def _get_matching_collections(
self, mode, tensor, tensor_type, ts_name, is_input_to_model=False, is_output_of_model=False
):
colls_with_tensor = set()
if tensor_type == "weight":
if match_inc(
tensor.name, self.collection_manager.get(CollectionKeys.BIASES).include_regex
):
colls_with_tensor.add(self.collection_manager.get(CollectionKeys.BIASES))
else:
colls_with_tensor.add(self.collection_manager.get(CollectionKeys.WEIGHTS))
elif is_input_to_model:
colls_with_tensor.add(self.collection_manager.get(CollectionKeys.INPUTS))
elif is_output_of_model:
colls_with_tensor.add(self.collection_manager.get(CollectionKeys.OUTPUTS))
for current_coll in self.collection_manager.get_collections().values():
if current_coll.name in [CollectionKeys.WEIGHTS, CollectionKeys.BIASES]:
# don't match regex for these as these are added specially above
# we also don't want users to make mistakes configuring these collections
continue
if match_inc(ts_name, current_coll.include_regex):
if not current_coll.has_tensor(tensor):
# tensor will be added to this coll below
colls_with_tensor.add(current_coll)
# don't recommend adding tensors externally as
# they will have different internal name
# but regardless, in such case we only use that tensor name to save data
# instead of the keras-style-internal-names
return colls_with_tensor
def _get_matching_collections(self,
mode,
tensor,
tensor_type,
ts_name,
is_input_to_model=False,
is_output_of_model=False):
colls_with_tensor = set()
if tensor_type == "weight":
if match_inc(
tensor.name,
self.collection_manager.get(
CollectionKeys.BIASES).include_regex):
colls_with_tensor.add(
self.collection_manager.get(CollectionKeys.BIASES))
else:
colls_with_tensor.add(
self.collection_manager.get(CollectionKeys.WEIGHTS))
elif is_input_to_model:
colls_with_tensor.add(
self.collection_manager.get(CollectionKeys.INPUTS))
elif is_output_of_model:
colls_with_tensor.add(
self.collection_manager.get(CollectionKeys.OUTPUTS))
for current_coll in self.collection_manager.get_collections().values():
if current_coll.name in [
CollectionKeys.WEIGHTS, CollectionKeys.BIASES
]:
# don't match regex for these as these are added specially above
# we also don't want users to make mistakes configuring these collections
continue
if match_inc(ts_name, current_coll.include_regex):
# In TF 2.x eager mode, we can't put tensors in a set/dictionary as tensor.__hash__()
# is no longer available. tensor.experimental_ref() returns a hashable reference
# object to this Tensor.
if is_tf_version_2x() and tf.executing_eagerly():
# tensor.experimental_ref is an experimental API
# and can be changed or removed.
# Ref: https://www.tensorflow.org/api_docs/python/tf/Tensor#experimental_ref
tensor = tensor.experimental_ref()
if not current_coll.has_tensor(tensor):
# tensor will be added to this coll below
colls_with_tensor.add(current_coll)
# don't recommend adding tensors externally as
# they will have different internal name
# but regardless, in such case we only use that tensor name to save data
# instead of the keras-style-internal-names
return colls_with_tensor
def _save_tensor_to_file(self, tensor_name, tensor_value, collections):
if isinstance(collections, set) is False:
collections = {collections}
# Since this function modifies the set, there is a possibility
# of bugs if calling functions attempt to re-use the set passed
# to this function
collections_to_write = collections.copy()
collections_to_save = self._get_collections_to_save_for_step()
for c in collections_to_save:
if match_inc(tensor_name, c.include_regex):
collections_to_write.add(c)
self._initialize_writers(only_initialize_if_missing=True)
tensor_refs = []
if isinstance(tensor_value, values.PerReplica):
for t in tensor_value._values:
tensor_ref = TensorRef.from_non_graph_var(tensor_name)
tensor_refs.append((tensor_ref, t))
else:
tensor_ref = TensorRef.from_non_graph_var(tensor_name)
tensor_refs.append((tensor_ref, tensor_value))
for tensor_ref, t in tensor_refs:
for collection in collections_to_write:
if isinstance(collection, str):
collection = self.get_collection(collection)
collection.set_tensor_ref(tensor_ref)
self._save_for_tensor(tensor_name, t, check_before_write=True)
def _add_tensor_to_matching_collections(self, tensor):
"""
Finds which collections to add this tensor to, and adds tensor to them
"""
colls_with_tensor = set()
for coll in sorted(self._get_all_collections_to_save(),
key=lambda x: x.name):
variable_collections_with_tensor, processed = self._process_tensor_from_variable_read_op(
tensor)
if processed:
colls_with_tensor.update(variable_collections_with_tensor)
# processed=True means this tensor was either a variable read tensor,
# or a tensor with same name as variable
# former will be added to collections such as weights, biases, opt_variables
# latter will be skipped as they refer to the same tensor
else:
# some collections are added automatically, don't match regex for these
if coll.name not in [
CollectionKeys.WEIGHTS,
CollectionKeys.BIASES,
CollectionKeys.OPTIMIZER_VARIABLES,
CollectionKeys.TENSORFLOW_SUMMARIES,
] and match_inc(tensor.name, coll.include_regex):
coll.add(tensor)
if coll.has_tensor(tensor.name):
# it must have been added when collection was added to
# from user(custom_coll)/library(losses, weights, grads)
tensor_ref = coll.get_tensor(tensor.name)
tensor_ref.tf_obj = tensor
colls_with_tensor.add(coll)
# create entry in hook's tensor_to_collections map for this tensor
self._create_tensors_for_matching_collections(tensor,
colls_with_tensor)
def _prepare_tensors_available_post_step(self):
# for gradients, optimizer_variables
custom_collections, _ = self._get_custom_and_default_collections()
for coll in [
self.get_collection(name=CollectionKeys.OPTIMIZER_VARIABLES),
self.get_collection(name=CollectionKeys.GRADIENTS),
self.get_collection(name=CollectionKeys.OUTPUTS),
self.get_collection(name=CollectionKeys.INPUTS),
]:
collection_values = coll.get_tensors()
for tensor_ref in collection_values:
if tensor_ref.name not in self.tensor_to_collections:
self.tensor_to_collections[tensor_ref.name] = {coll}
elif coll not in self.tensor_to_collections[tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(coll)
# Add tensor to custom collections
for custom_coll in custom_collections:
if (
match_inc(tensor_ref.name, custom_coll.include_regex)
and tensor_ref.tf_obj is not None
):
custom_coll.add_for_mode(tensor_ref.tf_obj, self.mode)
if custom_coll not in self.tensor_to_collections[tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(custom_coll)
def _tensors_matching_regex(self, regex_list) -> set:
matched_tensornames = set()
if not isinstance(regex_list, list):
regex_list = [regex_list]
regex_list = flatten(regex_list)
for tensorname in self._tensors.keys():
if match_inc(tensorname, regex_list):
matched_tensornames.add(tensorname)
return matched_tensornames
def _add_weights_and_biases(self):
wts = tf.trainable_variables()
# variable such as <tf.Var w1:0>
for w in wts:
if match_inc(w.name, self.collection_manager.get(CollectionKeys.BIASES).include_regex):
self.collection_manager.get(CollectionKeys.BIASES).add(w)
else:
# adds a tensor_ref with name `w1/read:0` and export_name `w1:0`
self.collection_manager.get(CollectionKeys.WEIGHTS).add(w)
def _get_collections_with_tensor(self, tensor_name) -> Set["Collection"]:
self._assert_prep()
# for tf this will be prepopulated in check_and_add_tensor
if tensor_name not in self.tensor_to_collections:
# for mxnet it is computed and then cached
matched_colls = set()
for coll in self._collections_to_save:
if tensor_name in coll.tensor_names:
# if being matched as reduction,
# it must be in reduction_tensor_name, not with regex
matched_colls.add(coll)
elif match_inc(tensor_name, coll.include_regex):
coll.add_tensor_name(tensor_name)
matched_colls.add(coll)
self.tensor_to_collections[tensor_name] = matched_colls
return self.tensor_to_collections[tensor_name]
def should_save_tensor_or_collection(self, tensor_name: str,
collection_name: str) -> bool:
if self.prepared_collections is False:
# always return false if an attempt to save a
# tensor is made before the collections are prepared.
# this can happen if the fn is called before callbacks are init.
self.logger.warning(
"Tensors cannot be saved with smdebug before callbacks are initialized."
)
return False
if collection_name == "gradients":
layer_name = tensor_name.split(":")[0]
tensor_name = "gradients/" + layer_name + "Grad"
if self._is_collection_being_saved_for_step(collection_name):
c = self.collection_manager.get(collection_name)
return match_inc(tensor_name,
c.include_regex) or c.include_regex == []
return self._is_tensor_saved_for_step(tensor_name)
def _prepare_non_layer_tensors(self):
# for gradients, optimizer_variables
custom_collections, _ = self._get_custom_and_default_collections()
for coll in [
self.get_collection(name=CollectionKeys.OPTIMIZER_VARIABLES),
self.get_collection(name=CollectionKeys.GRADIENTS),
]:
for tensor_ref in coll.get_tensors():
if tensor_ref.name not in self.tensor_to_collections:
self.tensor_to_collections[tensor_ref.name] = {coll}
elif coll not in self.tensor_to_collections[tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(coll)
# Add tensor to custom collections
for custom_coll in custom_collections:
if match_inc(tensor_ref.name, custom_coll.include_regex):
custom_coll.add_for_mode(tensor_ref.tf_obj, self.mode)
if custom_coll not in self.tensor_to_collections[tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(custom_coll)
def _prepare_non_layer_tensors(self):
# for gradients, optimizer_variables
custom_collections, default_tf_collection = self._get_custom_and_default_collections(
)
for default_coll in default_tf_collection:
for tensor_ref in default_coll.get_tensors():
if tensor_ref.name not in self.tensor_to_collections:
self.tensor_to_collections[tensor_ref.name] = {
default_coll
}
elif default_coll not in self.tensor_to_collections[
tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(
default_coll)
# Add tensor to custom collections
for custom_coll in custom_collections:
if match_inc(tensor_ref.name, custom_coll.include_regex):
custom_coll.add_for_mode(tensor_ref.tf_obj, self.mode)
if custom_coll not in self.tensor_to_collections[
tensor_ref.name]:
self.tensor_to_collections[tensor_ref.name].add(
custom_coll)
def _get_dataloader_profiler_data_by_time(self,
start_time_us,
end_time_us,
cache_metrics=False,
selected_framework_metrics=[]):
"""
Get metrics data within a time interval.
:param start_time_us: Start of the interval in microseconds
:param end_time_us: End of the interval in microseconds
:param cache_metrics: If True, collect and return all metrics requested so far, else,
:param framework_metrics_list: list of framework metrics. If not empty, function will only return framework events that are part of this list.
:return: Framework metrics DataFrame
"""
# get framework metrics
framework_metrics = []
# only fetch a subset of data to avoid out of memory issues
if end_time_us - start_time_us > self.interval:
current_time_us = start_time_us + self.interval
else:
current_time_us = end_time_us
while start_time_us < end_time_us:
# get all framework metrics from last to current timestamp
self.framework_metrics_reader.refresh_event_file_list()
events = self.framework_metrics_reader.get_events(
start_time_us, current_time_us)
# append new events to existing list
for event in events:
if len(selected_framework_metrics) > 0 and not (
match_inc(event.event_name, selected_framework_metrics)
or match_inc(event.event_phase,
selected_framework_metrics)):
continue
if event.event_args is not None and "step_num" in event.event_args:
step = int(event.event_args["step_num"])
else:
step = -1
if event.event_args is not None and "layer_name" in event.event_args:
name = event.event_args["layer_name"]
elif event.event_args is not None and "name" in event.event_args:
name = event.event_args["name"]
else:
name = event.event_name
if event.event_args is not None and "worker_id" in event.event_args:
worker_id = event.event_args["worker_id"]
else:
worker_id = -1
if event.event_args is not None and "num_workers" in event.event_args:
num_workers = event.event_args["num_workers"]
else:
num_workers = -1
if event.event_args is not None and "pin_memory" in event.event_args:
pin_memory = "True" if event.event_args[
"pin_memory"] is True else "False"
else:
pin_memory = "NA"
framework_metrics.append([
us_since_epoch_to_human_readable_time(event.start_time),
us_since_epoch_to_human_readable_time(event.end_time),
event.start_time,
event.end_time,
event.duration,
event.pid,
name,
step,
worker_id,
num_workers,
pin_memory,
event.event_phase,
event.node_id,
])
# read the next chunk of data
start_time_us = current_time_us
if current_time_us + self.interval < end_time_us:
current_time_us = current_time_us + self.interval
else:
current_time_us = end_time_us
if cache_metrics is True:
self.framework_metrics.extend(framework_metrics)
framework_metrics = self.framework_metrics
# create data frame for framework metrics
framework_metrics_df = pd.DataFrame(
framework_metrics,
columns=[
"start_time",
"end_time",
"start_time_us",
"end_time_us",
"duration_us",
"pid",
"framework_metric",
"step",
"worker_id",
"num_workers",
"pin_memory",
"process",
"node_id",
],
)
framework_metrics_df["start_time_us"] = (
framework_metrics_df["start_time_us"] - self.start_time)
framework_metrics_df["end_time_us"] = framework_metrics_df[
"end_time_us"] - self.start_time
return framework_metrics_df[framework_metrics_df.duplicated() == False]
def _is_tensor_saved_for_step(self, tensor_name):
collections_to_save = self._get_collections_to_save_for_step()
for c in collections_to_save:
if match_inc(tensor_name, c.include_regex):
return True
return False
