1586 lines
62 KiB
Python
1586 lines
62 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains the loss scaling optimizer class."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras import optimizers
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from keras.optimizers import optimizer
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from keras.optimizers import utils as optimizer_utils
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from keras.optimizers.legacy import optimizer_v2
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from keras.saving.legacy import serialization
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# isort: off
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from tensorflow.python.platform import tf_logging
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from tensorflow.python.util.tf_export import keras_export
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class _UnwrapPreventer:
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"""Wrapper that DistributionStrategy will not unwrap.
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Typically, DistributionStrategy will unwrap values when going from a cross-
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replica context to a replica context via `call_for_each_replica`. This class
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is a wrapper that DistributionStrategy will not unwrap, so it can be used to
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prevent it from unwrapping a value.
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TODO(reedwm): Find/implement a better way of preventing values from being
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unwrapped by DistributionStrategy
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"""
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__slots__ = ["value"]
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def __init__(self, value):
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self.value = value
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def _is_all_finite(grads):
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"""Returns a scalar boolean tensor indicating if all gradients are
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finite."""
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is_finite_per_grad = [
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tf.reduce_all(tf.math.is_finite(g)) for g in grads if g is not None
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]
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return tf.reduce_all(is_finite_per_grad)
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def _op_in_graph_mode(tensor):
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"""Returns the tensor's op in graph mode, or the tensor in eager mode.
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This is useful because sometimes an op is needed in graph mode instead of a
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tensor. In eager mode, there are no ops.
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Args:
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tensor: A tensor.
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Returns:
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The tensor's op in graph mode. The tensor in eager mode.
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"""
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if tf.executing_eagerly():
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return tensor
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return tensor.op
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def _assign_if_finite(var, value):
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"""Assigns a value to a variable if the value is finite."""
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return tf.cond(
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tf.math.is_finite(value),
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lambda: _op_in_graph_mode(var.assign(value)),
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tf.no_op,
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)
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def _maybe_warn_about_scaling(
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loss_has_been_scaled, gradients_have_been_unscaled
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):
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"""Warn if the loss or gradients hasn't been scaled or unscaled."""
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if loss_has_been_scaled and gradients_have_been_unscaled:
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return
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example_code = """
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with tf.GradientTape() as tape:
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loss = loss_fn()
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scaled_loss = opt.get_scaled_loss(loss)
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scaled_grads = tape.gradient(scaled_loss, vars)
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grads = opt.get_unscaled_gradients(scaled_grads)
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opt.apply_gradients([(grads, var)])"""
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if not loss_has_been_scaled and not gradients_have_been_unscaled:
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tf_logging.warning(
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"You forgot to call LossScaleOptimizer.get_scaled_loss() and "
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"LossScaleOptimizer.get_unscaled_gradients() before calling "
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"LossScaleOptimizer.apply_gradients(). This will likely result in "
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"worse model quality, so please call them in the correct places! "
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f"For example:{example_code}\nFor more information, see "
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"https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer" # noqa: E501
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)
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elif not loss_has_been_scaled:
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tf_logging.warning(
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"You forgot to call LossScaleOptimizer.get_scaled_loss() before "
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"calling LossScaleOptimizer.apply_gradients() (you did call "
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"get_unscaled_gradients() however). This will likely result in "
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"worse model quality, so please call get_scaled_loss() in the "
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f"correct place! For example:{example_code}\nFor more information, "
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"see "
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"https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer" # noqa: E501
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)
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elif not gradients_have_been_unscaled:
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tf_logging.warning(
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"You forgot to call LossScaleOptimizer.get_unscaled_gradients() "
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"before calling LossScaleOptimizer.apply_gradients() (you did call "
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"get_scaled_loss() however). This will likely result in worse "
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"model quality, so please call get_unscaled_gradients() in the "
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f"correct place! For example:{example_code}\nFor more information, "
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"see "
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"https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/LossScaleOptimizer" # noqa: E501
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)
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class _DynamicLossScaleState(tf.__internal__.tracking.Trackable):
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"""The state of a dynamic loss scale."""
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def __init__(self, initial_loss_scale, growth_steps, multiplier):
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"""Creates the dynamic loss scale."""
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super().__init__()
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self._initial_loss_scale = float(initial_loss_scale)
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self._growth_steps = int(growth_steps)
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self._multiplier = float(multiplier)
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self._weights = {}
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self._current_loss_scale = self._add_weight(
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name="current_loss_scale",
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dtype=tf.float32,
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initial_value=self._initial_loss_scale,
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)
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# The number of consecutive steps with finite gradients since the last
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# nonfinite gradient or change in loss scale. The name is 'good_steps'
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# for backwards compatibility with older checkpoints.
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self._counter = self._add_weight(
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name="good_steps", dtype=tf.int64, initial_value=0
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)
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def _add_weight(self, name, initial_value, dtype=None):
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"""Adds a weight to this loss scale.
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Args:
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name: Variable name.
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initial_value: The variable's initial value.
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dtype: The type of the variable.
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Returns:
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A variable.
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Raises:
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RuntimeError: If a weight with `name` has already been added.
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"""
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variable = tf.Variable(
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initial_value=initial_value,
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name=name,
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dtype=dtype,
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trainable=False,
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synchronization=tf.VariableSynchronization.AUTO,
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# Set aggregation to NONE, as loss scaling variables should never be
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# aggregated.
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aggregation=tf.VariableAggregation.NONE,
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)
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if tf.executing_eagerly():
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graph_key = None
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else:
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graph = tf.compat.v1.get_default_graph()
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graph_key = graph._graph_key
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key = (name, graph_key)
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self._weights[key] = variable
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self._handle_deferred_dependencies(name=name, trackable=variable)
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backend.track_variable(variable)
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return variable
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def _trackable_children(self, save_type="checkpoint", **kwargs):
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"""From Trackable. Gather graph-specific weights to save."""
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if tf.executing_eagerly():
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graph_key = None
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else:
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graph = tf.compat.v1.get_default_graph()
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graph_key = graph._graph_key
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weights = {}
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for (name, g), v in sorted(
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self._weights.items(), key=lambda i: i[0][0]
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):
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if g == graph_key:
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weights[name] = v
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weights.update(super()._trackable_children(save_type, **kwargs))
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return weights
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def _lookup_dependency(self, name):
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"""From Trackable. Find a weight in the current graph."""
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unconditional = super()._lookup_dependency(name)
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if unconditional is not None:
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return unconditional
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if tf.executing_eagerly():
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graph_key = None
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else:
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graph = tf.compat.v1.get_default_graph()
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graph_key = graph._graph_key
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return self._weights.get((name, graph_key), None)
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@property
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def initial_loss_scale(self):
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return self._initial_loss_scale
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@property
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def growth_steps(self):
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return self._growth_steps
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@property
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def multiplier(self):
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return self._multiplier
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@property
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def current_loss_scale(self):
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"""Returns the current loss scale as a float32 `tf.Variable`."""
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return self._current_loss_scale
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@property
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def counter(self):
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"""Returns the counter as a float32 `tf.Variable`."""
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return self._counter
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def __call__(self):
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"""Returns the current loss scale as a scalar `float32` tensor."""
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return tf.convert_to_tensor(self._current_loss_scale)
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def update(self, grads):
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"""Updates the value of the loss scale.
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Args:
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grads: A nested structure of unscaled gradients, each which is an
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all-reduced gradient of the loss with respect to a weight.
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Returns:
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update_op: In eager mode, None. In graph mode, an op to update the
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loss scale.
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should_apply_gradients: Either a bool or a scalar boolean tensor. If
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False, the caller should skip applying `grads` to the variables this
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step.
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"""
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grads = tf.nest.flatten(grads)
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if (
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tf.distribute.has_strategy()
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and tf.distribute.in_cross_replica_context()
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):
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distribution = tf.distribute.get_strategy()
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is_finite_per_replica = distribution.extended.call_for_each_replica(
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_is_all_finite, args=(grads,)
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)
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# Each replica computed the same `is_finite` value, since `grads` is
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# all-reduced across replicas. Arbitrarily take `is_finite` from the
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# first replica.
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is_finite = distribution.experimental_local_results(
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is_finite_per_replica
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)[0]
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else:
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is_finite = _is_all_finite(grads)
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def update_if_finite_grads():
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"""Update assuming the gradients are finite."""
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def incr_loss_scale():
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new_loss_scale = self.current_loss_scale * self.multiplier
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return tf.group(
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_assign_if_finite(self.current_loss_scale, new_loss_scale),
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self.counter.assign(0),
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)
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return tf.cond(
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self.counter + 1 >= self.growth_steps,
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incr_loss_scale,
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lambda: _op_in_graph_mode(self.counter.assign_add(1)),
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)
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def update_if_not_finite_grads():
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"""Update assuming the gradients are nonfinite."""
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new_loss_scale = tf.maximum(
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self.current_loss_scale / self.multiplier, 1
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)
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return tf.group(
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self.counter.assign(0),
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self.current_loss_scale.assign(new_loss_scale),
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)
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update_op = tf.cond(
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is_finite, update_if_finite_grads, update_if_not_finite_grads
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)
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should_apply_gradients = is_finite
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return update_op, should_apply_gradients
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# See LossScaleOptimizer docstring for why this is so big
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_DEFAULT_INITIAL_SCALE = 2**15
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_DEFAULT_GROWTH_STEPS = 2000
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# TODO(b/215389169): Delete this class after `OptimizerV2` is deprecated.
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class LossScaleOptimizerMetaclass(type):
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"""Metaclass that delegates LossScaleOptimizer instance creation.
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This metaclass causes a LossScaleOptimizer or LossScaleOptimizerV3 to be
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created when a BaseLossScaleOptimizer is constructed. As a result, when a
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user creates a loss scale optimizer with
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`tf.keras.mixed_precision.LossScaleOptimizer(opt)`, either a
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LossScaleOptimizer or LossScaleOptimizerV3 will be created, depending on the
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type of `opt`.
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"""
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def __call__(cls, inner_optimizer, *args, **kwargs):
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if cls is not BaseLossScaleOptimizer:
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return super(LossScaleOptimizerMetaclass, cls).__call__(
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inner_optimizer, *args, **kwargs
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)
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if isinstance(inner_optimizer, optimizer_v2.OptimizerV2):
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return LossScaleOptimizer(inner_optimizer, *args, **kwargs)
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elif isinstance(inner_optimizer, optimizer.Optimizer):
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return LossScaleOptimizerV3(inner_optimizer, *args, **kwargs)
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# Raise TypeError because inner_optimizer is not an optimizer
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msg = (
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'"inner_optimizer" must be an instance of '
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"`tf.keras.optimizers.Optimizer` or "
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"`tf.keras.optimizers.experimental.Optimizer`, but got: "
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f"{inner_optimizer}."
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)
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raise TypeError(msg)
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# TODO(b/215389169): Delete this class after `OptimizerV2` is deprecated.
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@keras_export("keras.mixed_precision.LossScaleOptimizer")
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class BaseLossScaleOptimizer(metaclass=LossScaleOptimizerMetaclass):
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"""An optimizer that applies loss scaling to prevent numeric underflow.
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Loss scaling is a technique to prevent numeric underflow in intermediate
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gradients when float16 is used. To prevent underflow, the loss is multiplied
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(or "scaled") by a certain factor called the "loss scale", which causes
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intermediate gradients to be scaled by the loss scale as well. The final
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gradients are divided (or "unscaled") by the loss scale to bring them back
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to their original value.
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`LossScaleOptimizer` wraps another optimizer and applies loss scaling to it.
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By default, the loss scale is dynamically updated over time so you do not
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have to choose the loss scale. The `minimize` method automatically scales
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the loss, unscales the gradients, and updates the loss scale so all you have
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to do is wrap your optimizer with a `LossScaleOptimizer` if you use
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`minimize`. For example:
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>>> opt = tf.keras.optimizers.experimental.SGD(0.25)
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>>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt)
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>>> var = tf.Variable(1.)
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>>> loss_fn = lambda: var ** 2
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>>> # 'minimize' applies loss scaling and updates the loss sale.
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>>> opt.minimize(loss_fn, var_list=[var])
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>>> var.numpy()
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0.5
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If a `tf.GradientTape` is used to compute gradients instead of `minimize`,
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you must scale the loss and gradients manually. This can be done with the
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`LossScaleOptimizer.get_scaled_loss` and
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`LossScaleOptimizer.get_unscaled_gradients` methods. For example:
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>>> with tf.GradientTape() as tape:
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... loss = loss_fn()
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... scaled_loss = opt.get_scaled_loss(loss)
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>>> scaled_grad = tape.gradient(scaled_loss, var)
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>>> (grad,) = opt.get_unscaled_gradients([scaled_grad])
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>>> opt.apply_gradients([(grad, var)]) # Loss scale is updated here
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>>> var.numpy()
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0.25
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Warning: If you forget to call `get_scaled_loss` or `get_unscaled_gradients`
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(or both) when using a `tf.GradientTape`, the model will likely converge to
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a worse quality. Please make sure you call each function exactly once.
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When mixed precision with float16 is used, there is typically no risk of
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underflow affecting model quality if loss scaling is properly used. See
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[the mixed precision guide](
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https://www.tensorflow.org/guide/keras/mixed_precision) for more information
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on how to use mixed precision.
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Args:
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inner_optimizer: The `tf.keras.optimizers.Optimizer` or
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`tf.keras.optimizers.experimental.Optimizer` instance to wrap.
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dynamic: Bool indicating whether dynamic loss scaling is used. Defaults to
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True. If True, the loss scale will be dynamically updated over time
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using an algorithm that keeps the loss scale at approximately its
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optimal value. If False, a single fixed loss scale is used and
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`initial_scale` must be specified, which is used as the loss scale.
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Recommended to keep as True, as choosing a fixed loss scale can be
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tricky. Currently, there is a small performance overhead to dynamic loss
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scaling compared to fixed loss scaling.
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||
|
initial_scale: The initial loss scale. If `dynamic` is True, this defaults
|
||
|
to `2 ** 15`. If `dynamic` is False, this must be specified and acts as
|
||
|
the sole loss scale, as the loss scale does not change over time. When
|
||
|
dynamic loss scaling is used, is better for this to be a very high
|
||
|
number, because a loss scale that is too high gets lowered far more
|
||
|
quickly than a loss scale that is too low gets raised.
|
||
|
dynamic_growth_steps: With dynamic loss scaling, every
|
||
|
`dynamic_growth_steps` steps with finite gradients, the loss scale is
|
||
|
doubled. Defaults to 2000. If a nonfinite gradient is encountered, the
|
||
|
count is reset back to zero, gradients are skipped that step, and the
|
||
|
loss scale is halved. The count can be queried with
|
||
|
`LossScaleOptimizer.dynamic_counter`. This argument can only be
|
||
|
specified if `dynamic` is True.
|
||
|
|
||
|
`LossScaleOptimizer` will occasionally skip applying gradients to the
|
||
|
variables, in which case the trainable variables will not change that step.
|
||
|
This is done because the dynamic loss scale will sometimes be raised too
|
||
|
high, causing overflow in the gradients. Typically, the first 2 to 15 steps
|
||
|
of the model are skipped as the initial loss scale is very high, but
|
||
|
afterwards steps will only be skipped on average 0.05% of the time (the
|
||
|
fraction of steps skipped is `1 / dynamic_growth_steps`).
|
||
|
|
||
|
`LossScaleOptimizer` delegates all public `Optimizer` methods to the inner
|
||
|
optimizer. Additionally, in methods `minimize` and `get_gradients`, it
|
||
|
scales the loss and unscales the gradients. In methods `minimize` and
|
||
|
`apply_gradients`, it additionally updates the loss scale and skips applying
|
||
|
gradients if any gradient has a nonfinite value.
|
||
|
|
||
|
### Hyperparameters
|
||
|
|
||
|
If wrapping a `tf.keras.optimizers.Optimizer`, hyperparameters can be
|
||
|
accessed and set on the LossScaleOptimizer, which will be delegated to the
|
||
|
wrapped optimizer.
|
||
|
|
||
|
>>> opt = tf.keras.optimizers.legacy.Adam(beta_1=0.8, epsilon=1e-5)
|
||
|
>>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt)
|
||
|
>>> opt.beta_1 # Equivalent to `opt.inner_optimizer.beta_1`
|
||
|
0.8
|
||
|
>>> opt.beta_1 = 0.7 # Equivalent to `opt.inner_optimizer.beta_1 = 0.7`
|
||
|
>>> opt.beta_1
|
||
|
0.7
|
||
|
>>> opt.inner_optimizer.beta_1
|
||
|
0.7
|
||
|
|
||
|
However, accessing or setting non-hyperparameters is not delegated to the
|
||
|
LossScaleOptimizer. In an Adam optimizer, `beta_1` is a hyperparameter but
|
||
|
`epsilon` is not, as the Adam optimizer only calls `Optimizer._set_hyper` on
|
||
|
`beta_1`.
|
||
|
|
||
|
>>> opt.inner_optimizer.epsilon
|
||
|
1e-5
|
||
|
>>> opt.epsilon
|
||
|
Traceback (most recent call last):
|
||
|
...
|
||
|
AttributeError: 'LossScaleOptimizer' object has no attribute 'epsilon'
|
||
|
>>> opt.epsilon = 1e-4 # This does NOT set epsilon on `opt.inner_optimizer`
|
||
|
>>> opt.inner_optimizer.epsilon
|
||
|
>>> 1e-5
|
||
|
|
||
|
In the above example, despite epsilon being set on the LossScaleOptimizer,
|
||
|
the old epsilon value will still be used when training as epsilon was not
|
||
|
set on the inner optimizer.
|
||
|
"""
|
||
|
|
||
|
@property
|
||
|
def dynamic(self):
|
||
|
"""Bool indicating whether dynamic loss scaling is used."""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
@property
|
||
|
def loss_scale(self):
|
||
|
"""The current loss scale as a float32 scalar tensor."""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
@property
|
||
|
def dynamic_counter(self):
|
||
|
"""The number of steps since the loss scale was last increased or
|
||
|
decreased.
|
||
|
|
||
|
This is None if `LossScaleOptimizer.dynamic` is False.
|
||
|
|
||
|
The counter is incremented every step. Once it reaches
|
||
|
`LossScaleOptimizer.dynamic_growth_steps`, the loss scale will be
|
||
|
doubled and the counter will be reset back to zero. If nonfinite
|
||
|
gradients are encountered, the loss scale will be halved and the counter
|
||
|
will be reset back to zero.
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
@property
|
||
|
def initial_scale(self):
|
||
|
"""The initial loss scale.
|
||
|
|
||
|
If `LossScaleOptimizer.dynamic` is False, this is the same number as
|
||
|
`LossScaleOptimizer.loss_scale`, as the loss scale never changes.
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
@property
|
||
|
def dynamic_growth_steps(self):
|
||
|
"""The number of steps it takes to increase the loss scale.
|
||
|
|
||
|
This is None if `LossScaleOptimizer.dynamic` is False.
|
||
|
|
||
|
Every `dynamic_growth_steps` consecutive steps with finite gradients,
|
||
|
the loss scale is increased.
|
||
|
"""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
@property
|
||
|
def inner_optimizer(self):
|
||
|
"""The optimizer that this LossScaleOptimizer is wrapping."""
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def get_scaled_loss(self, loss):
|
||
|
"""Scales the loss by the loss scale.
|
||
|
|
||
|
This method is only needed if you compute gradients manually, e.g. with
|
||
|
`tf.GradientTape`. In that case, call this method to scale the loss
|
||
|
before passing the loss to `tf.GradientTape`. If you use
|
||
|
`LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`,
|
||
|
loss scaling is automatically applied and this method is unneeded.
|
||
|
|
||
|
If this method is called, `get_unscaled_gradients` should also be
|
||
|
called. See the `tf.keras.mixed_precision.LossScaleOptimizer` doc for
|
||
|
an example.
|
||
|
|
||
|
Args:
|
||
|
loss: The loss, which will be multiplied by the loss scale. Can either
|
||
|
be a tensor or a callable returning a tensor.
|
||
|
|
||
|
Returns:
|
||
|
`loss` multiplied by `LossScaleOptimizer.loss_scale`.
|
||
|
"""
|
||
|
# Calls to this function would be delegated to `get_scaled_loss`
|
||
|
# of either `LossScaleOptimizer` or `LossScaleOptimizerV3`, depending on
|
||
|
# the type of `inner_optimizer`.
|
||
|
raise NotImplementedError
|
||
|
|
||
|
def get_unscaled_gradients(self, grads):
|
||
|
"""Unscales the gradients by the loss scale.
|
||
|
|
||
|
This method is only needed if you compute gradients manually, e.g. with
|
||
|
`tf.GradientTape`. In that case, call this method to unscale the
|
||
|
gradients after computing them with `tf.GradientTape`. If you use
|
||
|
`LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`,
|
||
|
loss scaling is automatically applied and this method is unneeded.
|
||
|
|
||
|
If this method is called, `get_scaled_loss` should also be called. See
|
||
|
the `tf.keras.mixed_precision.LossScaleOptimizer` doc for an
|
||
|
example.
|
||
|
|
||
|
Args:
|
||
|
grads: A list of tensors, each which will be divided by the loss
|
||
|
scale. Can have None values, which are ignored.
|
||
|
|
||
|
Returns:
|
||
|
A new list the same size as `grads`, where every non-None value in
|
||
|
`grads` is divided by `LossScaleOptimizer.loss_scale`.
|
||
|
"""
|
||
|
# Calls to this function would be delegated to `get_unscaled_gradients`
|
||
|
# of either `LossScaleOptimizer` or `LossScaleOptimizerV3`, depending on
|
||
|
# the type of `inner_optimizer`.
|
||
|
raise NotImplementedError
|
||
|
|
||
|
|
||
|
class LossScaleOptimizer(
|
||
|
tf.__internal__.tracking.DelegatingTrackableMixin,
|
||
|
optimizer_v2.OptimizerV2,
|
||
|
BaseLossScaleOptimizer,
|
||
|
):
|
||
|
"""An optimizer that applies loss scaling to prevent numeric underflow."""
|
||
|
|
||
|
_HAS_AGGREGATE_GRAD = True
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
inner_optimizer,
|
||
|
dynamic=True,
|
||
|
initial_scale=None,
|
||
|
dynamic_growth_steps=None,
|
||
|
):
|
||
|
if not isinstance(inner_optimizer, optimizer_v2.OptimizerV2):
|
||
|
if isinstance(inner_optimizer, optimizer.Optimizer):
|
||
|
# Give better error message if the new experimental optimizer is
|
||
|
# passed.
|
||
|
raise TypeError(
|
||
|
"You passed an instance of the new experimental "
|
||
|
"optimizer, `optimizer.Optimizer`, "
|
||
|
"to LossScaleOptimizer, but "
|
||
|
"only the classic optimizers subclassing from "
|
||
|
"`tf.keras.optimizers.Optimizer` can be passed. Please "
|
||
|
"use `loss_scale_optimizer.LossScaleOptimizerV3` "
|
||
|
"instead of "
|
||
|
"`tf.keras.mixed_precision.LossScaleOptimizer`, "
|
||
|
"as the former supports wrapping "
|
||
|
"instances of the new experimental optimizer. "
|
||
|
f"Got optimizer: {inner_optimizer}"
|
||
|
)
|
||
|
msg = (
|
||
|
'"inner_optimizer" must be an instance of '
|
||
|
"`tf.keras.optimizers.Optimizer`, but got: %s. "
|
||
|
% inner_optimizer
|
||
|
)
|
||
|
raise TypeError(msg)
|
||
|
if not isinstance(dynamic, bool):
|
||
|
# Catch errors if a user incorrectly passes a string or float to the
|
||
|
# second argument argument, as this was commonly done for the
|
||
|
# now-removed LossScaleOptimizerV1.
|
||
|
raise TypeError(
|
||
|
'"dynamic" argument to LossScaleOptimizer.__init__ must '
|
||
|
"be a bool, but got: %r" % (dynamic,)
|
||
|
)
|
||
|
if isinstance(inner_optimizer, LossScaleOptimizer):
|
||
|
raise TypeError(
|
||
|
"LossScaleOptimizer cannot wrap another "
|
||
|
"LossScaleOptimizer, but got: %s" % (inner_optimizer,)
|
||
|
)
|
||
|
_raise_if_strategy_unsupported()
|
||
|
if getattr(
|
||
|
inner_optimizer, "_is_wrapped_by_loss_scale_optimizer", False
|
||
|
):
|
||
|
# TODO(reedwm): Maybe support this. The difficulty is that LSO has
|
||
|
# the same checkpoint format as the inner optimizer, so multiple
|
||
|
# LSOs wrapping the same optimizer causes the checkpointing logic to
|
||
|
# become confused.
|
||
|
raise ValueError(
|
||
|
'"inner_optimizer" is already wrapped by a '
|
||
|
"LossScaleOptimizer. An optimizer can only be wrapped "
|
||
|
"by a single LossScaleOptimizer"
|
||
|
)
|
||
|
self._optimizer = inner_optimizer
|
||
|
self._optimizer._is_wrapped_by_loss_scale_optimizer = True
|
||
|
|
||
|
# We don't call super().__init__, since we do not want to call
|
||
|
# OptimizerV2's constructor.
|
||
|
tf.__internal__.tracking.DelegatingTrackableMixin.__init__(
|
||
|
self, self._optimizer
|
||
|
)
|
||
|
|
||
|
if dynamic:
|
||
|
if initial_scale is None:
|
||
|
initial_scale = _DEFAULT_INITIAL_SCALE
|
||
|
if dynamic_growth_steps is None:
|
||
|
dynamic_growth_steps = _DEFAULT_GROWTH_STEPS
|
||
|
self._loss_scale = _DynamicLossScaleState(
|
||
|
initial_scale, dynamic_growth_steps, multiplier=2
|
||
|
)
|
||
|
self._track_trackable(self._loss_scale, "loss_scale")
|
||
|
else:
|
||
|
if initial_scale is None:
|
||
|
raise ValueError(
|
||
|
'"initial_scale" must be specified if "dynamic" is False'
|
||
|
)
|
||
|
self._loss_scale = float(initial_scale)
|
||
|
if dynamic_growth_steps is not None:
|
||
|
raise ValueError(
|
||
|
'"dynamic_growth_steps" must be None if "dynamic" '
|
||
|
"is False, but got: %s" % (dynamic_growth_steps,)
|
||
|
)
|
||
|
|
||
|
# Used to track whether get_scaled_loss() and get_unscaled_gradients()
|
||
|
# have been called
|
||
|
self._loss_has_been_scaled = False
|
||
|
self._gradients_have_been_unscaled = False
|
||
|
|
||
|
# To support restoring TensorFlow 2.2 checkpoints.
|
||
|
self._track_trackable(
|
||
|
FakeOptimizerForRestoration(self._optimizer), "base_optimizer"
|
||
|
)
|
||
|
|
||
|
@property
|
||
|
def dynamic(self):
|
||
|
return isinstance(self._loss_scale, _DynamicLossScaleState)
|
||
|
|
||
|
@property
|
||
|
def loss_scale(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return tf.convert_to_tensor(self._loss_scale.current_loss_scale)
|
||
|
else:
|
||
|
return tf.convert_to_tensor(self._loss_scale)
|
||
|
|
||
|
@property
|
||
|
def dynamic_counter(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.counter
|
||
|
else:
|
||
|
return None
|
||
|
|
||
|
@property
|
||
|
def initial_scale(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.initial_loss_scale
|
||
|
else:
|
||
|
return self._loss_scale
|
||
|
|
||
|
@property
|
||
|
def dynamic_growth_steps(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.growth_steps
|
||
|
else:
|
||
|
return None
|
||
|
|
||
|
@property
|
||
|
def inner_optimizer(self):
|
||
|
return self._optimizer
|
||
|
|
||
|
def get_scaled_loss(self, loss):
|
||
|
self._loss_has_been_scaled = True
|
||
|
if callable(loss):
|
||
|
|
||
|
def new_loss():
|
||
|
loss_val = loss()
|
||
|
return loss_val * tf.cast(self.loss_scale, loss_val.dtype)
|
||
|
|
||
|
return new_loss
|
||
|
else:
|
||
|
return loss * tf.cast(self.loss_scale, loss.dtype)
|
||
|
|
||
|
def get_unscaled_gradients(self, grads):
|
||
|
self._gradients_have_been_unscaled = True
|
||
|
loss_scale_reciprocal = 1.0 / self.loss_scale
|
||
|
return [
|
||
|
_multiply_gradient(g, loss_scale_reciprocal)
|
||
|
if g is not None
|
||
|
else None
|
||
|
for g in grads
|
||
|
]
|
||
|
|
||
|
def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None):
|
||
|
tape = tf.GradientTape() if tape is None else tape
|
||
|
with tape:
|
||
|
loss = self.get_scaled_loss(loss)
|
||
|
grads_and_vars = self._optimizer._compute_gradients(
|
||
|
loss, var_list, grad_loss, tape=tape
|
||
|
)
|
||
|
grads = [g for g, _ in grads_and_vars]
|
||
|
weights = [v for _, v in grads_and_vars]
|
||
|
unscaled_grads = self.get_unscaled_gradients(grads)
|
||
|
return list(zip(unscaled_grads, weights))
|
||
|
|
||
|
def get_gradients(self, loss, params):
|
||
|
loss = self.get_scaled_loss(loss)
|
||
|
grads = self._optimizer.get_gradients(loss, params)
|
||
|
return self.get_unscaled_gradients(grads)
|
||
|
|
||
|
def _create_all_weights(self, var_list):
|
||
|
self._optimizer._create_all_weights(var_list)
|
||
|
|
||
|
def apply_gradients(
|
||
|
self, grads_and_vars, name=None, experimental_aggregate_gradients=True
|
||
|
):
|
||
|
if tf.distribute.in_cross_replica_context():
|
||
|
raise ValueError(
|
||
|
"apply_gradients() must be called in a replica context."
|
||
|
)
|
||
|
# We check for the strategy here despite already checking in the
|
||
|
# constructor as frequently the optimizer is created outside the
|
||
|
# strategy's scope.
|
||
|
_raise_if_strategy_unsupported()
|
||
|
_maybe_warn_about_scaling(
|
||
|
self._loss_has_been_scaled, self._gradients_have_been_unscaled
|
||
|
)
|
||
|
|
||
|
grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
|
||
|
if experimental_aggregate_gradients:
|
||
|
# We must aggregate the gradients here instead of in
|
||
|
# self.optimizer.apply_gradients, so that any NaN or Inf gradients
|
||
|
# are propagated to each replica. If any replica has a NaN or Inf
|
||
|
# gradient, they must all have a NaN or Inf gradient so that they
|
||
|
# all skip the step.
|
||
|
grads_and_vars = self._optimizer._transform_unaggregated_gradients(
|
||
|
grads_and_vars
|
||
|
)
|
||
|
grads_and_vars = self._optimizer._aggregate_gradients(
|
||
|
grads_and_vars
|
||
|
)
|
||
|
|
||
|
grads_and_vars = tuple(grads_and_vars)
|
||
|
grads = [g for g, _ in grads_and_vars]
|
||
|
# We do not want DistributionStrategy to unwrap any MirroredVariables in
|
||
|
# grads_and_vars, because even in a replica context, the wrapped
|
||
|
# optimizer expects mirrored variables. So we wrap the variables with an
|
||
|
# _UnwrapPreventer, preventing DistributionStrategy from unwrapping the
|
||
|
# MirroredVariables.
|
||
|
wrapped_vars = _UnwrapPreventer([v for _, v in grads_and_vars])
|
||
|
|
||
|
def do_not_apply_fn():
|
||
|
# Normally self._optimizer.iterations is incremented in
|
||
|
# self._optimizer.apply_gradients(). Since that is not called in
|
||
|
# this branch, we increment it here instead.
|
||
|
return self._optimizer.iterations.assign_add(1, read_value=False)
|
||
|
|
||
|
def _if_should_apply_grads(grads):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.update(grads)
|
||
|
else:
|
||
|
return (tf.no_op(), True)
|
||
|
|
||
|
if tf.__internal__.distribute.strategy_supports_no_merge_call():
|
||
|
loss_scale_update_op, should_apply_grads = _if_should_apply_grads(
|
||
|
grads
|
||
|
)
|
||
|
|
||
|
def apply_fn():
|
||
|
return self._apply_gradients(grads, wrapped_vars, name)
|
||
|
|
||
|
maybe_apply_op = tf.__internal__.smart_cond.smart_cond(
|
||
|
should_apply_grads, apply_fn, do_not_apply_fn
|
||
|
)
|
||
|
return tf.group(maybe_apply_op, loss_scale_update_op)
|
||
|
|
||
|
else:
|
||
|
|
||
|
def _apply_gradients_cross_replica(
|
||
|
distribution, grads, wrapped_vars, name
|
||
|
):
|
||
|
(
|
||
|
loss_scale_update_op,
|
||
|
should_apply_grads,
|
||
|
) = _if_should_apply_grads(grads)
|
||
|
|
||
|
def apply_fn():
|
||
|
return distribution.extended.call_for_each_replica(
|
||
|
self._apply_gradients, args=(grads, wrapped_vars, name)
|
||
|
)
|
||
|
|
||
|
# Note: We must call this cond() in a cross-replica context.
|
||
|
# DistributionStrategy does not support having a cond in a
|
||
|
# replica context with a branch that calls `merge_call`, and
|
||
|
# self._optimizer.apply_gradients calls `merge_call`.
|
||
|
maybe_apply_op = tf.__internal__.smart_cond.smart_cond(
|
||
|
should_apply_grads, apply_fn, do_not_apply_fn
|
||
|
)
|
||
|
return tf.group(maybe_apply_op, loss_scale_update_op)
|
||
|
|
||
|
return tf.distribute.get_replica_context().merge_call(
|
||
|
_apply_gradients_cross_replica, args=(grads, wrapped_vars, name)
|
||
|
)
|
||
|
|
||
|
def _apply_gradients(self, grads, wrapped_vars, name):
|
||
|
# Pass experimental_aggregate_gradients=False since LossScaleOptimizer
|
||
|
# already aggregated the gradients.
|
||
|
# TODO(reedwm): This will raise a fairly cryptic error message if
|
||
|
# self._optimizer.apply_gradients does not take
|
||
|
# experimental_aggregate_gradients.
|
||
|
return self._optimizer.apply_gradients(
|
||
|
list(zip(grads, wrapped_vars.value)),
|
||
|
name=name,
|
||
|
experimental_aggregate_gradients=False,
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
serialized_optimizer = optimizers.serialize(self._optimizer)
|
||
|
return {
|
||
|
"inner_optimizer": serialized_optimizer,
|
||
|
"dynamic": self.dynamic,
|
||
|
"initial_scale": self.initial_scale,
|
||
|
"dynamic_growth_steps": self.dynamic_growth_steps,
|
||
|
}
|
||
|
|
||
|
@classmethod
|
||
|
def from_config(cls, config, custom_objects=None):
|
||
|
config = config.copy() # Make a copy, since we mutate config
|
||
|
if "loss_scale" in config:
|
||
|
# If loss_scale is in config, we assume we are deserializing a
|
||
|
# LossScaleOptimizer from TF 2.3 or below. We convert the config so
|
||
|
# it can be deserialized in the current LossScaleOptimizer.
|
||
|
loss_scale = serialization.deserialize_keras_object(
|
||
|
config.pop("loss_scale"),
|
||
|
module_objects={
|
||
|
"FixedLossScale": tf.compat.v1.mixed_precision.FixedLossScale, # noqa: E501
|
||
|
"DynamicLossScale": tf.compat.v1.mixed_precision.DynamicLossScale, # noqa: E501
|
||
|
},
|
||
|
printable_module_name="loss scale",
|
||
|
)
|
||
|
|
||
|
if isinstance(
|
||
|
loss_scale, tf.compat.v1.mixed_precision.FixedLossScale
|
||
|
):
|
||
|
config["dynamic"] = False
|
||
|
config["initial_scale"] = loss_scale._loss_scale_value
|
||
|
elif isinstance(
|
||
|
loss_scale, tf.compat.v1.mixed_precision.DynamicLossScale
|
||
|
):
|
||
|
config["dynamic"] = True
|
||
|
config["initial_scale"] = loss_scale.initial_loss_scale
|
||
|
config["dynamic_growth_steps"] = loss_scale.increment_period
|
||
|
if loss_scale.multiplier != 2:
|
||
|
raise ValueError(
|
||
|
"Cannot deserialize LossScaleOptimizer with a "
|
||
|
"DynamicLossScale whose multiplier is not 2. Got "
|
||
|
"DynamicLossScale: %s" % (loss_scale,)
|
||
|
)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Serialized LossScaleOptimizers with a LossScale that is "
|
||
|
"neither a FixedLossScale nor a DynamicLossScale can no "
|
||
|
"longer be deserialized"
|
||
|
)
|
||
|
config["inner_optimizer"] = config.pop("optimizer")
|
||
|
if isinstance(config["inner_optimizer"], optimizer_v2.OptimizerV2):
|
||
|
inner_optimizer = config["inner_optimizer"]
|
||
|
else:
|
||
|
inner_optimizer = optimizers.deserialize(
|
||
|
config["inner_optimizer"],
|
||
|
custom_objects=custom_objects,
|
||
|
use_legacy_optimizer=True,
|
||
|
)
|
||
|
del config["inner_optimizer"]
|
||
|
return cls(inner_optimizer, **config)
|
||
|
|
||
|
# Delegations: We delegate most OptimizerV2 methods to the wrapped optimizer
|
||
|
# below.
|
||
|
|
||
|
@property
|
||
|
def iterations(self):
|
||
|
return self._optimizer.iterations
|
||
|
|
||
|
@iterations.setter
|
||
|
def iterations(self, variable):
|
||
|
self._optimizer.iterations = variable
|
||
|
|
||
|
def get_slot_names(self):
|
||
|
return self._optimizer.get_slot_names()
|
||
|
|
||
|
def variables(self):
|
||
|
return self._optimizer.variables()
|
||
|
|
||
|
@property
|
||
|
def weights(self):
|
||
|
return self._optimizer.weights
|
||
|
|
||
|
def get_weights(self):
|
||
|
return self._optimizer.get_weights()
|
||
|
|
||
|
def set_weights(self, weights):
|
||
|
return self._optimizer.set_weights(weights)
|
||
|
|
||
|
@property
|
||
|
def clipnorm(self):
|
||
|
return self._optimizer.clipnorm
|
||
|
|
||
|
@clipnorm.setter
|
||
|
def clipnorm(self, val):
|
||
|
self._optimizer.clipnorm = val
|
||
|
|
||
|
@property
|
||
|
def global_clipnorm(self):
|
||
|
return self._optimizer.global_clipnorm
|
||
|
|
||
|
@global_clipnorm.setter
|
||
|
def global_clipnorm(self, val):
|
||
|
self._optimizer.global_clipnorm = val
|
||
|
|
||
|
@property
|
||
|
def clipvalue(self):
|
||
|
return self._optimizer.clipvalue
|
||
|
|
||
|
@clipvalue.setter
|
||
|
def clipvalue(self, val):
|
||
|
self._optimizer.clipvalue = val
|
||
|
|
||
|
def _aggregate_gradients(self, grads_and_vars):
|
||
|
return self._optimizer._aggregate_gradients(grads_and_vars)
|
||
|
|
||
|
def _restore_slot_variable(self, slot_name, variable, slot_variable):
|
||
|
return self._optimizer._restore_slot_variable(
|
||
|
slot_name,
|
||
|
variable,
|
||
|
slot_variable,
|
||
|
)
|
||
|
|
||
|
def _create_or_restore_slot_variable(
|
||
|
self, slot_variable_position, slot_name, variable
|
||
|
):
|
||
|
return self._optimizer._create_or_restore_slot_variable(
|
||
|
slot_variable_position, slot_name, variable
|
||
|
)
|
||
|
|
||
|
def get_slot(self, var, slot_name):
|
||
|
return self._optimizer.get_slot(var, slot_name)
|
||
|
|
||
|
def add_slot(self, var, slot_name, initializer="zeros"):
|
||
|
return self._optimizer.add_slot(var, slot_name, initializer)
|
||
|
|
||
|
def __getattribute__(self, name):
|
||
|
try:
|
||
|
return object.__getattribute__(self, name)
|
||
|
except AttributeError as e:
|
||
|
if name == "_optimizer" or name == "_hyper":
|
||
|
# Avoid infinite recursion
|
||
|
raise e
|
||
|
|
||
|
# Delegate hyperparameter accesses to inner optimizer.
|
||
|
if name == "lr":
|
||
|
name = "learning_rate"
|
||
|
if name in self._optimizer._hyper:
|
||
|
return self._optimizer._get_hyper(name)
|
||
|
raise e
|
||
|
|
||
|
def __dir__(self):
|
||
|
result = set(super().__dir__())
|
||
|
if "_optimizer" in result:
|
||
|
result |= self._optimizer._hyper.keys()
|
||
|
if "learning_rate" in self._optimizer._hyper.keys():
|
||
|
result.add("lr")
|
||
|
return list(result)
|
||
|
|
||
|
def __setattr__(self, name, value):
|
||
|
if name == "lr":
|
||
|
name = "learning_rate"
|
||
|
# Delegate setting hyperparameter to inner optimizer if the attribute
|
||
|
# does not exist on the LossScaleOptimizer
|
||
|
try:
|
||
|
# We cannot check for the 'iterations' attribute as it cannot be set
|
||
|
# after it is accessed.
|
||
|
if name != "iterations":
|
||
|
object.__getattribute__(self, name)
|
||
|
has_attribute = True
|
||
|
except AttributeError:
|
||
|
has_attribute = False
|
||
|
if (
|
||
|
name != "_optimizer"
|
||
|
and name in self._optimizer._hyper
|
||
|
and not has_attribute
|
||
|
):
|
||
|
self._optimizer._set_hyper(name, value)
|
||
|
else:
|
||
|
super().__setattr__(name, value)
|
||
|
|
||
|
# Explicitly delegate learning_rate. Normally hyperparameters are delegated
|
||
|
# in __getattribute__, but if a hyperparameter is not in
|
||
|
# self._optimizer._hyper (e.g. because self._optimizer itself wraps another
|
||
|
# optimizer), then it won't be delegated. Since learning_rate is a very
|
||
|
# commonly accessed hyperparameter, we delegate it here.
|
||
|
@property
|
||
|
def learning_rate(self):
|
||
|
return self._optimizer.learning_rate
|
||
|
|
||
|
@learning_rate.setter
|
||
|
def learning_rate(self, value):
|
||
|
self._optimizer.learning_rate = value
|
||
|
|
||
|
@property
|
||
|
def lr(self):
|
||
|
return self._optimizer.learning_rate
|
||
|
|
||
|
@lr.setter
|
||
|
def lr(self, value):
|
||
|
self._optimizer.lr = value
|
||
|
|
||
|
# We do not override some OptimizerV2 methods. For each, we describe why we
|
||
|
# do not delegate them to self._optimizer:
|
||
|
# * get_updates: get_updates() calls get_gradients(). Since we override
|
||
|
# get_gradients(), we cannot delegate get_updates() to self._optimizer,
|
||
|
# otherwise the overridden get_gradients() method would not be called.
|
||
|
# Luckily, get_updates() does not access any OptimizerV2 fields, so
|
||
|
# inheriting the OptimizerV2 version works fine.
|
||
|
# * minimize: We don't delegate for a similar as get_updates(): it calls
|
||
|
# both self._compute_gradients() and self.apply_gradients(), and both need
|
||
|
# to have the LossScaleOptimizer version called.
|
||
|
|
||
|
# TODO(reedwm): Maybe throw an error if mixed precision is used without this
|
||
|
# optimizer being used.
|
||
|
|
||
|
|
||
|
class LossScaleOptimizerV3(
|
||
|
tf.__internal__.tracking.DelegatingTrackableMixin,
|
||
|
optimizer.Optimizer,
|
||
|
BaseLossScaleOptimizer,
|
||
|
):
|
||
|
"""An optimizer that applies loss scaling to prevent numeric underflow.
|
||
|
|
||
|
This is a copy of the `mixed_precision.LossScaleOptimizer` class
|
||
|
defined above, except it subclasses and wraps the new experimental Optimizer
|
||
|
class instead of the `tf.keras.optimizers.Optimizer` class. Some of the
|
||
|
methods this class defines and calls are different compared to
|
||
|
LossScaleOptimizer due to the differences between the two Optimizer base
|
||
|
classes. Additionally, this class does not support the legacy graph mode,
|
||
|
but LossScaleOptimizer does.
|
||
|
|
||
|
Since the new experimental Optimizer does not have a hyperparameter concept,
|
||
|
LossScaleOptimizerV3 does not delegate arbitrary hyperparameter accesses to
|
||
|
the inner optimizer, unlike LossScaleOptimizer. LossScaleOptimizerV3 does
|
||
|
delegate the "learning_rate" attribute, however.
|
||
|
"""
|
||
|
|
||
|
@tf.__internal__.tracking.no_automatic_dependency_tracking
|
||
|
def __init__(
|
||
|
self,
|
||
|
inner_optimizer,
|
||
|
dynamic=True,
|
||
|
initial_scale=None,
|
||
|
dynamic_growth_steps=None,
|
||
|
):
|
||
|
if not isinstance(inner_optimizer, optimizer.Optimizer):
|
||
|
if isinstance(inner_optimizer, optimizer_v2.OptimizerV2):
|
||
|
# Give better error message if the OptimizerV2 class is passed
|
||
|
# instead of the new experimental optimizer.
|
||
|
raise TypeError(
|
||
|
"You passed a `tf.keras.optimizers.Optimizer` instance to "
|
||
|
"LossScaleOptimizerV3, but only the new experimental "
|
||
|
"optimizer defined in "
|
||
|
"keras/optimizer_expeirmental/optimizer.py can be "
|
||
|
"passed. Please use "
|
||
|
"`tf.keras.mixed_precision.LossScaleOptimizer` "
|
||
|
"instead of LossScaleOptimizerV3, as the former supports "
|
||
|
"`tf.keras.optimizers.Optimizer`s. Got optimizer: "
|
||
|
f"{inner_optimizer}"
|
||
|
)
|
||
|
raise TypeError(
|
||
|
'"inner_optimizer" must be an instance of '
|
||
|
f"Optimizer, but got: {inner_optimizer}."
|
||
|
)
|
||
|
if not isinstance(dynamic, bool):
|
||
|
# Catch errors if a user incorrectly passes a string or float to the
|
||
|
# second argument argument, as this was commonly done for the
|
||
|
# now-removed LossScaleOptimizerV1.
|
||
|
raise TypeError(
|
||
|
'"dynamic" argument to LossScaleOptimizer.__init__ must '
|
||
|
f"be a bool, but got: {repr(dynamic)}"
|
||
|
)
|
||
|
if isinstance(inner_optimizer, LossScaleOptimizerV3):
|
||
|
raise TypeError(
|
||
|
"LossScaleOptimizer cannot wrap another "
|
||
|
f"LossScaleOptimizer, but got: {inner_optimizer}"
|
||
|
)
|
||
|
_raise_if_strategy_unsupported()
|
||
|
if getattr(
|
||
|
inner_optimizer, "_is_wrapped_by_loss_scale_optimizer", False
|
||
|
):
|
||
|
# TODO(reedwm): Maybe support this. The difficulty is that LSO has
|
||
|
# the same checkpoint format as the inner optimizer, so multiple
|
||
|
# LSOs wrapping the same optimizer causes the checkpointing logic to
|
||
|
# become confused.
|
||
|
raise ValueError(
|
||
|
'"inner_optimizer" is already wrapped by a '
|
||
|
"LossScaleOptimizer. An optimizer can only be wrapped "
|
||
|
"by a single LossScaleOptimizer"
|
||
|
)
|
||
|
self._optimizer = inner_optimizer
|
||
|
self._optimizer._is_wrapped_by_loss_scale_optimizer = True
|
||
|
|
||
|
# We don't call super().__init__, since we do not want to call
|
||
|
# Optimizer's constructor.
|
||
|
tf.__internal__.tracking.DelegatingTrackableMixin.__init__(
|
||
|
self, self._optimizer
|
||
|
)
|
||
|
|
||
|
if dynamic:
|
||
|
if initial_scale is None:
|
||
|
initial_scale = _DEFAULT_INITIAL_SCALE
|
||
|
if dynamic_growth_steps is None:
|
||
|
dynamic_growth_steps = _DEFAULT_GROWTH_STEPS
|
||
|
self._loss_scale = _DynamicLossScaleState(
|
||
|
initial_scale, dynamic_growth_steps, multiplier=2
|
||
|
)
|
||
|
self._track_trackable(self._loss_scale, "loss_scale")
|
||
|
else:
|
||
|
if initial_scale is None:
|
||
|
raise ValueError(
|
||
|
'"initial_scale" must be specified if "dynamic" is False'
|
||
|
)
|
||
|
self._loss_scale = float(initial_scale)
|
||
|
if dynamic_growth_steps is not None:
|
||
|
raise ValueError(
|
||
|
'"dynamic_growth_steps" must be None if "dynamic" '
|
||
|
f"is False, but got: {dynamic_growth_steps}"
|
||
|
)
|
||
|
|
||
|
# Used to track whether get_scaled_loss() and get_unscaled_gradients()
|
||
|
# have been called
|
||
|
self._loss_has_been_scaled = False
|
||
|
self._gradients_have_been_unscaled = False
|
||
|
|
||
|
@property
|
||
|
def dynamic(self):
|
||
|
return isinstance(self._loss_scale, _DynamicLossScaleState)
|
||
|
|
||
|
@property
|
||
|
def loss_scale(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return tf.convert_to_tensor(self._loss_scale.current_loss_scale)
|
||
|
else:
|
||
|
return tf.convert_to_tensor(self._loss_scale)
|
||
|
|
||
|
@property
|
||
|
def dynamic_counter(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.counter
|
||
|
else:
|
||
|
return None
|
||
|
|
||
|
@property
|
||
|
def initial_scale(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.initial_loss_scale
|
||
|
else:
|
||
|
return self._loss_scale
|
||
|
|
||
|
@property
|
||
|
def dynamic_growth_steps(self):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
return self._loss_scale.growth_steps
|
||
|
else:
|
||
|
return None
|
||
|
|
||
|
@property
|
||
|
def inner_optimizer(self):
|
||
|
return self._optimizer
|
||
|
|
||
|
def get_scaled_loss(self, loss):
|
||
|
self._loss_has_been_scaled = True
|
||
|
if callable(loss):
|
||
|
|
||
|
def new_loss():
|
||
|
loss_val = loss()
|
||
|
return loss_val * tf.cast(self.loss_scale, loss_val.dtype)
|
||
|
|
||
|
return new_loss
|
||
|
else:
|
||
|
return loss * tf.cast(self.loss_scale, loss.dtype)
|
||
|
|
||
|
def get_unscaled_gradients(self, grads):
|
||
|
self._gradients_have_been_unscaled = True
|
||
|
loss_scale_reciprocal = 1.0 / self.loss_scale
|
||
|
return [
|
||
|
_multiply_gradient(g, loss_scale_reciprocal)
|
||
|
if g is not None
|
||
|
else None
|
||
|
for g in grads
|
||
|
]
|
||
|
|
||
|
def compute_gradients(self, loss, var_list, tape=None):
|
||
|
tape = tf.GradientTape() if tape is None else tape
|
||
|
with tape:
|
||
|
loss = self.get_scaled_loss(loss)
|
||
|
grads_and_vars = self._optimizer.compute_gradients(
|
||
|
loss, var_list, tape=tape
|
||
|
)
|
||
|
grads = [g for g, _ in grads_and_vars]
|
||
|
weights = [v for _, v in grads_and_vars]
|
||
|
unscaled_grads = self.get_unscaled_gradients(grads)
|
||
|
return list(zip(unscaled_grads, weights))
|
||
|
|
||
|
def apply_gradients(
|
||
|
self, grads_and_vars, skip_gradients_aggregation=False, **kwargs
|
||
|
):
|
||
|
if tf.distribute.in_cross_replica_context():
|
||
|
raise ValueError(
|
||
|
"apply_gradients() must be called in a replica context."
|
||
|
)
|
||
|
# We check for the strategy here despite already checking in the
|
||
|
# constructor as frequently the optimizer is created outside the
|
||
|
# strategy's scope.
|
||
|
_raise_if_strategy_unsupported()
|
||
|
_maybe_warn_about_scaling(
|
||
|
self._loss_has_been_scaled, self._gradients_have_been_unscaled
|
||
|
)
|
||
|
|
||
|
grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
|
||
|
# `experimental_aggregate_gradients` is an arg in `apply_gradients` of
|
||
|
# v2 optimizer -- the reverse of `skip_gradients_aggregation`.
|
||
|
# We read it from kwargs for backward compatibility.
|
||
|
experimental_aggregate_gradients = kwargs.pop(
|
||
|
"experimental_aggregate_gradients", True
|
||
|
)
|
||
|
if not skip_gradients_aggregation and experimental_aggregate_gradients:
|
||
|
# We must aggregate the gradients here instead of in
|
||
|
# self.optimizer.apply_gradients, so that any NaN or Inf gradients
|
||
|
# are propagated to each replica. If any replica has a NaN or Inf
|
||
|
# gradient, they must all have a NaN or Inf gradient so that they
|
||
|
# all skip the step.
|
||
|
grads_and_vars = self._optimizer.aggregate_gradients(grads_and_vars)
|
||
|
|
||
|
grads_and_vars = tuple(grads_and_vars)
|
||
|
grads = [g for g, _ in grads_and_vars]
|
||
|
# We do not want DistributionStrategy to unwrap any MirroredVariables in
|
||
|
# grads_and_vars, because even in a replica context, the wrapped
|
||
|
# optimizer expects mirrored variables. So we wrap the variables with an
|
||
|
# _UnwrapPreventer, preventing DistributionStrategy from unwrapping the
|
||
|
# MirroredVariables.
|
||
|
wrapped_vars = _UnwrapPreventer([v for _, v in grads_and_vars])
|
||
|
|
||
|
def do_not_apply_fn():
|
||
|
# Normally self._optimizer.iterations is incremented in
|
||
|
# self._optimizer.apply_gradients(). Since that is not called in
|
||
|
# this branch, we increment it here instead.
|
||
|
self._optimizer.iterations.assign_add(1, read_value=False)
|
||
|
|
||
|
def _if_should_apply_grads(grads):
|
||
|
if isinstance(self._loss_scale, _DynamicLossScaleState):
|
||
|
_, should_apply_grad = self._loss_scale.update(grads)
|
||
|
return should_apply_grad
|
||
|
else:
|
||
|
return True
|
||
|
|
||
|
if tf.__internal__.distribute.strategy_supports_no_merge_call():
|
||
|
should_apply_grads = _if_should_apply_grads(grads)
|
||
|
|
||
|
def apply_fn():
|
||
|
return self._apply_gradients(grads, wrapped_vars)
|
||
|
|
||
|
tf.__internal__.smart_cond.smart_cond(
|
||
|
should_apply_grads, apply_fn, do_not_apply_fn
|
||
|
)
|
||
|
else:
|
||
|
|
||
|
def _apply_gradients_cross_replica(
|
||
|
distribution, grads, wrapped_vars
|
||
|
):
|
||
|
should_apply_grads = _if_should_apply_grads(grads)
|
||
|
|
||
|
def apply_fn():
|
||
|
distribution.extended.call_for_each_replica(
|
||
|
self._apply_gradients, args=(grads, wrapped_vars)
|
||
|
)
|
||
|
|
||
|
# Note: We must call this cond() in a cross-replica context.
|
||
|
# DistributionStrategy does not support having a cond in a
|
||
|
# replica context with a branch that calls `merge_call`, and
|
||
|
# self._optimizer.apply_gradients calls `merge_call`.
|
||
|
tf.__internal__.smart_cond.smart_cond(
|
||
|
should_apply_grads, apply_fn, do_not_apply_fn
|
||
|
)
|
||
|
|
||
|
tf.distribute.get_replica_context().merge_call(
|
||
|
_apply_gradients_cross_replica, args=(grads, wrapped_vars)
|
||
|
)
|
||
|
|
||
|
def _apply_gradients(self, grads, wrapped_vars):
|
||
|
# Pass skip_gradients_aggregation=True since LossScaleOptimizer
|
||
|
# already aggregated the gradients.
|
||
|
self._optimizer.apply_gradients(
|
||
|
list(zip(grads, wrapped_vars.value)),
|
||
|
skip_gradients_aggregation=True,
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
serialized_optimizer = optimizers.serialize(self._optimizer)
|
||
|
return {
|
||
|
"inner_optimizer": serialized_optimizer,
|
||
|
"dynamic": self.dynamic,
|
||
|
"initial_scale": self.initial_scale,
|
||
|
"dynamic_growth_steps": self.dynamic_growth_steps,
|
||
|
}
|
||
|
|
||
|
@classmethod
|
||
|
def from_config(cls, config, custom_objects=None):
|
||
|
config = config.copy() # Make a copy, since we mutate config
|
||
|
if isinstance(config["inner_optimizer"], optimizer.Optimizer):
|
||
|
inner_optimizer = config["inner_optimizer"]
|
||
|
else:
|
||
|
inner_optimizer = optimizers.deserialize(
|
||
|
config["inner_optimizer"],
|
||
|
custom_objects=custom_objects,
|
||
|
use_legacy_optimizer=False,
|
||
|
)
|
||
|
del config["inner_optimizer"]
|
||
|
return cls(inner_optimizer, **config)
|
||
|
|
||
|
@property
|
||
|
def iterations(self):
|
||
|
return self._optimizer.iterations
|
||
|
|
||
|
@iterations.setter
|
||
|
def iterations(self, variable):
|
||
|
self._optimizer.iterations = variable
|
||
|
|
||
|
@property
|
||
|
def variables(self):
|
||
|
return self._optimizer.variables
|
||
|
|
||
|
def build(self, var_list):
|
||
|
return self._optimizer.build(var_list)
|
||
|
|
||
|
@property
|
||
|
def learning_rate(self):
|
||
|
return self._optimizer.learning_rate
|
||
|
|
||
|
@learning_rate.setter
|
||
|
def learning_rate(self, learning_rate):
|
||
|
self._optimizer.learning_rate = learning_rate
|
||
|
|
||
|
@property
|
||
|
def use_ema(self):
|
||
|
return self._optimizer.use_ema
|
||
|
|
||
|
@use_ema.setter
|
||
|
def use_ema(self, use_ema):
|
||
|
self._optimizer.use_ema = use_ema
|
||
|
|
||
|
@property
|
||
|
def ema_momentum(self):
|
||
|
return self._optimizer.ema_momentum
|
||
|
|
||
|
@ema_momentum.setter
|
||
|
def ema_momentum(self, ema_momentum):
|
||
|
self._optimizer.ema_momentum = ema_momentum
|
||
|
|
||
|
def finalize_variable_values(self, var_list):
|
||
|
self._optimizer.finalize_variable_values(var_list)
|
||
|
|
||
|
|
||
|
class FakeOptimizerForRestoration(tf.__internal__.tracking.Trackable):
|
||
|
"""A fake optimizer used to support restoring TensorFlow 2.2 checkpoints.
|
||
|
|
||
|
The checkpoint format for LossScaleOptimizers changed after TF 2.2. This
|
||
|
class exists to support restoring TF 2.2 checkpoints in newer version of
|
||
|
TensorFlow.
|
||
|
|
||
|
In TF 2.2, LossScaleOptimizer would track the wrapped optimizer by calling
|
||
|
the following in LossScaleOptimizer.__init__
|
||
|
|
||
|
```
|
||
|
self._track_trackable(self._optimizer, 'base_optimizer')
|
||
|
```
|
||
|
|
||
|
This means a dependency from the LossScaleOptimizer to the wrapped optimizer
|
||
|
would be stored in the checkpoint. However now, the checkpoint format with a
|
||
|
LossScaleOptimizer is the same as the format without a LossScaleOptimizer,
|
||
|
except the loss scale is also stored. This means there is no dependency from
|
||
|
the LossScaleOptimizer to the wrapped optimizer. Instead, the
|
||
|
LossScaleOptimizer acts as if it is the wrapped optimizer, from a
|
||
|
checkpoint's perspective, by overriding all Trackable methods and delegating
|
||
|
them to the wrapped optimizer.
|
||
|
|
||
|
To allow restoring TF 2.2. checkpoints, LossScaleOptimizer adds a dependency
|
||
|
on this class instead of the inner optimizer. When restored, this class will
|
||
|
instead restore the slot variables of the inner optimizer. Since this class
|
||
|
has no variables, it does not affect the checkpoint when saved.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, optimizer):
|
||
|
self._optimizer = optimizer
|
||
|
|
||
|
def get_slot_names(self):
|
||
|
return self._optimizer.get_slot_names()
|
||
|
|
||
|
def _create_or_restore_slot_variable(
|
||
|
self, slot_variable_position, slot_name, variable
|
||
|
):
|
||
|
return self._optimizer._create_or_restore_slot_variable(
|
||
|
slot_variable_position, slot_name, variable
|
||
|
)
|
||
|
|
||
|
|
||
|
def _create_loss_scale_optimizer_from_v1_loss_scale(optimizer, loss_scale):
|
||
|
"""Creates an LSO from a tf.compat.v1.mixed_precision.LossScale.
|
||
|
|
||
|
This is only used to pass to
|
||
|
`tf.__internal__.mixed_precision.register_loss_scale_wrapper` below, which
|
||
|
is called so that
|
||
|
`tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite` can
|
||
|
wrap a Keras optimizer with a LossScaleOptimizer.
|
||
|
|
||
|
Args:
|
||
|
optimizer: An OptimizerV2 instance.
|
||
|
loss_scale: A `tf.compat.v1.mixed_precision.LossScale` instance
|
||
|
|
||
|
Returns:
|
||
|
A LossScaleOptimizer that wraps `optimizer` and uses the same loss scaling
|
||
|
algorithm as `loss_scale`.
|
||
|
"""
|
||
|
if isinstance(loss_scale, (int, float)):
|
||
|
return LossScaleOptimizer(
|
||
|
optimizer, dynamic=False, initial_scale=loss_scale
|
||
|
)
|
||
|
elif isinstance(loss_scale, tf.compat.v1.mixed_precision.FixedLossScale):
|
||
|
ls_val = loss_scale._loss_scale_value
|
||
|
return LossScaleOptimizer(
|
||
|
optimizer, dynamic=False, initial_scale=ls_val
|
||
|
)
|
||
|
elif loss_scale == "dynamic":
|
||
|
return LossScaleOptimizer(optimizer)
|
||
|
elif isinstance(loss_scale, tf.compat.v1.mixed_precision.DynamicLossScale):
|
||
|
if loss_scale.multiplier != 2:
|
||
|
raise ValueError(
|
||
|
'When passing a DynamicLossScale to "loss_scale", '
|
||
|
"DynamicLossScale.multiplier must be 2. Got: "
|
||
|
f"{loss_scale}"
|
||
|
)
|
||
|
return LossScaleOptimizer(
|
||
|
optimizer,
|
||
|
initial_scale=loss_scale.initial_loss_scale,
|
||
|
dynamic_growth_steps=loss_scale.increment_period,
|
||
|
)
|
||
|
elif isinstance(loss_scale, tf.compat.v1.mixed_precision.LossScale):
|
||
|
raise TypeError(
|
||
|
"Passing a LossScale that is not a FixedLossScale or a "
|
||
|
f"DynamicLossScale is not supported. Got: {loss_scale}"
|
||
|
)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Invalid value passed to loss_scale. loss_scale "
|
||
|
'must be the string "dynamic" (recommended), an int, '
|
||
|
"a float, a FixedLossScale, or a DynamicLossScale. Got "
|
||
|
f"value: {loss_scale}"
|
||
|
)
|
||
|
|
||
|
|
||
|
tf.__internal__.mixed_precision.register_loss_scale_wrapper(
|
||
|
optimizer_v2.OptimizerV2,
|
||
|
_create_loss_scale_optimizer_from_v1_loss_scale,
|
||
|
LossScaleOptimizer,
|
||
|
)
|
||
|
|
||
|
|
||
|
def _multiply_gradient(gradient, scale):
|
||
|
"""Multiply a (possibly sparse) gradient by the given scale factor."""
|
||
|
scale = tf.cast(scale, gradient.dtype)
|
||
|
if isinstance(gradient, tf.IndexedSlices):
|
||
|
return tf.IndexedSlices(
|
||
|
gradient.values * scale,
|
||
|
gradient.indices,
|
||
|
dense_shape=gradient.dense_shape,
|
||
|
)
|
||
|
else:
|
||
|
return gradient * scale
|
||
|
|
||
|
|
||
|
def strategy_supports_loss_scaling():
|
||
|
"""Returns True if the current Strategy supports loss scaling."""
|
||
|
if not tf.distribute.has_strategy():
|
||
|
return True
|
||
|
strategy = tf.distribute.get_strategy()
|
||
|
# Strategies are supported if either there is only one replica or if
|
||
|
# variables are replicated per device. Otherwise, the current model.fit()
|
||
|
# implementation and most custom training loops incorrectly unscale the
|
||
|
# gradients. Currently, gradients are unscaled once per compute replica, but
|
||
|
# they should be unscaled once per variable replica. When there is one
|
||
|
# variable replica for each compute replica, this works fine, but otherwise
|
||
|
# issues will occur.
|
||
|
# TODO(reedwm): Support all strategies.
|
||
|
return isinstance(
|
||
|
strategy,
|
||
|
(
|
||
|
tf.distribute.MultiWorkerMirroredStrategy,
|
||
|
tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy,
|
||
|
tf.distribute.OneDeviceStrategy,
|
||
|
tf.compat.v1.distribute.OneDeviceStrategy,
|
||
|
tf.distribute.MirroredStrategy,
|
||
|
tf.compat.v1.distribute.MirroredStrategy,
|
||
|
),
|
||
|
)
|
||
|
|
||
|
|
||
|
def _raise_if_strategy_unsupported():
|
||
|
"""Raise an exception if the current strategy doesn't support loss
|
||
|
scaling."""
|
||
|
if not strategy_supports_loss_scaling():
|
||
|
strategy = tf.distribute.get_strategy()
|
||
|
if isinstance(
|
||
|
strategy,
|
||
|
(
|
||
|
tf.distribute.experimental.TPUStrategy,
|
||
|
tf.compat.v1.distribute.experimental.TPUStrategy,
|
||
|
tf.distribute.TPUStrategy,
|
||
|
),
|
||
|
):
|
||
|
raise ValueError(
|
||
|
"Loss scaling is not supported with TPUStrategy. Loss scaling "
|
||
|
"is unnecessary with TPUs, since they support bfloat16 instead "
|
||
|
"of float16 and bfloat16 does not require loss scaling. You "
|
||
|
"should remove the use of the LossScaleOptimizer when TPUs are "
|
||
|
"used."
|
||
|
)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Loss scaling is not supported with the "
|
||
|
"tf.distribute.Strategy: "
|
||
|
f"{strategy.__class__.__name__}. Try using a different "
|
||
|
"Strategy, e.g. a MirroredStrategy"
|
||
|
)
|