def run_specify_abi(self, flags, configs, target_abi):
if target_abi not in self.target_abis:
six.print_('There is no device with soc: %s abi: %s' %
(self.target_socs, target_abi))
return
library_name = configs[YAMLKeyword.library_name]
mace_lib_type = flags.mace_lib_type
embed_model_data = \
configs[YAMLKeyword.model_data_format] == ModelFormat.code
build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi)
# get target name for run
if flags.example:
if mace_lib_type == MACELibType.static:
target_name = EXAMPLE_STATIC_NAME
else:
target_name = EXAMPLE_DYNAMIC_NAME
else:
if mace_lib_type == MACELibType.static:
target_name = MACE_RUN_STATIC_NAME
else:
target_name = MACE_RUN_DYNAMIC_NAME
link_dynamic = mace_lib_type == MACELibType.dynamic
model_output_dirs = []
for model_name in configs[YAMLKeyword.models]:
check_model_converted(library_name, model_name,
configs[YAMLKeyword.model_graph_format],
configs[YAMLKeyword.model_data_format],
target_abi)
if target_abi != ABIType.host:
self.clear_data_dir()
MaceLogger.header(
StringFormatter.block('Run model {} on {}'.format(
model_name, self.device_name)))
model_config = configs[YAMLKeyword.models][model_name]
model_runtime = model_config[YAMLKeyword.runtime]
subgraphs = model_config[YAMLKeyword.subgraphs]
if not configs[YAMLKeyword.target_socs] \
or target_abi == ABIType.host:
model_output_base_dir, model_output_dir, mace_model_dir = \
get_build_model_dirs(
library_name, model_name, target_abi, self,
model_config[YAMLKeyword.model_file_path])
else:
model_output_base_dir, model_output_dir, mace_model_dir = \
get_build_model_dirs(
library_name, model_name, target_abi, self,
model_config[YAMLKeyword.model_file_path])
# clear temp model output dir
if os.path.exists(model_output_dir):
sh.rm('-rf', model_output_dir)
os.makedirs(model_output_dir)
is_tuned = False
model_opencl_output_bin_path = ''
model_opencl_parameter_path = ''
if not flags.address_sanitizer \
and not flags.example \
and target_abi != ABIType.host \
and configs[YAMLKeyword.target_socs] \
and self.target_socs \
and model_runtime in [RuntimeType.gpu,
RuntimeType.cpu_gpu] \
and not flags.disable_tuning:
self.tuning(library_name, model_name, model_config,
configs[YAMLKeyword.model_graph_format],
configs[YAMLKeyword.model_data_format], target_abi,
mace_lib_type)
model_output_dirs.append(model_output_dir)
model_opencl_output_bin_path = \
'{}/{}/{}'.format(model_output_dir,
BUILD_TMP_OPENCL_BIN_DIR,
CL_COMPILED_BINARY_FILE_NAME)
model_opencl_parameter_path = \
'{}/{}/{}'.format(model_output_dir,
BUILD_TMP_OPENCL_BIN_DIR,
CL_TUNED_PARAMETER_FILE_NAME)
self.clear_data_dir()
is_tuned = True
elif target_abi != ABIType.host and self.target_socs:
model_opencl_output_bin_path = get_opencl_binary_output_path(
library_name, target_abi, self)
model_opencl_parameter_path = get_opencl_parameter_output_path(
library_name, target_abi, self)
sh_commands.gen_random_input(
model_output_dir,
subgraphs[0][YAMLKeyword.input_tensors],
subgraphs[0][YAMLKeyword.input_shapes],
subgraphs[0][YAMLKeyword.validation_inputs_data],
input_ranges=subgraphs[0][YAMLKeyword.input_ranges],
input_data_types=subgraphs[0][YAMLKeyword.input_data_types])
runtime_list = []
if target_abi == ABIType.host:
runtime_list.append(RuntimeType.cpu)
elif model_runtime == RuntimeType.cpu_gpu:
runtime_list.extend([RuntimeType.cpu, RuntimeType.gpu])
else:
runtime_list.append(model_runtime)
for runtime in runtime_list:
device_type = parse_device_type(runtime)
# run for specified soc
run_output = self.tuning_run(
abi=target_abi,
target_dir=build_tmp_binary_dir,
target_name=target_name,
vlog_level=flags.vlog_level,
embed_model_data=embed_model_data,
model_output_dir=model_output_dir,
input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
mace_model_dir=mace_model_dir,
model_tag=model_name,
device_type=device_type,
running_round=flags.round,
restart_round=flags.restart_round,
limit_opencl_kernel_time=model_config[
YAMLKeyword.limit_opencl_kernel_time],
tuning=False,
out_of_range_check=flags.gpu_out_of_range_check,
model_graph_format=configs[YAMLKeyword.model_graph_format],
omp_num_threads=flags.omp_num_threads,
cpu_affinity_policy=flags.cpu_affinity_policy,
gpu_perf_hint=flags.gpu_perf_hint,
gpu_priority_hint=flags.gpu_priority_hint,
runtime_failure_ratio=flags.runtime_failure_ratio,
address_sanitizer=flags.address_sanitizer,
opencl_binary_file=model_opencl_output_bin_path,
opencl_parameter_file=model_opencl_parameter_path,
libmace_dynamic_library_path=LIBMACE_DYNAMIC_PATH,
link_dynamic=link_dynamic,
quantize_stat=flags.quantize_stat,
)
if flags.validate:
model_file_path, weight_file_path = get_model_files(
model_config[YAMLKeyword.model_file_path],
model_config[YAMLKeyword.model_sha256_checksum],
BUILD_DOWNLOADS_DIR,
model_config[YAMLKeyword.weight_file_path],
model_config[YAMLKeyword.weight_sha256_checksum])
validate_type = device_type
if model_config[YAMLKeyword.quantize] == 1:
validate_type = device_type + '_QUANTIZE'
sh_commands.validate_model(
abi=target_abi,
device=self,
model_file_path=model_file_path,
weight_file_path=weight_file_path,
platform=model_config[YAMLKeyword.platform],
device_type=device_type,
input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
model_output_dir=model_output_dir,
input_data_types=subgraphs[0][
YAMLKeyword.input_data_types],
caffe_env=flags.caffe_env,
validation_threshold=subgraphs[0][
YAMLKeyword.validation_threshold][validate_type],
backend=subgraphs[0][YAMLKeyword.backend])
if flags.report and flags.round > 0:
tuned = is_tuned and device_type == DeviceType.GPU
self.report_run_statistics(target_abi=target_abi,
model_name=model_name,
device_type=device_type,
output_dir=flags.report_dir,
tuned=tuned)
if model_output_dirs:
opencl_output_bin_path = get_opencl_binary_output_path(
library_name, target_abi, self)
opencl_parameter_bin_path = get_opencl_parameter_output_path(
library_name, target_abi, self)
# clear opencl output dir
if os.path.exists(opencl_output_bin_path):
sh.rm('-rf', opencl_output_bin_path)
if os.path.exists(opencl_parameter_bin_path):
sh.rm('-rf', opencl_parameter_bin_path)
# merge all model's opencl binaries together
sh_commands.merge_opencl_binaries(model_output_dirs,
CL_COMPILED_BINARY_FILE_NAME,
opencl_output_bin_path)
# merge all model's opencl parameter together
sh_commands.merge_opencl_parameters(model_output_dirs,
CL_TUNED_PARAMETER_FILE_NAME,
opencl_parameter_bin_path)
def bm_specific_target(self, flags, configs, target_abi):
library_name = configs[YAMLKeyword.library_name]
embed_model_data = \
configs[YAMLKeyword.model_data_format] == ModelFormat.code
opencl_output_bin_path = ''
opencl_parameter_path = ''
link_dynamic = flags.mace_lib_type == MACELibType.dynamic
if link_dynamic:
bm_model_binary_name = BM_MODEL_DYNAMIC_NAME
else:
bm_model_binary_name = BM_MODEL_STATIC_NAME
build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi)
if configs[YAMLKeyword.target_socs] and target_abi != ABIType.host:
opencl_output_bin_path = get_opencl_binary_output_path(
library_name, target_abi, self)
opencl_parameter_path = get_opencl_parameter_output_path(
library_name, target_abi, self)
for model_name in configs[YAMLKeyword.models]:
check_model_converted(library_name, model_name,
configs[YAMLKeyword.model_graph_format],
configs[YAMLKeyword.model_data_format],
target_abi)
MaceLogger.header(
StringFormatter.block('Benchmark model %s on %s' %
(model_name, self.device_name)))
model_config = configs[YAMLKeyword.models][model_name]
model_runtime = model_config[YAMLKeyword.runtime]
subgraphs = model_config[YAMLKeyword.subgraphs]
model_output_base_dir, model_output_dir, mace_model_dir = \
get_build_model_dirs(library_name, model_name,
target_abi, self,
model_config[YAMLKeyword.model_file_path])
if os.path.exists(model_output_dir):
sh.rm('-rf', model_output_dir)
os.makedirs(model_output_dir)
if target_abi != ABIType.host:
self.clear_data_dir()
sh_commands.gen_random_input(
model_output_dir,
subgraphs[0][YAMLKeyword.input_tensors],
subgraphs[0][YAMLKeyword.input_shapes],
subgraphs[0][YAMLKeyword.validation_inputs_data],
input_ranges=subgraphs[0][YAMLKeyword.input_ranges],
input_data_types=subgraphs[0][YAMLKeyword.input_data_types])
runtime_list = []
if target_abi == ABIType.host:
runtime_list.append(RuntimeType.cpu)
elif model_runtime == RuntimeType.cpu_gpu:
runtime_list.extend([RuntimeType.cpu, RuntimeType.cpu_gpu])
else:
runtime_list.append(model_runtime)
for runtime in runtime_list:
device_type = parse_device_type(runtime)
self.benchmark_model(
abi=target_abi,
benchmark_binary_dir=build_tmp_binary_dir,
benchmark_binary_name=bm_model_binary_name,
vlog_level=0,
embed_model_data=embed_model_data,
model_output_dir=model_output_dir,
input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
mace_model_dir=mace_model_dir,
model_tag=model_name,
device_type=device_type,
model_graph_format=configs[YAMLKeyword.model_graph_format],
omp_num_threads=flags.omp_num_threads,
cpu_affinity_policy=flags.cpu_affinity_policy,
gpu_perf_hint=flags.gpu_perf_hint,
gpu_priority_hint=flags.gpu_priority_hint,
opencl_binary_file=opencl_output_bin_path,
opencl_parameter_file=opencl_parameter_path,
libmace_dynamic_library_path=LIBMACE_DYNAMIC_PATH,
link_dynamic=link_dynamic)
def tuning(self, library_name, model_name, model_config,
model_graph_format, model_data_format, target_abi,
mace_lib_type):
six.print_('* Tuning, it may take some time')
build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi)
mace_run_name = MACE_RUN_STATIC_NAME
link_dynamic = False
if mace_lib_type == MACELibType.dynamic:
mace_run_name = MACE_RUN_DYNAMIC_NAME
link_dynamic = True
embed_model_data = model_data_format == ModelFormat.code
# build for specified soc
# device_wrapper = DeviceWrapper(device)
model_output_base_dir, model_output_dir, mace_model_dir = \
get_build_model_dirs(
library_name, model_name, target_abi, self,
model_config[YAMLKeyword.model_file_path])
self.clear_data_dir()
subgraphs = model_config[YAMLKeyword.subgraphs]
# generate input data
sh_commands.gen_random_input(
model_output_dir,
subgraphs[0][YAMLKeyword.input_tensors],
subgraphs[0][YAMLKeyword.input_shapes],
subgraphs[0][YAMLKeyword.validation_inputs_data],
input_ranges=subgraphs[0][YAMLKeyword.input_ranges],
input_data_types=subgraphs[0][YAMLKeyword.input_data_types])
self.tuning_run(
abi=target_abi,
target_dir=build_tmp_binary_dir,
target_name=mace_run_name,
vlog_level=0,
embed_model_data=embed_model_data,
model_output_dir=model_output_dir,
input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
mace_model_dir=mace_model_dir,
model_tag=model_name,
device_type=DeviceType.GPU,
running_round=0,
restart_round=1,
limit_opencl_kernel_time=model_config[
YAMLKeyword.limit_opencl_kernel_time],
tuning=True,
out_of_range_check=False,
model_graph_format=model_graph_format,
opencl_binary_file='',
opencl_parameter_file='',
libmace_dynamic_library_path=LIBMACE_DYNAMIC_PATH,
link_dynamic=link_dynamic,
)
# pull opencl library
self.pull(self.interior_dir, CL_COMPILED_BINARY_FILE_NAME,
'{}/{}'.format(model_output_dir, BUILD_TMP_OPENCL_BIN_DIR))
# pull opencl parameter
self.pull_from_data_dir(
CL_TUNED_PARAMETER_FILE_NAME,
'{}/{}'.format(model_output_dir, BUILD_TMP_OPENCL_BIN_DIR))
six.print_('Tuning done! \n')
