def parse_files(self, filenames, events_data):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
"""
self._events_data = events_data
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
for filename in summary_files:
if self._latest_filename and \
(self._compare_summary_file(self._latest_filename, filename)):
continue
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
self._latest_file_size = new_size
try:
self._load_single_file(self._summary_file_handler)
except UnknownError as ex:
logger.warning("Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
def phase_pb_file(file_path: str) -> Union[MSGraph, None]:
"""
Parse pb file to graph
Args:
file_path (str): The file path of pb file.
Returns:
MSGraph, if load pb file and build graph success, will return the graph, else return None.
"""
if not CONFIG.VERBOSE:
logger.setLevel(logging.ERROR)
logger.info("Start to load graph from pb file, file path: %s.", file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(FileHandler(file_path).read())
except ParseError:
logger.warning("The given file is not a valid pb file, file path: %s.",
file_path)
return None
graph = MSGraph()
try:
graph.build_graph(model_proto.graph)
except Exception as ex:
logger.error("Build graph failed, file path: %s.", file_path)
logger.exception(ex)
raise UnknownError(str(ex))
logger.info("Build graph success, file path: %s.", file_path)
return graph
def parse(self):
"""Load summary file and parse file content."""
summary_file_handler = FileHandler(self.summary_file, 'rb')
self._file_size = os.path.getsize(self.summary_file)
# when current parsed size bigger than self._process_info, print process
self._process_info = self._file_size // INFO_INTERVAL
parse_summary_logger.info("Loading %s.", self.summary_file)
result = self._load(summary_file_handler)
if result:
warning = ''
scalar_path = FileHandler.join(self._output, "scalar.csv")
image_path = FileHandler.join(self._output, IMAGE)
if not self._image_check:
warning = warning + " The summary file contains no image."
else:
parse_summary_logger.info("Images are written in %s.", image_path)
if not self._scalar_check:
warning = warning + " The summary file contains no scalar value."
else:
parse_summary_logger.info("Writing scalar data into %s.", scalar_path)
self._scalar_writer.write()
if warning:
parse_summary_logger.warning(warning)
parse_summary_logger.info("Finished loading %s.", self.summary_file)
def _parse_pb_file(self, filename):
"""
Parse pb file and write content to `EventsData`.
Args:
filename (str): The file path of pb file.
"""
file_path = FileHandler.join(self._summary_dir, filename)
logger.info("Start to load graph from pb file, file path: %s.",
file_path)
filehandler = FileHandler(file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(filehandler.read())
except ParseError:
logger.warning(
"The given file is not a valid pb file, file path: %s.",
file_path)
return
graph = MSGraph()
graph.build_graph(model_proto.graph)
tensor_event = TensorEvent(wall_time=FileHandler.file_stat(file_path),
step=0,
tag=filename,
plugin_name=PluginNameEnum.GRAPH.value,
value=graph)
self._events_data.add_tensor_event(tensor_event)
def parse_files(self, executor, filenames, events_data):
"""
Load summary file and parse file content.
Args:
executor (Executor): The executor instance.
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
Returns:
bool, True if all the summary files are finished loading.
"""
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
if self._latest_filename in summary_files:
index = summary_files.index(self._latest_filename)
summary_files = summary_files[index:]
for filename in summary_files:
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
try:
if not self._load_single_file(self._summary_file_handler,
executor, events_data):
self._latest_file_size = self._summary_file_handler.offset
else:
self._latest_file_size = new_size
# Wait for data in this file to be processed to avoid loading multiple files at the same time.
logger.debug("Parse summary file offset %d, file path: %s.",
self._latest_file_size, file_path)
return False
except UnknownError as ex:
logger.warning(
"Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
return True
def _parse_pb_file(summary_dir, filename):
"""
Parse pb file and write content to `EventsData`.
Args:
filename (str): The file path of pb file.
Returns:
TensorEvent, if load pb file and build graph success, will return tensor event, else return None.
"""
file_path = FileHandler.join(summary_dir, filename)
logger.info("Start to load graph from pb file, file path: %s.",
file_path)
filehandler = FileHandler(file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(filehandler.read())
except ParseError:
logger.warning(
"The given file is not a valid pb file, file path: %s.",
file_path)
return None
graph = MSGraph()
try:
graph.build_graph(model_proto.graph)
except Exception as ex:
# Normally, there are no exceptions, and it is only possible for users on the MindSpore side
# to dump other non-default graphs.
logger.error("Build graph failed, file path: %s.", file_path)
logger.exception(ex)
raise UnknownError(str(ex))
tensor_event = TensorEvent(
wall_time=FileHandler.file_stat(file_path).mtime,
step=0,
tag=filename,
plugin_name=PluginNameEnum.GRAPH.value,
value=graph,
filename=filename)
logger.info("Build graph success, file path: %s.", file_path)
return tensor_event
def parse_files(self, executor, filenames, events_data):
"""
Load summary file and parse file content.
Args:
executor (Executor): The executor instance.
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
"""
self._events_data = events_data
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
for filename in summary_files:
if self._latest_filename and \
(self._compare_summary_file(self._latest_filename, filename)):
continue
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
self._latest_file_size = new_size
try:
self._load_single_file(self._summary_file_handler, executor)
# Wait for data in this file to be processed to avoid loading multiple files at the same time.
executor.wait_all_tasks_finish()
except UnknownError as ex:
logger.warning(
"Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
def list_events(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
tuple, the elements of the tuple are:
- file_changed (bool): True if the latest file is changed.
- is_end (bool): True if all the summary files are finished loading.
- event_data (dict): Event data where keys are explanation field.
"""
summary_files = self.sort_files(filenames)
is_end = False
file_changed = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_offset = 0
file_changed = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_offset:
is_end = True
return file_changed, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return file_changed, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.debug("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return file_changed, is_end, event_data
except exceptions.CRCLengthFailedError as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and reset offset, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return file_changed, is_end, event_data
except Exception as ex:
# Note: If an unknown error occurs, we will set the offset to the end of this file,
# which is equivalent to stopping parsing this file. We do not delete the current job
# and retain the data that has been successfully parsed.
self._summary_file_handler.reset_offset(new_size)
# Notice: If the current job is the latest one in the loader pool and the job is deleted,
# the job goes into an infinite cycle of load-fail-delete-reload-load-fail-delete.
# We need to prevent this infinite loop.
logger.error(
"Parse summary file failed, will set offset to the file end. file_path: %s, "
"offset: %d, detail: %s.", file_path,
self._summary_file_handler.offset, str(ex))
logger.exception(ex)
raise UnknownError(str(ex))
finally:
self._latest_offset = self._summary_file_handler.offset
def parse_explain(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
bool, True if all the summary files are finished loading.
"""
summary_files = self.sort_files(filenames)
is_end = False
is_clean = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
is_clean = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
is_end = True
return is_clean, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return is_clean, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.info("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return is_clean, is_end, event_data
except (exceptions.CRCFailedError,
exceptions.CRCLengthFailedError) as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and ignore this file, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return is_clean, is_end, event_data
except (OSError, DecodeError,
exceptions.MindInsightException) as ex:
is_end = True
logger.warning(
"Parse log file fail, and ignore this file, detail: %r,"
"file path: %s.", str(ex),
self._summary_file_handler.file_path)
return is_clean, is_end, event_data
except Exception as ex:
logger.exception(ex)
raise UnknownError(str(ex))