143 lines
5.0 KiB
Python
143 lines
5.0 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 os
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from absl.testing import parameterized
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from tensorflow.python.distribute import combinations as ds_combinations
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from tensorflow.python.distribute import strategy_combinations
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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.framework import ops
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from tensorflow.python.framework import test_combinations as combinations
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from tensorflow.python.keras.optimizer_v2 import adam
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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.platform import test
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from tensorflow.python.training.saving import checkpoint_options
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from tensorflow.python.training.tracking import util as trackable_utils
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class TrainingCheckpointTests(test.TestCase, parameterized.TestCase):
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@ds_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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@ds_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.cloud_tpu_strategy,
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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 testCheckpointSaveRestoreIoDevice(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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return v
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ckpt_options = checkpoint_options.CheckpointOptions(
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experimental_io_device="/job:localhost")
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def checkpoint():
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v = state()
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# Save random weights into checkpoint.
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checkpoint = trackable_utils.Checkpoint(v=v)
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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, options=ckpt_options)
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return save_path
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save_path = checkpoint()
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v = state()
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checkpoint = trackable_utils.Checkpoint(v=v)
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# Restore from the checkpoint inside a distribution.scope().
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# Check that restore works without error.
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with self.test_session():
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with distribution.scope():
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checkpoint.restore(save_path, options=ckpt_options)
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if __name__ == "__main__":
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ops.enable_eager_execution()
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test.main()
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