161 lines
5.5 KiB
Python
161 lines
5.5 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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"""Correctness tests for tf.keras RNN models using DistributionStrategy."""
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import numpy as np
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import tensorflow.compat.v2 as tf
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import keras
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from keras.distribute import keras_correctness_test_base
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from keras.layers.rnn import gru
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from keras.layers.rnn import gru_v1
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from keras.layers.rnn import lstm
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from keras.layers.rnn import lstm_v1
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from keras.mixed_precision import policy
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from keras.optimizers.legacy import gradient_descent as gradient_descent_keras
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from keras.testing_infra import test_utils
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class _DistributionStrategyRnnModelCorrectnessTest(
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keras_correctness_test_base.TestDistributionStrategyEmbeddingModelCorrectnessBase # noqa: E501
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):
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def _get_layer_class(self):
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raise NotImplementedError
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def get_model(
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self,
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max_words=10,
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initial_weights=None,
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distribution=None,
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input_shapes=None,
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):
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del input_shapes
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rnn_cls = self._get_layer_class()
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with keras_correctness_test_base.MaybeDistributionScope(distribution):
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word_ids = keras.layers.Input(
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shape=(max_words,), dtype=np.int32, name="words"
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)
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word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(
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word_ids
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)
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rnn_embed = rnn_cls(units=4, return_sequences=False)(word_embed)
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dense_output = keras.layers.Dense(2)(rnn_embed)
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preds = keras.layers.Softmax(dtype="float32")(dense_output)
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model = keras.Model(inputs=[word_ids], outputs=[preds])
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if initial_weights:
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model.set_weights(initial_weights)
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optimizer_fn = gradient_descent_keras.SGD
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model.compile(
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optimizer=optimizer_fn(learning_rate=0.1),
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loss="sparse_categorical_crossentropy",
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metrics=["sparse_categorical_accuracy"],
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)
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return model
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@test_utils.run_all_without_tensor_float_32(
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"Uses Dense layers, which call matmul"
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)
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class DistributionStrategyGruModelCorrectnessTest(
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_DistributionStrategyRnnModelCorrectnessTest
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):
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def _get_layer_class(self):
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if tf.__internal__.tf2.enabled():
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if not tf.executing_eagerly():
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self.skipTest(
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"GRU v2 and legacy graph mode don't work together."
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)
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return gru.GRU
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else:
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return gru_v1.GRU
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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def test_gru_model_correctness(
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self, distribution, use_numpy, use_validation_data
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):
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self.run_correctness_test(distribution, use_numpy, use_validation_data)
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@test_utils.run_all_without_tensor_float_32(
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"Uses Dense layers, which call matmul"
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)
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class DistributionStrategyLstmModelCorrectnessTest(
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_DistributionStrategyRnnModelCorrectnessTest
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):
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def _get_layer_class(self):
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if tf.__internal__.tf2.enabled():
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if not tf.executing_eagerly():
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self.skipTest(
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"LSTM v2 and legacy graph mode don't work together."
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)
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return lstm.LSTM
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else:
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return lstm_v1.LSTM
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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def test_lstm_model_correctness(
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self, distribution, use_numpy, use_validation_data
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):
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self.run_correctness_test(distribution, use_numpy, use_validation_data)
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@tf.__internal__.distribute.combinations.generate(
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keras_correctness_test_base.test_combinations_for_embedding_model()
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+ keras_correctness_test_base.multi_worker_mirrored_eager()
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)
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@test_utils.enable_v2_dtype_behavior
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def test_lstm_model_correctness_mixed_precision(
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self, distribution, use_numpy, use_validation_data
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):
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if isinstance(
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distribution,
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(
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tf.distribute.experimental.CentralStorageStrategy,
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tf.compat.v1.distribute.experimental.CentralStorageStrategy,
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),
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):
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self.skipTest(
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"CentralStorageStrategy is not supported by mixed precision."
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)
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if isinstance(
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distribution,
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(
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tf.distribute.experimental.TPUStrategy,
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tf.compat.v1.distribute.experimental.TPUStrategy,
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),
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):
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policy_name = "mixed_bfloat16"
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else:
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policy_name = "mixed_float16"
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with policy.policy_scope(policy_name):
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self.run_correctness_test(
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distribution, use_numpy, use_validation_data
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)
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if __name__ == "__main__":
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tf.__internal__.distribute.multi_process_runner.test_main()
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