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未验证 提交 3784ae63 编写于 作者: 张 张春乔 提交者： GitHub
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[Test Mv] quantization (#52353)

上级 8a4aee18
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python/CMakeLists.txt
浏览文件 @ 3784ae63
...@@ -150,7 +150,6 @@ set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/) ...@@ -150,7 +150,6 @@ set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/)
if(WITH_TESTING) if(WITH_TESTING)
add_subdirectory(paddle/fluid/tests) add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/static/quantization/tests)
endif() endif()
if(NOT WITH_SETUP_INSTALL) if(NOT WITH_SETUP_INSTALL)
......
python/paddle/static/quantization/tests/CMakeLists.txt 已删除 100755 → 0
浏览文件 @ 8a4aee18
file(
GLOB TEST_OPS
RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
"test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
function(_inference_analysis_python_api_int8_test target model_dir data_path
filename use_mkldnn)
py_test(
${target}
SRCS ${filename}
ENVS
CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=${use_mkldnn}
ARGS
--infer_model
${model_dir}/model
--infer_data
${data_path}
--int8_model_save_path
int8_models/${target}
--warmup_batch_size
${WARMUP_BATCH_SIZE}
--batch_size
50)
endfunction()
function(inference_analysis_python_api_int8_test target model_dir data_path
filename)
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
${filename} False)
endfunction()
function(inference_analysis_python_api_int8_test_custom_warmup_batch_size
target model_dir data_dir filename warmup_batch_size)
set(WARMUP_BATCH_SIZE ${warmup_batch_size})
inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir}
${filename})
endfunction()
function(inference_analysis_python_api_int8_test_mkldnn target model_dir
data_path filename)
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
${filename} True)
endfunction()
function(download_data install_dir url data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${url} ${data_file}
${check_sum})
endif()
endfunction()
function(download_quant_data install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_fp32_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file}
${check_sum})
endif()
endfunction()
function(download_lstm_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/lstm
${data_file} ${check_sum})
endif()
endfunction()
function(inference_quant_int8_image_classification_test target quant_model_dir
dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant_int8_image_classification_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--infer_data
${dataset_path}
--batch_size
25
--batch_num
2
--acc_diff_threshold
0.1)
endfunction()
# set batch_size 10 for UT only (avoid OOM).
# For whole dataset, use batch_size 25
function(inference_quant2_int8_image_classification_test target quant_model_dir
fp32_model_dir dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_image_classification_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--fp32_model
${fp32_model_dir}
--infer_data
${dataset_path}
--batch_size
50
--batch_num
2
--acc_diff_threshold
0.1)
endfunction()
# set batch_size 10 for UT only (avoid OOM).
# For whole dataset, use batch_size 20
function(
inference_quant2_int8_nlp_test
target
quant_model_dir
fp32_model_dir
dataset_path
labels_path
ops_to_quantize)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_nlp_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--fp32_model
${fp32_model_dir}
--infer_data
${dataset_path}
--labels
${labels_path}
--batch_size
10
--batch_num
2
--acc_diff_threshold
0.1
--ops_to_quantize
${ops_to_quantize})
endfunction()
function(inference_quant2_int8_lstm_model_test target fp32_model quant_model
dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_lstm_model.py"
ARGS
--fp32_model
${fp32_model}
--quant_model
${quant_model}
--infer_data
${dataset_path}
--num_threads
1
--mkldnn_cache_capacity
100
--warmup_iter
100
--acc_diff_threshold
0.11)
endfunction()
function(download_quant_data install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
endif()
endfunction()
function(convert_model2dot_test target model_path save_graph_dir
save_graph_name)
py_test(
${target}
SRCS ${CMAKE_CURRENT_SOURCE_DIR}/convert_model2dot.py
ARGS
--model_path
${model_path}
--save_graph_dir
${save_graph_dir}
--save_graph_name
${save_graph_name})
endfunction()
if(WIN32)
list(REMOVE_ITEM TEST_OPS test_light_nas)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mnist)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_while)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_program_resnet50)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_lstm_model)
list(REMOVE_ITEM TEST_OPS test_imperative_ptq)
list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_amp)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_lsq)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_matmul)
endif()
if(LINUX AND WITH_MKLDNN)
#### Image classification dataset: ImageNet (small)
# The dataset should already be downloaded for INT8v2 unit tests
set(IMAGENET_DATA_PATH "${INFERENCE_DEMO_INSTALL_DIR}/imagenet/data.bin")
#### INT8 image classification python api test
# Models should be already downloaded for INT8v2 unit tests
set(INT8_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
#### QUANT & INT8 comparison python api tests
set(QUANT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/quant")
### Quant1 for image classification
# Quant ResNet50
set(QUANT_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant")
set(QUANT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
download_quant_model(
${QUANT_RESNET50_MODEL_DIR} ${QUANT_RESNET50_MODEL_ARCHIVE}
ff89b934ab961c3a4a844193ece2e8a7)
inference_quant_int8_image_classification_test(
test_quant_int8_resnet50_mkldnn ${QUANT_RESNET50_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant ResNet101
set(QUANT_RESNET101_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet101_quant")
set(QUANT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
download_quant_model(
${QUANT_RESNET101_MODEL_DIR} ${QUANT_RESNET101_MODEL_ARCHIVE}
95c6d01e3aeba31c13efb2ba8057d558)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_resnet101_mkldnn \
# ${QUANT_RESNET101_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
# Quant GoogleNet
set(QUANT_GOOGLENET_MODEL_DIR "${QUANT_INSTALL_DIR}/GoogleNet_quant")
set(QUANT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
download_quant_model(
${QUANT_GOOGLENET_MODEL_DIR} ${QUANT_GOOGLENET_MODEL_ARCHIVE}
1d4a7383baa63e7d1c423e8db2b791d5)
inference_quant_int8_image_classification_test(
test_quant_int8_googlenet_mkldnn ${QUANT_GOOGLENET_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant MobileNetV1
set(QUANT_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant")
set(QUANT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
download_quant_model(
${QUANT_MOBILENETV1_MODEL_DIR} ${QUANT_MOBILENETV1_MODEL_ARCHIVE}
3b774d94a9fcbb604d09bdb731fc1162)
inference_quant_int8_image_classification_test(
test_quant_int8_mobilenetv1_mkldnn ${QUANT_MOBILENETV1_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant MobileNetV2
set(QUANT_MOBILENETV2_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV2_quant")
set(QUANT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
download_quant_model(
${QUANT_MOBILENETV2_MODEL_DIR} ${QUANT_MOBILENETV2_MODEL_ARCHIVE}
758a99d9225d8b73e1a8765883f96cdd)
inference_quant_int8_image_classification_test(
test_quant_int8_mobilenetv2_mkldnn ${QUANT_MOBILENETV2_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant VGG16
set(QUANT_VGG16_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG16_quant")
set(QUANT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
download_quant_model(${QUANT_VGG16_MODEL_DIR} ${QUANT_VGG16_MODEL_ARCHIVE}
c37e63ca82a102f47be266f8068b0b55)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_vgg16_mkldnn \
# ${QUANT_VGG16_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
# Quant VGG19
set(QUANT_VGG19_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG19_quant")
set(QUANT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
download_quant_model(${QUANT_VGG19_MODEL_DIR} ${QUANT_VGG19_MODEL_ARCHIVE}
62bcd4b6c3ca2af67e8251d1c96ea18f)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_vgg19_mkldnn ${QUANT_VGG19_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
### Quant2 for image classification
# Quant2 ResNet50 with input/output scales in
# `fake_quantize_moving_average_abs_max` operators,
# with weight scales in `fake_dequantize_max_abs` operators
set(QUANT2_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant2")
set(QUANT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
download_quant_model(
${QUANT2_RESNET50_MODEL_DIR} ${QUANT2_RESNET50_MODEL_ARCHIVE}
e87309457e8c462a579340607f064d66)
set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_mkldnn
${QUANT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
# operators and the `out_threshold` attributes,
# with weight scales in `fake_dequantize_max_abs` operators
set(QUANT2_RESNET50_RANGE_MODEL_DIR
"${QUANT_INSTALL_DIR}/ResNet50_quant2_range")
set(QUANT2_RESNET50_RANGE_MODEL_ARCHIVE "ResNet50_qat_range.tar.gz")
download_quant_model(
${QUANT2_RESNET50_RANGE_MODEL_DIR} ${QUANT2_RESNET50_RANGE_MODEL_ARCHIVE}
2fdc8a139f041c0d270abec826b2d304)
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_range_mkldnn
${QUANT2_RESNET50_RANGE_MODEL_DIR}/ResNet50_qat_range
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
# operators and the `out_threshold` attributes,
# with weight scales in `fake_channel_wise_dequantize_max_abs` operators
set(QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
"${QUANT_INSTALL_DIR}/ResNet50_quant2_channelwise")
set(QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
"ResNet50_qat_channelwise.tar.gz")
download_quant_model(
${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}
${QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE}
887a1b1b0e9a4efd10f263a43764db26)
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_channelwise_mkldnn
${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}/ResNet50_qat_channelwise
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 MobileNetV1
set(QUANT2_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant2")
set(QUANT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
download_quant_model(
${QUANT2_MOBILENETV1_MODEL_DIR} ${QUANT2_MOBILENETV1_MODEL_ARCHIVE}
7f626e453db2d56fed6c2538621ffacf)
set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
inference_quant2_int8_image_classification_test(
test_quant2_int8_mobilenetv1_mkldnn
${QUANT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf/float
${FP32_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
### Quant2 for NLP
set(NLP_DATA_ARCHIVE "Ernie_dataset.tar.gz")
set(NLP_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie_dataset")
set(NLP_DATA_PATH "${NLP_DATA_DIR}/Ernie_dataset/1.8w.bs1")
set(NLP_LABLES_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
download_quant_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE}
e650ce0cbc1fadbed5cc2c01d4e734dc)
# Quant2 Ernie
set(QUANT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
set(QUANT2_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_quant2")
download_quant_model(${QUANT2_ERNIE_MODEL_DIR} ${QUANT2_ERNIE_MODEL_ARCHIVE}
f7cdf4720755ecf66efbc8044e9922d9)
set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
set(FP32_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_float")
download_quant_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE}
114f38804a3ef8c45e7259e68bbd838b)
set(QUANT2_ERNIE_OPS_TO_QUANTIZE
"fc,reshape2,transpose2,matmul,elementwise_add,slice")
inference_quant2_int8_nlp_test(
test_quant2_int8_ernie_mkldnn ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH}
${NLP_LABLES_PATH} ${QUANT2_ERNIE_OPS_TO_QUANTIZE})
# Quant2 GRU
set(QUANT2_GRU_MODEL_DIR "${QUANT_INSTALL_DIR}/GRU_quant2")
set(QUANT2_GRU_OPS_TO_QUANTIZE "multi_gru")
# Quant2 LSTM
set(QUANT2_LSTM_MODEL_ARCHIVE "lstm_quant.tar.gz")
set(QUANT2_LSTM_MODEL_DIR "${QUANT_INSTALL_DIR}/lstm_quant_test")
download_quant_model(${QUANT2_LSTM_MODEL_DIR} ${QUANT2_LSTM_MODEL_ARCHIVE}
40a693803b12ee9e251258f32559abcb)
# Convert Quant2 model to dot and pdf files
set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH
"${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")
convert_model2dot_test(
convert_model2dot_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
${QUANT2_INT8_ERNIE_DOT_SAVE_PATH} "Ernie_quant2_int8")
### PTQ INT8
# PTQ int8 lstm model
set(QUANT2_INT8_LSTM_SAVE_PATH "${QUANT_INSTALL_DIR}/lstm_quant2_int8")
set(LSTM_DATA_FILE "quant_lstm_input_data.tar.gz")
set(LSTM_URL "${INFERENCE_URL}/int8/unittest_model_data")
download_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_URL} ${LSTM_DATA_FILE}
add84c754e9b792fea1fbd728d134ab7)
set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
download_lstm_model(
${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE}
eecd9f44d69a84acc1cf2235c4b8b743)
inference_quant2_int8_lstm_model_test(
test_quant2_int8_lstm_mkldnn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model
${QUANT2_LSTM_MODEL_DIR}/lstm_quant
${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)
endif()
# Since the tests for Quant & INT8 comparison support only testing on Linux
# with MKL-DNN, we remove it here to not test it on other systems.
list(REMOVE_ITEM TEST_OPS test_mkldnn_int8_quantization_strategy
quant_int8_image_classification_comparison quant_int8_nlp_comparison)
#TODO(wanghaoshuang): Fix this unitest failed on GCC8.
list(REMOVE_ITEM TEST_OPS test_auto_pruning)
list(REMOVE_ITEM TEST_OPS test_filter_pruning)
# fix
if(WIN32)
set(SINGLE_CARD_TEST_OPS
test_user_defined_quantization
test_quantization_scale_pass
test_quantization_pass
test_moving_average_abs_max_scale_op
test_imperative_qat_channelwise
test_imperative_qat
test_imperative_qat_lsq
test_imperative_qat_matmul
test_imperative_out_scale
test_graph)
list(REMOVE_ITEM TEST_OPS ${SINGLE_CARD_TEST_OPS})
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)
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_post_training_quantization_while PROPERTIES TIMEOUT
120)
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 120)
set_tests_properties(test_weight_quantization_mobilenetv1 PROPERTIES TIMEOUT
120)
endif()
set_tests_properties(test_graph PROPERTIES TIMEOUT 120)
set_tests_properties(test_quantization_pass PROPERTIES TIMEOUT 120)
set_tests_properties(test_imperative_qat_channelwise PROPERTIES TIMEOUT 200)
set_tests_properties(test_user_defined_quantization PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat_fuse PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_out_scale PROPERTIES TIMEOUT 200)
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)
if(LINUX AND WITH_MKLDNN)
set_tests_properties(test_quant2_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(convert_model2dot_ernie PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant2_int8_resnet50_channelwise_mkldnn
PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_mobilenetv2_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant2_int8_resnet50_range_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant_int8_resnet50_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant2_int8_ernie_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_googlenet_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant2_int8_resnet50_mkldnn PROPERTIES TIMEOUT 200)
set_tests_properties(test_quant2_int8_lstm_mkldnn PROPERTIES TIMEOUT 120)
endif()
if(APPLE)
set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
300)
set_tests_properties(test_post_training_quantization_while PROPERTIES TIMEOUT
300)
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 300)
set_tests_properties(test_imperative_skip_op PROPERTIES TIMEOUT 300)
endif()
test/quantization/CMakeLists.txt
浏览文件 @ 3784ae63
...@@ -4,6 +4,524 @@ file( ...@@ -4,6 +4,524 @@ file(
"test_*.py") "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
function(_inference_analysis_python_api_int8_test target model_dir data_path
filename use_mkldnn)
py_test(
${target}
SRCS ${filename}
ENVS
CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=${use_mkldnn}
ARGS
--infer_model
${model_dir}/model
--infer_data
${data_path}
--int8_model_save_path
int8_models/${target}
--warmup_batch_size
${WARMUP_BATCH_SIZE}
--batch_size
50)
endfunction()
function(inference_analysis_python_api_int8_test target model_dir data_path
filename)
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
${filename} False)
endfunction()
function(inference_analysis_python_api_int8_test_custom_warmup_batch_size
target model_dir data_dir filename warmup_batch_size)
set(WARMUP_BATCH_SIZE ${warmup_batch_size})
inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir}
${filename})
endfunction()
function(inference_analysis_python_api_int8_test_mkldnn target model_dir
data_path filename)
_inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_path}
${filename} True)
endfunction()
function(download_data install_dir url data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${url} ${data_file}
${check_sum})
endif()
endfunction()
function(download_quant_data install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_fp32_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file}
${check_sum})
endif()
endfunction()
function(download_lstm_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/lstm
${data_file} ${check_sum})
endif()
endfunction()
function(inference_quant_int8_image_classification_test target quant_model_dir
dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant_int8_image_classification_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--infer_data
${dataset_path}
--batch_size
25
--batch_num
2
--acc_diff_threshold
0.1)
endfunction()
# set batch_size 10 for UT only (avoid OOM).
# For whole dataset, use batch_size 25
function(inference_quant2_int8_image_classification_test target quant_model_dir
fp32_model_dir dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_image_classification_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--fp32_model
${fp32_model_dir}
--infer_data
${dataset_path}
--batch_size
50
--batch_num
2
--acc_diff_threshold
0.1)
endfunction()
# set batch_size 10 for UT only (avoid OOM).
# For whole dataset, use batch_size 20
function(
inference_quant2_int8_nlp_test
target
quant_model_dir
fp32_model_dir
dataset_path
labels_path
ops_to_quantize)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_nlp_comparison.py"
ENVS
FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS
--quant_model
${quant_model_dir}
--fp32_model
${fp32_model_dir}
--infer_data
${dataset_path}
--labels
${labels_path}
--batch_size
10
--batch_num
2
--acc_diff_threshold
0.1
--ops_to_quantize
${ops_to_quantize})
endfunction()
function(inference_quant2_int8_lstm_model_test target fp32_model quant_model
dataset_path)
py_test(
${target}
SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_lstm_model.py"
ARGS
--fp32_model
${fp32_model}
--quant_model
${quant_model}
--infer_data
${dataset_path}
--num_threads
1
--mkldnn_cache_capacity
100
--warmup_iter
100
--acc_diff_threshold
0.11)
endfunction()
function(download_quant_data install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8
${data_file} ${check_sum})
endif()
endfunction()
function(download_quant_model install_dir data_file check_sum)
if(NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(
${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file} ${check_sum})
endif()
endfunction()
function(convert_model2dot_test target model_path save_graph_dir
save_graph_name)
py_test(
${target}
SRCS ${CMAKE_CURRENT_SOURCE_DIR}/convert_model2dot.py
ARGS
--model_path
${model_path}
--save_graph_dir
${save_graph_dir}
--save_graph_name
${save_graph_name})
endfunction()
if(WIN32)
list(REMOVE_ITEM TEST_OPS test_light_nas)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mnist)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_while)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_program_resnet50)
list(REMOVE_ITEM TEST_OPS test_post_training_quantization_lstm_model)
list(REMOVE_ITEM TEST_OPS test_imperative_ptq)
list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_amp)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_lsq)
list(REMOVE_ITEM TEST_OPS test_imperative_qat_matmul)
endif()
if(LINUX AND WITH_MKLDNN)
#### Image classification dataset: ImageNet (small)
# The dataset should already be downloaded for INT8v2 unit tests
set(IMAGENET_DATA_PATH "${INFERENCE_DEMO_INSTALL_DIR}/imagenet/data.bin")
#### INT8 image classification python api test
# Models should be already downloaded for INT8v2 unit tests
set(INT8_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
#### QUANT & INT8 comparison python api tests
set(QUANT_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/quant")
### Quant1 for image classification
# Quant ResNet50
set(QUANT_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant")
set(QUANT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
download_quant_model(
${QUANT_RESNET50_MODEL_DIR} ${QUANT_RESNET50_MODEL_ARCHIVE}
ff89b934ab961c3a4a844193ece2e8a7)
inference_quant_int8_image_classification_test(
test_quant_int8_resnet50_mkldnn ${QUANT_RESNET50_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant ResNet101
set(QUANT_RESNET101_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet101_quant")
set(QUANT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
download_quant_model(
${QUANT_RESNET101_MODEL_DIR} ${QUANT_RESNET101_MODEL_ARCHIVE}
95c6d01e3aeba31c13efb2ba8057d558)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_resnet101_mkldnn \
# ${QUANT_RESNET101_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
# Quant GoogleNet
set(QUANT_GOOGLENET_MODEL_DIR "${QUANT_INSTALL_DIR}/GoogleNet_quant")
set(QUANT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
download_quant_model(
${QUANT_GOOGLENET_MODEL_DIR} ${QUANT_GOOGLENET_MODEL_ARCHIVE}
1d4a7383baa63e7d1c423e8db2b791d5)
inference_quant_int8_image_classification_test(
test_quant_int8_googlenet_mkldnn ${QUANT_GOOGLENET_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant MobileNetV1
set(QUANT_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant")
set(QUANT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
download_quant_model(
${QUANT_MOBILENETV1_MODEL_DIR} ${QUANT_MOBILENETV1_MODEL_ARCHIVE}
3b774d94a9fcbb604d09bdb731fc1162)
inference_quant_int8_image_classification_test(
test_quant_int8_mobilenetv1_mkldnn ${QUANT_MOBILENETV1_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant MobileNetV2
set(QUANT_MOBILENETV2_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV2_quant")
set(QUANT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
download_quant_model(
${QUANT_MOBILENETV2_MODEL_DIR} ${QUANT_MOBILENETV2_MODEL_ARCHIVE}
758a99d9225d8b73e1a8765883f96cdd)
inference_quant_int8_image_classification_test(
test_quant_int8_mobilenetv2_mkldnn ${QUANT_MOBILENETV2_MODEL_DIR}/model
${IMAGENET_DATA_PATH})
# Quant VGG16
set(QUANT_VGG16_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG16_quant")
set(QUANT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
download_quant_model(${QUANT_VGG16_MODEL_DIR} ${QUANT_VGG16_MODEL_ARCHIVE}
c37e63ca82a102f47be266f8068b0b55)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_vgg16_mkldnn \
# ${QUANT_VGG16_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
# Quant VGG19
set(QUANT_VGG19_MODEL_DIR "${QUANT_INSTALL_DIR}/VGG19_quant")
set(QUANT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
download_quant_model(${QUANT_VGG19_MODEL_DIR} ${QUANT_VGG19_MODEL_ARCHIVE}
62bcd4b6c3ca2af67e8251d1c96ea18f)
# inference_quant_int8_image_classification_test( \
# test_quant_int8_vgg19_mkldnn ${QUANT_VGG19_MODEL_DIR}/model \
# ${IMAGENET_DATA_PATH})
### Quant2 for image classification
# Quant2 ResNet50 with input/output scales in
# `fake_quantize_moving_average_abs_max` operators,
# with weight scales in `fake_dequantize_max_abs` operators
set(QUANT2_RESNET50_MODEL_DIR "${QUANT_INSTALL_DIR}/ResNet50_quant2")
set(QUANT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
download_quant_model(
${QUANT2_RESNET50_MODEL_DIR} ${QUANT2_RESNET50_MODEL_ARCHIVE}
e87309457e8c462a579340607f064d66)
set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_mkldnn
${QUANT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
# operators and the `out_threshold` attributes,
# with weight scales in `fake_dequantize_max_abs` operators
set(QUANT2_RESNET50_RANGE_MODEL_DIR
"${QUANT_INSTALL_DIR}/ResNet50_quant2_range")
set(QUANT2_RESNET50_RANGE_MODEL_ARCHIVE "ResNet50_qat_range.tar.gz")
download_quant_model(
${QUANT2_RESNET50_RANGE_MODEL_DIR} ${QUANT2_RESNET50_RANGE_MODEL_ARCHIVE}
2fdc8a139f041c0d270abec826b2d304)
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_range_mkldnn
${QUANT2_RESNET50_RANGE_MODEL_DIR}/ResNet50_qat_range
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max`
# operators and the `out_threshold` attributes,
# with weight scales in `fake_channel_wise_dequantize_max_abs` operators
set(QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
"${QUANT_INSTALL_DIR}/ResNet50_quant2_channelwise")
set(QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
"ResNet50_qat_channelwise.tar.gz")
download_quant_model(
${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}
${QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE}
887a1b1b0e9a4efd10f263a43764db26)
inference_quant2_int8_image_classification_test(
test_quant2_int8_resnet50_channelwise_mkldnn
${QUANT2_RESNET50_CHANNELWISE_MODEL_DIR}/ResNet50_qat_channelwise
${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# Quant2 MobileNetV1
set(QUANT2_MOBILENETV1_MODEL_DIR "${QUANT_INSTALL_DIR}/MobileNetV1_quant2")
set(QUANT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
download_quant_model(
${QUANT2_MOBILENETV1_MODEL_DIR} ${QUANT2_MOBILENETV1_MODEL_ARCHIVE}
7f626e453db2d56fed6c2538621ffacf)
set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
inference_quant2_int8_image_classification_test(
test_quant2_int8_mobilenetv1_mkldnn
${QUANT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf/float
${FP32_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
### Quant2 for NLP
set(NLP_DATA_ARCHIVE "Ernie_dataset.tar.gz")
set(NLP_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie_dataset")
set(NLP_DATA_PATH "${NLP_DATA_DIR}/Ernie_dataset/1.8w.bs1")
set(NLP_LABLES_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
download_quant_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE}
e650ce0cbc1fadbed5cc2c01d4e734dc)
# Quant2 Ernie
set(QUANT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
set(QUANT2_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_quant2")
download_quant_model(${QUANT2_ERNIE_MODEL_DIR} ${QUANT2_ERNIE_MODEL_ARCHIVE}
f7cdf4720755ecf66efbc8044e9922d9)
set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
set(FP32_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_float")
download_quant_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE}
114f38804a3ef8c45e7259e68bbd838b)
set(QUANT2_ERNIE_OPS_TO_QUANTIZE
"fc,reshape2,transpose2,matmul,elementwise_add,slice")
inference_quant2_int8_nlp_test(
test_quant2_int8_ernie_mkldnn ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH}
${NLP_LABLES_PATH} ${QUANT2_ERNIE_OPS_TO_QUANTIZE})
# Quant2 GRU
set(QUANT2_GRU_MODEL_DIR "${QUANT_INSTALL_DIR}/GRU_quant2")
set(QUANT2_GRU_OPS_TO_QUANTIZE "multi_gru")
# Quant2 LSTM
set(QUANT2_LSTM_MODEL_ARCHIVE "lstm_quant.tar.gz")
set(QUANT2_LSTM_MODEL_DIR "${QUANT_INSTALL_DIR}/lstm_quant_test")
download_quant_model(${QUANT2_LSTM_MODEL_DIR} ${QUANT2_LSTM_MODEL_ARCHIVE}
40a693803b12ee9e251258f32559abcb)
# Convert Quant2 model to dot and pdf files
set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH
"${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")
convert_model2dot_test(
convert_model2dot_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float
${QUANT2_INT8_ERNIE_DOT_SAVE_PATH} "Ernie_quant2_int8")
### PTQ INT8
# PTQ int8 lstm model
set(QUANT2_INT8_LSTM_SAVE_PATH "${QUANT_INSTALL_DIR}/lstm_quant2_int8")
set(LSTM_DATA_FILE "quant_lstm_input_data.tar.gz")
set(LSTM_URL "${INFERENCE_URL}/int8/unittest_model_data")
download_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_URL} ${LSTM_DATA_FILE}
add84c754e9b792fea1fbd728d134ab7)
set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
download_lstm_model(
${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE}
eecd9f44d69a84acc1cf2235c4b8b743)
inference_quant2_int8_lstm_model_test(
test_quant2_int8_lstm_mkldnn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model
${QUANT2_LSTM_MODEL_DIR}/lstm_quant
${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)
endif()
# Since the tests for Quant & INT8 comparison support only testing on Linux
# with MKL-DNN, we remove it here to not test it on other systems.
list(REMOVE_ITEM TEST_OPS test_mkldnn_int8_quantization_strategy
quant_int8_image_classification_comparison quant_int8_nlp_comparison)
#TODO(wanghaoshuang): Fix this unitest failed on GCC8.
list(REMOVE_ITEM TEST_OPS test_auto_pruning)
list(REMOVE_ITEM TEST_OPS test_filter_pruning)
# fix
if(WIN32)
set(SINGLE_CARD_TEST_OPS
test_user_defined_quantization
test_quantization_scale_pass
test_quantization_pass
test_moving_average_abs_max_scale_op
test_imperative_qat_channelwise
test_imperative_qat
test_imperative_qat_lsq
test_imperative_qat_matmul
test_imperative_out_scale
test_graph)
list(REMOVE_ITEM TEST_OPS ${SINGLE_CARD_TEST_OPS})
foreach(src ${SINGLE_CARD_TEST_OPS})
py_test(${src} SRCS ${src}.py ENVS CUDA_VISIBLE_DEVICES=0)
endforeach()
endif()
foreach(src ${TEST_OPS}) foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py) py_test(${src} SRCS ${src}.py)
endforeach() 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_post_training_quantization_while PROPERTIES TIMEOUT
120)
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 120)
set_tests_properties(test_weight_quantization_mobilenetv1 PROPERTIES TIMEOUT
120)
endif()
set_tests_properties(test_graph PROPERTIES TIMEOUT 120)
set_tests_properties(test_quantization_pass PROPERTIES TIMEOUT 120)
set_tests_properties(test_imperative_qat_channelwise PROPERTIES TIMEOUT 200)
set_tests_properties(test_user_defined_quantization PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_qat_fuse PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_out_scale PROPERTIES TIMEOUT 200)
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)
if(LINUX AND WITH_MKLDNN)
set_tests_properties(test_quant2_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(convert_model2dot_ernie PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant2_int8_resnet50_channelwise_mkldnn
PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_mobilenetv2_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant2_int8_resnet50_range_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant_int8_resnet50_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_mobilenetv1_mkldnn PROPERTIES TIMEOUT
120)
set_tests_properties(test_quant2_int8_ernie_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant_int8_googlenet_mkldnn PROPERTIES TIMEOUT 120)
set_tests_properties(test_quant2_int8_resnet50_mkldnn PROPERTIES TIMEOUT 200)
set_tests_properties(test_quant2_int8_lstm_mkldnn PROPERTIES TIMEOUT 120)
endif()
if(APPLE)
set_tests_properties(test_post_training_quantization_mnist PROPERTIES TIMEOUT
300)
set_tests_properties(test_post_training_quantization_while PROPERTIES TIMEOUT
300)
set_tests_properties(test_imperative_ptq PROPERTIES TIMEOUT 300)
set_tests_properties(test_imperative_skip_op PROPERTIES TIMEOUT 300)
endif()
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