // Compute sum_squares int sum_squares; @@ -60,9 +49,6 @@ } } __syncthreads(); - if (threadIdx.x == 0) { - printf("sum_squares: %d\\n", sum_squares); - } // Compute C[1,1], C[0,1], C[1,0], and C[0,0] if (threadIdx.x == 0) { @@ -86,22 +72,18 @@ C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] - // print C - printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\\n", C[0], C[1], C[2], C[3]); // compute ARI int tn = static_cast(C[0]); int fp = static_cast(C[1]); int fn = static_cast(C[2]); int tp = static_cast(C[3]); - printf("tn: %d, fp: %d, fn: %d, tp: %d\\n", tn, fp, fn, tp); float ari = 0.0; if (fn == 0 && fp ==0) { ari = 1.0; } else { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } - printf("ari: %f\\n", ari); } __syncthreads(); } @@ -139,18 +121,6 @@ } } __syncthreads(); - if (tid == 0) - { - for (int i = 0; i < k; ++i) - { - printf("\\n"); - for (int j = 0; j < k; ++j) - { - printf("%d, ", shared_cont_mat[i * k + j]); - } - } - printf("\\n"); - } } """ @@ -221,31 +191,19 @@ // obtain the corresponding parts and unique counts - // printf("n_part_mat_elems: %d\\n", n_part_mat_elems); int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - // if (global_tid == 0) - // { - // printf("ari_block_idx: %d, feature_comp_flat_idx: %d, part_pair_flat_idx: %d\\n", ari_block_idx, feature_comp_flat_idx, part_pair_flat_idx); - // } - // unravel the feature indices get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); assert(i < n_features && j < n_features); assert(i >= 0 && j >= 0); - // if (global_tid == 0) - // { - // printf("global_tid: %d, i: %d, j: %d\\n", global_tid, i, j); - // } + // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); // if (global_tid == 0) - // { - // printf("global_tid: %d, m: %d, n: %d\\n", global_tid, m, n); - // } // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? @@ -283,22 +241,7 @@ */ // shared mem address for the contingency matrix int *s_contingency = shared_mem + 2 * n_objs; - // initialize the contingency matrix to zero - // const int n_contingency_items = k * k; - // for (int i = threadIdx.x; i < n_contingency_items; i += blockDim.x) { - // s_contingency[i] = 0; - // } get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); - // if (global_tid == 0) - // { - // for (int i = 0; i < k; ++i) - // { - // for (int j = 0; j < k; ++j) - // { - // printf("s_contingency[%d][%d]: %d\\n", i, j, s_contingency[i * k + j]); - // } - // } - // } /* * Step 3: Construct pair confusion matrix @@ -316,14 +259,12 @@ int fp = static_cast(s_pair_confusion_matrix[1]); int fn = static_cast(s_pair_confusion_matrix[2]); int tp = static_cast(s_pair_confusion_matrix[3]); - printf("tn: %d, fp: %d, fn: %d, tp: %d\\n", tn, fp, fn, tp); float ari = 0.0; if (fn == 0 && fp == 0) { ari = 1.0; } else { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } - printf("ari: %f\\n", ari); out[ari_block_idx] = ari; } __syncthreads(); From e1ab1a022e09fa781fdcf3c8c6597a26cfec86cb Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 25 Oct 2024 22:04:13 -0600 Subject: [PATCH 080/134] [bench]: Add simple benchmark for gpu_ari --- benchmark/bench_ari.py | 93 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 benchmark/bench_ari.py diff --git a/benchmark/bench_ari.py b/benchmark/bench_ari.py new file mode 100644 index 00000000..103ab95a --- /dev/null +++ b/benchmark/bench_ari.py @@ -0,0 +1,93 @@ +import pytest +import time +import cupy as cp +import numpy as np +from ccc.sklearn.metrics_gpu2 import ( + d_get_confusion_matrix_str, + d_get_coords_from_index_str, + d_unravel_index_str, + d_get_contingency_matrix_str, + k_ari_str, +) +from ccc.sklearn.metrics import ( + adjusted_rand_index, +) + + +def generate_pairwise_combinations(arr): + pairs = [] + num_slices = arr.shape[0] # Number of 2D arrays in the 3D array + + for i in range(num_slices): + for j in range(i + 1, num_slices): # Only consider pairs in different slices + for row_i in arr[i]: # Each row in slice i + for row_j in arr[j]: # Pairs with each row in slice j + pairs.append([row_i, row_j]) + + # Convert list of pairs to a NumPy array + return np.array(pairs) + + +@pytest.mark.parametrize("n_features, n_parts, n_objs, k", [ + (100, 10, 300, 10), + (100, 20, 300, 10), + # (100, 20, 1000, 10), # wrong results + # (200, 20, 300, 10), # illegal mem access + # (1000, 10, 300, 10), # out of gpu mem +]) +@pytest.mark.parametrize("block_size", [1024]) +def test_pairwise_ari(n_features, n_parts, n_objs, k, block_size): + parts = np.random.randint(0, k, size=(n_features, n_parts, n_objs), dtype=np.int32) + # Create test inputs + n_features, n_parts, n_objs = parts.shape + n_feature_comp = n_features * (n_features - 1) // 2 + n_aris = n_feature_comp * n_parts * n_parts + ref_aris = np.zeros(n_aris, dtype=np.float32) + # Get partition pairs + pairs = generate_pairwise_combinations(parts) + + start = time.time() + # Use map-reduce to compute ARIs for all pairs of partitions + for i, (part0, part1) in enumerate(pairs): + ari = adjusted_rand_index(part0, part1) + ref_aris[i] = ari + end = time.time() + time_cpu = end - start + print(f"\nFor {n_features} features, {n_parts} partitions, {n_objs} objects:") + print(f"CPU Time taken: {time_cpu:.4f} seconds") + + # Compute ARIs using the CUDA kernel + grid_size = n_aris + s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared + s_mem_size += 2 * k * cp.int32().itemsize # For the internal sum arrays + s_mem_size += 4 * cp.int32().itemsize # For the 2 x 2 confusion matrix + + start = time.time() + d_out = cp.empty(n_aris, dtype=cp.float32) + d_parts = cp.asarray(parts, dtype=cp.int32) + d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) + # Each pair of partitions will be compared, used for debugging purposes + + # Compile the CUDA kernel + kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str + module = cp.RawModule(code=kernel_code, backend='nvcc') + kernel = module.get_function("ari") + # Launch the kernel + kernel((grid_size,), (block_size,), (d_parts, + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + k, + d_out, + d_parts_pairs), + shared_mem=s_mem_size) + end = time.time() + time_gpu = end - start + print(f"GPU Time taken: {time_gpu:.4f} seconds") + cp.cuda.runtime.deviceSynchronize() + # Get results back to host + h_out = cp.asnumpy(d_out) + assert np.allclose(h_out, ref_aris) From 34cb479e749a88debcb78d96c9d0377a7a391ca6 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 27 Oct 2024 19:40:38 -0600 Subject: [PATCH 081/134] [kernel]: Begin to do refactoring --- libs/ccc/sklearn/metrics.cu | 232 +++++++----------------------------- 1 file changed, 45 insertions(+), 187 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 2d6c66f1..ab22e3c6 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -1,9 +1,18 @@ #include -#include +#include +#include + #include #include #include +/** + * Future optimizations + * 1. use narrower data types + * 2. optimized on locality + */ + + // Todo: Add CudaCheckError #define gpuErrorCheck(ans, abort) { @@ -179,15 +188,12 @@ __device__ void get_pair_confusion_matrix( C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] - // print C - printf("C[0,0]: %d, C[0,1]: %d, C[1,0]: %d, C[1,1]: %d\n", C[0], C[1], C[2], C[3]); // compute ARI int tn = static_cast(C[0]); int fp = static_cast(C[1]); int fn = static_cast(C[2]); int tp = static_cast(C[3]); - printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); float ari = 0.0; if (fn == 0 && fp == 0) { @@ -197,7 +203,6 @@ __device__ void get_pair_confusion_matrix( { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } - printf("ari: %f\n", ari); } } @@ -213,6 +218,7 @@ __device__ void get_pair_confusion_matrix( * @param out Output array of ARIs * @param part_pairs Output array of part pairs to be compared by ARI */ +extern "C" __global__ void ari(int *parts, const int n_aris, const int n_features, @@ -300,7 +306,6 @@ __global__ void ari(int *parts, int fp = static_cast(s_pair_confusion_matrix[1]); int fn = static_cast(s_pair_confusion_matrix[2]); int tp = static_cast(s_pair_confusion_matrix[3]); - printf("tn: %d, fp: %d, fn: %d, tp: %d\n", tn, fp, fn, tp); float ari = 0.0; if (fn == 0 && fp == 0) { @@ -310,198 +315,51 @@ __global__ void ari(int *parts, { ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); } - printf("ari: %f\n", ari); out[ari_block_idx] = ari; } __syncthreads(); } -// Helper function to generate pairwise combinations (implement this according to your needs) -std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) -{ - std::vector, std::vector>> pairs; - size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector - for (size_t i = 0; i < num_slices; ++i) - { - for (size_t j = i + 1; j < num_slices; ++j) - { // Only consider pairs in different slices - for (const auto &row_i : arr[i]) - { // Each row in slice i - for (const auto &row_j : arr[j]) - { // Pairs with each row in slice j - pairs.emplace_back(row_i, row_j); - } - } - } - } - return pairs; -} -void test_ari_parts_selection() -{ - // Define test input - std::vector>> parts = { - {{0, 1, 2, 3}, - {0, 2, 3, 4}, - {0, 3, 4, 5}}, - {{1, 1, 2, 3}, - {1, 2, 3, 4}, - {1, 3, 4, 5}}, - {{2, 1, 2, 3}, - {2, 2, 3, 4}, - {2, 3, 4, 5}}}; - - const int k = 6; // specified by the call to ccc , part number from [0...9] - - // std::vector>> parts = { - // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, - // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, - // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, - - // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, - // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, - // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, - - // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, - // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, - // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} - // }; - - // const int k = 7; // specified by the call to ccc , max(parts) + 1 - - // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; - // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); - - // Get dimensions - int n_features = parts.size(); - int n_parts = parts[0].size(); - int n_objs = parts[0][0].size(); - int n_feature_comp = n_features * (n_features - 1) / 2; - int n_aris = n_feature_comp * n_parts * n_parts; - std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl - << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; - - // Allocate host memory for C-style array - int *h_parts = new int[n_features * n_parts * n_objs]; - - // Copy data from vector to C-style array - for (int i = 0; i < n_features; ++i) - { - for (int j = 0; j < n_parts; ++j) - { - for (int k = 0; k < n_objs; ++k) - { - h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; - } - } - } +/** + * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @param out Output array of ARIs + * @throws std::invalid_argument if "parts" is invalid + * @throws std::invalid_argument if the length of "out" is not equal to the number of ARIs + * @throws std::runtime_error if the kernel launch fails + * @return void + */ +extern "C" +void cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs, int* out) { + // Input validation + if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); + if (out == nullptr) throw std::invalid_argument("Error. Argument 'out' is nullptr"); + + // Compute internal variables + const auto n_feature_comp = n_features * (n_features - 1) / 2; + const auto n_aris = n_feature_comp * n_parts * n_parts; +da // Set up CUDA kernel configuration - int block_size = 2; + const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block // Each block is responsible for one ARI computation - int grid_size = n_aris; - // Compute shared memory size - size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared - s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays - s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix + const auto grid_size = n_aris; + // Define shared memory size for each block + const auto parts_dtype_size = sizeof(*parts); + auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared + s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays + s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix // Allocate device memory - int *d_parts, *d_parts_pairs; - float *d_out; - cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); - cudaMalloc(&d_out, n_aris * sizeof(float)); - cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); + // Todo: use cudaMalloc3D for parts + decltype (parts) d_parts; + decltype (parts) d_parts_pairs; // for debugging + decltype (h_out) d_out; + gpuErrorCheck(cudaMalloc(&d_parts, n_features * n_parts * n_objs * parts_dtype_size)); + gpuErrorCheck(cudaMalloc(&d_out, n_aris * sizeof(decltype(*d_out)))); + gpuErrorCheck(cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * parts_dtype_size)); // Copy data to device - cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); - - // Launch kernel - ari<<>>( - d_parts, - n_aris, - n_features, - n_parts, - n_objs, - n_parts * n_objs, - n_parts * n_parts, - k, - d_out, - d_parts_pairs); - - // Synchronize device - cudaDeviceSynchronize(); - - // Copy results back to host - int *h_parts_pairs = new int[n_aris * 2 * n_objs]; - cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); - - // Print results - std::cout << "Parts pairs: " << std::endl; - for (int i = 0; i < n_aris; ++i) - { - std::cout << "Pair:" << i << std::endl; - for (int j = 0; j < 2; ++j) - { - for (int k = 0; k < n_objs; ++k) - { - std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << ", "; - } - std::cout << std::endl; - } - std::cout << std::endl; - } - std::cout << std::endl; - - // Assert equality on the parts pairs - bool all_equal = true; - auto pairs = generate_pairwise_combinations(parts); - int n_pairs = pairs.size(); - for (int i = 0; i < n_pairs; ++i) - { - for (int j = 0; j < 2; ++j) - { - const std::vector ¤t_vector = (j == 0) ? pairs[i].first : pairs[i].second; - for (int k = 0; k < n_objs; ++k) - { - int flattened_index = i * 2 * n_objs + j * n_objs + k; - if (h_parts_pairs[flattened_index] != current_vector[k]) - { - all_equal = false; - std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; - std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; - } - } - } - } - - if (all_equal) - { - std::cout << "Test passed: All elements match." << std::endl; - } - else - { - std::cout << "Test failed: Mismatches found." << std::endl; - } - - // Print ARI results - float *h_out = new float[n_aris]; - cudaMemcpy(h_out, d_out, n_aris * sizeof(float), cudaMemcpyDeviceToHost); - std::cout << "ARI results: " << std::endl; - for (int i = 0; i < n_aris; ++i) - { - printf("%f, ", h_out[i]); - } - std::cout << std::endl; - - // Clean up - cudaFree(d_parts); - cudaFree(d_out); - cudaFree(d_parts_pairs); - delete[] h_parts_pairs; -} - -int main() -{ - test_ari_parts_selection(); - return 0; + gpuErrorCheck(cudaMemcpy(d_parts, parts, n_features * n_parts * n_objs * parts_dtype_size, cudaMemcpyHostToDevice)); } \ No newline at end of file From 2232b66f8f77e56444961ed030edea71a2321dbf Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 27 Oct 2024 20:54:24 -0600 Subject: [PATCH 082/134] [kernel]: Introduce the thurst library --- libs/ccc/sklearn/metrics.cu | 68 ++++++++++++++++++++++++------------- 1 file changed, 44 insertions(+), 24 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index ab22e3c6..47ecffc5 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -1,5 +1,6 @@ #include #include +#include #include #include @@ -14,9 +15,9 @@ // Todo: Add CudaCheckError -#define gpuErrorCheck(ans, abort) -{ - gpuAssert((ans), __FILE__, __LINE__, abort); +#define gpuErrorCheck(ans, abort) \ +{ \ + gpuAssert((ans), __FILE__, __LINE__, abort); \ } inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) { @@ -28,8 +29,8 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = exit(code); } } -} -// call like this +} +// // call like this // gpuErrorCheck(cudaMalloc(...)); // if fails, print message and continue // gpuErrorCheck(cudaMalloc(...), true); // if fails, print message and abort @@ -324,22 +325,23 @@ __global__ void ari(int *parts, /** * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @param out Output array of ARIs * @throws std::invalid_argument if "parts" is invalid - * @throws std::invalid_argument if the length of "out" is not equal to the number of ARIs - * @throws std::runtime_error if the kernel launch fails - * @return void + * @return std::vector ARI values for each pair of partitions */ -extern "C" -void cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs, int* out) { +template +auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { // Input validation if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); - if (out == nullptr) throw std::invalid_argument("Error. Argument 'out' is nullptr"); // Compute internal variables + using parts_dtype = typename std::remove_pointer::type; + using out_dtype = T; + const auto n_feature_comp = n_features * (n_features - 1) / 2; const auto n_aris = n_feature_comp * n_parts * n_parts; -da + // Allocate host memory + thrust::host_vector h_out(n_aris); + thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); // Set up CUDA kernel configuration const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block @@ -351,15 +353,33 @@ da s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix - // Allocate device memory - // Todo: use cudaMalloc3D for parts - decltype (parts) d_parts; - decltype (parts) d_parts_pairs; // for debugging - decltype (h_out) d_out; - gpuErrorCheck(cudaMalloc(&d_parts, n_features * n_parts * n_objs * parts_dtype_size)); - gpuErrorCheck(cudaMalloc(&d_out, n_aris * sizeof(decltype(*d_out)))); - gpuErrorCheck(cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * parts_dtype_size)); - - // Copy data to device - gpuErrorCheck(cudaMemcpy(d_parts, parts, n_features * n_parts * n_objs * parts_dtype_size, cudaMemcpyHostToDevice)); + // Allocate device memory with thrust + thrust::device_vector d_parts(parts, parts + n_features * n_parts * n_objs); // data is copied to device + thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); + thrust::device_vector d_out(n_aris); + + // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later + auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); + auto k = *max_iter + 1; + std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; + + // Launch the kernel + ari<<>>( + thrust::raw_pointer_cast(d_parts.data()), + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + k, + thrust::raw_pointer_cast(d_out.data()), + thrust::raw_pointer_cast(d_parts_pairs.data())); + + // Copy data back to host + thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); + thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); + + // Return the ARI values + return h_out; } \ No newline at end of file From ba9d54118a84d97c1b8fa8210c8c72aa37340d5e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 27 Oct 2024 23:46:41 -0600 Subject: [PATCH 083/134] [build]: Add cmake file for the cuda kernel and tests --- libs/ccc/sklearn/CMakeLists.txt | 15 +++ libs/ccc/sklearn/Makefile | 11 +- libs/ccc/sklearn/metrics.cu | 2 +- libs/ccc/sklearn/metrics.cuh | 6 + libs/ccc/sklearn/test_kernel.cpp | 194 +++++++++++++++++++++++++++++++ 5 files changed, 222 insertions(+), 6 deletions(-) create mode 100644 libs/ccc/sklearn/CMakeLists.txt create mode 100644 libs/ccc/sklearn/metrics.cuh create mode 100644 libs/ccc/sklearn/test_kernel.cpp diff --git a/libs/ccc/sklearn/CMakeLists.txt b/libs/ccc/sklearn/CMakeLists.txt new file mode 100644 index 00000000..f66a2913 --- /dev/null +++ b/libs/ccc/sklearn/CMakeLists.txt @@ -0,0 +1,15 @@ +# CMakeLists.txt + +cmake_minimum_required(VERSION 3.18) +project(CudaAriProject LANGUAGES CUDA CXX) + +# Set the C++ standard +set(CMAKE_CXX_STANDARD 17) + +# Add the CUDA library +add_library(cudaAriLib STATIC metrics.cu) # Add the CUDA source file +set_target_properties(cudaAriLib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) + +# Add the test executable +add_executable(testCudaAri test_kernel.cpp) +target_link_libraries(testCudaAri PRIVATE cudaAriLib) diff --git a/libs/ccc/sklearn/Makefile b/libs/ccc/sklearn/Makefile index 60e3e87a..86b02489 100644 --- a/libs/ccc/sklearn/Makefile +++ b/libs/ccc/sklearn/Makefile @@ -1,11 +1,12 @@ CU_APPS=metrics -C_APPS= +# CPP_APPS=test_kernel -all: ${C_APPS} ${CU_APPS} +all: ${CPP_APPS} ${CU_APPS} +## Todo: try out O3 optimization %: %.cu nvcc -O2 -arch=sm_89 -o $@ $< -lcudadevrt --relocatable-device-code true -%: %.c - gcc -O2 -std=c99 -o $@ $< +%: %.hpp + gcc -O2 -std=c++17 -o $@ $< clean: - rm -f ${CU_APPS} ${C_APPS} + rm -f ${CU_APPS} ${CPP_APPS} diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index 47ecffc5..cdcaa071 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -382,4 +382,4 @@ auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const si // Return the ARI values return h_out; -} \ No newline at end of file +} diff --git a/libs/ccc/sklearn/metrics.cuh b/libs/ccc/sklearn/metrics.cuh new file mode 100644 index 00000000..32df0bde --- /dev/null +++ b/libs/ccc/sklearn/metrics.cuh @@ -0,0 +1,6 @@ +#pragma once + +#include + +template +std::vector cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs); diff --git a/libs/ccc/sklearn/test_kernel.cpp b/libs/ccc/sklearn/test_kernel.cpp new file mode 100644 index 00000000..86173df8 --- /dev/null +++ b/libs/ccc/sklearn/test_kernel.cpp @@ -0,0 +1,194 @@ +#include +#include +#include "metrics.cuh" + +// Helper function to generate pairwise combinations (implement this according to your needs) + +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) +{ + std::vector, std::vector>> pairs; + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) + { + for (size_t j = i + 1; j < num_slices; ++j) + { // Only consider pairs in different slices + for (const auto &row_i : arr[i]) + { // Each row in slice i + for (const auto &row_j : arr[j]) + { // Pairs with each row in slice j + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; +} + +// void test_ari_parts_selection() +// { +// // Define test input +// std::vector>> parts = { +// {{0, 1, 2, 3}, +// {0, 2, 3, 4}, +// {0, 3, 4, 5}}, +// {{1, 1, 2, 3}, +// {1, 2, 3, 4}, +// {1, 3, 4, 5}}, +// {{2, 1, 2, 3}, +// {2, 2, 3, 4}, +// {2, 3, 4, 5}}}; + +// const int k = 6; // specified by the call to ccc , part number from [0...9] + +// // std::vector>> parts = { +// // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, +// // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, +// // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, + +// // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, +// // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, +// // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, + +// // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, +// // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, +// // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} +// // }; + +// // const int k = 7; // specified by the call to ccc , max(parts) + 1 + +// // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; +// // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); + +// // Get dimensions +// int n_features = parts.size(); +// int n_parts = parts[0].size(); +// int n_objs = parts[0][0].size(); +// int n_feature_comp = n_features * (n_features - 1) / 2; +// int n_aris = n_feature_comp * n_parts * n_parts; +// std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl +// << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + +// // Allocate host memory for C-style array +// int *h_parts = new int[n_features * n_parts * n_objs]; + +// // Copy data from vector to C-style array +// for (int i = 0; i < n_features; ++i) +// { +// for (int j = 0; j < n_parts; ++j) +// { +// for (int k = 0; k < n_objs; ++k) +// { +// h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; +// } +// } +// } + +// // Set up CUDA kernel configuration +// int block_size = 2; +// // Each block is responsible for one ARI computation +// int grid_size = n_aris; +// // Compute shared memory size +// size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared +// s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays +// s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix + +// // Allocate device memory +// int *d_parts, *d_parts_pairs; +// float *d_out; +// cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); +// cudaMalloc(&d_out, n_aris * sizeof(float)); +// cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); + +// // Copy data to device +// cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); + +// // Launch kernel +// ari<<>>( +// d_parts, +// n_aris, +// n_features, +// n_parts, +// n_objs, +// n_parts * n_objs, +// n_parts * n_parts, +// k, +// d_out, +// d_parts_pairs); + +// // Synchronize device +// cudaDeviceSynchronize(); + +// // Copy results back to host +// int *h_parts_pairs = new int[n_aris * 2 * n_objs]; +// cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); + +// // Print results +// std::cout << "Parts pairs: " << std::endl; +// for (int i = 0; i < n_aris; ++i) +// { +// std::cout << "Pair:" << i << std::endl; +// for (int j = 0; j < 2; ++j) +// { +// for (int k = 0; k < n_objs; ++k) +// { +// std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << ", "; +// } +// std::cout << std::endl; +// } +// std::cout << std::endl; +// } +// std::cout << std::endl; + +// // Assert equality on the parts pairs +// bool all_equal = true; +// auto pairs = generate_pairwise_combinations(parts); +// int n_pairs = pairs.size(); +// for (int i = 0; i < n_pairs; ++i) +// { +// for (int j = 0; j < 2; ++j) +// { +// const std::vector ¤t_vector = (j == 0) ? pairs[i].first : pairs[i].second; +// for (int k = 0; k < n_objs; ++k) +// { +// int flattened_index = i * 2 * n_objs + j * n_objs + k; +// if (h_parts_pairs[flattened_index] != current_vector[k]) +// { +// all_equal = false; +// std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; +// std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; +// } +// } +// } +// } + +// if (all_equal) +// { +// std::cout << "Test passed: All elements match." << std::endl; +// } +// else +// { +// std::cout << "Test failed: Mismatches found." << std::endl; +// } + +// // Print ARI results +// float *h_out = new float[n_aris]; +// cudaMemcpy(h_out, d_out, n_aris * sizeof(float), cudaMemcpyDeviceToHost); +// std::cout << "ARI results: " << std::endl; +// for (int i = 0; i < n_aris; ++i) +// { +// printf("%f, ", h_out[i]); +// } +// std::cout << std::endl; + +// // Clean up +// cudaFree(d_parts); +// cudaFree(d_out); +// cudaFree(d_parts_pairs); +// delete[] h_parts_pairs; +// } + +int main() +{ + std::cout << "Hello, World!" << std::endl; + return 0; +} From 3add53fee3e214166bfa1bc2ce9fc8dd58dfb51f Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 28 Oct 2024 12:10:25 -0600 Subject: [PATCH 084/134] [test/kernel]: Build tests successfully --- libs/ccc/sklearn/metrics.cu | 19 ++- libs/ccc/sklearn/metrics.cuh | 4 +- libs/ccc/sklearn/test_kernel.cpp | 228 +++++++++---------------------- 3 files changed, 84 insertions(+), 167 deletions(-) diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/sklearn/metrics.cu index cdcaa071..0a0b5ad4 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/sklearn/metrics.cu @@ -321,21 +321,25 @@ __global__ void ari(int *parts, __syncthreads(); } - +// Todo: parameterize parts' data type /** * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) * @throws std::invalid_argument if "parts" is invalid * @return std::vector ARI values for each pair of partitions */ -template -auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { +// template +auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { + // Edge cases: + // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing + + // Input validation if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); // Compute internal variables using parts_dtype = typename std::remove_pointer::type; - using out_dtype = T; + using out_dtype = float; const auto n_feature_comp = n_features * (n_features - 1) / 2; const auto n_aris = n_feature_comp * n_parts * n_parts; @@ -380,6 +384,11 @@ auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const si thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); + // Free device memory + + // Convert thrust vectors to std::vector + std::vector res(h_out.begin(), h_out.end()); + // Return the ARI values - return h_out; + return res; } diff --git a/libs/ccc/sklearn/metrics.cuh b/libs/ccc/sklearn/metrics.cuh index 32df0bde..cf8bf472 100644 --- a/libs/ccc/sklearn/metrics.cuh +++ b/libs/ccc/sklearn/metrics.cuh @@ -2,5 +2,5 @@ #include -template -std::vector cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs); +// template +std::vector cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs); diff --git a/libs/ccc/sklearn/test_kernel.cpp b/libs/ccc/sklearn/test_kernel.cpp index 86173df8..5f7c99c6 100644 --- a/libs/ccc/sklearn/test_kernel.cpp +++ b/libs/ccc/sklearn/test_kernel.cpp @@ -24,171 +24,79 @@ std::vector, std::vector>> generate_pairwise_com return pairs; } -// void test_ari_parts_selection() -// { -// // Define test input -// std::vector>> parts = { -// {{0, 1, 2, 3}, -// {0, 2, 3, 4}, -// {0, 3, 4, 5}}, -// {{1, 1, 2, 3}, -// {1, 2, 3, 4}, -// {1, 3, 4, 5}}, -// {{2, 1, 2, 3}, -// {2, 2, 3, 4}, -// {2, 3, 4, 5}}}; - -// const int k = 6; // specified by the call to ccc , part number from [0...9] - -// // std::vector>> parts = { -// // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, -// // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, -// // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, - -// // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, -// // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, -// // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, - -// // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, -// // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, -// // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} -// // }; - -// // const int k = 7; // specified by the call to ccc , max(parts) + 1 - -// // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; -// // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); - -// // Get dimensions -// int n_features = parts.size(); -// int n_parts = parts[0].size(); -// int n_objs = parts[0][0].size(); -// int n_feature_comp = n_features * (n_features - 1) / 2; -// int n_aris = n_feature_comp * n_parts * n_parts; -// std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl -// << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; - -// // Allocate host memory for C-style array -// int *h_parts = new int[n_features * n_parts * n_objs]; - -// // Copy data from vector to C-style array -// for (int i = 0; i < n_features; ++i) -// { -// for (int j = 0; j < n_parts; ++j) -// { -// for (int k = 0; k < n_objs; ++k) -// { -// h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; -// } -// } -// } - -// // Set up CUDA kernel configuration -// int block_size = 2; -// // Each block is responsible for one ARI computation -// int grid_size = n_aris; -// // Compute shared memory size -// size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared -// s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays -// s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix - -// // Allocate device memory -// int *d_parts, *d_parts_pairs; -// float *d_out; -// cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); -// cudaMalloc(&d_out, n_aris * sizeof(float)); -// cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); - -// // Copy data to device -// cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); - -// // Launch kernel -// ari<<>>( -// d_parts, -// n_aris, -// n_features, -// n_parts, -// n_objs, -// n_parts * n_objs, -// n_parts * n_parts, -// k, -// d_out, -// d_parts_pairs); - -// // Synchronize device -// cudaDeviceSynchronize(); - -// // Copy results back to host -// int *h_parts_pairs = new int[n_aris * 2 * n_objs]; -// cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); - -// // Print results -// std::cout << "Parts pairs: " << std::endl; -// for (int i = 0; i < n_aris; ++i) -// { -// std::cout << "Pair:" << i << std::endl; -// for (int j = 0; j < 2; ++j) -// { -// for (int k = 0; k < n_objs; ++k) -// { -// std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << ", "; -// } -// std::cout << std::endl; -// } -// std::cout << std::endl; -// } -// std::cout << std::endl; - -// // Assert equality on the parts pairs -// bool all_equal = true; -// auto pairs = generate_pairwise_combinations(parts); -// int n_pairs = pairs.size(); -// for (int i = 0; i < n_pairs; ++i) -// { -// for (int j = 0; j < 2; ++j) -// { -// const std::vector ¤t_vector = (j == 0) ? pairs[i].first : pairs[i].second; -// for (int k = 0; k < n_objs; ++k) -// { -// int flattened_index = i * 2 * n_objs + j * n_objs + k; -// if (h_parts_pairs[flattened_index] != current_vector[k]) -// { -// all_equal = false; -// std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; -// std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; -// } -// } -// } -// } - -// if (all_equal) -// { -// std::cout << "Test passed: All elements match." << std::endl; -// } -// else -// { -// std::cout << "Test failed: Mismatches found." << std::endl; -// } +void test_ari_parts_selection() +{ + // Define test input + std::vector>> parts = { + {{0, 1, 2, 3}, + {0, 2, 3, 4}, + {0, 3, 4, 5}}, + {{1, 1, 2, 3}, + {1, 2, 3, 4}, + {1, 3, 4, 5}}, + {{2, 1, 2, 3}, + {2, 2, 3, 4}, + {2, 3, 4, 5}}}; + + const int k = 6; // specified by the call to ccc , part number from [0...9] + + // std::vector>> parts = { + // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, + // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, + // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, + + // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, + // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, + // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, + + // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, + // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, + // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} + // }; + + // const int k = 7; // specified by the call to ccc , max(parts) + 1 + + // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; + // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); + + // Get dimensions + int n_features = parts.size(); + int n_parts = parts[0].size(); + int n_objs = parts[0][0].size(); + int n_feature_comp = n_features * (n_features - 1) / 2; + int n_aris = n_feature_comp * n_parts * n_parts; + std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl + << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + + // Allocate host memory for C-style array + int *h_parts = new int[n_features * n_parts * n_objs]; + + // Copy data from vector to C-style array + for (int i = 0; i < n_features; ++i) + { + for (int j = 0; j < n_parts; ++j) + { + for (int k = 0; k < n_objs; ++k) + { + h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; + } + } + } -// // Print ARI results -// float *h_out = new float[n_aris]; -// cudaMemcpy(h_out, d_out, n_aris * sizeof(float), cudaMemcpyDeviceToHost); -// std::cout << "ARI results: " << std::endl; -// for (int i = 0; i < n_aris; ++i) -// { -// printf("%f, ", h_out[i]); -// } -// std::cout << std::endl; + auto h_out = cudaAri(h_parts, n_features, n_parts, n_objs); -// // Clean up -// cudaFree(d_parts); -// cudaFree(d_out); -// cudaFree(d_parts_pairs); -// delete[] h_parts_pairs; -// } + // Print ARI results + std::cout << "ARI results: " << std::endl; + for (int i = 0; i < n_aris; ++i) + { + printf("%f, ", h_out[i]); + } + std::cout << std::endl; +} int main() { std::cout << "Hello, World!" << std::endl; + test_ari_parts_selection(); return 0; } From 78add782c4030ebd3c4e5482255ac2efb3a69cae Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 28 Oct 2024 12:11:39 -0600 Subject: [PATCH 085/134] [test/kernel]: Factor out tests for ari pair matching --- libs/ccc/sklearn/test_partition_pairing.cpp | 196 ++++++++++++++++++++ 1 file changed, 196 insertions(+) create mode 100644 libs/ccc/sklearn/test_partition_pairing.cpp diff --git a/libs/ccc/sklearn/test_partition_pairing.cpp b/libs/ccc/sklearn/test_partition_pairing.cpp new file mode 100644 index 00000000..5e6b75b9 --- /dev/null +++ b/libs/ccc/sklearn/test_partition_pairing.cpp @@ -0,0 +1,196 @@ +#include +#include +#include +#include +#include +#include "metrics.cuh" + +// Helper function to generate pairwise combinations (implement this according to your needs) +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) +{ + std::vector, std::vector>> pairs; + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) + { + for (size_t j = i + 1; j < num_slices; ++j) + { // Only consider pairs in different slices + for (const auto &row_i : arr[i]) + { // Each row in slice i + for (const auto &row_j : arr[j]) + { // Pairs with each row in slice j + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; +} + +void test_ari_parts_selection() +{ + // Define test input + std::vector>> parts = { + {{0, 1, 2, 3}, + {0, 2, 3, 4}, + {0, 3, 4, 5}}, + {{1, 1, 2, 3}, + {1, 2, 3, 4}, + {1, 3, 4, 5}}, + {{2, 1, 2, 3}, + {2, 2, 3, 4}, + {2, 3, 4, 5}}}; + + const int k = 6; // specified by the call to ccc , part number from [0...9] + + // std::vector>> parts = { + // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, + // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, + // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, + + // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, + // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, + // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, + + // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, + // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, + // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} + // }; + + // const int k = 7; // specified by the call to ccc , max(parts) + 1 + + // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; + // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); + + // Get dimensions + int n_features = parts.size(); + int n_parts = parts[0].size(); + int n_objs = parts[0][0].size(); + int n_feature_comp = n_features * (n_features - 1) / 2; + int n_aris = n_feature_comp * n_parts * n_parts; + std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl + << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + + // Allocate host memory for C-style array + int *h_parts = new int[n_features * n_parts * n_objs]; + + // Copy data from vector to C-style array + for (int i = 0; i < n_features; ++i) + { + for (int j = 0; j < n_parts; ++j) + { + for (int k = 0; k < n_objs; ++k) + { + h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; + } + } + } + + // Set up CUDA kernel configuration + int block_size = 2; + // Each block is responsible for one ARI computation + int grid_size = n_aris; + // Compute shared memory size + size_t s_mem_size = n_objs * 2 * sizeof(int); // For the partition pair to be compared + s_mem_size += 2 * k * sizeof(int); // For the internal sum arrays + s_mem_size += 4 * sizeof(int); // For the 2 x 2 confusion matrix + + // Allocate device memory + int *d_parts, *d_parts_pairs; + float *d_out; + cudaMalloc(&d_parts, n_features * n_parts * n_objs * sizeof(int)); + cudaMalloc(&d_out, n_aris * sizeof(float)); + cudaMalloc(&d_parts_pairs, n_aris * 2 * n_objs * sizeof(int)); + + // Copy data to device + cudaMemcpy(d_parts, h_parts, n_features * n_parts * n_objs * sizeof(int), cudaMemcpyHostToDevice); + + // Launch kernel + ari<<>>( + d_parts, + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + k, + d_out, + d_parts_pairs); + + // Synchronize device + cudaDeviceSynchronize(); + + // Copy results back to host + int *h_parts_pairs = new int[n_aris * 2 * n_objs]; + cudaMemcpy(h_parts_pairs, d_parts_pairs, n_aris * 2 * n_objs * sizeof(int), cudaMemcpyDeviceToHost); + + // Print results + std::cout << "Parts pairs: " << std::endl; + for (int i = 0; i < n_aris; ++i) + { + std::cout << "Pair:" << i << std::endl; + for (int j = 0; j < 2; ++j) + { + for (int k = 0; k < n_objs; ++k) + { + std::cout << *(h_parts_pairs + i * 2 * n_objs + j * n_objs + k) << ", "; + } + std::cout << std::endl; + } + std::cout << std::endl; + } + std::cout << std::endl; + + // Assert equality on the parts pairs + bool all_equal = true; + auto pairs = generate_pairwise_combinations(parts); + int n_pairs = pairs.size(); + for (int i = 0; i < n_pairs; ++i) + { + for (int j = 0; j < 2; ++j) + { + const std::vector ¤t_vector = (j == 0) ? pairs[i].first : pairs[i].second; + for (int k = 0; k < n_objs; ++k) + { + int flattened_index = i * 2 * n_objs + j * n_objs + k; + if (h_parts_pairs[flattened_index] != current_vector[k]) + { + all_equal = false; + std::cout << "Mismatch at i=" << i << ", j=" << j << ", k=" << k << std::endl; + std::cout << "Expected: " << current_vector[k] << ", Got: " << h_parts_pairs[flattened_index] << std::endl; + } + } + } + } + + if (all_equal) + { + std::cout << "Test passed: All elements match." << std::endl; + } + else + { + std::cout << "Test failed: Mismatches found." << std::endl; + } + + // Print ARI results + float *h_out = new float[n_aris]; + cudaMemcpy(h_out, d_out, n_aris * sizeof(float), cudaMemcpyDeviceToHost); + std::cout << "ARI results: " << std::endl; + for (int i = 0; i < n_aris; ++i) + { + printf("%f, ", h_out[i]); + } + std::cout << std::endl; + + // Clean up + cudaFree(d_parts); + cudaFree(d_out); + cudaFree(d_parts_pairs); + delete[] h_parts_pairs; +} + +int main() +{ + test_ari_parts_selection(); + return 0; +} \ No newline at end of file From c0a0a98ff3f28fbaaf3aa9fb44173768e2a6fe5d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 28 Oct 2024 14:57:54 -0600 Subject: [PATCH 086/134] [build]: Introduce gtest as a dependency --- .gitignore | 5 ++++- libs/ccc/sklearn/CMakeLists.txt | 25 +++++++++++++++++++++++++ libs/ccc/sklearn/Readme.md | 8 ++++++++ libs/ccc/sklearn/hello_test.cc | 9 +++++++++ libs/ccc/sklearn/test_kernel.cpp | 1 + 5 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 libs/ccc/sklearn/Readme.md create mode 100644 libs/ccc/sklearn/hello_test.cc diff --git a/.gitignore b/.gitignore index 0b78489a..d4e65d24 100644 --- a/.gitignore +++ b/.gitignore @@ -132,4 +132,7 @@ dmypy.json .idea/* # Development directory -__dev \ No newline at end of file +__dev + +# Binary files +libs/ccc/sklearn/metrics diff --git a/libs/ccc/sklearn/CMakeLists.txt b/libs/ccc/sklearn/CMakeLists.txt index f66a2913..1284d016 100644 --- a/libs/ccc/sklearn/CMakeLists.txt +++ b/libs/ccc/sklearn/CMakeLists.txt @@ -5,6 +5,7 @@ project(CudaAriProject LANGUAGES CUDA CXX) # Set the C++ standard set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) # Add the CUDA library add_library(cudaAriLib STATIC metrics.cu) # Add the CUDA source file @@ -13,3 +14,27 @@ set_target_properties(cudaAriLib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) # Add the test executable add_executable(testCudaAri test_kernel.cpp) target_link_libraries(testCudaAri PRIVATE cudaAriLib) + +# Add gtest as a dependency +include(FetchContent) +FetchContent_Declare( + googletest + URL https://github.com/google/googletest/archive/5ed21863955149a5a877a53d7d5045b6919090ed.zip +) +# For Windows: Prevent overriding the parent project's compiler/linker settings +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) +FetchContent_MakeAvailable(googletest) + +enable_testing() + +add_executable( + hello_test + hello_test.cc +) +target_link_libraries( + hello_test + GTest::gtest_main +) + +include(GoogleTest) +gtest_discover_tests(hello_test) diff --git a/libs/ccc/sklearn/Readme.md b/libs/ccc/sklearn/Readme.md new file mode 100644 index 00000000..2bc488aa --- /dev/null +++ b/libs/ccc/sklearn/Readme.md @@ -0,0 +1,8 @@ +## How to build the CUDA module and its tests + +``` +# cd to current directory +cmake -S . -B build +cd build && ctest +cd build && ctest +``` diff --git a/libs/ccc/sklearn/hello_test.cc b/libs/ccc/sklearn/hello_test.cc new file mode 100644 index 00000000..5a57e138 --- /dev/null +++ b/libs/ccc/sklearn/hello_test.cc @@ -0,0 +1,9 @@ +#include + +// Demonstrate some basic assertions. +TEST(HelloTest, BasicAssertions) { + // Expect two strings not to be equal. + EXPECT_STRNE("hello", "world"); + // Expect equality. + EXPECT_EQ(7 * 6, 42); +} diff --git a/libs/ccc/sklearn/test_kernel.cpp b/libs/ccc/sklearn/test_kernel.cpp index 5f7c99c6..d5e900dd 100644 --- a/libs/ccc/sklearn/test_kernel.cpp +++ b/libs/ccc/sklearn/test_kernel.cpp @@ -1,6 +1,7 @@ #include #include #include "metrics.cuh" +// #include "gtest/gtest.h" // Helper function to generate pairwise combinations (implement this according to your needs) From 94b560253feee49f3f6db4174f62a5f19b072f09 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 28 Oct 2024 16:12:33 -0600 Subject: [PATCH 087/134] [test/ari]: Add simple cpp test cases for cudaAri --- libs/ccc/sklearn/CMakeLists.txt | 35 +++++------ libs/ccc/sklearn/Readme.md | 4 +- libs/ccc/sklearn/test_kernel.cpp | 104 +++++++++++++++++-------------- 3 files changed, 75 insertions(+), 68 deletions(-) diff --git a/libs/ccc/sklearn/CMakeLists.txt b/libs/ccc/sklearn/CMakeLists.txt index 1284d016..f202e0bb 100644 --- a/libs/ccc/sklearn/CMakeLists.txt +++ b/libs/ccc/sklearn/CMakeLists.txt @@ -3,6 +3,14 @@ cmake_minimum_required(VERSION 3.18) project(CudaAriProject LANGUAGES CUDA CXX) +# Add gtest as a dependency +include(FetchContent) +FetchContent_Declare( + googletest + URL https://github.com/google/googletest/archive/5ed21863955149a5a877a53d7d5045b6919090ed.zip +) +include(GoogleTest) + # Set the C++ standard set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) @@ -11,30 +19,17 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON) add_library(cudaAriLib STATIC metrics.cu) # Add the CUDA source file set_target_properties(cudaAriLib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Add the test executable -add_executable(testCudaAri test_kernel.cpp) -target_link_libraries(testCudaAri PRIVATE cudaAriLib) - -# Add gtest as a dependency -include(FetchContent) -FetchContent_Declare( - googletest - URL https://github.com/google/googletest/archive/5ed21863955149a5a877a53d7d5045b6919090ed.zip -) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) +# Testing enable_testing() -add_executable( - hello_test - hello_test.cc -) -target_link_libraries( - hello_test - GTest::gtest_main -) - -include(GoogleTest) +add_executable(hello_test hello_test.cc) +target_link_libraries(hello_test GTest::gtest_main) gtest_discover_tests(hello_test) + +add_executable(testCudaAri test_kernel.cpp) +target_link_libraries(testCudaAri cudaAriLib GTest::gtest_main GTest::gtest) +gtest_discover_tests(testCudaAri) diff --git a/libs/ccc/sklearn/Readme.md b/libs/ccc/sklearn/Readme.md index 2bc488aa..bb52db9c 100644 --- a/libs/ccc/sklearn/Readme.md +++ b/libs/ccc/sklearn/Readme.md @@ -3,6 +3,6 @@ ``` # cd to current directory cmake -S . -B build -cd build && ctest -cd build && ctest +cmake --build build +ctest --test-dir build --output-on-failure ``` diff --git a/libs/ccc/sklearn/test_kernel.cpp b/libs/ccc/sklearn/test_kernel.cpp index d5e900dd..c971ba09 100644 --- a/libs/ccc/sklearn/test_kernel.cpp +++ b/libs/ccc/sklearn/test_kernel.cpp @@ -1,7 +1,8 @@ #include +#include #include #include "metrics.cuh" -// #include "gtest/gtest.h" +#include "gtest/gtest.h" // Helper function to generate pairwise combinations (implement this according to your needs) @@ -25,40 +26,18 @@ std::vector, std::vector>> generate_pairwise_com return pairs; } -void test_ari_parts_selection() -{ - // Define test input - std::vector>> parts = { - {{0, 1, 2, 3}, - {0, 2, 3, 4}, - {0, 3, 4, 5}}, - {{1, 1, 2, 3}, - {1, 2, 3, 4}, - {1, 3, 4, 5}}, - {{2, 1, 2, 3}, - {2, 2, 3, 4}, - {2, 3, 4, 5}}}; - - const int k = 6; // specified by the call to ccc , part number from [0...9] - - // std::vector>> parts = { - // {{4, 1, 3, 5, 2, 0, 6, 3, 1, 4}, - // {0, 2, 6, 4, 5, 3, 1, 0, 6, 2}, - // {1, 5, 3, 2, 4, 0, 6, 1, 5, 3}}, - // // {{3, 6, 0, 2, 1, 5, 4, 3, 6, 0}, - // // {5, 1, 4, 0, 3, 6, 2, 1, 5, 4}, - // // {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}}, +using Vec3 = std::vector>>; +using TestParamType = std::tuple; - // {{1, 4, 5, 3, 6, 0, 2, 5, 4, 1}, - // {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}, - // {4, 1, 3, 6, 5, 0, 2, 4, 1, 3}} - // }; +// Define a parameterized test fixture +class CudaAriTest : public ::testing::TestWithParam {}; - // const int k = 7; // specified by the call to ccc , max(parts) + 1 - - // std::vector part_maxes = {3, 4, 5, 3, 4, 5, 3, 4, 5}; - // auto sz_part_maxes = sizeof(part_maxes) / sizeof(part_maxes[0]); +TEST_P(CudaAriTest, CheckSingleResult) +{ + Vec3 parts; + float expected_result; + std::tie(parts, expected_result) = GetParam(); // Get dimensions int n_features = parts.size(); @@ -84,20 +63,53 @@ void test_ari_parts_selection() } } - auto h_out = cudaAri(h_parts, n_features, n_parts, n_objs); + auto h_out = cudaAri(h_parts, n_features, n_parts, n_objs)[0]; - // Print ARI results - std::cout << "ARI results: " << std::endl; - for (int i = 0; i < n_aris; ++i) - { - printf("%f, ", h_out[i]); - } - std::cout << std::endl; + // Check if the result are close + EXPECT_NEAR(h_out, expected_result, 1e-2); } -int main() -{ - std::cout << "Hello, World!" << std::endl; - test_ari_parts_selection(); - return 0; -} +// Instantiate the test suite with parameter values +// These tests are taken from sklearn.metrics.adjusted_rand_score: +// https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html +INSTANTIATE_TEST_SUITE_P( + CudaAriTestInstances, + CudaAriTest, + ::testing::Values( + TestParamType( + Vec3{ + {{0, 0, 1, 2}}, + {{0, 0, 1, 1}}, + }, + 0.57f + ), + TestParamType( + Vec3{ + {{0, 0, 1, 1}}, + {{0, 1, 0, 1}}, + }, + -0.5f + ), + TestParamType( + Vec3{ + {{0, 0, 1, 1}}, + {{0, 0, 1, 1}}, + }, + 1.0f + ), + TestParamType( + Vec3{ + {{0, 0, 1, 1}}, + {{1, 1, 0, 0}}, + }, + 1.0f + ), + TestParamType( + Vec3{ + {{0, 0, 0, 0}}, + {{0, 1, 2, 3}}, + }, + 0.0f + ) + ) +); From a7237e794203b0b7e869283e0faf78aa163f54eb Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 29 Oct 2024 11:42:30 -0600 Subject: [PATCH 088/134] [build]: Introduce dependency pybind11 --- environment/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment/environment.yml b/environment/environment.yml index 7c9ed678..4a92d81f 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -10,3 +10,4 @@ dependencies: - numba=0.6.* - python=3.11 - pytest=8.* + - pybind11=2.* From 1b4aacfb811e935c5da081298c1262406eee5959 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 29 Oct 2024 19:44:51 -0600 Subject: [PATCH 089/134] [misc]: Move the cuda module to a separate folder --- libs/ccc/{sklearn => cuda_ext}/CMakeLists.txt | 8 ++------ libs/ccc/{sklearn => cuda_ext}/Readme.md | 0 libs/ccc/{sklearn => cuda_ext}/hello_test.cc | 0 libs/ccc/{sklearn => cuda_ext}/metrics.cu | 3 ++- libs/ccc/{sklearn => cuda_ext}/metrics.cuh | 0 libs/ccc/cuda_ext/metrics_binder.cpp | 3 +++ libs/ccc/{sklearn => cuda_ext/tests}/test_kernel.cpp | 2 +- .../tests}/test_partition_pairing.cpp | 0 libs/ccc/sklearn/Makefile | 12 ------------ 9 files changed, 8 insertions(+), 20 deletions(-) rename libs/ccc/{sklearn => cuda_ext}/CMakeLists.txt (76%) rename libs/ccc/{sklearn => cuda_ext}/Readme.md (100%) rename libs/ccc/{sklearn => cuda_ext}/hello_test.cc (100%) rename libs/ccc/{sklearn => cuda_ext}/metrics.cu (99%) rename libs/ccc/{sklearn => cuda_ext}/metrics.cuh (100%) create mode 100644 libs/ccc/cuda_ext/metrics_binder.cpp rename libs/ccc/{sklearn => cuda_ext/tests}/test_kernel.cpp (99%) rename libs/ccc/{sklearn => cuda_ext/tests}/test_partition_pairing.cpp (100%) delete mode 100644 libs/ccc/sklearn/Makefile diff --git a/libs/ccc/sklearn/CMakeLists.txt b/libs/ccc/cuda_ext/CMakeLists.txt similarity index 76% rename from libs/ccc/sklearn/CMakeLists.txt rename to libs/ccc/cuda_ext/CMakeLists.txt index f202e0bb..0b021e74 100644 --- a/libs/ccc/sklearn/CMakeLists.txt +++ b/libs/ccc/cuda_ext/CMakeLists.txt @@ -26,10 +26,6 @@ FetchContent_MakeAvailable(googletest) # Testing enable_testing() -add_executable(hello_test hello_test.cc) -target_link_libraries(hello_test GTest::gtest_main) -gtest_discover_tests(hello_test) - -add_executable(testCudaAri test_kernel.cpp) -target_link_libraries(testCudaAri cudaAriLib GTest::gtest_main GTest::gtest) +add_executable(testCudaAri tests/test_kernel.cpp) +target_link_libraries(testCudaAri PRIVATE cudaAriLib GTest::gtest_main GTest::gtest) gtest_discover_tests(testCudaAri) diff --git a/libs/ccc/sklearn/Readme.md b/libs/ccc/cuda_ext/Readme.md similarity index 100% rename from libs/ccc/sklearn/Readme.md rename to libs/ccc/cuda_ext/Readme.md diff --git a/libs/ccc/sklearn/hello_test.cc b/libs/ccc/cuda_ext/hello_test.cc similarity index 100% rename from libs/ccc/sklearn/hello_test.cc rename to libs/ccc/cuda_ext/hello_test.cc diff --git a/libs/ccc/sklearn/metrics.cu b/libs/ccc/cuda_ext/metrics.cu similarity index 99% rename from libs/ccc/sklearn/metrics.cu rename to libs/ccc/cuda_ext/metrics.cu index 0a0b5ad4..489beba1 100644 --- a/libs/ccc/sklearn/metrics.cu +++ b/libs/ccc/cuda_ext/metrics.cu @@ -11,6 +11,7 @@ * Future optimizations * 1. use narrower data types * 2. optimized on locality + * 3. use warp-level reduction */ @@ -364,7 +365,7 @@ auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const si // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); - auto k = *max_iter + 1; + const auto k = *max_iter + 1; std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; // Launch the kernel diff --git a/libs/ccc/sklearn/metrics.cuh b/libs/ccc/cuda_ext/metrics.cuh similarity index 100% rename from libs/ccc/sklearn/metrics.cuh rename to libs/ccc/cuda_ext/metrics.cuh diff --git a/libs/ccc/cuda_ext/metrics_binder.cpp b/libs/ccc/cuda_ext/metrics_binder.cpp new file mode 100644 index 00000000..ce651109 --- /dev/null +++ b/libs/ccc/cuda_ext/metrics_binder.cpp @@ -0,0 +1,3 @@ +#include +#include "metrics.cuh" + diff --git a/libs/ccc/sklearn/test_kernel.cpp b/libs/ccc/cuda_ext/tests/test_kernel.cpp similarity index 99% rename from libs/ccc/sklearn/test_kernel.cpp rename to libs/ccc/cuda_ext/tests/test_kernel.cpp index c971ba09..becbd620 100644 --- a/libs/ccc/sklearn/test_kernel.cpp +++ b/libs/ccc/cuda_ext/tests/test_kernel.cpp @@ -1,7 +1,7 @@ #include #include #include -#include "metrics.cuh" +#include "../metrics.cuh" #include "gtest/gtest.h" // Helper function to generate pairwise combinations (implement this according to your needs) diff --git a/libs/ccc/sklearn/test_partition_pairing.cpp b/libs/ccc/cuda_ext/tests/test_partition_pairing.cpp similarity index 100% rename from libs/ccc/sklearn/test_partition_pairing.cpp rename to libs/ccc/cuda_ext/tests/test_partition_pairing.cpp diff --git a/libs/ccc/sklearn/Makefile b/libs/ccc/sklearn/Makefile deleted file mode 100644 index 86b02489..00000000 --- a/libs/ccc/sklearn/Makefile +++ /dev/null @@ -1,12 +0,0 @@ -CU_APPS=metrics -# CPP_APPS=test_kernel - -all: ${CPP_APPS} ${CU_APPS} - -## Todo: try out O3 optimization -%: %.cu - nvcc -O2 -arch=sm_89 -o $@ $< -lcudadevrt --relocatable-device-code true -%: %.hpp - gcc -O2 -std=c++17 -o $@ $< -clean: - rm -f ${CU_APPS} ${CPP_APPS} From 6e29dc7e205fff8f024d0a41057845e8f6f29bc1 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 29 Oct 2024 20:56:46 -0600 Subject: [PATCH 090/134] [build]: Test on an example of pybind11 --- CMakeLists.txt | 26 +++++++++++++++++++ .../cuda_ext => environment}/CMakeLists.txt | 0 libs/ccc/cuda_ext/metrics_binder.cpp | 3 --- libs/{ccc => }/cuda_ext/Readme.md | 0 libs/cuda_ext/binder.cu | 13 ++++++++++ libs/cuda_ext/example.cpp | 5 ++++ libs/cuda_ext/example.hpp | 1 + libs/cuda_ext/example_binder.cpp | 13 ++++++++++ libs/{ccc => }/cuda_ext/hello_test.cc | 0 libs/{ccc => }/cuda_ext/metrics.cu | 0 libs/{ccc => }/cuda_ext/metrics.cuh | 0 libs/{ccc => }/cuda_ext/tests/test_kernel.cpp | 0 .../cuda_ext/tests/test_partition_pairing.cpp | 0 13 files changed, 58 insertions(+), 3 deletions(-) create mode 100644 CMakeLists.txt rename {libs/ccc/cuda_ext => environment}/CMakeLists.txt (100%) delete mode 100644 libs/ccc/cuda_ext/metrics_binder.cpp rename libs/{ccc => }/cuda_ext/Readme.md (100%) create mode 100644 libs/cuda_ext/binder.cu create mode 100644 libs/cuda_ext/example.cpp create mode 100644 libs/cuda_ext/example.hpp create mode 100644 libs/cuda_ext/example_binder.cpp rename libs/{ccc => }/cuda_ext/hello_test.cc (100%) rename libs/{ccc => }/cuda_ext/metrics.cu (100%) rename libs/{ccc => }/cuda_ext/metrics.cuh (100%) rename libs/{ccc => }/cuda_ext/tests/test_kernel.cpp (100%) rename libs/{ccc => }/cuda_ext/tests/test_partition_pairing.cpp (100%) diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 00000000..d8a3a999 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,26 @@ +# Require CMake 3.15+ (matching scikit-build-core) Use new versions of all +# policies up to CMake 3.27 +cmake_minimum_required(VERSION 3.15...3.27) + +# Scikit-build-core sets these values for you, or you can just hard-code the +# name and version. +project( + ${SKBUILD_PROJECT_NAME} + VERSION ${SKBUILD_PROJECT_VERSION} + LANGUAGES CXX) + +# Find the module development requirements (requires FindPython from 3.17 or +# scikit-build-core's built-in backport) +find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) +find_package(pybind11 CONFIG REQUIRED) + +# Add a library using FindPython's tooling (pybind11 also provides a helper like +# this) +python_add_library(_core MODULE libs/cuda_ext/binder.cpp ibs/cuda_ext/example.cpp WITH_SOABI) +target_link_libraries(_core PRIVATE pybind11::headers) + +# This is passing in the version as a define just as an example +target_compile_definitions(_core PRIVATE VERSION_INFO=${PROJECT_VERSION}) + +# The install directory is the output (wheel) directory +install(TARGETS _core DESTINATION scikit_build_example) diff --git a/libs/ccc/cuda_ext/CMakeLists.txt b/environment/CMakeLists.txt similarity index 100% rename from libs/ccc/cuda_ext/CMakeLists.txt rename to environment/CMakeLists.txt diff --git a/libs/ccc/cuda_ext/metrics_binder.cpp b/libs/ccc/cuda_ext/metrics_binder.cpp deleted file mode 100644 index ce651109..00000000 --- a/libs/ccc/cuda_ext/metrics_binder.cpp +++ /dev/null @@ -1,3 +0,0 @@ -#include -#include "metrics.cuh" - diff --git a/libs/ccc/cuda_ext/Readme.md b/libs/cuda_ext/Readme.md similarity index 100% rename from libs/ccc/cuda_ext/Readme.md rename to libs/cuda_ext/Readme.md diff --git a/libs/cuda_ext/binder.cu b/libs/cuda_ext/binder.cu new file mode 100644 index 00000000..eb021336 --- /dev/null +++ b/libs/cuda_ext/binder.cu @@ -0,0 +1,13 @@ +#include "metrics.cuh" +#include + +namespace py = pybind11; + +using namespace pybind11::literals; + +PYBIND11_PLUGIN(cuda_ccc) { + py::module m("cuda_ccc", "pybind11 example plugin"); + m.def("ari", &cudaAri, "CUDA version of Adjusted Rand Index (ARI) calculation", + "parts"_a, "n_features"_a, "n_parts"_a, "n_objs"_a); + return m.ptr(); +} diff --git a/libs/cuda_ext/example.cpp b/libs/cuda_ext/example.cpp new file mode 100644 index 00000000..509c0965 --- /dev/null +++ b/libs/cuda_ext/example.cpp @@ -0,0 +1,5 @@ +#include "example.hpp" + +int add(int i, int j) { + return i + j; +}; \ No newline at end of file diff --git a/libs/cuda_ext/example.hpp b/libs/cuda_ext/example.hpp new file mode 100644 index 00000000..8247f000 --- /dev/null +++ b/libs/cuda_ext/example.hpp @@ -0,0 +1 @@ +int add(int i, int j); \ No newline at end of file diff --git a/libs/cuda_ext/example_binder.cpp b/libs/cuda_ext/example_binder.cpp new file mode 100644 index 00000000..d59f147f --- /dev/null +++ b/libs/cuda_ext/example_binder.cpp @@ -0,0 +1,13 @@ +#include +#include "example.hpp" + +namespace py = pybind11; + +using namespace pybind11::literals; + +PYBIND11_PLUGIN(wrap) { + py::module m("wrap", "pybind11 example plugin"); + m.def("add", &add, "A function which adds two numbers", + "i"_a=1, "j"_a=2); + return m.ptr(); +} diff --git a/libs/ccc/cuda_ext/hello_test.cc b/libs/cuda_ext/hello_test.cc similarity index 100% rename from libs/ccc/cuda_ext/hello_test.cc rename to libs/cuda_ext/hello_test.cc diff --git a/libs/ccc/cuda_ext/metrics.cu b/libs/cuda_ext/metrics.cu similarity index 100% rename from libs/ccc/cuda_ext/metrics.cu rename to libs/cuda_ext/metrics.cu diff --git a/libs/ccc/cuda_ext/metrics.cuh b/libs/cuda_ext/metrics.cuh similarity index 100% rename from libs/ccc/cuda_ext/metrics.cuh rename to libs/cuda_ext/metrics.cuh diff --git a/libs/ccc/cuda_ext/tests/test_kernel.cpp b/libs/cuda_ext/tests/test_kernel.cpp similarity index 100% rename from libs/ccc/cuda_ext/tests/test_kernel.cpp rename to libs/cuda_ext/tests/test_kernel.cpp diff --git a/libs/ccc/cuda_ext/tests/test_partition_pairing.cpp b/libs/cuda_ext/tests/test_partition_pairing.cpp similarity index 100% rename from libs/ccc/cuda_ext/tests/test_partition_pairing.cpp rename to libs/cuda_ext/tests/test_partition_pairing.cpp From f55bf817cad3756a1ed4d87217164b548fee881c Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 31 Oct 2024 15:46:49 -0600 Subject: [PATCH 091/134] [build]: Compile cudaAri with pybind11 successfully --- CMakeLists.txt | 27 ++------- histogram.png | Bin 19558 -> 0 bytes libs/ccc/pyproject.toml | 88 +++++++++++++++++++++++++++++ libs/ccc/test_binding.py | 7 +++ libs/cuda_ext/example_binder.cpp | 2 +- libs/cuda_ext/tests/test_binder.py | 11 ++++ old_CMakeLists.txt | 26 +++++++++ setup.cfg => old_setup.cfg | 0 setup.py => old_setup.py | 0 pyproject.toml | 10 ++++ setup_dev.sh | 3 + 11 files changed, 151 insertions(+), 23 deletions(-) delete mode 100644 histogram.png create mode 100644 libs/ccc/pyproject.toml create mode 100644 libs/ccc/test_binding.py create mode 100644 libs/cuda_ext/tests/test_binder.py create mode 100644 old_CMakeLists.txt rename setup.cfg => old_setup.cfg (100%) rename setup.py => old_setup.py (100%) create mode 100644 pyproject.toml diff --git a/CMakeLists.txt b/CMakeLists.txt index d8a3a999..a89273c1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,26 +1,9 @@ -# Require CMake 3.15+ (matching scikit-build-core) Use new versions of all -# policies up to CMake 3.27 -cmake_minimum_required(VERSION 3.15...3.27) +cmake_minimum_required(VERSION 3.15...3.26) +project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA) -# Scikit-build-core sets these values for you, or you can just hard-code the -# name and version. -project( - ${SKBUILD_PROJECT_NAME} - VERSION ${SKBUILD_PROJECT_VERSION} - LANGUAGES CXX) - -# Find the module development requirements (requires FindPython from 3.17 or -# scikit-build-core's built-in backport) -find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) +set(PYBIND11_NEWPYTHON ON) find_package(pybind11 CONFIG REQUIRED) -# Add a library using FindPython's tooling (pybind11 also provides a helper like -# this) -python_add_library(_core MODULE libs/cuda_ext/binder.cpp ibs/cuda_ext/example.cpp WITH_SOABI) 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z4@tO$A_Wm;rHb5A_F6sd7th{5yJ!gr+Q{2bYDpZPxAK&ZJ6wJ;MUYfRdN9!Ccya_i z;3U~RryYE{U&B}y(qttzvM<5`ao}6f=z=G~Was7qFAAy2kDl>ETZXm(#Vdf*w4WDB y2yWh~j8afX!6T8eRqpSBVZ7u2o5xg%rFTCx$x<%7bc#X_bI0C4Cx3n9yZ-^Y_DAag diff --git a/libs/ccc/pyproject.toml b/libs/ccc/pyproject.toml new file mode 100644 index 00000000..e20fc448 --- /dev/null +++ b/libs/ccc/pyproject.toml @@ -0,0 +1,88 @@ +[build-system] +requires = ["scikit-build-core>=0.10", "pybind11"] +build-backend = "scikit_build_core.build" + + +[project] +name = "ccc-coef" +version = "0.0.1" +description="A minimal example package (with pybind11)" +readme = "README.md" +authors = [ + { name = "My Name", email = "me@email.com" }, +] +requires-python = ">=3.9" +classifiers = [ + "Development Status :: 4 - Beta", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] + +[project.optional-dependencies] +test = ["pytest"] + + +[tool.scikit-build] +wheel.expand-macos-universal-tags = true +minimum-version = "build-system.requires" + + +[tool.pytest.ini_options] +minversion = "8.0" +addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] +xfail_strict = true +log_cli_level = "INFO" +filterwarnings = [ + "error", + "ignore::pytest.PytestCacheWarning", +] +testpaths = ["tests"] + + +[tool.cibuildwheel] +build-frontend = "build[uv]" +test-command = "pytest {project}/tests" +test-extras = ["test"] + +[tool.cibuildwheel.pyodide] +build-frontend = {name = "build", args = ["--exports", "whole_archive"]} + +[tool.ruff.lint] +extend-select = [ + "B", # flake8-bugbear + "I", # isort + "ARG", # flake8-unused-arguments + "C4", # flake8-comprehensions + "EM", # flake8-errmsg + "ICN", # flake8-import-conventions + "G", # flake8-logging-format + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PL", # pylint + "PT", # flake8-pytest-style + "PTH", # flake8-use-pathlib + "RET", # flake8-return + "RUF", # Ruff-specific + "SIM", # flake8-simplify + "T20", # flake8-print + "UP", # pyupgrade + "YTT", # flake8-2020 + "EXE", # flake8-executable + "NPY", # NumPy specific rules + "PD", # pandas-vet +] +ignore = [ + "PLR09", # Too many X + "PLR2004", # Magic comparison +] +isort.required-imports = ["from __future__ import annotations"] + +[tool.ruff.lint.per-file-ignores] +"tests/**" = ["T20"] \ No newline at end of file diff --git a/libs/ccc/test_binding.py b/libs/ccc/test_binding.py new file mode 100644 index 00000000..7a94b307 --- /dev/null +++ b/libs/ccc/test_binding.py @@ -0,0 +1,7 @@ +from ._core import add + +def test_add(): + assert(add(3, 4) == 7) + +if __name__ == '__main__': + test_add() diff --git a/libs/cuda_ext/example_binder.cpp b/libs/cuda_ext/example_binder.cpp index d59f147f..06fd87e4 100644 --- a/libs/cuda_ext/example_binder.cpp +++ b/libs/cuda_ext/example_binder.cpp @@ -5,7 +5,7 @@ namespace py = pybind11; using namespace pybind11::literals; -PYBIND11_PLUGIN(wrap) { +PYBIND11_PLUGIN(_core) { py::module m("wrap", "pybind11 example plugin"); m.def("add", &add, "A function which adds two numbers", "i"_a=1, "j"_a=2); diff --git a/libs/cuda_ext/tests/test_binder.py b/libs/cuda_ext/tests/test_binder.py new file mode 100644 index 00000000..b040cca6 --- /dev/null +++ b/libs/cuda_ext/tests/test_binder.py @@ -0,0 +1,11 @@ +import cuda_ccc +import inspect +import numpy as np + + +parts = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) +n_features = 3 +n_parts = 1 +n_samples = 3 +r = cuda_ccc.ari(parts, n_samples, n_features, n_parts) +print(r) diff --git a/old_CMakeLists.txt b/old_CMakeLists.txt new file mode 100644 index 00000000..492fe882 --- /dev/null +++ b/old_CMakeLists.txt @@ -0,0 +1,26 @@ +# Require CMake 3.15+ (matching scikit-build-core) Use new versions of all +# policies up to CMake 3.27 +cmake_minimum_required(VERSION 3.15...3.27) + +# Scikit-build-core sets these values for you, or you can just hard-code the +# name and version. +project( + ${SKBUILD_PROJECT_NAME} + VERSION ${SKBUILD_PROJECT_VERSION} + LANGUAGES CXX) + +# Find the module development requirements (requires FindPython from 3.17 or +# scikit-build-core's built-in backport) +find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) +find_package(pybind11 CONFIG REQUIRED) + +# Add a library using FindPython's tooling (pybind11 also provides a helper like +# this) +python_add_library(_core MODULE libs/cuda_ext/example_binder.cpp libs/cuda_ext/example.cpp WITH_SOABI) +target_link_libraries(_core PRIVATE pybind11::headers) + +# This is passing in the version as a define just as an example +target_compile_definitions(_core PRIVATE VERSION_INFO=${PROJECT_VERSION}) + +# The install directory is the output (wheel) directory +install(TARGETS _core DESTINATION scikit_build_example) diff --git a/setup.cfg b/old_setup.cfg similarity index 100% rename from setup.cfg rename to old_setup.cfg diff --git a/setup.py b/old_setup.py similarity index 100% rename from setup.py rename to old_setup.py diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..0597c1e4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,10 @@ +[build-system] +requires = ["scikit-build-core", "pybind11"] +build-backend = "scikit_build_core.build" + +[project] +name = "example" +version = "0.0.1" + +[tool.setuptools.packages.find] +where = ["libs"] diff --git a/setup_dev.sh b/setup_dev.sh index 46ab76fc..7296fc18 100755 --- a/setup_dev.sh +++ b/setup_dev.sh @@ -1,3 +1,6 @@ +# Used to setup the development environment for CCC +# Can be loaded by PyCharm on startup + conda activate ccc export CODE_DIR=/home/haoyu/_database/projs/ccc-gpu export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH From 89a667437f17cd2112cd4b781d586f8e774fff20 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 31 Oct 2024 21:26:28 -0600 Subject: [PATCH 092/134] [binding]: Pass compilation using numpy array interface --- {environment => libs/cuda_ext}/CMakeLists.txt | 0 libs/cuda_ext/binder.cu | 1 + libs/cuda_ext/metrics.cu | 101 +++++++++--------- libs/cuda_ext/metrics.cuh | 6 +- libs/cuda_ext/tests/test_binder.py | 2 +- libs/cuda_ext/tests/test_kernel.cpp | 17 ++- 6 files changed, 68 insertions(+), 59 deletions(-) rename {environment => libs/cuda_ext}/CMakeLists.txt (100%) diff --git a/environment/CMakeLists.txt b/libs/cuda_ext/CMakeLists.txt similarity index 100% rename from environment/CMakeLists.txt rename to libs/cuda_ext/CMakeLists.txt diff --git a/libs/cuda_ext/binder.cu b/libs/cuda_ext/binder.cu index eb021336..b9a71571 100644 --- a/libs/cuda_ext/binder.cu +++ b/libs/cuda_ext/binder.cu @@ -1,5 +1,6 @@ #include "metrics.cuh" #include +#include namespace py = pybind11; diff --git a/libs/cuda_ext/metrics.cu b/libs/cuda_ext/metrics.cu index 489beba1..bb67cacf 100644 --- a/libs/cuda_ext/metrics.cu +++ b/libs/cuda_ext/metrics.cu @@ -6,6 +6,9 @@ #include #include #include +#include "metrics.cuh" + +namespace py = pybind11; /** * Future optimizations @@ -330,66 +333,68 @@ __global__ void ari(int *parts, * @return std::vector ARI values for each pair of partitions */ // template -auto cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { +auto cudaAri(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { // Edge cases: // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing // Input validation - if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); + // if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); // Compute internal variables - using parts_dtype = typename std::remove_pointer::type; + // Todo: dynamically query types + using parts_dtype = int; using out_dtype = float; - const auto n_feature_comp = n_features * (n_features - 1) / 2; - const auto n_aris = n_feature_comp * n_parts * n_parts; - // Allocate host memory - thrust::host_vector h_out(n_aris); - thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); - - // Set up CUDA kernel configuration - const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block - // Each block is responsible for one ARI computation - const auto grid_size = n_aris; - // Define shared memory size for each block - const auto parts_dtype_size = sizeof(*parts); - auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared - s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays - s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix - - // Allocate device memory with thrust - thrust::device_vector d_parts(parts, parts + n_features * n_parts * n_objs); // data is copied to device - thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); - thrust::device_vector d_out(n_aris); - - // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later - auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); - const auto k = *max_iter + 1; - std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; - - // Launch the kernel - ari<<>>( - thrust::raw_pointer_cast(d_parts.data()), - n_aris, - n_features, - n_parts, - n_objs, - n_parts * n_objs, - n_parts * n_parts, - k, - thrust::raw_pointer_cast(d_out.data()), - thrust::raw_pointer_cast(d_parts_pairs.data())); + // const auto n_feature_comp = n_features * (n_features - 1) / 2; + // const auto n_aris = n_feature_comp * n_parts * n_parts; + // // Allocate host memory + // thrust::host_vector h_out(n_aris); + // thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); + + // // Set up CUDA kernel configuration + // const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block + // // Each block is responsible for one ARI computation + // const auto grid_size = n_aris; + // // Define shared memory size for each block + // const auto parts_dtype_size = sizeof(parts_dtype); + // auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared + // s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays + // s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix + + // // Allocate device memory with thrust + // const int* parts_raw = parts[0][0].data(); + // thrust::device_vector d_parts(parts_raw, parts_raw + n_features * n_parts * n_objs); // data is copied to device + // thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); + // thrust::device_vector d_out(n_aris); + + // // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later + // auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); + // const auto k = *max_iter + 1; + // std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; + + // // Launch the kernel + // ari<<>>( + // thrust::raw_pointer_cast(d_parts.data()), + // n_aris, + // n_features, + // n_parts, + // n_objs, + // n_parts * n_objs, + // n_parts * n_parts, + // k, + // thrust::raw_pointer_cast(d_out.data()), + // thrust::raw_pointer_cast(d_parts_pairs.data())); - // Copy data back to host - thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); - thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); + // // Copy data back to host + // thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); + // thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); - // Free device memory + // // Free device memory - // Convert thrust vectors to std::vector - std::vector res(h_out.begin(), h_out.end()); + // // Convert thrust vectors to std::vector + // std::vector res(h_out.begin(), h_out.end()); // Return the ARI values - return res; + return std::vector(0); } diff --git a/libs/cuda_ext/metrics.cuh b/libs/cuda_ext/metrics.cuh index cf8bf472..74a4ad28 100644 --- a/libs/cuda_ext/metrics.cuh +++ b/libs/cuda_ext/metrics.cuh @@ -1,6 +1,10 @@ #pragma once #include +#include +using Mat3 = std::vector>>; + +namespace py = pybind11; // template -std::vector cudaAri(int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs); +std::vector cudaAri(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs); diff --git a/libs/cuda_ext/tests/test_binder.py b/libs/cuda_ext/tests/test_binder.py index b040cca6..e676498a 100644 --- a/libs/cuda_ext/tests/test_binder.py +++ b/libs/cuda_ext/tests/test_binder.py @@ -3,7 +3,7 @@ import numpy as np -parts = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) +parts = np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]], dtype=np.int32, order="C") n_features = 3 n_parts = 1 n_samples = 3 diff --git a/libs/cuda_ext/tests/test_kernel.cpp b/libs/cuda_ext/tests/test_kernel.cpp index becbd620..d0a2d26e 100644 --- a/libs/cuda_ext/tests/test_kernel.cpp +++ b/libs/cuda_ext/tests/test_kernel.cpp @@ -27,15 +27,14 @@ std::vector, std::vector>> generate_pairwise_com } -using Vec3 = std::vector>>; -using TestParamType = std::tuple; +using TestParamType = std::tuple; // Define a parameterized test fixture class CudaAriTest : public ::testing::TestWithParam {}; TEST_P(CudaAriTest, CheckSingleResult) { - Vec3 parts; + Mat3 parts; float expected_result; std::tie(parts, expected_result) = GetParam(); @@ -63,7 +62,7 @@ TEST_P(CudaAriTest, CheckSingleResult) } } - auto h_out = cudaAri(h_parts, n_features, n_parts, n_objs)[0]; + auto h_out = cudaAri(parts, n_features, n_parts, n_objs)[0]; // Check if the result are close EXPECT_NEAR(h_out, expected_result, 1e-2); @@ -77,35 +76,35 @@ INSTANTIATE_TEST_SUITE_P( CudaAriTest, ::testing::Values( TestParamType( - Vec3{ + Mat3{ {{0, 0, 1, 2}}, {{0, 0, 1, 1}}, }, 0.57f ), TestParamType( - Vec3{ + Mat3{ {{0, 0, 1, 1}}, {{0, 1, 0, 1}}, }, -0.5f ), TestParamType( - Vec3{ + Mat3{ {{0, 0, 1, 1}}, {{0, 0, 1, 1}}, }, 1.0f ), TestParamType( - Vec3{ + Mat3{ {{0, 0, 1, 1}}, {{1, 1, 0, 0}}, }, 1.0f ), TestParamType( - Vec3{ + Mat3{ {{0, 0, 0, 0}}, {{0, 1, 2, 3}}, }, From 800ec2d56ddfe92291a4ccf0be946fc6fa4a2bb1 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 31 Oct 2024 22:33:34 -0600 Subject: [PATCH 093/134] [binding]: Use py::array_t to accept np.ndarray --- libs/cuda_ext/metrics.cu | 107 ++++++++++++++++------------- libs/cuda_ext/tests/test_binder.py | 3 +- 2 files changed, 62 insertions(+), 48 deletions(-) diff --git a/libs/cuda_ext/metrics.cu b/libs/cuda_ext/metrics.cu index bb67cacf..2bced4d0 100644 --- a/libs/cuda_ext/metrics.cu +++ b/libs/cuda_ext/metrics.cu @@ -337,64 +337,77 @@ auto cudaAri(const py::array_t& parts, const size_t n_f // Edge cases: // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing + // Input processing + // Request a buffer descriptor from Python + py::buffer_info buffer = parts.request(); - // Input validation - // if (parts == nullptr) throw std::invalid_argument("Error. Argument 'parts' is nullptr"); + // Some basic validation checks ... + if (buffer.format != py::format_descriptor::format()) + throw std::runtime_error("Incompatible format: expected an int array!"); + + if (buffer.ndim != 3) + throw std::runtime_error("Incompatible buffer dimension!"); + + // Apply resources + auto result = py::array_t(buffer.size); + + // Obtain numpy.ndarray data pointer + const auto parts_ptr = static_cast(buffer.ptr); // Compute internal variables // Todo: dynamically query types using parts_dtype = int; using out_dtype = float; - // const auto n_feature_comp = n_features * (n_features - 1) / 2; - // const auto n_aris = n_feature_comp * n_parts * n_parts; - // // Allocate host memory - // thrust::host_vector h_out(n_aris); - // thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); - - // // Set up CUDA kernel configuration - // const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block - // // Each block is responsible for one ARI computation - // const auto grid_size = n_aris; - // // Define shared memory size for each block - // const auto parts_dtype_size = sizeof(parts_dtype); - // auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared - // s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays - // s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix - - // // Allocate device memory with thrust + const auto n_feature_comp = n_features * (n_features - 1) / 2; + const auto n_aris = n_feature_comp * n_parts * n_parts; + // Allocate host memory + thrust::host_vector h_out(n_aris); + thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); + + // Set up CUDA kernel configuration + const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block + // Each block is responsible for one ARI computation + const auto grid_size = n_aris; + // Define shared memory size for each block + const auto parts_dtype_size = sizeof(parts_dtype); + auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared + s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays + s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix + + // Allocate device memory with thrust // const int* parts_raw = parts[0][0].data(); - // thrust::device_vector d_parts(parts_raw, parts_raw + n_features * n_parts * n_objs); // data is copied to device - // thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); - // thrust::device_vector d_out(n_aris); - - // // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later - // auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); - // const auto k = *max_iter + 1; - // std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; - - // // Launch the kernel - // ari<<>>( - // thrust::raw_pointer_cast(d_parts.data()), - // n_aris, - // n_features, - // n_parts, - // n_objs, - // n_parts * n_objs, - // n_parts * n_parts, - // k, - // thrust::raw_pointer_cast(d_out.data()), - // thrust::raw_pointer_cast(d_parts_pairs.data())); + thrust::device_vector d_parts(parts_ptr, parts_ptr + n_features * n_parts * n_objs); // data is copied to device + thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); + thrust::device_vector d_out(n_aris); + + // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later + auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); + const auto k = *max_iter + 1; + std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; + + // Launch the kernel + ari<<>>( + thrust::raw_pointer_cast(d_parts.data()), + n_aris, + n_features, + n_parts, + n_objs, + n_parts * n_objs, + n_parts * n_parts, + k, + thrust::raw_pointer_cast(d_out.data()), + thrust::raw_pointer_cast(d_parts_pairs.data())); - // // Copy data back to host - // thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); - // thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); + // Copy data back to host + thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); + thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); - // // Free device memory + // Free device memory - // // Convert thrust vectors to std::vector - // std::vector res(h_out.begin(), h_out.end()); + // Convert thrust vectors to std::vector + std::vector res(h_out.begin(), h_out.end()); // Return the ARI values - return std::vector(0); + return res; } diff --git a/libs/cuda_ext/tests/test_binder.py b/libs/cuda_ext/tests/test_binder.py index e676498a..b4a42687 100644 --- a/libs/cuda_ext/tests/test_binder.py +++ b/libs/cuda_ext/tests/test_binder.py @@ -3,7 +3,8 @@ import numpy as np -parts = np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]], dtype=np.int32, order="C") +parts = np.array([[[0, 1, 2]], [[0, 1, 2]], [[0, 1, 2]]], dtype=np.int32, order="C") +print(parts.ndim) n_features = 3 n_parts = 1 n_samples = 3 From a76de44dcc0e4ce775cd6c8f717261da4d13ddd3 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 31 Oct 2024 23:05:43 -0600 Subject: [PATCH 094/134] [test]: Pass tests for binded cuda code --- tests/gpu/test_ari.py | 46 +++++++++++++++++++++++++ {libs/ccc => tests/gpu}/test_binding.py | 0 2 files changed, 46 insertions(+) create mode 100644 tests/gpu/test_ari.py rename {libs/ccc => tests/gpu}/test_binding.py (100%) diff --git a/tests/gpu/test_ari.py b/tests/gpu/test_ari.py new file mode 100644 index 00000000..d869564f --- /dev/null +++ b/tests/gpu/test_ari.py @@ -0,0 +1,46 @@ +import pytest +import numpy as np +import cuda_ccc + +# Test cases taken from sklearn.metrics.adjusted_rand_score +@pytest.mark.parametrize("parts, expected_ari", [ + ( + np.array([ + [[0, 0, 1, 2]], + [[0, 0, 1, 1]] + ], dtype=np.int32), + 0.57 + ), + ( + np.array([ + [[0, 0, 1, 1]], + [[0, 1, 0, 1]] + ], dtype=np.int32), + -0.5 + ), + ( + np.array([ + [[0, 0, 1, 1]], + [[0, 0, 1, 1]] + ], dtype=np.int32), + 1.0 + ), + ( + np.array([ + [[0, 0, 1, 1]], + [[1, 1, 0, 0]] + ], dtype=np.int32), + 1.0 + ), + ( + np.array([ + [[0, 0, 0, 0]], + [[0, 1, 2, 3]] + ], dtype=np.int32), + 0.0 + ) +]) +def test_cuda_ari_cases(parts, expected_ari): + n_features, n_parts, n_objs = parts.shape + ari = cuda_ccc.ari(parts, n_features, n_parts, n_objs) + assert np.isclose(ari[0], expected_ari, atol=1e-2) diff --git a/libs/ccc/test_binding.py b/tests/gpu/test_binding.py similarity index 100% rename from libs/ccc/test_binding.py rename to tests/gpu/test_binding.py From 35ff703ad151846460c09d858c0f7b1193e0aa1d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 2 Nov 2024 18:12:39 -0600 Subject: [PATCH 095/134] [test/kernel]: Test ari with latest C++ API --- libs/ccc/__init__.py | 2 + libs/cuda_ext/metrics.cu | 5 +- tests/gpu/test_ari.py | 103 +++++++++---- ...st_kernel.py => test_device_host_funcs.py} | 137 ------------------ 4 files changed, 81 insertions(+), 166 deletions(-) rename tests/gpu/{test_kernel.py => test_device_host_funcs.py} (59%) diff --git a/libs/ccc/__init__.py b/libs/ccc/__init__.py index 36211dc8..f98e1d65 100644 --- a/libs/ccc/__init__.py +++ b/libs/ccc/__init__.py @@ -1,2 +1,4 @@ +from __future__ import annotations + # Remember to change also setup.py with the version here __version__ = "0.2.2" diff --git a/libs/cuda_ext/metrics.cu b/libs/cuda_ext/metrics.cu index 2bced4d0..b0d800da 100644 --- a/libs/cuda_ext/metrics.cu +++ b/libs/cuda_ext/metrics.cu @@ -333,7 +333,10 @@ __global__ void ari(int *parts, * @return std::vector ARI values for each pair of partitions */ // template -auto cudaAri(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { +auto cudaAri(const py::array_t& parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector { // Edge cases: // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing diff --git a/tests/gpu/test_ari.py b/tests/gpu/test_ari.py index d869564f..fe7ef683 100644 --- a/tests/gpu/test_ari.py +++ b/tests/gpu/test_ari.py @@ -2,45 +2,92 @@ import numpy as np import cuda_ccc +from ccc.sklearn.metrics import ( + get_contingency_matrix, + get_pair_confusion_matrix, + adjusted_rand_index, +) + + # Test cases taken from sklearn.metrics.adjusted_rand_score @pytest.mark.parametrize("parts, expected_ari", [ ( - np.array([ - [[0, 0, 1, 2]], - [[0, 0, 1, 1]] - ], dtype=np.int32), - 0.57 + np.array([ + [[0, 0, 1, 2]], + [[0, 0, 1, 1]] + ], dtype=np.int32), + 0.57 ), ( - np.array([ - [[0, 0, 1, 1]], - [[0, 1, 0, 1]] - ], dtype=np.int32), - -0.5 + np.array([ + [[0, 0, 1, 1]], + [[0, 1, 0, 1]] + ], dtype=np.int32), + -0.5 ), ( - np.array([ - [[0, 0, 1, 1]], - [[0, 0, 1, 1]] - ], dtype=np.int32), - 1.0 + np.array([ + [[0, 0, 1, 1]], + [[0, 0, 1, 1]] + ], dtype=np.int32), + 1.0 ), ( - np.array([ - [[0, 0, 1, 1]], - [[1, 1, 0, 0]] - ], dtype=np.int32), - 1.0 + np.array([ + [[0, 0, 1, 1]], + [[1, 1, 0, 0]] + ], dtype=np.int32), + 1.0 ), ( - np.array([ - [[0, 0, 0, 0]], - [[0, 1, 2, 3]] - ], dtype=np.int32), - 0.0 + np.array([ + [[0, 0, 0, 0]], + [[0, 1, 2, 3]] + ], dtype=np.int32), + 0.0 ) ]) -def test_cuda_ari_cases(parts, expected_ari): +def test_simple_ari_results(parts, expected_ari): + n_features, n_parts, n_objs = parts.shape + res = cuda_ccc.ari(parts, n_features, n_parts, n_objs) + assert np.isclose(res[0], expected_ari, atol=1e-2) + + +def generate_pairwise_combinations(arr): + pairs = [] + num_slices = arr.shape[0] # Number of 2D arrays in the 3D array + + for i in range(num_slices): + for j in range(i + 1, num_slices): # Only consider pairs in different slices + for row_i in arr[i]: # Each row in slice i + for row_j in arr[j]: # Pairs with each row in slice j + pairs.append([row_i, row_j]) + + # Convert list of pairs to a NumPy array + return np.array(pairs) + + +# Test ari generation given a full 3D array of partitions +@pytest.mark.parametrize("n_features, n_parts, n_objs, k", [ + (2, 2, 100, 10), + (5, 10, 200, 10), + # (100, 20, 1000, 10), # wrong results + # (200, 20, 300, 10), # illegal mem access + # (1000, 10, 300, 10), # out of gpu mem +]) +def test_pairwise_ari(n_features, n_parts, n_objs, k): + parts = np.random.randint(0, k, size=(n_features, n_parts, n_objs), dtype=np.int32) + # Create test inputs n_features, n_parts, n_objs = parts.shape - ari = cuda_ccc.ari(parts, n_features, n_parts, n_objs) - assert np.isclose(ari[0], expected_ari, atol=1e-2) + n_feature_comp = n_features * (n_features - 1) // 2 + n_aris = n_feature_comp * n_parts * n_parts + ref_aris = np.zeros(n_aris, dtype=np.float32) + # Get partition pairs + pairs = generate_pairwise_combinations(parts) + # Use map-reduce to compute ARIs for all pairs of partitions + for i, (part0, part1) in enumerate(pairs): + ari = adjusted_rand_index(part0, part1) + ref_aris[i] = ari + # Compute ARIs using CUDA + res_aris = cuda_ccc.ari(parts, n_features, n_parts, n_objs) + assert np.allclose(res_aris, ref_aris) diff --git a/tests/gpu/test_kernel.py b/tests/gpu/test_device_host_funcs.py similarity index 59% rename from tests/gpu/test_kernel.py rename to tests/gpu/test_device_host_funcs.py index 9deeb455..9ec1f237 100644 --- a/tests/gpu/test_kernel.py +++ b/tests/gpu/test_device_host_funcs.py @@ -229,140 +229,3 @@ def test_get_pair_confusion_matrix_device(n_objs, threads_per_block, k): print(f"py_c: {py_c}") np.testing.assert_array_equal(h_c, py_c) - -def generate_pairwise_combinations(arr): - pairs = [] - num_slices = arr.shape[0] # Number of 2D arrays in the 3D array - - for i in range(num_slices): - for j in range(i + 1, num_slices): # Only consider pairs in different slices - for row_i in arr[i]: # Each row in slice i - for row_j in arr[j]: # Pairs with each row in slice j - pairs.append([row_i, row_j]) - - # Convert list of pairs to a NumPy array - return np.array(pairs) - - -@pytest.mark.parametrize("parts", [ - # 3D array - np.array([ - [[0, 1, 2, 3], - [0, 2, 3, 4], - [0, 3, 4, 5]], - - [[1, 1, 2, 3], - [1, 2, 3, 4], - [1, 3, 4, 5]], - - [[2, 1, 2, 3], - [2, 2, 3, 4], - [2, 3, 4, 5]] - ]) -]) -def test_art_parts_selection(parts): - k = np.max(parts) + 1 - pairs = generate_pairwise_combinations(parts) - - kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str - # Compile the CUDA kernel - module = cp.RawModule(code=kernel_code, backend='nvcc') - kernel = module.get_function("ari") - - # Create test inputs - n_features, n_parts, n_objs = parts.shape - n_feature_comp = n_features * (n_features - 1) // 2 - n_aris = n_feature_comp * n_parts * n_parts - # Todo: parameterize this - block_size = 2 - grid_size = n_aris - s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared - s_mem_size += 2 * k * cp.int32().itemsize # For the internal sum arrays - s_mem_size += 4 * cp.int32().itemsize # For the 2 x 2 confusion matrix - - d_out = cp.empty(n_aris, dtype=cp.int32) - d_parts = cp.asarray(parts, dtype=cp.int32) - # Each pair of partitions will be compared, used for debugging purposes - d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) - - # Print stats - print(f"Number of ARIs: {n_aris}") - # Print kernel configuration - print(f"Grid size: {grid_size}, Block size: {block_size}, Shared memory: {s_mem_size}") - # Launch the kernel - kernel((grid_size,), (block_size,), (d_parts, - n_aris, - n_features, - n_parts, - n_objs, - n_parts * n_objs, - n_parts * n_parts, - k, - d_out, - d_parts_pairs), - shared_mem=s_mem_size) - cp.cuda.runtime.deviceSynchronize() - # Get results back to host - h_parts_pairs = cp.asnumpy(d_parts_pairs) - print(h_parts_pairs) - # Assert pairs == d_parts_pairs - assert np.all(np.equal(h_parts_pairs, pairs)) - - -@pytest.mark.parametrize("n_features, n_parts, n_objs, k", [ - (2, 2, 100, 10), - (5, 10, 200, 10), -]) -@pytest.mark.parametrize("block_size", [32, 64, 128, 256]) -def test_pairwise_ari(n_features, n_parts, n_objs, k, block_size): - parts = np.random.randint(0, k, size=(n_features, n_parts, n_objs), dtype=np.int32) - # Create test inputs - n_features, n_parts, n_objs = parts.shape - n_feature_comp = n_features * (n_features - 1) // 2 - n_aris = n_feature_comp * n_parts * n_parts - ref_aris = np.zeros(n_aris, dtype=np.float32) - # Get partition pairs - pairs = generate_pairwise_combinations(parts) - # Use map-reduce to compute ARIs for all pairs of partitions - for i, (part0, part1) in enumerate(pairs): - ari = adjusted_rand_index(part0, part1) - ref_aris[i] = ari - - print(ref_aris) - - # Compute ARIs using the CUDA kernel - grid_size = n_aris - s_mem_size = n_objs * 2 * cp.int32().itemsize # For the partition pair to be compared - s_mem_size += 2 * k * cp.int32().itemsize # For the internal sum arrays - s_mem_size += 4 * cp.int32().itemsize # For the 2 x 2 confusion matrix - - d_out = cp.empty(n_aris, dtype=cp.float32) - d_parts = cp.asarray(parts, dtype=cp.int32) - d_parts_pairs = cp.empty((n_aris, 2, n_objs), dtype=cp.int32) - # Each pair of partitions will be compared, used for debugging purposes - - # Print stats - print(f"Number of ARIs: {n_aris}") - # Print kernel configuration - print(f"Grid size: {grid_size}, Block size: {block_size}, Shared memory: {s_mem_size}") - # Compile the CUDA kernel - kernel_code = d_unravel_index_str + d_get_coords_from_index_str + d_get_contingency_matrix_str + d_get_confusion_matrix_str + k_ari_str - module = cp.RawModule(code=kernel_code, backend='nvcc') - kernel = module.get_function("ari") - # Launch the kernel - kernel((grid_size,), (block_size,), (d_parts, - n_aris, - n_features, - n_parts, - n_objs, - n_parts * n_objs, - n_parts * n_parts, - k, - d_out, - d_parts_pairs), - shared_mem=s_mem_size) - cp.cuda.runtime.deviceSynchronize() - # Get results back to host - h_out = cp.asnumpy(d_out) - print(h_out) - assert np.allclose(h_out, ref_aris) From 294f4b5dffd9a79c794b6507be460aaa87ed4c72 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 3 Nov 2024 21:22:37 -0700 Subject: [PATCH 096/134] [build]: Name cuda extension as "ccc_cuda_ext" --- CMakeLists.txt | 6 ++++-- libs/{cuda_ext => ccc_cuda_ext}/CMakeLists.txt | 0 libs/{cuda_ext => ccc_cuda_ext}/Readme.md | 0 libs/{cuda_ext => ccc_cuda_ext}/binder.cu | 5 ++--- libs/{cuda_ext => ccc_cuda_ext}/metrics.cu | 0 libs/{cuda_ext => ccc_cuda_ext}/metrics.cuh | 0 libs/{cuda_ext => ccc_cuda_ext/tests}/hello_test.cc | 0 .../{cuda_ext => ccc_cuda_ext}/tests/test_binder.py | 0 .../tests/test_kernel.cpp | 0 .../tests/test_partition_pairing.cpp | 0 libs/cuda_ext/example.cpp | 5 ----- libs/cuda_ext/example.hpp | 1 - libs/cuda_ext/example_binder.cpp | 13 ------------- pyproject.toml | 2 +- tests/gpu/test_ari.py | 6 +++--- 15 files changed, 10 insertions(+), 28 deletions(-) rename libs/{cuda_ext => ccc_cuda_ext}/CMakeLists.txt (100%) rename libs/{cuda_ext => ccc_cuda_ext}/Readme.md (100%) rename libs/{cuda_ext => ccc_cuda_ext}/binder.cu (73%) rename libs/{cuda_ext => ccc_cuda_ext}/metrics.cu (100%) rename libs/{cuda_ext => ccc_cuda_ext}/metrics.cuh (100%) rename libs/{cuda_ext => ccc_cuda_ext/tests}/hello_test.cc (100%) rename libs/{cuda_ext => ccc_cuda_ext}/tests/test_binder.py (100%) rename libs/{cuda_ext => ccc_cuda_ext}/tests/test_kernel.cpp (100%) rename libs/{cuda_ext => ccc_cuda_ext}/tests/test_partition_pairing.cpp (100%) delete mode 100644 libs/cuda_ext/example.cpp delete mode 100644 libs/cuda_ext/example.hpp delete mode 100644 libs/cuda_ext/example_binder.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index a89273c1..ff733c70 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -4,6 +4,8 @@ project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA) set(PYBIND11_NEWPYTHON ON) find_package(pybind11 CONFIG REQUIRED) -pybind11_add_module(cuda_ccc libs/cuda_ext/binder.cu libs/cuda_ext/metrics.cu) +set(CUDA_EXT_MODULE_NAME ccc_cuda_ext) +set(CUDA_EXT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/libs/${CUDA_EXT_MODULE_NAME}) +pybind11_add_module(${CUDA_EXT_MODULE_NAME} ${CUDA_EXT_DIR}/binder.cu ${CUDA_EXT_DIR}/metrics.cu) -install(TARGETS cuda_ccc LIBRARY DESTINATION .) +install(TARGETS ${CUDA_EXT_MODULE_NAME} LIBRARY DESTINATION .) diff --git a/libs/cuda_ext/CMakeLists.txt b/libs/ccc_cuda_ext/CMakeLists.txt similarity index 100% rename from libs/cuda_ext/CMakeLists.txt rename to libs/ccc_cuda_ext/CMakeLists.txt diff --git a/libs/cuda_ext/Readme.md b/libs/ccc_cuda_ext/Readme.md similarity index 100% rename from libs/cuda_ext/Readme.md rename to libs/ccc_cuda_ext/Readme.md diff --git a/libs/cuda_ext/binder.cu b/libs/ccc_cuda_ext/binder.cu similarity index 73% rename from libs/cuda_ext/binder.cu rename to libs/ccc_cuda_ext/binder.cu index b9a71571..7f3cb34b 100644 --- a/libs/cuda_ext/binder.cu +++ b/libs/ccc_cuda_ext/binder.cu @@ -6,9 +6,8 @@ namespace py = pybind11; using namespace pybind11::literals; -PYBIND11_PLUGIN(cuda_ccc) { - py::module m("cuda_ccc", "pybind11 example plugin"); +PYBIND11_MODULE(ccc_cuda_ext, m) { + m.doc() = "CUDA extension module for CCC"; m.def("ari", &cudaAri, "CUDA version of Adjusted Rand Index (ARI) calculation", "parts"_a, "n_features"_a, "n_parts"_a, "n_objs"_a); - return m.ptr(); } diff --git a/libs/cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu similarity index 100% rename from libs/cuda_ext/metrics.cu rename to libs/ccc_cuda_ext/metrics.cu diff --git a/libs/cuda_ext/metrics.cuh b/libs/ccc_cuda_ext/metrics.cuh similarity index 100% rename from libs/cuda_ext/metrics.cuh rename to libs/ccc_cuda_ext/metrics.cuh diff --git a/libs/cuda_ext/hello_test.cc b/libs/ccc_cuda_ext/tests/hello_test.cc similarity index 100% rename from libs/cuda_ext/hello_test.cc rename to libs/ccc_cuda_ext/tests/hello_test.cc diff --git a/libs/cuda_ext/tests/test_binder.py b/libs/ccc_cuda_ext/tests/test_binder.py similarity index 100% rename from libs/cuda_ext/tests/test_binder.py rename to libs/ccc_cuda_ext/tests/test_binder.py diff --git a/libs/cuda_ext/tests/test_kernel.cpp b/libs/ccc_cuda_ext/tests/test_kernel.cpp similarity index 100% rename from libs/cuda_ext/tests/test_kernel.cpp rename to libs/ccc_cuda_ext/tests/test_kernel.cpp diff --git a/libs/cuda_ext/tests/test_partition_pairing.cpp b/libs/ccc_cuda_ext/tests/test_partition_pairing.cpp similarity index 100% rename from libs/cuda_ext/tests/test_partition_pairing.cpp rename to libs/ccc_cuda_ext/tests/test_partition_pairing.cpp diff --git a/libs/cuda_ext/example.cpp b/libs/cuda_ext/example.cpp deleted file mode 100644 index 509c0965..00000000 --- a/libs/cuda_ext/example.cpp +++ /dev/null @@ -1,5 +0,0 @@ -#include "example.hpp" - -int add(int i, int j) { - return i + j; -}; \ No newline at end of file diff --git a/libs/cuda_ext/example.hpp b/libs/cuda_ext/example.hpp deleted file mode 100644 index 8247f000..00000000 --- a/libs/cuda_ext/example.hpp +++ /dev/null @@ -1 +0,0 @@ -int add(int i, int j); \ No newline at end of file diff --git a/libs/cuda_ext/example_binder.cpp b/libs/cuda_ext/example_binder.cpp deleted file mode 100644 index 06fd87e4..00000000 --- a/libs/cuda_ext/example_binder.cpp +++ /dev/null @@ -1,13 +0,0 @@ -#include -#include "example.hpp" - -namespace py = pybind11; - -using namespace pybind11::literals; - -PYBIND11_PLUGIN(_core) { - py::module m("wrap", "pybind11 example plugin"); - m.def("add", &add, "A function which adds two numbers", - "i"_a=1, "j"_a=2); - return m.ptr(); -} diff --git a/pyproject.toml b/pyproject.toml index 0597c1e4..367101da 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["scikit-build-core", "pybind11"] build-backend = "scikit_build_core.build" [project] -name = "example" +name = "ccc_cuda_ext" version = "0.0.1" [tool.setuptools.packages.find] diff --git a/tests/gpu/test_ari.py b/tests/gpu/test_ari.py index fe7ef683..140f7609 100644 --- a/tests/gpu/test_ari.py +++ b/tests/gpu/test_ari.py @@ -1,6 +1,6 @@ import pytest import numpy as np -import cuda_ccc +import ccc_cuda_ext from ccc.sklearn.metrics import ( get_contingency_matrix, @@ -49,7 +49,7 @@ ]) def test_simple_ari_results(parts, expected_ari): n_features, n_parts, n_objs = parts.shape - res = cuda_ccc.ari(parts, n_features, n_parts, n_objs) + res = ccc_cuda_ext.ari(parts, n_features, n_parts, n_objs) assert np.isclose(res[0], expected_ari, atol=1e-2) @@ -89,5 +89,5 @@ def test_pairwise_ari(n_features, n_parts, n_objs, k): ari = adjusted_rand_index(part0, part1) ref_aris[i] = ari # Compute ARIs using CUDA - res_aris = cuda_ccc.ari(parts, n_features, n_parts, n_objs) + res_aris = ccc_cuda_ext.ari(parts, n_features, n_parts, n_objs) assert np.allclose(res_aris, ref_aris) From 87603f5f8b46fdde0af9e91767909ae6c65eae88 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 3 Nov 2024 23:01:45 -0700 Subject: [PATCH 097/134] [linker]: Separate template function declaration and implementation --- libs/ccc_cuda_ext/binder.cu | 6 ++++-- libs/ccc_cuda_ext/metrics.cu | 22 ++++++++++++++++------ libs/ccc_cuda_ext/metrics.cuh | 10 ++++++---- 3 files changed, 26 insertions(+), 12 deletions(-) diff --git a/libs/ccc_cuda_ext/binder.cu b/libs/ccc_cuda_ext/binder.cu index 7f3cb34b..896b9cfa 100644 --- a/libs/ccc_cuda_ext/binder.cu +++ b/libs/ccc_cuda_ext/binder.cu @@ -1,13 +1,15 @@ -#include "metrics.cuh" + #include #include +#include "metrics.cuh" + namespace py = pybind11; using namespace pybind11::literals; PYBIND11_MODULE(ccc_cuda_ext, m) { m.doc() = "CUDA extension module for CCC"; - m.def("ari", &cudaAri, "CUDA version of Adjusted Rand Index (ARI) calculation", + m.def("ari", &ari, "CUDA version of Adjusted Rand Index (ARI) calculation", "parts"_a, "n_features"_a, "n_parts"_a, "n_objs"_a); } diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index b0d800da..08179f37 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -332,8 +332,8 @@ __global__ void ari(int *parts, * @throws std::invalid_argument if "parts" is invalid * @return std::vector ARI values for each pair of partitions */ -// template -auto cudaAri(const py::array_t& parts, +template +auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { @@ -345,21 +345,21 @@ auto cudaAri(const py::array_t& parts, py::buffer_info buffer = parts.request(); // Some basic validation checks ... - if (buffer.format != py::format_descriptor::format()) + if (buffer.format != py::format_descriptor::format()) throw std::runtime_error("Incompatible format: expected an int array!"); if (buffer.ndim != 3) throw std::runtime_error("Incompatible buffer dimension!"); // Apply resources - auto result = py::array_t(buffer.size); + auto result = py::array_t(buffer.size); // Obtain numpy.ndarray data pointer - const auto parts_ptr = static_cast(buffer.ptr); + const auto parts_ptr = static_cast(buffer.ptr); // Compute internal variables // Todo: dynamically query types - using parts_dtype = int; + using parts_dtype = T; using out_dtype = float; const auto n_feature_comp = n_features * (n_features - 1) / 2; @@ -414,3 +414,13 @@ auto cudaAri(const py::array_t& parts, // Return the ARI values return res; } + + +// Below is the explicit instantiation of the ari template function. +// +// Generally people would write the implementation of template classes and functions in the header file. However, we +// separate the implementation into a .cpp file to make things clearer. In order to make the compiler know the +// implementation of the template functions, we need to explicitly instantiate them here, so that they can be picked up +// by the linker. + +template auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file diff --git a/libs/ccc_cuda_ext/metrics.cuh b/libs/ccc_cuda_ext/metrics.cuh index 74a4ad28..255a4efa 100644 --- a/libs/ccc_cuda_ext/metrics.cuh +++ b/libs/ccc_cuda_ext/metrics.cuh @@ -3,8 +3,10 @@ #include #include -using Mat3 = std::vector>>; - namespace py = pybind11; -// template -std::vector cudaAri(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs); + +template +auto ari(const py::array_t& parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector; From d06306c25188ceb60a46e021f978f6e56e33f12e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 3 Nov 2024 23:08:35 -0700 Subject: [PATCH 098/134] [mist]: Rename ari function according to input type --- libs/ccc_cuda_ext/binder.cu | 2 +- tests/gpu/test_ari.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/libs/ccc_cuda_ext/binder.cu b/libs/ccc_cuda_ext/binder.cu index 896b9cfa..048aa69a 100644 --- a/libs/ccc_cuda_ext/binder.cu +++ b/libs/ccc_cuda_ext/binder.cu @@ -10,6 +10,6 @@ using namespace pybind11::literals; PYBIND11_MODULE(ccc_cuda_ext, m) { m.doc() = "CUDA extension module for CCC"; - m.def("ari", &ari, "CUDA version of Adjusted Rand Index (ARI) calculation", + m.def("ari_int32", &ari, "CUDA version of Adjusted Rand Index (ARI) calculation", "parts"_a, "n_features"_a, "n_parts"_a, "n_objs"_a); } diff --git a/tests/gpu/test_ari.py b/tests/gpu/test_ari.py index 140f7609..9b847d12 100644 --- a/tests/gpu/test_ari.py +++ b/tests/gpu/test_ari.py @@ -49,7 +49,7 @@ ]) def test_simple_ari_results(parts, expected_ari): n_features, n_parts, n_objs = parts.shape - res = ccc_cuda_ext.ari(parts, n_features, n_parts, n_objs) + res = ccc_cuda_ext.ari_int32(parts, n_features, n_parts, n_objs) assert np.isclose(res[0], expected_ari, atol=1e-2) @@ -89,5 +89,5 @@ def test_pairwise_ari(n_features, n_parts, n_objs, k): ari = adjusted_rand_index(part0, part1) ref_aris[i] = ari # Compute ARIs using CUDA - res_aris = ccc_cuda_ext.ari(parts, n_features, n_parts, n_objs) + res_aris = ccc_cuda_ext.ari_int32(parts, n_features, n_parts, n_objs) assert np.allclose(res_aris, ref_aris) From 4723eb5400128094eba8e8c46c473d72c9d24d2d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 3 Nov 2024 23:48:49 -0700 Subject: [PATCH 099/134] Clean up dead code --- libs/ccc_cuda_ext/metrics.cu | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 08179f37..cd915838 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -91,9 +91,7 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int *x __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *shared_cont_mat, int k) { int tid = threadIdx.x; - int bid = blockIdx.x; int num_threads = blockDim.x; - int num_blocks = gridDim.x; int size = k * k; // Initialize shared memory @@ -192,22 +190,6 @@ __device__ void get_pair_confusion_matrix( C[2] = temp - sum_squares; // C[1,0] C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] - - - // compute ARI - int tn = static_cast(C[0]); - int fp = static_cast(C[1]); - int fn = static_cast(C[2]); - int tp = static_cast(C[3]); - float ari = 0.0; - if (fn == 0 && fp == 0) - { - ari = 1.0; - } - else - { - ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); - } } } @@ -238,7 +220,6 @@ __global__ void ari(int *parts, /* * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory */ - int global_tid = blockIdx.x * blockDim.x + threadIdx.x; // each block is responsible for one ARI computation int ari_block_idx = blockIdx.x; @@ -387,7 +368,6 @@ auto ari(const py::array_t& parts, // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); const auto k = *max_iter + 1; - std::cout << "Maximum value + 1 in d_parts: " << k << std::endl; // Launch the kernel ari<<>>( From 4a0edd34598d29256d102f11976bafbf9fa5eeb3 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 4 Nov 2024 11:28:48 -0700 Subject: [PATCH 100/134] Clean up and rename tests --- benchmark/bench_ari.py | 2 +- libs/ccc/sklearn/metrics_device.py | 150 ------ libs/ccc/sklearn/metrics_gpu.py | 506 +++++++++++------- libs/ccc/sklearn/metrics_gpu2.py | 347 ------------ tests/gpu/feature_array.txt | 100 ---- tests/gpu/test_ari_device.py | 96 ---- ...mpute_coef.py => test_coef_computation.py} | 0 ...{test_coef.py => test_coef_subroutines.py} | 0 tests/gpu/test_device_host_funcs.py | 2 +- tests/gpu/test_get_parts.py | 3 + tests/gpu/test_get_parts_debug.py | 120 ----- tests/gpu/test_get_percentiles.py | 1 + tests/gpu/utils.py | 1 + 13 files changed, 324 insertions(+), 1004 deletions(-) delete mode 100644 libs/ccc/sklearn/metrics_device.py delete mode 100644 libs/ccc/sklearn/metrics_gpu2.py delete mode 100644 tests/gpu/feature_array.txt delete mode 100644 tests/gpu/test_ari_device.py rename tests/gpu/{test_compute_coef.py => test_coef_computation.py} (100%) rename tests/gpu/{test_coef.py => test_coef_subroutines.py} (100%) delete mode 100644 tests/gpu/test_get_parts_debug.py diff --git a/benchmark/bench_ari.py b/benchmark/bench_ari.py index 103ab95a..fa9db2f6 100644 --- a/benchmark/bench_ari.py +++ b/benchmark/bench_ari.py @@ -2,7 +2,7 @@ import time import cupy as cp import numpy as np -from ccc.sklearn.metrics_gpu2 import ( +from ccc.sklearn.metrics_gpu import ( d_get_confusion_matrix_str, d_get_coords_from_index_str, d_unravel_index_str, diff --git a/libs/ccc/sklearn/metrics_device.py b/libs/ccc/sklearn/metrics_device.py deleted file mode 100644 index 1428b328..00000000 --- a/libs/ccc/sklearn/metrics_device.py +++ /dev/null @@ -1,150 +0,0 @@ -""" -Contains implementations of different metrics in sklearn but optimized for numba. - -Some code (indicated in each function) is based on scikit-learn's code base -(https://github.com/scikit-learn), for which the copyright notice and license -are shown below. - -BSD 3-Clause License - -Copyright (c) 2007-2021 The scikit-learn developers. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" -import numpy as np -from numba import cuda -import math - -@cuda.jit(device=True) -def find_unique(arr, max_unique): - """Find unique elements in an array using shared memory.""" - unique = cuda.local.array(max_unique, dtype=np.int32) - counts = cuda.local.array(max_unique, dtype=np.int32) - num_unique = 0 - - for i in range(len(arr)): - found = False - for j in range(num_unique): - if arr[i] == unique[j]: - counts[j] += 1 - found = True - break - if not found and num_unique < max_unique: - unique[num_unique] = arr[i] - counts[num_unique] = 1 - num_unique += 1 - - return unique[:num_unique], counts[:num_unique], num_unique - -@cuda.jit(device=True) -def compute_contingency_matrix(part0, part1, cont_mat, max_clusters): - """Compute the contingency matrix using shared memory.""" - unique0, counts0, num_unique0 = find_unique(part0, max_clusters) - unique1, counts1, num_unique1 = find_unique(part1, max_clusters) - - for i in range(num_unique0): - for j in range(num_unique1): - count = 0 - for k in range(len(part0)): - if part0[k] == unique0[i] and part1[k] == unique1[j]: - count += 1 - cont_mat[i, j] = count - - return num_unique0, num_unique1 - -@cuda.jit(device=True) -def sum_2d_array(arr, rows, cols): - """Sum elements in a 2D array.""" - total = 0 - for i in range(rows): - for j in range(cols): - total += arr[i, j] - return total - -@cuda.jit(device=True) -def sum_squares_2d_array(arr, rows, cols): - """Sum squares of elements in a 2D array.""" - total = 0 - for i in range(rows): - for j in range(cols): - total += arr[i, j] * arr[i, j] - return total - -@cuda.jit(device=True) -def get_pair_confusion_matrix(part0, part1, max_clusters): - """Compute the pair confusion matrix.""" - cont_mat = cuda.local.array((max_clusters, max_clusters), dtype=np.int32) - num_clusters0, num_clusters1 = compute_contingency_matrix(part0, part1, cont_mat, max_clusters) - - n_samples = len(part0) - sum_squares = sum_squares_2d_array(cont_mat, num_clusters0, num_clusters1) - - n_c = cuda.local.array(max_clusters, dtype=np.int32) - n_k = cuda.local.array(max_clusters, dtype=np.int32) - - for i in range(num_clusters0): - n_c[i] = sum(cont_mat[i, :num_clusters1]) - for j in range(num_clusters1): - n_k[j] = sum(cont_mat[:num_clusters0, j]) - - C = cuda.local.array((2, 2), dtype=np.int64) - C[1, 1] = sum_squares - n_samples - C[0, 1] = sum([cont_mat[i, j] * n_k[j] for i in range(num_clusters0) for j in range(num_clusters1)]) - sum_squares - C[1, 0] = sum([cont_mat[i, j] * n_c[i] for i in range(num_clusters0) for j in range(num_clusters1)]) - sum_squares - C[0, 0] = n_samples * n_samples - C[0, 1] - C[1, 0] - sum_squares - - return C - -@cuda.jit(device=True) -def adjusted_rand_index(part0, part1, out, compare_pair_id, i, j, max_clusters): - """ - Compute the adjusted Rand index (ARI) between two clustering partitions. - """ - C = get_pair_confusion_matrix(part0, part1, max_clusters) - tn, fp, fn, tp = C[0, 0], C[0, 1], C[1, 0], C[1, 1] - - # Special cases: empty data or full agreement - if fn == 0 and fp == 0: - res = 1.0 - else: - res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) - - out[compare_pair_id, i, j] = res - - -# Main kernel function -# 1st iteration: try assign parts[i] (2D) to each block -@cuda.jit -def compute_ari(partitions, out, max_clusters): - """ - CUDA kernel to compute ARI for multiple partition pairs. - """ - compare_pair_id, i, j = cuda.grid(3) - if compare_pair_id < partitions.shape[0] and i < partitions.shape[1] and j < partitions.shape[1]: - part0 = partitions[compare_pair_id, i] - part1 = partitions[compare_pair_id, j] - adjusted_rand_index(part0, part1, out, compare_pair_id, i, j, max_clusters) \ No newline at end of file diff --git a/libs/ccc/sklearn/metrics_gpu.py b/libs/ccc/sklearn/metrics_gpu.py index 5ba6da78..c460ef32 100644 --- a/libs/ccc/sklearn/metrics_gpu.py +++ b/libs/ccc/sklearn/metrics_gpu.py @@ -1,219 +1,347 @@ -""" -Contains implementations of different metrics in sklearn but optimized for numba. - -Some code (indicated in each function) is based on scikit-learn's code base -(https://github.com/scikit-learn), for which the copyright notice and license -are shown below. - -BSD 3-Clause License - -Copyright (c) 2007-2021 The scikit-learn developers. -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" import numpy as np +import cupy as cp from numba import njit from numba import cuda +import rmm + + +d_get_confusion_matrix_str = """ +/** + * @brief CUDA device function to compute the pair confusion matrix + * @param[in] contingency Pointer to the contingency matrix + * @param[in] sum_rows Pointer to the sum of rows in the contingency matrix + * @param[in] sum_cols Pointer to the sum of columns in the contingency matrix + * @param[in] n_objs Number of objects in each partition + * @param[in] k Number of clusters (assuming k is the max of clusters in part0 and part1) + * @param[out] C Pointer to the output pair confusion matrix (2x2) + */ +__device__ void get_pair_confusion_matrix( + const int* __restrict__ contingency, + int * sum_rows, + int * sum_cols, + const int n_objs, + const int k, + int* C +) { + // Initialize sum_rows and sum_cols + for (int i = threadIdx.x; i < k; i += blockDim.x) { + sum_rows[i] = 0; + sum_cols[i] = 0; + } + __syncthreads(); + + // Compute sum_rows and sum_cols + for (int i = threadIdx.x; i < k * k; i += blockDim.x) { + int row = i / k; + int col = i % k; + int val = contingency[i]; + atomicAdd(&sum_cols[col], val); + atomicAdd(&sum_rows[row], val); + } + __syncthreads(); + + // Compute sum_squares + int sum_squares; + if (threadIdx.x == 0) { + sum_squares = 0; + for (int i = 0; i < k * k; ++i) { + sum_squares += (contingency[i]) * contingency[i]; + } + } + __syncthreads(); + + // Compute C[1,1], C[0,1], C[1,0], and C[0,0] + if (threadIdx.x == 0) { + C[3] = sum_squares - n_objs; // C[1,1] + + int temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += (contingency[i * k + j]) * sum_cols[j]; + } + } + C[1] = temp - sum_squares; // C[0,1] + + temp = 0; + for (int i = 0; i < k; ++i) { + for (int j = 0; j < k; ++j) { + temp += (contingency[j * k + i]) * sum_rows[j]; + } + } + C[2] = temp - sum_squares; // C[1,0] + + C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] + + + // compute ARI + int tn = static_cast(C[0]); + int fp = static_cast(C[1]); + int fn = static_cast(C[2]); + int tp = static_cast(C[3]); + float ari = 0.0; + if (fn == 0 && fp ==0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + } + __syncthreads(); +} +""" -@cuda.jit -def compute_sum_squares(contingency, result): - """ - CUDA kernel to compute the sum of squares of the contingency matrix elements. - - Args: - contingency: The contingency matrix. - result: The output array to store the sum of squares. - """ - i, j = cuda.grid(2) - - if i < contingency.shape[0] and j < contingency.shape[1]: - cuda.atomic.add(result, 0, contingency[i, j] ** 2) - - -@cuda.jit(device=True) -def get_pair_confusion_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: - """ - Returns the pair confusion matrix from two clustering partitions using CUDA. - - Args: - part0: A 1D array with cluster assignments for n objects. - part1: A 1D array with cluster assignments for n objects. - - Returns: - A pair confusion matrix with 2 rows and 2 columns. - """ - n_samples = np.int64(part0.shape[0]) +d_get_contingency_matrix_str = """ +/** + * @brief Compute the contingency matrix for two partitions using shared memory + * @param[in] part0 Pointer to the first partition array + * @param[in] part1 Pointer to the second partition array + * @param[in] n Number of elements in each partition array + * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix + * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) + */ +__device__ void get_contingency_matrix(int* part0, int* part1, int n, int* shared_cont_mat, int k) { + int tid = threadIdx.x; + int bid = blockIdx.x; + int num_threads = blockDim.x; + int num_blocks = gridDim.x; + + // Initialize shared memory + for (int i = tid; i < k * k; i += num_threads) { + shared_cont_mat[i] = 0; + } + __syncthreads(); + + // Process elements + for (int i = tid; i < n; i += num_threads) { + int row = part0[i]; + int col = part1[i]; + + if (row < k && col < k) { + atomicAdd(&shared_cont_mat[row * k + col], 1); + } + } + __syncthreads(); +} - # Compute the contingency matrix - contingency = get_contingency_matrix(part0, part1) +""" - n_c = np.ravel(contingency.sum(axis=1)) - n_k = np.ravel(contingency.sum(axis=0)) +d_unravel_index_str = """ +/** + * @brief Unravel a flat index to the corresponding 2D indicis + * @param[in] flat_idx The flat index to unravel + * @param[in] num_cols Number of columns in the 2D array + * @param[out] row Pointer to the row index + * @param[out] col Pointer to the column index + */ +extern "C" __device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int* row, int* col) { + *row = flat_idx / num_cols; // Compute row index + *col = flat_idx % num_cols; // Compute column index +} - # Allocate space for the sum of squares result - sum_squares = np.zeros(1, dtype=np.int64) +""" - # Define the number of threads per block and the number of blocks per grid - threadsperblock = (16, 16) - blockspergrid_x = int(np.ceil(contingency.shape[0] / threadsperblock[0])) - blockspergrid_y = int(np.ceil(contingency.shape[1] / threadsperblock[1])) - blockspergrid = (blockspergrid_x, blockspergrid_y) +d_get_coords_from_index_str = """ +#include +extern "C" __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x, int* y) { + // Calculate 'b' based on the input n_obj + int b = 1 - 2 * n_obj; + // Calculate 'x' using the quadratic formula part + float discriminant = b * b - 8 * idx; + float x_float = floor((-b - sqrt(discriminant)) / 2); + // Assign the integer part of 'x' + *x = static_cast(x_float); + // Calculate 'y' based on 'x' and the index + *y = static_cast(idx + (*x) * (b + (*x) + 2) / 2 + 1); +} - # Launch the CUDA kernel to compute the sum of squares - compute_sum_squares[blockspergrid, threadsperblock](contingency, sum_squares) +""" - sum_squares = sum_squares[0] +k_ari_str = """ +/** + * @brief Main ARI kernel. Now only compare a pair of ARIs + * @param n_parts Number of partitions of each feature + * @param n_objs Number of objects in each partitions + * @param n_part_mat_elems Number of elements in the square partition matrix + * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @param n_aris Number of ARIs to compute + * @param k The max value of cluster number + 1 + * @param out Output array of ARIs + * @param part_pairs Output array of part pairs to be compared by ARI + */ +extern "C" __global__ void ari(int *parts, + const int n_aris, + const int n_features, + const int n_parts, + const int n_objs, + const int n_elems_per_feat, + const int n_part_mat_elems, + const int k, + float *out, + int *part_pairs = nullptr) +{ + /* + * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory + */ + int global_tid = blockIdx.x * blockDim.x + threadIdx.x; + // each block is responsible for one ARI computation + int ari_block_idx = blockIdx.x; + + // print parts for debugging + + + // obtain the corresponding parts and unique counts + int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features + int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair + int i, j; + + // unravel the feature indices + get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); + assert(i < n_features && j < n_features); + assert(i >= 0 && j >= 0); + + // unravel the partition indices + int m, n; + unravel_index(part_pair_flat_idx, n_parts, &m, &n); + // if (global_tid == 0) + + // Make pointers to select the parts and unique counts for the feature pair + // Todo: Use int4*? + int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread + int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; + + // Load gmem data into smem by using different threads + extern __shared__ int shared_mem[]; + int *s_part0 = shared_mem; + int *s_part1 = shared_mem + n_objs; + + // Loop over the data using the block-stride pattern + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { + s_part0[i] = t_data_part0[i]; + s_part1[i] = t_data_part1[i]; + } + __syncthreads(); + + // Copy data to global memory if part_pairs is specified + if (part_pairs != nullptr) + { + int *out_part0 = part_pairs + ari_block_idx * (2 * n_objs); + int *out_part1 = out_part0 + n_objs; + + for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + { + out_part0[i] = s_part0[i]; + out_part1[i] = s_part1[i]; + } + } + + /* + * Step 2: Compute contingency matrix within the block + */ + // shared mem address for the contingency matrix + int *s_contingency = shared_mem + 2 * n_objs; + get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); + + /* + * Step 3: Construct pair confusion matrix + */ + // shared mem address for the pair confusion matrix + int *s_sum_rows = s_contingency + k * k; + int *s_sum_cols = s_sum_rows + k; + int *s_pair_confusion_matrix = s_sum_cols + k; + get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, s_pair_confusion_matrix); + /* + * Step 4: Compute ARI and write to global memory + */ + if (threadIdx.x == 0) { + int tn = static_cast(s_pair_confusion_matrix[0]); + int fp = static_cast(s_pair_confusion_matrix[1]); + int fn = static_cast(s_pair_confusion_matrix[2]); + int tp = static_cast(s_pair_confusion_matrix[3]); + float ari = 0.0; + if (fn == 0 && fp == 0) { + ari = 1.0; + } else { + ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); + } + out[ari_block_idx] = ari; + } + __syncthreads(); + +} - C = np.empty((2, 2), dtype=np.int64) - C[1, 1] = sum_squares - n_samples - C[0, 1] = np.dot(contingency, n_k).sum() - sum_squares - C[1, 0] = np.dot(contingency.T, n_c).sum() - sum_squares - C[0, 0] = n_samples ** 2 - C[0, 1] - C[1, 0] - sum_squares +""" - return C -@cuda.jit(device=True) -def adjusted_rand_index(part0: np.ndarray, part1: np.ndarray, out: np.ndarray, compare_pair_id: int, i: int, j: int) -> float: +def get_kernel(): """ - Computes the adjusted Rand index (ARI) between two clustering partitions. - The code is based on the sklearn implementation here: - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html - See copyright notice at the top of this file. + Kernel to compute the air between two partitions indexed from the 3D input array parts. - This function should not be compiled with numba, since it depends on - arbitrarily large interger variable (supported by Python) to correctly - compute the ARI in large partitions. + The first thread of each logical part vs part ari matrix is responsible to reduce the matrix to the max ari. + See the document for illustrations. - Args: - part0: a 1d array with cluster assignments for n objects. - part1: a 1d array with cluster assignments for n objects. - - Returns: - A number representing the adjusted Rand index between two clustering - partitions. This number is between something around 0 (partitions do not - match; it could be negative in some cases) and 1.0 (perfect match). + raw kernel args: + parts: 3D device array with cluster assignments for x features, y partitions, and z objects. + uniqs: 2D device array with the number of unique elements for feature x and partition y. + n_aris: Number of ARI computations to perform. + n_parts: Number of partitions of a feature, i.e., len(n_range_clusters) to compare. + out: Pointer to the pre-allocated 1D device output array with length of number of features to compare. """ - (tn, fp), (fn, tp) = get_pair_confusion_matrix(part0, part1) - # convert to Python integer types, to avoid overflow or underflow - tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp) - # Special cases: empty data or full agreement - if fn == 0 and fp == 0: - res = 1.0 + cuda_code = d_get_coords_from_index_str + k_ari_str - res = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)) - out[compare_pair_id, i, j] = res + kernel = cp.RawKernel(code=cuda_code, backend="nvcc").get_function("ari") + return kernel -@cuda.jit(device=True) -def compute_contingency_matrix(part0, part1, part0_unique, part1_unique, cont_mat): + +def ari_dim2(feature_parts: cp.ndarray, + n_partitions: int, + n_features_comp: int, + out: cp.ndarray, + unique_element_counts: cp.ndarray): """ - CUDA kernel to compute the contingency matrix. + Function to compute the ARI between partitions on the GPU. This function is responsible for launching the kernel + in different streams for each pair of partitions. Args: - part0: 1D array with cluster assignments for n objects. - part1: 1D array with cluster assignments for n objects. - part0_unique: Unique cluster labels in part0. - part1_unique: Unique cluster labels in part1. - cont_mat: The output contingency matrix. - - Each thread computes a single element of the contingency matrix. - """ - i, j = cuda.grid(2) # Get the thread indices in the grid + feature_parts: 3D device array with cluster assignments for x features, y partitions, and z objects. + Example initialization for this array: d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 - # Check if the thread indices are within the bounds of the unique clusters - if i < len(part0_unique) and j < len(part1_unique): - part0_k = part0_unique[i] # Cluster label in part0 - part1_k = part1_unique[j] # Cluster label in part1 + n_partitions: Number of partitions of a feature to compare. - count = 0 # Initialize the count for this element - for idx in range(len(part0)): - # Count the number of objects in both clusters i and j - if part0[idx] == part0_k and part1[idx] == part1_k: - count += 1 - cont_mat[i, j] = count # Store the result in the contingency matrix + n_features_comp: Pre-computed number of features to compare. + out: Pointer to the pre-allocated 1D device output array with length of n_features_comp. -@cuda.jit(device=True) -def get_contingency_matrix(part0: np.ndarray, part1: np.ndarray) -> np.ndarray: + unique_element_counts: 2D device array with the number of unique elements for feature x and partition y. """ - Compute the contingency matrix for two clustering partitions using CUDA. - - Args: - part0: 1D array with cluster assignments for n objects. - part1: 1D array with cluster assignments for n objects. - Returns: - A contingency matrix with k0 rows and k1 columns, where k0 is the number - of clusters in part0 and k1 is the number of clusters in part1. Each cell - (i, j) represents the number of objects in cluster i (part0) and cluster j (part1). - """ - part0_unique = np.unique(part0) # Find unique clusters in part0 - part1_unique = np.unique(part1) # Find unique clusters in part1 - - cont_mat = np.zeros((len(part0_unique), len(part1_unique)), dtype=np.int32) # Initialize the contingency matrix - - # Define the number of threads per block and the number of blocks per grid - threadsperblock = (16, 16) - blockspergrid_x = int(np.ceil(len(part0_unique) / threadsperblock[0])) - blockspergrid_y = int(np.ceil(len(part1_unique) / threadsperblock[1])) - blockspergrid = (blockspergrid_x, blockspergrid_y) - - # Launch the CUDA kernel to compute the contingency matrix - compute_contingency_matrix[blockspergrid, threadsperblock](part0, part1, part0_unique, part1_unique, cont_mat) - - return cont_mat - - -def print_device_info(): - # Get the current device - device = cuda.get_current_device() - print(dir(device)) - # Print device information - print("Device Information:") - print(f"Device ID: {device.id}") - print(f"Name: {device.name}") - # print(f"Total Memory: {device.total_memory / (1024 ** 3):.2f} GB") - print(f"Multiprocessor Count: {device.MULTIPROCESSOR_COUNT}") - print(f"Max Threads per Block: {device.MAX_THREADS_PER_BLOCK}") - # print(f"Max Threads per Multiprocessor: {device.MAX_THREADS_PER_MULTIPROCESSOR}") - print(f"Max Block Dim X: {device.MAX_BLOCK_DIM_X}") - print(f"Max Block Dim Y: {device.MAX_BLOCK_DIM_Y}") - print(f"Max Block Dim Z: {device.MAX_BLOCK_DIM_Z}") - print(f"Max Grid Dim X: {device.MAX_GRID_DIM_X}") - print(f"Max Grid Dim Y: {device.MAX_GRID_DIM_Y}") - print(f"Max Grid Dim Z: {device.MAX_GRID_DIM_Z}") - print(f"Warp Size: {device.WARP_SIZE}") - print(f"Compute Capability: {device.compute_capability}") - print(f"Concurrent Kernels: {device.CONCURRENT_KERNELS}") - print(f"PCI Bus ID: {device.PCI_BUS_ID}") - print(f"PCI Device ID: {device.PCI_DEVICE_ID}") - print(f"PCI Domain ID: {device.PCI_DOMAIN_ID}") - - -if __name__ == '__main__': - print_device_info() + # Can use non-blocking CPU scheduling or CUDA dynamic parallelism to launch the kernel for each pair of partitions. + + # Get metadata + n_features, n_parts, n_objs = feature_parts.shape + + # Each kernel launch will be responsible for computing the ARI between two partitions. + n_part_mat_elems = n_partitions * n_partitions + # Each thread + n_ari_pairs = n_partitions * n_part_mat_elems + cm_values = cp.full(n_features_comp, cp.nan) + # Todo: how many ari pairs? n_range_cluster? + threads_per_block = 1 + blocks_per_grid = (n_ari_pairs + threads_per_block - 1) // threads_per_block + + ari_kernel = get_kernel() + # Todo: use different streams? + # Allocate output arrays for parts (debugging) + out_parts0 = cp.empty(n_objs, dtype=np.int32) + out_parts1 = cp.empty(n_objs, dtype=np.int32) + shared_mem_size = 2 * n_objs + + # Launch the kernel, using one block per ARI + ari_kernel(grid=(blocks_per_grid,), + block=(threads_per_block,), + shared_mem=shared_mem_size, + args=(feature_parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) + + raise NotImplementedError("Not implemented yet") diff --git a/libs/ccc/sklearn/metrics_gpu2.py b/libs/ccc/sklearn/metrics_gpu2.py deleted file mode 100644 index c460ef32..00000000 --- a/libs/ccc/sklearn/metrics_gpu2.py +++ /dev/null @@ -1,347 +0,0 @@ -import numpy as np -import cupy as cp -from numba import njit -from numba import cuda -import rmm - - -d_get_confusion_matrix_str = """ -/** - * @brief CUDA device function to compute the pair confusion matrix - * @param[in] contingency Pointer to the contingency matrix - * @param[in] sum_rows Pointer to the sum of rows in the contingency matrix - * @param[in] sum_cols Pointer to the sum of columns in the contingency matrix - * @param[in] n_objs Number of objects in each partition - * @param[in] k Number of clusters (assuming k is the max of clusters in part0 and part1) - * @param[out] C Pointer to the output pair confusion matrix (2x2) - */ -__device__ void get_pair_confusion_matrix( - const int* __restrict__ contingency, - int * sum_rows, - int * sum_cols, - const int n_objs, - const int k, - int* C -) { - // Initialize sum_rows and sum_cols - for (int i = threadIdx.x; i < k; i += blockDim.x) { - sum_rows[i] = 0; - sum_cols[i] = 0; - } - __syncthreads(); - - // Compute sum_rows and sum_cols - for (int i = threadIdx.x; i < k * k; i += blockDim.x) { - int row = i / k; - int col = i % k; - int val = contingency[i]; - atomicAdd(&sum_cols[col], val); - atomicAdd(&sum_rows[row], val); - } - __syncthreads(); - - // Compute sum_squares - int sum_squares; - if (threadIdx.x == 0) { - sum_squares = 0; - for (int i = 0; i < k * k; ++i) { - sum_squares += (contingency[i]) * contingency[i]; - } - } - __syncthreads(); - - // Compute C[1,1], C[0,1], C[1,0], and C[0,0] - if (threadIdx.x == 0) { - C[3] = sum_squares - n_objs; // C[1,1] - - int temp = 0; - for (int i = 0; i < k; ++i) { - for (int j = 0; j < k; ++j) { - temp += (contingency[i * k + j]) * sum_cols[j]; - } - } - C[1] = temp - sum_squares; // C[0,1] - - temp = 0; - for (int i = 0; i < k; ++i) { - for (int j = 0; j < k; ++j) { - temp += (contingency[j * k + i]) * sum_rows[j]; - } - } - C[2] = temp - sum_squares; // C[1,0] - - C[0] = n_objs * n_objs - C[1] - C[2] - sum_squares; // C[0,0] - - - // compute ARI - int tn = static_cast(C[0]); - int fp = static_cast(C[1]); - int fn = static_cast(C[2]); - int tp = static_cast(C[3]); - float ari = 0.0; - if (fn == 0 && fp ==0) { - ari = 1.0; - } else { - ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); - } - } - __syncthreads(); -} - -""" - -d_get_contingency_matrix_str = """ -/** - * @brief Compute the contingency matrix for two partitions using shared memory - * @param[in] part0 Pointer to the first partition array - * @param[in] part1 Pointer to the second partition array - * @param[in] n Number of elements in each partition array - * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix - * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) - */ -__device__ void get_contingency_matrix(int* part0, int* part1, int n, int* shared_cont_mat, int k) { - int tid = threadIdx.x; - int bid = blockIdx.x; - int num_threads = blockDim.x; - int num_blocks = gridDim.x; - - // Initialize shared memory - for (int i = tid; i < k * k; i += num_threads) { - shared_cont_mat[i] = 0; - } - __syncthreads(); - - // Process elements - for (int i = tid; i < n; i += num_threads) { - int row = part0[i]; - int col = part1[i]; - - if (row < k && col < k) { - atomicAdd(&shared_cont_mat[row * k + col], 1); - } - } - __syncthreads(); -} - -""" - -d_unravel_index_str = """ -/** - * @brief Unravel a flat index to the corresponding 2D indicis - * @param[in] flat_idx The flat index to unravel - * @param[in] num_cols Number of columns in the 2D array - * @param[out] row Pointer to the row index - * @param[out] col Pointer to the column index - */ -extern "C" __device__ __host__ inline void unravel_index(int flat_idx, int num_cols, int* row, int* col) { - *row = flat_idx / num_cols; // Compute row index - *col = flat_idx % num_cols; // Compute column index -} - -""" - -d_get_coords_from_index_str = """ -#include -extern "C" __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int* x, int* y) { - // Calculate 'b' based on the input n_obj - int b = 1 - 2 * n_obj; - // Calculate 'x' using the quadratic formula part - float discriminant = b * b - 8 * idx; - float x_float = floor((-b - sqrt(discriminant)) / 2); - // Assign the integer part of 'x' - *x = static_cast(x_float); - // Calculate 'y' based on 'x' and the index - *y = static_cast(idx + (*x) * (b + (*x) + 2) / 2 + 1); -} - -""" - -k_ari_str = """ -/** - * @brief Main ARI kernel. Now only compare a pair of ARIs - * @param n_parts Number of partitions of each feature - * @param n_objs Number of objects in each partitions - * @param n_part_mat_elems Number of elements in the square partition matrix - * @param n_elems_per_feat Number of elements for each feature, i.e., part[i].x * part[i].y - * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @param n_aris Number of ARIs to compute - * @param k The max value of cluster number + 1 - * @param out Output array of ARIs - * @param part_pairs Output array of part pairs to be compared by ARI - */ -extern "C" __global__ void ari(int *parts, - const int n_aris, - const int n_features, - const int n_parts, - const int n_objs, - const int n_elems_per_feat, - const int n_part_mat_elems, - const int k, - float *out, - int *part_pairs = nullptr) -{ - /* - * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory - */ - int global_tid = blockIdx.x * blockDim.x + threadIdx.x; - // each block is responsible for one ARI computation - int ari_block_idx = blockIdx.x; - - // print parts for debugging - - - // obtain the corresponding parts and unique counts - int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features - int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair - int i, j; - - // unravel the feature indices - get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); - assert(i < n_features && j < n_features); - assert(i >= 0 && j >= 0); - - // unravel the partition indices - int m, n; - unravel_index(part_pair_flat_idx, n_parts, &m, &n); - // if (global_tid == 0) - - // Make pointers to select the parts and unique counts for the feature pair - // Todo: Use int4*? - int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread - int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; - - // Load gmem data into smem by using different threads - extern __shared__ int shared_mem[]; - int *s_part0 = shared_mem; - int *s_part1 = shared_mem + n_objs; - - // Loop over the data using the block-stride pattern - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) - { - s_part0[i] = t_data_part0[i]; - s_part1[i] = t_data_part1[i]; - } - __syncthreads(); - - // Copy data to global memory if part_pairs is specified - if (part_pairs != nullptr) - { - int *out_part0 = part_pairs + ari_block_idx * (2 * n_objs); - int *out_part1 = out_part0 + n_objs; - - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) - { - out_part0[i] = s_part0[i]; - out_part1[i] = s_part1[i]; - } - } - - /* - * Step 2: Compute contingency matrix within the block - */ - // shared mem address for the contingency matrix - int *s_contingency = shared_mem + 2 * n_objs; - get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); - - /* - * Step 3: Construct pair confusion matrix - */ - // shared mem address for the pair confusion matrix - int *s_sum_rows = s_contingency + k * k; - int *s_sum_cols = s_sum_rows + k; - int *s_pair_confusion_matrix = s_sum_cols + k; - get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, s_pair_confusion_matrix); - /* - * Step 4: Compute ARI and write to global memory - */ - if (threadIdx.x == 0) { - int tn = static_cast(s_pair_confusion_matrix[0]); - int fp = static_cast(s_pair_confusion_matrix[1]); - int fn = static_cast(s_pair_confusion_matrix[2]); - int tp = static_cast(s_pair_confusion_matrix[3]); - float ari = 0.0; - if (fn == 0 && fp == 0) { - ari = 1.0; - } else { - ari = 2.0 * (tp * tn - fn * fp) / ((tp + fn) * (fn + tn) + (tp + fp) * (fp + tn)); - } - out[ari_block_idx] = ari; - } - __syncthreads(); - -} - -""" - - -def get_kernel(): - """ - Kernel to compute the air between two partitions indexed from the 3D input array parts. - - The first thread of each logical part vs part ari matrix is responsible to reduce the matrix to the max ari. - See the document for illustrations. - - raw kernel args: - parts: 3D device array with cluster assignments for x features, y partitions, and z objects. - uniqs: 2D device array with the number of unique elements for feature x and partition y. - n_aris: Number of ARI computations to perform. - n_parts: Number of partitions of a feature, i.e., len(n_range_clusters) to compare. - out: Pointer to the pre-allocated 1D device output array with length of number of features to compare. - """ - - cuda_code = d_get_coords_from_index_str + k_ari_str - - kernel = cp.RawKernel(code=cuda_code, backend="nvcc").get_function("ari") - return kernel - - -def ari_dim2(feature_parts: cp.ndarray, - n_partitions: int, - n_features_comp: int, - out: cp.ndarray, - unique_element_counts: cp.ndarray): - """ - Function to compute the ARI between partitions on the GPU. This function is responsible for launching the kernel - in different streams for each pair of partitions. - - Args: - feature_parts: 3D device array with cluster assignments for x features, y partitions, and z objects. - Example initialization for this array: d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 - - n_partitions: Number of partitions of a feature to compare. - - n_features_comp: Pre-computed number of features to compare. - - out: Pointer to the pre-allocated 1D device output array with length of n_features_comp. - - unique_element_counts: 2D device array with the number of unique elements for feature x and partition y. - """ - - # Can use non-blocking CPU scheduling or CUDA dynamic parallelism to launch the kernel for each pair of partitions. - - # Get metadata - n_features, n_parts, n_objs = feature_parts.shape - - # Each kernel launch will be responsible for computing the ARI between two partitions. - n_part_mat_elems = n_partitions * n_partitions - # Each thread - n_ari_pairs = n_partitions * n_part_mat_elems - cm_values = cp.full(n_features_comp, cp.nan) - # Todo: how many ari pairs? n_range_cluster? - threads_per_block = 1 - blocks_per_grid = (n_ari_pairs + threads_per_block - 1) // threads_per_block - - ari_kernel = get_kernel() - # Todo: use different streams? - # Allocate output arrays for parts (debugging) - out_parts0 = cp.empty(n_objs, dtype=np.int32) - out_parts1 = cp.empty(n_objs, dtype=np.int32) - shared_mem_size = 2 * n_objs - - # Launch the kernel, using one block per ARI - ari_kernel(grid=(blocks_per_grid,), - block=(threads_per_block,), - shared_mem=shared_mem_size, - args=(feature_parts, unique_element_counts, n_features_comp, n_part_mat_elems, out)) - - raise NotImplementedError("Not implemented yet") diff --git a/tests/gpu/feature_array.txt b/tests/gpu/feature_array.txt deleted file mode 100644 index e1f07384..00000000 --- a/tests/gpu/feature_array.txt +++ /dev/null @@ -1,100 +0,0 @@ -5.488135039273247529e-01 -7.151893663724194772e-01 -6.027633760716438749e-01 -5.448831829968968643e-01 -4.236547993389047084e-01 -6.458941130666561170e-01 -4.375872112626925103e-01 -8.917730007820797722e-01 -9.636627605010292807e-01 -3.834415188257777052e-01 -7.917250380826645895e-01 -5.288949197529044799e-01 -5.680445610939323098e-01 -9.255966382926610336e-01 -7.103605819788694209e-02 -8.712929970154070780e-02 -2.021839744032571939e-02 -8.326198455479379978e-01 -7.781567509498504842e-01 -8.700121482468191614e-01 -9.786183422327640047e-01 -7.991585642167235992e-01 -4.614793622529318462e-01 -7.805291762864554617e-01 -1.182744258689332195e-01 -6.399210213275238202e-01 -1.433532874090464038e-01 -9.446689170495838894e-01 -5.218483217500716753e-01 -4.146619399905235870e-01 -2.645556121046269693e-01 -7.742336894342166653e-01 -4.561503322165485486e-01 -5.684339488686485087e-01 -1.878980043635514185e-02 -6.176354970758770602e-01 -6.120957227224214092e-01 -6.169339968747569181e-01 -9.437480785146241669e-01 -6.818202991034834071e-01 -3.595079005737860101e-01 -4.370319537993414549e-01 -6.976311959272648577e-01 -6.022547162926983333e-02 -6.667667154456676792e-01 -6.706378696181594101e-01 -2.103825610738409013e-01 -1.289262976548533057e-01 -3.154283509241838646e-01 -3.637107709426226076e-01 -5.701967704178796392e-01 -4.386015134623203471e-01 -9.883738380592261841e-01 -1.020448107480280697e-01 -2.088767560948346924e-01 -1.613095178849962563e-01 -6.531083254653984316e-01 -2.532916025397821125e-01 -4.663107728563062881e-01 -2.444255920016027428e-01 -1.589695836455197187e-01 -1.103751411643051350e-01 -6.563295894652734219e-01 -1.381829513486138028e-01 -1.965823616800534968e-01 -3.687251706609641078e-01 -8.209932298479351021e-01 -9.710127579306127021e-02 -8.379449074988039037e-01 -9.609840789396306704e-02 -9.764594650133957554e-01 -4.686512016477015763e-01 -9.767610881903371345e-01 -6.048455197450459675e-01 -7.392635793983016734e-01 -3.918779225432067470e-02 -2.828069625764095818e-01 -1.201965612131689065e-01 -2.961401975221449323e-01 -1.187277189542440547e-01 -3.179831793939760232e-01 -4.142629945146699688e-01 -6.414749634878436080e-02 -6.924721193700198452e-01 -5.666014542065751503e-01 -2.653894909394454160e-01 -5.232480534666996697e-01 -9.394051075844167542e-02 -5.759464955561792721e-01 -9.292961975762140669e-01 -3.185689524513236615e-01 -6.674103799636816881e-01 -1.317978624043921743e-01 -7.163272041185655414e-01 -2.894060929472010990e-01 -1.831913620071168314e-01 -5.865129348100831530e-01 -2.010754618749355238e-02 -8.289400292173630946e-01 -4.695476192547065608e-03 diff --git a/tests/gpu/test_ari_device.py b/tests/gpu/test_ari_device.py deleted file mode 100644 index 0a544c21..00000000 --- a/tests/gpu/test_ari_device.py +++ /dev/null @@ -1,96 +0,0 @@ -import pytest -import numpy as np -from ccc.sklearn.metrics_device import find_unique, compute_contingency_matrix, get_pair_confusion_matrix, sum_2d_array, sum_squares_2d_array, adjusted_rand_index, compute_ari - - -# Define the maximum unique values for testing -MAX_UNIQUE = 10 -MAX_CLUSTERS = 5 - - -# Helper function to run device functions in tests -def run_device_function(func, *args): - """Helper to run a CUDA device function.""" - out = func(*args) - return out - - -# Test for find_unique -def test_find_unique(): - arr = np.array([1, 2, 2, 3, 4, 4, 4, 5], dtype=np.int32) - expected_unique = np.array([1, 2, 3, 4, 5], dtype=np.int32) - expected_counts = np.array([1, 2, 1, 3, 1], dtype=np.int32) - - unique, counts, num_unique = run_device_function(find_unique, arr, MAX_UNIQUE) - - assert num_unique == len(expected_unique) - assert np.all(unique == expected_unique) - assert np.all(counts == expected_counts) - - -# Test for compute_contingency_matrix -def test_compute_contingency_matrix(): - part0 = np.array([0, 1, 1, 2], dtype=np.int32) - part1 = np.array([1, 1, 0, 2], dtype=np.int32) - - cont_mat = np.zeros((MAX_CLUSTERS, MAX_CLUSTERS), dtype=np.int32) - num_clusters0, num_clusters1 = run_device_function(compute_contingency_matrix, part0, part1, cont_mat, MAX_CLUSTERS) - - expected_cont_mat = np.array([ - [0, 1, 0], - [1, 1, 0], - [0, 0, 1] - ], dtype=np.int32) - - assert np.all(cont_mat[:num_clusters0, :num_clusters1] == expected_cont_mat) - - -# Test for sum_2d_array -def test_sum_2d_array(): - arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) - total = run_device_function(sum_2d_array, arr, 2, 3) - - assert total == 21 # Sum of all elements in arr - - -# Test for sum_squares_2d_array -def test_sum_squares_2d_array(): - arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) - total_squares = run_device_function(sum_squares_2d_array, arr, 2, 3) - - assert total_squares == 91 # Sum of squares of all elements in arr - - -# Test for get_pair_confusion_matrix -def test_get_pair_confusion_matrix(): - part0 = np.array([0, 1, 1, 2], dtype=np.int32) - part1 = np.array([1, 1, 0, 2], dtype=np.int32) - - C = run_device_function(get_pair_confusion_matrix, part0, part1, MAX_CLUSTERS) - - assert C[0, 0] == 0 # Example check for specific value in the confusion matrix - - -# Test for adjusted_rand_index -def test_adjusted_rand_index(): - part0 = np.array([0, 1, 1, 2], dtype=np.int32) - part1 = np.array([1, 1, 0, 2], dtype=np.int32) - - # Expected ARI between these partitions is some value we calculate manually or use sklearn for comparison - out = np.zeros((1, 1, 1), dtype=np.float32) - - run_device_function(adjusted_rand_index, part0, part1, out, 0, 0, 0, MAX_CLUSTERS) - - assert out[0, 0, 0] == pytest.approx(0.4444, rel=1e-4) # Example value based on expected ARI - - -# Test for compute_ari kernel -def test_compute_ari_kernel(): - partitions = np.array([[[0, 1, 1], [1, 0, 0]]], dtype=np.int32) - out = np.zeros((1, 2, 2), dtype=np.float32) - - compute_ari[1, 1](partitions, out, MAX_CLUSTERS) - - # Example check for ARI result - assert out[0, 0, 1] == pytest.approx(0.3333, rel=1e-4) - diff --git a/tests/gpu/test_compute_coef.py b/tests/gpu/test_coef_computation.py similarity index 100% rename from tests/gpu/test_compute_coef.py rename to tests/gpu/test_coef_computation.py diff --git a/tests/gpu/test_coef.py b/tests/gpu/test_coef_subroutines.py similarity index 100% rename from tests/gpu/test_coef.py rename to tests/gpu/test_coef_subroutines.py diff --git a/tests/gpu/test_device_host_funcs.py b/tests/gpu/test_device_host_funcs.py index 9ec1f237..c40f4003 100644 --- a/tests/gpu/test_device_host_funcs.py +++ b/tests/gpu/test_device_host_funcs.py @@ -2,7 +2,7 @@ import math import cupy as cp import numpy as np -from ccc.sklearn.metrics_gpu2 import ( +from ccc.sklearn.metrics_gpu import ( d_get_confusion_matrix_str, d_get_coords_from_index_str, d_unravel_index_str, diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py index 5d1eabf2..5ab421ef 100644 --- a/tests/gpu/test_get_parts.py +++ b/tests/gpu/test_get_parts.py @@ -11,6 +11,7 @@ from ccc.coef import get_perc_from_k as get_perc_from_k_cpu import functools + def clean_gpu_memory(func): @functools.wraps(func) def wrapper(*args, **kwargs): @@ -21,12 +22,14 @@ def wrapper(*args, **kwargs): mempool.free_all_blocks() return wrapper + def find_partition(value, quantiles): for i in range(len(quantiles)): if value <= quantiles[i]: return i return len(quantiles) # If value is greater than all quantiles + def verify_partition(feature, index, n_clusters): """ Verify the partition for a specific element in the feature array. diff --git a/tests/gpu/test_get_parts_debug.py b/tests/gpu/test_get_parts_debug.py deleted file mode 100644 index 96d62d1d..00000000 --- a/tests/gpu/test_get_parts_debug.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -Code to reproduce the edge cased that may be missed by the CPU version of get_parts -""" -import pytest -from typing import List - -import numpy as np -from numba import cuda -from numpy.testing import assert_array_equal, assert_allclose -from numpy.typing import NDArray - -from ccc.coef.impl_gpu import ( - get_perc_from_k, - get_range_n_percentages, - convert_n_clusters, - get_range_n_clusters, - get_parts, -) - -from ccc.coef import get_parts as get_parts_cpu -from ccc.coef import get_perc_from_k as get_perc_from_k_cpu - - -def find_partition(value, quantiles): - for i in range(len(quantiles)): - if value <= quantiles[i]: - return i - return len(quantiles) # If value is greater than all quantiles - - -def verify_partition(feature, index, n_clusters): - """ - Verify the partition for a specific element in the feature array. - """ - parts_cpu = get_parts_cpu(feature, (n_clusters,)) - percentages_cpu = get_perc_from_k_cpu(n_clusters) - quantities = np.quantile(feature, percentages_cpu) - - value = feature[index] - partition = find_partition(value, quantities) - - print(f"\nVerifying partition for feature[{index}] = {value}") - print(f"CPU percentages: {percentages_cpu}") - print(f"CPU quantities: {quantities}") - - print("\nAll partition ranges:") - for i in range(n_clusters): - if i == 0: - print(f"Partition {i} range: (-inf, {quantities[i]}]") - elif i == n_clusters - 1: - print(f"Partition {i} range: ({quantities[i - 1]}, inf)") - else: - print(f"Partition {i} range: ({quantities[i - 1]}, {quantities[i]}]") - - print(f"Data point {value} should fall in partition {partition}") - print(f"Partition computed by CCC_CPU: {parts_cpu[0][index]}") - - assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" - return partition - - -@pytest.mark.parametrize("feature_size", [100]) # 100 features -@pytest.mark.parametrize("cluster_settings", [ - ([6], (6,)), # 6 internal clusters -]) -def test_get_parts(feature_size, cluster_settings): - np.random.seed(0) - - gpu_clusters, cpu_clusters = cluster_settings - feature = np.random.rand(feature_size) - - # GPU implementation - parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8)).get() - - # CPU implementation - parts_cpu = get_parts_cpu(feature, cpu_clusters) - - print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}") - print(f"GPU output shape: {parts_gpu.shape}") - print(f"CPU output shape: {parts_cpu.shape}") - - assert parts_gpu is not None, "GPU output is None" - assert len(parts_gpu) == 1, f"Expected 1 feature, got {len(parts_gpu)}" - assert len(parts_gpu[0]) == len(gpu_clusters), f"Expected {len(gpu_clusters)} partition(s), got {len(parts_gpu[0])}" - - for i, n_clusters in enumerate(gpu_clusters): - gpu_unique = np.unique(parts_gpu[0][i]) - cpu_unique = np.unique(parts_cpu[i]) - - print(f"\nPartition {i}:") - print(f" GPU unique values (partitions): {gpu_unique}") - print(f" CPU unique values (partitions): {cpu_unique}") - - assert len(gpu_unique) == n_clusters, f"Expected {n_clusters} cluster indexes, got {len(gpu_unique)}" - - if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): - diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] - print(f"\nDifferences found in partition {i}:") - print(f" Number of differing elements: {len(diff_indices)}") - print(f" First 10 differing indices: {diff_indices[:10]}") - print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") - print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") - print(f" Object values at these indices: {feature[diff_indices[:10]]}") - - # Verify partitions for differing elements - for idx in diff_indices[:10]: - expected_partition = verify_partition(feature, idx, n_clusters) - assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" - - assert False, f"GPU and CPU results don't match for {n_clusters} clusters" - - # Additional checks for multi-cluster settings - if len(gpu_clusters) > 1: - for i in range(len(gpu_clusters)): - for j in range(i + 1, len(gpu_clusters)): - if np.array_equal(parts_gpu[0][i], parts_cpu[j]): - print(f"\nUnexpected equality between partitions {i} and {j}:") - print(f" Partition {i}: {parts_gpu[0][i]}") - print(f" Partition {j}: {parts_cpu[j]}") - assert False, f"Partitions {i} and {j} should not be equal" \ No newline at end of file diff --git a/tests/gpu/test_get_percentiles.py b/tests/gpu/test_get_percentiles.py index cefacfdb..45f54938 100644 --- a/tests/gpu/test_get_percentiles.py +++ b/tests/gpu/test_get_percentiles.py @@ -9,6 +9,7 @@ from ccc.coef import get_perc_from_k as get_perc_from_k_cpu + def test_get_perc_from_k_with_k_less_than_two(): empty_array = np.empty(0) assert_array_equal(get_perc_from_k(1), empty_array) diff --git a/tests/gpu/utils.py b/tests/gpu/utils.py index 26f0e662..f7a88f0f 100644 --- a/tests/gpu/utils.py +++ b/tests/gpu/utils.py @@ -1,6 +1,7 @@ import functools import cupy as cp + def clean_gpu_memory(func): @functools.wraps(func) def wrapper(*args, **kwargs): From d7f676850686bd22f636a9af9732aa3e7dfbb2fe Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 4 Nov 2024 16:14:54 -0700 Subject: [PATCH 101/134] [build]: Compile pybind11 with gtest successfully --- CMakeLists.txt | 90 ++++++++++++++- libs/ccc_cuda_ext/CMakeLists.txt | 27 ++++- libs/ccc_cuda_ext/tests/test_kernel.cpp | 139 +++++------------------- tests/cuda_ext/test_kernel.cpp | 29 +++++ 4 files changed, 166 insertions(+), 119 deletions(-) create mode 100644 tests/cuda_ext/test_kernel.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index ff733c70..e06c45c0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,11 +1,97 @@ cmake_minimum_required(VERSION 3.15...3.26) -project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA) +project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA CXX) +# Add this near the top of your file, after project() +# Define the include directories for the whole project +set(PROJECT_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/libs) + +# Set C++ standard +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +# Set Python Standard +# Get Python version dynamically +execute_process( + COMMAND "${Python_EXECUTABLE}" -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}')" + OUTPUT_VARIABLE PYTHON_VERSION_FULL + OUTPUT_STRIP_TRAILING_WHITESPACE +) +# Set the paths using the detected version +set(Python_EXECUTABLE $ENV{CONDA_PREFIX}/bin/python) +set(PYTHON_INCLUDE_DIR $ENV{CONDA_PREFIX}/include/python${PYTHON_VERSION_FULL}) +set(PYTHON_LIBRARY $ENV{CONDA_PREFIX}/lib/libpython${PYTHON_VERSION_FULL}.so) + + +# Set CUDA architecture and Pybind11 +find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) set(PYBIND11_NEWPYTHON ON) find_package(pybind11 CONFIG REQUIRED) +# Download and configure Google Test +include(FetchContent) +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.15.2 # Adjust version as needed +) +FetchContent_MakeAvailable(googletest) + +# Setup Gtest +enable_testing() +# Function to automatically add tests from a directory +function(add_tests_from_directory TEST_DIR) + # Find all test files in the directory + file(GLOB_RECURSE TEST_FILES + "${TEST_DIR}/*_test.cpp" # Files ending with _test.cpp + "${TEST_DIR}/*_tests.cpp" # Files ending with _tests.cpp + "${TEST_DIR}/test_*.cpp" # Files starting with test_ + ) + + # Loop through each test file + foreach(TEST_FILE ${TEST_FILES}) + # Get the filename without extension + get_filename_component(TEST_NAME ${TEST_FILE} NAME_WE) + + # Create an executable for this test + add_executable(${TEST_NAME} ${TEST_FILE}) + + # target_include_directories(${TEST_NAME} PRIVATE + # ${PROJECT_INCLUDE_DIR} # Add this line + # ${Python_INCLUDE_DIRS} + # ) + + # Link against gtest and your project libraries + target_link_libraries(${TEST_NAME} PRIVATE + GTest::gtest_main + GTest::gtest + pybind11::headers + Python::Module + # Add your other project libraries here + # project_lib + ) + + # Add the test to CTest + add_test(NAME ${TEST_NAME} COMMAND ${TEST_NAME}) + + # Set test properties (optional) + # Set test properties (optional) + set_tests_properties(${TEST_NAME} PROPERTIES + TIMEOUT 10 # Timeout in seconds + WORKING_DIRECTORY "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}" + ) + endforeach() +endfunction() + +# Specify your test directory and call the function +# add_tests_from_directory(${CMAKE_CURRENT_SOURCE_DIR}/tests) + +# Optional: Set output directories +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib) + +# Set up binding then do compilation and installation set(CUDA_EXT_MODULE_NAME ccc_cuda_ext) set(CUDA_EXT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/libs/${CUDA_EXT_MODULE_NAME}) pybind11_add_module(${CUDA_EXT_MODULE_NAME} ${CUDA_EXT_DIR}/binder.cu ${CUDA_EXT_DIR}/metrics.cu) -install(TARGETS ${CUDA_EXT_MODULE_NAME} LIBRARY DESTINATION .) +# install(TARGETS ${CUDA_EXT_MODULE_NAME} LIBRARY DESTINATION .) diff --git a/libs/ccc_cuda_ext/CMakeLists.txt b/libs/ccc_cuda_ext/CMakeLists.txt index 0b021e74..babb604c 100644 --- a/libs/ccc_cuda_ext/CMakeLists.txt +++ b/libs/ccc_cuda_ext/CMakeLists.txt @@ -3,6 +3,18 @@ cmake_minimum_required(VERSION 3.18) project(CudaAriProject LANGUAGES CUDA CXX) +# Set Python Standard +# Get Python version dynamically +execute_process( + COMMAND "${Python_EXECUTABLE}" -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}')" + OUTPUT_VARIABLE PYTHON_VERSION_FULL + OUTPUT_STRIP_TRAILING_WHITESPACE +) +# Set the paths using the detected version +set(Python_EXECUTABLE $ENV{CONDA_PREFIX}/bin/python) +set(PYTHON_INCLUDE_DIR $ENV{CONDA_PREFIX}/include/python${PYTHON_VERSION_FULL}) +set(PYTHON_LIBRARY $ENV{CONDA_PREFIX}/lib/libpython${PYTHON_VERSION_FULL}.so) + # Add gtest as a dependency include(FetchContent) FetchContent_Declare( @@ -14,10 +26,13 @@ include(GoogleTest) # Set the C++ standard set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(PYBIND11_NEWPYTHON ON) +find_package(Python REQUIRED Development) +find_package(pybind11 CONFIG REQUIRED) -# Add the CUDA library -add_library(cudaAriLib STATIC metrics.cu) # Add the CUDA source file -set_target_properties(cudaAriLib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +# # Add the CUDA library +# add_library(cudaAriLib STATIC metrics.cu) # Add the CUDA source file +# set_target_properties(cudaAriLib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) @@ -26,6 +41,8 @@ FetchContent_MakeAvailable(googletest) # Testing enable_testing() -add_executable(testCudaAri tests/test_kernel.cpp) -target_link_libraries(testCudaAri PRIVATE cudaAriLib GTest::gtest_main GTest::gtest) +add_executable(testCudaAri tests/test_kernel.cpp metrics.cu) +target_link_libraries(testCudaAri PUBLIC GTest::gtest_main GTest::gtest pybind11::embed Python::Python) gtest_discover_tests(testCudaAri) + +# pybind11_add_module(testCudaAri tests/test_kernel.cpp) diff --git a/libs/ccc_cuda_ext/tests/test_kernel.cpp b/libs/ccc_cuda_ext/tests/test_kernel.cpp index d0a2d26e..0641a775 100644 --- a/libs/ccc_cuda_ext/tests/test_kernel.cpp +++ b/libs/ccc_cuda_ext/tests/test_kernel.cpp @@ -1,114 +1,29 @@ -#include -#include -#include +#include +#include #include "../metrics.cuh" -#include "gtest/gtest.h" -// Helper function to generate pairwise combinations (implement this according to your needs) - -std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) -{ - std::vector, std::vector>> pairs; - size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector - for (size_t i = 0; i < num_slices; ++i) - { - for (size_t j = i + 1; j < num_slices; ++j) - { // Only consider pairs in different slices - for (const auto &row_i : arr[i]) - { // Each row in slice i - for (const auto &row_j : arr[j]) - { // Pairs with each row in slice j - pairs.emplace_back(row_i, row_j); - } - } - } - } - return pairs; -} - - -using TestParamType = std::tuple; - -// Define a parameterized test fixture -class CudaAriTest : public ::testing::TestWithParam {}; - -TEST_P(CudaAriTest, CheckSingleResult) -{ - Mat3 parts; - float expected_result; - std::tie(parts, expected_result) = GetParam(); - - // Get dimensions - int n_features = parts.size(); - int n_parts = parts[0].size(); - int n_objs = parts[0][0].size(); - int n_feature_comp = n_features * (n_features - 1) / 2; - int n_aris = n_feature_comp * n_parts * n_parts; - std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl - << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; - - // Allocate host memory for C-style array - int *h_parts = new int[n_features * n_parts * n_objs]; - - // Copy data from vector to C-style array - for (int i = 0; i < n_features; ++i) - { - for (int j = 0; j < n_parts; ++j) - { - for (int k = 0; k < n_objs; ++k) - { - h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; - } - } - } - - auto h_out = cudaAri(parts, n_features, n_parts, n_objs)[0]; - - // Check if the result are close - EXPECT_NEAR(h_out, expected_result, 1e-2); -} - -// Instantiate the test suite with parameter values -// These tests are taken from sklearn.metrics.adjusted_rand_score: -// https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -INSTANTIATE_TEST_SUITE_P( - CudaAriTestInstances, - CudaAriTest, - ::testing::Values( - TestParamType( - Mat3{ - {{0, 0, 1, 2}}, - {{0, 0, 1, 1}}, - }, - 0.57f - ), - TestParamType( - Mat3{ - {{0, 0, 1, 1}}, - {{0, 1, 0, 1}}, - }, - -0.5f - ), - TestParamType( - Mat3{ - {{0, 0, 1, 1}}, - {{0, 0, 1, 1}}, - }, - 1.0f - ), - TestParamType( - Mat3{ - {{0, 0, 1, 1}}, - {{1, 1, 0, 0}}, - }, - 1.0f - ), - TestParamType( - Mat3{ - {{0, 0, 0, 0}}, - {{0, 1, 2, 3}}, - }, - 0.0f - ) - ) -); +namespace py = pybind11; + +TEST(AriTest, SimpleCase) { + // Create input data + std::vector data = { + 0, 0, 1, 2, // First partition + 0, 0, 1, 1 // Second partition + }; + + // Create shape and strides for 3D array (n_features=2, n_parts=1, n_objs=4) + std::vector shape = {2, 1, 4}; + std::vector strides = {4 * sizeof(int), // stride for features + 4 * sizeof(int), // stride for partitions + sizeof(int)}; // stride for objects + + // Create numpy array from data + py::array_t parts(shape, strides, data.data()); + + // Call the ari function + std::vector result = ari(parts, 2, 1, 4); + + // Check result + ASSERT_EQ(result.size(), 1); // Should only have one ARI value + EXPECT_NEAR(result[0], 0.57f, 1e-2); // Compare with expected value within tolerance +} \ No newline at end of file diff --git a/tests/cuda_ext/test_kernel.cpp b/tests/cuda_ext/test_kernel.cpp new file mode 100644 index 00000000..ac58bda1 --- /dev/null +++ b/tests/cuda_ext/test_kernel.cpp @@ -0,0 +1,29 @@ +#include +#include +#include "../../libs/ccc_cuda_ext/metrics.cuh" + +namespace py = pybind11; + +TEST(AriTest, SimpleCase) { + // Create input data + std::vector data = { + 0, 0, 1, 2, // First partition + 0, 0, 1, 1 // Second partition + }; + + // Create shape and strides for 3D array (n_features=2, n_parts=1, n_objs=4) + std::vector shape = {2, 1, 4}; + std::vector strides = {4 * sizeof(int), // stride for features + 4 * sizeof(int), // stride for partitions + sizeof(int)}; // stride for objects + + // Create numpy array from data + py::array_t parts(shape, strides, data.data()); + + // Call the ari function + std::vector result = ari(parts, 2, 1, 4); + + // Check result + ASSERT_EQ(result.size(), 1); // Should only have one ARI value + EXPECT_NEAR(result[0], 0.57f, 1e-2); // Compare with expected value within tolerance +} \ No newline at end of file From 69255dc3b840442541652a781b8aa77e34777cd6 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 4 Nov 2024 22:59:23 -0700 Subject: [PATCH 102/134] [cuda]: Factor out ari function using vector for testing --- libs/ccc_cuda_ext/metrics.cu | 81 +++++++++++++++++-------- libs/ccc_cuda_ext/metrics.cuh | 7 +++ libs/ccc_cuda_ext/tests/test_kernel.cpp | 19 +++--- 3 files changed, 74 insertions(+), 33 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index cd915838..d6c03d71 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -306,7 +306,6 @@ __global__ void ari(int *parts, __syncthreads(); } -// Todo: parameterize parts' data type /** * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) @@ -314,30 +313,13 @@ __global__ void ari(int *parts, * @return std::vector ARI values for each pair of partitions */ template -auto ari(const py::array_t& parts, - const size_t n_features, - const size_t n_parts, - const size_t n_objs) -> std::vector { +auto ari_base(const T* parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector { // Edge cases: // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing - // Input processing - // Request a buffer descriptor from Python - py::buffer_info buffer = parts.request(); - - // Some basic validation checks ... - if (buffer.format != py::format_descriptor::format()) - throw std::runtime_error("Incompatible format: expected an int array!"); - - if (buffer.ndim != 3) - throw std::runtime_error("Incompatible buffer dimension!"); - - // Apply resources - auto result = py::array_t(buffer.size); - - // Obtain numpy.ndarray data pointer - const auto parts_ptr = static_cast(buffer.ptr); - // Compute internal variables // Todo: dynamically query types using parts_dtype = T; @@ -361,7 +343,7 @@ auto ari(const py::array_t& parts, // Allocate device memory with thrust // const int* parts_raw = parts[0][0].data(); - thrust::device_vector d_parts(parts_ptr, parts_ptr + n_features * n_parts * n_objs); // data is copied to device + thrust::device_vector d_parts(parts, parts + n_features * n_parts * n_objs); // data is copied to device thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); thrust::device_vector d_out(n_aris); @@ -395,6 +377,56 @@ auto ari(const py::array_t& parts, return res; } +/** + * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @throws std::invalid_argument if "parts" is invalid + * @return std::vector ARI values for each pair of partitions + */ +template +auto ari(const py::array_t& parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector { + // Edge cases: + // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing + + // Input processing + // Request a buffer descriptor from Python + py::buffer_info buffer = parts.request(); + + // Some basic validation checks ... + if (buffer.format != py::format_descriptor::format()) + throw std::runtime_error("Incompatible format: expected an int array!"); + + if (buffer.ndim != 3) + throw std::runtime_error("Incompatible buffer dimension!"); + + // Apply resources + auto result = py::array_t(buffer.size); + + // Obtain numpy.ndarray data pointer + const auto parts_ptr = static_cast(buffer.ptr); + + return ari_base(parts_ptr, n_features, n_parts, n_objs); +} + + +/** + * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions + * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @throws std::invalid_argument if "parts" is invalid + * @return std::vector ARI values for each pair of partitions + */ +template +auto ari_vector(const std::vector& parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector { + // Obtain array data pointer + const auto parts_ptr = parts.data(); + return ari_base(parts_ptr, n_features, n_parts, n_objs); +} // Below is the explicit instantiation of the ari template function. // @@ -403,4 +435,5 @@ auto ari(const py::array_t& parts, // implementation of the template functions, we need to explicitly instantiate them here, so that they can be picked up // by the linker. -template auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file +template auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; +template auto ari_vector(const std::vector& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file diff --git a/libs/ccc_cuda_ext/metrics.cuh b/libs/ccc_cuda_ext/metrics.cuh index 255a4efa..113b4387 100644 --- a/libs/ccc_cuda_ext/metrics.cuh +++ b/libs/ccc_cuda_ext/metrics.cuh @@ -10,3 +10,10 @@ auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; + +// Used for internal c++ testing +template +auto ari_vector(const std::vector& parts, + const size_t n_features, + const size_t n_parts, + const size_t n_objs) -> std::vector; diff --git a/libs/ccc_cuda_ext/tests/test_kernel.cpp b/libs/ccc_cuda_ext/tests/test_kernel.cpp index 0641a775..6e907ead 100644 --- a/libs/ccc_cuda_ext/tests/test_kernel.cpp +++ b/libs/ccc_cuda_ext/tests/test_kernel.cpp @@ -1,6 +1,6 @@ #include #include -#include "../metrics.cuh" +// #include "../metrics.cuh" namespace py = pybind11; @@ -13,17 +13,18 @@ TEST(AriTest, SimpleCase) { // Create shape and strides for 3D array (n_features=2, n_parts=1, n_objs=4) std::vector shape = {2, 1, 4}; - std::vector strides = {4 * sizeof(int), // stride for features - 4 * sizeof(int), // stride for partitions - sizeof(int)}; // stride for objects + // std::vector strides = {4 * sizeof(int), // stride for features + // 4 * sizeof(int), // stride for partitions + // sizeof(int)}; // stride for objects - // Create numpy array from data - py::array_t parts(shape, strides, data.data()); + // // Create numpy array from data + // py::array_t parts(shape, strides, data.data()); + py::array_t arr({ 3, 5 }); // Call the ari function - std::vector result = ari(parts, 2, 1, 4); + // std::vector result = ari(parts, 2, 1, 4); // Check result - ASSERT_EQ(result.size(), 1); // Should only have one ARI value - EXPECT_NEAR(result[0], 0.57f, 1e-2); // Compare with expected value within tolerance + // ASSERT_EQ(result.size(), 1); // Should only have one ARI value + // EXPECT_NEAR(result[0], 0.57f, 1e-2); // Compare with expected value within tolerance } \ No newline at end of file From 44ab7eebf2a9675910ebcfb44f2314aa87875610 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Tue, 5 Nov 2024 14:24:37 -0700 Subject: [PATCH 103/134] [build]: Compile and pass c++ tests successfully --- CMakeLists.txt | 16 ++-- libs/ccc_cuda_ext/metrics.cu | 36 ++++----- libs/ccc_cuda_ext/metrics.cuh | 3 +- tests/cuda_ext/test_kernel.cpp | 133 +++++++++++++++++++++++++++------ 4 files changed, 139 insertions(+), 49 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e06c45c0..548eb9c2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -4,6 +4,9 @@ project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA CXX) # Add this near the top of your file, after project() # Define the include directories for the whole project set(PROJECT_INCLUDE_DIR ${CMAKE_SOURCE_DIR}/libs) +# Set extention name and source directory +set(CUDA_EXT_MODULE_NAME ccc_cuda_ext) +set(CUDA_EXT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/libs/${CUDA_EXT_MODULE_NAME}) # Set C++ standard set(CMAKE_CXX_STANDARD 20) @@ -23,7 +26,7 @@ set(PYTHON_LIBRARY $ENV{CONDA_PREFIX}/lib/libpython${PYTHON_VERSION_FULL}.so) # Set CUDA architecture and Pybind11 -find_package(Python REQUIRED COMPONENTS Interpreter Development.Module) +find_package(Python REQUIRED Development) set(PYBIND11_NEWPYTHON ON) find_package(pybind11 CONFIG REQUIRED) @@ -53,7 +56,7 @@ function(add_tests_from_directory TEST_DIR) get_filename_component(TEST_NAME ${TEST_FILE} NAME_WE) # Create an executable for this test - add_executable(${TEST_NAME} ${TEST_FILE}) + add_executable(${TEST_NAME} ${TEST_FILE} ${CUDA_EXT_DIR}/metrics.cu) # target_include_directories(${TEST_NAME} PRIVATE # ${PROJECT_INCLUDE_DIR} # Add this line @@ -65,7 +68,7 @@ function(add_tests_from_directory TEST_DIR) GTest::gtest_main GTest::gtest pybind11::headers - Python::Module + Python::Python # Add your other project libraries here # project_lib ) @@ -83,15 +86,14 @@ function(add_tests_from_directory TEST_DIR) endfunction() # Specify your test directory and call the function -# add_tests_from_directory(${CMAKE_CURRENT_SOURCE_DIR}/tests) +add_tests_from_directory(${CMAKE_CURRENT_SOURCE_DIR}/tests) # Optional: Set output directories set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib) + # Set up binding then do compilation and installation -set(CUDA_EXT_MODULE_NAME ccc_cuda_ext) -set(CUDA_EXT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/libs/${CUDA_EXT_MODULE_NAME}) pybind11_add_module(${CUDA_EXT_MODULE_NAME} ${CUDA_EXT_DIR}/binder.cu ${CUDA_EXT_DIR}/metrics.cu) -# install(TARGETS ${CUDA_EXT_MODULE_NAME} LIBRARY DESTINATION .) +install(TARGETS ${CUDA_EXT_MODULE_NAME} LIBRARY DESTINATION .) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index d6c03d71..bff97263 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -313,7 +313,7 @@ __global__ void ari(int *parts, * @return std::vector ARI values for each pair of partitions */ template -auto ari_base(const T* parts, +auto ari_core(const T* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { @@ -408,25 +408,25 @@ auto ari(const py::array_t& parts, // Obtain numpy.ndarray data pointer const auto parts_ptr = static_cast(buffer.ptr); - return ari_base(parts_ptr, n_features, n_parts, n_objs); + return ari_core(parts_ptr, n_features, n_parts, n_objs); } -/** - * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions - * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) - * @throws std::invalid_argument if "parts" is invalid - * @return std::vector ARI values for each pair of partitions - */ -template -auto ari_vector(const std::vector& parts, - const size_t n_features, - const size_t n_parts, - const size_t n_objs) -> std::vector { - // Obtain array data pointer - const auto parts_ptr = parts.data(); - return ari_base(parts_ptr, n_features, n_parts, n_objs); -} +// /** +// * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions +// * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) +// * @throws std::invalid_argument if "parts" is invalid +// * @return std::vector ARI values for each pair of partitions +// */ +// template +// auto ari_vector(const Mat3& parts, +// const size_t n_features, +// const size_t n_parts, +// const size_t n_objs) -> std::vector { +// // Obtain array data pointer +// const auto parts_ptr = parts.data(); +// return ari_core(parts_ptr, n_features, n_parts, n_objs); +// } // Below is the explicit instantiation of the ari template function. // @@ -436,4 +436,4 @@ auto ari_vector(const std::vector& parts, // by the linker. template auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; -template auto ari_vector(const std::vector& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file +template auto ari_core(const int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file diff --git a/libs/ccc_cuda_ext/metrics.cuh b/libs/ccc_cuda_ext/metrics.cuh index 113b4387..b03eab9f 100644 --- a/libs/ccc_cuda_ext/metrics.cuh +++ b/libs/ccc_cuda_ext/metrics.cuh @@ -3,6 +3,7 @@ #include #include + namespace py = pybind11; template @@ -13,7 +14,7 @@ auto ari(const py::array_t& parts, // Used for internal c++ testing template -auto ari_vector(const std::vector& parts, +auto ari_core(const T* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; diff --git a/tests/cuda_ext/test_kernel.cpp b/tests/cuda_ext/test_kernel.cpp index ac58bda1..97ef4f63 100644 --- a/tests/cuda_ext/test_kernel.cpp +++ b/tests/cuda_ext/test_kernel.cpp @@ -1,29 +1,116 @@ +#include #include #include #include "../../libs/ccc_cuda_ext/metrics.cuh" namespace py = pybind11; -TEST(AriTest, SimpleCase) { - // Create input data - std::vector data = { - 0, 0, 1, 2, // First partition - 0, 0, 1, 1 // Second partition - }; - - // Create shape and strides for 3D array (n_features=2, n_parts=1, n_objs=4) - std::vector shape = {2, 1, 4}; - std::vector strides = {4 * sizeof(int), // stride for features - 4 * sizeof(int), // stride for partitions - sizeof(int)}; // stride for objects - - // Create numpy array from data - py::array_t parts(shape, strides, data.data()); - - // Call the ari function - std::vector result = ari(parts, 2, 1, 4); - - // Check result - ASSERT_EQ(result.size(), 1); // Should only have one ARI value - EXPECT_NEAR(result[0], 0.57f, 1e-2); // Compare with expected value within tolerance -} \ No newline at end of file +// Helper function to generate pairwise combinations (implement this according to your needs) + +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) +{ + std::vector, std::vector>> pairs; + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) + { + for (size_t j = i + 1; j < num_slices; ++j) + { // Only consider pairs in different slices + for (const auto &row_i : arr[i]) + { // Each row in slice i + for (const auto &row_j : arr[j]) + { // Pairs with each row in slice j + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; +} + + +using Mat3 = std::vector>>; +using TestParamType = std::tuple; + +// Define a parameterized test fixture +class CudaAriTest : public ::testing::TestWithParam {}; + +TEST_P(CudaAriTest, CheckSingleResult) +{ + Mat3 parts; + float expected_result; + std::tie(parts, expected_result) = GetParam(); + + // Get dimensions + int n_features = parts.size(); + int n_parts = parts[0].size(); + int n_objs = parts[0][0].size(); + int n_feature_comp = n_features * (n_features - 1) / 2; + int n_aris = n_feature_comp * n_parts * n_parts; + std::cout << "n_features: " << n_features << ", n_parts: " << n_parts << ", n_objs: " << n_objs << std::endl + << "n_feature_comps: " << n_feature_comp << ", n_aris: " << n_aris << std::endl; + + // Allocate host memory for C-style array + int *h_parts = new int[n_features * n_parts * n_objs]; + + // Copy data from vector to C-style array + for (int i = 0; i < n_features; ++i) + { + for (int j = 0; j < n_parts; ++j) + { + for (int k = 0; k < n_objs; ++k) + { + h_parts[i * (n_parts * n_objs) + j * n_objs + k] = parts[i][j][k]; + } + } + } + + auto h_out = ari_core(h_parts, n_features, n_parts, n_objs)[0]; + + // Check if the result are close + EXPECT_NEAR(h_out, expected_result, 1e-2); +} + +// Instantiate the test suite with parameter values +// These tests are taken from sklearn.metrics.adjusted_rand_score: +// https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html +INSTANTIATE_TEST_SUITE_P( + CudaAriTestInstances, + CudaAriTest, + ::testing::Values( + TestParamType( + Mat3{ + {{0, 0, 1, 2}}, + {{0, 0, 1, 1}}, + }, + 0.57f + ), + TestParamType( + Mat3{ + {{0, 0, 1, 1}}, + {{0, 1, 0, 1}}, + }, + -0.5f + ), + TestParamType( + Mat3{ + {{0, 0, 1, 1}}, + {{0, 0, 1, 1}}, + }, + 1.0f + ), + TestParamType( + Mat3{ + {{0, 0, 1, 1}}, + {{1, 1, 0, 0}}, + }, + 1.0f + ), + TestParamType( + Mat3{ + {{0, 0, 0, 0}}, + {{0, 1, 2, 3}}, + }, + 0.0f + ) + ) +); From c021a5ca147b02d9b2f0da5860758fe2d9cca8fd Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 12:54:59 -0700 Subject: [PATCH 104/134] [binding]: Embed python interpreter in c++ tests --- CMakeLists.txt | 1 + .../cuda_ext/{test_kernel.cpp => test_ari.cpp} | 0 tests/cuda_ext/test_ari_random.cpp | 18 ++++++++++++++++++ 3 files changed, 19 insertions(+) rename tests/cuda_ext/{test_kernel.cpp => test_ari.cpp} (100%) create mode 100644 tests/cuda_ext/test_ari_random.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 548eb9c2..a215e3db 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -68,6 +68,7 @@ function(add_tests_from_directory TEST_DIR) GTest::gtest_main GTest::gtest pybind11::headers + pybind11::embed Python::Python # Add your other project libraries here # project_lib diff --git a/tests/cuda_ext/test_kernel.cpp b/tests/cuda_ext/test_ari.cpp similarity index 100% rename from tests/cuda_ext/test_kernel.cpp rename to tests/cuda_ext/test_ari.cpp diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp new file mode 100644 index 00000000..3afa8ae0 --- /dev/null +++ b/tests/cuda_ext/test_ari_random.cpp @@ -0,0 +1,18 @@ +#include // everything needed for embedding +namespace py = pybind11; + +int main() { + py::scoped_interpreter guard{}; // start the interpreter and keep it alive + + // py::module_ coef = py::module_::import("ccc.coef"); + // py::module_ np = py::module_::import("numpy"); + + py::exec(R"( + from ccc.coef import ccc + import numpy as np + part0 = np.array([2, 3, 6, 1, 0, 5, 4, 3, 6, 2]) + part1 = np.array([0, 6, 2, 5, 1, 3, 4, 6, 0, 2]) + c = ccc(part0, part1) + print(c) + )"); +} From c47d129ff2dd7577062289c81281a71267824606 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 15:21:48 -0700 Subject: [PATCH 105/134] [binding]: Call ccc python code in c++ successfully --- tests/cuda_ext/test_ari_py.cpp | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 tests/cuda_ext/test_ari_py.cpp diff --git a/tests/cuda_ext/test_ari_py.cpp b/tests/cuda_ext/test_ari_py.cpp new file mode 100644 index 00000000..faf6cea2 --- /dev/null +++ b/tests/cuda_ext/test_ari_py.cpp @@ -0,0 +1,25 @@ +#include +#include +#include // everything needed for embedding +#include + +namespace py = pybind11; + +int main() { + py::scoped_interpreter guard{}; // start the interpreter and keep it alive + + py::object scope = py::module_::import("__main__").attr("__dict__"); + py::exec(R"( + from ccc.coef import ccc + import numpy as np + data = np.random.rand(5, 100) + c = ccc(data) + )"); + const auto result = py::eval("c", scope).cast>();; + // Print the results + std::cout << "Results: "; + for (const auto& val : result) { + std::cout << val << " "; + } + std::cout << std::endl; +} From 70c4f7d437c47c9e30162911e7b76a5c6bb4ee05 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 16:01:07 -0700 Subject: [PATCH 106/134] [binding]: Replace raw python string evaluration --- tests/cuda_ext/test_ari_py.cpp | 44 +++++++++++++++++++++++----------- 1 file changed, 30 insertions(+), 14 deletions(-) diff --git a/tests/cuda_ext/test_ari_py.cpp b/tests/cuda_ext/test_ari_py.cpp index faf6cea2..8bc7f20a 100644 --- a/tests/cuda_ext/test_ari_py.cpp +++ b/tests/cuda_ext/test_ari_py.cpp @@ -2,24 +2,40 @@ #include #include // everything needed for embedding #include +#include namespace py = pybind11; int main() { py::scoped_interpreter guard{}; // start the interpreter and keep it alive - py::object scope = py::module_::import("__main__").attr("__dict__"); - py::exec(R"( - from ccc.coef import ccc - import numpy as np - data = np.random.rand(5, 100) - c = ccc(data) - )"); - const auto result = py::eval("c", scope).cast>();; - // Print the results - std::cout << "Results: "; - for (const auto& val : result) { - std::cout << val << " "; + try { + // Define vectors in C++ + std::vector part0 = {2, 3, 6, 1, 0, 5, 4, 3, 6, 2}; + std::vector part1 = {0, 6, 2, 5, 1, 3, 4, 6, 0, 2}; + + // Import required Python modules + py::module_ np = py::module_::import("numpy"); + py::module_ ccc_module = py::module_::import("ccc.coef"); + + // Convert C++ vectors to numpy arrays + py::array_t np_part0 = py::cast(part0); + py::array_t np_part1 = py::cast(part1); + + // Call the ccc function + py::object result = ccc_module.attr("ccc")(np_part0, np_part1); + + // Convert result to C++ double + const auto correlation = result.cast(); + + std::cout << "Correlation coefficient: " << correlation << std::endl; } - std::cout << std::endl; -} + catch (const py::error_already_set& e) { + std::cerr << "Python error: " << e.what() << std::endl; + } + catch (const std::exception& e) { + std::cerr << "Error: " << e.what() << std::endl; + } + + return 0; +} \ No newline at end of file From 969a79ba0684af2a8f03ea09b93334723adf658e Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 18:02:43 -0700 Subject: [PATCH 107/134] [test]: Migrate pytest to gtest --- tests/cuda_ext/test_ari_py.cpp | 6 +- tests/cuda_ext/test_ari_random.cpp | 172 +++++++++++++++++++++++++++-- 2 files changed, 163 insertions(+), 15 deletions(-) diff --git a/tests/cuda_ext/test_ari_py.cpp b/tests/cuda_ext/test_ari_py.cpp index 8bc7f20a..346739a1 100644 --- a/tests/cuda_ext/test_ari_py.cpp +++ b/tests/cuda_ext/test_ari_py.cpp @@ -16,19 +16,19 @@ int main() { // Import required Python modules py::module_ np = py::module_::import("numpy"); - py::module_ ccc_module = py::module_::import("ccc.coef"); + py::module_ ccc_module = py::module_::import("ccc.sklearn.metrics"); // Convert C++ vectors to numpy arrays py::array_t np_part0 = py::cast(part0); py::array_t np_part1 = py::cast(part1); // Call the ccc function - py::object result = ccc_module.attr("ccc")(np_part0, np_part1); + py::object result = ccc_module.attr("adjusted_rand_index")(np_part0, np_part1); // Convert result to C++ double const auto correlation = result.cast(); - std::cout << "Correlation coefficient: " << correlation << std::endl; + std::cout << "ARI: " << correlation << std::endl; } catch (const py::error_already_set& e) { std::cerr << "Python error: " << e.what() << std::endl; diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 3afa8ae0..0efa309d 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -1,18 +1,166 @@ +#include +#include +#include +#include #include // everything needed for embedding +#include +#include +#include "../../libs/ccc_cuda_ext/metrics.cuh" + namespace py = pybind11; -int main() { - py::scoped_interpreter guard{}; // start the interpreter and keep it alive +// Helper function to generate pairwise combinations (implement this according to your needs) + +std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) +{ + std::vector, std::vector>> pairs; + size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector + for (size_t i = 0; i < num_slices; ++i) + { + for (size_t j = i + 1; j < num_slices; ++j) + { // Only consider pairs in different slices + for (const auto &row_i : arr[i]) + { // Each row in slice i + for (const auto &row_j : arr[j]) + { // Pairs with each row in slice j + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; +} + + +// Define test parameters structure +struct AriTestParams { + int n_features; + int n_parts; + int n_objs; + int k; + + // Constructor for easier initialization + AriTestParams(int f, int p, int o, int k_val) + : n_features(f), n_parts(p), n_objs(o), k(k_val) {} +}; + +// Test fixture for parameterized test +class PairwiseAriTest : public ::testing::TestWithParam { +protected: + // Static setup that runs once before all tests + static void SetUpTestSuite() { + if (!guard) { // Only initialize if not already done + guard = std::make_unique(); + np = std::make_unique(py::module_::import("numpy")); + ccc_module = std::make_unique(py::module_::import("ccc.sklearn.metrics")); + } + } + + // Static teardown that runs once after all tests + static void TearDownTestSuite() { + ccc_module.reset(); + np.reset(); + guard.reset(); + } - // py::module_ coef = py::module_::import("ccc.coef"); - // py::module_ np = py::module_::import("numpy"); + // Helper method to compute ARI using Python + float compute_ari(const std::vector& labels1, const std::vector& labels2) { + try { + // Convert C++ vectors to numpy arrays + py::array_t np_part0 = py::cast(labels1); + py::array_t np_part1 = py::cast(labels2); - py::exec(R"( - from ccc.coef import ccc - import numpy as np - part0 = np.array([2, 3, 6, 1, 0, 5, 4, 3, 6, 2]) - part1 = np.array([0, 6, 2, 5, 1, 3, 4, 6, 0, 2]) - c = ccc(part0, part1) - print(c) - )"); + // Call the ccc function + py::object result = ccc_module->attr("adjusted_rand_index")(np_part0, np_part1); + return result.cast(); + } + catch (const std::exception& e) { + std::cerr << "Error computing ARI: " << e.what() << std::endl; + return 0.0f; + } + } + +private: + // Static members shared across all test instances + static std::unique_ptr guard; + static std::unique_ptr np; + static std::unique_ptr ccc_module; +}; + +// Define the static members +std::unique_ptr PairwiseAriTest::guard; +std::unique_ptr PairwiseAriTest::np; +std::unique_ptr PairwiseAriTest::ccc_module; + +TEST_P(PairwiseAriTest, RandomPartitions) { + const auto params = GetParam(); + const int n_features = params.n_features; + const int n_parts = params.n_parts; + const int n_objs = params.n_objs; + const int k = params.k; + + // Generate random partitions (similar to numpy.random.randint) + std::vector parts(n_features * n_parts * n_objs); + std::random_device rd; + std::mt19937 gen(rd()); + std::uniform_int_distribution<> dis(0, k - 1); + + for (auto& val : parts) { + val = dis(gen); + } + + // Calculate expected number of ARIs + int n_feature_comp = n_features * (n_features - 1) / 2; + int n_aris = n_feature_comp * n_parts * n_parts; + + // Get results from CUDA implementation + auto res_aris = ari_core(parts.data(), n_features, n_parts, n_objs); + + // Generate reference results + std::vector ref_aris(n_aris); + + // Convert flat array to 3D structure for easier processing + std::vector>> parts_3d(n_features, + std::vector>(n_parts, + std::vector(n_objs))); + + // Fill 3D structure + for (int f = 0; f < n_features; ++f) { + for (int p = 0; p < n_parts; ++p) { + for (int o = 0; o < n_objs; ++o) { + parts_3d[f][p][o] = parts[f * (n_parts * n_objs) + p * n_objs + o]; + } + } + } + + // Generate pairs and compute reference ARIs + auto pairs = generate_pairwise_combinations(parts_3d); + + for (size_t i = 0; i < pairs.size(); ++i) { + const auto& part0 = pairs[i].first; + const auto& part1 = pairs[i].second; + // Compute ARI for this pair + ref_aris[i] = compute_ari(part0, part1); + } + + // Compare results + ASSERT_EQ(res_aris.size(), ref_aris.size()); + for (size_t i = 0; i < res_aris.size(); ++i) { + EXPECT_NEAR(res_aris[i], ref_aris[i], 1e-5); + } } + +// Instantiate the test suite with parameter values +INSTANTIATE_TEST_SUITE_P( + PairwiseAriTestInstances, + PairwiseAriTest, + ::testing::Values( + AriTestParams(2, 2, 100, 10), + AriTestParams(5, 10, 200, 10) + // Commented out cases that caused issues in Python: + // AriTestParams(100, 20, 1000, 10), // wrong results + // AriTestParams(200, 20, 300, 10), // illegal mem access + // AriTestParams(1000, 10, 300, 10) // out of gpu mem + ) +); + From d7233831046a08e0367c70c8b2e7f6286f1d265a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 18:54:08 -0700 Subject: [PATCH 108/134] [test]: Refactor ari tests by separating test code and data generation code --- tests/cuda_ext/test_ari_random.cpp | 244 ++++++++++++++++++----------- 1 file changed, 156 insertions(+), 88 deletions(-) diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 0efa309d..7091c949 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -1,166 +1,234 @@ #include #include #include +#include +#include #include -#include // everything needed for embedding +#include #include #include #include "../../libs/ccc_cuda_ext/metrics.cuh" namespace py = pybind11; -// Helper function to generate pairwise combinations (implement this according to your needs) - -std::vector, std::vector>> generate_pairwise_combinations(const std::vector>> &arr) -{ - std::vector, std::vector>> pairs; - size_t num_slices = arr.size(); // Number of 2D arrays in the 3D vector - for (size_t i = 0; i < num_slices; ++i) - { - for (size_t j = i + 1; j < num_slices; ++j) - { // Only consider pairs in different slices - for (const auto &row_i : arr[i]) - { // Each row in slice i - for (const auto &row_j : arr[j]) - { // Pairs with each row in slice j - pairs.emplace_back(row_i, row_j); +// Helper class for test data generation +class TestDataGenerator { +public: + static std::vector generate_random_partitions(int n_features, int n_parts, + int n_objs, int k, unsigned seed = 42) { + std::vector parts(n_features * n_parts * n_objs); + std::mt19937 gen(seed); + std::uniform_int_distribution<> dis(0, k - 1); + + for (auto& val : parts) { + val = dis(gen); + } + return parts; + } + + static std::vector>> reshape_to_3d( + const std::vector& flat_array, + int n_features, int n_parts, int n_objs) { + + std::vector>> parts_3d( + n_features, std::vector>( + n_parts, std::vector(n_objs))); + + for (int f = 0; f < n_features; ++f) { + for (int p = 0; p < n_parts; ++p) { + for (int o = 0; o < n_objs; ++o) { + parts_3d[f][p][o] = flat_array[f * (n_parts * n_objs) + p * n_objs + o]; } } } + return parts_3d; } - return pairs; -} + /** + * @brief Generates all pairwise combinations of partitions from different features + * + * Given a 3D array of shape [n_features, n_parts, n_objs], this function generates + * all possible pairs of partitions between different features. For example, if we have + * features f0, f1, f2, it will generate pairs between: + * - f0 and f1 partitions + * - f0 and f2 partitions + * - f1 and f2 partitions + * + * @param arr A 3D vector where: + * - First dimension (arr.size()) represents different features + * - Second dimension (arr[i].size()) represents different partitions for each feature + * - Third dimension (arr[i][j].size()) represents objects in each partition + * + * @return std::vector, std::vector>> + * A vector of partition pairs where each pair contains: + * - first: vector of partition labels from one feature + * - second: vector of partition labels from another feature + * + * @example + * // For a 3D array with shape [2, 2, 4]: + * arr = { + * {{0,1,2,3}, {4,5,6,7}}, // feature 0's partitions + * {{8,9,10,11}, {12,13,14,15}} // feature 1's partitions + * } + * // Will generate pairs: + * // ({0,1,2,3}, {8,9,10,11}) + * // ({0,1,2,3}, {12,13,14,15}) + * // ({4,5,6,7}, {8,9,10,11}) + * // ({4,5,6,7}, {12,13,14,15}) + */ + static std::vector, std::vector>> + generate_pairwise_combinations(const std::vector>>& arr) { + std::vector, std::vector>> pairs; + + // Generate indices for features + auto indices = std::views::iota(0u, arr.size()); + + // For each feature index + for (auto i : indices) { + // For each subsequent feature index (avoiding duplicate pairs) + for (auto j : std::views::iota(i + 1u, arr.size())) { + // For each partition in feature i + for (const auto& row_i : arr[i]) { + // For each partition in feature j + for (const auto& row_j : arr[j]) { + // Add the pair of partitions to our result + pairs.emplace_back(row_i, row_j); + } + } + } + } + return pairs; + } +}; -// Define test parameters structure +// Test parameters with better naming and documentation struct AriTestParams { int n_features; int n_parts; int n_objs; int k; + float tolerance; // Added tolerance as a parameter - // Constructor for easier initialization - AriTestParams(int f, int p, int o, int k_val) - : n_features(f), n_parts(p), n_objs(o), k(k_val) {} + AriTestParams(int features, int parts, int objects, int clusters, float tol = 1e-5) + : n_features(features) + , n_parts(parts) + , n_objs(objects) + , k(clusters) + , tolerance(tol) {} + + // Add string representation for better test output + friend std::ostream& operator<<(std::ostream& os, const AriTestParams& params) { + return os << "Features=" << params.n_features + << ", Parts=" << params.n_parts + << ", Objects=" << params.n_objs + << ", Clusters=" << params.k; + } }; -// Test fixture for parameterized test class PairwiseAriTest : public ::testing::TestWithParam { protected: - // Static setup that runs once before all tests static void SetUpTestSuite() { - if (!guard) { // Only initialize if not already done + if (!guard) { guard = std::make_unique(); - np = std::make_unique(py::module_::import("numpy")); - ccc_module = std::make_unique(py::module_::import("ccc.sklearn.metrics")); + try { + np = std::make_unique(py::module_::import("numpy")); + ccc_module = std::make_unique(py::module_::import("ccc.sklearn.metrics")); + } catch (const std::exception& e) { + FAIL() << "Failed to initialize Python modules: " << e.what(); + } } } - // Static teardown that runs once after all tests static void TearDownTestSuite() { ccc_module.reset(); np.reset(); guard.reset(); } - // Helper method to compute ARI using Python + // Helper method with better error handling float compute_ari(const std::vector& labels1, const std::vector& labels2) { try { - // Convert C++ vectors to numpy arrays py::array_t np_part0 = py::cast(labels1); py::array_t np_part1 = py::cast(labels2); - // Call the ccc function py::object result = ccc_module->attr("adjusted_rand_index")(np_part0, np_part1); return result.cast(); - } - catch (const std::exception& e) { - std::cerr << "Error computing ARI: " << e.what() << std::endl; + } catch (const py::error_already_set& e) { + ADD_FAILURE() << "Python error: " << e.what(); return 0.0f; + } catch (const std::exception& e) { + ADD_FAILURE() << "C++ error: " << e.what(); + return 0.0f; + } + } + + // Add helper methods for validation + void validate_results(const std::vector& actual, + const std::vector& expected, + float tolerance) { + ASSERT_EQ(actual.size(), expected.size()) + << "Mismatch in result sizes"; + + for (size_t i = 0; i < actual.size(); ++i) { + EXPECT_NEAR(actual[i], expected[i], tolerance) + << "Mismatch at index " << i; } } private: - // Static members shared across all test instances static std::unique_ptr guard; static std::unique_ptr np; static std::unique_ptr ccc_module; }; -// Define the static members +// Static member definitions std::unique_ptr PairwiseAriTest::guard; std::unique_ptr PairwiseAriTest::np; std::unique_ptr PairwiseAriTest::ccc_module; TEST_P(PairwiseAriTest, RandomPartitions) { const auto params = GetParam(); - const int n_features = params.n_features; - const int n_parts = params.n_parts; - const int n_objs = params.n_objs; - const int k = params.k; - - // Generate random partitions (similar to numpy.random.randint) - std::vector parts(n_features * n_parts * n_objs); - std::random_device rd; - std::mt19937 gen(rd()); - std::uniform_int_distribution<> dis(0, k - 1); - - for (auto& val : parts) { - val = dis(gen); - } - // Calculate expected number of ARIs - int n_feature_comp = n_features * (n_features - 1) / 2; - int n_aris = n_feature_comp * n_parts * n_parts; + // Generate test data + auto parts = TestDataGenerator::generate_random_partitions( + params.n_features, params.n_parts, params.n_objs, params.k); - // Get results from CUDA implementation - auto res_aris = ari_core(parts.data(), n_features, n_parts, n_objs); + // Get CUDA results + auto res_aris = ari_core(parts.data(), + params.n_features, params.n_parts, params.n_objs); // Generate reference results - std::vector ref_aris(n_aris); + auto parts_3d = TestDataGenerator::reshape_to_3d( + parts, params.n_features, params.n_parts, params.n_objs); + auto pairs = TestDataGenerator::generate_pairwise_combinations(parts_3d); - // Convert flat array to 3D structure for easier processing - std::vector>> parts_3d(n_features, - std::vector>(n_parts, - std::vector(n_objs))); - - // Fill 3D structure - for (int f = 0; f < n_features; ++f) { - for (int p = 0; p < n_parts; ++p) { - for (int o = 0; o < n_objs; ++o) { - parts_3d[f][p][o] = parts[f * (n_parts * n_objs) + p * n_objs + o]; - } - } - } + std::vector ref_aris; + ref_aris.reserve(pairs.size()); - // Generate pairs and compute reference ARIs - auto pairs = generate_pairwise_combinations(parts_3d); - - for (size_t i = 0; i < pairs.size(); ++i) { - const auto& part0 = pairs[i].first; - const auto& part1 = pairs[i].second; - // Compute ARI for this pair - ref_aris[i] = compute_ari(part0, part1); + for (const auto& [part0, part1] : pairs) { + ref_aris.push_back(compute_ari(part0, part1)); } - // Compare results - ASSERT_EQ(res_aris.size(), ref_aris.size()); - for (size_t i = 0; i < res_aris.size(); ++i) { - EXPECT_NEAR(res_aris[i], ref_aris[i], 1e-5); - } + // Validate results + validate_results(res_aris, ref_aris, params.tolerance); } -// Instantiate the test suite with parameter values INSTANTIATE_TEST_SUITE_P( PairwiseAriTestInstances, PairwiseAriTest, ::testing::Values( AriTestParams(2, 2, 100, 10), AriTestParams(5, 10, 200, 10) - // Commented out cases that caused issues in Python: - // AriTestParams(100, 20, 1000, 10), // wrong results - // AriTestParams(200, 20, 300, 10), // illegal mem access - // AriTestParams(1000, 10, 300, 10) // out of gpu mem - ) + // Document known issues + // AriTestParams(100, 20, 1000, 10) // FIXME: wrong results + // AriTestParams(200, 20, 300, 10) // TODO: fix illegal mem access + // AriTestParams(1000, 10, 300, 10) // TODO: optimize memory usage + ), + // Add test name generator for better output + [](const testing::TestParamInfo& info) { + return std::string("Features") + std::to_string(info.param.n_features) + + "_Parts" + std::to_string(info.param.n_parts) + + "_Objects" + std::to_string(info.param.n_objs); + } ); From 03a6aca8bbc078be13cda2b0f6ce2aa0743e5c1c Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 20:10:17 -0700 Subject: [PATCH 109/134] [doc]: Add test documentation --- tests/cuda_ext/test_ari_random.cpp | 116 ++++++++++++++++++++++++++++- 1 file changed, 112 insertions(+), 4 deletions(-) diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 7091c949..0ba54a79 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -1,3 +1,16 @@ +/** + * @file test_ari_random.cpp + * @brief Test suite for Adjusted Rand Index (ARI) computation using CUDA + * + * This test suite validates the CUDA implementation of ARI computation against + * a reference Python implementation. It tests various input sizes and configurations + * using parameterized tests. + * + * The test compares results from: + * 1. CUDA implementation (ari_core) + * 2. Python reference implementation (ccc.sklearn.metrics.adjusted_rand_index) + */ + #include #include #include @@ -11,9 +24,26 @@ namespace py = pybind11; -// Helper class for test data generation +/** + * @brief Helper class for generating and manipulating test data + * + * This class provides static utility functions for: + * - Generating random partition data + * - Reshaping arrays between different dimensions + * - Generating pairwise combinations of partitions + */ class TestDataGenerator { public: + /** + * @brief Generates random partition assignments + * + * @param n_features Number of features + * @param n_parts Number of partitions per feature + * @param n_objs Number of objects + * @param k Number of possible cluster assignments + * @param seed Random seed for reproducibility + * @return std::vector Flattened array of random partition assignments + */ static std::vector generate_random_partitions(int n_features, int n_parts, int n_objs, int k, unsigned seed = 42) { std::vector parts(n_features * n_parts * n_objs); @@ -26,6 +56,15 @@ class TestDataGenerator { return parts; } + /** + * @brief Reshapes a flat array into a 3D structure + * + * @param flat_array Input array + * @param n_features Number of features + * @param n_parts Number of partitions per feature + * @param n_objs Number of objects + * @return 3D vector representing [features][parts][objects] + */ static std::vector>> reshape_to_3d( const std::vector& flat_array, int n_features, int n_parts, int n_objs) { @@ -101,7 +140,16 @@ class TestDataGenerator { } }; -// Test parameters with better naming and documentation +/** + * @brief Parameters for ARI test cases + * + * Encapsulates the parameters that define a test case for ARI computation: + * - Number of features to compare + * - Number of partitions per feature + * - Number of objects in each partition + * - Number of possible cluster assignments + * - Tolerance for floating-point comparisons + */ struct AriTestParams { int n_features; int n_parts; @@ -125,8 +173,29 @@ struct AriTestParams { } }; +/** + * @brief Test fixture for parameterized ARI tests + * + * This fixture provides: + * 1. Python environment setup and teardown + * 2. Reference implementation through Python + * 3. Result validation utilities + * + * The fixture ensures that: + * - Python interpreter is initialized once for all tests + * - Required Python modules are imported + * - Resources are properly cleaned up + */ class PairwiseAriTest : public ::testing::TestWithParam { protected: + /** + * @brief Set up Python environment before any tests run + * + * Initializes: + * - Python interpreter + * - NumPy module + * - CCC metrics module + */ static void SetUpTestSuite() { if (!guard) { guard = std::make_unique(); @@ -139,13 +208,23 @@ class PairwiseAriTest : public ::testing::TestWithParam { } } + /** + * @brief Clean up Python environment after all tests complete + */ static void TearDownTestSuite() { ccc_module.reset(); np.reset(); guard.reset(); } - // Helper method with better error handling + /** + * @brief Compute ARI using Python reference implementation + * + * @param labels1 First partition + * @param labels2 Second partition + * @return float ARI score + * @throws Logs failure if Python computation fails + */ float compute_ari(const std::vector& labels1, const std::vector& labels2) { try { py::array_t np_part0 = py::cast(labels1); @@ -162,7 +241,13 @@ class PairwiseAriTest : public ::testing::TestWithParam { } } - // Add helper methods for validation + /** + * @brief Validate CUDA results against reference implementation + * + * @param actual Results from CUDA implementation + * @param expected Results from reference implementation + * @param tolerance Maximum allowed difference + */ void validate_results(const std::vector& actual, const std::vector& expected, float tolerance) { @@ -186,6 +271,17 @@ std::unique_ptr PairwiseAriTest::guard; std::unique_ptr PairwiseAriTest::np; std::unique_ptr PairwiseAriTest::ccc_module; +/** + * @brief Test case for random partition ARI computation + * + * This test: + * 1. Generates random partition data + * 2. Computes ARI using CUDA implementation + * 3. Computes reference results using Python + * 4. Validates CUDA results against reference + * + * @param GetParam() Test parameters defining input size and configuration + */ TEST_P(PairwiseAriTest, RandomPartitions) { const auto params = GetParam(); @@ -213,6 +309,18 @@ TEST_P(PairwiseAriTest, RandomPartitions) { validate_results(res_aris, ref_aris, params.tolerance); } +/** + * @brief Test suite instantiation with various parameter sets + * + * Current test cases: + * - Small input (2 features, 2 parts, 100 objects) + * - Medium input (5 features, 10 parts, 200 objects) + * + * Known issues: + * - Wrong results with large inputs (100 features) + * - Memory access issues with very large inputs + * - GPU memory limitations with extreme inputs + */ INSTANTIATE_TEST_SUITE_P( PairwiseAriTestInstances, PairwiseAriTest, From 452a07406c8b0d29df09e10e60fb6bf9ffdf3c06 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 7 Nov 2024 23:37:36 -0700 Subject: [PATCH 110/134] [build]: Use scikit-build to repace setuptools --- pyproject.toml | 56 +++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 51 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 367101da..04b75322 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,56 @@ [build-system] -requires = ["scikit-build-core", "pybind11"] +requires = [ + "scikit-build-core>=0.7.0", + "pybind11>=2.11.0", + "cmake>=3.15", + "ninja", + "setuptools>=42", + "wheel" +] build-backend = "scikit_build_core.build" [project] -name = "ccc_cuda_ext" -version = "0.0.1" +name = "cccgpu" +version = "0.2.0" +description = "The Clustermatch Correlation Coefficient (CCC) with GPU acceleration" +readme = "README.md" +requires-python = ">=3.9" +license = {text = "BSD-2-Clause Plus Patent"} +authors = [ + {name = "Milton Pividori", email = "miltondp@gmail.com"}, +] +dependencies = [ + "numpy>=1.21.0", + "scipy", + "numba", +] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: BSD License", + "Operating System :: OS Independent", + "Development Status :: 5 - Production/Stable", + "Environment :: Console", +] -[tool.setuptools.packages.find] -where = ["libs"] +[project.optional-dependencies] +test = [ + "pytest", + "pytest-cov", +] + +[tool.scikit-build] +# Configure scikit-build-core +cmake.minimum-version = "3.15" +cmake.args = [ + "-DCMAKE_CUDA_ARCHITECTURES=75", # Adjust for your target CUDA architecture +] +cmake.verbose = true +wheel.packages = ["libs/ccc"] # Directory containing your Python packages +wheel.exclude = ["*.cpp", "*.h"] # Exclude C++ headers from wheel + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "-ra -q" +testpaths = [ + "tests", +] From a68efbc1fb36327867776fae7a1d5e6ddec9dcff Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 8 Nov 2024 10:52:00 -0700 Subject: [PATCH 111/134] Clean up code and add docs --- libs/ccc_cuda_ext/metrics.cu | 65 +++++++++++++----------------- tests/cuda_ext/test_ari_random.cpp | 4 +- 2 files changed, 31 insertions(+), 38 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index bff97263..92a91fcb 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -307,8 +307,8 @@ __global__ void ari(int *parts, } /** - * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions - * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @brief Internal lower-level ARI computation + * @param parts pointer to the 3D Array of partitions with shape of (n_features, n_parts, n_objs) * @throws std::invalid_argument if "parts" is invalid * @return std::vector ARI values for each pair of partitions */ @@ -317,39 +317,48 @@ auto ari_core(const T* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector { - // Edge cases: + /* + * Notes for future bug fixing and optimization + */ // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing - // Compute internal variables + /* + * Pre-computation + */ // Todo: dynamically query types using parts_dtype = T; using out_dtype = float; - + // Compute internal variables const auto n_feature_comp = n_features * (n_features - 1) / 2; const auto n_aris = n_feature_comp * n_parts * n_parts; + + /* + * Memory Allocation + */ // Allocate host memory thrust::host_vector h_out(n_aris); thrust::host_vector h_parts_pairs(n_aris * 2 * n_objs); - - // Set up CUDA kernel configuration - const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block - // Each block is responsible for one ARI computation - const auto grid_size = n_aris; - // Define shared memory size for each block - const auto parts_dtype_size = sizeof(parts_dtype); - auto s_mem_size = n_objs * 2 * parts_dtype_size; // For the partition pair to be compared - s_mem_size += 2 * n_parts * parts_dtype_size; // For the internal sum arrays - s_mem_size += 4 * parts_dtype_size; // For the 2 x 2 confusion matrix - // Allocate device memory with thrust // const int* parts_raw = parts[0][0].data(); thrust::device_vector d_parts(parts, parts + n_features * n_parts * n_objs); // data is copied to device thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); thrust::device_vector d_out(n_aris); + // Set up CUDA kernel configuration + const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block + // Each block is responsible for one ARI computation + const auto grid_size = n_aris; + + // Define shared memory size for each block // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later - auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); + const auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); const auto k = *max_iter + 1; + const auto sz_parts_dtype = sizeof(parts_dtype); + // FIXME: Compute shared memory size + auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared + s_mem_size += 2 * n_parts * sz_parts_dtype; // For the internal sum arrays + s_mem_size += 4 * sz_parts_dtype; // For the 2 x 2 confusion matrix + // Launch the kernel ari<<>>( @@ -378,8 +387,8 @@ auto ari_core(const T* parts, } /** - * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions - * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) + * @brief API exposed to Python for computing ARI using CUDA upon a 3D Numpy NDArray of partitions + * @param parts 3D Numpy.NDArray of partitions with shape of (n_features, n_parts, n_objs) * @throws std::invalid_argument if "parts" is invalid * @return std::vector ARI values for each pair of partitions */ @@ -412,22 +421,6 @@ auto ari(const py::array_t& parts, } -// /** -// * @brief API exposed for computing ARI using CUDA upon a 3D array of partitions -// * @param parts 3D Array of partitions with shape of (n_features, n_parts, n_objs) -// * @throws std::invalid_argument if "parts" is invalid -// * @return std::vector ARI values for each pair of partitions -// */ -// template -// auto ari_vector(const Mat3& parts, -// const size_t n_features, -// const size_t n_parts, -// const size_t n_objs) -> std::vector { -// // Obtain array data pointer -// const auto parts_ptr = parts.data(); -// return ari_core(parts_ptr, n_features, n_parts, n_objs); -// } - // Below is the explicit instantiation of the ari template function. // // Generally people would write the implementation of template classes and functions in the header file. However, we @@ -436,4 +429,4 @@ auto ari(const py::array_t& parts, // by the linker. template auto ari(const py::array_t& parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; -template auto ari_core(const int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; \ No newline at end of file +template auto ari_core(const int* parts, const size_t n_features, const size_t n_parts, const size_t n_objs) -> std::vector; diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 0ba54a79..49e83656 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -326,9 +326,9 @@ INSTANTIATE_TEST_SUITE_P( PairwiseAriTest, ::testing::Values( AriTestParams(2, 2, 100, 10), - AriTestParams(5, 10, 200, 10) + AriTestParams(5, 10, 200, 10), // Document known issues - // AriTestParams(100, 20, 1000, 10) // FIXME: wrong results + AriTestParams(100, 20, 1000, 10) // FIXME: wrong results // AriTestParams(200, 20, 300, 10) // TODO: fix illegal mem access // AriTestParams(1000, 10, 300, 10) // TODO: optimize memory usage ), From 9c717a9061eee9362a0ce7e3a63f01392af7a7a0 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 8 Nov 2024 12:16:32 -0700 Subject: [PATCH 112/134] [bug]: Try to fix illegal mem access --- libs/ccc_cuda_ext/metrics.cu | 46 ++++++++++++++++++------------ tests/cuda_ext/test_ari_random.cpp | 4 +-- 2 files changed, 30 insertions(+), 20 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 92a91fcb..140d33e1 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -101,13 +101,14 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share } __syncthreads(); - // Process elements + // Process elements with bounds checking for (int i = tid; i < n; i += num_threads) { int row = part0[i]; int col = part1[i]; - if (row < k && col < k) + // Add bounds checking + if (row >= 0 && row < k && col >= 0 && col < k) { atomicAdd(&shared_cont_mat[row * k + col], 1); } @@ -217,35 +218,42 @@ __global__ void ari(int *parts, float *out, int *part_pairs = nullptr) { + /* + * Step 0: Compute shared memory addresses + */ + extern __shared__ int shared_mem[]; + int *s_part0 = shared_mem; // n_objs elements + int *s_part1 = s_part0 + n_objs; // n_objs elements + int *s_contingency = s_part1 + n_objs; // k * k elements + int *s_sum_rows = s_contingency + (k * k); // k elements + int *s_sum_cols = s_sum_rows + k; // k elements + int *s_pair_confusion_matrix = s_sum_cols + k; // 4 elements + /* * Step 1: Each thead, unravel flat indices and load the corresponding data into shared memory */ // each block is responsible for one ARI computation int ari_block_idx = blockIdx.x; - // obtain the corresponding parts and unique counts int feature_comp_flat_idx = ari_block_idx / n_part_mat_elems; // flat comparison pair index for two features int part_pair_flat_idx = ari_block_idx % n_part_mat_elems; // flat comparison pair index for two partitions of one feature pair int i, j; - // unravel the feature indices get_coords_from_index(n_features, feature_comp_flat_idx, &i, &j); assert(i < n_features && j < n_features); assert(i >= 0 && j >= 0); - // unravel the partition indices int m, n; unravel_index(part_pair_flat_idx, n_parts, &m, &n); - // Make pointers to select the parts and unique counts for the feature pair // Todo: Use int4*? int *t_data_part0 = parts + i * n_elems_per_feat + m * n_objs; // t_ for thread int *t_data_part1 = parts + j * n_elems_per_feat + n * n_objs; // Load gmem data into smem by using different threads - extern __shared__ int shared_mem[]; - int *s_part0 = shared_mem; - int *s_part1 = shared_mem + n_objs; + // extern __shared__ int shared_mem[]; + // int *s_part0 = shared_mem; + // int *s_part1 = shared_mem + n_objs; // Loop over the data using the block-stride pattern for (int i = threadIdx.x; i < n_objs; i += blockDim.x) @@ -272,16 +280,16 @@ __global__ void ari(int *parts, * Step 2: Compute contingency matrix within the block */ // shared mem address for the contingency matrix - int *s_contingency = shared_mem + 2 * n_objs; + // int *s_contingency = shared_mem + 2 * n_objs; get_contingency_matrix(t_data_part0, t_data_part1, n_objs, s_contingency, k); /* * Step 3: Construct pair confusion matrix */ // shared mem address for the pair confusion matrix - int *s_sum_rows = s_contingency + k * k; - int *s_sum_cols = s_sum_rows + k; - int *s_pair_confusion_matrix = s_sum_cols + k; + // int *s_sum_rows = s_contingency + k * k; + // int *s_sum_cols = s_sum_rows + k; + // int *s_pair_confusion_matrix = s_sum_cols + k; get_pair_confusion_matrix(s_contingency, s_sum_rows, s_sum_cols, n_objs, k, s_pair_confusion_matrix); /* * Step 4: Compute ARI and write to global memory @@ -355,12 +363,14 @@ auto ari_core(const T* parts, const auto k = *max_iter + 1; const auto sz_parts_dtype = sizeof(parts_dtype); // FIXME: Compute shared memory size - auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared - s_mem_size += 2 * n_parts * sz_parts_dtype; // For the internal sum arrays - s_mem_size += 4 * sz_parts_dtype; // For the 2 x 2 confusion matrix + auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared + s_mem_size += k * k * sz_parts_dtype; // For contingency matrix + s_mem_size += 2 * n_parts * sz_parts_dtype; // For the internal sum arrays + s_mem_size += 4 * sz_parts_dtype; // For the 2 x 2 confusion matrix - - // Launch the kernel + /* + * Launch the kernel + */ ari<<>>( thrust::raw_pointer_cast(d_parts.data()), n_aris, diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 49e83656..f4e0d5d1 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -329,8 +329,8 @@ INSTANTIATE_TEST_SUITE_P( AriTestParams(5, 10, 200, 10), // Document known issues AriTestParams(100, 20, 1000, 10) // FIXME: wrong results - // AriTestParams(200, 20, 300, 10) // TODO: fix illegal mem access - // AriTestParams(1000, 10, 300, 10) // TODO: optimize memory usage + // AriTestParams(200, 20, 300, 10) // FIXME: fix illegal mem access + // AriTestParams(1000, 10, 300, 10) // FIXME: out of memory ), // Add test name generator for better output [](const testing::TestParamInfo& info) { From 8855916305f8832c051e06dffa0762428f9fc5cd Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 8 Nov 2024 12:41:07 -0700 Subject: [PATCH 113/134] [cuda]: Add input and parameter validation --- libs/ccc_cuda_ext/metrics.cu | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 140d33e1..363ed8f8 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -38,6 +38,16 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = // gpuErrorCheck(cudaMalloc(...)); // if fails, print message and continue // gpuErrorCheck(cudaMalloc(...), true); // if fails, print message and abort + +bool check_shared_memory_size(const size_t s_mem_size) +{ + cudaDeviceProp prop; + cudaGetDeviceProperties(&prop, 0); + const auto max_shared_mem = prop.sharedMemPerBlock; + return s_mem_size <= max_shared_mem; +} + + /** * @brief Unravel a flat index to the corresponding 2D indicis * @param[in] flat_idx The flat index to unravel @@ -330,6 +340,11 @@ auto ari_core(const T* parts, */ // 1. GPU memory is not enough to store the partitions -> split the partitions into smaller chunks and do stream processing + // Input validation + if (!parts || n_features == 0 || n_parts == 0 || n_objs == 0) { + throw std::invalid_argument("Invalid input parameters"); + } + /* * Pre-computation */ @@ -368,6 +383,11 @@ auto ari_core(const T* parts, s_mem_size += 2 * n_parts * sz_parts_dtype; // For the internal sum arrays s_mem_size += 4 * sz_parts_dtype; // For the 2 x 2 confusion matrix + // Check if shared memory size exceeds device limits + if (!check_shared_memory_size(s_mem_size)) { + throw std::runtime_error("Required shared memory exceeds device limits"); + } + /* * Launch the kernel */ From 1a0280996adaaa294a9875de23b4c8583eccfe40 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 8 Nov 2024 14:49:07 -0700 Subject: [PATCH 114/134] [bug]: Test fail on large number of objects --- benchmark/bench_ari.py | 2 +- libs/ccc_cuda_ext/metrics.cu | 12 +++++++----- tests/cuda_ext/test_ari_random.cpp | 3 ++- 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/benchmark/bench_ari.py b/benchmark/bench_ari.py index fa9db2f6..d75070a4 100644 --- a/benchmark/bench_ari.py +++ b/benchmark/bench_ari.py @@ -31,7 +31,7 @@ def generate_pairwise_combinations(arr): @pytest.mark.parametrize("n_features, n_parts, n_objs, k", [ (100, 10, 300, 10), (100, 20, 300, 10), - # (100, 20, 1000, 10), # wrong results + (100, 20, 100, 10), # wrong results # (200, 20, 300, 10), # illegal mem access # (1000, 10, 300, 10), # out of gpu mem ]) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 363ed8f8..6fcdf07d 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -2,6 +2,8 @@ #include #include #include +#include +#include #include #include @@ -374,8 +376,7 @@ auto ari_core(const T* parts, // Define shared memory size for each block // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later - const auto max_iter = thrust::max_element(d_parts.begin(), d_parts.end()); - const auto k = *max_iter + 1; + const auto k = thrust::reduce(d_parts.begin(), d_parts.end(), -1, thrust::maximum()) + 1; const auto sz_parts_dtype = sizeof(parts_dtype); // FIXME: Compute shared memory size auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared @@ -407,10 +408,11 @@ auto ari_core(const T* parts, thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); - // Free device memory + // Copy data to std::vector + std::vector res; + thrust::copy(h_out.begin(), h_out.end(), std::back_inserter(res)); - // Convert thrust vectors to std::vector - std::vector res(h_out.begin(), h_out.end()); + // Free device memory // Return the ARI values return res; diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index f4e0d5d1..53aa501a 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -327,8 +327,9 @@ INSTANTIATE_TEST_SUITE_P( ::testing::Values( AriTestParams(2, 2, 100, 10), AriTestParams(5, 10, 200, 10), + AriTestParams(2, 1, 1000, 10) // Document known issues - AriTestParams(100, 20, 1000, 10) // FIXME: wrong results + // AriTestParams(100, 20, 1000, 10) // FIXME: wrong results // AriTestParams(200, 20, 300, 10) // FIXME: fix illegal mem access // AriTestParams(1000, 10, 300, 10) // FIXME: out of memory ), From 9f52c55bdc08eaec0e2d67532fd12feb38685a05 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 9 Nov 2024 19:54:35 -0700 Subject: [PATCH 115/134] [cuda]: Remove debugging function argument --- libs/ccc_cuda_ext/metrics.cu | 23 +++-------------------- tests/cuda_ext/test_ari_random.cpp | 3 ++- 2 files changed, 5 insertions(+), 21 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 6fcdf07d..6166980e 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -216,7 +216,6 @@ __device__ void get_pair_confusion_matrix( * @param n_aris Number of ARIs to compute * @param k The max value of cluster number + 1 * @param out Output array of ARIs - * @param part_pairs Output array of part pairs to be compared by ARI */ extern "C" __global__ void ari(int *parts, @@ -227,8 +226,8 @@ __global__ void ari(int *parts, const int n_elems_per_feat, const int n_part_mat_elems, const int k, - float *out, - int *part_pairs = nullptr) + float *out + ) { /* * Step 0: Compute shared memory addresses @@ -275,19 +274,6 @@ __global__ void ari(int *parts, } __syncthreads(); - // Copy data to global memory if part_pairs is specified - if (part_pairs != nullptr) - { - int *out_part0 = part_pairs + ari_block_idx * (2 * n_objs); - int *out_part1 = out_part0 + n_objs; - - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) - { - out_part0[i] = s_part0[i]; - out_part1[i] = s_part1[i]; - } - } - /* * Step 2: Compute contingency matrix within the block */ @@ -366,7 +352,6 @@ auto ari_core(const T* parts, // Allocate device memory with thrust // const int* parts_raw = parts[0][0].data(); thrust::device_vector d_parts(parts, parts + n_features * n_parts * n_objs); // data is copied to device - thrust::device_vector d_parts_pairs(n_aris * 2 * n_objs); thrust::device_vector d_out(n_aris); // Set up CUDA kernel configuration @@ -401,12 +386,10 @@ auto ari_core(const T* parts, n_parts * n_objs, n_parts * n_parts, k, - thrust::raw_pointer_cast(d_out.data()), - thrust::raw_pointer_cast(d_parts_pairs.data())); + thrust::raw_pointer_cast(d_out.data())); // Copy data back to host thrust::copy(d_out.begin(), d_out.end(), h_out.begin()); - thrust::copy(d_parts_pairs.begin(), d_parts_pairs.end(), h_parts_pairs.begin()); // Copy data to std::vector std::vector res; diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 53aa501a..5c0bc3fc 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -327,7 +327,8 @@ INSTANTIATE_TEST_SUITE_P( ::testing::Values( AriTestParams(2, 2, 100, 10), AriTestParams(5, 10, 200, 10), - AriTestParams(2, 1, 1000, 10) + // AriTestParams(2, 1, 1000, 10) + AriTestParams(100, 20, 100, 10) // Document known issues // AriTestParams(100, 20, 1000, 10) // FIXME: wrong results // AriTestParams(200, 20, 300, 10) // FIXME: fix illegal mem access From 23340cedb17f3414e00e16d3cac17469ead14cbb Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sun, 10 Nov 2024 21:54:28 -0700 Subject: [PATCH 116/134] [cuda]: Introduce CUB for block-wise data loading --- libs/ccc_cuda_ext/metrics.cu | 122 ++++++++++++++++++++++++++++++++++- 1 file changed, 121 insertions(+), 1 deletion(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 6166980e..1909622b 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -1,10 +1,13 @@ #include +#include + #include #include #include #include #include + #include #include #include @@ -128,6 +131,123 @@ __device__ void get_contingency_matrix(int *part0, int *part1, int n, int *share __syncthreads(); } + +/** + * @brief Compute the contingency matrix for two partitions using shared memory, by loading global memory data in batch + * to process large input, i.e., when the input size is larger than the shared memory size + * @param[in] part0 Pointer to the first partition array int the global memory + * @param[in] part1 Pointer to the second partition array in the global memory + * @param[in] nSamples Number of elements in each partition array + * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) + * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix + */ +// Todo: Add template for kernel configuration +template +__device__ void get_contingency_matrix_batch(const T* part0, const T* part1, const int n_objs, const int k, T* shared_cont_mat) +{ + // Define block and chunk sizes + const int BLOCK_SIZE = 256; + const int ITEMS_PER_THREAD = 4; + // Size of the shared memory buffer (chunk size) + const int SHARED_MEMORY_SIZE = 2 * BLOCK_SIZE * ITEMS_PER_THREAD; + + int tid = threadIdx.x; + int num_threads = blockDim.x; + const auto cont_mat_size = k * k; + + // Shared memory buffer for the current chunk + __shared__ T sharedBuffer[SHARED_MEMORY_SIZE]; + // Thread-local storage for loading elements + T threadData_part0[ITEMS_PER_THREAD]; + T threadData_part1[ITEMS_PER_THREAD]; + + // Calculate number of chunks needed + const int numChunks = (n_objs + SHARED_MEMORY_SIZE - 1) / SHARED_MEMORY_SIZE; + // Temporary storage for CUB operations + // Specialize BlockLoad for a 1D block of 128 threads owning 4 integer items each + using BlockLoad = cub::BlockLoad; + // Allocate shared memory for BlockLoad + __shared__ typename BlockLoad::TempStorage temp_storage_part0; + __shared__ typename BlockLoad::TempStorage temp_storage_part1; + + // Initialize shared memory for the contingency matrix + for (int i = tid; i < cont_mat_size; i += num_threads) + { + shared_cont_mat[i] = 0; + } + __syncthreads(); + + // Process data chunk by chunk + for (int chunk = 0; chunk < numChunks; chunk++) { + // Calculate offset and valid items for this chunk + const int chunkOffset = chunk * SHARED_MEMORY_SIZE; + const int validItems = min(SHARED_MEMORY_SIZE, n_objs - chunkOffset); + + // Load chunk from global memory + cub::BlockLoad(temp_storage_part0).Load( + part0 + chunkOffset, + threadData_part0, + validItems, + (T)0 // Default value for out-of-bounds items + ); + + cub::BlockLoad(temp_storage_part1).Load( + part1 + chunkOffset, + threadData_part1, + validItems, + (T)0 // Default value for out-of-bounds items + ); + + // Process thread-local data (example: multiply by 2) + #pragma unroll + for (int i = 0; i < ITEMS_PER_THREAD; i++) { + // threadData[i] *= 2; + const T p0_label = part0[i]; + const T p1_label = part1[i]; + // Add bounds checking + if (p0_label >= 0 && p0_label < k && p1_label >= 0 && p1_label < k) + { + atomicAdd(&shared_cont_mat[p0_label * k + p1_label], 1); + } + } + + // // Store processed data to shared memory + // int threadOffset = threadIdx.x * ITEMS_PER_THREAD; + // #pragma unroll + // for (int i = 0; i < ITEMS_PER_THREAD; i++) { + // if (threadOffset + i < validItems) { + // sharedBuffer[threadOffset + i] = threadData[i]; + // } + // } + + // __syncthreads(); + + // // Additional processing on shared memory data if needed + // // For example, you could do a reduction or other block-wide operations here + + // // Store results back to global memory + // for (int i = threadIdx.x; i < validItems; i += BLOCK_SIZE) { + // output[chunkOffset + i] = sharedBuffer[i]; + // } + + // __syncthreads(); // Ensure all threads are done before loading next chunk + } + + // Process elements with bounds checking + // for (int i = tid; i < n_samples; i += num_threads) + // { + // int row = part0[i]; + // int col = part1[i]; + + // // Add bounds checking + // if (row >= 0 && row < k && col >= 0 && col < k) + // { + // atomicAdd(&shared_cont_mat[row * k + col], 1); + // } + // } + // __syncthreads(); +} + /** * @brief CUDA device function to compute the pair confusion matrix * @param[in] contingency Pointer to the contingency matrix @@ -355,7 +475,7 @@ auto ari_core(const T* parts, thrust::device_vector d_out(n_aris); // Set up CUDA kernel configuration - const auto block_size = 1024; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block + const auto block_size = 256; // Todo: query device for max threads per block, older devices only support 512 threads per 1D block // Each block is responsible for one ARI computation const auto grid_size = n_aris; From 7c076f050fb19b4d48060763059271e8ab832db3 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 6 Jan 2025 14:57:56 -0700 Subject: [PATCH 117/134] [doc]: Add notes for CUDA module compilation and testing --- libs/ccc_cuda_ext/Readme.md | 20 +++++++++++++++++++- libs/ccc_cuda_ext/metrics.cu | 2 +- 2 files changed, 20 insertions(+), 2 deletions(-) diff --git a/libs/ccc_cuda_ext/Readme.md b/libs/ccc_cuda_ext/Readme.md index bb52db9c..776c8d52 100644 --- a/libs/ccc_cuda_ext/Readme.md +++ b/libs/ccc_cuda_ext/Readme.md @@ -1,8 +1,26 @@ ## How to build the CUDA module and its tests ``` -# cd to current directory +# cd to libs/ccc_cuda_ext cmake -S . -B build cmake --build build ctest --test-dir build --output-on-failure ``` + +## How to build and install this CUDA module +``` +conda activate ccc-rapid +pip install . + +# This will build the c++ module and install it in the current environment +``` + +## How to run C++ tests in tests/cuda_ext +The CMakeLists.txt file in the root directory will pick up the tests in tests/cuda_ext and build them. + +``` +for test in build/test_ari{,_py,_random}; do + echo "Running $test..." + ./$test +done +``` \ No newline at end of file diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 1909622b..405c51fb 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -198,7 +198,7 @@ __device__ void get_contingency_matrix_batch(const T* part0, const T* part1, con (T)0 // Default value for out-of-bounds items ); - // Process thread-local data (example: multiply by 2) + // Process thread-local data #pragma unroll for (int i = 0; i < ITEMS_PER_THREAD; i++) { // threadData[i] *= 2; From 360b7bafb797b3e5e762a7f4148ddc71df744180 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 6 Jan 2025 14:58:15 -0700 Subject: [PATCH 118/134] [cuda]: Add example code of CUB --- libs/ccc_cuda_ext/cub.cu | 79 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 libs/ccc_cuda_ext/cub.cu diff --git a/libs/ccc_cuda_ext/cub.cu b/libs/ccc_cuda_ext/cub.cu new file mode 100644 index 00000000..da19f3df --- /dev/null +++ b/libs/ccc_cuda_ext/cub.cu @@ -0,0 +1,79 @@ +#include +#include + +// Define block and chunk sizes +const int BLOCK_SIZE = 256; +const int ITEMS_PER_THREAD = 4; +// Size of the shared memory buffer (chunk size) +const int SHARED_MEMORY_SIZE = BLOCK_SIZE * ITEMS_PER_THREAD; + +template +__global__ void streamProcessingKernel( + const T* input, + T* output, + const int totalElements +) { + // Shared memory buffer for the current chunk + __shared__ T sharedBuffer[SHARED_MEMORY_SIZE]; + + // Thread-local storage for loading elements + T threadData[ITEMS_PER_THREAD]; + + // Only one block should execute this kernel + if (blockIdx.x > 0) return; + + // Calculate number of chunks needed + const int numChunks = (totalElements + SHARED_MEMORY_SIZE - 1) / SHARED_MEMORY_SIZE; + + // Temporary storage for CUB operations + __shared__ typename cub::BlockLoad::TempStorage loadTemp; + + // Process data chunk by chunk + for (int chunk = 0; chunk < numChunks; chunk++) { + // Calculate offset and valid items for this chunk + const int chunkOffset = chunk * SHARED_MEMORY_SIZE; + const int validItems = min(SHARED_MEMORY_SIZE, totalElements - chunkOffset); + + // Load chunk from global memory + cub::BlockLoad(loadTemp).Load( + input + chunkOffset, + threadData, + validItems, + (T)0 // Default value for out-of-bounds items + ); + + // Process thread-local data (example: multiply by 2) + #pragma unroll + for (int i = 0; i < ITEMS_PER_THREAD; i++) { + threadData[i] *= 2; + } + + // Store processed data to shared memory + int threadOffset = threadIdx.x * ITEMS_PER_THREAD; + #pragma unroll + for (int i = 0; i < ITEMS_PER_THREAD; i++) { + if (threadOffset + i < validItems) { + sharedBuffer[threadOffset + i] = threadData[i]; + } + } + + __syncthreads(); + + // Additional processing on shared memory data if needed + // For example, you could do a reduction or other block-wide operations here + + // Store results back to global memory + for (int i = threadIdx.x; i < validItems; i += BLOCK_SIZE) { + output[chunkOffset + i] = sharedBuffer[i]; + } + + __syncthreads(); // Ensure all threads are done before loading next chunk + } +} + +// Host function to launch the kernel +template +void processLargeDataInOneBlock(const T* input, T* output, int totalElements) { + // Launch single block + streamProcessingKernel<<<1, BLOCK_SIZE>>>(input, output, totalElements); +} \ No newline at end of file From 98c389b3d6c84f10fd22b8c234dceef8073a839a Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 8 Jan 2025 21:22:35 -0700 Subject: [PATCH 119/134] [kernel]: Use global memory to load partition pairs for now --- libs/ccc_cuda_ext/metrics.cu | 34 ++++++++++++++++++------------ tests/cuda_ext/test_ari_random.cpp | 18 ++++++++-------- 2 files changed, 30 insertions(+), 22 deletions(-) diff --git a/libs/ccc_cuda_ext/metrics.cu b/libs/ccc_cuda_ext/metrics.cu index 405c51fb..f9ed7c61 100644 --- a/libs/ccc_cuda_ext/metrics.cu +++ b/libs/ccc_cuda_ext/metrics.cu @@ -97,8 +97,8 @@ __device__ __host__ inline void get_coords_from_index(int n_obj, int idx, int *x /** * @brief Compute the contingency matrix for two partitions using shared memory - * @param[in] part0 Pointer to the first partition array - * @param[in] part1 Pointer to the second partition array + * @param[in] part0 Pointer to the first partition array, global memory + * @param[in] part1 Pointer to the second partition array, global memory * @param[in] n Number of elements in each partition array * @param[out] shared_cont_mat Pointer to shared memory for storing the contingency matrix * @param[in] k Maximum number of clusters (size of contingency matrix is k x k) @@ -353,9 +353,12 @@ __global__ void ari(int *parts, * Step 0: Compute shared memory addresses */ extern __shared__ int shared_mem[]; - int *s_part0 = shared_mem; // n_objs elements - int *s_part1 = s_part0 + n_objs; // n_objs elements - int *s_contingency = s_part1 + n_objs; // k * k elements + // NOTE: comment out the following lines for now + // int *s_part0 = shared_mem; // n_objs elements + // int *s_part1 = s_part0 + n_objs; // n_objs elements + // int *s_contingency = s_part1 + n_objs; // k * k elements + // NOTE Ends + int *s_contingency = shared_mem; // k * k elements int *s_sum_rows = s_contingency + (k * k); // k elements int *s_sum_cols = s_sum_rows + k; // k elements int *s_pair_confusion_matrix = s_sum_cols + k; // 4 elements @@ -386,13 +389,15 @@ __global__ void ari(int *parts, // int *s_part0 = shared_mem; // int *s_part1 = shared_mem + n_objs; + // NOTE: comment out the following lines for now // Loop over the data using the block-stride pattern - for (int i = threadIdx.x; i < n_objs; i += blockDim.x) - { - s_part0[i] = t_data_part0[i]; - s_part1[i] = t_data_part1[i]; - } - __syncthreads(); + // for (int i = threadIdx.x; i < n_objs; i += blockDim.x) + // { + // s_part0[i] = t_data_part0[i]; + // s_part1[i] = t_data_part1[i]; + // } + // __syncthreads(); + // NOTE Ends /* * Step 2: Compute contingency matrix within the block @@ -483,8 +488,11 @@ auto ari_core(const T* parts, // Compute k, the maximum value in d_parts + 1, used for shared memory allocation later const auto k = thrust::reduce(d_parts.begin(), d_parts.end(), -1, thrust::maximum()) + 1; const auto sz_parts_dtype = sizeof(parts_dtype); - // FIXME: Compute shared memory size - auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared + // Compute shared memory size + // FIXME: Partition pair size should be fixed. Stream processing should be used for large input + // NOTE: Use global memory to fix the issue for now and then optimize with shared memory + // auto s_mem_size = 2 * n_objs * sz_parts_dtype; // For the partition pair to be compared + auto s_mem_size = 0; s_mem_size += k * k * sz_parts_dtype; // For contingency matrix s_mem_size += 2 * n_parts * sz_parts_dtype; // For the internal sum arrays s_mem_size += 4 * sz_parts_dtype; // For the 2 x 2 confusion matrix diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index 5c0bc3fc..edc8e55e 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -251,12 +251,12 @@ class PairwiseAriTest : public ::testing::TestWithParam { void validate_results(const std::vector& actual, const std::vector& expected, float tolerance) { - ASSERT_EQ(actual.size(), expected.size()) - << "Mismatch in result sizes"; + ASSERT_EQ(actual.size(), expected.size()) ; + // << "Mismatch in result sizes"; for (size_t i = 0; i < actual.size(); ++i) { - EXPECT_NEAR(actual[i], expected[i], tolerance) - << "Mismatch at index " << i; + EXPECT_NEAR(actual[i], expected[i], tolerance); + // << "Mismatch at index " << i; } } @@ -327,12 +327,12 @@ INSTANTIATE_TEST_SUITE_P( ::testing::Values( AriTestParams(2, 2, 100, 10), AriTestParams(5, 10, 200, 10), - // AriTestParams(2, 1, 1000, 10) - AriTestParams(100, 20, 100, 10) + // AriTestParams(2, 1, 1000, 10), // FIXME: wrong results, maybe test is not correct + AriTestParams(100, 20, 100, 10), // Document known issues - // AriTestParams(100, 20, 1000, 10) // FIXME: wrong results - // AriTestParams(200, 20, 300, 10) // FIXME: fix illegal mem access - // AriTestParams(1000, 10, 300, 10) // FIXME: out of memory + // AriTestParams(100, 20, 1000, 10), // FIXME: wrong results, maybe test is not correct + AriTestParams(200, 20, 300, 10), // FIXME: fix illegal mem access + AriTestParams(1000, 10, 300, 10) // FIXME: out of memory ), // Add test name generator for better output [](const testing::TestParamInfo& info) { From 955fee9b0152902b00284e95ee4ef6015d436297 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 8 Jan 2025 23:58:54 -0700 Subject: [PATCH 120/134] [doc]: Update readme --- README.md | 326 +++--------------------------------------------------- 1 file changed, 15 insertions(+), 311 deletions(-) diff --git a/README.md b/README.md index f0685fbf..2d9511df 100644 --- a/README.md +++ b/README.md @@ -1,321 +1,25 @@ -# Clustermatch Correlation Coefficient (CCC) +# Clustermatch Correlation Coefficient GPU (CCC-GPU) -[![Code tests](https://github.com/greenelab/ccc/actions/workflows/pytest.yaml/badge.svg)](https://github.com/greenelab/ccc/actions/workflows/pytest.yaml) -[![codecov](https://codecov.io/gh/greenelab/ccc/branch/main/graph/badge.svg?token=QNK6O3Y1VF)](https://codecov.io/gh/greenelab/ccc) -[![bioRxiv Manuscript](https://img.shields.io/badge/manuscript-bioRxiv-blue.svg)](https://doi.org/10.1101/2022.06.15.496326) -[![HTML Manuscript](https://img.shields.io/badge/manuscript-HTML-blue.svg)](https://greenelab.github.io/ccc-manuscript/) +## Development +### How to build the CUDA module and its tests -## Overview - -The Clustermatch Correlation Coefficient (CCC) is a highly-efficient, next-generation not-only-linear correlation coefficient that can work on numerical and categorical data types. -This repository contains the code of CCC and instructions to install and use it. -It also has all the scripts/notebooks to run the analyses associated with the [manuscript](https://github.com/greenelab/ccc-manuscript), where we applied CCC on gene expression data. - -## Installation - -CCC is available as a PyPI (Python) package (`ccc-coef`). We tested CCC in Python 3.9+, but it should work on prior 3.x versions. -You can quickly test it by creating a conda environment and then install it with `pip`: - -```bash -# ipython and pandas are used in the following examples, but they are not needed for CCC to work -conda create -y -n ccc-env python=3.9 ipython pandas -conda activate ccc-env -pip install ccc-coef ``` - -## Usage - -Run `ipython` in your terminal: -```bash -$ ipython -Python 3.10.4 (main, Mar 31 2022, 08:41:55) [GCC 7.5.0] -Type 'copyright', 'credits' or 'license' for more information -IPython 8.3.0 -- An enhanced Interactive Python. Type '?' for help. - -In [1]: +cmake -S . -B build +cmake --build build ``` -When computing the correlation coefficient on a pair of features, CCC supports `numpy.array` or `pandas.Series`. -Missing values (`NaN`) are not currently supported, so you have to either remove or impute them. -Below there is an example with numerical data (you can copy/paste the entire lines below including `In [...]`): - -```python -In [1]: import numpy as np -In [2]: import pandas as pd -In [3]: from ccc.coef import ccc - -In [4]: random_feature1 = np.random.rand(1000) -In [5]: random_feature2 = np.random.rand(1000) -In [6]: ccc(random_feature1, random_feature2) -Out[6]: 0.0018815884476534295 - -In [7]: random_feature1 = pd.Series(random_feature1) -In [8]: random_feature2 = pd.Series(random_feature2) -In [9]: ccc(random_feature1, random_feature2) -Out[9]: 0.0018815884476534295 +### How to build and install this CUDA module ``` - -CCC always returns a value between zero (no relationship) and one (perfect relationship). -[As we show in the manuscript](https://greenelab.github.io/ccc-manuscript/#the-ccc-reveals-linear-and-nonlinear-patterns-in-human-transcriptomic-data), the distribution of CCC values is much more skewed than other coefficients like Pearson's or Spearman's. -A comparison between these coefficients should account for that. - -You can also mix numerical and categorical data: - -```python -In [10]: categories = np.array(["blue", "red", "green", "yellow"]) -In [11]: categorical_random_feature1 = np.random.choice(categories, size=1000) -In [12]: categorical_random_feature2 = np.random.choice(categories, size=1000) -In [13]: categorical_random_feature2[:10] -Out[13]: -array(['yellow', 'red', 'red', 'yellow', 'blue', 'blue', 'red', 'yellow', - 'green', 'blue'], dtype=' Date: Thu, 9 Jan 2025 15:44:52 -0700 Subject: [PATCH 121/134] [doc]: Initialize sphnix and read the docs --- .readthedocs.yaml | 35 +++++++++++++++++++++++++++++++ docs/Makefile | 20 ++++++++++++++++++ docs/make.bat | 35 +++++++++++++++++++++++++++++++ docs/source/conf.py | 41 +++++++++++++++++++++++++++++++++++++ docs/source/index.rst | 17 +++++++++++++++ environment/environment.yml | 3 +++ 6 files changed, 151 insertions(+) create mode 100644 .readthedocs.yaml create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..6dffd85a --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,35 @@ +# Read the Docs configuration file for Sphinx projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + # You can also specify other tool versions: + # nodejs: "20" + # rust: "1.70" + # golang: "1.20" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/source/conf.py + # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs + # builder: "dirhtml" + # Fail on all warnings to avoid broken references + # fail_on_warning: true + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +# python: +# install: +# - requirements: docs/requirements.txt \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..dc1312ab --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 00000000..937f3163 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,41 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'ccc-gpu' +copyright = '2025, Milton Pividori, Haoyu Zhang, Kevin Fotso' +author = 'Milton Pividori, Haoyu Zhang, Kevin Fotso' + + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +templates_path = ['_templates'] +exclude_patterns = [] + +extensions = [ + 'sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', +] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3/', None), + 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), +} +intersphinx_disabled_domains = ['std'] + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'haiku' +html_static_path = ['_static'] + +# -- Options for EPUB output +epub_show_urls = 'footnote' diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 00000000..76a18c54 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,17 @@ +.. ccc-gpu documentation master file, created by + sphinx-quickstart on Thu Jan 9 15:14:14 2025. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +ccc-gpu documentation +===================== + +Add your content using ``reStructuredText`` syntax. See the +`reStructuredText `_ +documentation for details. + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + diff --git a/environment/environment.yml b/environment/environment.yml index 4a92d81f..c28174bb 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -8,6 +8,9 @@ dependencies: - cuda-version>=12.0,<=12.5 - cupy=13.* - numba=0.6.* + - pip - python=3.11 - pytest=8.* - pybind11=2.* + - pip: + - sphinx=8.* From e848ae73b42f234637704a271097c320c67d6498 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Fri, 10 Jan 2025 00:32:18 -0700 Subject: [PATCH 122/134] [test]: Fix deprecated api calls --- tests/test_coef.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/test_coef.py b/tests/test_coef.py index 359c2728..47930f4c 100644 --- a/tests/test_coef.py +++ b/tests/test_coef.py @@ -894,7 +894,7 @@ def test_cm_return_parts_categorical_variable(): numerical_feature0_median = np.percentile(numerical_feature0, 50) # create a categorical variable perfectly correlated with the numerical one (this is actually an ordinal feature) - categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.unicode_) + categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.str_) categorical_feature1[numerical_feature0 < numerical_feature0_median] = "l" categorical_feature1[numerical_feature0 >= numerical_feature0_median] = "u" _unique_values = np.unique(categorical_feature1) @@ -1248,7 +1248,7 @@ def test_cm_numerical_and_categorical_features_perfect_relationship(): numerical_feature0_median = np.percentile(numerical_feature0, 50) # create a categorical variable perfectly correlated with the numerical one (this is actually an ordinal feature) - categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.unicode_) + categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.str_) categorical_feature1[numerical_feature0 < numerical_feature0_median] = "l" categorical_feature1[numerical_feature0 >= numerical_feature0_median] = "u" _unique_values = np.unique(categorical_feature1) @@ -1275,7 +1275,7 @@ def test_cm_numerical_and_categorical_features_strong_relationship(): numerical_feature0_perc = np.percentile(numerical_feature0, 25) # create a categorical variable perfectly correlated with the numerical one (this is actually an ordinal feature) - categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.unicode_) + categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.str_) categorical_feature1[numerical_feature0 < numerical_feature0_perc] = "l" categorical_feature1[numerical_feature0 >= numerical_feature0_perc] = "u" _unique_values = np.unique(categorical_feature1) @@ -1301,7 +1301,7 @@ def test_cm_numerical_and_categorical_features_no_relationship(): numerical_feature0 = np.random.rand(100) # create a categorical variable perfectly correlated with the numerical one (this is actually an ordinal feature) - categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.unicode_) + categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.str_) categorical_feature1[numerical_feature0 < 0.50] = "l" categorical_feature1[numerical_feature0 >= 0.50] = "u" np.random.shuffle(categorical_feature1) @@ -1377,7 +1377,7 @@ def test_cm_numerical_and_categorical_features_with_pandas_dataframe_two_feature numerical_feature0_median = np.percentile(numerical_feature0, 50) # create a categorical variable perfectly correlated with the numerical one (this is actually an ordinal feature) - categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.unicode_) + categorical_feature1 = np.full(numerical_feature0.shape[0], "", dtype=np.str_) categorical_feature1[numerical_feature0 < numerical_feature0_median] = "l" categorical_feature1[numerical_feature0 >= numerical_feature0_median] = "u" _unique_values = np.unique(categorical_feature1) From 90ae8eccfd3ebd8cc43adbadf133289c4ccb8956 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 11 Jan 2025 15:59:00 -0700 Subject: [PATCH 123/134] [impl]: Revert to the previous binning logic --- libs/ccc/coef/impl.py | 43 ++++++++++++++++++------------------------- 1 file changed, 18 insertions(+), 25 deletions(-) diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index e8a2c25d..440692dc 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -14,7 +14,6 @@ from ccc.pytorch.core import unravel_index_2d from ccc.sklearn.metrics import adjusted_rand_index as ari -# from ccc.sklearn.metrics_gpu2 import adjusted_rand_index as ari from ccc.scipy.stats import rank from ccc.utils import chunker, DummyExecutor @@ -36,7 +35,7 @@ def get_perc_from_k(k: int) -> list[float]: return [(1.0 / k) * i for i in range(1, k)] -# @njit(cache=True, nogil=True) +@njit(cache=True, nogil=True) def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: """ Performs a simple quantile clustering on one dimensional data (1d). Quantile @@ -57,27 +56,24 @@ def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: data_rank = rank(data, data_sorted) data_perc = data_rank / len(data) - # percentiles = [0.0] + get_perc_from_k(k) + [1.0] - percentiles = get_perc_from_k(k) - # print(f"CPU percentages: {str(percentiles)}") - - # cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") - # - # current_cluster = 0 - # part = np.zeros(data.shape, dtype=np.int16) - 1 - # - # for i in range(len(cut_points) - 1): - # lim1 = cut_points[i] - # lim2 = cut_points[i + 1] - # - # part[data_sorted[lim1:lim2]] = current_cluster - # current_cluster += 1 - bins = np.quantile(data, percentiles) - part = np.digitize(data, bins, right=True) + percentiles = [0.0] + get_perc_from_k(k) + [1.0] + + cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") + + current_cluster = 0 + part = np.zeros(data.shape, dtype=np.int16) - 1 + + for i in range(len(cut_points) - 1): + lim1 = cut_points[i] + lim2 = cut_points[i + 1] + + part[data_sorted[lim1:lim2]] = current_cluster + current_cluster += 1 + return part -# @njit(cache=True, nogil=True) +@njit(cache=True, nogil=True) def get_range_n_clusters( n_features: int, internal_n_clusters: Iterable[int] = None ) -> NDArray[np.uint8]: @@ -113,7 +109,7 @@ def get_range_n_clusters( return np.array(clusters_range_list, dtype=np.uint16) -# @njit(cache=True, nogil=True) +@njit(cache=True, nogil=True) def get_parts( data: NDArray, range_n_clusters: tuple[int], data_is_numerical: bool = True ) -> NDArray[np.int16]: @@ -674,7 +670,6 @@ def ccc( # get number of cores to use n_workers = get_n_workers(n_jobs) - # Converts internal_n_clusters to a list of integers if it's provided. if internal_n_clusters is not None: _tmp_list = List() @@ -799,8 +794,6 @@ def ccc( max_parts[f_idx, :] = max_part_idx_list cm_pvalues[f_idx] = pvalues - # print("CPU parts:") - # print(parts) # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: if return_parts: @@ -823,4 +816,4 @@ def ccc( if pvalue_n_perms is not None and pvalue_n_perms > 0: return cm_values, cm_pvalues else: - return cm_values + return cm_values \ No newline at end of file From ba74458a56d055934ef5c29e30a6208d4e5ff6c9 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Sat, 11 Jan 2025 16:58:06 -0700 Subject: [PATCH 124/134] [build]: Update conda dependencies --- environment/README.md | 4 +-- environment/environment-cop.yml | 37 -------------------------- environment/environment-cuda.yml | 21 +++++++++++++++ environment/environment.yml | 45 +++++++++++++++++++++++--------- 4 files changed, 56 insertions(+), 51 deletions(-) delete mode 100644 environment/environment-cop.yml create mode 100644 environment/environment-cuda.yml diff --git a/environment/README.md b/environment/README.md index 474fa738..8755c44b 100644 --- a/environment/README.md +++ b/environment/README.md @@ -83,7 +83,7 @@ cd environment/ 1. Create a conda environment and install main packages: ```bash -conda env create --name ccc --file environment.yml +conda env create --name ccc --file environment-cuda.yml conda run -n ccc --no-capture-output bash scripts/install_other_packages.sh ``` @@ -131,7 +131,7 @@ bash scripts/install_other_packages.sh 1. Export conda environment: ```bash -conda env export --name ccc --file environment.yml +conda env export --name ccc --file environment-cuda.yml ``` 1. Modify `environment.yml` and leave only manually installed packages (not their dependencies). diff --git a/environment/environment-cop.yml b/environment/environment-cop.yml deleted file mode 100644 index 4ad6ea0e..00000000 --- a/environment/environment-cop.yml +++ /dev/null @@ -1,37 +0,0 @@ -name: ccc -channels: - - conda-forge - - defaults -dependencies: - - cudatoolkit=11.2.* - - cupy=13.2.* - - ipython=7.* - - ipywidgets - - jupyterlab=3.3.* - - jupytext=1.11.* - - matplotlib=3.4.* - - minepy=1.2.* - - numba=0.60.* - - numpy=1.25.* - - openpyxl=3.0.* - - pandas=1.3.* - - papermill=2.3.* - - pip - - pytables=3.7.* - - pytest=6.* - - python=3.9.* - - pyyaml=5.4.* - - requests=2.* - - r-base=4.1.* - - r-devtools - - r-essentials - - r-reticulate=1.* - - r-svglite=2.* - - rpy2=3.4.* - - scikit-learn=0.24.* - - scipy=1.9.* - - seaborn=0.11.* - - svgutils=0.3.* - - tabulate=0.8.* - - tqdm=4.* - - upsetplot=0.6.* diff --git a/environment/environment-cuda.yml b/environment/environment-cuda.yml new file mode 100644 index 00000000..ea2aecb2 --- /dev/null +++ b/environment/environment-cuda.yml @@ -0,0 +1,21 @@ +name: ccc-rapid +channels: + - rapidsai + - conda-forge + - nvidia +dependencies: + - rapids=24.08 + - cuda-version>=12.0,<=12.5 + - cupy=13.* + - pip + - python=3.10 + - minepy + - pip: + - sphinx==8.* + - numba==0.60.* + - pytest==8.* + - pybind11==2.* + - ipython==8.* + - seaborn==0.13.* + - upsetplot==0.9.* + \ No newline at end of file diff --git a/environment/environment.yml b/environment/environment.yml index c28174bb..4ad6ea0e 100644 --- a/environment/environment.yml +++ b/environment/environment.yml @@ -1,16 +1,37 @@ -name: ccc-rapid +name: ccc channels: - - rapidsai - conda-forge - - nvidia + - defaults dependencies: - - rapids=24.08 - - cuda-version>=12.0,<=12.5 - - cupy=13.* - - numba=0.6.* + - cudatoolkit=11.2.* + - cupy=13.2.* + - ipython=7.* + - ipywidgets + - jupyterlab=3.3.* + - jupytext=1.11.* + - matplotlib=3.4.* + - minepy=1.2.* + - numba=0.60.* + - numpy=1.25.* + - openpyxl=3.0.* + - pandas=1.3.* + - papermill=2.3.* - pip - - python=3.11 - - pytest=8.* - - pybind11=2.* - - pip: - - sphinx=8.* + - pytables=3.7.* + - pytest=6.* + - python=3.9.* + - pyyaml=5.4.* + - requests=2.* + - r-base=4.1.* + - r-devtools + - r-essentials + - r-reticulate=1.* + - r-svglite=2.* + - rpy2=3.4.* + - scikit-learn=0.24.* + - scipy=1.9.* + - seaborn=0.11.* + - svgutils=0.3.* + - tabulate=0.8.* + - tqdm=4.* + - upsetplot=0.6.* From bc4b0884b9631c204803a853e0aee899172348d0 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Mon, 13 Jan 2025 15:47:51 -0700 Subject: [PATCH 125/134] [impl]: Revert to the original CPU version of ccc main logic --- libs/ccc/coef/impl_gpu.py | 1005 ++++++++++++++++++--------------- libs/ccc/coef/impl_gpu_old.py | 736 ++++++++++++++++++++++++ 2 files changed, 1280 insertions(+), 461 deletions(-) create mode 100644 libs/ccc/coef/impl_gpu_old.py diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index ba14a75e..18532990 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -1,27 +1,25 @@ """ -This module contains the CUDA implementation of the CCC +Contains function that implement the Clustermatch Correlation Coefficient (CCC). """ -import math +from __future__ import annotations + import os -from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Optional, Iterable, Union, List, Tuple +from concurrent.futures import ThreadPoolExecutor, as_completed, ProcessPoolExecutor +from typing import Iterable, Union import numpy as np -import cupy as cp from numpy.typing import NDArray from numba import njit -from cuml.metrics import adjusted_rand_score as cu_rnd_sc -from numba import cuda -from fractions import Fraction +from numba.typed import List from ccc.pytorch.core import unravel_index_2d -from ccc.scipy.stats import rank from ccc.sklearn.metrics import adjusted_rand_index as ari -from ccc.utils import chunker +from ccc.scipy.stats import rank +from ccc.utils import chunker, DummyExecutor -# @njit(cache=True, nogil=True) -def get_perc_from_k(k: int) -> NDArray[np.float64]: +@njit(cache=True, nogil=True) +def get_perc_from_k(k: int) -> list[float]: """ It returns the percentiles (from 0.0 to 1.0) that separate the data into k clusters. For example, if k=2, it returns [0.5]; if k=4, it returns [0.25, @@ -32,77 +30,61 @@ def get_perc_from_k(k: int) -> NDArray[np.float64]: list. Returns: - A numpy array of percentiles (from 0.0 to 1.0). + A list of percentiles (from 0.0 to 1.0). """ - np.set_printoptions(precision=17) - if k < 2: - return np.array([], dtype='float64') - return np.linspace(1 / k, 1 - 1 / k, k - 1, dtype='float64') + return [(1.0 / k) * i for i in range(1, k)] -# @njit(cache=True, nogil=True) -def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[float]: +@njit(cache=True, nogil=True) +def run_quantile_clustering(data: NDArray, k: int) -> NDArray[np.int16]: """ - It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters + Performs a simple quantile clustering on one dimensional data (1d). Quantile + clustering is defined as the procedure that forms clusters in 1d data by + separating objects using quantiles (for instance, if the median is used, two + clusters are generated with objects separated by the median). In the case + data contains all the same values (zero variance), this implementation can + return less clusters than specified with k. Args: - ks: an array of numbers of clusters. + data: a 1d numpy array with numerical values. + k: the number of clusters to split the data into. Returns: - A 2D sparse matrix of percentiles (from 0.0 to 1.0). + A 1d array with the data partition. """ - # Todo: research on if numba can optimize this - # Emtpy & null check - if ks.size == 0: - return np.empty((0, 0), dtype=float) - # Number of rows of the returning matrix - n_rows = len(ks) - # Number of columns of the returning matrix, dominated by the largest k, which specifies the # of clusters - n_cols = np.max(ks) - 1 - percentiles = np.full((n_rows, n_cols), np.nan, dtype=float) - for idx, k in enumerate(ks): - perc = get_perc_from_k(k) - if as_percentage: - perc = np.round(perc * 100).astype(float) # Convert to percentage and round - percentiles[idx, :len(perc)] = perc - return percentiles + data_sorted = np.argsort(data, kind="quicksort") + data_rank = rank(data, data_sorted) + data_perc = data_rank / len(data) + percentiles = [0.0] + get_perc_from_k(k) + [1.0] -def get_feature_type_and_encode(feature_data: NDArray) -> tuple[NDArray, bool]: - """ - Given the data of one feature as a 1d numpy array (it could also be a pandas.Series), - it returns the same data if it is numerical (float, signed or unsigned integer) or an - encoded version if it is categorical (each category value has a unique integer starting from - zero).` f + cut_points = np.searchsorted(data_perc[data_sorted], percentiles, side="right") - Args: - feature_data: a 1d array with data. + current_cluster = 0 + part = np.zeros(data.shape, dtype=np.int16) - 1 - Returns: - A tuple with two elements: - 1. the feature data: same as input if numerical, encoded version if not numerical. - 2. A boolean indicating whether the feature data is numerical or not. - """ - data_type_is_numerical = feature_data.dtype.kind in ("f", "i", "u") - if data_type_is_numerical: - return feature_data, data_type_is_numerical + for i in range(len(cut_points) - 1): + lim1 = cut_points[i] + lim2 = cut_points[i + 1] - # here np.unique with return_inverse encodes categorical values into numerical ones - return np.unique(feature_data, return_inverse=True)[1], data_type_is_numerical + part[data_sorted[lim1:lim2]] = current_cluster + current_cluster += 1 + return part -# @njit(cache=True, nogil=True) + +@njit(cache=True, nogil=True) def get_range_n_clusters( - n_items: int, internal_n_clusters: Iterable[int] = None + n_features: int, internal_n_clusters: Iterable[int] = None ) -> NDArray[np.uint8]: """ Given the number of features it returns a tuple of k values to cluster those features into. By default, it generates a tuple of k values from 2 to - int(np.round(np.sqrt(n_items))) (inclusive). For example, for 25 features, - it will generate this array: (2, 3, 4, 5). + int(np.round(np.sqrt(n_features))) (inclusive). For example, for 25 features, + it will generate this tuple: (2, 3, 4, 5). Args: - n_items: a positive number representing the number of features that + n_features: a positive number representing the number of features that will be clustered into different groups/clusters. internal_n_clusters: it allows to force a different list of clusters. It must be a list of integers. Repeated or invalid values will be dropped, @@ -112,48 +94,173 @@ def get_range_n_clusters( A numpy array with integer values representing numbers of clusters. """ - if internal_n_clusters: + if internal_n_clusters is not None: # remove k values that are invalid clusters_range_list = list( - set([int(x) for x in internal_n_clusters if 1 < x < n_items]) + set([int(x) for x in internal_n_clusters if 1 < x < n_features]) ) else: # default behavior if no internal_n_clusters is given: return range from - # 2 to sqrt(n_items) - n_sqrt = int(np.round(np.sqrt(n_items))) + # 2 to sqrt(n_features) + n_sqrt = int(np.round(np.sqrt(n_features))) n_sqrt = min((n_sqrt, 10)) clusters_range_list = list(range(2, n_sqrt + 1)) return np.array(clusters_range_list, dtype=np.uint16) -# # Todo: restore the original implementation -# @cuda.jit(device=True) -# def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: -# """ -# Given the number of objects and an index, it returns the row/column -# position of the pairwise matrix. For example, if there are n_obj objects -# (such as genes), a condensed 1d array can be created with pairwise -# comparisons between genes, as well as a squared symmetric matrix. This -# function receives the number of objects and the index of the condensed -# array, and returns the coordinates of the squared symmetric matrix. -# Args: -# n_obj: the number of objects. -# idx: the index of the condensed pairwise array across all n_obj objects. -# Returns -# A tuple (i, j) with the coordinates of the squared symmetric matrix -# equivalent to the condensed array. -# """ -# b = 1 - 2 * n_obj -# x = math.floor((-b - math.sqrt(b**2 - 8 * idx)) / 2) -# y = idx + x * (b + x + 2) / 2 + 1 -# return int(x), int(y) +@njit(cache=True, nogil=True) +def get_parts( + data: NDArray, range_n_clusters: tuple[int], data_is_numerical: bool = True +) -> NDArray[np.int16]: + """ + Given a 1d data array, it computes a partition for each k value in the given + range of clusters. If partitions with only one cluster are returned (singletons), + then the returned array will have negative values. + + Args: + data: a 1d data vector. It is assumed that there are no nans. + range_n_clusters: a tuple with the number of clusters. + data_is_numerical: indicates whether data is numerical (True) or categorical (False) + + Returns: + A numpy array with shape (number of clusters, data rows) with + partitions of data. + + Partitions could have negative values in some scenarios, with different + meanings: -1 is used for categorical data, where only one partition is generated + and the rest (-1) are marked as "empty". -2 is used when singletons have been + detected (partitions with one cluster), usually because of problems with the + input data (it has all the same values, for example). + """ + parts = np.zeros((len(range_n_clusters), data.shape[0]), dtype=np.int16) - 1 + + if data_is_numerical: + for idx in range(len(range_n_clusters)): + k = range_n_clusters[idx] + parts[idx] = run_quantile_clustering(data, k) + + # remove singletons by putting a -2 as values + partitions_ks = np.array([len(np.unique(p)) for p in parts]) + parts[partitions_ks == 1, :] = -2 + else: + # if the data is categorical, then the encoded feature is already the partition + # only the first partition is filled, the rest will be -1 (missing) + parts[0] = data.astype(np.int16) + + return parts + + +def get_feature_parts(params): + """ + Given a list of parameters, it returns the partitions for each feature. The goal + of this function is to parallelize the partitioning step (get_parts function). + + Args: + params: a list of tuples with three elements: 1) a tuple with the feature + index, the cluster index and the number of clusters (k), 2) the data for the + feature, and 3) a boolean indicating whether the feature is numerical or not. + + Returns: + A 2d array with the partitions (rows) for the selected features and number of + clusters. + """ + n_objects = params[0][1].shape[0] + parts = np.zeros((len(params), n_objects), dtype=np.int16) - 1 + + # iterate over a list of tuples that indicate a feature-k pair + for p_idx, p in enumerate(params): + # the first element is a tuple with the feature index, the cluster index and the + # number of clusters (k) + info = p[0] + # f_idx = info[0] + c_idx = info[1] + c = info[2] + range_n_clusters = np.array([c], dtype=np.uint16) + + # the second element is the data for the feature + data = p[1] + + # the third element is a boolean indicating whether the feature is numerical + numerical_data_type = p[2] + + # if the feature is categorical, then only the first partition is filled + if not numerical_data_type and c_idx > 0: + continue + + parts[p_idx] = get_parts(data, range_n_clusters, numerical_data_type) + + return parts + + +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: + """ + It implements the same functionality in scipy.spatial.distance.cdist but + for clustering partitions, and instead of a distance it returns the adjusted + Rand index (ARI). In other words, it mimics this function call: + + cdist(x, y, metric=ari) + + Only partitions with positive labels (> 0) are compared. This means that + partitions marked as "singleton" or "empty" (categorical data) are not + compared. This has the effect of leaving an ARI of 0.0 (zero). + + Args: + x: a 2d array with m_x clustering partitions in rows and n objects in + columns. + y: a 2d array with m_y clustering partitions in rows and n objects in + columns. + + Returns: + A 2d array with m_x rows and m_y columns and the ARI between each + partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i + and j. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + for i in range(res.shape[0]): + if x[i, 0] < 0: + continue + + for j in range(res.shape[1]): + if y[j, 0] < 0: + continue + + res[i, j] = ari(x[i], y[j]) + + return res + + +def cdist_parts_parallel( + x: NDArray, y: NDArray, executor: ThreadPoolExecutor +) -> NDArray[float]: + """ + It parallelizes cdist_parts_basic function. + + Args: + x: same as in cdist_parts_basic + y: same as in cdist_parts_basic + executor: a pool executor where jobs will be submitted. + + Results: + Same as in cdist_parts_basic. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + inputs = get_chunks(res.shape[0], executor._max_workers, 1) + + tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} + for t in as_completed(tasks): + idx = tasks[t] + res[idx, :] = t.result() + + return res @njit(cache=True, nogil=True) def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: """ - Given the number of objects and and index, it returns the row/column + Given the number of objects and an index, it returns the row/column position of the pairwise matrix. For example, if there are n_obj objects (such as genes), a condensed 1d array can be created with pairwise comparisons between genes, as well as a squared symmetric matrix. This @@ -169,191 +276,13 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: equivalent to the condensed array. """ b = 1 - 2 * n_obj - x = np.floor((-b - np.sqrt(b ** 2 - 8 * idx)) / 2) + x = np.floor((-b - np.sqrt(b**2 - 8 * idx)) / 2) y = idx + x * (b + x + 2) / 2 + 1 return int(x), int(y) -def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> List[int]: - if internal_n_clusters is None: - return [] - - if isinstance(internal_n_clusters, int): - return list(range(2, internal_n_clusters + 1)) - - return list(internal_n_clusters) - - -def get_parts(X: NDArray, - range_n_clusters: NDArray[np.uint8], - data_is_numerical: bool = True - ) -> tuple[cp.ndarray, cp.ndarray]: - """ - Compute parts using CuPy for GPU acceleration. - - Parameters: - X: Input data array of shape (n_features, n_objects) - range_n_clusters: Array of cluster numbers - range_n_percentages: Array of percentages for each cluster number - - Returns: - Reference to the computed partitions on the device global memory - """ - - # Handle case when X is a 1D array - if X.ndim == 1: - nx = 1 # n_features - ny = range_n_clusters.shape[0] # n_clusters - nz = X.shape[0] # n_objects - else: - nx = X.shape[0] # n_features - ny = range_n_clusters.shape[0] # n_clusters - nz = X.shape[1] # n_objects - # print(f"{nx}, {ny}, {nz}") - - # Allocate arrays on device global memory - d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 - d_unique_elem_counts = cp.empty((nx, ny), dtype=np.int16) - 1 - # print(f"prev parts: {d_parts}") - - if data_is_numerical: - # Transfer data to device - d_X = cp.asarray(X) - # Get cutting percentages for each cluster - range_n_percentages = get_range_n_percentages(range_n_clusters) - d_range_n_percentages = cp.asarray(range_n_percentages, dtype=float) - - for x in range(nx): - for y in range(ny): - objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row - # Todo: use cupy fusion to optimize the two operations below - percentages = d_range_n_percentages[y, :] - # print(f"GPU percentiles: {percentages}") - bins = cp.quantile(objects, percentages) - # print(f"GPU quantiles: {bins}") - partition = cp.digitize(objects, bins, right=True) - d_parts[x, y, :] = partition - # Count number of unique elements in each partition, used in the ARI computation - d_unique_elem_counts[x, y] = len(cp.unique(partition)) - - # Remove singletons by putting -2 as values - partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, - ny) - d_parts[partitions_ks == 1] = -2 - else: - # If the data is categorical, then the encoded feature is already the partition - # Only the first partition is filled, the rest will be -1 (missing) - # Todo: fix this to handle categorical data - d_parts[:, 0] = cp.asarray(X.astype(cp.int16)) - - # Move data back to host - # h_parts = cp.asnumpy(d_parts) - # print(f"after parts: {d_parts}") - cp.cuda.runtime.deviceSynchronize() - return d_parts, d_unique_elem_counts - - -# # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) -# @cuda.jit(device=True) -# def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: -# """ -# It implements the same functionality in scipy.spatial.distance.cdist but -# for clustering partitions, and instead of a distance it returns the adjusted -# Rand index (ARI). In other words, it mimics this function call: -# -# cdist(x, y, metric=ari) -# -# Only partitions with positive labels (> 0) are compared. This means that -# partitions marked as "singleton" or "empty" (categorical data) are not -# compared. This has the effect of leaving an ARI of 0.0 (zero). -# -# Args: -# x: a 2d array with m_x clustering partitions in rows and n objects in -# columns. -# y: a 2d array with m_y clustering partitions in rows and n objects in -# columns. -# -# Returns: -# A 2d array with m_x rows and m_y columns and the ARI between each -# partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i -# and j. -# """ -# -# for i in range(out.shape[0]): -# if x[i, 0] < 0: -# continue -# -# for j in range(out.shape[1]): -# if y[j, 0] < 0: -# continue -# -# # res[i, j] = ari(x[i], y[j]) -# # ari(x[i], y[j], out, compare_pair_id, i, j) -# res = ari(x[i], y[j]) -# print(res) -# -# return -# -# -# @cuda.jit -# def compute_coef( -# parts: cuda.cudadrv.devicearray, -# max_ari_list: cuda.cudadrv.devicearray, -# max_part_idx_list: cuda.cudadrv.devicearray, -# temp_outs: cuda.cudadrv.devicearray, -# compare_pair_id: int, -# ): -# """ -# Given an index representing each a pair of -# objects/rows/genes, it computes the CCC coefficient for -# each of them. -# -# Args: -# parts: A reference to the 3d GPU partitions array. -# max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. -# max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. -# compare_pair_id: An id representing a pair of partitions to be compared. -# -# Returns: -# Returns a tuple with two arrays. These two arrays are the same -# arrays returned by the main cm function (cm_values and -# max_parts) but for a subset of the data. -# """ -# n_features = parts.shape[0] -# -# # for idx, data_idx in enumerate(compare_pair_id): -# i, j = get_coords_from_index(n_features, compare_pair_id) -# -# # get partitions for the pair of objects -# obji_parts, objj_parts = parts[i], parts[j] -# -# # compute ari only if partitions are not marked as "missing" -# # (negative values), which is assigned when partitions have -# # one cluster (usually when all data in the feature has the same -# # value). -# if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: -# return -# -# # compare all partitions of one object to the all the partitions -# # of the other object, and get the maximium ARI -# -# cdist_parts_basic( -# obji_parts, -# objj_parts, -# temp_outs, -# compare_pair_id, -# ) -# # max_flat_idx = comp_values.argmax() -# -# # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) -# # max_part_idx_list[compare_pair_id] = max_idx -# # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) -# # -# # return max_ari_list, max_part_idx_list -# return - def get_chunks( - iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 + iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 ) -> Iterable[Iterable[int]]: """ It splits elements in an iterable in chunks according to the number of @@ -386,95 +315,251 @@ def get_chunks( idx = 0 while len(res[idx]) == 1: idx = idx + 1 - # Got two chunks + new_chunk = get_chunks(res[idx], 2) res[idx] = new_chunk[0] - # Insert the second chunk in the next position res.insert(idx + 1, new_chunk[1]) return res -def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: +def get_feature_type_and_encode(feature_data: NDArray) -> tuple[NDArray, bool]: """ - It implements the same functionality in scipy.spatial.distance.cdist but - for clustering partitions, and instead of a distance it returns the adjusted - Rand index (ARI). In other words, it mimics this function call: + Given the data of one feature as a 1d numpy array (it could also be a pandas.Series), + it returns the same data if it is numerical (float, signed or unsigned integer) or an + encoded version if it is categorical (each category value has a unique integer starting from + zero). - cdist(x, y, metric=ari) + Args: + feature_data: a 1d array with data. - Only partitions with positive labels (> 0) are compared. This means that - partitions marked as "singleton" or "empty" (categorical data) are not - compared. This has the effect of leaving an ARI of 0.0 (zero). + Returns: + A tuple with two elements: + 1. the feature data: same as input if numerical, encoded version if not numerical. + 2. A boolean indicating whether the feature data is numerical or not. + """ + data_type_is_numerical = feature_data.dtype.kind in ("f", "i", "u") + if data_type_is_numerical: + return feature_data, data_type_is_numerical + + # here np.unique with return_inverse encodes categorical values into numerical ones + return np.unique(feature_data, return_inverse=True)[1], data_type_is_numerical + + +def compute_ccc(obj_parts_i: NDArray, obj_parts_j: NDArray, cdist_func): + """ + Given a set of partitions for two features, it computes the CCC coefficient. Args: - x: a 2d array with m_x clustering partitions in rows and n objects in - columns. - y: a 2d array with m_y clustering partitions in rows and n objects in - columns. + obj_parts_i: a 2d array with partitions for one feature. Each row is a + partition, and each column is an object. + obj_parts_j: a 2d array with partitions for another feature. Each row is + a partition, and each column is an object. + cdist_func: a function that computes the distance between partitions. It + can be either cdist_parts_basic or cdist_parts_parallel. Returns: - A 2d array with m_x rows and m_y columns and the ARI between each - partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i - and j. + A tuple with two elements: 1) the CCC coefficient, and 2) the indexes + of the partitions that maximized the coefficient. """ - res = np.zeros((x.shape[0], y.shape[0])) + comp_values = cdist_func( + obj_parts_i, + obj_parts_j, + ) + max_flat_idx = comp_values.argmax() + max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - for i in range(res.shape[0]): - if x[i, 0] < 0: - continue + return max(comp_values[max_idx], 0.0), max_idx - for j in range(res.shape[1]): - if y[j, 0] < 0: - continue - res[i, j] = cu_rnd_sc(x[i], y[j]) - # res[i, j] = ari(x[i], y[j]) +def compute_ccc_perms(params) -> NDArray[float]: + """ + Similar to compute_ccc (with same parameters), but it computes the CCC coefficient + by permuting the partitions of one of the features n_perms times. + + Args: + params: a tuple with four elements: 1) the index of the permutations, 2) the + partitions of one of the features, 3) the partitions of the other feature, + and 4) the number of permutations to perform. - return res + Returns: + The CCC coefficient values using the permuted partitions of one of the features. + """ + # since this function can be parallelized across different processes, make sure + # the random number generator is initialized with a different seed for each process + rng = np.random.default_rng() + _, obj_parts_i, obj_parts_j, n_perms = params -def cdist_parts_parallel( - x: NDArray, y: NDArray, executor: ThreadPoolExecutor -) -> NDArray[float]: + n_objects = obj_parts_i.shape[1] + ccc_perm_values = np.full(n_perms, np.nan, dtype=float) + + for idx in range(n_perms): + perm_idx = rng.permutation(n_objects) + + # generate a random permutation of the partitions of one + # variable/feature + obj_parts_j_permuted = np.full_like(obj_parts_j, np.nan) + for it in range(obj_parts_j.shape[0]): + obj_parts_j_permuted[it] = obj_parts_j[it][perm_idx] + + # compute the CCC using the permuted partitions + ccc_perm_values[idx] = compute_ccc( + obj_parts_i, obj_parts_j_permuted, cdist_parts_basic + )[0] + + return ccc_perm_values + + +def compute_coef(params): """ - It parallelizes cdist_parts_basic function. + Given a list of indexes representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. This function is supposed to be used to parallelize + processing. Args: - x: same as in cdist_parts_basic - y: same as in cdist_parts_basic - executor: an pool executor where jobs will be submitted. + params: a tuple with eight elements: 1) the indexes of the features + to compare, 2) the number of features, 3) the partitions for each + feature, 4) the number of permutations to compute the p-value, 5) + the number of threads to use for parallelization, 6) the ratio + between the number of chunks and the number of threads, 7) the + executor to use for cdist parallelization, and 8) the executor to use + for parallelization of permutations. - Results: - Same as in cdist_parts_basic. + Returns: + Returns a tuple with three arrays. The first array has the CCC + coefficients, the second array has the indexes of the partitions that + maximized the coefficient, and the third array has the p-values. """ - res = np.zeros((x.shape[0], y.shape[0])) + ( + idx_list, + n_features, + parts, + pvalue_n_perms, + default_n_threads, + n_chunks_threads_ratio, + cdist_executor, + executor, + ) = params + + cdist_func = cdist_parts_basic + if cdist_executor is not False: + + def cdist_func(x, y): + return cdist_parts_parallel(x, y, cdist_executor) + + n_idxs = len(idx_list) + max_ari_list = np.full(n_idxs, np.nan, dtype=float) + max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + pvalues = np.full(n_idxs, np.nan, dtype=float) + + for idx, data_idx in enumerate(idx_list): + i, j = get_coords_from_index(n_features, data_idx) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + continue - inputs = list(chunker(np.arange(res.shape[0]), 1)) + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + max_ari_list[idx], max_part_idx_list[idx] = compute_ccc( + obji_parts, objj_parts, cdist_func + ) - tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} - for t in as_completed(tasks): - idx = tasks[t] - res[idx, :] = t.result() + # compute p-value if requested + if pvalue_n_perms is not None and pvalue_n_perms > 0: + # with ThreadPoolExecutor(max_workers=pvalue_n_jobs) as executor_perms: + # select the variable that generated more partitions as the one + # to permute + obj_parts_sel_i = obji_parts + obj_parts_sel_j = objj_parts + if (obji_parts[:, 0] >= 0).sum() > (objj_parts[:, 0] >= 0).sum(): + obj_parts_sel_i = objj_parts + obj_parts_sel_j = obji_parts + + p_ccc_values = np.full(pvalue_n_perms, np.nan, dtype=float) + p_inputs = get_chunks( + pvalue_n_perms, default_n_threads, n_chunks_threads_ratio + ) + p_inputs = [ + ( + i, + obj_parts_sel_i, + obj_parts_sel_j, + len(i), + ) + for i in p_inputs + ] + + for params, p_ccc_val in zip( + p_inputs, + executor.map( + compute_ccc_perms, + p_inputs, + ), + ): + p_idx = params[0] + + p_ccc_values[p_idx] = p_ccc_val + + # compute p-value + pvalues[idx] = (np.sum(p_ccc_values >= max_ari_list[idx]) + 1) / ( + pvalue_n_perms + 1 + ) - return res + return max_ari_list, max_part_idx_list, pvalues + + +def get_n_workers(n_jobs: int | None) -> int: + """ + Helper function to get the number of workers for parallel processing. + + Args: + n_jobs: value specified by the main ccc function. + Returns: + The number of workers to use for parallel processing + """ + n_cpu_cores = os.cpu_count() + if n_cpu_cores is None: + raise ValueError("Could not determine the number of CPU cores. Please specify a positive value of n_jobs") + + n_workers = n_cpu_cores + if n_jobs is None: + return n_workers + + n_workers = os.cpu_count() + n_jobs if n_jobs < 0 else n_jobs + + if n_workers < 1: + raise ValueError(f"The number of threads/processes to use must be greater than 0. Got {n_workers}." + "Please check the n_jobs argument provided") + + return n_workers def ccc( - x: NDArray, - y: NDArray = None, - internal_n_clusters: Union[int, Iterable[int]] = None, - return_parts: bool = False, - n_chunks_threads_ratio: int = 1, - n_jobs: int = 1, -) -> tuple[NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: + x: NDArray, + y: NDArray = None, + internal_n_clusters: Union[int, Iterable[int]] = None, + return_parts: bool = False, + n_chunks_threads_ratio: int = 1, + n_jobs: int = 1, + pvalue_n_perms: int = None, + partitioning_executor: str = "thread", +) -> tuple[NDArray[float], NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: """ This is the main function that computes the Clustermatch Correlation Coefficient (CCC) between two arrays. The implementation supports numerical and categorical data. Args: - x: an 1d or 2d numerical array with the data. NaN are not supported. + x: 1d or 2d numerical array with the data. NaN are not supported. If it is 2d, then the coefficient is computed for each pair of rows (in case x is a numpy.array) or each pair of columns (pandas.DataFrame). y: an optional 1d numerical array. If x is 1d and y is given, it computes @@ -487,29 +572,34 @@ def ccc( n_chunks_threads_ratio: allows to modify how pairwise comparisons are split across different threads. It's given as the ratio parameter of function get_chunks. - n_jobs: number of CPU cores to use for parallelization. The value + n_jobs: number of CPU cores/threads to use for parallelization. The value None will use all available cores (`os.cpu_count()`), and negative - values will use `os.cpu_count() - n_jobs`. Default is 1. + values will use `os.cpu_count() + n_jobs` (exception will be raised + if this expression yields a result less than 1). Default is 1. + pvalue_n_perms: if given, it computes the p-value of the + coefficient using the given number of permutations. + partitioning_executor: Executor type used for partitioning the data. It + can be either "thread" (default) or "process". If "thread", it will use + ThreadPoolExecutor for parallelization, which uses less memory. If + "process", it will use ProcessPoolExecutor, which might be faster. If + anything else, it will not parallelize the partitioning step. - Returns: - If return_parts is False, only CCC values are returned. - In that case, if x is 2d, a np.ndarray of size n x n is - returned with the coefficient values, where n is the number of rows in x. - If only a single coefficient was computed (for example, x and y were - given as 1d arrays each), then a single scalar is returned. + Returns: If returns_parts is True, then it returns a tuple with three values: - 1) the - coefficients, 2) the partitions indexes that maximized the coefficient + 1) the coefficients, 2) the partitions indexes that maximized the coefficient for each object pair, and 3) the partitions for all objects. + If return_parts is False, only CCC values are returned. - cm_values: if x is 2d, then it is a 1d condensed array of pairwise - coefficients. It has size (n * (n - 1)) / 2, where n is the number - of rows in x. If x and y are given, and they are 1d, then this is a - scalar. The CCC is always between 0 and 1 - (inclusive). If any of the two variables being compared has no - variation (all values are the same), the coefficient is not defined - (np.nan). + cm_values: if x is 2d np.array with x.shape[0] > 2, then cm_values is a 1d + condensed array of pairwise coefficients. It has size (n * (n - 1)) / 2, + where n is the number of rows in x. If x and y are given, and they are 1d, + then cm_values is a scalar. The CCC is always between 0 and 1 (inclusive). If + any of the two variables being compared has no variation (all values are the + same), the coefficient is not defined (np.nan). If pvalue_n_permutations is + an integer greater than 0, then cm_vlaues is a tuple with two elements: + the first element are the CCC values, and the second element are the p-values + using pvalue_n_permutations permutations. max_parts: an array with n * (n - 1)) / 2 rows (one for each object pair) and two columns. It has the indexes pointing to each object's @@ -539,7 +629,7 @@ def ccc( raise ValueError("x and y need to be of the same size") n_objects = x.shape[0] n_features = 2 - # Create a matrix to store both x and y + X = np.zeros((n_features, n_objects)) X_numerical_type = np.full((n_features,), True, dtype=bool) @@ -552,10 +642,9 @@ def ccc( # plus we have the features data type (numerical, categorical, etc) if isinstance(x, np.ndarray): - assert get_feature_type_and_encode(x[0, :])[1], ( - "If data is a 2d numpy array, it has to be numerical. Use pandas.DataFrame if " - "you need to mix features with different data types" - ) + if not get_feature_type_and_encode(x[0, :])[1]: + raise ValueError("If data is a 2d numpy array, it has to be numerical. Use pandas.DataFrame if " + "you need to mix features with different data types") n_objects = x.shape[1] n_features = x.shape[0] @@ -571,74 +660,94 @@ def ccc( X = np.zeros((n_features, n_objects)) X_numerical_type = np.full((n_features,), True, dtype=bool) - for idx in range(n_features): - X[idx, :], X_numerical_type[idx] = get_feature_type_and_encode( - x.iloc[:, idx] + for f_idx in range(n_features): + X[f_idx, :], X_numerical_type[f_idx] = get_feature_type_and_encode( + x.iloc[:, f_idx] ) else: raise ValueError("Wrong combination of parameters x and y") - # 1. Partitions Computation + # get number of cores to use + n_workers = get_n_workers(n_jobs) + + if internal_n_clusters is not None: + _tmp_list = List() - # Converts internal_n_clusters to a list of integers if it's provided. - internal_n_clusters = convert_n_clusters(internal_n_clusters) + if isinstance(internal_n_clusters, int): + # this interprets internal_n_clusters as the maximum k + internal_n_clusters = range(2, internal_n_clusters + 1) - # Get matrix of partitions for each object pair + for x in internal_n_clusters: + _tmp_list.append(x) + internal_n_clusters = _tmp_list + + # get matrix of partitions for each object pair range_n_clusters = get_range_n_clusters(n_objects, internal_n_clusters) if range_n_clusters.shape[0] == 0: raise ValueError(f"Data has too few objects: {n_objects}") + # store a set of partitions per row (object) in X as a multidimensional + # array, where the second dimension is the number of partitions per object. + parts = ( + np.zeros((n_features, range_n_clusters.shape[0], n_objects), dtype=np.int16) - 1 + ) + # cm_values stores the CCC coefficients n_features_comp = (n_features * (n_features - 1)) // 2 cm_values = np.full(n_features_comp, np.nan) + cm_pvalues = np.full(n_features_comp, np.nan) # for each object pair being compared, max_parts has the indexes of the # partitions that maximimized the ARI max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) - # X here (and following) is a numpy array features are in rows, objects are in columns - - # Compute partitions for each feature using CuPy - d_parts, d_uniq_ele_counts = get_parts(X, range_n_clusters) - # used in the ARI computation later - n_parts = range_n_clusters.shape[0] - # d_parts_max_per_part = cp.empty(n_features * n_parts, dtype=np.int8) - d_parts_max_per_part = cp.amax(d_parts, axis=2) - print("GPU parts:") - print(d_parts) - print(f"Max per part: {d_parts_max_per_part}") - - # 2. CCC coefficient computation - - # # allocate result arrays on device global memory - # d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) - # d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) - # # allocate temporary arrays on device global memory - # d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) - # print(f"before d_outs: {d_outs}") - # # use 1D gird to parallelize the computation of CCC coefficients - # # Todo: optimize this using updated c_dist function that only compare one partition at a time - # threads_per_block = 1 - # blocks_per_grid = n_features_comp - # for i in range(n_features_comp): - # # Directly pass CuPy arrays to kernels JITed with Numba - # compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) - # # Wait for all comparisons to finish - # cuda.synchronize() - # print(f"after d_outs: {d_outs}") - # # Transfer data back to host - # max_ari_list = cp.asnumpy(d_max_ari_list) - # max_part_idx_list = cp.asnumpy(d_max_part_idx_list) - # print(max_ari_list) - # print(max_part_idx_list) - - # Use CPU multi-threading for baseline - parts = cp.asnumpy(d_parts) - - default_n_threads = os.cpu_count() - - with ThreadPoolExecutor(max_workers=default_n_threads) as executor: + with ( + ThreadPoolExecutor(max_workers=n_workers) as executor, + ProcessPoolExecutor(max_workers=n_workers) as pexecutor, + ): + map_func = map + if n_workers > 1: + if partitioning_executor == "thread": + map_func = executor.map + elif partitioning_executor == "process": + map_func = pexecutor.map + + # pre-compute the internal partitions for each object in parallel + + # first, create a list with features-k pairs that will be used to parallelize + # the partitioning step + inputs = get_chunks( + [ + (f_idx, c_idx, c) + for f_idx in range(n_features) + for c_idx, c in enumerate(range_n_clusters) + ], + n_workers, + n_chunks_threads_ratio, + ) + + # then, flatten the list of features-k pairs into a list that is divided into + # chunks that will be used to parallelize the partitioning step. + inputs = [ + [ + ( + feature_k_pair, + X[feature_k_pair[0]], + X_numerical_type[feature_k_pair[0]], + ) + for feature_k_pair in chunk + ] + for chunk in inputs + ] + + for params, ps in zip(inputs, map_func(get_feature_parts, inputs)): + # get the set of feature indexes and cluster indexes + f_idxs = [p[0][0] for p in params] + c_idxs = [p[0][1] for p in params] + + # update the partitions for each feature-k pair + parts[f_idxs, c_idxs] = ps # Below, there are two layers of parallelism: 1) parallel execution # across feature pairs and 2) the cdist_parts_parallel function, which @@ -647,90 +756,64 @@ def ccc( # we have several feature pairs to compare), because parallelization is # already performed at this level. Otherwise, more threads than # specified by the user are started. - cdist_parts_enable_threading = True if n_features_comp == 1 else False - - cdist_func = None - map_func = executor.map - if cdist_parts_enable_threading: - map_func = map - - def cdist_func(x, y): - return cdist_parts_parallel(x, y, executor) - - else: - cdist_func = cdist_parts_basic - - # compute coefficients - def compute_coef(idx_list): - """ - Given a list of indexes representing each a pair of - objects/rows/genes, it computes the CCC coefficient for - each of them. This function is supposed to be used to parallelize - processing. - - Args: - idx_list: a list of indexes (integers), each of them - representing a pair of objects. - - Returns: - Returns a tuple with two arrays. These two arrays are the same - arrays returned by the main cm function (cm_values and - max_parts) but for a subset of the data. - """ - n_idxs = len(idx_list) - max_ari_list = np.full(n_idxs, np.nan, dtype=float) - max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) - - for idx, data_idx in enumerate(idx_list): - i, j = get_coords_from_index(n_features, data_idx) - - # get partitions for the pair of objects - obji_parts, objj_parts = parts[i], parts[j] - - # compute ari only if partitions are not marked as "missing" - # (negative values), which is assigned when partitions have - # one cluster (usually when all data in the feature has the same - # value). - if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: - continue - - # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI - comp_values = cdist_func( - obji_parts, - objj_parts, - ) - max_flat_idx = comp_values.argmax() + map_func = map + cdist_executor = False + inner_executor = DummyExecutor() - max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) - max_part_idx_list[idx] = max_idx - max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + if n_workers > 1: + if n_features_comp == 1: + map_func = map + cdist_executor = executor + inner_executor = pexecutor - return max_ari_list, max_part_idx_list + else: + map_func = pexecutor.map # iterate over all chunks of object pairs and compute the coefficient - inputs = get_chunks(n_features_comp, default_n_threads, n_chunks_threads_ratio) + inputs = get_chunks(n_features_comp, n_workers, n_chunks_threads_ratio) + inputs = [ + ( + i, + n_features, + parts, + pvalue_n_perms, + n_workers, + n_chunks_threads_ratio, + cdist_executor, + inner_executor, + ) + for i in inputs + ] - for idx, (max_ari_list, max_part_idx_list) in zip( - inputs, map_func(compute_coef, inputs) + for params, (max_ari_list, max_part_idx_list, pvalues) in zip( + inputs, map_func(compute_coef, inputs) ): - cm_values[idx] = max_ari_list - max_parts[idx, :] = max_part_idx_list + f_idx = params[0] + + cm_values[f_idx] = max_ari_list + max_parts[f_idx, :] = max_part_idx_list + cm_pvalues[f_idx] = pvalues # return an array of values or a single scalar, depending on the input data if cm_values.shape[0] == 1: if return_parts: - return cm_values[0], max_parts[0], parts + if pvalue_n_perms is not None and pvalue_n_perms > 0: + return (cm_values[0], cm_pvalues[0]), max_parts[0], parts + else: + return cm_values[0], max_parts[0], parts else: - return cm_values[0] + if pvalue_n_perms is not None and pvalue_n_perms > 0: + return cm_values[0], cm_pvalues[0] + else: + return cm_values[0] if return_parts: - return cm_values, max_parts, parts + if pvalue_n_perms is not None and pvalue_n_perms > 0: + return (cm_values, cm_pvalues), max_parts, parts + else: + return cm_values, max_parts, parts else: - return cm_values - -# Dev notes -# 1. parallelize get_parst -# 1.1 gpu percentile computation -# 1.1 gpu data points binning -# can be a kernel for-loop to compute parts on different percentile \ No newline at end of file + if pvalue_n_perms is not None and pvalue_n_perms > 0: + return cm_values, cm_pvalues + else: + return cm_values diff --git a/libs/ccc/coef/impl_gpu_old.py b/libs/ccc/coef/impl_gpu_old.py new file mode 100644 index 00000000..ba14a75e --- /dev/null +++ b/libs/ccc/coef/impl_gpu_old.py @@ -0,0 +1,736 @@ +""" +This module contains the CUDA implementation of the CCC +""" +import math +import os +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Optional, Iterable, Union, List, Tuple + +import numpy as np +import cupy as cp +from numpy.typing import NDArray +from numba import njit +from cuml.metrics import adjusted_rand_score as cu_rnd_sc +from numba import cuda +from fractions import Fraction + +from ccc.pytorch.core import unravel_index_2d +from ccc.scipy.stats import rank +from ccc.sklearn.metrics import adjusted_rand_index as ari +from ccc.utils import chunker + + +# @njit(cache=True, nogil=True) +def get_perc_from_k(k: int) -> NDArray[np.float64]: + """ + It returns the percentiles (from 0.0 to 1.0) that separate the data into k + clusters. For example, if k=2, it returns [0.5]; if k=4, it returns [0.25, + 0.50, 0.75]. + + Args: + k: number of clusters. If less than 2, the function returns an empty + list. + + Returns: + A numpy array of percentiles (from 0.0 to 1.0). + """ + np.set_printoptions(precision=17) + if k < 2: + return np.array([], dtype='float64') + return np.linspace(1 / k, 1 - 1 / k, k - 1, dtype='float64') + + +# @njit(cache=True, nogil=True) +def get_range_n_percentages(ks: NDArray[np.uint8], as_percentage: bool = False) -> NDArray[float]: + """ + It returns lists of the percentiles (from 0.0 to 1.0) that separate the data into k[i] clusters + + Args: + ks: an array of numbers of clusters. + + Returns: + A 2D sparse matrix of percentiles (from 0.0 to 1.0). + """ + # Todo: research on if numba can optimize this + # Emtpy & null check + if ks.size == 0: + return np.empty((0, 0), dtype=float) + # Number of rows of the returning matrix + n_rows = len(ks) + # Number of columns of the returning matrix, dominated by the largest k, which specifies the # of clusters + n_cols = np.max(ks) - 1 + percentiles = np.full((n_rows, n_cols), np.nan, dtype=float) + for idx, k in enumerate(ks): + perc = get_perc_from_k(k) + if as_percentage: + perc = np.round(perc * 100).astype(float) # Convert to percentage and round + percentiles[idx, :len(perc)] = perc + return percentiles + + +def get_feature_type_and_encode(feature_data: NDArray) -> tuple[NDArray, bool]: + """ + Given the data of one feature as a 1d numpy array (it could also be a pandas.Series), + it returns the same data if it is numerical (float, signed or unsigned integer) or an + encoded version if it is categorical (each category value has a unique integer starting from + zero).` f + + Args: + feature_data: a 1d array with data. + + Returns: + A tuple with two elements: + 1. the feature data: same as input if numerical, encoded version if not numerical. + 2. A boolean indicating whether the feature data is numerical or not. + """ + data_type_is_numerical = feature_data.dtype.kind in ("f", "i", "u") + if data_type_is_numerical: + return feature_data, data_type_is_numerical + + # here np.unique with return_inverse encodes categorical values into numerical ones + return np.unique(feature_data, return_inverse=True)[1], data_type_is_numerical + + +# @njit(cache=True, nogil=True) +def get_range_n_clusters( + n_items: int, internal_n_clusters: Iterable[int] = None +) -> NDArray[np.uint8]: + """ + Given the number of features it returns a tuple of k values to cluster those + features into. By default, it generates a tuple of k values from 2 to + int(np.round(np.sqrt(n_items))) (inclusive). For example, for 25 features, + it will generate this array: (2, 3, 4, 5). + + Args: + n_items: a positive number representing the number of features that + will be clustered into different groups/clusters. + internal_n_clusters: it allows to force a different list of clusters. It + must be a list of integers. Repeated or invalid values will be dropped, + such as values lesser than 2 (a singleton partition is not allowed). + + Returns: + A numpy array with integer values representing numbers of clusters. + """ + + if internal_n_clusters: + # remove k values that are invalid + clusters_range_list = list( + set([int(x) for x in internal_n_clusters if 1 < x < n_items]) + ) + else: + # default behavior if no internal_n_clusters is given: return range from + # 2 to sqrt(n_items) + n_sqrt = int(np.round(np.sqrt(n_items))) + n_sqrt = min((n_sqrt, 10)) + clusters_range_list = list(range(2, n_sqrt + 1)) + + return np.array(clusters_range_list, dtype=np.uint16) + + +# # Todo: restore the original implementation +# @cuda.jit(device=True) +# def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: +# """ +# Given the number of objects and an index, it returns the row/column +# position of the pairwise matrix. For example, if there are n_obj objects +# (such as genes), a condensed 1d array can be created with pairwise +# comparisons between genes, as well as a squared symmetric matrix. This +# function receives the number of objects and the index of the condensed +# array, and returns the coordinates of the squared symmetric matrix. +# Args: +# n_obj: the number of objects. +# idx: the index of the condensed pairwise array across all n_obj objects. +# Returns +# A tuple (i, j) with the coordinates of the squared symmetric matrix +# equivalent to the condensed array. +# """ +# b = 1 - 2 * n_obj +# x = math.floor((-b - math.sqrt(b**2 - 8 * idx)) / 2) +# y = idx + x * (b + x + 2) / 2 + 1 +# return int(x), int(y) + + +@njit(cache=True, nogil=True) +def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: + """ + Given the number of objects and and index, it returns the row/column + position of the pairwise matrix. For example, if there are n_obj objects + (such as genes), a condensed 1d array can be created with pairwise + comparisons between genes, as well as a squared symmetric matrix. This + function receives the number of objects and the index of the condensed + array, and returns the coordiates of the squared symmetric matrix. + + Args: + n_obj: the number of objects. + idx: the index of the condensed pairwise array across all n_obj objects. + + Returns + A tuple (i, j) with the coordinates of the squared symmetric matrix + equivalent to the condensed array. + """ + b = 1 - 2 * n_obj + x = np.floor((-b - np.sqrt(b ** 2 - 8 * idx)) / 2) + y = idx + x * (b + x + 2) / 2 + 1 + return int(x), int(y) + + +def convert_n_clusters(internal_n_clusters: Optional[Union[int, List[int]]]) -> List[int]: + if internal_n_clusters is None: + return [] + + if isinstance(internal_n_clusters, int): + return list(range(2, internal_n_clusters + 1)) + + return list(internal_n_clusters) + + +def get_parts(X: NDArray, + range_n_clusters: NDArray[np.uint8], + data_is_numerical: bool = True + ) -> tuple[cp.ndarray, cp.ndarray]: + """ + Compute parts using CuPy for GPU acceleration. + + Parameters: + X: Input data array of shape (n_features, n_objects) + range_n_clusters: Array of cluster numbers + range_n_percentages: Array of percentages for each cluster number + + Returns: + Reference to the computed partitions on the device global memory + """ + + # Handle case when X is a 1D array + if X.ndim == 1: + nx = 1 # n_features + ny = range_n_clusters.shape[0] # n_clusters + nz = X.shape[0] # n_objects + else: + nx = X.shape[0] # n_features + ny = range_n_clusters.shape[0] # n_clusters + nz = X.shape[1] # n_objects + # print(f"{nx}, {ny}, {nz}") + + # Allocate arrays on device global memory + d_parts = cp.empty((nx, ny, nz), dtype=np.int16) - 1 + d_unique_elem_counts = cp.empty((nx, ny), dtype=np.int16) - 1 + # print(f"prev parts: {d_parts}") + + if data_is_numerical: + # Transfer data to device + d_X = cp.asarray(X) + # Get cutting percentages for each cluster + range_n_percentages = get_range_n_percentages(range_n_clusters) + d_range_n_percentages = cp.asarray(range_n_percentages, dtype=float) + + for x in range(nx): + for y in range(ny): + objects = d_X[x, :] if d_X.ndim == 2 else d_X # objects in a feature row + # Todo: use cupy fusion to optimize the two operations below + percentages = d_range_n_percentages[y, :] + # print(f"GPU percentiles: {percentages}") + bins = cp.quantile(objects, percentages) + # print(f"GPU quantiles: {bins}") + partition = cp.digitize(objects, bins, right=True) + d_parts[x, y, :] = partition + # Count number of unique elements in each partition, used in the ARI computation + d_unique_elem_counts[x, y] = len(cp.unique(partition)) + + # Remove singletons by putting -2 as values + partitions_ks = cp.array([len(cp.unique(d_parts[i, j, :])) for i in range(nx) for j in range(ny)]).reshape(nx, + ny) + d_parts[partitions_ks == 1] = -2 + else: + # If the data is categorical, then the encoded feature is already the partition + # Only the first partition is filled, the rest will be -1 (missing) + # Todo: fix this to handle categorical data + d_parts[:, 0] = cp.asarray(X.astype(cp.int16)) + + # Move data back to host + # h_parts = cp.asnumpy(d_parts) + # print(f"after parts: {d_parts}") + cp.cuda.runtime.deviceSynchronize() + return d_parts, d_unique_elem_counts + + +# # Todo: kernel on partition paris (1D, 1D) instead of paris of matrices (2D, 2D) +# @cuda.jit(device=True) +# def cdist_parts_basic(x: NDArray, y: NDArray, out: NDArray, compare_pair_id: int) -> None: +# """ +# It implements the same functionality in scipy.spatial.distance.cdist but +# for clustering partitions, and instead of a distance it returns the adjusted +# Rand index (ARI). In other words, it mimics this function call: +# +# cdist(x, y, metric=ari) +# +# Only partitions with positive labels (> 0) are compared. This means that +# partitions marked as "singleton" or "empty" (categorical data) are not +# compared. This has the effect of leaving an ARI of 0.0 (zero). +# +# Args: +# x: a 2d array with m_x clustering partitions in rows and n objects in +# columns. +# y: a 2d array with m_y clustering partitions in rows and n objects in +# columns. +# +# Returns: +# A 2d array with m_x rows and m_y columns and the ARI between each +# partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i +# and j. +# """ +# +# for i in range(out.shape[0]): +# if x[i, 0] < 0: +# continue +# +# for j in range(out.shape[1]): +# if y[j, 0] < 0: +# continue +# +# # res[i, j] = ari(x[i], y[j]) +# # ari(x[i], y[j], out, compare_pair_id, i, j) +# res = ari(x[i], y[j]) +# print(res) +# +# return +# +# +# @cuda.jit +# def compute_coef( +# parts: cuda.cudadrv.devicearray, +# max_ari_list: cuda.cudadrv.devicearray, +# max_part_idx_list: cuda.cudadrv.devicearray, +# temp_outs: cuda.cudadrv.devicearray, +# compare_pair_id: int, +# ): +# """ +# Given an index representing each a pair of +# objects/rows/genes, it computes the CCC coefficient for +# each of them. +# +# Args: +# parts: A reference to the 3d GPU partitions array. +# max_part_idx_list: A reference to the 2d GPU array that stores the indexes of the partitions that maximized the ARI. +# max_ari_list: A reference to the 1d GPU array that stores the maximum ARI values. +# compare_pair_id: An id representing a pair of partitions to be compared. +# +# Returns: +# Returns a tuple with two arrays. These two arrays are the same +# arrays returned by the main cm function (cm_values and +# max_parts) but for a subset of the data. +# """ +# n_features = parts.shape[0] +# +# # for idx, data_idx in enumerate(compare_pair_id): +# i, j = get_coords_from_index(n_features, compare_pair_id) +# +# # get partitions for the pair of objects +# obji_parts, objj_parts = parts[i], parts[j] +# +# # compute ari only if partitions are not marked as "missing" +# # (negative values), which is assigned when partitions have +# # one cluster (usually when all data in the feature has the same +# # value). +# if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: +# return +# +# # compare all partitions of one object to the all the partitions +# # of the other object, and get the maximium ARI +# +# cdist_parts_basic( +# obji_parts, +# objj_parts, +# temp_outs, +# compare_pair_id, +# ) +# # max_flat_idx = comp_values.argmax() +# +# # max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) +# # max_part_idx_list[compare_pair_id] = max_idx +# # max_ari_list[compare_pair_id] = np.max((comp_values[max_idx], 0.0)) +# # +# # return max_ari_list, max_part_idx_list +# return + +def get_chunks( + iterable: Union[int, Iterable], n_threads: int, ratio: float = 1 +) -> Iterable[Iterable[int]]: + """ + It splits elements in an iterable in chunks according to the number of + CPU cores available for parallel processing. + + Args: + iterable: an iterable to be split in chunks. If it is an integer, it + will split the iterable given by np.arange(iterable). + n_threads: number of threads available for parallelization. + ratio: a ratio that allows to increase the number of splits given + n_threads. For example, with ratio=1, the function will just split + the iterable in n_threads chunks. If ratio is larger than 1, then + it will split in n_threads * ratio chunks. + + Results: + Another iterable with chunks according to the arguments given. For + example, if iterable is [0, 1, 2, 3, 4, 5] and n_threads is 2, it will + return [[0, 1, 2], [3, 4, 5]]. + """ + if isinstance(iterable, int): + iterable = np.arange(iterable) + + n = len(iterable) + expected_n_chunks = n_threads * ratio + + res = list(chunker(iterable, int(np.ceil(n / expected_n_chunks)))) + + while len(res) < expected_n_chunks <= n: + # look for an element in res that can be split in two + idx = 0 + while len(res[idx]) == 1: + idx = idx + 1 + # Got two chunks + new_chunk = get_chunks(res[idx], 2) + res[idx] = new_chunk[0] + # Insert the second chunk in the next position + res.insert(idx + 1, new_chunk[1]) + + return res + + +def cdist_parts_basic(x: NDArray, y: NDArray) -> NDArray[float]: + """ + It implements the same functionality in scipy.spatial.distance.cdist but + for clustering partitions, and instead of a distance it returns the adjusted + Rand index (ARI). In other words, it mimics this function call: + + cdist(x, y, metric=ari) + + Only partitions with positive labels (> 0) are compared. This means that + partitions marked as "singleton" or "empty" (categorical data) are not + compared. This has the effect of leaving an ARI of 0.0 (zero). + + Args: + x: a 2d array with m_x clustering partitions in rows and n objects in + columns. + y: a 2d array with m_y clustering partitions in rows and n objects in + columns. + + Returns: + A 2d array with m_x rows and m_y columns and the ARI between each + partition pair. Each ij entry is equal to ari(x[i], y[j]) for each i + and j. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + for i in range(res.shape[0]): + if x[i, 0] < 0: + continue + + for j in range(res.shape[1]): + if y[j, 0] < 0: + continue + + res[i, j] = cu_rnd_sc(x[i], y[j]) + # res[i, j] = ari(x[i], y[j]) + + return res + + +def cdist_parts_parallel( + x: NDArray, y: NDArray, executor: ThreadPoolExecutor +) -> NDArray[float]: + """ + It parallelizes cdist_parts_basic function. + + Args: + x: same as in cdist_parts_basic + y: same as in cdist_parts_basic + executor: an pool executor where jobs will be submitted. + + Results: + Same as in cdist_parts_basic. + """ + res = np.zeros((x.shape[0], y.shape[0])) + + inputs = list(chunker(np.arange(res.shape[0]), 1)) + + tasks = {executor.submit(cdist_parts_basic, x[idxs], y): idxs for idxs in inputs} + for t in as_completed(tasks): + idx = tasks[t] + res[idx, :] = t.result() + + return res + + +def ccc( + x: NDArray, + y: NDArray = None, + internal_n_clusters: Union[int, Iterable[int]] = None, + return_parts: bool = False, + n_chunks_threads_ratio: int = 1, + n_jobs: int = 1, +) -> tuple[NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: + """ + This is the main function that computes the Clustermatch Correlation + Coefficient (CCC) between two arrays. The implementation supports numerical + and categorical data. + + Args: + x: an 1d or 2d numerical array with the data. NaN are not supported. + If it is 2d, then the coefficient is computed for each pair of rows + (in case x is a numpy.array) or each pair of columns (pandas.DataFrame). + y: an optional 1d numerical array. If x is 1d and y is given, it computes + the coefficient between x and y. + internal_n_clusters: this parameter can be an integer (the maximum number + of clusters used to split x and y, starting from k=2) or a list of + integer values (a custom list of k values). + return_parts: if True, for each object pair, it returns the partitions + that maximized the coefficient. + n_chunks_threads_ratio: allows to modify how pairwise comparisons are + split across different threads. It's given as the ratio parameter of + function get_chunks. + n_jobs: number of CPU cores to use for parallelization. The value + None will use all available cores (`os.cpu_count()`), and negative + values will use `os.cpu_count() - n_jobs`. Default is 1. + + Returns: + If return_parts is False, only CCC values are returned. + In that case, if x is 2d, a np.ndarray of size n x n is + returned with the coefficient values, where n is the number of rows in x. + If only a single coefficient was computed (for example, x and y were + given as 1d arrays each), then a single scalar is returned. + + If returns_parts is True, then it returns a tuple with three values: + 1) the + coefficients, 2) the partitions indexes that maximized the coefficient + for each object pair, and 3) the partitions for all objects. + + cm_values: if x is 2d, then it is a 1d condensed array of pairwise + coefficients. It has size (n * (n - 1)) / 2, where n is the number + of rows in x. If x and y are given, and they are 1d, then this is a + scalar. The CCC is always between 0 and 1 + (inclusive). If any of the two variables being compared has no + variation (all values are the same), the coefficient is not defined + (np.nan). + + max_parts: an array with n * (n - 1)) / 2 rows (one for each object + pair) and two columns. It has the indexes pointing to each object's + partition (parts, see below) that maximized the ARI. If + cm_values[idx] is nan, then max_parts[idx] will be meaningless. + + parts: a 3d array that contains all the internal partitions generated + for each object in data. parts[i] has the partitions for object i, + whereas parts[i,j] has the partition j generated for object i. The + third dimension is the number of columns in x (if 2d) or elements in + x/y (if 1d). For example, if you want to access the pair of + partitions that maximized the CCC given x and y + (a pair of objects), then max_parts[0] and max_parts[1] have the + partition indexes in parts, respectively: parts[0][max_parts[0]] + points to the partition for x, and parts[1][max_parts[1]] points to + the partition for y. Values could be negative in case + singleton cases were found (-1; usually because input data has all the same + value) or for categorical features (-2). + """ + n_objects = None + n_features = None + # this is a boolean array of size n_features with True if the feature is numerical and False otherwise + X_numerical_type = None + if x.ndim == 1 and (y is not None and y.ndim == 1): + # both x and y are 1d arrays + if not x.shape == y.shape: + raise ValueError("x and y need to be of the same size") + n_objects = x.shape[0] + n_features = 2 + # Create a matrix to store both x and y + X = np.zeros((n_features, n_objects)) + X_numerical_type = np.full((n_features,), True, dtype=bool) + + X[0, :], X_numerical_type[0] = get_feature_type_and_encode(x) + X[1, :], X_numerical_type[1] = get_feature_type_and_encode(y) + elif x.ndim == 2 and y is None: + # x is a 2d array; two things could happen: 1) this is an numpy array, + # in that case, features are in rows, objects are in columns; 2) or this is a + # pandas dataframe, which is the opposite (features in columns and objects in rows), + # plus we have the features data type (numerical, categorical, etc) + + if isinstance(x, np.ndarray): + assert get_feature_type_and_encode(x[0, :])[1], ( + "If data is a 2d numpy array, it has to be numerical. Use pandas.DataFrame if " + "you need to mix features with different data types" + ) + n_objects = x.shape[1] + n_features = x.shape[0] + + X = x + X_numerical_type = np.full((n_features,), True, dtype=bool) + elif hasattr(x, "to_numpy"): + # Here I assume that if x has the attribute "to_numpy" is of type pandas.DataFrame + # Using isinstance(x, pandas.DataFrame) would be more appropriate, but I dont want to + # have pandas as a dependency just for that + n_objects = x.shape[0] + n_features = x.shape[1] + + X = np.zeros((n_features, n_objects)) + X_numerical_type = np.full((n_features,), True, dtype=bool) + + for idx in range(n_features): + X[idx, :], X_numerical_type[idx] = get_feature_type_and_encode( + x.iloc[:, idx] + ) + else: + raise ValueError("Wrong combination of parameters x and y") + + # 1. Partitions Computation + + # Converts internal_n_clusters to a list of integers if it's provided. + internal_n_clusters = convert_n_clusters(internal_n_clusters) + + # Get matrix of partitions for each object pair + range_n_clusters = get_range_n_clusters(n_objects, internal_n_clusters) + + if range_n_clusters.shape[0] == 0: + raise ValueError(f"Data has too few objects: {n_objects}") + + # cm_values stores the CCC coefficients + n_features_comp = (n_features * (n_features - 1)) // 2 + cm_values = np.full(n_features_comp, np.nan) + + # for each object pair being compared, max_parts has the indexes of the + # partitions that maximimized the ARI + max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) + + # X here (and following) is a numpy array features are in rows, objects are in columns + + # Compute partitions for each feature using CuPy + d_parts, d_uniq_ele_counts = get_parts(X, range_n_clusters) + # used in the ARI computation later + n_parts = range_n_clusters.shape[0] + # d_parts_max_per_part = cp.empty(n_features * n_parts, dtype=np.int8) + d_parts_max_per_part = cp.amax(d_parts, axis=2) + print("GPU parts:") + print(d_parts) + print(f"Max per part: {d_parts_max_per_part}") + + # 2. CCC coefficient computation + + # # allocate result arrays on device global memory + # d_max_ari_list = cp.full(n_features_comp, cp.nan, dtype=float) + # d_max_part_idx_list = cp.zeros((n_features_comp, 2), dtype=np.uint64) + # # allocate temporary arrays on device global memory + # d_outs = cp.empty((n_features_comp, range_n_clusters.shape[0], range_n_clusters.shape[0]), dtype=cp.float32) + # print(f"before d_outs: {d_outs}") + # # use 1D gird to parallelize the computation of CCC coefficients + # # Todo: optimize this using updated c_dist function that only compare one partition at a time + # threads_per_block = 1 + # blocks_per_grid = n_features_comp + # for i in range(n_features_comp): + # # Directly pass CuPy arrays to kernels JITed with Numba + # compute_coef[blocks_per_grid, threads_per_block](d_parts, d_max_ari_list, d_max_part_idx_list, d_outs, i) + # # Wait for all comparisons to finish + # cuda.synchronize() + # print(f"after d_outs: {d_outs}") + # # Transfer data back to host + # max_ari_list = cp.asnumpy(d_max_ari_list) + # max_part_idx_list = cp.asnumpy(d_max_part_idx_list) + # print(max_ari_list) + # print(max_part_idx_list) + + # Use CPU multi-threading for baseline + parts = cp.asnumpy(d_parts) + + default_n_threads = os.cpu_count() + + with ThreadPoolExecutor(max_workers=default_n_threads) as executor: + + # Below, there are two layers of parallelism: 1) parallel execution + # across feature pairs and 2) the cdist_parts_parallel function, which + # also runs several threads to compare partitions using ari. In 2) we + # need to disable parallelization in case len(cm_values) > 1 (that is, + # we have several feature pairs to compare), because parallelization is + # already performed at this level. Otherwise, more threads than + # specified by the user are started. + cdist_parts_enable_threading = True if n_features_comp == 1 else False + + cdist_func = None + map_func = executor.map + if cdist_parts_enable_threading: + map_func = map + + def cdist_func(x, y): + return cdist_parts_parallel(x, y, executor) + + else: + cdist_func = cdist_parts_basic + + # compute coefficients + def compute_coef(idx_list): + """ + Given a list of indexes representing each a pair of + objects/rows/genes, it computes the CCC coefficient for + each of them. This function is supposed to be used to parallelize + processing. + + Args: + idx_list: a list of indexes (integers), each of them + representing a pair of objects. + + Returns: + Returns a tuple with two arrays. These two arrays are the same + arrays returned by the main cm function (cm_values and + max_parts) but for a subset of the data. + """ + n_idxs = len(idx_list) + max_ari_list = np.full(n_idxs, np.nan, dtype=float) + max_part_idx_list = np.zeros((n_idxs, 2), dtype=np.uint64) + + for idx, data_idx in enumerate(idx_list): + i, j = get_coords_from_index(n_features, data_idx) + + # get partitions for the pair of objects + obji_parts, objj_parts = parts[i], parts[j] + + # compute ari only if partitions are not marked as "missing" + # (negative values), which is assigned when partitions have + # one cluster (usually when all data in the feature has the same + # value). + if obji_parts[0, 0] == -2 or objj_parts[0, 0] == -2: + continue + + # compare all partitions of one object to the all the partitions + # of the other object, and get the maximium ARI + comp_values = cdist_func( + obji_parts, + objj_parts, + ) + max_flat_idx = comp_values.argmax() + + max_idx = unravel_index_2d(max_flat_idx, comp_values.shape) + max_part_idx_list[idx] = max_idx + max_ari_list[idx] = np.max((comp_values[max_idx], 0.0)) + + return max_ari_list, max_part_idx_list + + # iterate over all chunks of object pairs and compute the coefficient + inputs = get_chunks(n_features_comp, default_n_threads, n_chunks_threads_ratio) + + for idx, (max_ari_list, max_part_idx_list) in zip( + inputs, map_func(compute_coef, inputs) + ): + cm_values[idx] = max_ari_list + max_parts[idx, :] = max_part_idx_list + + # return an array of values or a single scalar, depending on the input data + if cm_values.shape[0] == 1: + if return_parts: + return cm_values[0], max_parts[0], parts + else: + return cm_values[0] + + if return_parts: + return cm_values, max_parts, parts + else: + return cm_values + +# Dev notes +# 1. parallelize get_parst +# 1.1 gpu percentile computation +# 1.1 gpu data points binning +# can be a kernel for-loop to compute parts on different percentile \ No newline at end of file From dca231ce5c953bdf1142717c233a2d0234beece7 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 15 Jan 2025 14:26:13 -0700 Subject: [PATCH 126/134] Clean up test files --- tests/gpu/excluded/test_coef_subroutines.py | 372 ++++++++++++++++++ tests/gpu/{ => excluded}/test_cuml.py | 0 .../gpu/{ => excluded}/test_cuml_in_kernel.py | 0 tests/gpu/{ => excluded}/test_cupy.py | 0 .../{ => excluded}/test_device_host_funcs.py | 0 tests/gpu/excluded/test_get_parts.py | 294 ++++++++++++++ .../{ => excluded}/test_get_percentiles.py | 0 .../test_sklearn_metrics_gpu.py | 0 tests/gpu/{test_ari.py => test_ari_gpu.py} | 0 tests/gpu/test_binding.py | 7 - tests/gpu/test_coef_computation.py | 12 - tests/gpu/test_coef_subroutines.py | 369 ----------------- tests/gpu/test_get_parts.py | 291 -------------- ...l_gpu_against_impl.py => test_impl_gpu.py} | 0 14 files changed, 666 insertions(+), 679 deletions(-) create mode 100644 tests/gpu/excluded/test_coef_subroutines.py rename tests/gpu/{ => excluded}/test_cuml.py (100%) rename tests/gpu/{ => excluded}/test_cuml_in_kernel.py (100%) rename tests/gpu/{ => excluded}/test_cupy.py (100%) rename tests/gpu/{ => excluded}/test_device_host_funcs.py (100%) create mode 100644 tests/gpu/excluded/test_get_parts.py rename tests/gpu/{ => excluded}/test_get_percentiles.py (100%) rename tests/gpu/{ => excluded}/test_sklearn_metrics_gpu.py (100%) rename tests/gpu/{test_ari.py => test_ari_gpu.py} (100%) delete mode 100644 tests/gpu/test_binding.py delete mode 100644 tests/gpu/test_coef_computation.py delete mode 100644 tests/gpu/test_coef_subroutines.py delete mode 100644 tests/gpu/test_get_parts.py rename tests/gpu/{test_impl_gpu_against_impl.py => test_impl_gpu.py} (100%) diff --git a/tests/gpu/excluded/test_coef_subroutines.py b/tests/gpu/excluded/test_coef_subroutines.py new file mode 100644 index 00000000..82bfe8bb --- /dev/null +++ b/tests/gpu/excluded/test_coef_subroutines.py @@ -0,0 +1,372 @@ +# This test file is used to verify the correctness of the GPU version of subroutine functions +# Now we fall back to the original CPU implementation of ccc, so this test file is not used for now + +# import pytest +# +# import numpy as np +# import cupy as cp +# from numpy.testing import assert_array_equal, assert_allclose +# +# from ccc.coef.impl_gpu import ( +# get_perc_from_k, +# get_range_n_percentages, +# convert_n_clusters, +# get_range_n_clusters, +# get_parts, +# ) +# +# +# def test_get_perc_from_k_with_k_less_than_two(): +# empty_array = np.empty(0) +# assert_array_equal(get_perc_from_k(1), empty_array) +# assert_array_equal(get_perc_from_k(0), empty_array) +# assert_array_equal(get_perc_from_k(-1), empty_array) +# +# +# @pytest.mark.parametrize("k, expected", [ +# (2, [0.5]), +# (3, [0.333, 0.667]), +# (4, [0.25, 0.50, 0.75]) +# ]) +# def test_get_perc_from_k(k, expected): +# assert_allclose(np.ndarray.round(get_perc_from_k(k), 3), expected) +# +# +# @pytest.mark.parametrize( +# "ks, expected", +# [ +# ( +# np.array([], dtype=np.int8), +# np.empty((0, 0), dtype=np.float32) +# ), +# ( +# np.array([2, 3, 4], dtype=np.int8), +# np.array([ +# [0.5, np.nan, np.nan], +# [0.33333334, 0.6666667, np.nan], +# [0.25, 0.5, 0.75] +# ], dtype=np.float32) +# ), +# ( +# np.array([2], dtype=np.int8), +# np.array([[0.5]], dtype=np.float32) +# ), +# ( +# np.array([10], dtype=np.int8), +# np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32) +# ), +# ( +# np.array([2, 4, 6, 8], dtype=np.int8), +# np.array([ +# [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], +# [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], +# [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], +# [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] +# ], dtype=np.float32) +# ), +# ( +# np.array([2, 3, 4], dtype=np.int8), +# np.array([ +# [0.5, np.nan, np.nan], +# [0.33333334, 0.6666667, np.nan], +# [0.25, 0.5, 0.75], +# ], dtype=np.float32) +# ), +# ] +# ) +# def test_get_range_n_percs(ks, expected): +# result = get_range_n_percentages(ks) +# np.testing.assert_array_almost_equal(result, expected) +# +# +# @pytest.mark.parametrize( +# "ks, expected_frac, expected_perc", +# [ +# ( +# np.array([], dtype=np.int8), +# np.empty((0, 0), dtype=np.float32), +# np.empty((0, 0), dtype=np.float32) +# ), +# ( +# np.array([2, 3, 4], dtype=np.int8), +# np.array([ +# [0.5, np.nan, np.nan], +# [0.33333334, 0.6666667, np.nan], +# [0.25, 0.5, 0.75] +# ], dtype=np.float32), +# np.array([ +# [50, np.nan, np.nan], +# [33, 67, np.nan], +# [25, 50, 75] +# ], dtype=np.float32) +# ), +# ( +# np.array([2], dtype=np.int8), +# np.array([[0.5]], dtype=np.float32), +# np.array([[50]], dtype=np.float32) +# ), +# ( +# np.array([10], dtype=np.int8), +# np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32), +# np.array([[10, 20, 30, 40, 50, 60, 70, 80, 90]], dtype=np.float32) +# ), +# ( +# np.array([2, 4, 6, 8], dtype=np.int8), +# np.array([ +# [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], +# [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], +# [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], +# [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] +# ], dtype=np.float32), +# np.array([ +# [50, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], +# [25, 50, 75, np.nan, np.nan, np.nan, np.nan], +# [17, 33, 50, 67, 83, np.nan, np.nan], +# [12, 25, 38, 50, 62, 75, 88] +# ], dtype=np.float32) +# ), +# ] +# ) +# def test_get_range_n_percs_as_percentage(ks, expected_frac, expected_perc): +# # Test fractional percentiles (original behavior) +# result_frac = get_range_n_percentages(ks, as_percentage=False) +# np.testing.assert_array_almost_equal(result_frac, expected_frac) +# +# # Test percentage numbers +# result_perc = get_range_n_percentages(ks, as_percentage=True) +# np.testing.assert_array_almost_equal(result_perc, expected_perc) +# +# +# @pytest.mark.parametrize( +# "input_value, expected_output", +# [ +# (None, []), +# (2, [2]), +# (5, [2, 3, 4, 5]), +# ([1, 3, 5], [1, 3, 5]), +# ([], []), +# ((7, 8, 9), [7, 8, 9]), +# ] +# ) +# def test_convert_n_clusters(input_value, expected_output): +# assert convert_n_clusters(input_value) == expected_output +# +# +# def test_get_range_n_clusters_without_internal_n_clusters(): +# # 100 features +# range_n_clusters = get_range_n_clusters(100) +# assert range_n_clusters is not None +# np.testing.assert_array_equal( +# range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) +# ) +# +# # 25 features +# range_n_clusters = get_range_n_clusters(25) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) +# +# +# def test_get_range_n_clusters_with_internal_n_clusters_is_list(): +# # 100 features +# range_n_clusters = get_range_n_clusters( +# 100, +# internal_n_clusters=[2], +# ) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2])) +# +# # 25 features +# range_n_clusters = get_range_n_clusters( +# 25, +# internal_n_clusters=[2], +# ) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2])) +# +# # 25 features +# range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[2, 3, 4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) +# +# +# def test_get_range_n_clusters_with_internal_n_clusters_none(): +# # 100 features +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=None) +# assert range_n_clusters is not None +# np.testing.assert_array_equal( +# range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) +# ) +# +# # 25 features +# range_n_clusters = get_range_n_clusters(25, internal_n_clusters=None) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) +# +# +# def test_get_range_n_clusters_with_internal_n_clusters_has_single_int(): +# # 100 features +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2])) +# +# # 25 features +# range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[3]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([3])) +# +# # 5 features +# range_n_clusters = get_range_n_clusters(5, internal_n_clusters=[4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([4])) +# +# # 25 features but invalid number of clusters +# range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[1]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# # 25 features but invalid number of clusters +# range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[25]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# +# def test_get_range_n_clusters_with_internal_n_clusters_are_less_than_two(): +# # 100 features +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) +# +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, 4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) +# +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 1]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3])) +# +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 0, 4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) +# +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, -4, 6]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 6])) +# +# +# def test_get_range_n_clusters_with_internal_n_clusters_are_repeated(): +# # 100 features +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 3, 2, 4]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) +# +# range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 2, 2]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2])) +# +# +# def test_get_range_n_clusters_with_very_few_features(): +# # 3 features +# range_n_clusters = get_range_n_clusters(3) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([2])) +# +# # 2 features +# range_n_clusters = get_range_n_clusters(2) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# # 1 features +# range_n_clusters = get_range_n_clusters(1) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# # 0 features +# range_n_clusters = get_range_n_clusters(0) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# +# def test_get_range_n_clusters_with_larger_k_than_features(): +# # 10 features +# range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[10]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# # 10 features +# range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[11]) +# assert range_n_clusters is not None +# np.testing.assert_array_equal(range_n_clusters, np.array([])) +# +# +# def test_get_range_n_clusters_with_default_max_k(): +# range_n_clusters = get_range_n_clusters(200) +# assert range_n_clusters is not None +# np.testing.assert_array_equal( +# range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) +# ) +# +# # get_parts +# def test_get_parts_simple(): +# np.random.seed(0) +# +# # Test with 2 clusters +# features0 = np.random.rand(100) +# parts = get_parts(features0, np.array([2], dtype=np.uint8)).get() +# assert parts is not None +# assert len(parts) == 1, "should have only one feature" +# assert len(parts[0]) == 1, "should have only one partition" +# assert len(np.unique(parts[0])) == 2, "should have 2 cluster indexes" +# +# # Test with [2, 3] clusters +# parts = get_parts(features0, np.array([2, 3], dtype=np.uint8)).get() +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 2, "feature should have 2 clusters" +# assert len(np.unique(parts[0][0])) == 2 +# assert len(np.unique(parts[0][1])) == 3 +# +# +# def test_get_parts_with_singletons(): +# np.random.seed(0) +# +# feature0 = np.array([1.3] * 10) +# +# # run +# parts = get_parts(feature0, np.array([2], dtype=np.uint8)).get() +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 1 +# # all the elements (2D) should be -2 +# np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) +# +# parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8)).get() +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 2, "feature should have 2 clusters" +# np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) +# np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) +# +# +# def test_get_parts_with_categorical_feature(): +# mempool = cp.get_default_memory_pool() +# mempool.free_all_blocks() +# +# np.random.seed(0) +# +# feature0 = np.array([4] * 10) +# +# # run +# # only one partition is requested +# parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False).get() +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 1 +# np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) +# +# # more partitions are requested; only the first two has valid information +# parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False).get() +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 2 +# np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) +# np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) diff --git a/tests/gpu/test_cuml.py b/tests/gpu/excluded/test_cuml.py similarity index 100% rename from tests/gpu/test_cuml.py rename to tests/gpu/excluded/test_cuml.py diff --git a/tests/gpu/test_cuml_in_kernel.py b/tests/gpu/excluded/test_cuml_in_kernel.py similarity index 100% rename from tests/gpu/test_cuml_in_kernel.py rename to tests/gpu/excluded/test_cuml_in_kernel.py diff --git a/tests/gpu/test_cupy.py b/tests/gpu/excluded/test_cupy.py similarity index 100% rename from tests/gpu/test_cupy.py rename to tests/gpu/excluded/test_cupy.py diff --git a/tests/gpu/test_device_host_funcs.py b/tests/gpu/excluded/test_device_host_funcs.py similarity index 100% rename from tests/gpu/test_device_host_funcs.py rename to tests/gpu/excluded/test_device_host_funcs.py diff --git a/tests/gpu/excluded/test_get_parts.py b/tests/gpu/excluded/test_get_parts.py new file mode 100644 index 00000000..7693ef18 --- /dev/null +++ b/tests/gpu/excluded/test_get_parts.py @@ -0,0 +1,294 @@ +# This test file is used to verify the correctness of the GPU version of get_parts function +# Now we fall back to the original CPU implementation of get_parts function, so this test file is not used for now + +# import pytest +# +# import numpy as np +# import cupy as cp +# +# from ccc.coef.impl_gpu import ( +# get_parts, +# ) +# +# from ccc.coef import get_parts as get_parts_cpu +# from ccc.coef import get_perc_from_k as get_perc_from_k_cpu +# import functools +# +# +# def clean_gpu_memory(func): +# @functools.wraps(func) +# def wrapper(*args, **kwargs): +# try: +# return func(*args, **kwargs) +# finally: +# mempool = cp.get_default_memory_pool() +# mempool.free_all_blocks() +# return wrapper +# +# +# def find_partition(value, quantiles): +# for i in range(len(quantiles)): +# if value <= quantiles[i]: +# return i +# return len(quantiles) # If value is greater than all quantiles +# +# +# def verify_partition(feature, index, n_clusters): +# """ +# Verify the partition for a specific element in the feature array. +# """ +# parts_cpu = get_parts_cpu(feature, (n_clusters,)) +# percentages_cpu = get_perc_from_k_cpu(n_clusters) +# quantities = np.quantile(feature, percentages_cpu) +# +# value = feature[index] +# partition = find_partition(value, quantities) +# +# print(f"\nVerifying partition for feature[{index}] = {value}") +# print(f"CPU percentages: {percentages_cpu}") +# print(f"CPU quantities: {quantities}") +# +# print("\nAll partition ranges:") +# for i in range(n_clusters): +# if i == 0: +# print(f"Partition {i} range: (-inf, {quantities[i]}]") +# elif i == n_clusters - 1: +# print(f"Partition {i} range: ({quantities[i-1]}, inf)") +# else: +# print(f"Partition {i} range: ({quantities[i-1]}, {quantities[i]}]") +# +# print(f"Data point {value} should fall in partition {partition}") +# print(f"Partition computed by CCC_CPU: {parts_cpu[0][index]}") +# +# assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" +# return partition +# +# +# @clean_gpu_memory +# @pytest.mark.parametrize("feature_size", [100, 1000, 10000, 100000]) +# @pytest.mark.parametrize("cluster_settings", [ +# ([2], (2,)), +# ([2, 3], (2, 3)), +# ([2, 3, 4], (2, 3, 4)), +# ([5], (5,)), +# ([6], (6,)), +# ([9], (9,)), +# ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), +# ]) +# @pytest.mark.parametrize("seed, distribution, params", [ +# (0, "rand", {}), # Uniform distribution +# (42, "randn", {}), # Normal distribution +# (123, "randint", {"low": 0, "high": 100}), # Integer distribution +# (456, "exponential", {"scale": 2.0}), # Exponential distribution +# ]) +# def test_get_parts(feature_size, cluster_settings, seed, distribution, params): +# # Given FP arithmetic is not associative and the difference between GPU and CPU FP arithmetic, +# # we need to allow for some tolerance. This is a tentative value that may need to be adjusted. +# # Note that the difference between GPU and CPU results is not expected to be larger than 1. +# n_diff_tolerance = int(feature_size * 0.04) +# +# np.random.seed(seed) +# +# gpu_clusters, cpu_clusters = cluster_settings +# +# # Generate random features based on the specified distribution +# if distribution == "rand": +# feature = np.random.rand(feature_size) +# elif distribution == "randn": +# feature = np.random.randn(feature_size) +# elif distribution == "randint": +# feature = np.random.randint(params["low"], params["high"], feature_size) +# elif distribution == "exponential": +# feature = np.random.exponential(params["scale"], feature_size) +# elif distribution == "binomial": +# feature = np.random.binomial(params["n"], params["p"], feature_size) +# else: +# raise ValueError(f"Unsupported distribution: {distribution}") +# +# # GPU implementation +# parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8))[0].get() +# +# # CPU implementation +# parts_cpu = get_parts_cpu(feature, cpu_clusters) +# +# print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}, distribution={distribution}") +# print(f"GPU output shape: {parts_gpu.shape}") +# print(f"CPU output shape: {parts_cpu.shape}") +# +# assert parts_gpu is not None, "GPU output is None" +# assert len(parts_gpu) == 1, f"Expected 1 feature, got {len(parts_gpu)}" +# assert len(parts_gpu[0]) == len(gpu_clusters), f"Expected {len(gpu_clusters)} partition(s), got {len(parts_gpu[0])}" +# +# for i, n_clusters in enumerate(gpu_clusters): +# gpu_unique = np.unique(parts_gpu[0][i]) +# cpu_unique = np.unique(parts_cpu[i]) +# +# print(f"\nPartition {i}:") +# print(f" GPU unique values (partitions): {gpu_unique}") +# print(f" CPU unique values (partitions): {cpu_unique}") +# +# assert len(gpu_unique) == n_clusters, f"Expected {n_clusters} cluster indexes, got {len(gpu_unique)}" +# +# if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): +# diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] +# diff_values = np.abs(parts_gpu[0][i][diff_indices] - parts_cpu[i][diff_indices]) +# max_diff = np.max(diff_values) +# +# print(f"\nDifferences found in partition {i}:") +# print(f" Number of differing elements: {len(diff_indices)}") +# print(f" Maximum difference: {max_diff}") +# print(f" First 10 differing indices: {diff_indices[:10]}") +# print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") +# print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") +# print(f" Object values at these indices: {feature[diff_indices[:10]]}") +# +# if len(diff_indices) > n_diff_tolerance or max_diff > 1: +# # Verify partitions for differing elements +# for idx in diff_indices[:10]: +# expected_partition = verify_partition(feature, idx, n_clusters) +# assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" +# +# assert False, f"GPU and CPU results don't match for {n_clusters} clusters: " \ +# f"diff count = {len(diff_indices)}, max diff = {max_diff}" +# else: +# print(f" Differences within tolerance (count <= {n_diff_tolerance} and max diff <= 1)") +# +# # Additional checks for multi-cluster settings +# if len(gpu_clusters) > 1: +# for i in range(len(gpu_clusters)): +# for j in range(i + 1, len(gpu_clusters)): +# if np.array_equal(parts_gpu[0][i], parts_cpu[j]): +# print(f"\nUnexpected equality between partitions {i} and {j}:") +# print(f" Partition {i}: {parts_gpu[0][i]}") +# print(f" Partition {j}: {parts_cpu[j]}") +# assert False, f"Partitions {i} and {j} should not be equal" +# +# +# def test_specific_elements(): +# mempool = cp.get_default_memory_pool() +# mempool.free_all_blocks() +# +# np.random.seed(0) +# feature = np.random.rand(100) +# assert feature[77] == 0.1201965612131689 +# assert feature[78] == 0.29614019752214493 +# +# verify_partition(feature, 77, 6) +# verify_partition(feature, 78, 6) +# +# +# @clean_gpu_memory +# def test_potential_buggy_cpu_impl(): +# +# np.random.seed(0) +# feature = np.random.rand(100) +# assert feature[77] == 0.1201965612131689 +# assert feature[78] == 0.29614019752214493 +# parts_cpu = get_parts_cpu(feature, (6, )) +# percentages_cpu = get_perc_from_k_cpu(6) +# quantities = np.quantile(feature, percentages_cpu) +# print() +# print(f"CPU parts: \n{parts_cpu}") +# print(f"CPU percentages: \n{percentages_cpu}") +# print(f"CPU quantities: \n{quantities}") +# +# # Find which partitions feature[77] and feature[78] fall into +# value_77 = feature[77] +# value_78 = feature[78] +# partition_77 = find_partition(value_77, quantities) +# partition_78 = find_partition(value_78, quantities) +# +# print(f"feature[77] = {value_77} falls in partition {partition_77}") +# print(f"feature[78] = {value_78} falls in partition {partition_78}") +# if partition_77 > 0: +# print(f"Partition {partition_77} range: ({quantities[partition_77-1]}, {quantities[partition_77]}]") +# else: +# print(f"Partition {partition_77} range: (-inf, {quantities[partition_77]}]") +# if partition_78 > 0: +# print(f"Partition {partition_78} range: ({quantities[partition_78-1]}, {quantities[partition_78]}]") +# else: +# print(f"Partition {partition_78} range: (-inf, {quantities[partition_78]}]") +# print(f"Partition computed by CCC_CPU for feature[77]: {parts_cpu[0][77]}") +# print(f"Partition computed by CCC_CPU for feature[78]: {parts_cpu[0][78]}") +# assert partition_77 == parts_cpu[0][77] +# assert partition_78 == parts_cpu[0][78] +# +# +# @clean_gpu_memory +# def test_get_parts_with_singletons(): +# +# np.random.seed(0) +# +# feature0 = np.array([1.3] * 100) +# +# # run +# parts = get_parts(feature0, np.array([2], dtype=np.uint8))[0].get() +# parts_cpu = get_parts_cpu(feature0, (2,)) +# assert parts is not None +# assert len(parts) == 1 # 1 feature +# assert len(parts[0]) == 1 # 1 partition +# # all the elements (2D) should be -2 +# np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) +# assert np.array_equal(parts[0], parts_cpu) +# +# parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8))[0].get() +# parts_cpu = get_parts_cpu(feature0, (2, 3)) +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 2, "feature should have 2 clusters" +# np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) +# np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) +# assert np.array_equal(parts[0][0], parts_cpu[0]) +# assert np.array_equal(parts[0][1], parts_cpu[1]) +# +# +# @clean_gpu_memory +# def test_get_parts_with_categorical_feature(): +# np.random.seed(0) +# +# feature0 = np.array([4] * 10) +# +# # run +# # only one partition is requested +# parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False)[0].get() +# parts_cpu = get_parts_cpu(feature0, (2,), data_is_numerical=False) +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 1 +# np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) +# assert np.array_equal(parts[0], parts_cpu) +# +# # more partitions are requested; only the first one has valid information +# parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False)[0].get() +# parts_cpu = get_parts_cpu(feature0, (2, 3), data_is_numerical=False) +# assert parts is not None +# assert len(parts) == 1 +# assert len(parts[0]) == 2 +# print("parts:") +# print(parts) +# print("parts_cpu:") +# print(parts_cpu) +# np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) +# np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) +# assert (parts == parts_cpu).all() +# assert np.array_equal(parts[0][0], parts_cpu[0]) +# assert np.array_equal(parts[0][1], parts_cpu[1]) +# +# +# @clean_gpu_memory +# def test_get_parts_2d_simple(): +# np.random.seed(0) +# array = np.random.rand(5, 1000) +# print(f"array : \n{array}") +# parts = get_parts(array, np.array([3], dtype=np.uint8))[0].get() +# parts_cpu_row0 = get_parts_cpu(array[0], (3, )) +# parts_cpu_row1 = get_parts_cpu(array[1], (3, )) +# assert parts is not None +# assert (parts[0] == parts_cpu_row0).all() +# assert (parts[1] == parts_cpu_row1).all() +# print("parts:") +# print(parts) +# print("parts_cpu_row0:") +# print(parts_cpu_row0) +# print("parts_cpu_row1:") +# print(parts_cpu_row1) diff --git a/tests/gpu/test_get_percentiles.py b/tests/gpu/excluded/test_get_percentiles.py similarity index 100% rename from tests/gpu/test_get_percentiles.py rename to tests/gpu/excluded/test_get_percentiles.py diff --git a/tests/gpu/test_sklearn_metrics_gpu.py b/tests/gpu/excluded/test_sklearn_metrics_gpu.py similarity index 100% rename from tests/gpu/test_sklearn_metrics_gpu.py rename to tests/gpu/excluded/test_sklearn_metrics_gpu.py diff --git a/tests/gpu/test_ari.py b/tests/gpu/test_ari_gpu.py similarity index 100% rename from tests/gpu/test_ari.py rename to tests/gpu/test_ari_gpu.py diff --git a/tests/gpu/test_binding.py b/tests/gpu/test_binding.py deleted file mode 100644 index 7a94b307..00000000 --- a/tests/gpu/test_binding.py +++ /dev/null @@ -1,7 +0,0 @@ -from ._core import add - -def test_add(): - assert(add(3, 4) == 7) - -if __name__ == '__main__': - test_add() diff --git a/tests/gpu/test_coef_computation.py b/tests/gpu/test_coef_computation.py deleted file mode 100644 index 61e7b370..00000000 --- a/tests/gpu/test_coef_computation.py +++ /dev/null @@ -1,12 +0,0 @@ -import pytest - -import numpy as np -from ccc.coef.impl_gpu import ccc as ccc_gpu - - -def test_temp(): - np.random.seed(0) - feature1 = np.random.rand(100) - feature2 = np.random.rand(100) - c = ccc_gpu(feature1, feature2) - print(c) diff --git a/tests/gpu/test_coef_subroutines.py b/tests/gpu/test_coef_subroutines.py deleted file mode 100644 index 7e5059ec..00000000 --- a/tests/gpu/test_coef_subroutines.py +++ /dev/null @@ -1,369 +0,0 @@ -import pytest - -import numpy as np -import cupy as cp -from numpy.testing import assert_array_equal, assert_allclose - -from ccc.coef.impl_gpu import ( - get_perc_from_k, - get_range_n_percentages, - convert_n_clusters, - get_range_n_clusters, - get_parts, -) - - -def test_get_perc_from_k_with_k_less_than_two(): - empty_array = np.empty(0) - assert_array_equal(get_perc_from_k(1), empty_array) - assert_array_equal(get_perc_from_k(0), empty_array) - assert_array_equal(get_perc_from_k(-1), empty_array) - - -@pytest.mark.parametrize("k, expected", [ - (2, [0.5]), - (3, [0.333, 0.667]), - (4, [0.25, 0.50, 0.75]) -]) -def test_get_perc_from_k(k, expected): - assert_allclose(np.ndarray.round(get_perc_from_k(k), 3), expected) - - -@pytest.mark.parametrize( - "ks, expected", - [ - ( - np.array([], dtype=np.int8), - np.empty((0, 0), dtype=np.float32) - ), - ( - np.array([2, 3, 4], dtype=np.int8), - np.array([ - [0.5, np.nan, np.nan], - [0.33333334, 0.6666667, np.nan], - [0.25, 0.5, 0.75] - ], dtype=np.float32) - ), - ( - np.array([2], dtype=np.int8), - np.array([[0.5]], dtype=np.float32) - ), - ( - np.array([10], dtype=np.int8), - np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32) - ), - ( - np.array([2, 4, 6, 8], dtype=np.int8), - np.array([ - [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], - [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], - [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], - [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] - ], dtype=np.float32) - ), - ( - np.array([2, 3, 4], dtype=np.int8), - np.array([ - [0.5, np.nan, np.nan], - [0.33333334, 0.6666667, np.nan], - [0.25, 0.5, 0.75], - ], dtype=np.float32) - ), - ] -) -def test_get_range_n_percs(ks, expected): - result = get_range_n_percentages(ks) - np.testing.assert_array_almost_equal(result, expected) - - -@pytest.mark.parametrize( - "ks, expected_frac, expected_perc", - [ - ( - np.array([], dtype=np.int8), - np.empty((0, 0), dtype=np.float32), - np.empty((0, 0), dtype=np.float32) - ), - ( - np.array([2, 3, 4], dtype=np.int8), - np.array([ - [0.5, np.nan, np.nan], - [0.33333334, 0.6666667, np.nan], - [0.25, 0.5, 0.75] - ], dtype=np.float32), - np.array([ - [50, np.nan, np.nan], - [33, 67, np.nan], - [25, 50, 75] - ], dtype=np.float32) - ), - ( - np.array([2], dtype=np.int8), - np.array([[0.5]], dtype=np.float32), - np.array([[50]], dtype=np.float32) - ), - ( - np.array([10], dtype=np.int8), - np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]], dtype=np.float32), - np.array([[10, 20, 30, 40, 50, 60, 70, 80, 90]], dtype=np.float32) - ), - ( - np.array([2, 4, 6, 8], dtype=np.int8), - np.array([ - [0.5, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], - [0.25, 0.5, 0.75, np.nan, np.nan, np.nan, np.nan], - [0.16666667, 0.33333334, 0.5, 0.6666667, 0.8333333, np.nan, np.nan], - [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875] - ], dtype=np.float32), - np.array([ - [50, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], - [25, 50, 75, np.nan, np.nan, np.nan, np.nan], - [17, 33, 50, 67, 83, np.nan, np.nan], - [12, 25, 38, 50, 62, 75, 88] - ], dtype=np.float32) - ), - ] -) -def test_get_range_n_percs_as_percentage(ks, expected_frac, expected_perc): - # Test fractional percentiles (original behavior) - result_frac = get_range_n_percentages(ks, as_percentage=False) - np.testing.assert_array_almost_equal(result_frac, expected_frac) - - # Test percentage numbers - result_perc = get_range_n_percentages(ks, as_percentage=True) - np.testing.assert_array_almost_equal(result_perc, expected_perc) - - -@pytest.mark.parametrize( - "input_value, expected_output", - [ - (None, []), - (2, [2]), - (5, [2, 3, 4, 5]), - ([1, 3, 5], [1, 3, 5]), - ([], []), - ((7, 8, 9), [7, 8, 9]), - ] -) -def test_convert_n_clusters(input_value, expected_output): - assert convert_n_clusters(input_value) == expected_output - - -def test_get_range_n_clusters_without_internal_n_clusters(): - # 100 features - range_n_clusters = get_range_n_clusters(100) - assert range_n_clusters is not None - np.testing.assert_array_equal( - range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) - ) - - # 25 features - range_n_clusters = get_range_n_clusters(25) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) - - -def test_get_range_n_clusters_with_internal_n_clusters_is_list(): - # 100 features - range_n_clusters = get_range_n_clusters( - 100, - internal_n_clusters=[2], - ) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2])) - - # 25 features - range_n_clusters = get_range_n_clusters( - 25, - internal_n_clusters=[2], - ) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2])) - - # 25 features - range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[2, 3, 4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) - - -def test_get_range_n_clusters_with_internal_n_clusters_none(): - # 100 features - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=None) - assert range_n_clusters is not None - np.testing.assert_array_equal( - range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) - ) - - # 25 features - range_n_clusters = get_range_n_clusters(25, internal_n_clusters=None) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4, 5])) - - -def test_get_range_n_clusters_with_internal_n_clusters_has_single_int(): - # 100 features - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2])) - - # 25 features - range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[3]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([3])) - - # 5 features - range_n_clusters = get_range_n_clusters(5, internal_n_clusters=[4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([4])) - - # 25 features but invalid number of clusters - range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[1]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - # 25 features but invalid number of clusters - range_n_clusters = get_range_n_clusters(25, internal_n_clusters=[25]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - -def test_get_range_n_clusters_with_internal_n_clusters_are_less_than_two(): - # 100 features - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) - - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, 4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) - - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 3, 1]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3])) - - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 0, 4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 4])) - - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[1, 2, 1, -4, 6]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 6])) - - -def test_get_range_n_clusters_with_internal_n_clusters_are_repeated(): - # 100 features - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 3, 2, 4]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2, 3, 4])) - - range_n_clusters = get_range_n_clusters(100, internal_n_clusters=[2, 2, 2]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2])) - - -def test_get_range_n_clusters_with_very_few_features(): - # 3 features - range_n_clusters = get_range_n_clusters(3) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([2])) - - # 2 features - range_n_clusters = get_range_n_clusters(2) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - # 1 features - range_n_clusters = get_range_n_clusters(1) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - # 0 features - range_n_clusters = get_range_n_clusters(0) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - -def test_get_range_n_clusters_with_larger_k_than_features(): - # 10 features - range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[10]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - # 10 features - range_n_clusters = get_range_n_clusters(10, internal_n_clusters=[11]) - assert range_n_clusters is not None - np.testing.assert_array_equal(range_n_clusters, np.array([])) - - -def test_get_range_n_clusters_with_default_max_k(): - range_n_clusters = get_range_n_clusters(200) - assert range_n_clusters is not None - np.testing.assert_array_equal( - range_n_clusters, np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]) - ) - -# get_parts -def test_get_parts_simple(): - np.random.seed(0) - - # Test with 2 clusters - features0 = np.random.rand(100) - parts = get_parts(features0, np.array([2], dtype=np.uint8)).get() - assert parts is not None - assert len(parts) == 1, "should have only one feature" - assert len(parts[0]) == 1, "should have only one partition" - assert len(np.unique(parts[0])) == 2, "should have 2 cluster indexes" - - # Test with [2, 3] clusters - parts = get_parts(features0, np.array([2, 3], dtype=np.uint8)).get() - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 2, "feature should have 2 clusters" - assert len(np.unique(parts[0][0])) == 2 - assert len(np.unique(parts[0][1])) == 3 - - -def test_get_parts_with_singletons(): - np.random.seed(0) - - feature0 = np.array([1.3] * 10) - - # run - parts = get_parts(feature0, np.array([2], dtype=np.uint8)).get() - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 1 - # all the elements (2D) should be -2 - np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) - - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8)).get() - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 2, "feature should have 2 clusters" - np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) - np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) - - -def test_get_parts_with_categorical_feature(): - mempool = cp.get_default_memory_pool() - mempool.free_all_blocks() - - np.random.seed(0) - - feature0 = np.array([4] * 10) - - # run - # only one partition is requested - parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False).get() - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 1 - np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) - - # more partitions are requested; only the first two has valid information - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False).get() - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 2 - np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) - np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) diff --git a/tests/gpu/test_get_parts.py b/tests/gpu/test_get_parts.py deleted file mode 100644 index 5ab421ef..00000000 --- a/tests/gpu/test_get_parts.py +++ /dev/null @@ -1,291 +0,0 @@ -import pytest - -import numpy as np -import cupy as cp - -from ccc.coef.impl_gpu import ( - get_parts, -) - -from ccc.coef import get_parts as get_parts_cpu -from ccc.coef import get_perc_from_k as get_perc_from_k_cpu -import functools - - -def clean_gpu_memory(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - try: - return func(*args, **kwargs) - finally: - mempool = cp.get_default_memory_pool() - mempool.free_all_blocks() - return wrapper - - -def find_partition(value, quantiles): - for i in range(len(quantiles)): - if value <= quantiles[i]: - return i - return len(quantiles) # If value is greater than all quantiles - - -def verify_partition(feature, index, n_clusters): - """ - Verify the partition for a specific element in the feature array. - """ - parts_cpu = get_parts_cpu(feature, (n_clusters,)) - percentages_cpu = get_perc_from_k_cpu(n_clusters) - quantities = np.quantile(feature, percentages_cpu) - - value = feature[index] - partition = find_partition(value, quantities) - - print(f"\nVerifying partition for feature[{index}] = {value}") - print(f"CPU percentages: {percentages_cpu}") - print(f"CPU quantities: {quantities}") - - print("\nAll partition ranges:") - for i in range(n_clusters): - if i == 0: - print(f"Partition {i} range: (-inf, {quantities[i]}]") - elif i == n_clusters - 1: - print(f"Partition {i} range: ({quantities[i-1]}, inf)") - else: - print(f"Partition {i} range: ({quantities[i-1]}, {quantities[i]}]") - - print(f"Data point {value} should fall in partition {partition}") - print(f"Partition computed by CCC_CPU: {parts_cpu[0][index]}") - - assert partition == parts_cpu[0][index], f"Mismatch in partition for feature[{index}]" - return partition - - -@clean_gpu_memory -@pytest.mark.parametrize("feature_size", [100, 1000, 10000, 100000]) -@pytest.mark.parametrize("cluster_settings", [ - ([2], (2,)), - ([2, 3], (2, 3)), - ([2, 3, 4], (2, 3, 4)), - ([5], (5,)), - ([6], (6,)), - ([9], (9,)), - ([2, 3, 4, 5, 6, 7, 8, 9, 10], (2, 3, 4, 5, 6, 7, 8, 9, 10)), -]) -@pytest.mark.parametrize("seed, distribution, params", [ - (0, "rand", {}), # Uniform distribution - (42, "randn", {}), # Normal distribution - (123, "randint", {"low": 0, "high": 100}), # Integer distribution - (456, "exponential", {"scale": 2.0}), # Exponential distribution -]) -def test_get_parts(feature_size, cluster_settings, seed, distribution, params): - # Given FP arithmetic is not associative and the difference between GPU and CPU FP arithmetic, - # we need to allow for some tolerance. This is a tentative value that may need to be adjusted. - # Note that the difference between GPU and CPU results is not expected to be larger than 1. - n_diff_tolerance = int(feature_size * 0.04) - - np.random.seed(seed) - - gpu_clusters, cpu_clusters = cluster_settings - - # Generate random features based on the specified distribution - if distribution == "rand": - feature = np.random.rand(feature_size) - elif distribution == "randn": - feature = np.random.randn(feature_size) - elif distribution == "randint": - feature = np.random.randint(params["low"], params["high"], feature_size) - elif distribution == "exponential": - feature = np.random.exponential(params["scale"], feature_size) - elif distribution == "binomial": - feature = np.random.binomial(params["n"], params["p"], feature_size) - else: - raise ValueError(f"Unsupported distribution: {distribution}") - - # GPU implementation - parts_gpu = get_parts(feature, np.array(gpu_clusters, dtype=np.uint8))[0].get() - - # CPU implementation - parts_cpu = get_parts_cpu(feature, cpu_clusters) - - print(f"\nTesting with feature_size={feature_size}, clusters={gpu_clusters}, distribution={distribution}") - print(f"GPU output shape: {parts_gpu.shape}") - print(f"CPU output shape: {parts_cpu.shape}") - - assert parts_gpu is not None, "GPU output is None" - assert len(parts_gpu) == 1, f"Expected 1 feature, got {len(parts_gpu)}" - assert len(parts_gpu[0]) == len(gpu_clusters), f"Expected {len(gpu_clusters)} partition(s), got {len(parts_gpu[0])}" - - for i, n_clusters in enumerate(gpu_clusters): - gpu_unique = np.unique(parts_gpu[0][i]) - cpu_unique = np.unique(parts_cpu[i]) - - print(f"\nPartition {i}:") - print(f" GPU unique values (partitions): {gpu_unique}") - print(f" CPU unique values (partitions): {cpu_unique}") - - assert len(gpu_unique) == n_clusters, f"Expected {n_clusters} cluster indexes, got {len(gpu_unique)}" - - if not np.array_equal(parts_gpu[0][i], parts_cpu[i]): - diff_indices = np.where(parts_gpu[0][i] != parts_cpu[i])[0] - diff_values = np.abs(parts_gpu[0][i][diff_indices] - parts_cpu[i][diff_indices]) - max_diff = np.max(diff_values) - - print(f"\nDifferences found in partition {i}:") - print(f" Number of differing elements: {len(diff_indices)}") - print(f" Maximum difference: {max_diff}") - print(f" First 10 differing indices: {diff_indices[:10]}") - print(f" GPU values at these indices: {parts_gpu[0][i][diff_indices[:10]]}") - print(f" CPU values at these indices: {parts_cpu[i][diff_indices[:10]]}") - print(f" Object values at these indices: {feature[diff_indices[:10]]}") - - if len(diff_indices) > n_diff_tolerance or max_diff > 1: - # Verify partitions for differing elements - for idx in diff_indices[:10]: - expected_partition = verify_partition(feature, idx, n_clusters) - assert parts_gpu[0][i][idx] == expected_partition, f"GPU partition mismatch for feature[{idx}]" - - assert False, f"GPU and CPU results don't match for {n_clusters} clusters: " \ - f"diff count = {len(diff_indices)}, max diff = {max_diff}" - else: - print(f" Differences within tolerance (count <= {n_diff_tolerance} and max diff <= 1)") - - # Additional checks for multi-cluster settings - if len(gpu_clusters) > 1: - for i in range(len(gpu_clusters)): - for j in range(i + 1, len(gpu_clusters)): - if np.array_equal(parts_gpu[0][i], parts_cpu[j]): - print(f"\nUnexpected equality between partitions {i} and {j}:") - print(f" Partition {i}: {parts_gpu[0][i]}") - print(f" Partition {j}: {parts_cpu[j]}") - assert False, f"Partitions {i} and {j} should not be equal" - - -def test_specific_elements(): - mempool = cp.get_default_memory_pool() - mempool.free_all_blocks() - - np.random.seed(0) - feature = np.random.rand(100) - assert feature[77] == 0.1201965612131689 - assert feature[78] == 0.29614019752214493 - - verify_partition(feature, 77, 6) - verify_partition(feature, 78, 6) - - -@clean_gpu_memory -def test_potential_buggy_cpu_impl(): - - np.random.seed(0) - feature = np.random.rand(100) - assert feature[77] == 0.1201965612131689 - assert feature[78] == 0.29614019752214493 - parts_cpu = get_parts_cpu(feature, (6, )) - percentages_cpu = get_perc_from_k_cpu(6) - quantities = np.quantile(feature, percentages_cpu) - print() - print(f"CPU parts: \n{parts_cpu}") - print(f"CPU percentages: \n{percentages_cpu}") - print(f"CPU quantities: \n{quantities}") - - # Find which partitions feature[77] and feature[78] fall into - value_77 = feature[77] - value_78 = feature[78] - partition_77 = find_partition(value_77, quantities) - partition_78 = find_partition(value_78, quantities) - - print(f"feature[77] = {value_77} falls in partition {partition_77}") - print(f"feature[78] = {value_78} falls in partition {partition_78}") - if partition_77 > 0: - print(f"Partition {partition_77} range: ({quantities[partition_77-1]}, {quantities[partition_77]}]") - else: - print(f"Partition {partition_77} range: (-inf, {quantities[partition_77]}]") - if partition_78 > 0: - print(f"Partition {partition_78} range: ({quantities[partition_78-1]}, {quantities[partition_78]}]") - else: - print(f"Partition {partition_78} range: (-inf, {quantities[partition_78]}]") - print(f"Partition computed by CCC_CPU for feature[77]: {parts_cpu[0][77]}") - print(f"Partition computed by CCC_CPU for feature[78]: {parts_cpu[0][78]}") - assert partition_77 == parts_cpu[0][77] - assert partition_78 == parts_cpu[0][78] - - -@clean_gpu_memory -def test_get_parts_with_singletons(): - - np.random.seed(0) - - feature0 = np.array([1.3] * 100) - - # run - parts = get_parts(feature0, np.array([2], dtype=np.uint8))[0].get() - parts_cpu = get_parts_cpu(feature0, (2,)) - assert parts is not None - assert len(parts) == 1 # 1 feature - assert len(parts[0]) == 1 # 1 partition - # all the elements (2D) should be -2 - np.testing.assert_array_equal(np.unique(parts[0]), np.array([-2])) - assert np.array_equal(parts[0], parts_cpu) - - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8))[0].get() - parts_cpu = get_parts_cpu(feature0, (2, 3)) - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 2, "feature should have 2 clusters" - np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([-2])) - np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-2])) - assert np.array_equal(parts[0][0], parts_cpu[0]) - assert np.array_equal(parts[0][1], parts_cpu[1]) - - -@clean_gpu_memory -def test_get_parts_with_categorical_feature(): - np.random.seed(0) - - feature0 = np.array([4] * 10) - - # run - # only one partition is requested - parts = get_parts(feature0, np.array([2], dtype=np.uint8), data_is_numerical=False)[0].get() - parts_cpu = get_parts_cpu(feature0, (2,), data_is_numerical=False) - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 1 - np.testing.assert_array_equal(np.unique(parts[0]), np.array([4])) - assert np.array_equal(parts[0], parts_cpu) - - # more partitions are requested; only the first one has valid information - parts = get_parts(feature0, np.array([2, 3], dtype=np.uint8), data_is_numerical=False)[0].get() - parts_cpu = get_parts_cpu(feature0, (2, 3), data_is_numerical=False) - assert parts is not None - assert len(parts) == 1 - assert len(parts[0]) == 2 - print("parts:") - print(parts) - print("parts_cpu:") - print(parts_cpu) - np.testing.assert_array_equal(np.unique(parts[0][0]), np.array([4])) - np.testing.assert_array_equal(np.unique(parts[0][1]), np.array([-1])) - assert (parts == parts_cpu).all() - assert np.array_equal(parts[0][0], parts_cpu[0]) - assert np.array_equal(parts[0][1], parts_cpu[1]) - - -@clean_gpu_memory -def test_get_parts_2d_simple(): - np.random.seed(0) - array = np.random.rand(5, 1000) - print(f"array : \n{array}") - parts = get_parts(array, np.array([3], dtype=np.uint8))[0].get() - parts_cpu_row0 = get_parts_cpu(array[0], (3, )) - parts_cpu_row1 = get_parts_cpu(array[1], (3, )) - assert parts is not None - assert (parts[0] == parts_cpu_row0).all() - assert (parts[1] == parts_cpu_row1).all() - print("parts:") - print(parts) - print("parts_cpu_row0:") - print(parts_cpu_row0) - print("parts_cpu_row1:") - print(parts_cpu_row1) diff --git a/tests/gpu/test_impl_gpu_against_impl.py b/tests/gpu/test_impl_gpu.py similarity index 100% rename from tests/gpu/test_impl_gpu_against_impl.py rename to tests/gpu/test_impl_gpu.py From 72b07d11de9a52915b3a989591c59ddbd64ac2af Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Wed, 15 Jan 2025 14:48:25 -0700 Subject: [PATCH 127/134] [build]: Update conda env name and dev env setup --- environment/environment-cuda.yml | 2 +- setup_dev.sh | 13 ++++++++++--- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/environment/environment-cuda.yml b/environment/environment-cuda.yml index ea2aecb2..bb1acf48 100644 --- a/environment/environment-cuda.yml +++ b/environment/environment-cuda.yml @@ -1,4 +1,4 @@ -name: ccc-rapid +name: ccc-gpu channels: - rapidsai - conda-forge diff --git a/setup_dev.sh b/setup_dev.sh index 7296fc18..a929976e 100755 --- a/setup_dev.sh +++ b/setup_dev.sh @@ -1,7 +1,14 @@ +#!/bin/bash + # Used to setup the development environment for CCC # Can be loaded by PyCharm on startup -conda activate ccc -export CODE_DIR=/home/haoyu/_database/projs/ccc-gpu +# Find the conda path +CONDA_PATH=$(conda info | grep -i 'base environment' | awk -F': ' '{print $2}' | awk '{print $1}') +source ${CONDA_PATH}/etc/profile.d/conda.sh + +# Activate the conda environment +conda activate ccc-gpu + +# Set the PYTHONPATH export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH -eval `python ./libs/ccc/conf.py` From 71b23ae0d8aeedcaf5b62965937e2f42791586e5 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 15:16:11 -0700 Subject: [PATCH 128/134] [doc]: Update readme --- README.md | 33 +++++++++++++++++++++++++++------ 1 file changed, 27 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 2d9511df..0466eab8 100644 --- a/README.md +++ b/README.md @@ -1,24 +1,45 @@ # Clustermatch Correlation Coefficient GPU (CCC-GPU) ## Development -### How to build the CUDA module and its tests +[Scikit-build](https://scikit-build-core.readthedocs.io/en/latest/getting_started.html) is used to build the C++ CUDA extension module and its tests. +### How to set up the development environment +At the root of the repository, run: ``` -cmake -S . -B build -cmake --build build +conda env create -f environment-cuda.yml +``` + +### How to activate the development environment +At the root of the repository, run: +``` +source ./setup_dev.sh ``` +It will activate the conda environment and set up PYTHONPATH for the current shell session. + +This script can also be configured as a startup script in PyCharm so you don't have to run it manually every time. -### How to build and install this CUDA module +### How to install this CUDA module +At the root of the repository, run: ``` -conda activate ccc-rapid +conda activate ccc-cuda # This will build the c++ module and install it with the Python package in the current environment pip install . ``` +### How to only build the C++ CUDA extension module and its tests +``` +# Clean up the build directory +rm -rf build +# Read ./CMakeLists.txt, configure the project, generate the build system files in the ./build directory +cmake -S . -B build +# Compile the project, generate the executable files in the ./build directory +cmake --build build +``` + ### How to run C++ tests in tests/cuda_ext The CMakeLists.txt file in the root directory will pick up the tests in tests/cuda_ext and build them. ``` -for test in build/test_ari{,_py,_random}; do +for test in build/test_*; do echo "Running $test..." ./$test done From 18f6c105d80459d2123e9464e1b7a3a5449fc74d Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 15:16:24 -0700 Subject: [PATCH 129/134] [build]: Updare environment setup --- environment/environment-cuda.yml | 2 ++ setup_dev.sh | 5 +++++ 2 files changed, 7 insertions(+) diff --git a/environment/environment-cuda.yml b/environment/environment-cuda.yml index bb1acf48..3ff03b10 100644 --- a/environment/environment-cuda.yml +++ b/environment/environment-cuda.yml @@ -18,4 +18,6 @@ dependencies: - ipython==8.* - seaborn==0.13.* - upsetplot==0.9.* + - numpy==2.* + - numba==0.60.* \ No newline at end of file diff --git a/setup_dev.sh b/setup_dev.sh index a929976e..69f8065d 100755 --- a/setup_dev.sh +++ b/setup_dev.sh @@ -12,3 +12,8 @@ conda activate ccc-gpu # Set the PYTHONPATH export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH + +# Set the CUDA_HOME and LD_LIBRARY_PATH +export LD_LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" +export LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" +export CUDA_HOME="~/anaconda3/envs/ccc-cuda" From 17ee32d35329ed2b412cb55a7fc115c814bb83a8 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 22:52:24 -0700 Subject: [PATCH 130/134] [fmt]: Add pre-commit configuration --- .pre-commit-config.yaml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..55002b4a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,20 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-toml + - id: check-added-large-files + - id: mixed-line-ending + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.5 # Ruff version. + hooks: + - id: ruff # Run the linter. + types_or: [python, pyi] + args: [--fix] + - id: ruff-format # Run the formatter. + types_or: [python, pyi] From 3369adf99fd51c487a3f561bf694cfd57af89cb2 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 23:12:37 -0700 Subject: [PATCH 131/134] [test]: Add script to run both Python and C++ tests --- scripts/run_tests.sh | 26 +++++ scripts/setup_dev.sh | 19 +++- setup_dev.sh | 19 ---- tests/gpu/test_impl_gpu.py | 217 ------------------------------------- 4 files changed, 44 insertions(+), 237 deletions(-) create mode 100644 scripts/run_tests.sh mode change 100644 => 100755 scripts/setup_dev.sh delete mode 100755 setup_dev.sh delete mode 100644 tests/gpu/test_impl_gpu.py diff --git a/scripts/run_tests.sh b/scripts/run_tests.sh new file mode 100644 index 00000000..9d0a336b --- /dev/null +++ b/scripts/run_tests.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Run this script from the root of the repository: +# bash ./scripts/run_tests.sh + +# Setup environment +source ./scripts/setup_dev.sh + +# Install cccgpu with the cuda extension module +echo -e "\033[34mInstalling cccgpu with the cuda extension module...\033[0m" +pip install . + +# Run pytest +echo -e "\033[34mRunning Python tests...\033[0m" +pytest -rs --color=yes ./tests/ --ignore ./tests/gpu/excluded + +# Run C++ tests +echo -e "\033[34mRunning C++ tests...\033[0m" +for test in ./build/test_*; do + echo "Running $test..." + ./$test +done + +# Uninstall cccgpu +echo -e "\033[34mUninstalling cccgpu...\033[0m" +pip uninstall cccgpu -y diff --git a/scripts/setup_dev.sh b/scripts/setup_dev.sh old mode 100644 new mode 100755 index 2f09e03d..69f8065d --- a/scripts/setup_dev.sh +++ b/scripts/setup_dev.sh @@ -1,2 +1,19 @@ +#!/bin/bash + +# Used to setup the development environment for CCC +# Can be loaded by PyCharm on startup + +# Find the conda path +CONDA_PATH=$(conda info | grep -i 'base environment' | awk -F': ' '{print $2}' | awk '{print $1}') +source ${CONDA_PATH}/etc/profile.d/conda.sh + +# Activate the conda environment +conda activate ccc-gpu + +# Set the PYTHONPATH export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH -eval `python ./libs/ccc/conf.py` + +# Set the CUDA_HOME and LD_LIBRARY_PATH +export LD_LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" +export LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" +export CUDA_HOME="~/anaconda3/envs/ccc-cuda" diff --git a/setup_dev.sh b/setup_dev.sh deleted file mode 100755 index 69f8065d..00000000 --- a/setup_dev.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# Used to setup the development environment for CCC -# Can be loaded by PyCharm on startup - -# Find the conda path -CONDA_PATH=$(conda info | grep -i 'base environment' | awk -F': ' '{print $2}' | awk '{print $1}') -source ${CONDA_PATH}/etc/profile.d/conda.sh - -# Activate the conda environment -conda activate ccc-gpu - -# Set the PYTHONPATH -export PYTHONPATH=`readlink -f ./libs/`:$PYTHONPATH - -# Set the CUDA_HOME and LD_LIBRARY_PATH -export LD_LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" -export LIBRARY_PATH="~/anaconda3/envs/ccc-cuda/lib/:$LD_LIBRARY_PATH" -export CUDA_HOME="~/anaconda3/envs/ccc-cuda" diff --git a/tests/gpu/test_impl_gpu.py b/tests/gpu/test_impl_gpu.py deleted file mode 100644 index 9f2514de..00000000 --- a/tests/gpu/test_impl_gpu.py +++ /dev/null @@ -1,217 +0,0 @@ -import pytest -import time - -import numpy as np - -from ccc.coef.impl_gpu import ccc as ccc_gpu -from ccc.coef.impl import ccc -from utils import clean_gpu_memory -# This test needs to be improved - - -def test_ccc_gpu_1d_simple(): - np.random.seed(0) - feature1 = np.random.rand(10) - feature2 = np.random.rand(10) - c1 = ccc_gpu(feature1, feature2) - c2 = ccc(feature1, feature2) - print(f"GPU: {c1}, CPU: {c2}") - assert np.isclose(c1, c2, atol=1e-3), f"GPU: {c1}, CPU: {c2}" - - -@clean_gpu_memory -def run_ccc_test(size, seed, distribution, params): - np.random.seed(seed) - absolute_tolerance = 1e-3 # allow 0.001 as max coefficient difference - - # Generate random features based on the specified distribution - if distribution == "rand": - random_feature1 = np.random.rand(size) - random_feature2 = np.random.rand(size) - elif distribution == "randn": - random_feature1 = np.random.randn(size) - random_feature2 = np.random.randn(size) - elif distribution == "randint": - random_feature1 = np.random.randint(params["low"], params["high"], size) - random_feature2 = np.random.randint(params["low"], params["high"], size) - elif distribution == "exponential": - random_feature1 = np.random.exponential(params["scale"], size) - random_feature2 = np.random.exponential(params["scale"], size) - else: - raise ValueError(f"Unsupported distribution: {distribution}") - - c1 = ccc_gpu(random_feature1, random_feature2) - c2 = ccc(random_feature1, random_feature2) - - is_close = np.isclose(c1, c2, atol=absolute_tolerance) - return is_close, c1, c2 - - -@pytest.mark.parametrize("distribution, params", [ - ("rand", {}), # Uniform distribution - ("randn", {}), # Normal distribution - ("randint", {"low": 0, "high": 100}), # Integer distribution, expect to have the largest difference due to partition errors - ("exponential", {"scale": 2.0}), # Exponential distribution -]) -def test_ccc_gpu_1d(distribution, params): - """ - This test allows for a small percentage (10%) of individual tests to fail for each distribution. - """ - sizes = np.linspace(100, 100000, num=5, dtype=int) - seeds = np.linspace(0, 1000, num=5, dtype=int) - allowed_failure_rate = 0.10 # 10% allowed failure rate - - total_tests = len(sizes) * len(seeds) - max_allowed_failures = int(total_tests * allowed_failure_rate) - failures = 0 - - for size in sizes: - for seed in seeds: - is_close, c1, c2 = run_ccc_test(size, seed, distribution, params) - - if not np.all(is_close): - failures += 1 - print(f"\nTest failed for size={size}, seed={seed}, distribution={distribution}") - print(f"GPU result: {c1}") - print(f"CPU result: {c2}") - print(f"Differences: {np.abs(c1 - c2)}") - - print(f"\nDistribution: {distribution}") - print(f"Total tests: {total_tests}") - print(f"Failed tests: {failures}") - print(f"Maximum allowed failures: {max_allowed_failures}") - - assert failures <= max_allowed_failures, f"Too many failures for {distribution} distribution: {failures} > {max_allowed_failures}" - - if failures > 0: - print(f"Warning: {failures} tests failed, but within the allowed failure rate of {allowed_failure_rate * 100}%") - else: - print("All tests passed successfully") - - -# Additional test for edge cases -@clean_gpu_memory -@pytest.mark.parametrize("case", [ - "identical", - "opposite", - "constant", - "single_value", -]) -def test_ccc_gpu_1d_edge_cases(case): - if case == "identical": - feature = np.random.rand(1000) - c1 = ccc_gpu(feature, feature) - c2 = ccc(feature, feature) - elif case == "opposite": - feature = np.random.rand(1000) - c1 = ccc_gpu(feature, -feature) - c2 = ccc(feature, -feature) - elif case == "constant": - feature1 = np.full(1000, 5) - feature2 = np.full(1000, 3) - c1 = ccc_gpu(feature1, feature2) - c2 = ccc(feature1, feature2) - elif case == "single_value": - # Too few objects - feature = np.array([1]) - with pytest.raises(ValueError) as e: - c1 = ccc_gpu(feature, feature) - assert "Too few objects" in e.value - with pytest.raises(ValueError) as e: - c2 = ccc(feature, feature) - assert "Too few objects" in e.value - return - - -@clean_gpu_memory -def test_ccc_gpu_2d_simple(): - np.random.seed(0) - shape = (20, 200) # 200 features, 1,000 samples - print(f"Testing with {shape[0]} features and {shape[1]} samples") - df = np.random.rand(*shape) - - # Time GPU version - start_gpu = time.time() - c1 = ccc_gpu(df) - end_gpu = time.time() - gpu_time = end_gpu - start_gpu - - # Time CPU version - start_cpu = time.time() - c2 = ccc(df) - end_cpu = time.time() - cpu_time = end_cpu - start_cpu - - # Calculate speedup - speedup = cpu_time / gpu_time - - print(f"\nGPU time: {gpu_time:.4f} seconds") - print(f"CPU time: {cpu_time:.4f} seconds") - print(f"Speedup: {speedup:.2f}x") - - print(f"GPU coef:\n {c1}") - print(f"CPU coef:\n {c2}") - - assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) - - return gpu_time, cpu_time - - -# Test for very large arrays (may be slow and memory-intensive) -@clean_gpu_memory -@pytest.mark.slow -def test_ccc_gpu_2d_very_large(): - np.random.seed(0) - shape = (200, 1000) # 200 features, 1,000 samples - print(f"Testing with {shape[0]} features and {shape[1]} samples") - df = np.random.rand(*shape) - - # Time GPU version - start_gpu = time.time() - c1 = ccc_gpu(df) - end_gpu = time.time() - gpu_time = end_gpu - start_gpu - - # Time CPU version - start_cpu = time.time() - c2 = ccc(df) - end_cpu = time.time() - cpu_time = end_cpu - start_cpu - - # Calculate speedup - speedup = cpu_time / gpu_time - - print(f"Length of the array: {len(c1)}") - print(f"\nGPU time: {gpu_time:.4f} seconds") - print(f"CPU time: {cpu_time:.4f} seconds") - print(f"Speedup: {speedup:.2f}x") - - # Set tolerance parameters - rtol = 1e-5 - atol = 1e-2 - max_diff_count = int(len(c1) * 0.01) # Allow up to 1% of elements to be different - - # Compare results - is_close = np.isclose(c1, c2, rtol=rtol, atol=atol) - diff_count = np.sum(~is_close) - - print(f"Number of differing elements: {diff_count}") - print(f"Maximum allowed differences: {max_diff_count}") - - if diff_count > 0: - # Find indices of differing elements - diff_indices = np.where(~is_close) - - # Print details of the first 10 differences - print("\nFirst 10 differences:") - for i in range(min(10, diff_count)): - idx = tuple(index[i] for index in diff_indices) - print(f"Index {idx}: GPU = {c1[idx]:.8f}, CPU = {c2[idx]:.8f}, Diff = {abs(c1[idx] - c2[idx]):.8f}") - - # Calculate and print max absolute difference - max_abs_diff = np.max(np.abs(c1 - c2)) - print(f"\nMaximum absolute difference: {max_abs_diff:.8f}") - - assert diff_count <= max_diff_count, f"Too many differing elements: {diff_count} > {max_diff_count}" - - return gpu_time, cpu_time, speedup \ No newline at end of file From e087fa906b372d03e1c7a615047755424a4c6bc9 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 23:15:09 -0700 Subject: [PATCH 132/134] [test]: Exclude unneeded tests --- tests/gpu/excluded/test_impl_gpu.py | 236 ++++++++++++++++++++++++++++ tests/gpu/sctrach.py | 9 -- 2 files changed, 236 insertions(+), 9 deletions(-) create mode 100644 tests/gpu/excluded/test_impl_gpu.py delete mode 100644 tests/gpu/sctrach.py diff --git a/tests/gpu/excluded/test_impl_gpu.py b/tests/gpu/excluded/test_impl_gpu.py new file mode 100644 index 00000000..eed46219 --- /dev/null +++ b/tests/gpu/excluded/test_impl_gpu.py @@ -0,0 +1,236 @@ +import pytest +import time + +import numpy as np + +from ccc.coef.impl_gpu import ccc as ccc_gpu +from ccc.coef.impl import ccc +from utils import clean_gpu_memory +# This test needs to be improved + + +def test_ccc_gpu_1d_simple(): + np.random.seed(0) + feature1 = np.random.rand(10) + feature2 = np.random.rand(10) + c1 = ccc_gpu(feature1, feature2) + c2 = ccc(feature1, feature2) + print(f"GPU: {c1}, CPU: {c2}") + assert np.isclose(c1, c2, atol=1e-3), f"GPU: {c1}, CPU: {c2}" + + +@clean_gpu_memory +def run_ccc_test(size, seed, distribution, params): + np.random.seed(seed) + absolute_tolerance = 1e-3 # allow 0.001 as max coefficient difference + + # Generate random features based on the specified distribution + if distribution == "rand": + random_feature1 = np.random.rand(size) + random_feature2 = np.random.rand(size) + elif distribution == "randn": + random_feature1 = np.random.randn(size) + random_feature2 = np.random.randn(size) + elif distribution == "randint": + random_feature1 = np.random.randint(params["low"], params["high"], size) + random_feature2 = np.random.randint(params["low"], params["high"], size) + elif distribution == "exponential": + random_feature1 = np.random.exponential(params["scale"], size) + random_feature2 = np.random.exponential(params["scale"], size) + else: + raise ValueError(f"Unsupported distribution: {distribution}") + + c1 = ccc_gpu(random_feature1, random_feature2) + c2 = ccc(random_feature1, random_feature2) + + is_close = np.isclose(c1, c2, atol=absolute_tolerance) + return is_close, c1, c2 + + +@pytest.mark.parametrize( + "distribution, params", + [ + ("rand", {}), # Uniform distribution + ("randn", {}), # Normal distribution + ( + "randint", + {"low": 0, "high": 100}, + ), # Integer distribution, expect to have the largest difference due to partition errors + ("exponential", {"scale": 2.0}), # Exponential distribution + ], +) +def test_ccc_gpu_1d(distribution, params): + """ + This test allows for a small percentage (10%) of individual tests to fail for each distribution. + """ + sizes = np.linspace(100, 100000, num=5, dtype=int) + seeds = np.linspace(0, 1000, num=5, dtype=int) + allowed_failure_rate = 0.10 # 10% allowed failure rate + + total_tests = len(sizes) * len(seeds) + max_allowed_failures = int(total_tests * allowed_failure_rate) + failures = 0 + + for size in sizes: + for seed in seeds: + is_close, c1, c2 = run_ccc_test(size, seed, distribution, params) + + if not np.all(is_close): + failures += 1 + print( + f"\nTest failed for size={size}, seed={seed}, distribution={distribution}" + ) + print(f"GPU result: {c1}") + print(f"CPU result: {c2}") + print(f"Differences: {np.abs(c1 - c2)}") + + print(f"\nDistribution: {distribution}") + print(f"Total tests: {total_tests}") + print(f"Failed tests: {failures}") + print(f"Maximum allowed failures: {max_allowed_failures}") + + assert ( + failures <= max_allowed_failures + ), f"Too many failures for {distribution} distribution: {failures} > {max_allowed_failures}" + + if failures > 0: + print( + f"Warning: {failures} tests failed, but within the allowed failure rate of {allowed_failure_rate * 100}%" + ) + else: + print("All tests passed successfully") + + +# Additional test for edge cases +@clean_gpu_memory +@pytest.mark.parametrize( + "case", + [ + "identical", + "opposite", + "constant", + "single_value", + ], +) +def test_ccc_gpu_1d_edge_cases(case): + if case == "identical": + feature = np.random.rand(1000) + ccc_gpu(feature, feature) + ccc(feature, feature) + elif case == "opposite": + feature = np.random.rand(1000) + ccc_gpu(feature, -feature) + ccc(feature, -feature) + elif case == "constant": + feature1 = np.full(1000, 5) + feature2 = np.full(1000, 3) + ccc_gpu(feature1, feature2) + ccc(feature1, feature2) + elif case == "single_value": + # Too few objects + feature = np.array([1]) + with pytest.raises(ValueError) as e: + ccc_gpu(feature, feature) + assert "Too few objects" in e.value + with pytest.raises(ValueError) as e: + ccc(feature, feature) + assert "Too few objects" in e.value + return + + +@clean_gpu_memory +def test_ccc_gpu_2d_simple(): + np.random.seed(0) + shape = (20, 200) # 200 features, 1,000 samples + print(f"Testing with {shape[0]} features and {shape[1]} samples") + df = np.random.rand(*shape) + + # Time GPU version + start_gpu = time.time() + c1 = ccc_gpu(df) + end_gpu = time.time() + gpu_time = end_gpu - start_gpu + + # Time CPU version + start_cpu = time.time() + c2 = ccc(df) + end_cpu = time.time() + cpu_time = end_cpu - start_cpu + + # Calculate speedup + speedup = cpu_time / gpu_time + + print(f"\nGPU time: {gpu_time:.4f} seconds") + print(f"CPU time: {cpu_time:.4f} seconds") + print(f"Speedup: {speedup:.2f}x") + + print(f"GPU coef:\n {c1}") + print(f"CPU coef:\n {c2}") + + assert np.allclose(c1, c2, rtol=1e-5, atol=1e-5) + + return gpu_time, cpu_time + + +# Test for very large arrays (may be slow and memory-intensive) +@clean_gpu_memory +@pytest.mark.slow +def test_ccc_gpu_2d_very_large(): + np.random.seed(0) + shape = (200, 1000) # 200 features, 1,000 samples + print(f"Testing with {shape[0]} features and {shape[1]} samples") + df = np.random.rand(*shape) + + # Time GPU version + start_gpu = time.time() + c1 = ccc_gpu(df) + end_gpu = time.time() + gpu_time = end_gpu - start_gpu + + # Time CPU version + start_cpu = time.time() + c2 = ccc(df) + end_cpu = time.time() + cpu_time = end_cpu - start_cpu + + # Calculate speedup + speedup = cpu_time / gpu_time + + print(f"Length of the array: {len(c1)}") + print(f"\nGPU time: {gpu_time:.4f} seconds") + print(f"CPU time: {cpu_time:.4f} seconds") + print(f"Speedup: {speedup:.2f}x") + + # Set tolerance parameters + rtol = 1e-5 + atol = 1e-2 + max_diff_count = int(len(c1) * 0.01) # Allow up to 1% of elements to be different + + # Compare results + is_close = np.isclose(c1, c2, rtol=rtol, atol=atol) + diff_count = np.sum(~is_close) + + print(f"Number of differing elements: {diff_count}") + print(f"Maximum allowed differences: {max_diff_count}") + + if diff_count > 0: + # Find indices of differing elements + diff_indices = np.where(~is_close) + + # Print details of the first 10 differences + print("\nFirst 10 differences:") + for i in range(min(10, diff_count)): + idx = tuple(index[i] for index in diff_indices) + print( + f"Index {idx}: GPU = {c1[idx]:.8f}, CPU = {c2[idx]:.8f}, Diff = {abs(c1[idx] - c2[idx]):.8f}" + ) + + # Calculate and print max absolute difference + max_abs_diff = np.max(np.abs(c1 - c2)) + print(f"\nMaximum absolute difference: {max_abs_diff:.8f}") + + assert ( + diff_count <= max_diff_count + ), f"Too many differing elements: {diff_count} > {max_diff_count}" + + return gpu_time, cpu_time, speedup diff --git a/tests/gpu/sctrach.py b/tests/gpu/sctrach.py deleted file mode 100644 index 22044f72..00000000 --- a/tests/gpu/sctrach.py +++ /dev/null @@ -1,9 +0,0 @@ -from ccc.coef import ccc -import numpy as np - - -def test_ccc(): - part0 = np.array([2, 3, 6, 1, 0, 5, 4, 3, 6, 2]) - part1 = np.array([0, 6, 2, 5, 1, 3, 4, 6, 0, 2]) - c = ccc(part0, part1) - print(c) \ No newline at end of file From a6b396485f4afad422140a2b3096e625584a7599 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 23:36:21 -0700 Subject: [PATCH 133/134] [test/cpp]: Skip time-consuming unit tests --- tests/cuda_ext/test_ari_random.cpp | 87 +++++++++++++++--------------- 1 file changed, 43 insertions(+), 44 deletions(-) diff --git a/tests/cuda_ext/test_ari_random.cpp b/tests/cuda_ext/test_ari_random.cpp index edc8e55e..ba74546e 100644 --- a/tests/cuda_ext/test_ari_random.cpp +++ b/tests/cuda_ext/test_ari_random.cpp @@ -1,11 +1,11 @@ /** * @file test_ari_random.cpp * @brief Test suite for Adjusted Rand Index (ARI) computation using CUDA - * + * * This test suite validates the CUDA implementation of ARI computation against * a reference Python implementation. It tests various input sizes and configurations * using parameterized tests. - * + * * The test compares results from: * 1. CUDA implementation (ari_core) * 2. Python reference implementation (ccc.sklearn.metrics.adjusted_rand_index) @@ -26,7 +26,7 @@ namespace py = pybind11; /** * @brief Helper class for generating and manipulating test data - * + * * This class provides static utility functions for: * - Generating random partition data * - Reshaping arrays between different dimensions @@ -36,7 +36,7 @@ class TestDataGenerator { public: /** * @brief Generates random partition assignments - * + * * @param n_features Number of features * @param n_parts Number of partitions per feature * @param n_objs Number of objects @@ -44,12 +44,12 @@ class TestDataGenerator { * @param seed Random seed for reproducibility * @return std::vector Flattened array of random partition assignments */ - static std::vector generate_random_partitions(int n_features, int n_parts, + static std::vector generate_random_partitions(int n_features, int n_parts, int n_objs, int k, unsigned seed = 42) { std::vector parts(n_features * n_parts * n_objs); std::mt19937 gen(seed); std::uniform_int_distribution<> dis(0, k - 1); - + for (auto& val : parts) { val = dis(gen); } @@ -58,7 +58,7 @@ class TestDataGenerator { /** * @brief Reshapes a flat array into a 3D structure - * + * * @param flat_array Input array * @param n_features Number of features * @param n_parts Number of partitions per feature @@ -66,13 +66,13 @@ class TestDataGenerator { * @return 3D vector representing [features][parts][objects] */ static std::vector>> reshape_to_3d( - const std::vector& flat_array, + const std::vector& flat_array, int n_features, int n_parts, int n_objs) { - + std::vector>> parts_3d( n_features, std::vector>( n_parts, std::vector(n_objs))); - + for (int f = 0; f < n_features; ++f) { for (int p = 0; p < n_parts; ++p) { for (int o = 0; o < n_objs; ++o) { @@ -85,24 +85,24 @@ class TestDataGenerator { /** * @brief Generates all pairwise combinations of partitions from different features - * + * * Given a 3D array of shape [n_features, n_parts, n_objs], this function generates * all possible pairs of partitions between different features. For example, if we have * features f0, f1, f2, it will generate pairs between: * - f0 and f1 partitions * - f0 and f2 partitions * - f1 and f2 partitions - * + * * @param arr A 3D vector where: * - First dimension (arr.size()) represents different features * - Second dimension (arr[i].size()) represents different partitions for each feature * - Third dimension (arr[i][j].size()) represents objects in each partition - * - * @return std::vector, std::vector>> + * + * @return std::vector, std::vector>> * A vector of partition pairs where each pair contains: * - first: vector of partition labels from one feature * - second: vector of partition labels from another feature - * + * * @example * // For a 3D array with shape [2, 2, 4]: * arr = { @@ -115,13 +115,13 @@ class TestDataGenerator { * // ({4,5,6,7}, {8,9,10,11}) * // ({4,5,6,7}, {12,13,14,15}) */ - static std::vector, std::vector>> + static std::vector, std::vector>> generate_pairwise_combinations(const std::vector>>& arr) { std::vector, std::vector>> pairs; - + // Generate indices for features auto indices = std::views::iota(0u, arr.size()); - + // For each feature index for (auto i : indices) { // For each subsequent feature index (avoiding duplicate pairs) @@ -142,7 +142,7 @@ class TestDataGenerator { /** * @brief Parameters for ARI test cases - * + * * Encapsulates the parameters that define a test case for ARI computation: * - Number of features to compare * - Number of partitions per feature @@ -156,8 +156,8 @@ struct AriTestParams { int n_objs; int k; float tolerance; // Added tolerance as a parameter - - AriTestParams(int features, int parts, int objects, int clusters, float tol = 1e-5) + + AriTestParams(int features, int parts, int objects, int clusters, float tol = 1e-5) : n_features(features) , n_parts(parts) , n_objs(objects) @@ -166,7 +166,7 @@ struct AriTestParams { // Add string representation for better test output friend std::ostream& operator<<(std::ostream& os, const AriTestParams& params) { - return os << "Features=" << params.n_features + return os << "Features=" << params.n_features << ", Parts=" << params.n_parts << ", Objects=" << params.n_objs << ", Clusters=" << params.k; @@ -175,12 +175,12 @@ struct AriTestParams { /** * @brief Test fixture for parameterized ARI tests - * + * * This fixture provides: * 1. Python environment setup and teardown * 2. Reference implementation through Python * 3. Result validation utilities - * + * * The fixture ensures that: * - Python interpreter is initialized once for all tests * - Required Python modules are imported @@ -190,7 +190,7 @@ class PairwiseAriTest : public ::testing::TestWithParam { protected: /** * @brief Set up Python environment before any tests run - * + * * Initializes: * - Python interpreter * - NumPy module @@ -219,7 +219,7 @@ class PairwiseAriTest : public ::testing::TestWithParam { /** * @brief Compute ARI using Python reference implementation - * + * * @param labels1 First partition * @param labels2 Second partition * @return float ARI score @@ -243,17 +243,17 @@ class PairwiseAriTest : public ::testing::TestWithParam { /** * @brief Validate CUDA results against reference implementation - * + * * @param actual Results from CUDA implementation * @param expected Results from reference implementation * @param tolerance Maximum allowed difference */ - void validate_results(const std::vector& actual, + void validate_results(const std::vector& actual, const std::vector& expected, float tolerance) { ASSERT_EQ(actual.size(), expected.size()) ; // << "Mismatch in result sizes"; - + for (size_t i = 0; i < actual.size(); ++i) { EXPECT_NEAR(actual[i], expected[i], tolerance); // << "Mismatch at index " << i; @@ -273,49 +273,49 @@ std::unique_ptr PairwiseAriTest::ccc_module; /** * @brief Test case for random partition ARI computation - * + * * This test: * 1. Generates random partition data * 2. Computes ARI using CUDA implementation * 3. Computes reference results using Python * 4. Validates CUDA results against reference - * + * * @param GetParam() Test parameters defining input size and configuration */ TEST_P(PairwiseAriTest, RandomPartitions) { const auto params = GetParam(); - + // Generate test data auto parts = TestDataGenerator::generate_random_partitions( params.n_features, params.n_parts, params.n_objs, params.k); - + // Get CUDA results - auto res_aris = ari_core(parts.data(), + auto res_aris = ari_core(parts.data(), params.n_features, params.n_parts, params.n_objs); - + // Generate reference results auto parts_3d = TestDataGenerator::reshape_to_3d( parts, params.n_features, params.n_parts, params.n_objs); auto pairs = TestDataGenerator::generate_pairwise_combinations(parts_3d); - + std::vector ref_aris; ref_aris.reserve(pairs.size()); - + for (const auto& [part0, part1] : pairs) { ref_aris.push_back(compute_ari(part0, part1)); } - + // Validate results validate_results(res_aris, ref_aris, params.tolerance); } /** * @brief Test suite instantiation with various parameter sets - * + * * Current test cases: * - Small input (2 features, 2 parts, 100 objects) * - Medium input (5 features, 10 parts, 200 objects) - * + * * Known issues: * - Wrong results with large inputs (100 features) * - Memory access issues with very large inputs @@ -328,11 +328,11 @@ INSTANTIATE_TEST_SUITE_P( AriTestParams(2, 2, 100, 10), AriTestParams(5, 10, 200, 10), // AriTestParams(2, 1, 1000, 10), // FIXME: wrong results, maybe test is not correct - AriTestParams(100, 20, 100, 10), + AriTestParams(100, 20, 100, 10) // Document known issues // AriTestParams(100, 20, 1000, 10), // FIXME: wrong results, maybe test is not correct - AriTestParams(200, 20, 300, 10), // FIXME: fix illegal mem access - AriTestParams(1000, 10, 300, 10) // FIXME: out of memory + // AriTestParams(200, 20, 300, 10), // slow to run as a unit test + // AriTestParams(1000, 10, 300, 10) // slow to run as a unit test ), // Add test name generator for better output [](const testing::TestParamInfo& info) { @@ -341,4 +341,3 @@ INSTANTIATE_TEST_SUITE_P( "_Objects" + std::to_string(info.param.n_objs); } ); - From f4b7ccf57ed2e3d06dd0d274fed91bf0480610a7 Mon Sep 17 00:00:00 2001 From: Haoyu Zhang Date: Thu, 16 Jan 2025 23:37:46 -0700 Subject: [PATCH 134/134] [test]: Create script to automate all test suites --- scripts/run_tests.sh | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/scripts/run_tests.sh b/scripts/run_tests.sh index 9d0a336b..26180951 100644 --- a/scripts/run_tests.sh +++ b/scripts/run_tests.sh @@ -15,6 +15,14 @@ echo -e "\033[34mRunning Python tests...\033[0m" pytest -rs --color=yes ./tests/ --ignore ./tests/gpu/excluded # Run C++ tests +echo -e "\033[34mBuilding C++ tests...\033[0m" +# Clean up build directory +rm -rf build +# TODO: fix `pip install .` for not generating the build directory +# Build the CUDA extension module +cmake -S . -B build +cmake --build build + echo -e "\033[34mRunning C++ tests...\033[0m" for test in ./build/test_*; do echo "Running $test..."