From 6cf2684199e70e7a52cb2bcf4b6c055a1b4ee79e Mon Sep 17 00:00:00 2001 From: Xinqi Li Date: Wed, 4 Mar 2026 08:32:49 +0000 Subject: [PATCH] add select_representive_sample_uids.py --- .../sqlite_util}/filter_valid_groups.py | 0 .../graph_net_sample_groups_util.py | 0 .../sqlite_util}/graph_net_sample_util.py | 0 .../sqlite_util}/select_evaluation_subset.py | 17 +- .../select_representive_sample_uids.py | 108 + .../sqlite_util}/vectorize_op_seq.py | 0 .../sqlite_util/sample_uids_and_op_seq.txt | 2878 +++++++++++++++++ .../test/sqlite_util/selected_op_seq.txt | 200 ++ .../test_select_representive_sample_uids.sh | 2 + 9 files changed, 3202 insertions(+), 3 deletions(-) rename {sqlite/util => graph_net/sqlite_util}/filter_valid_groups.py (100%) rename {sqlite/util => graph_net/sqlite_util}/graph_net_sample_groups_util.py (100%) rename {sqlite/util => graph_net/sqlite_util}/graph_net_sample_util.py (100%) rename {sqlite/util => graph_net/sqlite_util}/select_evaluation_subset.py (89%) create mode 100644 graph_net/sqlite_util/select_representive_sample_uids.py rename {sqlite/util => graph_net/sqlite_util}/vectorize_op_seq.py (100%) create mode 100644 graph_net/test/sqlite_util/sample_uids_and_op_seq.txt create mode 100644 graph_net/test/sqlite_util/selected_op_seq.txt create mode 100644 graph_net/test/sqlite_util/test_select_representive_sample_uids.sh diff --git a/sqlite/util/filter_valid_groups.py b/graph_net/sqlite_util/filter_valid_groups.py similarity index 100% rename from sqlite/util/filter_valid_groups.py rename to graph_net/sqlite_util/filter_valid_groups.py diff --git a/sqlite/util/graph_net_sample_groups_util.py b/graph_net/sqlite_util/graph_net_sample_groups_util.py similarity index 100% rename from sqlite/util/graph_net_sample_groups_util.py rename to graph_net/sqlite_util/graph_net_sample_groups_util.py diff --git a/sqlite/util/graph_net_sample_util.py b/graph_net/sqlite_util/graph_net_sample_util.py similarity index 100% rename from sqlite/util/graph_net_sample_util.py rename to graph_net/sqlite_util/graph_net_sample_util.py diff --git a/sqlite/util/select_evaluation_subset.py b/graph_net/sqlite_util/select_evaluation_subset.py similarity index 89% rename from sqlite/util/select_evaluation_subset.py rename to graph_net/sqlite_util/select_evaluation_subset.py index 7aeccbf20..3f3aa496c 100644 --- a/sqlite/util/select_evaluation_subset.py +++ b/graph_net/sqlite_util/select_evaluation_subset.py @@ -4,6 +4,7 @@ import math from collections import Counter +from typing import Callable import numpy as np from scipy import sparse @@ -88,12 +89,17 @@ def _compute_metrics( return result -def _greedy_select(metrics: list[dict], k: int, rarity_weight: float) -> list[int]: +def _greedy_select( + metrics: list[dict], k: int, rarity_weight: float, selected: list[int] = None +) -> list[int]: """Greedily maximise edge coverage, weighted by rarity score.""" target = min(k, len(metrics)) rarity_norm = _min_max_normalize([m["rarity_score"] for m in metrics]) - selected: list[int] = [] + if selected is None: + selected: list[int] = [] + else: + assert isinstance(selected, list) selected_set: set[int] = set() covered_edges: set[tuple[str, str]] = set() @@ -123,6 +129,7 @@ def select_evaluation_subset( *, smoothing_alpha: float = 1e-3, rarity_weight: float = 1, + is_selected: Callable[tuple[str, ...], bool] = lambda x: False, ) -> list[tuple[str, ...]]: """Select k sequences from op_seqs using Markov-based greedy coverage. @@ -140,4 +147,8 @@ def select_evaluation_subset( op_to_id, count_matrix, row_sums = _build_markov_model(seqs) metrics = _compute_metrics(seqs, op_to_id, count_matrix, row_sums, smoothing_alpha) - return [seqs[i] for i in _greedy_select(metrics, k, rarity_weight)] + selected_indexes = [i for i, seq in enumerate(seqs) if is_selected(seq)] + return [ + seqs[i] + for i in _greedy_select(metrics, k, rarity_weight, selected=selected_indexes) + ] diff --git a/graph_net/sqlite_util/select_representive_sample_uids.py b/graph_net/sqlite_util/select_representive_sample_uids.py new file mode 100644 index 000000000..4d5155945 --- /dev/null +++ b/graph_net/sqlite_util/select_representive_sample_uids.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +""" +select_representative_sample_uids.py + +Reads a TSV file of (sample_uid, op_seq) pairs and a file listing selected op_seqs. +For each selected op_seq, picks the sample_uid with the maximum string length from its group, +and outputs them in the same order as the selected op_seqs. +""" + +import argparse +import sys +from collections import defaultdict + + +def read_tsv_pairs(file_path): + """ + Read a tab-separated file where each line contains sample_uid and op_seq. + Returns a list of (sample_uid, op_seq) tuples. + """ + pairs = [] + with open(file_path, "r", encoding="utf-8") as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + if not line: + continue + parts = line.split("\t") + if len(parts) != 2: + print( + f"Warning: {file_path}:{line_num} invalid format, skipped: {line}", + file=sys.stderr, + ) + continue + sample_uid, op_seq = parts + pairs.append((sample_uid, op_seq)) + return pairs + + +def read_op_seq_list(file_path): + """ + Read a file with one op_seq per line. Returns a list preserving order. + """ + op_seqs = [] + with open(file_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + op_seqs.append(line) + return op_seqs + + +def get_max_len_string(strings): + """ + Return the string with the maximum length from a list. + If multiple strings have the same max length, return the first encountered. + """ + if not strings: + return None + # max with key returns the first element in case of ties (Python's max is stable) + return max(strings, key=lambda s: len(s)) + + +def main(): + parser = argparse.ArgumentParser( + description="Select representative sample UIDs by max string length per op_seq group." + ) + parser.add_argument( + "pairs_file", help="TSV file with two columns: sample_uid and op_seq" + ) + parser.add_argument( + "opseq_file", help="File with one op_seq per line (order matters)" + ) + parser.add_argument("-o", "--output", help="Output file path (default: stdout)") + args = parser.parse_args() + + # 1. Load input data + pairs = read_tsv_pairs(args.pairs_file) + selected_op_seqs = read_op_seq_list(args.opseq_file) + + # 2. Group sample_uids by op_seq + groups = defaultdict(list) + for sample_uid, op_seq in pairs: + groups[op_seq].append(sample_uid) + + # 3. For each op_seq, find the longest sample_uid + op_seq_to_max_uid = {} + for op_seq, uid_list in groups.items(): + max_uid = get_max_len_string(uid_list) + if max_uid is not None: + op_seq_to_max_uid[op_seq] = max_uid + + # 4. Collect results in the order of selected_op_seqs + result_uids = [] + for op_seq in selected_op_seqs: + if op_seq in op_seq_to_max_uid: + result_uids.append(op_seq_to_max_uid[op_seq]) + + # 5. 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"Tensor.fill_", "getitem", "Tensor.fill_", "Tensor.reshape", "Tensor.transpose", "Tensor.reshape", "Tensor.transpose", "Tensor.reshape", "Tensor.unsqueeze", "Tensor.unsqueeze", "sub"] diff --git a/graph_net/test/sqlite_util/selected_op_seq.txt b/graph_net/test/sqlite_util/selected_op_seq.txt new file mode 100644 index 000000000..a2845793d --- /dev/null +++ b/graph_net/test/sqlite_util/selected_op_seq.txt @@ -0,0 +1,200 @@ +["cat", "arange", "arange", "meshgrid", "getitem", "getitem", "stack", "flatten", "getitem", "getitem", "sub", "Tensor.permute", "Tensor.contiguous", "getitem", "iadd", "setitem", "getitem", "iadd", "setitem", "getitem", "imul", "setitem", "zeros", "Tensor.sum", "setitem", "setitem", "setitem", "setitem", "Tensor.view"] +["add", "layer_norm", "zeros", "arange", "Tensor.view", "arange", "Tensor.view", "sub", "Tensor.repeat", "Tensor.repeat_interleave", "Tensor.repeat_interleave", "pow", "pow", "add", "Tensor.unsqueeze", "setitem", "Tensor.unsqueeze", "setitem", "Tensor.unsqueeze", "setitem"] +["cat", "Tensor.cos", "mul", "Tensor.sin", "mul", "Tensor.to", "Tensor.to", "_set_grad_enabled", "_log_api_usage_once", "Tensor.to", "Tensor.pow", "Tensor.mean", "add", "rsqrt", "mul", "Tensor.to", "mul"] +["Tensor.cumsum", "sub", "Tensor.__eq__", "Tensor.masked_fill_", "Tensor.unsqueeze", "Tensor.expand", "Tensor.to", "Tensor.max", "getitem", "Tensor.max", "getitem", "add", "sub"] +["arange", "Tensor.type_as", "outer", "cat", "Tensor.to", "Tensor.cos", "getitem", "Tensor.sin", "getitem", "getitem", "getitem", "mul", "Tensor.chunk", "getitem", "getitem"] +["embedding", "Tensor.__eq__", "Tensor.unsqueeze", "Tensor.masked_fill", "Tensor.sum", "Tensor.__eq__", "Tensor.sum", "Tensor.float", "truediv", "mul", "sub", "getitem"] +["conv2d", "Tensor.flatten", "Tensor.transpose", "layer_norm", "dropout", "layer_norm", "Tensor.view", "pad", "Tensor.view", "Tensor.permute", "Tensor.contiguous", "Tensor.view", "Tensor.view"] +["zeros", "getitem", "Tensor.fill_", "getitem", "Tensor.fill_", "Tensor.reshape", "Tensor.transpose", "Tensor.reshape", "Tensor.transpose", "Tensor.reshape", "Tensor.unsqueeze", "Tensor.unsqueeze", "sub"] +["neg", "getitem", "stack", "Tensor.reshape", "mul", "add", "cat", "Tensor.type_as", "getitem", "getitem", "Tensor.tensor_split", "getitem", "getitem"] +["mul", "softmax", "Tensor.to", "dropout", "matmul", "Tensor.transpose", "Tensor.contiguous", "Tensor.reshape", "Tensor.contiguous"] +["add", "Tensor.float", "Tensor.mean", "sub", "Tensor.pow", "Tensor.mean", "sub", "add", "sqrt", "truediv", "Tensor.to", "mul", "add"] +["conv2d", "sigmoid", "mul", "cat", "cat", "Tensor.view", "transpose", "Tensor.contiguous", "Tensor.view", "Tensor.view", "transpose", "Tensor.contiguous", "Tensor.view", "Tensor.chunk", "getitem", "getitem", "Tensor.chunk", "getitem", "getitem"] +["arange", "meshgrid", "getitem", "getitem", "Tensor.flatten", "Tensor.flatten", "Tensor.reshape", "Tensor.unsqueeze", "truediv", "Tensor.unsqueeze", "truediv", "Tensor.cos", "Tensor.sin"] +["conv2d", "Tensor.view", "Tensor.permute", "layer_norm", "Tensor.permute", "Tensor.view", "dropout", "Tensor.view", "Tensor.permute", "Tensor.expand"] +["getitem", "Tensor.view", "Tensor.permute", "Tensor.contiguous", "Tensor.unsqueeze", "add", "Tensor.view", "Tensor.unsqueeze", "Tensor.unsqueeze", "add", "Tensor.view", "softmax", "dropout"] +["linear", "Tensor.reshape", "Tensor.split", "getitem", "getitem", "getitem", "Tensor.permute", "Tensor.permute", "Tensor.permute", "Tensor.to", "Tensor.transpose"] +["Tensor.sigmoid", "Tensor.view", "Tensor.expand_as", "mul", "iadd", "relu", "adaptive_avg_pool2d", "Tensor.flatten"] +["Tensor.ne", "Tensor.int", "cumsum", "Tensor.type_as", "add", "mul", "Tensor.long", "add"] +["softmax", "Tensor.reshape", "Tensor.mul", "Tensor.reshape", "sum", "Tensor.mul", "Tensor.reshape", "sum", "cat"] +["conv2d", "Tensor.flatten", "Tensor.transpose", "Tensor.expand", "cat", "dropout", "getitem", "Tensor.reshape", "Tensor.permute"] +["linear", "getitem", "Tensor.view", "getitem", "Tensor.view", "Tensor.reshape", "linear", "getitem", "getitem", "Tensor.unsqueeze"] +["max", "getitem", "Tensor.expand_as", "sub", "softmax", "Tensor.view"] +["conv2d", "add", "interpolate", "add", "batch_norm", "relu"] +["conv2d", "Tensor.sigmoid", "mul", "gelu", "adaptive_avg_pool2d", "Tensor.flatten", "dropout"] +["relu", "flatten", "norm", "mul", "Tensor.clamp", "truediv", "mul"] +["add", "Tensor.mean", "dropout", "dropout", "batch_norm"] +["conv2d", "Tensor.view", "cat", "Tensor.sigmoid", "sub", "mul"] +["Tensor.reshape", "add", "add", "Tensor.transpose", "Tensor.transpose", "Tensor.transpose"] +["softmax", "Tensor.reshape", "Tensor.view", "Tensor.view", "mul", "sum", "Tensor.contiguous"] +["Tensor.to", "tensor", "sub", "Tensor.to", "Tensor.masked_fill"] +["mul", "pow", "mul", "add", "mul", "tanh", "add", "mul"] +["iadd", "Tensor.float", "softmax", "Tensor.type_as", "dropout"] +["truediv", "Tensor.to", "embedding", "add", "Tensor.unsqueeze", "mul", "Tensor.to", "layer_norm"] +["add", "embedding", "iadd", "layer_norm", "dropout", "getitem", "Tensor.expand"] +["Tensor.contiguous", "Tensor.view", "roll", "getitem", "Tensor.contiguous", "Tensor.view", "add", "layer_norm"] +["linear", "Tensor.permute", "Tensor.reshape", "interpolate", "Tensor.flatten", "Tensor.transpose"] +["neg", "cat", "mul", "add", "Tensor.to", "getitem", "getitem", "mul", "Tensor.chunk", "getitem", "getitem"] +["linear", "Tensor.view", "Tensor.transpose", "Tensor.view", "Tensor.transpose", "Tensor.view", "Tensor.transpose", "Tensor.reshape", "Tensor.reshape", "Tensor.reshape", "Tensor.transpose"] +["matmul", "getitem", "getitem", "Tensor.transpose", "Tensor.reshape", "split", "getitem", "getitem", "getitem"] +["cat", "batch_norm", "prelu", "adaptive_avg_pool2d", "Tensor.view"] +["cat", "Tensor.reshape", "Tensor.transpose", "mul", "pad"] +["einsum", "cat", "softmax", "getitem"] +["mul", "Tensor.unsqueeze", "add", "Tensor.softmax", "dropout"] +["iadd", "gelu", "batch_norm", "add"] +["conv2d", "stack", "Tensor.sum", "cat"] +["mul", "truediv", "erf", "add", "mul"] +["conv2d", "add", "Tensor.flatten", "Tensor.transpose", "cat", "layer_norm"] +["conv2d", "Tensor.flatten", "Tensor.transpose", "add", "dropout", "Tensor.expand"] +["add", "add", "truediv", "add", "softmax", "dropout"] +["cat", "Tensor.cos", "Tensor.sin", "cat", "stack", "Tensor.transpose"] +["add", "getitem", "Tensor.reshape", "Tensor.permute", "Tensor.contiguous", "layer_norm", "getitem"] +["conv2d", "interpolate", "sigmoid", "sigmoid", "sigmoid", "sigmoid", "sigmoid", "sigmoid"] +["Tensor.sum", "Tensor.unsqueeze", "itruediv", "dropout"] +["conv2d", "hardsigmoid", "mul", "adaptive_avg_pool2d", "Tensor.flatten", "dropout"] +["cat", "adaptive_avg_pool2d", "flatten", "dropout"] +["relu", "max_pool2d", "max_pool2d", "max_pool2d", "cat"] +["conv3d", "Tensor.flatten", "Tensor.transpose", "Tensor.detach", "Tensor.type_as"] +["mul", "as_tensor", "as_tensor", "as_tensor", "as_tensor", "cat"] +["conv2d", "mul", "Tensor.reshape"] +["linear", "add", "Tensor.relu_"] +["Tensor.norm", "truediv", "Tensor.t", "Tensor.to"] +["cat", "getitem", "Tensor.mean"] +["conv2d", "layer_norm", "relu"] +["sym_sum", "floordiv", "sym_sum", "Tensor.view"] +["truediv", "relu", "square"] +["conv2d", "silu", "dropout"] +["conv2d", "Tensor.permute", "Tensor.reshape", "sigmoid"] +["truediv", "Tensor.to", "add", "_log_api_usage_once"] +["matmul", "mul", "Tensor.t"] +["arange", "mul", "Tensor.view", "Tensor.unsqueeze", "add", "Tensor.view"] +["relu", "batch_norm", "dropout"] +["conv2d", "batch_norm", "leaky_relu"] +["conv2d", "relu", "dropout"] +["iadd", "mul", "add", "Tensor.contiguous"] +["layer_norm", "Tensor.sigmoid", "Tensor.sigmoid"] +["conv2d", "sigmoid", "Tensor.view", "mul", "Tensor.contiguous"] +["add", "iadd", "Tensor.mean"] +["mul", "batch_norm", "silu"] +["Tensor.reshape", "getitem", "Tensor.contiguous", "Tensor.contiguous", "Tensor.contiguous"] +["relu", "mul", "add", "pad"] +["conv2d", "Tensor.view", "batch_norm", "relu", "cat"] +["mul", "sum", "Tensor.unsqueeze", "sigmoid"] +["relu", "add", "adaptive_avg_pool2d"] +["arange", "Tensor.view", "sub", "add", "sub", "embedding", "Tensor.to"] +["batch_norm", "add", "relu", "Tensor.mean"] +["gelu", "mul", "dropout"] +["conv2d", "hardswish", "Tensor.flatten"] +["conv2d", "adaptive_avg_pool2d", "batch_norm", "relu"] +["conv2d", "iadd", "Tensor.permute", "Tensor.contiguous", "Tensor.view"] +["conv2d", "unfold", "Tensor.reshape"] +["mul", "softmax", "matmul", "Tensor.permute"] +["Tensor.exp", "mul", "add", "Tensor.t"] +["conv2d", "gelu", "dropout"] +["add", "mul", "add", "tensor", "getitem"] +["mul", "Tensor.softmax", "Tensor.transpose"] +["interpolate", "mul", "interpolate", "mul"] +["conv2d", "dropout", "mul", "add", "batch_norm"] +["arange", "Tensor.expand", "add", "embedding", "add", "layer_norm", "dropout"] +["linear", "dropout", "add"] +["Tensor.to", "sub", "mul", "getitem"] +["Tensor.exp", "Tensor.to", "Tensor.to", "Tensor.t"] +["conv2d", "add", "truediv", "Tensor.clamp_", "mul"] +["mul", "add", "unbind", "getitem", "getitem", "Tensor.permute"] +["hardtanh", "adaptive_avg_pool2d", "Tensor.view", "flatten"] +["add", "silu", "adaptive_avg_pool2d", "Tensor.flatten", "dropout"] +["conv2d", "Tensor.flatten", "Tensor.transpose", "Tensor.expand", "cat", "add", "dropout", "layer_norm", "layer_norm"] +["add", "layer_norm", "Tensor.reshape", "Tensor.permute", "Tensor.contiguous", "Tensor.flatten", "Tensor.transpose"] +["avg_pool2d", "sub", "Tensor.unsqueeze", "Tensor.unsqueeze", "mul", "add", "Tensor.unsqueeze", "Tensor.unsqueeze"] +["linear", "Tensor.view", "Tensor.transpose", "scaled_dot_product_attention", "Tensor.transpose", "Tensor.reshape"] +["max_pool2d", "interpolate", "cat", "batch_norm", "relu"] +["silu", "split", "getitem", "getitem", "getitem", "Tensor.unsqueeze", "getitem"] +["softmax", "dropout", "bmm", "Tensor.view", "Tensor.transpose", "Tensor.reshape"] +["silu", "Tensor.detach", "Tensor.detach", "Tensor.detach"] +["linear", "Tensor.reshape", "Tensor.permute", "Tensor.unbind", "getitem", "getitem", "getitem", "Tensor.transpose"] +["iadd", "iadd", "relu", "Tensor.chunk", "getitem", "getitem", "Tensor.chunk", "getitem", "getitem"] +["ne", "Tensor.masked_fill", "eq"] +["getitem", "batch_norm"] +["mul", "Tensor.transpose"] +["conv2d", "getitem"] +["conv2d", "Tensor.mean"] +["conv2d", "max_pool2d"] +["conv2d", "avg_pool2d"] +["conv2d", "hardtanh"] +["getitem", "unsqueeze"] +["cat", "normalize"] +["relu", "dropout2d"] +["relu", "sigmoid"] +["conv2d", "flatten"] +["getitem", "dropout", "dropout"] +["add", "getitem", "cat"] +["adaptive_avg_pool2d", "cat"] +["conv2d", "cat"] +["linear", "mul"] +["Tensor.flatten", "Tensor.permute"] +["linear", "Tensor.mean"] +["linear", "batch_norm"] +["linear", "Tensor.transpose"] +["conv2d", "Tensor.view", "Tensor.mean"] +["conv2d", "Tensor.view", "Tensor.softmax"] +["relu", "Tensor.flatten"] +["add", "Tensor.permute", "Tensor.view"] +["cat", "Tensor.view", "layer_norm"] +["conv2d", "add", "dropout2d"] +["matmul", "mul", "getattr"] +["conv2d", "batch_norm", "avg_pool2d"] +["softmax", "mul", "sum"] +["conv2d", "Tensor.view", "softmax", "Tensor.unsqueeze"] +["relu", "dropout", "Tensor.flatten"] +["silu", "add"] +["truediv", "Tensor.transpose"] +["truediv", "Tensor.view"] +["conv2d", "batch_norm", "iadd"] +["relu", "Tensor.view", "Tensor.unsqueeze"] +["linear", "Tensor.permute", "Tensor.transpose"] +["add", "layer_norm", "add"] +["getitem", "Tensor.expand", "getitem"] +["arange", "iadd"] +["arange", "lazy_load_decompositions"] +["arange", "Tensor.to"] +["interpolate", "interpolate"] +["conv2d", "add", "add", "batch_norm", "Tensor.mean"] +["layer_norm", "Tensor.transpose"] +["conv2d", "Tensor.sigmoid", "mul", "hardtanh"] +["matmul", "Tensor.view"] +["silu", "mul", "dropout"] +["embedding", "getitem"] +["embedding", "Tensor.long"] +["embedding", "mul"] +["iadd", "dropout", "layer_norm"] +["arange", "Tensor.unsqueeze", "Tensor.expand"] +["add", "softmax", "dropout", "Tensor.to"] +["mul", "sigmoid", "mul", "dropout"] +["truediv", "Tensor.to", "Tensor.unsqueeze", "mul", "Tensor.to", "layer_norm"] +["conv2d", "Tensor.sigmoid", "mul", "relu", "batch_norm"] +["relu", "Tensor.mean", "Tensor.view"] +["conv2d", "Tensor.reshape", "Tensor.permute", "layer_norm"] +["matmul", "Tensor.to", "Tensor.to"] +["add", "layer_norm", "getitem", "Tensor.reshape", "Tensor.permute", "relu"] +["silu", "Tensor.mean"] +["silu", "max_pool2d"] +["relu", "Tensor.reshape", "Tensor.reshape", "Tensor.permute"] +["getitem", "Tensor.view", "Tensor.permute", "Tensor.contiguous", "Tensor.unsqueeze", "layer_norm"] +["Tensor.view", "Tensor.transpose", "Tensor.permute", "Tensor.reshape"] +["embedding", "embedding", "add", "Tensor.unsqueeze", "mul", "Tensor.to"] +["gelu", "Tensor.flatten"] +["gelu", "Tensor.mean"] +["Tensor.contiguous", "Tensor.view", "roll", "Tensor.view", "add", "layer_norm"] +["conv2d", "add", "batch_norm", "adaptive_avg_pool2d", "Tensor.flatten"] +["linear", "Tensor.view", "Tensor.transpose", "getitem", "Tensor.expand"] +["iadd", "batch_norm", "relu"] +["conv2d", "Tensor.flatten", "Tensor.transpose", "layer_norm", "Tensor.expand"] +["interpolate", "Tensor.permute", "Tensor.reshape", "getitem"] +["add", "split", "getitem", "getitem", "Tensor.permute", "Tensor.view"] +["conv2d", "dropout", "Tensor.unsqueeze", "Tensor.unsqueeze", "mul", "add", "Tensor.flatten", "Tensor.transpose", "layer_norm", "Tensor.view", "Tensor.permute"] +["linear", "Tensor.view", "Tensor.transpose", "Tensor.unsqueeze", "Tensor.unsqueeze"] +["Tensor.sum", "Tensor.mean"] +["Tensor.sum", "adaptive_avg_pool2d"] +["add", "getitem", "add", "Tensor.to", "Tensor.pow", "Tensor.mean", "add", "rsqrt", "mul", "mul"] +["embedding", "layer_norm", "dropout"] +["cat", "Tensor.cos", "mul", "Tensor.sin", "mul", "Tensor.to", "Tensor.to", "_set_grad_enabled", "_log_api_usage_once", "layer_norm"] diff --git a/graph_net/test/sqlite_util/test_select_representive_sample_uids.sh b/graph_net/test/sqlite_util/test_select_representive_sample_uids.sh new file mode 100644 index 000000000..a3a67805f --- /dev/null +++ b/graph_net/test/sqlite_util/test_select_representive_sample_uids.sh @@ -0,0 +1,2 @@ +#!/bin/bash +python3 -m graph_net.sqlite_util.select_representive_sample_uids ./sample_uids_and_op_seq.txt ./selected_op_seq.txt