454 lines
17 KiB
Python
454 lines
17 KiB
Python
|
# Copyright 2019 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Contains LossScale classes."""
|
||
|
|
import abc
|
||
|
|
|
||
|
|
from tensorflow.python.distribute import distribute_lib
|
||
|
|
from tensorflow.python.distribute import reduce_util
|
||
|
|
from tensorflow.python.eager import context
|
||
|
|
from tensorflow.python.framework import dtypes
|
||
|
|
from tensorflow.python.framework import indexed_slices
|
||
|
|
from tensorflow.python.framework import ops
|
||
|
|
from tensorflow.python.ops import cond
|
||
|
|
from tensorflow.python.ops import control_flow_ops
|
||
|
|
from tensorflow.python.ops import math_ops
|
||
|
|
from tensorflow.python.ops import variable_v1
|
||
|
|
from tensorflow.python.ops import variables
|
||
|
|
from tensorflow.python.trackable import base as trackable
|
||
|
|
from tensorflow.python.util import deprecation
|
||
|
|
from tensorflow.python.util import nest
|
||
|
|
from tensorflow.python.util.tf_export import tf_export
|
||
|
|
|
||
|
|
|
||
|
|
@deprecation.deprecated_endpoints('mixed_precision.experimental.LossScale',
|
||
|
|
'train.experimental.LossScale')
|
||
|
|
@tf_export(
|
||
|
|
v1=[
|
||
|
|
'mixed_precision.LossScale',
|
||
|
|
'mixed_precision.experimental.LossScale',
|
||
|
|
'train.experimental.LossScale'
|
||
|
|
])
|
||
|
|
class LossScale(trackable.Trackable, metaclass=abc.ABCMeta):
|
||
|
|
"""Base class for all TF1 loss scales.
|
||
|
|
|
||
|
|
This is an abstract base class, so you cannot instantiate it directly.
|
||
|
|
Instead, use one of its concrete subclasses:
|
||
|
|
* `tf.compat.v1.mixed_precision.DynamicLossScale`
|
||
|
|
* `tf.compat.v1.mixed_precision.FixedLossScale`
|
||
|
|
|
||
|
|
Loss scaling is a process that multiplies the loss by a multiplier called the
|
||
|
|
loss scale, and divides each gradient by the same multiplier. The pseudocode
|
||
|
|
for this process is:
|
||
|
|
|
||
|
|
```
|
||
|
|
loss = ...
|
||
|
|
loss *= loss_scale
|
||
|
|
grads = gradients(loss, vars)
|
||
|
|
grads /= loss_scale
|
||
|
|
```
|
||
|
|
|
||
|
|
Mathematically, loss scaling has no effect, but can help avoid numerical
|
||
|
|
underflow in intermediate gradients when float16 tensors are used for mixed
|
||
|
|
precision training. By multiplying the loss, each intermediate gradient will
|
||
|
|
have the same multiplier applied.
|
||
|
|
|
||
|
|
Instances of this class represent a loss scale. Calling instances of this
|
||
|
|
class returns the loss scale as a scalar float32 tensor, while method
|
||
|
|
`update()` updates the loss scale depending on the values of the gradients.
|
||
|
|
Optimizers use instances of this class to scale loss and gradients.
|
||
|
|
|
||
|
|
In most functions that accept a LossScale, you can also pass an int (such as
|
||
|
|
8) to create a `FixedLossScale` or the string `"dynamic"` to create a dynamic
|
||
|
|
loss scale.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
"""Initializes the loss scale class."""
|
||
|
|
self._weights = {}
|
||
|
|
|
||
|
|
@abc.abstractmethod
|
||
|
|
def __call__(self):
|
||
|
|
"""Returns the current loss scale as a scalar `float32` tensor."""
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abc.abstractmethod
|
||
|
|
def update(self, grads):
|
||
|
|
"""Updates the value of the loss scale.
|
||
|
|
|
||
|
|
The loss scale will be potentially updated, based on the value of `grads`.
|
||
|
|
The tensor returned by calling this class is only updated when this function
|
||
|
|
is evaluated.
|
||
|
|
|
||
|
|
In eager mode, this directly updates the loss scale, so that calling
|
||
|
|
`__call__` will return the newly updated loss scale. In graph mode,
|
||
|
|
this returns an op that, when evaluated, updates the loss scale.
|
||
|
|
|
||
|
|
This function also returns a `should_apply_gradients` bool. If False,
|
||
|
|
gradients should not be applied to the variables that step, as nonfinite
|
||
|
|
gradients were found, and the loss scale has been be updated to reduce the
|
||
|
|
chance of finding nonfinite gradients in the next step. Some loss scale
|
||
|
|
classes will always return True, as they cannot adjust themselves in
|
||
|
|
response to nonfinite gradients.
|
||
|
|
|
||
|
|
When a DistributionStrategy is used, this function may only be called in a
|
||
|
|
cross-replica context.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
grads: A nested structure of unscaled gradients, each which is the
|
||
|
|
gradient of the loss with respect to a weight. The gradients should have
|
||
|
|
already been divided by the loss scale being before passed to this
|
||
|
|
function. 'None' gradients are accepted, and are ignored.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
update_op: In eager mode, None. In graph mode, an op to update the loss
|
||
|
|
scale.
|
||
|
|
should_apply_gradients: Either a bool or a scalar boolean tensor. If
|
||
|
|
False, the caller should skip applying `grads` to the variables this
|
||
|
|
step.
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
def _add_weight(self, name, initial_value, dtype=None):
|
||
|
|
"""Adds a weight to this loss scale.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
name: Variable name.
|
||
|
|
initial_value: The variable's initial value.
|
||
|
|
dtype: The type of the variable.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A variable.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
RuntimeError: If a weight with `name` has already been added.
|
||
|
|
"""
|
||
|
|
variable = variable_v1.VariableV1(
|
||
|
|
initial_value=initial_value,
|
||
|
|
name=name,
|
||
|
|
dtype=dtype,
|
||
|
|
trainable=False,
|
||
|
|
use_resource=True,
|
||
|
|
synchronization=variables.VariableSynchronization.AUTO,
|
||
|
|
# Set aggregation to NONE, as loss scaling variables should never be
|
||
|
|
# aggregated.
|
||
|
|
aggregation=variables.VariableAggregation.NONE)
|
||
|
|
if context.executing_eagerly():
|
||
|
|
graph_key = None
|
||
|
|
else:
|
||
|
|
graph = ops.get_default_graph()
|
||
|
|
graph_key = graph._graph_key # pylint: disable=protected-access
|
||
|
|
|
||
|
|
key = (name, graph_key)
|
||
|
|
if self._weights.get(key, None) is not None:
|
||
|
|
raise RuntimeError('Duplicate variables detected. {}'.format(key))
|
||
|
|
self._weights[key] = variable
|
||
|
|
self._handle_deferred_dependencies(name=name, trackable=variable)
|
||
|
|
return variable
|
||
|
|
|
||
|
|
def _trackable_children(self,
|
||
|
|
save_type=trackable.SaveType.CHECKPOINT,
|
||
|
|
**kwargs):
|
||
|
|
"""From Trackable. Gather graph-specific weights to save."""
|
||
|
|
if context.executing_eagerly():
|
||
|
|
graph_key = None
|
||
|
|
else:
|
||
|
|
graph = ops.get_default_graph()
|
||
|
|
graph_key = graph._graph_key # pylint: disable=protected-access
|
||
|
|
weights = {}
|
||
|
|
for (name, g), v in sorted(self._weights.items(), key=lambda i: i[0][0]):
|
||
|
|
if g == graph_key:
|
||
|
|
weights[name] = v
|
||
|
|
weights.update(
|
||
|
|
super(LossScale, self)._trackable_children(save_type, **kwargs))
|
||
|
|
return weights
|
||
|
|
|
||
|
|
def _lookup_dependency(self, name, cached_dependencies=None):
|
||
|
|
"""From Trackable. Find a weight in the current graph."""
|
||
|
|
unconditional = super(LossScale, self)._lookup_dependency(
|
||
|
|
name, cached_dependencies)
|
||
|
|
if unconditional is not None:
|
||
|
|
return unconditional
|
||
|
|
if context.executing_eagerly():
|
||
|
|
graph_key = None
|
||
|
|
else:
|
||
|
|
graph = ops.get_default_graph()
|
||
|
|
graph_key = graph._graph_key # pylint: disable=protected-access
|
||
|
|
return self._weights.get((name, graph_key), None)
|
||
|
|
|
||
|
|
@abc.abstractmethod
|
||
|
|
def get_config(self):
|
||
|
|
"""Returns the config of this loss scale."""
|
||
|
|
pass
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_config(cls, config):
|
||
|
|
"""Creates the LossScale from its config."""
|
||
|
|
return cls(**config)
|
||
|
|
|
||
|
|
|
||
|
|
@deprecation.deprecated_endpoints('mixed_precision.experimental.FixedLossScale',
|
||
|
|
'train.experimental.FixedLossScale')
|
||
|
|
@tf_export(
|
||
|
|
v1=[
|
||
|
|
'mixed_precision.FixedLossScale',
|
||
|
|
'mixed_precision.experimental.FixedLossScale',
|
||
|
|
'train.experimental.FixedLossScale'
|
||
|
|
])
|
||
|
|
class FixedLossScale(LossScale):
|
||
|
|
"""Loss scale with a fixed value.
|
||
|
|
|
||
|
|
The loss scale is not updated for the lifetime of instances of this class.
|
||
|
|
A given instance of this class always returns the same number when called.
|
||
|
|
"""
|
||
|
|
|
||
|
|
@deprecation.deprecated(
|
||
|
|
None, 'Use tf.keras.mixed_precision.LossScaleOptimizer instead. '
|
||
|
|
'LossScaleOptimizer now has all the functionality of '
|
||
|
|
'FixedLossScale')
|
||
|
|
def __init__(self, loss_scale_value):
|
||
|
|
"""Creates the fixed loss scale.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
loss_scale_value: A Python float. Its ideal value varies depending on
|
||
|
|
models to run. Choosing a too small loss_scale might affect model
|
||
|
|
quality; a too big loss_scale might cause inf or nan. There is no single
|
||
|
|
right loss_scale to apply. There is no harm choosing a relatively big
|
||
|
|
number as long as no nan or inf is encountered in training.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If loss_scale_value is less than 1.
|
||
|
|
"""
|
||
|
|
super(FixedLossScale, self).__init__()
|
||
|
|
if not isinstance(loss_scale_value, (int, float)):
|
||
|
|
raise ValueError('loss_scale_value must be a Python int or float.')
|
||
|
|
if loss_scale_value < 1:
|
||
|
|
raise ValueError('loss_scale_value must be at least 1.')
|
||
|
|
# It's important we do not create tensors in the constructor, as such
|
||
|
|
# tensors might be on a different device or tf.function vs when the tensor
|
||
|
|
# is used. This would hurt performance. Therefore, we do not create a tensor
|
||
|
|
# from loss_scale_value, but instead leave it as a Python float.
|
||
|
|
# TODO(reedwm): Also do not create tensors in the DynamicLossScale
|
||
|
|
# constructor.
|
||
|
|
self._loss_scale_value = float(loss_scale_value)
|
||
|
|
|
||
|
|
def __call__(self):
|
||
|
|
return ops.convert_to_tensor(self._loss_scale_value)
|
||
|
|
|
||
|
|
def update(self, grads):
|
||
|
|
del grads
|
||
|
|
return control_flow_ops.no_op(), True
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return 'FixedLossScale(%s)' % self._loss_scale_value
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {'loss_scale_value': self._loss_scale_value}
|
||
|
|
|
||
|
|
|
||
|
|
def _is_all_finite(grads):
|
||
|
|
"""Returns a scalar boolean tensor indicating if all gradients are finite."""
|
||
|
|
def raw_values(g):
|
||
|
|
return g.values if isinstance(g, indexed_slices.IndexedSlices) else g
|
||
|
|
|
||
|
|
is_finite_per_grad = [
|
||
|
|
math_ops.reduce_all(math_ops.is_finite(raw_values(g)))
|
||
|
|
for g in grads
|
||
|
|
if g is not None
|
||
|
|
]
|
||
|
|
return math_ops.reduce_all(is_finite_per_grad)
|
||
|
|
|
||
|
|
|
||
|
|
def _op_in_graph_mode(tensor):
|
||
|
|
"""Returns the tensor's op in graph mode, or the tensor in eager mode.
|
||
|
|
|
||
|
|
This is useful because sometimes an op is needed in graph mode instead of a
|
||
|
|
tensor. In eager mode, there are no ops.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tensor: A tensor.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The tensor's op in graph mode. The tensor in eager mode.
|
||
|
|
"""
|
||
|
|
if context.executing_eagerly():
|
||
|
|
return tensor
|
||
|
|
return tensor.op
|
||
|
|
|
||
|
|
|
||
|
|
def _assign_if_finite(var, value):
|
||
|
|
"""Assigns a value to a variable if the value is finite."""
|
||
|
|
return cond.cond(
|
||
|
|
math_ops.is_finite(value), lambda: _op_in_graph_mode(var.assign(value)),
|
||
|
|
control_flow_ops.no_op)
|
||
|
|
|
||
|
|
|
||
|
|
@deprecation.deprecated_endpoints(
|
||
|
|
'mixed_precision.experimental.DynamicLossScale',
|
||
|
|
'train.experimental.DynamicLossScale')
|
||
|
|
@tf_export(
|
||
|
|
v1=[
|
||
|
|
'mixed_precision.DynamicLossScale',
|
||
|
|
'mixed_precision.experimental.DynamicLossScale',
|
||
|
|
'train.experimental.DynamicLossScale'
|
||
|
|
])
|
||
|
|
class DynamicLossScale(LossScale):
|
||
|
|
"""Loss scale that dynamically adjusts itself.
|
||
|
|
|
||
|
|
Dynamic loss scaling works by adjusting the loss scale as training progresses.
|
||
|
|
The goal is to keep the loss scale as high as possible without overflowing the
|
||
|
|
gradients. As long as the gradients do not overflow, raising the loss scale
|
||
|
|
never hurts.
|
||
|
|
|
||
|
|
The algorithm starts by setting the loss scale to an initial value. Every N
|
||
|
|
steps that the gradients are finite, the loss scale is increased by some
|
||
|
|
factor. However, if a NaN or Inf gradient is found, the gradients for that
|
||
|
|
step are not applied, and the loss scale is decreased by the factor. This
|
||
|
|
process tends to keep the loss scale as high as possible without gradients
|
||
|
|
overflowing.
|
||
|
|
"""
|
||
|
|
|
||
|
|
@deprecation.deprecated(
|
||
|
|
None, 'Use tf.keras.mixed_precision.LossScaleOptimizer instead. '
|
||
|
|
'LossScaleOptimizer now has all the functionality of '
|
||
|
|
'DynamicLossScale')
|
||
|
|
def __init__(self,
|
||
|
|
initial_loss_scale=2 ** 15, # See docstring for why this is big.
|
||
|
|
increment_period=2000,
|
||
|
|
multiplier=2.):
|
||
|
|
"""Creates the dynamic loss scale.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
initial_loss_scale: A Python float. The loss scale to use at the
|
||
|
|
beginning. It's better to start this at 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. The default is 2 ** 15, which is
|
||
|
|
approximately half the maximum float16 value.
|
||
|
|
increment_period: Increases loss scale every `increment_period`
|
||
|
|
consecutive steps that finite gradients are encountered. If a nonfinite
|
||
|
|
gradient is encountered, the count is reset back to zero.
|
||
|
|
multiplier: The multiplier to use when increasing or decreasing the loss
|
||
|
|
scale.
|
||
|
|
"""
|
||
|
|
super(DynamicLossScale, self).__init__()
|
||
|
|
self._initial_loss_scale = float(initial_loss_scale)
|
||
|
|
self._increment_period = int(increment_period)
|
||
|
|
self._multiplier = float(multiplier)
|
||
|
|
|
||
|
|
self._current_loss_scale = self._add_weight(
|
||
|
|
name='current_loss_scale',
|
||
|
|
dtype=dtypes.float32,
|
||
|
|
initial_value=self._initial_loss_scale)
|
||
|
|
# The number of consecutive steps with finite gradients since the last
|
||
|
|
# nonfinite gradient or change in loss scale.
|
||
|
|
self._num_good_steps = self._add_weight(
|
||
|
|
name='good_steps', dtype=dtypes.int64, initial_value=0)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def initial_loss_scale(self):
|
||
|
|
return self._initial_loss_scale
|
||
|
|
|
||
|
|
@property
|
||
|
|
def increment_period(self):
|
||
|
|
return self._increment_period
|
||
|
|
|
||
|
|
@property
|
||
|
|
def multiplier(self):
|
||
|
|
return self._multiplier
|
||
|
|
|
||
|
|
def __call__(self):
|
||
|
|
return ops.convert_to_tensor(self._current_loss_scale)
|
||
|
|
|
||
|
|
def update(self, grads):
|
||
|
|
"""Updates loss scale based on if gradients are finite in current step."""
|
||
|
|
grads = nest.flatten(grads)
|
||
|
|
if distribute_lib.has_strategy():
|
||
|
|
distribution = distribute_lib.get_cross_replica_context()
|
||
|
|
|
||
|
|
def get_is_finite(grads):
|
||
|
|
is_finite = _is_all_finite(grads)
|
||
|
|
# We cast to float, because we cannot reduce booleans with
|
||
|
|
# DistributionStrategy.
|
||
|
|
return math_ops.cast(is_finite, dtypes.float32)
|
||
|
|
|
||
|
|
is_finite_float = distribution.extended.call_for_each_replica(
|
||
|
|
get_is_finite, args=(grads,))
|
||
|
|
reduced_is_finite_float = distribution.reduce(reduce_util.ReduceOp.SUM,
|
||
|
|
is_finite_float, axis=None)
|
||
|
|
is_finite = math_ops.equal(reduced_is_finite_float,
|
||
|
|
distribution.num_replicas_in_sync)
|
||
|
|
else:
|
||
|
|
is_finite = _is_all_finite(grads)
|
||
|
|
|
||
|
|
def update_if_finite_grads():
|
||
|
|
"""Update assuming the gradients are finite."""
|
||
|
|
|
||
|
|
def incr_loss_scale():
|
||
|
|
new_loss_scale = self._current_loss_scale * self._multiplier
|
||
|
|
return control_flow_ops.group(
|
||
|
|
_assign_if_finite(self._current_loss_scale, new_loss_scale),
|
||
|
|
self._num_good_steps.assign(0))
|
||
|
|
|
||
|
|
return cond.cond(
|
||
|
|
self._num_good_steps + 1 >= self._increment_period,
|
||
|
|
incr_loss_scale, lambda: _op_in_graph_mode(
|
||
|
|
self._num_good_steps.assign_add(1)))
|
||
|
|
|
||
|
|
def update_if_not_finite_grads():
|
||
|
|
"""Update assuming the gradients are nonfinite."""
|
||
|
|
|
||
|
|
new_loss_scale = math_ops.maximum(
|
||
|
|
self._current_loss_scale / self._multiplier, 1)
|
||
|
|
return control_flow_ops.group(
|
||
|
|
self._num_good_steps.assign(0),
|
||
|
|
self._current_loss_scale.assign(new_loss_scale))
|
||
|
|
|
||
|
|
update_op = cond.cond(is_finite, update_if_finite_grads,
|
||
|
|
update_if_not_finite_grads)
|
||
|
|
should_apply_gradients = is_finite
|
||
|
|
return update_op, should_apply_gradients
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
if context.executing_eagerly():
|
||
|
|
return ('DynamicLossScale(current_loss_scale=%s, num_good_steps=%s, '
|
||
|
|
'initial_loss_scale=%s, increment_period=%s, multiplier=%s)' %
|
||
|
|
(self._current_loss_scale.numpy(), self._num_good_steps.numpy(),
|
||
|
|
self.initial_loss_scale, self.increment_period, self.multiplier))
|
||
|
|
else:
|
||
|
|
return ('DynamicLossScale(initial_loss_scale=%s, increment_period=%s, '
|
||
|
|
'multiplier=%s)' %
|
||
|
|
(self.initial_loss_scale, self.increment_period, self.multiplier))
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {
|
||
|
|
'initial_loss_scale': self.initial_loss_scale,
|
||
|
|
'increment_period': self.increment_period,
|
||
|
|
'multiplier': self.multiplier,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def get(identifier):
|
||
|
|
"""Get a loss scale object."""
|
||
|
|
if isinstance(identifier, (int, float)):
|
||
|
|
return FixedLossScale(identifier)
|
||
|
|
if identifier == 'dynamic':
|
||
|
|
return DynamicLossScale()
|
||
|
|
if isinstance(identifier, LossScale):
|
||
|
|
return identifier
|
||
|
|
elif identifier is None:
|
||
|
|
return None
|
||
|
|
else:
|
||
|
|
raise ValueError('Could not interpret loss scale identifier: %s' %
|
||
|
|
identifier)
|