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# Copyright 2018 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.
# ==============================================================================
"""The step function abstraction represents a single training step."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import backprop
from tensorflow.python.training import optimizer as optimizer_lib
class Step(object):
"""Interface for performing each step of a training algorithm."""
def __init__(self, distribution):
self._distribution = distribution
@property
def distribution(self):
return self._distribution
def __call__(self):
"""Perform one step of this training algorithm."""
raise NotImplementedError("must be implemented in descendants")
# TODO(priyag): Add an method to access initialization and finalize ops.
class StandardInputStep(Step):
"""Step with a standard implementation of input handling.
Args:
dataset_fn: a function that returns a tf.data Dataset that produces the
input for the model.
"""
def __init__(self, dataset_fn, distribution):
super(StandardInputStep, self).__init__(distribution)
self._distributed_input = distribution.distribute_dataset(dataset_fn)
self._iterator = self._distributed_input.make_one_shot_iterator()
class StandardSingleLossStep(StandardInputStep):
"""A step function that implements a training step for a feed forward network.
An instance of this class is intended to be used as a callable:
```python
...
step = step_fn.StandardSingleLossStep(
dataset, loss_fn, optimizer, distribution)
# Run a single training step on a given DistributionStrategy:
step(distribution)
...
```
Args:
dataset_fn: a function that returns a tf.data Dataset that produces the
input for the model.
loss_fn: a function that takes a context and inputs as arguments. It returns
the loss for those inputs. `context` is an instance of
`values.MultiStepContext` that will be passed when `loss_fn` is run.
`context` can be used to specify the outputs to be returned from
`loss_fn`, among other things.
optimizer: an optimizer that implements an update rule.
distribution: a `DistributionStrategy` object.
"""
def __init__(self, dataset_fn, loss_fn, optimizer, distribution,
iterations_per_step=1):
super(StandardSingleLossStep, self).__init__(dataset_fn, distribution)
self._loss_fn = loss_fn
self._optimizer = optimizer
self._is_run_concurrently = False
self._iterations_per_step = iterations_per_step
def __call__(self):
with self._distribution.scope():
def step_fn(ctx, *inputs):
"""Function to run one iteration with one input."""
gradients_fn = backprop.implicit_grad(self._loss_fn)
gradients_fn = optimizer_lib.get_filtered_grad_fn(gradients_fn)
grads_and_vars = self.distribution.call_for_each_tower(
gradients_fn,
ctx, *inputs,
run_concurrently=self._is_run_concurrently)
# If threads use layers, then we need to run the first step
# sequentially, so that layers.build() is not executed in parallel.
# Otherwise, multiple sets of mirrored variables are going to be
# created.
self._is_run_concurrently = True
return self._optimizer._distributed_apply( # pylint: disable=protected-access
self.distribution, grads_and_vars)
# TODO(priyag): Return the outputs, context, etc as well.
ctx = self.distribution.run_steps_on_dataset(
step_fn, self._iterator, self._iterations_per_step)
return ctx.run_op
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