a861238b03
TF distribute shouldn't rely on any keras code. PiperOrigin-RevId: 340483958 Change-Id: I4c3774dce1e914dc1f257d13117420a3fb9b3406
112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
# Copyright 2019 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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"""Tests for ShardedVariable."""
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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.compat import v2_compat
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from tensorflow.python.distribute import sharded_variable
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.ops import variable_scope
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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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class ShardedVariableTest(test.TestCase):
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def test_keras_layer_setattr(self):
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class Layer(base_layer.Layer):
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def __init__(self):
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super().__init__()
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variables1 = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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]
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variables2 = [
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variables_lib.Variable([2], trainable=False),
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variables_lib.Variable([3], trainable=False),
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]
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self.w = sharded_variable.ShardedVariable(variables1)
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self.b = sharded_variable.ShardedVariable(variables2)
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layer = Layer()
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self.assertLen(layer.trainable_weights, 2)
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self.assertEqual(layer.trainable_weights[0], [0])
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self.assertEqual(layer.trainable_weights[1], [1])
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self.assertLen(layer.non_trainable_weights, 2)
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self.assertEqual(layer.non_trainable_weights[0], [2])
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self.assertEqual(layer.non_trainable_weights[1], [3])
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self.assertAllEqual(layer.weights,
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layer.trainable_weights + layer.non_trainable_weights)
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self.assertAllEqual(layer.trainable_weights, layer.trainable_variables)
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self.assertAllEqual(layer.weights, layer.variables)
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checkpoint_deps = set(dep.ref for dep in layer._checkpoint_dependencies)
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self.assertEqual(checkpoint_deps, set([layer.w, layer.b]))
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def test_keras_layer_add_weight(self):
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class Layer(base_layer.Layer):
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def __init__(self):
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super().__init__()
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self.w = self.add_weight(
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shape=(2,), initializer=lambda shape, dtype: [0, 1], trainable=True)
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self.b = self.add_weight(
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shape=(2,),
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initializer=lambda shape, dtype: [2, 3],
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trainable=False)
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def sharded_variable_creator(next_creator, **kwargs):
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v1_value = kwargs['initial_value']()[0:1]
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v2_value = kwargs['initial_value']()[1:]
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kwargs['initial_value'] = v1_value
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kwargs['shape'] = (1,)
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v1 = next_creator(**kwargs)
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kwargs['initial_value'] = v2_value
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kwargs['shape'] = (1,)
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v2 = next_creator(**kwargs)
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return sharded_variable.ShardedVariable([v1, v2])
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with variable_scope.variable_creator_scope(sharded_variable_creator):
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layer = Layer()
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self.assertLen(layer.trainable_weights, 2)
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self.assertEqual(layer.trainable_weights[0], [0])
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self.assertEqual(layer.trainable_weights[1], [1])
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self.assertLen(layer.non_trainable_weights, 2)
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self.assertEqual(layer.non_trainable_weights[0], [2])
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self.assertEqual(layer.non_trainable_weights[1], [3])
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self.assertAllEqual(layer.weights,
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layer.trainable_weights + layer.non_trainable_weights)
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self.assertAllEqual(layer.trainable_weights, layer.trainable_variables)
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self.assertAllEqual(layer.weights, layer.variables)
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checkpoint_deps = set(dep.ref for dep in layer._checkpoint_dependencies)
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self.assertEqual(checkpoint_deps, set([layer.w, layer.b]))
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if __name__ == '__main__':
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v2_compat.enable_v2_behavior()
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test.main()
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