Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/optimizers/utils.py

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2023-06-19 00:49:18 +02:00
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Optimizer utilities."""
import tensorflow.compat.v2 as tf
# isort: off
from tensorflow.python.platform import tf_logging as logging
def all_reduce_sum_gradients(grads_and_vars):
"""Returns all-reduced gradients aggregated via summation.
Args:
grads_and_vars: List of (gradient, variable) pairs.
Returns:
List of (gradient, variable) pairs where gradients have been all-reduced.
"""
grads_and_vars = list(grads_and_vars)
filtered_grads_and_vars = filter_empty_gradients(grads_and_vars)
if filtered_grads_and_vars:
if tf.__internal__.distribute.strategy_supports_no_merge_call():
grads = [pair[0] for pair in filtered_grads_and_vars]
reduced = tf.distribute.get_replica_context().all_reduce(
tf.distribute.ReduceOp.SUM, grads
)
else:
# TODO(b/183257003): Remove this branch
reduced = tf.distribute.get_replica_context().merge_call(
_all_reduce_sum_fn, args=(filtered_grads_and_vars,)
)
else:
reduced = []
# Copy 'reduced' but add None gradients back in
reduced_with_nones = []
reduced_pos = 0
for g, v in grads_and_vars:
if g is None:
reduced_with_nones.append((None, v))
else:
reduced_with_nones.append((reduced[reduced_pos], v))
reduced_pos += 1
assert reduced_pos == len(reduced), "Failed to add all gradients"
return reduced_with_nones
def filter_empty_gradients(grads_and_vars):
"""Filter out `(grad, var)` pairs that have a gradient equal to `None`."""
grads_and_vars = tuple(grads_and_vars)
if not grads_and_vars:
return grads_and_vars
filtered = []
vars_with_empty_grads = []
for grad, var in grads_and_vars:
if grad is None:
vars_with_empty_grads.append(var)
else:
filtered.append((grad, var))
filtered = tuple(filtered)
if not filtered:
variable = ([v.name for _, v in grads_and_vars],)
raise ValueError(
f"No gradients provided for any variable: {variable}. "
f"Provided `grads_and_vars` is {grads_and_vars}."
)
if vars_with_empty_grads:
logging.warning(
"Gradients do not exist for variables %s when minimizing the "
"loss. If you're using `model.compile()`, did you forget to "
"provide a `loss` argument?",
([v.name for v in vars_with_empty_grads]),
)
return filtered
def make_gradient_clipnorm_fn(clipnorm):
"""Creates a gradient transformation function for clipping by norm."""
if clipnorm is None:
return lambda grads_and_vars: grads_and_vars
def gradient_clipnorm_fn(grads_and_vars):
if isinstance(
tf.distribute.get_strategy(),
(
tf.distribute.experimental.CentralStorageStrategy,
tf.compat.v1.distribute.experimental.CentralStorageStrategy,
),
):
raise ValueError(
"`clipnorm` is not supported with `CenteralStorageStrategy`. "
f"The strategy used is {tf.distribute.get_strategy()}."
)
clipped_grads_and_vars = [
(tf.clip_by_norm(g, clipnorm), v) for g, v in grads_and_vars
]
return clipped_grads_and_vars
return gradient_clipnorm_fn
def make_global_gradient_clipnorm_fn(clipnorm):
"""Creates a gradient transformation function for clipping by norm."""
if clipnorm is None:
return lambda grads_and_vars: grads_and_vars
def gradient_clipnorm_fn(grads_and_vars):
if isinstance(
tf.distribute.get_strategy(),
(
tf.distribute.experimental.CentralStorageStrategy,
tf.compat.v1.distribute.experimental.CentralStorageStrategy,
),
):
raise ValueError(
"`global_clipnorm` is not supported with "
"`CenteralStorageStrategy`. "
f"The strategy used is {tf.distribute.get_strategy()}."
)
grads, variables = zip(*grads_and_vars)
clipped_grads, _ = tf.clip_by_global_norm(grads, clipnorm)
clipped_grads_and_vars = list(zip(clipped_grads, variables))
return clipped_grads_and_vars
return gradient_clipnorm_fn
def make_gradient_clipvalue_fn(clipvalue):
"""Creates a gradient transformation function for clipping by value."""
if clipvalue is None:
return lambda grads_and_vars: grads_and_vars
def gradient_clipvalue_fn(grads_and_vars):
if isinstance(
tf.distribute.get_strategy(),
(
tf.distribute.experimental.CentralStorageStrategy,
tf.compat.v1.distribute.experimental.CentralStorageStrategy,
),
):
raise ValueError(
"`clipvalue` is not supported with `CenteralStorageStrategy`. "
f"The strategy used is {tf.distribute.get_strategy()}."
)
clipped_grads_and_vars = [
(tf.clip_by_value(g, -clipvalue, clipvalue), v)
for g, v in grads_and_vars
]
return clipped_grads_and_vars
return gradient_clipvalue_fn
def _all_reduce_sum_fn(distribution, grads_and_vars):
return distribution.extended.batch_reduce_to(
tf.distribute.ReduceOp.SUM, grads_and_vars
)