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258 lines
12 KiB
258 lines
12 KiB
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
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string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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function(_inference_analysis_python_api_int8_test target model_dir data_path filename use_mkldnn)
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py_test(${target} SRCS ${filename}
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ENVS CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_mkldnn=${use_mkldnn}
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ARGS --infer_model ${model_dir}/model
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--infer_data ${data_path}
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--int8_model_save_path int8_models/${target}
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--warmup_batch_size ${WARMUP_BATCH_SIZE}
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--batch_size 50)
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endfunction()
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function(inference_analysis_python_api_int8_test target model_dir data_path filename)
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_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path} ${filename} False)
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endfunction()
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function(inference_analysis_python_api_int8_test_custom_warmup_batch_size 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} ${filename})
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endfunction()
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function(inference_analysis_python_api_int8_test_mkldnn target model_dir data_path filename)
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_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path} ${filename} True)
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endfunction()
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function(download_qat_data install_dir data_file)
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if (NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
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endif()
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endfunction()
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function(download_qat_model install_dir data_file)
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if (NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
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endif()
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endfunction()
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function(inference_qat_int8_image_classification_test target model_dir dataset_path)
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py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_image_classification_comparison.py"
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ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_mkldnn=true
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ARGS --qat_model ${model_dir}/model
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--infer_data ${dataset_path}
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--batch_size 25
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--batch_num 2
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--acc_diff_threshold 0.1)
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endfunction()
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# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25
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function(inference_qat2_int8_image_classification_test target model_dir data_path quantized_ops)
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py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_image_classification_comparison.py"
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ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_mkldnn=true
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ARGS --qat_model ${model_dir}/float
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--infer_data ${data_path}
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--batch_size 10
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--batch_num 2
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--acc_diff_threshold 0.1
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--quantized_ops ${quantized_ops}
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--qat2)
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endfunction()
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# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 20
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function(inference_qat2_int8_nlp_test target model_dir data_path labels_path quantized_ops)
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py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_nlp_comparison.py"
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ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
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FLAGS_use_mkldnn=true
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ARGS --qat_model ${model_dir}/float
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--infer_data ${data_path}
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--labels ${labels_path}
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--batch_size 10
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--batch_num 2
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--quantized_ops ${quantized_ops}
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--acc_diff_threshold 0.1)
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endfunction()
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function(download_qat_data install_dir data_file)
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if (NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
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endif()
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endfunction()
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function(download_qat_model install_dir data_file)
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if (NOT EXISTS ${install_dir}/${data_file})
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inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
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endif()
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endfunction()
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function(save_qat_model_test target qat_model_dir fp32_model_save_path int8_model_save_path quantized_ops)
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py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/save_qat_model.py
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ARGS --qat_model_path ${qat_model_dir}
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--fp32_model_save_path ${fp32_model_save_path}
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--int8_model_save_path ${int8_model_save_path}
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--quantized_ops ${quantized_ops})
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endfunction()
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if(WIN32)
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list(REMOVE_ITEM TEST_OPS test_light_nas)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
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list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
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list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
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endif()
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if(LINUX AND WITH_MKLDNN)
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#### Image classification dataset: ImageNet (small)
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# The dataset should already be downloaded for INT8v2 unit tests
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set(IMAGENET_DATA_PATH "${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 "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
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set(INT8_IC_TEST_FILE "test_mkldnn_int8_quantization_strategy.py")
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set(INT8_IC_TEST_FILE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${INT8_IC_TEST_FILE}")
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# googlenet int8
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set(INT8_GOOGLENET_MODEL_DIR "${INT8_INSTALL_DIR}/googlenet")
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inference_analysis_python_api_int8_test_custom_warmup_batch_size(test_slim_int8_googlenet ${INT8_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH} 10)
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# mobilenet int8
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set(INT8_MOBILENET_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
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inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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inference_analysis_python_api_int8_test_mkldnn(test_slim_int8_mobilenet_mkldnn ${INT8_MOBILENET_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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# temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally,
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# since the following UTs cost too much time on CI test.
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if (WITH_SLIM_MKLDNN_FULL_TEST)
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# resnet50 int8
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set(INT8_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
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inference_analysis_python_api_int8_test(test_slim_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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# mobilenetv2 int8
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set(INT8_MOBILENETV2_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv2")
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inference_analysis_python_api_int8_test(test_slim_int8_mobilenetv2 ${INT8_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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# resnet101 int8
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set(INT8_RESNET101_MODEL_DIR "${INT8_INSTALL_DIR}/resnet101")
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inference_analysis_python_api_int8_test(test_slim_int8_resnet101 ${INT8_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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# vgg16 int8
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set(INT8_VGG16_MODEL_DIR "${INT8_INSTALL_DIR}/vgg16")
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inference_analysis_python_api_int8_test(test_slim_int8_vgg16 ${INT8_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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# vgg19 int8
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set(INT8_VGG19_MODEL_DIR "${INT8_INSTALL_DIR}/vgg19")
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inference_analysis_python_api_int8_test(test_slim_int8_vgg19 ${INT8_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH} ${INT8_IC_TEST_FILE_PATH})
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endif()
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#### QAT FP32 & INT8 comparison python api tests
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set(QAT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/qat")
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### QATv1 for image classification
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# QAT ResNet50
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set(QAT_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_QAT")
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set(QAT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
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download_qat_model(${QAT_RESNET50_MODEL_DIR} ${QAT_RESNET50_MODEL_ARCHIVE})
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inference_qat_int8_image_classification_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT ResNet101
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set(QAT_RESNET101_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet101_QAT")
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set(QAT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
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download_qat_model(${QAT_RESNET101_MODEL_DIR} ${QAT_RESNET101_MODEL_ARCHIVE})
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# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT GoogleNet
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set(QAT_GOOGLENET_MODEL_DIR "${QAT_INSTALL_DIR}/GoogleNet_QAT")
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set(QAT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
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download_qat_model(${QAT_GOOGLENET_MODEL_DIR} ${QAT_GOOGLENET_MODEL_ARCHIVE})
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inference_qat_int8_image_classification_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT MobileNetV1
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set(QAT_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV1_QAT")
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set(QAT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
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download_qat_model(${QAT_MOBILENETV1_MODEL_DIR} ${QAT_MOBILENETV1_MODEL_ARCHIVE})
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inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT MobileNetV2
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set(QAT_MOBILENETV2_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV2_QAT")
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set(QAT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
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download_qat_model(${QAT_MOBILENETV2_MODEL_DIR} ${QAT_MOBILENETV2_MODEL_ARCHIVE})
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inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT VGG16
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set(QAT_VGG16_MODEL_DIR "${QAT_INSTALL_DIR}/VGG16_QAT")
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set(QAT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
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download_qat_model(${QAT_VGG16_MODEL_DIR} ${QAT_VGG16_MODEL_ARCHIVE})
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# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH})
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# QAT VGG19
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set(QAT_VGG19_MODEL_DIR "${QAT_INSTALL_DIR}/VGG19_QAT")
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set(QAT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
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download_qat_model(${QAT_VGG19_MODEL_DIR} ${QAT_VGG19_MODEL_ARCHIVE})
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# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH})
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### QATv2 for image classification
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set(QAT2_IC_QUANTIZED_OPS "conv2d,pool2d")
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# QAT2 ResNet50
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set(QAT2_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_qat_perf")
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set(QAT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
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download_qat_model(${QAT2_RESNET50_MODEL_DIR} ${QAT2_RESNET50_MODEL_ARCHIVE})
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inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_mkldnn ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
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# QAT2 MobileNetV1
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set(QAT2_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNet_qat_perf")
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set(QAT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
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download_qat_model(${QAT2_MOBILENETV1_MODEL_DIR} ${QAT2_MOBILENETV1_MODEL_ARCHIVE})
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inference_qat2_int8_image_classification_test(test_qat2_int8_mobilenetv1_mkldnn ${QAT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
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### QATv2 for NLP
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set(QAT2_NLP_QUANTIZED_OPS "fc,reshape2,transpose2")
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set(NLP_DATA_ARCHIVE "Ernie_dataset.tar.gz")
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set(NLP_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie_dataset")
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set(NLP_DATA_PATH "${NLP_DATA_DIR}/Ernie_dataset/1.8w.bs1")
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set(NLP_LABLES_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
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download_qat_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE})
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# QAT2 Ernie
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set(QAT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
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set(QAT2_ERNIE_MODEL_DIR "${QAT_INSTALL_DIR}/Ernie_qat")
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download_qat_model(${QAT2_ERNIE_MODEL_DIR} ${QAT2_ERNIE_MODEL_ARCHIVE})
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inference_qat2_int8_nlp_test(test_qat2_int8_ernie_mkldnn ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat ${NLP_DATA_PATH} ${NLP_LABLES_PATH} ${QAT2_NLP_QUANTIZED_OPS})
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### Save QAT2 FP32 model or QAT2 INT8 model
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set(QAT2_INT8_RESNET50_SAVE_PATH "${QAT_INSTALL_DIR}/ResNet50_qat2_int8")
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set(QAT2_FP32_RESNET50_SAVE_PATH "${QAT_INSTALL_DIR}/ResNet50_qat2_fp32")
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save_qat_model_test(save_qat2_model_resnet50 ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${QAT2_FP32_RESNET50_SAVE_PATH} ${QAT2_INT8_RESNET50_SAVE_PATH} ${QAT2_IC_QUANTIZED_OPS})
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set(QAT2_INT8_ERNIE_SAVE_PATH "${QAT_INSTALL_DIR}/Ernie_qat2_int8")
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set(QAT2_FP32_ERNIE_SAVE_PATH "${QAT_INSTALL_DIR}/Ernie_qat2_fp32")
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save_qat_model_test(save_qat2_model_ernie ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QAT2_FP32_ERNIE_SAVE_PATH} ${QAT2_INT8_ERNIE_SAVE_PATH} ${QAT2_NLP_QUANTIZED_OPS})
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endif()
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# Since the tests for QAT FP32 & INT8 comparison support only testing on Linux
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# with MKL-DNN, we remove it here to not test it on other systems.
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list(REMOVE_ITEM TEST_OPS
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test_mkldnn_int8_quantization_strategy
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qat_int8_image_classification_comparison
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qat_int8_nlp_comparison)
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foreach(src ${TEST_OPS})
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py_test(${src} SRCS ${src}.py)
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endforeach()
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