wehub-resource-sync
157 lines
5.8 KiB
Python
157 lines
5.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import sys
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def classify_cases_by_mem(rootPath):
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"""classify cases by mem"""
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case_filename = f'{rootPath}/build/classify_case_by_cardNum.txt'
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case_exec_100 = [
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'test_conv_eltwiseadd_bn_fuse_pass',
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'test_trt_convert_pool2d',
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'test_fc_fuse_pass',
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'test_trt_convert_depthwise_conv2d',
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'test_quant2_int8_resnet50_onednn',
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'test_conv_elementwise_add_act_fuse_pass',
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'test_trt_convert_conv2d',
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'test_paddle_save_load',
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'test_logical_op',
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'test_pool2d_op',
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'test_conv3d_transpose_op',
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'test_lstmp_op',
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'test_cross_entropy2_op',
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'test_sgd_op',
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'test_imperative_ptq',
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'test_model',
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'test_custom_relu_op_setup',
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'test_dropout_op',
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'test_concat_op',
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] # 木桶原理 70s-100s之间的case
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case_exec_200 = [
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'test_post_training_quantization_mnist',
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'test_trt_dynamic_shape_ernie_fp16_ser_deser',
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'test_trt_dynamic_shape_ernie',
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'test_layer_norm_op',
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'trt_quant_int8_yolov3_r50_test',
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'test_gru_op',
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'test_post_training_quantization_while',
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'test_onednn_log_softmax_op',
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'test_onednn_matmulv2_op',
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'test_onednn_shape_op',
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'interceptor_pipeline_short_path_test',
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'interceptor_pipeline_long_path_test',
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'test_cpuonly_spawn',
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] # 木桶原理 110s-200s之间的case 以及容易timeout
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case_always_timeout = [
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'test_quant2_int8_resnet50_channelwise_onednn',
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'test_parallel_dygraph_unused_variables_gloo',
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'test_seq2seq',
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'test_pool3d_op',
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'test_trilinear_interp_v2_op',
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'test_dropout_op',
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'test_parallel_dygraph_sync_batch_norm',
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'test_conv3d_op',
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'test_quant2_int8_resnet50_range_onednn',
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] # always timeout
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f = open(case_filename)
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lines = f.readlines()
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all_tests_by_card = {}
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for line in lines:
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if line.startswith('single_card_tests:'):
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all_tests_by_card['single_card_tests'] = []
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line = line.split('single_card_tests: ^job$|')[1].split('|')
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for case in line:
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case = case.replace('^', '').replace('$', '').strip()
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all_tests_by_card['single_card_tests'].append(case)
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elif line.startswith('multiple_card_tests:'):
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all_tests_by_card['multiple_card_tests'] = []
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line = line.split('multiple_card_tests: ^job$|')[1].split('|')
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for case in line:
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case = case.replace('^', '').replace('$', '').strip()
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all_tests_by_card['multiple_card_tests'].append(case)
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elif line.startswith('exclusive_card_tests:'):
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all_tests_by_card['exclusive_card_tests'] = []
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line = line.split('exclusive_card_tests: ^job$')[1].split('|')
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for case in line:
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case = case.replace('^', '').replace('$', '').strip()
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all_tests_by_card['exclusive_card_tests'].append(case)
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if not os.path.exists("/pre_test"):
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os.mkdir("/pre_test")
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with open("/pre_test/classify_case_by_cardNum.json", "w") as f:
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json.dump(all_tests_by_card, f)
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with open("/pre_test/ut_mem_map.json", 'r') as load_f:
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new_latest_mem = json.load(load_f)
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no_parallel_case = '^job$'
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for cardType in all_tests_by_card:
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case_mem_0 = '^job$'
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case_mem_1 = {}
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for case in all_tests_by_card[cardType]:
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if case in case_exec_100 or case in case_exec_200:
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continue
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if case in case_always_timeout:
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no_parallel_case = no_parallel_case + '|^' + case + '$'
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continue
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if case not in new_latest_mem:
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continue
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# mem = 0
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if new_latest_mem[case]["mem_nvidia"] == 0:
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case_mem_0 = case_mem_0 + '|^' + case + '$'
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# mem != 0
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else:
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case_mem_1[case] = new_latest_mem[case]["mem_nvidia"]
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with open(f'/pre_test/{cardType}_mem0', 'w') as f:
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f.write(case_mem_0)
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f.close()
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case_mem_1_sort = sorted(case_mem_1.items(), key=lambda x: x[1])
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case_mem_1_line = '^job$'
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mem_1_sum = 0
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with open(f'/pre_test/{cardType}', 'w') as f_not_0:
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for index in case_mem_1_sort:
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if mem_1_sum < 14 * 1024 * 2:
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mem_1_sum += index[1]
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case_mem_1_line = case_mem_1_line + '|^' + index[0] + '$'
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else:
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f_not_0.write(case_mem_1_line + '\n')
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case_mem_1_line = '^job$|^' + index[0] + '$'
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mem_1_sum = index[1]
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f_not_0.write(case_mem_1_line + '\n')
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if cardType == 'single_card_tests':
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for cases in [case_exec_100, case_exec_200]:
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case_mem_1_line = '^job$'
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for case in cases:
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case_mem_1_line = case_mem_1_line + '|^' + case + '$'
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f_not_0.write(case_mem_1_line + '\n')
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f_not_0.close()
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os.system(f'cp {rootPath}/build/nightly_case /pre_test/')
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if __name__ == '__main__':
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rootPath = sys.argv[1]
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classify_cases_by_mem(rootPath)
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