198 lines
7.4 KiB
Python
198 lines
7.4 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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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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import functools
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import os
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from absl.testing import parameterized
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.distribute import tpu_strategy
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import def_function
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from tensorflow.python.eager import test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.optimizer_v2 import adam
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables as variables_lib
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from tensorflow.python.training import adam as adam_v1
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from tensorflow.python.training import checkpoint_management
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from tensorflow.python.training import training_util
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from tensorflow.python.training.tracking import tracking
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from tensorflow.python.training.tracking import util as trackable_utils
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class NonLayerTrackable(tracking.AutoTrackable):
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def __init__(self):
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super(NonLayerTrackable, self).__init__()
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self.a_variable = trackable_utils.add_variable(
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self, name="a_variable", shape=[])
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class Subclassed(training.Model):
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"""A concrete Model for testing."""
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def __init__(self):
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super(Subclassed, self).__init__()
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self._named_dense = core.Dense(1, use_bias=True)
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self._second = core.Dense(1, use_bias=False)
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# We can still track Trackables which aren't Layers.
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self._non_layer = NonLayerTrackable()
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def call(self, values):
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ret = self._second(self._named_dense(values))
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return ret
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class TrainingCheckpointTests(test.TestCase, parameterized.TestCase):
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def testEagerTPUDistributionStrategy(self):
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self.skipTest("b/121387144")
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num_training_steps = 10
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checkpoint_directory = self.get_temp_dir()
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checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
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def _train_fn(optimizer, model):
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input_value = constant_op.constant([[3.]])
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optimizer.minimize(
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functools.partial(model, input_value),
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global_step=root.optimizer_step)
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for training_continuation in range(3):
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strategy = tpu_strategy.TPUStrategy()
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with strategy.scope():
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model = Subclassed()
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optimizer = adam_v1.AdamOptimizer(0.001)
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root = trackable_utils.Checkpoint(
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optimizer=optimizer, model=model,
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optimizer_step=training_util.get_or_create_global_step())
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root.restore(checkpoint_management.latest_checkpoint(
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checkpoint_directory))
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for _ in range(num_training_steps):
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strategy.extended.call_for_each_replica(
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functools.partial(_train_fn, optimizer, model))
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root.save(file_prefix=checkpoint_prefix)
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self.assertEqual((training_continuation + 1) * num_training_steps,
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root.optimizer_step.numpy())
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.mirrored_strategy_with_one_cpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.tpu_strategy,
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strategy_combinations.tpu_strategy_packed_var,
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strategy_combinations.central_storage_strategy_with_two_gpus,
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],
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mode=["eager"]))
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def testInitializeFromCheckpoint(self, distribution):
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variable_shape = [5]
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save_checkpoint = trackable_utils.Checkpoint(v=variables_lib.Variable(
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array_ops.ones(variable_shape)))
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save_path = save_checkpoint.save(
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os.path.join(self.get_temp_dir(), "checkpoint"))
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with distribution.scope():
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restore_checkpoint = trackable_utils.Checkpoint()
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restore_checkpoint.restore(save_path)
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initial_value = restore_checkpoint._preload_simple_restoration(
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"v", variable_shape)
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v = variables_lib.Variable(initial_value)
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# Check that the variable is now tagged as restored. `Checkpoint` then
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# knows it doesn't have to restore `v`'s value when it's assigned to an
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# object.
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self.assertGreater(v._update_uid, 0)
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self.assertAllClose(array_ops.ones(variable_shape), v)
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v.assign(array_ops.zeros(variable_shape))
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# Assignment to an object should not trigger restoration, since we already
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# restored the object through an initializer. This wouldn't be a
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# correctness issue, but it would mean that models would use twice as much
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# memory when loading (the buffer already assigned to the variable, and
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# the new restoration).
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restore_checkpoint.v = v
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self.assertAllClose(array_ops.zeros(variable_shape), v)
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.mirrored_strategy_with_one_cpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.tpu_strategy,
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strategy_combinations.tpu_strategy_packed_var,
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strategy_combinations.central_storage_strategy_with_two_gpus,
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],
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mode=["eager"]))
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def testCheckpointRestoreOptimizerSlots(self, distribution):
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def state():
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with distribution.scope():
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v = variables_lib.Variable(random_ops.random_normal([]))
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opt = adam.Adam(0.001)
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@def_function.function
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def step():
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def f():
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with backprop.GradientTape() as tape:
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loss = v + v
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gradients = tape.gradient(loss, [v])
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opt.apply_gradients(zip(gradients, [v]))
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distribution.run(f)
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return v, opt, step
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def checkpoint():
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v, opt, step = state()
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step()
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# Save random weights into checkpoint.
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checkpoint = trackable_utils.Checkpoint(v=v, opt=opt)
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prefix = os.path.join(self.get_temp_dir(), "ckpt")
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with self.test_session():
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save_path = checkpoint.save(prefix)
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return save_path
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save_path = checkpoint()
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v, opt, step = state()
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checkpoint = trackable_utils.Checkpoint(v=v, opt=opt)
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# Restore from the checkpoint inside a distribution.scope().
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with self.test_session():
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with distribution.scope():
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checkpoint.restore(save_path)
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step()
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slot = opt.get_slot(v, "m")
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self.assertEqual(v._distribute_strategy, slot._distribute_strategy)
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v, opt, step = state()
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checkpoint = trackable_utils.Checkpoint(v=v, opt=opt)
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# Restore from the checkpoint outside a distribution.scope().
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with self.test_session():
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with self.assertRaisesRegex(
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ValueError, "optimizer slot variable under the scope"):
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checkpoint.restore(save_path)
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if __name__ == "__main__":
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test.main()
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