| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Correctness tests for tf.keras RNN models using DistributionStrategy.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| from tensorflow.python import keras |
| from tensorflow.python import tf2 |
| from tensorflow.python.distribute import central_storage_strategy |
| from tensorflow.python.distribute import combinations as ds_combinations |
| from tensorflow.python.distribute import multi_process_runner |
| from tensorflow.python.distribute import tpu_strategy |
| from tensorflow.python.eager import context |
| from tensorflow.python.keras import testing_utils |
| from tensorflow.python.keras.distribute import keras_correctness_test_base |
| from tensorflow.python.keras.layers import recurrent as rnn_v1 |
| from tensorflow.python.keras.layers import recurrent_v2 as rnn_v2 |
| from tensorflow.python.keras.mixed_precision import policy |
| from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_keras |
| |
| |
| class _DistributionStrategyRnnModelCorrectnessTest( |
| keras_correctness_test_base |
| .TestDistributionStrategyEmbeddingModelCorrectnessBase): |
| |
| def _get_layer_class(self): |
| raise NotImplementedError |
| |
| def get_model(self, |
| max_words=10, |
| initial_weights=None, |
| distribution=None, |
| input_shapes=None): |
| del input_shapes |
| rnn_cls = self._get_layer_class() |
| |
| with keras_correctness_test_base.MaybeDistributionScope(distribution): |
| word_ids = keras.layers.Input( |
| shape=(max_words,), dtype=np.int32, name='words') |
| word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(word_ids) |
| rnn_embed = rnn_cls(units=4, return_sequences=False)(word_embed) |
| |
| dense_output = keras.layers.Dense(2)(rnn_embed) |
| preds = keras.layers.Softmax(dtype='float32')(dense_output) |
| model = keras.Model(inputs=[word_ids], outputs=[preds]) |
| |
| if initial_weights: |
| model.set_weights(initial_weights) |
| |
| optimizer_fn = gradient_descent_keras.SGD |
| |
| model.compile( |
| optimizer=optimizer_fn(learning_rate=0.1), |
| loss='sparse_categorical_crossentropy', |
| metrics=['sparse_categorical_accuracy']) |
| return model |
| |
| |
| @testing_utils.run_all_without_tensor_float_32( |
| 'Uses Dense layers, which call matmul') |
| class DistributionStrategyGruModelCorrectnessTest( |
| _DistributionStrategyRnnModelCorrectnessTest): |
| |
| def _get_layer_class(self): |
| if tf2.enabled(): |
| if not context.executing_eagerly(): |
| self.skipTest("GRU v2 and legacy graph mode don't work together.") |
| return rnn_v2.GRU |
| else: |
| return rnn_v1.GRU |
| |
| @ds_combinations.generate( |
| keras_correctness_test_base.test_combinations_for_embedding_model() + |
| keras_correctness_test_base.multi_worker_mirrored_eager()) |
| def test_gru_model_correctness(self, distribution, use_numpy, |
| use_validation_data): |
| self.run_correctness_test(distribution, use_numpy, use_validation_data) |
| |
| |
| @testing_utils.run_all_without_tensor_float_32( |
| 'Uses Dense layers, which call matmul') |
| class DistributionStrategyLstmModelCorrectnessTest( |
| _DistributionStrategyRnnModelCorrectnessTest): |
| |
| def _get_layer_class(self): |
| if tf2.enabled(): |
| if not context.executing_eagerly(): |
| self.skipTest("LSTM v2 and legacy graph mode don't work together.") |
| return rnn_v2.LSTM |
| else: |
| return rnn_v1.LSTM |
| |
| @ds_combinations.generate( |
| keras_correctness_test_base.test_combinations_for_embedding_model() + |
| keras_correctness_test_base.multi_worker_mirrored_eager()) |
| def test_lstm_model_correctness(self, distribution, use_numpy, |
| use_validation_data): |
| self.run_correctness_test(distribution, use_numpy, use_validation_data) |
| |
| @ds_combinations.generate( |
| keras_correctness_test_base.test_combinations_for_embedding_model() + |
| keras_correctness_test_base.multi_worker_mirrored_eager()) |
| @testing_utils.enable_v2_dtype_behavior |
| def test_lstm_model_correctness_mixed_precision(self, distribution, use_numpy, |
| use_validation_data): |
| if isinstance(distribution, |
| (central_storage_strategy.CentralStorageStrategy, |
| central_storage_strategy.CentralStorageStrategyV1)): |
| self.skipTest('CentralStorageStrategy is not supported by ' |
| 'mixed precision.') |
| if isinstance(distribution, |
| (tpu_strategy.TPUStrategy, tpu_strategy.TPUStrategyV1)): |
| policy_name = 'mixed_bfloat16' |
| else: |
| policy_name = 'mixed_float16' |
| |
| with policy.policy_scope(policy_name): |
| self.run_correctness_test(distribution, use_numpy, use_validation_data) |
| |
| |
| if __name__ == '__main__': |
| multi_process_runner.test_main() |