Add distribution strategy tests for premade models.
PiperOrigin-RevId: 261197895
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@ -97,6 +97,21 @@ py_library(
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],
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)
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distribute_py_test(
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name = "keras_premade_models_test",
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srcs = ["keras_premade_models_test.py"],
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full_precision = True,
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main = "keras_premade_models_test.py",
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shard_count = 4,
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tags = [
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"multi_and_single_gpu",
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],
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deps = [
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":distribute_strategy_test_lib",
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":keras_correctness_test_lib",
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],
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)
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distribute_py_test(
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name = "distribute_strategy_test",
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srcs = ["distribute_strategy_test.py"],
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@ -0,0 +1,96 @@
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# 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 keras premade models using tf.distribute.Strategy."""
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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 absl.testing import parameterized
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import numpy as np
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from tensorflow.python.data.ops import dataset_ops
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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.eager import test
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from tensorflow.python.keras.engine import sequential
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.optimizer_v2 import adagrad
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from tensorflow.python.keras.optimizer_v2 import gradient_descent
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from tensorflow.python.keras.premade import linear
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from tensorflow.python.keras.premade import wide_deep
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def strategy_combinations_eager_data_fn():
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return combinations.combine(
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distribution=[
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strategy_combinations.default_strategy,
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strategy_combinations.one_device_strategy,
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strategy_combinations.one_device_strategy_gpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_two_gpus
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],
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mode=['eager'],
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data_fn=[get_numpy, get_dataset])
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def get_numpy():
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inputs = np.random.uniform(low=-5, high=5, size=(64, 2)).astype(np.float32)
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output = .3 * inputs[:, 0] + .2 * inputs[:, 1]
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return inputs, output
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def get_dataset():
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inputs, output = get_numpy()
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dataset = dataset_ops.Dataset.from_tensor_slices((inputs, output))
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dataset = dataset.batch(10).repeat(10)
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return dataset
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class KerasPremadeModelsTest(test.TestCase, parameterized.TestCase):
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@combinations.generate(strategy_combinations_eager_data_fn())
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def test_linear_model(self, distribution, data_fn):
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with distribution.scope():
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model = linear.LinearModel()
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opt = gradient_descent.SGD(learning_rate=0.1)
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model.compile(opt, 'mse', experimental_run_tf_function=True)
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if data_fn == get_numpy:
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inputs, output = get_numpy()
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hist = model.fit(inputs, output, epochs=5)
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else:
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hist = model.fit(get_dataset(), epochs=5)
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self.assertLess(hist.history['loss'][4], 0.1)
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@combinations.generate(strategy_combinations_eager_data_fn())
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def test_wide_deep_model(self, distribution, data_fn):
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with distribution.scope():
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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linear_opt = gradient_descent.SGD(learning_rate=0.1)
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dnn_opt = adagrad.Adagrad(learning_rate=0.2)
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wide_deep_model.compile(
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optimizer=[linear_opt, dnn_opt],
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loss='mse',
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experimental_run_tf_function=True)
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if data_fn == get_numpy:
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inputs, output = get_numpy()
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hist = wide_deep_model.fit(inputs, output, epochs=5)
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else:
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hist = wide_deep_model.fit(get_dataset(), epochs=5)
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self.assertLess(hist.history['loss'][4], 0.2)
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if __name__ == '__main__':
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test.main()
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