def _gpu_analyse(self):
"""Collect and analyse gpu performance data"""
self._dev_id = context.get_context("device_id")
if GlobalComm.WORLD_COMM_GROUP == "nccl_world_group":
self._dev_id = str(get_rank())
self._gpu_profiler.stop()
timeline_generator = self._generate_timeline()
# parse minddata pipeline operator and queue for GPU
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(is_training_mode_flag=timeline_generator.
check_op_name('Gradients'))
except ProfilerException as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
logger.warning(
'\nMemory Usage is not supported on GPU currently.\n'
'Please running on Ascend if you would like to see memory analysis, '
'otherwise, this warning can be ignored.')
def _gpu_analyse(self):
"""Collect and analyse gpu performance data"""
if GlobalComm.WORLD_COMM_GROUP == "nccl_world_group" and self._dev_id != str(get_rank()):
self._dev_id = str(get_rank())
logger.error('Please check the Profiler object initialized after mindspore.context.set_auto_parallel_'
'context() and mindspore.communication.management.init(). Profiler should be initialized'
' after these code.')
self._gpu_profiler.stop()
timeline_generator = self._generate_timeline()
# parse minddata pipeline operator and queue for GPU
try:
pipeline_parser = MinddataPipelineParser(self._output_path, self._dev_id, self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(is_training_mode_flag=timeline_generator.check_op_name('Gradients'))
except ProfilerException as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
logger.warning(
'\nMemory Usage is not supported on GPU currently.\n'
'Please running on Ascend if you would like to see memory analysis, '
'otherwise, this warning can be ignored.'
)
def _gpu_analyse(self):
"""Collect and analyse gpu performance data"""
if context.get_auto_parallel_context(
'device_num') > 1 and self._dev_id != get_rank():
self._dev_id = get_rank()
logger.error(
'Please check the Profiler object initialized after set_auto_parallel_context() '
'and init(). Profiler should be initialized after these code. '
)
self._gpu_profiler.stop()
timeline_generator = self._generate_timeline()
# parse minddata pipeline operator and queue for GPU
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(is_training_mode_flag=timeline_generator.
check_op_name('Gradients'))
except ProfilerException as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
def _ascend_analyse(self):
"""Collect and analyse ascend performance data"""
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(self._output_path, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
source_path = validate_and_normalize_path(source_path)
hwts_output_filename = validate_and_normalize_path(
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
hwtslog_parser.execute()
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(self._output_path,
opcompute_output_filename)
opcompute_output_filename = validate_and_normalize_path(
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict, self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
output_data_preprocess_aicpu = validate_and_normalize_path(
output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(source_path,
output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path, self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
points = None
try:
points = self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser,
source_path)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)
# analyse memory usage info
try:
self._analyse_memory_usage(points)
except (ProfilerIOException, ProfilerFileNotFoundException,
ProfilerRawFileException) as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
context.set_context(enable_profiling=False)
def analyse(self):
"""
Collect and analyse performance data, called after training or during training.
Examples:
>>> from mindspore.profiler import Profiler
>>> import mindspore.context
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler()
>>> model = Model()
>>> model.train()
>>> profiler.analyse()
"""
if self._device_target and self._device_target == "GPU":
if context.get_auto_parallel_context(
'device_num') > 1 and self._dev_id != get_rank():
self._dev_id = get_rank()
logger.error(
'Please check the Profiler object initialized after set_auto_parallel_context() '
'and init(). Profiler should be initialized after these code. '
)
self._gpu_profiler.stop()
timeline_generator = self._generate_timeline()
# parse minddata pipeline operator and queue for GPU
try:
pipeline_parser = MinddataPipelineParser(
self._output_path, self._dev_id, self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(
is_training_mode_flag=timeline_generator.check_op_name(
'Gradients'))
except ProfilerException as err:
logger.warning(err.message)
os.environ['PROFILING_MODE'] = str("false")
elif self._device_target and self._device_target == "Ascend":
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(self._output_path, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
source_path = validate_and_normalize_path(source_path)
hwts_output_filename = validate_and_normalize_path(
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
_ = hwtslog_parser.execute()
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(
self._output_path, opcompute_output_filename)
opcompute_output_filename = validate_and_normalize_path(
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict,
self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
output_data_preprocess_aicpu = validate_and_normalize_path(
output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(
source_path, output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path,
self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(
self._output_path, self._dev_id, self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)
os.environ['PROFILING_MODE'] = str("false")
context.set_context(enable_profiling=False)
def analyse(self):
"""
Collect and analyse performance data, called after training or during training.
Examples:
>>> from mindspore.profiler import Profiler
>>> import mindspore.context
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
>>> model = Model()
>>> model.train()
>>> profiler.analyse()
"""
release()
job_id = self._get_profiling_job_id()
logger.info("Profiling: job id is %s ", job_id)
source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id)
# parse hwts.log.data.45.dev file, and get task profiling data
hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
hwts_output_filename = os.path.join(self._output_path,
hwts_output_filename)
hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
result = hwtslog_parser.execute()
if not result:
logger.error("Profiling: fail to parse hwts log file.")
return
# parse Framework file, and get the relation of op and tasks
framework_parser = FrameworkParser(job_id, self._dev_id,
self._output_path)
framework_parser.parse()
op_task_dict = framework_parser.to_task_id_full_op_name_dict()
if not op_task_dict:
logger.error("Profiling: fail to parse framework files.")
return
# get op compute time from hwts data and framework data, write output_op_compute_time.txt
opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
opcompute_output_filename = os.path.join(self._output_path,
opcompute_output_filename)
optime_parser = OPComputeTimeParser(hwts_output_filename,
opcompute_output_filename,
op_task_dict, self._output_path,
self._dev_id)
optime_parser.execute()
# parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
output_data_preprocess_aicpu = os.path.join(
self._output_path, output_data_preprocess_aicpu)
aicpu_data_parser = DataPreProcessParser(source_path,
output_data_preprocess_aicpu)
aicpu_data_parser.execute()
# Parsing minddata AICPU profiling
MinddataParser.execute(source_path, self._output_path, self._dev_id)
# parse minddata pipeline operator and queue
try:
pipeline_parser = MinddataPipelineParser(self._output_path,
self._dev_id,
self._output_path)
pipeline_parser.parse()
except ProfilerException as err:
logger.warning(err.message)
# analyse op compute time info
try:
self._analyser_op_info()
except ProfilerException as err:
logger.warning(err.message)
# analyse step trace info
try:
self._analyse_step_trace(source_path, framework_parser)
except ProfilerException as err:
logger.warning(err.message)
# analyse timeline info
try:
self._analyse_timeline(aicpu_data_parser, optime_parser)
except (ProfilerIOException, ProfilerFileNotFoundException,
RuntimeError) as err:
logger.warning('Fail to write timeline data: %s', err)