def flush(self):
"""
Flush the event file to disk.
Call it to make sure that all pending events have been written to disk.
Examples:
>>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
>>> summary_record.flush()
"""
if self._closed:
logger.error("The record writer is closed and can not flush.")
elif self._event_writer:
self._event_writer.flush()
def _aicore_trace_data_load(self):
"""Load data according to the parsed AICORE operator types file."""
file_path = query_latest_trace_time_file(self._profiling_dir,
int(self._device_id))
if not file_path:
logger.error("Failed to find parsed trace time file.")
raise ProfilerFileNotFoundException('parsed step trace time file')
with open(file_path, 'r') as handle:
csv_reader = csv.reader(handle)
self.__column__ = next(csv_reader)
self._aicore_trace_data = list(csv_reader)
self._size = len(self._aicore_trace_data) - 1
self._display_col_names = self._col_names[:]
self._load_point_info()
def record(self, step, train_network=None):
"""
Record the summary.
Args:
step (int): Represents training step number.
train_network (Cell): The network that called the callback.
Examples:
>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>> file_prefix="xxx_", file_suffix="_yyy")
>>> summary_record.record(step=2)
Returns:
bool, whether the record process is successful or not.
"""
logger.info("SummaryRecord step is %r.", step)
if self._closed:
logger.error("The record writer is closed.")
return False
if not isinstance(step, int) or isinstance(step, bool):
raise ValueError("`step` should be int")
# Set the current summary of train step
self.step = step
if self.network is not None and self.has_graph is False:
graph_proto = self.network.get_func_graph_proto()
if graph_proto is None and train_network is not None:
graph_proto = train_network.get_func_graph_proto()
if graph_proto is None:
logger.error("Failed to get proto for graph")
else:
self.event_writer.write_event_to_file(
package_graph_event(graph_proto).SerializeToString())
self.event_writer.flush()
self.has_graph = True
data = _summary_tensor_cache.get("SummaryRecord")
if data is None:
logger.error("The step(%r) does not have record data.", self.step)
return False
if self.queue_max_size > 0 and len(data) > self.queue_max_size:
logger.error(
"The size of data record is %r, which is greater than queue_max_size %r.",
len(data), self.queue_max_size)
# clean the data of cache
del _summary_tensor_cache["SummaryRecord"]
# process the data
self.worker_scheduler.dispatch(self.step, data)
# count & flush
self.event_writer.count_event()
self.event_writer.flush_cycle()
logger.debug(
"Send the summary data to scheduler for saving, step = %d",
self.step)
return True
def _compile_akg_task_ascend(*json_strs):
"""
compile func called in single process
Parameters:
json_strs: list. List contains multiple kernel infos, suitable for json compile api.
"""
akg_compiler = os.path.join(os.path.split(
os.path.realpath(__file__))[0], "compiler.py")
for json_str in json_strs:
try:
subprocess.run([sys.executable, akg_compiler, json_str], text=True, check=True)
except BaseException as e:
logger.error(e, "Failed, args: {}!".format(json_str))
def parse(self):
"""Parse the function or method."""
logger.debug("fn = %r", self.fn)
tree = None
if isinstance(self.fn, (types.FunctionType, types.MethodType)):
original_src = inspect.getsource(self.fn)
src = dedent(original_src)
self.col_offset = \
len(original_src.split('\n')[0]) - len(src.split('\n')[0])
logger.debug("get source = %s", src)
tree = asttokens.ASTTokens(src, parse=True).tree
else:
logger.error("Fn type is invalid")
return tree
def to_tensor(self):
"""Get the tensor format data of this Initializer."""
arr = None
try:
arr = np.ndarray(self.shape)
except ValueError:
msg = "Error shape={}".format(self.shape)
logger.error(msg)
raise ValueError(msg)
if self._seed is not None:
np.random.seed(self.seed)
self.__call__(arr)
self._seed = None
return Tensor(arr, dtype=self.dtype)
def _fill_image_summary(tag: str,
np_value,
summary_image,
input_format='NCHW'):
"""
Package the image summary.
Args:
tag (str): Summary tag describe.
np_value (Type): Summary data type.
summary_image (Tensor): The tensor of summary.
input_format (str): Data sort order index. Default: 'NCHW'.
Returns:
Summary, return image summary content.
"""
logger.debug(f"Set({tag}) the image summary value")
if np_value.ndim != 4 or np_value.shape[1] not in (1, 3):
logger.error(
f"The value is not Image, tag = {tag}, ndim = {np_value.ndim}, shape={np_value.shape}"
)
return False
if np_value.ndim != len(input_format):
logger.error(
f"The tensor with dim({np_value.ndim}) can't convert the format({input_format}) because dim not same"
)
return False
# convert the tensor format
tensor = _convert_image_format(np_value, input_format)
# convert the tensor dtype
# Do not assume that user passes in values in [0, 255], use data type to detect
scale_factor = 1
if tensor.dtype == np.uint8:
scale_factor = 1
elif np.max(tensor) <= 1 and np.min(tensor) >= 0:
scale_factor = 255
tensor = tensor.astype(np.float32)
tensor = (tensor * scale_factor).astype(np.uint8)
# create the image summary
height, width, channel, image_string = _make_image(tensor)
summary_image.height = height
summary_image.width = width
summary_image.colorspace = channel
summary_image.encoded_image = image_string
return True
def _set_fusion_strategy_by_size(dataSizeList, group="hccl_world_group"):
"""
A function set gradient segment strategy according to the data size percentage list.
Note:
In the back propagation,
the fusion of the allreduce operators with a fusion attribute equals 1,
will be performed according to dataSizeList,
to achieve the effect of parallel between calculation and communication.
Args:
dataSizeList (list): The data size percentage list of the gradient.
group (str): The hccl communication group.
Raises:
TypeError: If group is not a python str.
TypeError: If dataSizeList is not a python list.
TypeError: If type of dataSizeList item is not int or float.
ValueError: If group name length is out of range.
ValueError: If dataSizeList length is 0.
ValueError: If dataSizeList item is less than 0.
RuntimeError: If allreduce split failed.
"""
try:
lib_ctype = _load_lib()
except RuntimeError:
logger.error('Load HCCL lib failed')
if isinstance(group, (str)):
group_len = len(group)
if group_len > _MAX_GROUP_NAME_LEN or group_len == 0:
raise ValueError('Group name is out of range {_MAX_GROUP_NAME_LEN}')
else:
raise TypeError('Group must be a python str')
if isinstance(dataSizeList, (list)):
len_data_size = len(dataSizeList)
if len_data_size == 0:
raise ValueError('DataSizeList length is 0')
else:
raise TypeError('DataSizeList must be a python list')
for dataSize in dataSizeList:
if not isinstance(dataSize, (int, float)):
raise TypeError('DataSize in dataSizeList is invalid')
c_array_sizeList = _c_array(ctypes.c_float, dataSizeList)
c_size_num = ctypes.c_uint(len(dataSizeList))
c_group = _c_str(group)
ret = lib_ctype.hcom_set_split_strategy_by_size(c_group, c_size_num, c_array_sizeList)
if ret != 0:
raise RuntimeError('Allreduce split error')
def flush(self):
"""
Flush the event file to disk.
Call it to make sure that all pending events have been written to disk.
Examples:
>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>> file_prefix="xxx_", file_suffix="_yyy")
>>> summary_record.flush()
"""
if self._closed:
logger.error("The record writer is closed and can not flush.")
else:
self.event_writer.flush()
def _write(self, plugin, data):
"""Write the data in the subprocess."""
for writer in self._writers[:]:
try:
writer.write(plugin, data)
except RuntimeError as exc:
logger.error(str(exc))
self._writers.remove(writer)
writer.close()
if self._raise_exception:
raise
except RuntimeWarning as exc:
logger.warning(str(exc))
self._writers.remove(writer)
writer.close()
def write_to_db(self):
"""
Create index field in table for reading data.
Returns:
MSRStatus, SUCCESS or FAILED.
Raises:
MRMGenerateIndexError: If failed to write to database.
"""
ret = self._generator.write_to_db()
if ret != ms.MSRStatus.SUCCESS:
logger.error("Failed to write to database.")
raise MRMGenerateIndexError
return ret
def get_category_fields(self):
"""
Get candidate category fields.
Returns:
list[str], by which data could be grouped.
Raises:
MRMFetchCandidateFieldsError: If failed to get candidate category fields.
"""
ret, fields = self._segment.get_category_fields()
if ret != SUCCESS:
logger.error("Failed to get candidate category fields.")
raise MRMFetchCandidateFieldsError
return fields
def build(self):
"""
Build index generator.
Returns:
MSRStatus, SUCCESS or FAILED.
Raises:
MRMGenerateIndexError: If failed to build index generator.
"""
ret = self._generator.build()
if ret != ms.MSRStatus.SUCCESS:
logger.error("Failed to build index generator.")
raise MRMGenerateIndexError
return ret
def estimate_ops(json_str: str):
"""Call costmodel to estimate ops."""
try:
json_obj = json.loads(json_str)
graph_descs = json_obj["graph_desc"]
graphs = []
for gd in graph_descs:
graphs.append(model.load_composite(gd).graph)
estimation = model.parallel_estimate(graphs)
res = (estimation.block_assign, estimation.gain,
estimation.fusion_type, estimation.type_info)
return res
except jd.JSONDecodeError:
logger.error(traceback.format_exc())
return None
def commit(self):
"""
Flush data to disk.
Returns:
Class MSRStatus, SUCCESS or FAILED.
Raises:
MRMCommitError: If failed to flush data to disk.
"""
ret = self._writer.commit()
if ret != ms.MSRStatus.SUCCESS:
logger.error("Failed to commit.")
raise MRMCommitError
return ret
def launch(self):
"""
Launch the worker threads to load data.
Returns:
MSRStatus, SUCCESS or FAILED.
Raises:
MRMLaunchError: If failed to launch worker threads.
"""
ret = self._reader.launch(False)
if ret != ms.MSRStatus.SUCCESS:
logger.error("Failed to launch worker threads.")
raise MRMLaunchError
return ret
def finish(self):
"""
stop the worker threads.
Returns:
MSRStatus, SUCCESS or FAILED.
Raises:
MRMFinishError: If failed to finish worker threads.
"""
ret = self._reader.finish()
if ret != ms.MSRStatus.SUCCESS:
logger.error("Failed to finish worker threads.")
raise MRMFinishError
return ret
def read_category_info(self):
"""
Get the group info by the current category field.
Returns:
str, description fo group information.
Raises:
MRMReadCategoryInfoError: If failed to read category information.
"""
ret, category_info = self._segment.read_category_info()
if ret != SUCCESS:
logger.error("Failed to read category information.")
raise MRMReadCategoryInfoError
return category_info
def record(self, step, train_network=None, plugin_filter=None):
"""
Record the summary.
Args:
step (int): Represents training step number.
train_network (Cell): The network to call the callback.
plugin_filter (Optional[Callable[[str], bool]]): The filter function, \
which is used to filter out plugins from being written by returning False.
Returns:
bool, whether the record process is successful or not.
Examples:
>>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
>>> summary_record.record(step=2)
"""
logger.debug("SummaryRecord step is %r.", step)
if self._closed:
logger.error("The record writer is closed.")
return False
if not isinstance(step, int) or isinstance(step, bool):
raise ValueError("`step` should be int")
# Set the current summary of train step
if self.network is not None and not self.has_graph:
graph_proto = self.network.get_func_graph_proto()
if graph_proto is None and train_network is not None:
graph_proto = train_network.get_func_graph_proto()
if graph_proto is None:
logger.error("Failed to get proto for graph")
else:
self._event_writer.write({'graph': [{'step': step, 'value': graph_proto}]})
self.has_graph = True
if not _summary_tensor_cache:
return True
if self._mode == 'train':
self._add_summary_tensor_data()
if not plugin_filter:
self._event_writer.write(self._consume_data_pool(step))
else:
filtered = {}
for plugin, datalist in self._consume_data_pool(step).items():
if plugin_filter(plugin):
filtered[plugin] = datalist
self._event_writer.write(filtered)
return True
def _run_hoc(self, summary, sample_id, sample_input, prob):
"""
Run HOC search for a sample image, and then save the result to summary.
Args:
summary (SummaryRecord): The summary object to store the data.
sample_id (int): The sample ID.
sample_input (Union[Tensor, np.ndarray]): Sample image tensor in CHW or NCWH(N=1).
prob (Union[Tensor, np.ndarray]): List of sample's classification prediction output, HOC will run for
labels with prediction output strictly larger then HOC searcher's threshold(0.5 by default).
"""
if isinstance(sample_input, ms.Tensor):
sample_input = sample_input.asnumpy()
if len(sample_input.shape) == 3:
sample_input = np.expand_dims(sample_input, axis=0)
has_rec = False
explain = Explain()
explain.sample_id = sample_id
str_mask = hoc.auto_str_mask(sample_input)
compiled_mask = None
for label_idx, label_prob in enumerate(prob):
if label_prob > self._hoc_searcher.threshold:
if compiled_mask is None:
compiled_mask = hoc.compile_mask(str_mask, sample_input)
try:
edit_tree, layer_outputs = self._hoc_searcher.search(
sample_input, label_idx, compiled_mask)
except hoc.NoValidResultError as ex:
log.error(
f"HOC cannot find result for sample:{sample_id} error:{ex}"
)
continue
has_rec = True
hoc_rec = explain.hoc.add()
hoc_rec.label = label_idx
hoc_rec.mask = str_mask
layer_count = edit_tree.max_layer + 1
for layer in range(layer_count):
steps = edit_tree.get_layer_or_leaf_steps(layer)
layer_output = layer_outputs[layer]
hoc_layer = hoc_rec.layer.add()
hoc_layer.prob = layer_output
for step in steps:
hoc_layer.box.extend(list(step.box))
if has_rec:
summary.add_value("explainer", "hoc", explain)
summary.record(1)
self._manifest['hierarchical_occlusion'] = True
def to_tensor(self, slice_index=None, shape=None):
"""
Get the tensor format data of this MetaTensor.
Args:
slice_index (int): Slice index of a parameter's slices.
It is used when initialize a slice of a parameter, it guarantees that devices
using the same slice can generate the same tensor.
shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
"""
if self.init is None:
raise TypeError(
"to_dense must be set MetaTensor.init, init can't be None")
if shape is None:
shape = self.shape
try:
arr = np.ndarray(shape, dtype=mstype.dtype_to_nptype(self.dtype))
except ValueError:
msg = "Error shape={}".format(shape)
logger.error(msg)
raise ValueError(msg)
class seed_context:
'''set and restore seed'''
def __init__(self, init):
self.init = init
from .seed import get_seed
global_seed = get_seed()
self._np_seed = np.random.get_state()[1][0]
self.need_set_seed = ((slice_index is not None)
and (global_seed is None))
def __enter__(self):
if self.need_set_seed:
self.seed = self.init.seed
np.random.seed(slice_index)
self.init.seed = slice_index
def __exit__(self, ptype, value, trace):
if self.need_set_seed:
np.random.seed(self._np_seed)
self.init.seed = self.seed
with seed_context(self.init):
self.init(arr)
return Tensor(arr, dtype=self.dtype)
def check_input_data(*data, data_class):
"""Input data check."""
for item in data:
if isinstance(item, (list, tuple)):
for v in item:
check_input_data(v, data_class=data_class)
else:
if not isinstance(item, data_class):
raise ValueError(f'Please provide as model inputs'
f' either a single'
f' or a list of {data_class.__name__},'
f' but got part data type is {str(type(item))}.')
if item.size() == 0:
msg = "Please provide non-empty data."
logger.error(msg)
raise ValueError(msg)
def flush(self):
"""
Flush the event file to disk.
Call it to make sure that all pending events have been written to disk.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... summary_record.flush()
"""
if self._closed:
logger.error("The record writer is closed and can not flush.")
elif self._event_writer:
self._event_writer.flush()
def write_timeline_summary(self):
"""Write timeline summary to json."""
timeline_summary_file_path = os.path.join(
self._profiling_dir,
self._timeline_summary_filename.format(self._device_id)
)
timeline_summary_file_path = validate_and_normalize_path(timeline_summary_file_path)
try:
with open(timeline_summary_file_path, 'w') as json_file:
json.dump(self._timeline_summary, json_file)
os.chmod(timeline_summary_file_path, stat.S_IREAD | stat.S_IWRITE)
except (IOError, OSError) as err:
logger.error('Error occurred when write timeline summary file: %s', err)
raise ProfilerIOException
def process_check(cycle_time, cmd, wait_time=5):
for i in range(cycle_time):
time.sleep(wait_time)
sub = subprocess.Popen(args="{}".format(cmd),
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True)
stdout_data, _ = sub.communicate()
if not stdout_data:
logger.info("process execute success.")
return True
logger.warning("process is running, please wait {}".format(i))
logger.error("process execute execute timeout.")
return False
def _get_next(self):
"""
Returns the next record in the dataset as dictionary
Returns:
Dict, the next record in the dataset.
"""
try:
return {k: self._transform_tensor(t) for k, t in self._iterator.GetNextAsMap().items()}
except RuntimeError as err:
## maybe "Out of memory" / "MemoryError" error
err_info = str(err)
if err_info.find("Out of memory") >= 0 or err_info.find("MemoryError") >= 0:
logger.error("Memory error occurred, process will exit.")
os.kill(os.getpid(), signal.SIGKILL)
raise err
def validate_ui_proc(proc_name):
"""
Validate proc name in restful request.
Args:
proc_name (str): The proc name to query. Acceptable value is in
[`iteration_interval`, `fp_and_bp`, `tail`].
Raises:
ProfilerParamValueErrorException: If the proc_name is invalid.
"""
accept_names = ['iteration_interval', 'fp_and_bp', 'tail']
if proc_name not in accept_names:
log.error("Invalid proc_name. The proc_name for restful api is in %s",
accept_names)
raise ProfilerParamValueErrorException(
f'proc_name should be in {accept_names}.')
def split_with_json(json_str: str):
"""Call costmodel to split GraphKernel"""
try:
graph_desc = json.loads(json_str)
comp = model.load_composite(graph_desc)
graph_split, graph_mode = model.split(comp.graph)
is_multi_graph = len(graph_split) > 1
graph_list = list(map(comp.dump, graph_split))
result = {
"multi_graph": is_multi_graph,
"graph_desc": graph_list,
"graph_mode": graph_mode
}
return json.dumps(result)
except jd.JSONDecodeError:
logger.error(traceback.format_exc())
return None
def _get_profiling_job_id(self):
"""Get profiling job id, which was generated by ada service.
Returns:
str, profiling job id.
"""
job_id = ""
for item in os.listdir(self._output_path):
if item.startswith('JOB'):
path = os.path.join(self._output_path, item)
log_file = get_file_names(path, "host_start.log")
if not log_file:
logger.error(
"Profiling: job path %s, host_start.log not exist.",
path)
continue
training_device_id = log_file[0].split('.')[-1]
if self._dev_id == training_device_id:
log_file = os.path.join(path, log_file[0])
job_start_time = self._parse_host_start_log(log_file)
if not job_start_time:
logger.error(
"Profiling: job path %s, fail to get job start info.",
path)
break
job_id = item
if self._start_time > int(job_start_time):
logger.info(
"Profiling: job path %s, start_time %s, training start_time %d.",
path, job_start_time, self._start_time)
break
else:
logger.info(
"Profiling: job path %s, dev id %s, training device id %s.",
path, training_device_id, self._dev_id)
if not job_id:
msg = "Fail to get profiling job, please check whether job dir was generated, " \
"or may be the device id from job dir dismatch the device_id in current process."
raise RuntimeError(msg)
return job_id
def _get_profiling_job_id(self):
"""Get profiling job id, which was generated by ada service.
Returns:
str, profiling job id.
"""
job_id = ""
for item in os.listdir(self._output_path):
if item.startswith('JOB'):
path = os.path.join(self._output_path, item)
log_file = get_file_names(path, "host_start.log")
if not log_file:
logger.error(
"Profiling: job path %s, host_start.log not exist.",
path)
break
log_file = os.path.join(path, log_file[0])
item_dict = self._parse_host_start_log(log_file)
if not item_dict:
logger.error(
"Profiling: job path %s, fail to get job start info.",
path)
break
job_id = item
if self._dev_id != item_dict["device_id"]:
logger.info(
"Profiling: job path %s, dev id %s, training device id %s.",
path, item_dict["device_id"], self._dev_id)
if self._start_time > int(item_dict["start_time"]):
logger.info(
"Profiling: job path %s, start_time %s, training start_time %d.",
path, item_dict["start_time"], self._start_time)
break
if not job_id:
msg = "Fail to get profiling job, please check whether job dir was generated"
raise RuntimeError(msg)
return job_id