# -*- coding: utf-8 -*- # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import json import sys def classify_cases_by_mem(rootPath): """classify cases by mem""" case_filename = '%s/build/classify_case_by_cardNum.txt' % rootPath case_exec_100 = [ 'test_conv_eltwiseadd_bn_fuse_pass', 'test_trt_convert_pool2d', 'test_fc_fuse_pass', 'test_trt_convert_depthwise_conv2d', 'test_quant2_int8_resnet50_mkldnn', 'test_conv_elementwise_add_act_fuse_pass', 'test_trt_convert_conv2d', 'test_paddle_save_load', 'test_logical_op', 'test_nearest_interp_op', 'test_pool2d_op', 'test_conv3d_transpose_op', 'test_lstmp_op', 'test_cross_entropy2_op', 'test_sgd_op', 'test_imperative_ptq', 'test_model', 'test_custom_relu_op_setup', 'test_dropout_op', 'test_concat_op' ] #木桶原理 70s-100s之间的case case_exec_200 = [ 'test_post_training_quantization_mnist', 'test_imperative_auto_mixed_precision', 'test_trt_dynamic_shape_ernie_fp16_ser_deser', 'test_trt_dynamic_shape_ernie', 'test_layer_norm_op', 'trt_quant_int8_yolov3_r50_test', 'test_gru_op', 'test_post_training_quantization_while', 'test_mkldnn_log_softmax_op', 'test_mkldnn_matmulv2_op', 'test_mkldnn_shape_op', 'interceptor_pipeline_short_path_test', 'interceptor_pipeline_long_path_test', 'test_cpuonly_spawn' ] #木桶原理 110s-200s之间的case 以及容易timeout case_always_timeout = [ 'test_quant2_int8_resnet50_channelwise_mkldnn', 'test_parallel_dygraph_unused_variables_gloo', 'test_seq2seq', 'test_pool3d_op', 'test_trilinear_interp_op', 'test_trilinear_interp_v2_op', 'test_dropout_op', 'test_parallel_dygraph_sync_batch_norm', 'test_conv3d_op', 'test_quant2_int8_resnet50_range_mkldnn', ] # always timeout f = open(case_filename) lines = f.readlines() all_tests_by_card = {} for line in lines: if line.startswith('single_card_tests:'): all_tests_by_card['single_card_tests'] = [] line = line.split('single_card_tests: ^job$|')[1].split('|') for case in line: case = case.replace('^', '').replace('$', '').strip() all_tests_by_card['single_card_tests'].append(case) elif line.startswith('multiple_card_tests:'): all_tests_by_card['multiple_card_tests'] = [] line = line.split('multiple_card_tests: ^job$|')[1].split('|') for case in line: case = case.replace('^', '').replace('$', '').strip() all_tests_by_card['multiple_card_tests'].append(case) elif line.startswith('exclusive_card_tests:'): all_tests_by_card['exclusive_card_tests'] = [] line = line.split('exclusive_card_tests: ^job$')[1].split('|') for case in line: case = case.replace('^', '').replace('$', '').strip() all_tests_by_card['exclusive_card_tests'].append(case) if not os.path.exists("/pre_test"): os.mkdir("/pre_test") with open("/pre_test/classify_case_by_cardNum.json", "w") as f: json.dump(all_tests_by_card, f) with open("/pre_test/ut_mem_map.json", 'r') as load_f: new_lastest_mem = json.load(load_f) no_parallel_case = '^job$' for cardType in all_tests_by_card: case_mem_0 = '^job$' case_mem_1 = {} for case in all_tests_by_card[cardType]: if case in case_exec_100 or case in case_exec_200: continue if case in case_always_timeout: no_parallel_case = no_parallel_case + '|^' + case + '$' continue if case not in new_lastest_mem: continue #mem = 0 if new_lastest_mem[case]["mem_nvidia"] == 0: case_mem_0 = case_mem_0 + '|^' + case + '$' #mem != 0 else: case_mem_1[case] = new_lastest_mem[case]["mem_nvidia"] with open('/pre_test/%s_mem0' % cardType, 'w') as f: f.write(case_mem_0) f.close() case_mem_1_sort = sorted(case_mem_1.items(), key=lambda x: x[1]) case_mem_1_line = '^job$' mem_1_sum = 0 with open('/pre_test/%s' % cardType, 'w') as f_not_0: for index in case_mem_1_sort: if mem_1_sum < 14 * 1024 * 2: mem_1_sum += index[1] case_mem_1_line = case_mem_1_line + '|^' + index[0] + '$' else: f_not_0.write(case_mem_1_line + '\n') ''' if len(always_timeout_list ) != 0 and cardType == 'single_card_tests' and count > 25: f.write(case_mem_1_line + '|^%s$\n' % always_timeout_list[0]) always_timeout_list.pop(0) else: f.write(case_mem_1_line + '\n') count += 1 ''' case_mem_1_line = '^job$|^' + index[0] + '$' mem_1_sum = index[1] f_not_0.write(case_mem_1_line + '\n') if cardType == 'single_card_tests': for cases in [case_exec_100, case_exec_200]: case_mem_1_line = '^job$' for case in cases: case_mem_1_line = case_mem_1_line + '|^' + case + '$' f_not_0.write(case_mem_1_line + '\n') f_not_0.close() os.system('cp %s/build/nightly_case /pre_test/' % rootPath) if __name__ == '__main__': rootPath = sys.argv[1] classify_cases_by_mem(rootPath)