wehub-resource-sync
501 lines
18 KiB
CMake
501 lines
18 KiB
CMake
include(../cpp/inference/test.cmake)
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file(
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GLOB TEST_OPS
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RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
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"test_*.py")
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string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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function(_inference_analysis_python_api_int8_test target model_dir data_path
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filename use_onednn)
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py_test(
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${target}
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SRCS ${filename}
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ENVS
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CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_onednn=${use_onednn}
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ARGS
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--infer_model
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${model_dir}/model
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--infer_data
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${data_path}
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--int8_model_save_path
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int8_models/${target}
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--warmup_batch_size
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${WARMUP_BATCH_SIZE}
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--batch_size
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50)
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endfunction()
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function(inference_analysis_python_api_int8_test target model_dir data_path
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filename)
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_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
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${filename} False)
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endfunction()
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function(inference_analysis_python_api_int8_test_custom_warmup_batch_size
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target model_dir data_dir filename warmup_batch_size)
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set(WARMUP_BATCH_SIZE ${warmup_batch_size})
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inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir}
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${filename})
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endfunction()
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function(inference_analysis_python_api_int8_test_onednn target model_dir
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data_path filename)
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_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
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${filename} True)
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endfunction()
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function(download_data install_dir url data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${url} ${data_file}
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${check_sum})
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endif()
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endfunction()
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function(download_quant_data install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
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${data_file} ${check_sum})
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endif()
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endfunction()
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function(download_quant_model install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(
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${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
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endif()
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endfunction()
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function(download_quant_fp32_model install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(
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${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file}
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${check_sum})
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endif()
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endfunction()
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function(download_lstm_model install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/lstm
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${data_file} ${check_sum})
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endif()
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endfunction()
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function(inference_quant_int8_image_classification_test target quant_model_dir
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dataset_path)
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py_test(
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${target}
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SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant_int8_image_classification_comparison.py"
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ENVS
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FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_onednn=true
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ARGS
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--quant_model
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${quant_model_dir}
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--infer_data
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${dataset_path}
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--batch_size
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25
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--batch_num
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2
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--acc_diff_threshold
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0.1)
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endfunction()
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# set batch_size 10 for UT only (avoid OOM).
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# For whole dataset, use batch_size 25
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function(inference_quant2_int8_image_classification_test target quant_model_dir
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fp32_model_dir dataset_path)
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py_test(
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${target}
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SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_image_classification_comparison.py"
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ENVS
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FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_onednn=true
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ARGS
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--quant_model
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${quant_model_dir}
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--fp32_model
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${fp32_model_dir}
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--infer_data
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${dataset_path}
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--batch_size
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50
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--batch_num
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2
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--acc_diff_threshold
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0.1)
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endfunction()
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# set batch_size 10 for UT only (avoid OOM).
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# For whole dataset, use batch_size 20
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function(
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inference_quant2_int8_nlp_test
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target
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quant_model_dir
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fp32_model_dir
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dataset_path
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labels_path
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ops_to_quantize)
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py_test(
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${target}
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SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_nlp_comparison.py"
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ENVS
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FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_onednn=true
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ARGS
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--quant_model
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${quant_model_dir}
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--fp32_model
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${fp32_model_dir}
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--infer_data
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${dataset_path}
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--labels
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${labels_path}
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--batch_size
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10
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--batch_num
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2
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--acc_diff_threshold
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0.1
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--ops_to_quantize
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${ops_to_quantize})
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endfunction()
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function(inference_quant2_int8_lstm_model_test target fp32_model quant_model
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dataset_path)
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py_test(
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${target}
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SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_lstm_model.py"
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ARGS
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--fp32_model
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${fp32_model}
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--quant_model
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${quant_model}
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--infer_data
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${dataset_path}
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--num_threads
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1
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--onednn_cache_capacity
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100
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--warmup_iter
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100
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--acc_diff_threshold
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0.11)
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endfunction()
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function(download_quant_data install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
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${data_file} ${check_sum})
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endif()
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endfunction()
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function(download_quant_model install_dir data_file check_sum)
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if(NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(
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${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
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endif()
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endfunction()
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function(convert_model2dot_test target model_path save_graph_dir
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save_graph_name)
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py_test(
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${target}
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SRCS ${CMAKE_CURRENT_SOURCE_DIR}/convert_model2dot.py
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ARGS
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--model_path
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${model_path}
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--save_graph_dir
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${save_graph_dir}
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--save_graph_name
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${save_graph_name})
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endfunction()
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if(WIN32)
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list(REMOVE_ITEM TEST_OPS test_light_nas)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mnist)
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list(REMOVE_ITEM TEST_OPS test_ptq)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_program_resnet50)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_lstm_model)
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list(REMOVE_ITEM TEST_OPS test_imperative_ptq)
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list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
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list(REMOVE_ITEM TEST_OPS test_imperative_qat_amp)
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list(REMOVE_ITEM TEST_OPS test_imperative_qat_lsq)
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list(REMOVE_ITEM TEST_OPS test_imperative_qat_matmul)
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list(REMOVE_ITEM TEST_OPS test_weight_only_linear)
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list(REMOVE_ITEM TEST_OPS test_weight_quantize)
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list(REMOVE_ITEM TEST_OPS test_llm_int8_linear)
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list(REMOVE_ITEM TEST_OPS test_quant_aware)
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list(REMOVE_ITEM TEST_OPS test_quant_post_quant_aware)
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list(REMOVE_ITEM TEST_OPS test_quant_aware_user_defined)
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list(REMOVE_ITEM TEST_OPS test_quant_aware_config)
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list(REMOVE_ITEM TEST_OPS test_quant_amp)
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list(REMOVE_ITEM TEST_OPS test_apply_per_channel_scale)
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endif()
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if(NOT WITH_GPU)
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list(REMOVE_ITEM TEST_OPS test_weight_only_linear)
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list(REMOVE_ITEM TEST_OPS test_weight_quantize)
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list(REMOVE_ITEM TEST_OPS test_llm_int8_linear)
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list(REMOVE_ITEM TEST_OPS test_apply_per_channel_scale)
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endif()
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#
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if(WITH_GPU)
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if(${CUDA_ARCH_NAME} STREQUAL "Hopper")
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list(REMOVE_ITEM TEST_OPS test_weight_only_linear)
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endif()
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endif()
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if(LINUX AND WITH_ONEDNN)
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#### Image classification dataset: ImageNet (small)
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# The dataset should already be downloaded for INT8v2 unit tests
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set(IMAGENET_DATA_PATH "${INFERENCE_DEMO_INSTALL_DIR}/imagenet/data.bin")
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#### INT8 image classification python api test
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# Models should be already downloaded for INT8v2 unit tests
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set(INT8_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
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#### QUANT & INT8 comparison python api tests
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set(QUANT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/quant")
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### Quant1 for image classification
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# Quant ResNet50
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set(QUANT_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant")
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set(QUANT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
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download_quant_model(
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${QUANT_RESNET50_MODEL_DIR} ${QUANT_RESNET50_MODEL_ARCHIVE}
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ff89b934ab961c3a4a844193ece2e8a7)
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inference_quant_int8_image_classification_test(
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test_quant_int8_resnet50_onednn ${QUANT_RESNET50_MODEL_DIR}/model
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${IMAGENET_DATA_PATH})
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# Quant ResNet101
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set(QUANT_RESNET101_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet101_quant")
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set(QUANT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
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download_quant_model(
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${QUANT_RESNET101_MODEL_DIR} ${QUANT_RESNET101_MODEL_ARCHIVE}
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95c6d01e3aeba31c13efb2ba8057d558)
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# inference_quant_int8_image_classification_test( \
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# test_quant_int8_resnet101_onednn \
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# ${QUANT_RESNET101_MODEL_DIR}/model \
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# ${IMAGENET_DATA_PATH})
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# Quant GoogleNet
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set(QUANT_GOOGLENET_MODEL_DIR "${QUANT_INSTALL_DIR}/GoogleNet_quant")
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set(QUANT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
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download_quant_model(
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${QUANT_GOOGLENET_MODEL_DIR} ${QUANT_GOOGLENET_MODEL_ARCHIVE}
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1d4a7383baa63e7d1c423e8db2b791d5)
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#inference_quant_int8_image_classification_test(
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# test_quant_int8_googlenet_onednn ${QUANT_GOOGLENET_MODEL_DIR}/model
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# ${IMAGENET_DATA_PATH})
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# Quant MobileNetV1
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set(QUANT_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant")
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set(QUANT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
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download_quant_model(
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${QUANT_MOBILENETV1_MODEL_DIR} ${QUANT_MOBILENETV1_MODEL_ARCHIVE}
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3b774d94a9fcbb604d09bdb731fc1162)
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# Quant MobileNetV2
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set(QUANT_MOBILENETV2_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV2_quant")
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set(QUANT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
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download_quant_model(
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${QUANT_MOBILENETV2_MODEL_DIR} ${QUANT_MOBILENETV2_MODEL_ARCHIVE}
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758a99d9225d8b73e1a8765883f96cdd)
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inference_quant_int8_image_classification_test(
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test_quant_int8_mobilenetv2_onednn ${QUANT_MOBILENETV2_MODEL_DIR}/model
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${IMAGENET_DATA_PATH})
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# Quant VGG16
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set(QUANT_VGG16_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG16_quant")
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set(QUANT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
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download_quant_model(${QUANT_VGG16_MODEL_DIR} ${QUANT_VGG16_MODEL_ARCHIVE}
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c37e63ca82a102f47be266f8068b0b55)
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# inference_quant_int8_image_classification_test( \
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# test_quant_int8_vgg16_onednn \
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# ${QUANT_VGG16_MODEL_DIR}/model \
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# ${IMAGENET_DATA_PATH})
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# Quant VGG19
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set(QUANT_VGG19_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG19_quant")
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set(QUANT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
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download_quant_model(${QUANT_VGG19_MODEL_DIR} ${QUANT_VGG19_MODEL_ARCHIVE}
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62bcd4b6c3ca2af67e8251d1c96ea18f)
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# inference_quant_int8_image_classification_test( \
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# test_quant_int8_vgg19_onednn ${QUANT_VGG19_MODEL_DIR}/model \
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# ${IMAGENET_DATA_PATH})
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### Quant2 for image classification
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# Quant2 ResNet50 with input/output scales in
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# `fake_quantize_moving_average_abs_max` operators,
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# with weight scales in `fake_dequantize_max_abs` operators
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set(QUANT2_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant2")
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set(QUANT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
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download_quant_model(
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${QUANT2_RESNET50_MODEL_DIR} ${QUANT2_RESNET50_MODEL_ARCHIVE}
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e87309457e8c462a579340607f064d66)
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set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
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# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
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# operators and the `out_threshold` attributes,
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# with weight scales in `fake_dequantize_max_abs` operators
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set(QUANT2_RESNET50_RANGE_MODEL_DIR
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"${QUANT_INSTALL_DIR}/ResNet50_quant2_range")
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set(QUANT2_RESNET50_RANGE_MODEL_ARCHIVE "ResNet50_qat_range.tar.gz")
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download_quant_model(
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${QUANT2_RESNET50_RANGE_MODEL_DIR} ${QUANT2_RESNET50_RANGE_MODEL_ARCHIVE}
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2fdc8a139f041c0d270abec826b2d304)
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# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
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# operators and the `out_threshold` attributes,
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# with weight scales in `fake_channel_wise_dequantize_max_abs` operators
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set(QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
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"${QUANT_INSTALL_DIR}/ResNet50_quant2_channelwise")
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set(QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
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"ResNet50_qat_channelwise.tar.gz")
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download_quant_model(
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${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}
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${QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE}
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887a1b1b0e9a4efd10f263a43764db26)
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# Quant2 MobileNetV1
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set(QUANT2_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant2")
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set(QUANT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
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download_quant_model(
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${QUANT2_MOBILENETV1_MODEL_DIR} ${QUANT2_MOBILENETV1_MODEL_ARCHIVE}
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7f626e453db2d56fed6c2538621ffacf)
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set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
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### Quant2 for NLP
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set(NLP_DATA_ARCHIVE "Ernie_dataset.tar.gz")
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set(NLP_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie_dataset")
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set(NLP_DATA_PATH "${NLP_DATA_DIR}/Ernie_dataset/1.8w.bs1")
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set(NLP_LABELS_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
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download_quant_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE}
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e650ce0cbc1fadbed5cc2c01d4e734dc)
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# Quant2 Ernie
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set(QUANT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
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set(QUANT2_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_quant2")
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download_quant_model(${QUANT2_ERNIE_MODEL_DIR} ${QUANT2_ERNIE_MODEL_ARCHIVE}
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f7cdf4720755ecf66efbc8044e9922d9)
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set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
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set(FP32_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_float")
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download_quant_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE}
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114f38804a3ef8c45e7259e68bbd838b)
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set(QUANT2_ERNIE_OPS_TO_QUANTIZE "fused_matmul,matmul,matmul_v2,slice")
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# Quant2 GRU
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set(QUANT2_GRU_MODEL_DIR "${QUANT_INSTALL_DIR}/GRU_quant2")
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set(QUANT2_GRU_OPS_TO_QUANTIZE "multi_gru")
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# Quant2 LSTM
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set(QUANT2_LSTM_MODEL_ARCHIVE "lstm_quant.tar.gz")
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set(QUANT2_LSTM_MODEL_DIR "${QUANT_INSTALL_DIR}/lstm_quant_test")
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download_quant_model(${QUANT2_LSTM_MODEL_DIR} ${QUANT2_LSTM_MODEL_ARCHIVE}
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40a693803b12ee9e251258f32559abcb)
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# Convert Quant2 model to dot and pdf files
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set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH
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"${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")
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### PTQ INT8
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# PTQ int8 lstm model
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set(QUANT2_INT8_LSTM_SAVE_PATH "${QUANT_INSTALL_DIR}/lstm_quant2_int8")
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set(LSTM_DATA_FILE "quant_lstm_input_data.tar.gz")
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set(LSTM_URL "${INFERENCE_URL}/int8/unittest_model_data")
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download_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_URL} ${LSTM_DATA_FILE}
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add84c754e9b792fea1fbd728d134ab7)
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set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
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download_lstm_model(
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${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE}
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|
eecd9f44d69a84acc1cf2235c4b8b743)
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|
inference_quant2_int8_lstm_model_test(
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test_quant2_int8_lstm_onednn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model
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${QUANT2_LSTM_MODEL_DIR}/lstm_quant
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${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)
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|
|
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endif()
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|
|
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# Since the tests for Quant & INT8 comparison support only testing on Linux
|
|
# with One-DNN, we remove it here to not test it on other systems.
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|
list(REMOVE_ITEM TEST_OPS test_onednn_int8_quantization_strategy
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|
quant_int8_image_classification_comparison quant_int8_nlp_comparison)
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|
|
|
#TODO(wanghaoshuang): Fix this unittest failed on GCC8.
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|
list(REMOVE_ITEM TEST_OPS test_auto_pruning)
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|
list(REMOVE_ITEM TEST_OPS test_filter_pruning)
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|
|
|
# fix
|
|
if(WIN32)
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|
set(SINGLE_CARD_TEST_OPS
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test_imperative_qat_channelwise test_imperative_qat
|
|
test_imperative_qat_fuse test_imperative_qat_lsq
|
|
test_imperative_qat_matmul test_imperative_out_scale)
|
|
list(REMOVE_ITEM TEST_OPS ${SINGLE_CARD_TEST_OPS})
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|
foreach(src ${SINGLE_CARD_TEST_OPS})
|
|
py_test(${src} SRCS ${src}.py ENVS CUDA_VISIBLE_DEVICES=0)
|
|
endforeach()
|
|
endif()
|
|
|
|
foreach(src ${TEST_OPS})
|
|
py_test(${src} SRCS ${src}.py)
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|
endforeach()
|
|
|
|
# setting timeout value for old unittests
|
|
if(NOT WIN32)
|
|
set_tests_properties(test_post_training_quantization_lstm_model
|
|
PROPERTIES TIMEOUT 120)
|
|
set_tests_properties(test_post_training_quantization_program_resnet50
|
|
PROPERTIES TIMEOUT 240)
|
|
set_tests_properties(test_post_training_quantization_mobilenetv1
|
|
PROPERTIES TIMEOUT 900 LABELS "RUN_TYPE=NIGHTLY")
|
|
set_tests_properties(test_post_training_quantization_resnet50
|
|
PROPERTIES TIMEOUT 600 LABELS "RUN_TYPE=NIGHTLY")
|
|
set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
|
|
150)
|
|
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 120)
|
|
set_tests_properties(test_ptq PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_quant_aware_config PROPERTIES TIMEOUT 200)
|
|
endif()
|
|
|
|
set_tests_properties(test_imperative_qat_user_defined PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_imperative_qat_lsq PROPERTIES TIMEOUT 300)
|
|
set_tests_properties(test_imperative_qat_matmul PROPERTIES TIMEOUT 300)
|
|
set_tests_properties(test_imperative_qat PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_imperative_qat_fuse PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_imperative_qat_channelwise PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_imperative_out_scale PROPERTIES TIMEOUT 200)
|
|
set_tests_properties(test_imperative_skip_op PROPERTIES TIMEOUT 300)
|
|
if(LINUX AND WITH_ONEDNN)
|
|
set_tests_properties(test_quant_int8_mobilenetv2_onednn PROPERTIES TIMEOUT
|
|
120)
|
|
set_tests_properties(test_quant_int8_resnet50_onednn PROPERTIES TIMEOUT 120)
|
|
#set_tests_properties(test_quant_int8_googlenet_onednn PROPERTIES TIMEOUT 120)
|
|
set_tests_properties(test_quant2_int8_lstm_onednn PROPERTIES TIMEOUT 120)
|
|
endif()
|
|
|
|
if(APPLE)
|
|
set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
|
|
300)
|
|
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 300)
|
|
set_tests_properties(test_imperative_skip_op PROPERTIES TIMEOUT 300)
|
|
set_tests_properties(test_ptq PROPERTIES TIMEOUT 300)
|
|
endif()
|