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