diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index da9c2c7..5bf187a 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -27,7 +27,7 @@ jobs: - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - poetry install --all-extras + poetry install --all-extras -vvv - name: Type-checking package with mypy run: | # Run this mypy instance against our main package. diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 96564e6..fa67967 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -27,7 +27,7 @@ jobs: - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - poetry install --all-extras + poetry install --all-extras -vvv - name: Test with pytest env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e077366 --- /dev/null +++ b/.gitignore @@ -0,0 +1,189 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/source/getting_started/examples/*.rst +!**/*.template.rst + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# VSCode +.vscode/ + +# DS Store +.DS_Store + +# Results +*.csv + +# Python pickle files +*.pkl + +# Sphinx documentation +_build/ + +# vim swap files +*.swo +*.swp + +# hip files generated by PyTorch +*.hip +*_hip* +hip_compat.h + +# Benchmark dataset +*.json diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..6493014 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "3rdparty/flashinfer"] + path = 3rdparty/flashinfer + url = https://github.com/flashinfer-ai/flashinfer.git diff --git a/3rdparty/flashinfer b/3rdparty/flashinfer new file mode 160000 index 0000000..58d3593 --- /dev/null +++ b/3rdparty/flashinfer @@ -0,0 +1 @@ +Subproject commit 58d35930740083f27e65c9818ab857f9f4880aff diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..9b95fc3 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,52 @@ +cmake_minimum_required(VERSION 3.23.1) +project(deft + VERSION 2024 + DESCRIPTION "An IO-aware fast attention kernel for efficient tree-structured interactions with LLMs" + LANGUAGES CUDA CXX) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CUDA_STANDARD 17) +set(CMAKE_EXPORT_COMPILE_COMMANDS ON) +set(FLASHINFER_DIR ${PROJECT_SOURCE_DIR}/3rdparty/flashinfer) +set(FLASHINFER_INCLUDE_DIR ${FLASHINFER_DIR}/include) + +add_subdirectory(${FLASHINFER_DIR}) + +set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g -lineinfo -Xptxas -v -lineinfo") +find_package(Python3 REQUIRED) +find_package(CUDAToolkit REQUIRED) +if(NOT Python3_FOUND) + message(FATAL_ERROR "Python3 not found.") +endif() +if(NOT CUDAToolkit_FOUND) + message(FATAL_ERROR "CUDA not found.") +endif() +message(STATUS "Python3 found at ${Python3_EXECUTABLE}") +message(STATUS "CUDA version is ${CUDAToolkit_VERSION}") +set(DEFT_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/csrc) +include_directories(${DEFT_SOURCE_DIR}) +include_directories(${CUDAToolkit_INCLUDE_DIRS}) +include_directories(${FLASHINFER_INCLUDE_DIR}) + +link_directories(${CUDAToolkit_LIBRARY_DIR}) + +set(DEFT_ENABLE_TESTS CACHE BOOL "Enable tests for DEFT" ON) + +if(DEFT_ENABLE_TESTS) + enable_testing() + set(DEFT_TEST_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/tests/csrc) + file(GLOB_RECURSE TEST_DUMMY_SRC ${DEFT_TEST_SOURCE_DIR}/test_dummy.cu) + add_executable(test_dummy ${TEST_DUMMY_SRC}) + target_link_libraries(test_dummy gtest_main gtest) + target_link_libraries(test_dummy ${CUDAToolkit_LIBRARIES}) + add_test(NAME test_dummy COMMAND test_dummy) + + file(GLOB_RECURSE TEST_DEFT_SRC ${DEFT_TEST_SOURCE_DIR}/test_deft_attention.cu) + add_executable(test_deft_attention ${TEST_DEFT_SRC}) + target_link_libraries(test_deft_attention gtest_main gtest) + target_link_libraries(test_deft_attention ${CUDAToolkit_LIBRARIES}) + target_include_directories(test_deft_attention PRIVATE ${FLASHINFER_DIR}/src) + add_dependencies(test_deft_attention dispatch_inc) + target_link_libraries(test_deft_attention decode_kernels) + add_test(NAME test_deft_attention COMMAND test_deft_attention) +endif() diff --git a/README.md b/README.md index b9b506a..f25af83 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ We propose DeFT, an IO-aware attention algorithm for efficient tree-structured i - [2024/05] We update the second version of DeFT paper with a better algorithm for general tree-structured LLM inference: [DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference](https://arxiv.org/abs/2404.00242)! - [2024/03] [DeFT: Flash Tree-Attention With IO-Awareness for Efficient Tree-Search-Based LLM Inference](https://openreview.net/pdf?id=HqfLHoX8bR) has been accepted as Oral presentation in [ICLR'24 AGI Workshop](https://iclr.cc/virtual/2024/23126)! - +**** ## Abstract Given the increasing demand for tree-structured interactions with LLMs, we introduce DeFT (Decoding with Flash Tree-Attention), an IO-aware tree attention algorithm tailored for tree-structured inference. Unlike traditional sequence-based decoding, tree-structured decoding better accommodates modern task requirements, including self-consistency, few-shot prompting, multi-step reasoning, and multi-model/head coordination. However, existing sequence-based inference systems are ill-suited for tree-structured decoding, resulting in redundancy in computation, memory footprints, and memory access, thereby undermining inference efficiency. To address this challenge, DeFT maintains memory-efficient attention calculation with low memory footprints through two key stages: (1) QKV Preparation: We propose a KV-Guided Grouping Strategy with Tree Split to intelligently group QKV, optimizing GPU resource utilization while minimizing memory reads/writes for KV cache between GPU global memory and on-chip shared memory; (2)Attention Calculation: We compute partial attention of each QKV group in a fused kernel and employ a Tree-topology-aware Global Reduction strategy to obtain final attention. By reducing 73-99% KV cache IO and nearly 100% IO for partial results during attention calculation (e.g., Softmax), DeFT achieves up to 2.52/3.82x speedup in the end-to-end/attention latency across three practical tree-based workloads: namely, few-shot prompting, multi-step reasoning, and speculative decoding, over state-of-the-art attention algorithms. @@ -42,10 +42,23 @@ poetry install CUDA_VISIBLE_DEVICES=0 python examples/ ``` + +### Run Tests + + + +```bash +git submodule update --init --recursive # clone flashinfer +cmake -B build +cmake --build build +cd build +ctest +``` + ## FAQ 1. **What is the difference between two versions of DeFT papers in arXiv?** - + DeFT-v1 @@ -72,4 +85,4 @@ If you find DeFT useful or relevant to your project and research, please kindly journal={arXiv preprint arXiv:2404.00242}, year={2024} } -``` \ No newline at end of file +``` diff --git a/build.py b/build.py new file mode 100644 index 0000000..36bdbae --- /dev/null +++ b/build.py @@ -0,0 +1,29 @@ +import logging +from typing import Any, Dict + +from torch.utils.cpp_extension import CUDAExtension + +logger = logging.getLogger(__name__) + + +ext_modules = [] +ext_modules.append( + CUDAExtension( + name='deft._kernels', + sources=['csrc/deft_api.cpp', 'csrc/deft/ops.cu'], + include_dirs=[ + 'csrc', + ], + ) +) + + +def build(setup_kwargs: Dict[str, Any]) -> None: + setup_kwargs.update( + { + # 'ext_modules': ext_modules, + # 'cmdclass': { + # 'build_ext': BuildExtension.with_options(use_ninja=False), + # }, + } + ) diff --git a/config.cmake b/config.cmake new file mode 100644 index 0000000..49d9dcd --- /dev/null +++ b/config.cmake @@ -0,0 +1,41 @@ +# Whether to compile fp8 kernels or not. +set(FLASHINFER_ENABLE_FP8 OFF) +# Whether to compile bf16 kernels or not. +set(FLASHINFER_ENABLE_BF16 OFF) +# Whether to compile tvm bindings or not. +set(FLASHINFER_TVM_BINDING OFF) +# Whether to compile prefill kernel tests/benchmarks or not. +set(FLASHINFER_PREFILL ON) +# Whether to compile decode kernel tests/benchmarks or not. +set(FLASHINFER_DECODE ON) +# Whether to compile page kernel tests/benchmarks or not. +set(FLASHINFER_PAGE ON) +# Whether to compile cascade kernel tests/benchmarks or not. +set(FLASHINFER_CASCADE ON) +# Whether to compile sampling kernel tests/benchmarks or not. +set(FLASHINFER_SAMPLING OFF) +# Whether to compile normalization kernel tests/benchmarks or not. +set(FLASHINFER_NORMALIZATION OFF) +# Whether to compile fastdiv tests +set(FLASHINFER_FASTDIV_TEST ON) +# Whether to compile distributed tests +set(FLASHINFER_DISTRIBUTED OFF) +# The following configurations can impact the binary +# size of the generated library +set(FLASHINFER_GEN_LOGITS_POST_HOOKS 0) +set(FLASHINFER_GEN_HEAD_DIMS 128) +set(FLASHINFER_GEN_KV_LAYOUTS 0) +set(FLASHINFER_GEN_POS_ENCODING_MODES 0) +set(FLASHINFER_GEN_ALLOW_FP16_QK_REDUCTIONS "false") +set(FLASHINFER_GEN_MASK_MODES 0) + +# Set target cuda architectures for tests/benchmarks, defaults to native. +# "native" is a special value for CMAKE_CUDA_ARCHITECTURES which means use the architectures of the host's GPU. +# it's new in CMake 3.24, if you are using an older of CMake or you want to use a different value, you can +# set its value here. Supported CUDA architctures include 80;86;89;90 +# NOTE(Zihao): using "native" might be slow because whenever compile a cuda file with `-arch=native`, nvcc will spawn +# a `__nvcc_device_query` process to get the architecture of the host's GPU, which could stall the compilation process. +# So it's recommended to set it to a specific value if you know the architecture of the target GPU. +# Example: +# set(FLASHINFER_CUDA_ARCHITECTURES 80) +set(FLASHINFER_CUDA_ARCHITECTURES 80) diff --git a/csrc/deft/attention.cuh b/csrc/deft/attention.cuh new file mode 100644 index 0000000..81d0e0a --- /dev/null +++ b/csrc/deft/attention.cuh @@ -0,0 +1,442 @@ +#ifndef DEFT_ATTENTION_CUH_ +#define DEFT_ATTENTION_CUH_ +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "deft/chunk.cuh" +#include "deft/layout.cuh" +#include "deft/utils.cuh" + +namespace deft { +__global__ void dummy_kernel(float* __restrict__ output, + const float* __restrict__ input, const int size) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < size) { + output[idx] = input[idx]; + } +} + +void dummy(float* output, const float* input, int size) { + dim3 block(256); + dim3 grid((size + block.x - 1) / block.x); + dummy_kernel<<>>(output, input, size); +} + +typedef half DTypeQ; +typedef half DTypeKV; +typedef half DTypeOut; +typedef uint32_t IdType; +using flashinfer::ceil_div; +using flashinfer::paged_kv_t; +using flashinfer::PageStorage; +using flashinfer::state_t; +using flashinfer::vec_t; +namespace cp_async = flashinfer::cp_async; +namespace math = flashinfer::math; +using cp_async::PrefetchMode; +using cp_async::SharedMemFillMode; + +namespace cg = cooperative_groups; + +template +__device__ __forceinline__ void compute_qk(const T* smem, + uint32_t compute_stage_idx, + const vec_t& q_vec, + uint32_t iter_base, + uint32_t iter_bound, float* s, + state_t& st) { + uint32_t tx = threadIdx.x, tz = threadIdx.z; + float m_prev = st.m; +#pragma unroll + for (uint32_t j = 0; j < tile_size; ++j) { + vec_t k_vec; + k_vec.cast_load(smem + (j * bdx + tx) * vec_size); + s[j] = 0.f; +#pragma unroll + for (uint32_t i = 0; i < vec_size; ++i) { + s[j] += q_vec[i] * k_vec[i]; + } +#pragma unroll + for (uint32_t offset = bdx / 2; offset > 0; offset /= 2) { + s[j] += math::shfl_xor_sync(s[j], offset); + } + s[j] = (iter_base + tz * tile_size + j < iter_bound) ? s[j] : -5e4; + st.m = max(st.m, s[j]); + } + + float o_scale = math::ptx_exp2(m_prev - st.m); + st.d *= o_scale; +#pragma unroll + for (uint32_t j = 0; j < tile_size; ++j) { + s[j] = math::ptx_exp2(s[j] - st.m); + st.d += s[j]; + } +#pragma unroll + for (uint32_t i = 0; i < vec_size; ++i) { + st.o[i] = st.o[i] * o_scale; + } +} + +template +__device__ __forceinline__ void update_local_state(const T* smem, + const float* s, + uint32_t compute_stage_idx, + state_t& st) { + uint32_t tx = threadIdx.x; +#pragma unroll + for (uint32_t j = 0; j < tile_size; ++j) { + vec_t v_vec; + v_vec.cast_load(smem + (j * bdx + tx) * vec_size); +#pragma unroll + for (uint32_t i = 0; i < vec_size; ++i) { + st.o[i] = st.o[i] + s[j] * v_vec[i]; + } + } +} + +template +__device__ __forceinline__ void sync_state(state_t& st, float* smem, + float* smem_md) { + if constexpr (bdz > 1) { + constexpr uint32_t head_dim = bdx * vec_size; + auto block = cg::this_thread_block(); + uint32_t tx = threadIdx.x, ty = threadIdx.y, tz = threadIdx.z; + st.o.store(smem + (tz * bdy + ty) * head_dim + tx * vec_size); + smem_md[(tz * bdy + ty) * 2] = st.m; + smem_md[(tz * bdy + ty) * 2 + 1] = st.d; + block.sync(); + st.init(); +#pragma unroll + for (uint32_t j = 0; j < bdz; ++j) { + float mz = smem_md[(j * bdy + ty) * 2], + dz = smem_md[(j * bdy + ty) * 2 + 1]; + vec_t oz; + oz.load(smem + (j * bdy + ty) * head_dim + tx * vec_size); + st.merge(oz, mz, dz); + } + } +} + +template +__global__ void deft_attention_stage_1_paged_kernel( + const chunk_t* chunks, float* __restrict__ partial_lse, + float* __restrict__ partial_o, float* __restrict__ row_max, + tensor_info_t info, float sm_scale) { + auto block = cg::this_thread_block(); + auto grid = cg::this_grid(); + sm_scale *= math::log2e; + const uint32_t head_dim = bdx * vec_size; + const uint32_t kv_head_idx = blockIdx.y; + const uint32_t qo_head_idx = blockIdx.y * bdy + threadIdx.y; + const uint32_t num_qo_heads = gridDim.y * bdy; + const uint32_t chunk_idx = blockIdx.x; + const chunk_t& chunk = chunks[chunk_idx]; + const uint32_t kv_chunk_len = chunk.kv_len; + const uint32_t q_chunk_len = chunk.q_len; + + extern __shared__ uint8_t smem[]; + DTypeKV* k_smem = reinterpret_cast(smem); + DTypeKV* v_smem = + (DTypeKV*)(smem + num_stages_smem * tile_size_per_bdx * bdy * bdz * + head_dim * sizeof(DTypeKV)); + DTypeKV** k_ptrs_smem = + (DTypeKV**)(smem + 2 * num_stages_smem * tile_size_per_bdx * bdy * bdz * + head_dim * sizeof(DTypeKV)); + float* smem_md = (float*)(smem + 2 * num_stages_smem * tile_size_per_bdx * + bdy * bdz * head_dim * sizeof(DTypeKV)); + + const uint32_t tx = threadIdx.x, ty = threadIdx.y, tz = threadIdx.z; + + uint32_t stage_idx = 0; + constexpr uint32_t vec_bits = sizeof(DTypeKV) * vec_size * 8; + + static_assert(num_stages_smem <= bdx); +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + k_ptrs_smem[((j * bdz + tz) * bdy + ty) * bdx + tx] = + chunk.protective_get_k_ptr(((j * bdz + tz) * bdy + ty) * bdx + tx, + kv_head_idx); + } + block.sync(); + + DTypeKV* k_ptrs[tile_size_per_bdx]; +#pragma unroll + for (uint32_t iter = 0; iter < num_stages_smem; ++iter) { +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + k_ptrs[j] = + k_ptrs_smem[((iter * bdz + tz) * bdy + ty) * tile_size_per_bdx + j] + + tx * vec_size; + } +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + cp_async::pred_load( + k_smem + + (((stage_idx * bdz + tz) * bdy + ty) * tile_size_per_bdx + j) * + head_dim + + tx * vec_size, + k_ptrs[j], + ((iter * bdz + tz) * bdy + ty) * tile_size_per_bdx + j < + kv_chunk_len); + } + cp_async::commit_group(); +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + DTypeKV* v_ptr = k_ptrs[j] + chunk.kv_ptr_delta(); + cp_async::pred_load( + v_smem + + (((stage_idx * bdz + tz) * bdy + ty) * tile_size_per_bdx + j) * + head_dim + + tx * vec_size, + v_ptr, + ((iter * bdz + tz) * bdy + ty) * tile_size_per_bdx + j < + kv_chunk_len); + } + cp_async::commit_group(); + stage_idx = (stage_idx + 1) % num_stages_smem; + } + state_t st[block_q]; + float s[block_q][bdy * tile_size_per_bdx]; + +#pragma unroll 2 + for (uint32_t iter = 0; + iter < ceil_div(kv_chunk_len, tile_size_per_bdx * bdy * bdz); ++iter) { + if ((iter + num_stages_smem) % bdx == 0) { +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + k_ptrs_smem[((j * bdz + tz) * bdy + ty) * bdx + tx] = + chunk.protective_get_k_ptr( + ((iter + num_stages_smem) * tile_size_per_bdx * bdy * bdz + + ((j * bdz + tz) * bdy + ty) * bdx + tx), + kv_head_idx); + } + } + // compute qk + cp_async::wait_group<2 * num_stages_smem - 1>(); + block.sync(); +#pragma unroll 1 + for (uint32_t q_idx = 0; q_idx < q_chunk_len; ++q_idx) { + vec_t q_vec; + q_vec.cast_load(chunk.protective_get_q_ptr(q_idx, qo_head_idx) + + tx * vec_size); + +#pragma unroll + for (uint32_t i = 0; i < vec_size; ++i) { + q_vec[i] *= sm_scale; + } + compute_qk( + k_smem + (stage_idx * bdz + tz) * bdy * tile_size_per_bdx * head_dim, + stage_idx, q_vec, iter * tile_size_per_bdx * bdy * bdz, kv_chunk_len, + s[q_idx], st[q_idx]); + block.sync(); + } + + block.sync(); + +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + k_ptrs[j] = + k_ptrs_smem[((((iter + num_stages_smem) % bdx) * bdz + tz) * bdy + + ty) * + tile_size_per_bdx + + j] + + tx * vec_size; + } + // load k tiles +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + cp_async::pred_load( + k_smem + + (((stage_idx * bdz + tz) * bdy + ty) * tile_size_per_bdx + j) * + head_dim + + tx * vec_size, + k_ptrs[j], + (((iter + num_stages_smem) * bdz + tz) * bdy + ty) * + tile_size_per_bdx + + j < + kv_chunk_len); + } + cp_async::commit_group(); + + // update m/d/o states + cp_async::wait_group<2 * num_stages_smem - 1>(); + block.sync(); +#pragma unroll 1 + for (uint32_t q_idx = 0; q_idx < q_chunk_len; ++q_idx) { + update_local_state( + v_smem + (stage_idx * bdz + tz) * bdy * tile_size_per_bdx * head_dim, + s[q_idx], stage_idx, st[q_idx]); + } + block.sync(); + + // load v tiles +#pragma unroll + for (uint32_t j = 0; j < tile_size_per_bdx; ++j) { + DTypeKV* v_ptr = k_ptrs[j] + chunk.kv_ptr_delta(); + cp_async::pred_load( + v_smem + + (((stage_idx * bdz + tz) * bdy + ty) * tile_size_per_bdx + j) * + head_dim + + tx * vec_size, + v_ptr, + (((iter + num_stages_smem) * bdz + tz) * bdy + ty) * + tile_size_per_bdx + + j < + kv_chunk_len); + } + cp_async::commit_group(); + stage_idx = (stage_idx + 1) % num_stages_smem; + } + cp_async::wait_group<0>(); + block.sync(); + +#pragma unroll 1 + for (uint32_t q_idx = 0; q_idx < q_chunk_len; ++q_idx) { + sync_state( + st[q_idx], reinterpret_cast(smem), smem_md); + if (tz == 0) { + uint32_t global_q_idx = chunk.get_q_idx(q_idx); + st[q_idx].normalize(); + st[q_idx].o.cast_store(partial_o + qo_head_idx * info.partial_o_stride_h + + (chunk.partial_o_offset + q_idx) * + info.partial_o_stride_n + + tx * vec_size); + if (tx == 0) { + partial_lse[qo_head_idx * info.partial_lse_stride_h + + chunk.partial_o_offset + q_idx] = st[q_idx].get_lse(); + atomicMax(row_max + qo_head_idx * info.row_max_stride_h + global_q_idx, + st[q_idx].get_lse()); + } + } + block.sync(); + } +} +template +__global__ void deft_attention_reduction_kernel( + const chunk_t* chunks, DTypeOut* __restrict__ o, float* __restrict__ L, + float* __restrict__ row_max, const float* __restrict__ partial_lse, + const float* __restrict__ partial_o, tensor_info_t info) { + auto block = cg::this_thread_block(); + auto grid = cg::this_grid(); + const uint32_t head_dim = bdx * vec_size; + const uint32_t chunk_idx = blockIdx.x; + const chunk_t chunk = chunks[chunk_idx]; + const uint32_t q_chunk_len = chunk.q_len; + const uint32_t qo_head_idx = blockIdx.y; + const uint32_t tx = threadIdx.x; +#pragma unroll 1 + for (uint32_t q_idx = 0; q_idx < q_chunk_len; ++q_idx) { + if (tx == 0) { + uint32_t global_q_idx = chunk.get_q_idx(q_idx); + float global_max = + row_max[qo_head_idx * info.row_max_stride_h + global_q_idx]; + float lse = partial_lse[qo_head_idx * info.partial_lse_stride_h + + chunk.partial_o_offset + q_idx]; + float new_exp = math::ptx_exp2(lse - global_max); + atomicAdd(L + qo_head_idx * info.L_stride_h + global_q_idx, new_exp); + } + block.sync(); + } + grid.sync(); +#pragma unroll 1 + for (uint32_t q_idx = 0; q_idx < q_chunk_len; ++q_idx) { + uint32_t global_q_idx = chunk.get_q_idx(q_idx); + float global_max = + row_max[qo_head_idx * info.row_max_stride_h + global_q_idx]; + float lse = partial_lse[qo_head_idx * info.partial_lse_stride_h + + chunk.partial_o_offset + q_idx]; + float new_exp = math::ptx_exp2(lse - global_max); + const float* partial_o_ptr = + partial_o + qo_head_idx * info.partial_o_stride_h + + (chunk.partial_o_offset + q_idx) * info.partial_o_stride_n + + tx * vec_size; + vec_t o_vec; + o_vec.cast_load(partial_o_ptr); + DTypeOut* o_ptr = o + qo_head_idx * info.q_stride_h + + global_q_idx * info.q_stride_n + tx * vec_size; +#pragma unroll + for (uint32_t i = 0; i < vec_size; ++i) { + o_vec[i] *= new_exp; + atomicAdd(o_ptr + i, o_vec[i]); + } + block.sync(); + } +} + +cudaError_t deft_attention(DTypeOut* o, + const thrust::device_vector& chunks, + uint32_t partial_num, tensor_info_t info, + float sm_scale, cudaStream_t stream) { + const uint32_t num_qo_heads = info.num_qo_heads; + const uint32_t num_kv_heads = info.num_kv_heads; + constexpr uint32_t HEAD_DIM = 128; + constexpr uint32_t GROUP_SIZE = 4; + + constexpr uint32_t vec_size = + std::max(16UL / sizeof(DTypeKV), HEAD_DIM / 32UL); + constexpr uint32_t num_stages_smem = 2U; + constexpr uint32_t bdx = HEAD_DIM / vec_size; + static_assert(bdx <= 32); + assert(num_qo_heads == num_kv_heads * GROUP_SIZE); + constexpr uint32_t bdy = GROUP_SIZE; + constexpr uint32_t num_threads = std::max(128U, bdx * bdy); + constexpr uint32_t bdz = num_threads / (bdx * bdy); + constexpr uint32_t tile_size_per_bdx = + GROUP_SIZE == 1 ? (sizeof(DTypeKV) == 1 ? 2U : 4U) : 1U; + constexpr uint32_t block_q = 32; + const uint32_t smem_size = + 2 * num_stages_smem * tile_size_per_bdx * bdy * bdz * HEAD_DIM * + sizeof(DTypeKV) + + std::max(tile_size_per_bdx * num_threads * sizeof(DTypeKV*), + 2 * bdy * bdz * sizeof(float)); + + thrust::device_vector L(num_qo_heads * info.qo_len, 0.0); + thrust::device_vector row_max(num_qo_heads * info.qo_len, 0.0); + thrust::device_vector partial_lse(num_qo_heads * partial_num, 0.0); + thrust::device_vector partial_o(num_qo_heads * partial_num * HEAD_DIM, + 0.0); + + auto L_ptr = L.data().get(); + auto row_max_ptr = row_max.data().get(); + auto partial_lse_ptr = partial_lse.data().get(); + auto partial_o_ptr = partial_o.data().get(); + auto chunk_ptr = chunks.data().get(); + dim3 block(bdx, bdy, bdz); + dim3 grid(chunks.size(), num_kv_heads); + auto stage_1_kernel = + deft_attention_stage_1_paged_kernel; + auto reduction_kernel = deft_attention_reduction_kernel<4, HEAD_DIM / 4>; + FLASHINFER_CUDA_CALL(cudaFuncSetAttribute( + stage_1_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size)); + stage_1_kernel<<>>( + chunk_ptr, partial_lse_ptr, partial_o_ptr, row_max_ptr, info, sm_scale); + + void* args[] = { + &chunk_ptr, &o, &L_ptr, &row_max_ptr, &partial_lse_ptr, + &partial_o_ptr, &info, + }; + FLASHINFER_CUDA_CALL(cudaLaunchCooperativeKernel( + (void*)reduction_kernel, dim3(chunks.size(), num_qo_heads), + dim3(HEAD_DIM / 4), args, 0, stream)) + return cudaSuccess; +} + +} // namespace deft +#endif // DEFT_ATTENTION_CUH_ diff --git a/csrc/deft/chunk.cuh b/csrc/deft/chunk.cuh new file mode 100644 index 0000000..3203ef0 --- /dev/null +++ b/csrc/deft/chunk.cuh @@ -0,0 +1,108 @@ +#ifndef DEFT_CHUNK_CUH_ +#define DEFT_CHUNK_CUH_ + +#include + +namespace deft { + +struct chunk_t { + typedef uint32_t IdType; + typedef half DType; + uint32_t num_kv_heads; + uint32_t num_qo_heads; + uint32_t head_dim; + uint32_t kv_stride_n; + uint32_t kv_stride_h; + uint32_t q_stride_n; + uint32_t q_stride_h; + + uint32_t q_len; + uint32_t kv_len; + IdType* q_idx; + IdType* kv_idx; + uint32_t partial_o_offset; + + DType* k_data; + DType* v_data; + DType* q_data; + + __host__ __device__ __forceinline__ + chunk_t(uint32_t num_kv_heads, uint32_t num_qo_heads, uint32_t head_dim, + uint32_t kv_stride_n, uint32_t kv_stride_h, uint32_t q_stride_n, + uint32_t q_stride_h, uint32_t q_len, uint32_t kv_len, IdType* q_idx, + IdType* kv_idx, uint32_t partial_o_offset, DType* k_data, + DType* v_data, DType* q_data) + : num_kv_heads(num_kv_heads), + num_qo_heads(num_qo_heads), + head_dim(head_dim), + kv_stride_n(kv_stride_n), + kv_stride_h(kv_stride_h), + q_stride_n(q_stride_n), + q_stride_h(q_stride_h), + q_len(q_len), + kv_len(kv_len), + q_idx(q_idx), + kv_idx(kv_idx), + partial_o_offset(partial_o_offset), + k_data(k_data), + v_data(v_data), + q_data(q_data) {} + + __host__ __device__ __forceinline__ chunk_t() + : num_kv_heads(0), + num_qo_heads(0), + head_dim(0), + kv_stride_n(0), + kv_stride_h(0), + q_stride_n(0), + q_stride_h(0), + q_len(0), + kv_len(0), + q_idx(nullptr), + kv_idx(nullptr), + partial_o_offset(0), + k_data(nullptr), + v_data(nullptr), + q_data(nullptr) {} + + __device__ __forceinline__ DType* protective_get_k_ptr( + IdType idx, uint32_t head_idx) const { + if (idx < kv_len) { + return k_data + kv_stride_n * __ldg(kv_idx + idx) + + head_idx * kv_stride_h; + } else { + return k_data; + } + } + + __device__ __forceinline__ DType* protective_get_v_ptr( + IdType idx, uint32_t head_idx) const { + if (idx < kv_len) { + return v_data + kv_stride_n * __ldg(kv_idx + idx) + + head_idx * kv_stride_h; + } else { + return v_data; + } + } + + __device__ __forceinline__ DType* protective_get_q_ptr( + IdType idx, uint32_t head_idx) const { + if (idx < q_len) { + return q_data + q_stride_n * __ldg(q_idx + idx) + head_idx * q_stride_h; + } else { + return q_data; + } + } + + __device__ __forceinline__ uint32_t get_q_idx(IdType idx) const { + return __ldg(q_idx + idx); + } + + __host__ __device__ __forceinline__ int64_t kv_ptr_delta() const { + return (int64_t(v_data) - int64_t(k_data)) / sizeof(DType); + } +}; + +} // namespace deft + +#endif // DEFT_CHUNK_CUH_ diff --git a/csrc/deft/layout.cuh b/csrc/deft/layout.cuh new file mode 100644 index 0000000..8aff9ff --- /dev/null +++ b/csrc/deft/layout.cuh @@ -0,0 +1,28 @@ +#ifndef DEFT_LAYOUT_CUH_ +#define DEFT_LAYOUT_CUH_ + +#include +namespace deft { +struct tensor_info_t : public flashinfer::tensor_info_t { + uint32_t partial_o_stride_n; + uint32_t partial_o_stride_h; + uint32_t partial_lse_stride_h; + uint32_t L_stride_h; + uint32_t row_max_stride_h; + uint32_t partial_num; + __host__ __device__ __forceinline__ + tensor_info_t(uint32_t qo_len, uint32_t kv_len, uint32_t num_qo_heads, + uint32_t num_kv_heads, flashinfer::QKVLayout kv_layout, + uint32_t head_dim, uint32_t partial_num) + : flashinfer::tensor_info_t(qo_len, kv_len, num_qo_heads, num_kv_heads, + kv_layout, head_dim), + partial_num(partial_num) { + partial_o_stride_n = head_dim; + partial_o_stride_h = partial_num * head_dim; + partial_lse_stride_h = partial_num; + L_stride_h = qo_len; + row_max_stride_h = qo_len; + } +}; +} // namespace deft +#endif // DEFT_LAYOUT_CUH_ diff --git a/csrc/deft/ops.cu b/csrc/deft/ops.cu new file mode 100644 index 0000000..8e5b38a --- /dev/null +++ b/csrc/deft/ops.cu @@ -0,0 +1,18 @@ +#include +#include "deft/ops.h" +#include "attention.cuh" +#include + +namespace deft { + torch::Tensor dummy( + torch::Tensor output, + torch::Tensor input + ) { + dummy( + output.data_ptr(), + input.data_ptr(), + input.numel() + ); + return output; + } +} diff --git a/csrc/deft/ops.h b/csrc/deft/ops.h new file mode 100644 index 0000000..b7a6e81 --- /dev/null +++ b/csrc/deft/ops.h @@ -0,0 +1,12 @@ +#pragma once + +#include +#include +#include + +namespace deft { + torch::Tensor dummy( + torch::Tensor output, + torch::Tensor input + ); +} diff --git a/csrc/deft/utils.cuh b/csrc/deft/utils.cuh new file mode 100644 index 0000000..c5d503b --- /dev/null +++ b/csrc/deft/utils.cuh @@ -0,0 +1,15 @@ +#ifndef DEFT_UTILS_CUH_ +#define DEFT_UTILS_CUH_ +__device__ __forceinline__ float atomicMax(float *addr, float value) { + float old = *addr, assumed; + if (old >= value) return old; + do { + assumed = old; + if (assumed > value) break; + old = atomicCAS((unsigned int *)addr, __float_as_int(assumed), + __float_as_int(value)); + + } while (old != assumed); + return old; +} +#endif // DEFT_UTILS_CUH_ diff --git a/csrc/deft_api.cpp b/csrc/deft_api.cpp new file mode 100644 index 0000000..a308c90 --- /dev/null +++ b/csrc/deft_api.cpp @@ -0,0 +1,8 @@ +#include +#include "deft/ops.h" + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.doc() = "DeFT"; + m.def("dummy", &deft::dummy, "Dummy"); +} diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..214c57c --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3557 @@ +# This file is automatically @generated by 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a/pyproject.toml +++ b/pyproject.toml @@ -6,6 +6,10 @@ authors = ["Jinwei Yao "] license = "Apache 2.0 License" readme = "README.md" +[tool.poetry.build] +script = "build.py" +generate-setup-file = false + [tool.poetry.dependencies] python = ">=3.8, <3.12" @@ -17,7 +21,7 @@ pydantic = "^2.7.2" arxiv = "^2.1.0" faiss-cpu = "^1.8.0" types-requests = "^2.31.0" -torch = ">=2.1.0 <2.3.0" +torch = "^2.3.0" transformers = "^4.41.2" litellm = "~1.23.12" beartype = "^0.18.5" @@ -37,7 +41,15 @@ pytest-asyncio = "*" [build-system] -requires = ["poetry-core"] +requires = [ + "poetry-core", + "cmake>=3.21", + "ninja", + "packaging", + "setuptools >= 49.4.0", + "torch == 2.3.1", + "wheel", +] build-backend = "poetry.core.masonry.api" [tool.mypy] diff --git a/tests/csrc/test_deft_attention.cu b/tests/csrc/test_deft_attention.cu new file mode 100644 index 0000000..bd053e8 --- /dev/null +++ b/tests/csrc/test_deft_attention.cu @@ -0,0 +1,101 @@ +#include +#include + +#include + +#include "deft/attention.cuh" +#include "flashinfer_ops.cuh" + +constexpr uint32_t chunk_size = 256; +constexpr uint32_t branch_size = 10; +constexpr uint32_t chunk_num = branch_size + 1; +constexpr uint32_t total_kv_len = chunk_size * chunk_num; +constexpr uint32_t num_kv_heads = 8; +constexpr uint32_t num_qo_heads = 32; +constexpr uint32_t head_dim = 128; +constexpr uint32_t partial_num = branch_size + branch_size; + +typedef half DTypeQ; +typedef half DTypeKV; +typedef half DTypeO; +typedef uint32_t IdType; + +using namespace flashinfer; + +__global__ void init_chunks(deft::chunk_t* chunks, uint32_t* q_idx[], + uint32_t* q_len, uint32_t* kv_idx[], + uint32_t* kv_len, DTypeQ* q_data, DTypeKV* k_data, + DTypeKV* v_data) { + int bid = blockIdx.x; + int offset = 0; + deft::chunk_t chunk( + num_kv_heads, num_qo_heads, head_dim, num_kv_heads * head_dim, head_dim, + num_qo_heads * head_dim, head_dim, q_len[bid], kv_len[bid], q_idx[bid], + kv_idx[bid], offset, k_data, v_data, q_data); + for (uint32_t i = 0; i < chunk.q_len; ++i) { + chunk.q_idx[i] = i; + } + for (uint32_t i = 0; i < chunk.kv_len; ++i) { + chunk.kv_idx[i] = bid * chunk_size + i; + } + // printf("q_len = %d, kv_len = %d, q_idx = %x, kv_idx = %x\n", chunk.q_len, + // chunk.kv_len, chunk.q_idx, chunk.kv_idx); + // printf("q_len = %d, kv_len = %d, q_idx = %x\n", t_q_len, t_kv_len, + // t_q_idx); +} + +TEST(Attention, DeftNodeKernel) { + thrust::device_vector chunks(chunk_num); + thrust::device_vector q_idx[chunk_num]; + thrust::device_vector kv_idx[chunk_num]; + constexpr uint32_t q_size = branch_size * num_qo_heads * head_dim; + constexpr uint32_t kv_size = total_kv_len * num_kv_heads * head_dim; + thrust::device_vector q_data(q_size); + thrust::device_vector k_data(kv_size); + thrust::device_vector v_data(kv_size); + thrust::device_vector o_data(q_size); + + uint32_t **d_q_idx_ptr, **d_kv_idx_ptr; + uint32_t *q_idx_ptr[chunk_num], *kv_idx_ptr[chunk_num]; + uint32_t *d_q_len, *d_kv_len; + uint32_t q_len[chunk_num], kv_len[chunk_num]; + + cudaMalloc(&d_q_idx_ptr, chunk_num * sizeof(uint32_t*)); + cudaMalloc(&d_kv_idx_ptr, chunk_num * sizeof(uint32_t*)); + cudaMalloc(&d_q_len, chunk_num * sizeof(uint32_t)); + cudaMalloc(&d_kv_len, chunk_num * sizeof(uint32_t)); + + for (uint32_t i = 0; i < chunk_num; ++i) { + if (i == 0) { + q_idx[0].resize(branch_size); + } else { + q_idx[i].resize(1); + } + kv_idx[i].resize(chunk_size); + q_idx_ptr[i] = q_idx[i].data().get(); + kv_idx_ptr[i] = kv_idx[i].data().get(); + q_len[i] = q_idx[i].size(); + kv_len[i] = kv_idx[i].size(); + } + + cudaMemcpy(d_q_idx_ptr, q_idx_ptr, chunk_num * sizeof(uint32_t*), + cudaMemcpyHostToDevice); + cudaMemcpy(d_kv_idx_ptr, kv_idx_ptr, chunk_num * sizeof(uint32_t*), + cudaMemcpyHostToDevice); + cudaMemcpy(d_q_len, q_len, chunk_num * sizeof(uint32_t), + cudaMemcpyHostToDevice); + cudaMemcpy(d_kv_len, kv_len, chunk_num * sizeof(uint32_t), + cudaMemcpyHostToDevice); + + deft::tensor_info_t info(branch_size, total_kv_len, num_qo_heads, + num_kv_heads, flashinfer::QKVLayout::kNHD, head_dim, + partial_num); + init_chunks<<>>(chunks.data().get(), d_q_idx_ptr, d_q_len, + d_kv_idx_ptr, d_kv_len, q_data.data().get(), + k_data.data().get(), v_data.data().get()); + cudaError_t e = + deft::deft_attention(o_data.data().get(), chunks, partial_num, info, + 1. / sqrtf(head_dim), cudaStreamDefault); + cudaDeviceSynchronize(); + ASSERT_EQ(e, cudaSuccess); +} diff --git a/tests/csrc/test_dummy.cu b/tests/csrc/test_dummy.cu new file mode 100644 index 0000000..8af1aeb --- /dev/null +++ b/tests/csrc/test_dummy.cu @@ -0,0 +1,41 @@ +#include +#include "deft/attention.cuh" +#include +#include + +__global__ void init_kernel(float* input, int size) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < size) { + input[idx] = idx; + } +} + +void init(float* input, int size) { + int block_size = 256; + int grid_size = (size + block_size - 1) / block_size; + init_kernel<<>>(input, size); +} + +TEST(Attention, Dummy) { + const int size = 1024; + float* input_device; + float* output_device; + float* output_host = new float[size]; + + cudaMalloc(&input_device, size * sizeof(float)); + cudaMalloc(&output_device, size * sizeof(float)); + + init(input_device, size); + + deft::dummy(output_device, input_device, size); + + cudaMemcpy(output_host, output_device, size * sizeof(float), cudaMemcpyDeviceToHost); + + for (int i = 0; i < size; i++) { + EXPECT_EQ(output_host[i], i); + } + + cudaFree(input_device); + cudaFree(output_device); + delete[] output_host; +} diff --git a/tests/test_example.py b/tests/test_example.py index feee70b..8f2dd01 100644 --- a/tests/test_example.py +++ b/tests/test_example.py @@ -1,2 +1,10 @@ -def test_example()-> None: - assert 1 + 1 == 2 \ No newline at end of file +# import torch + +# from deft._kernels import dummy + + +# def test_example() -> None: +# a = torch.randn(1024, device='cuda') +# b = torch.zeros_like(a) +# dummy(b, a) +# assert torch.allclose(a, b)