Add distribution strategy tests for premade models.

PiperOrigin-RevId: 261197895

tensorflow/python/keras/distribute/BUILD

@@ -97,6 +97,21 @@ py_library(
     ],
 )
 
+distribute_py_test(
+    name = "keras_premade_models_test",
+    srcs = ["keras_premade_models_test.py"],
+    full_precision = True,
+    main = "keras_premade_models_test.py",
+    shard_count = 4,
+    tags = [
+        "multi_and_single_gpu",
+    ],
+    deps = [
+        ":distribute_strategy_test_lib",
+        ":keras_correctness_test_lib",
+    ],
+)
+
 distribute_py_test(
     name = "distribute_strategy_test",
     srcs = ["distribute_strategy_test.py"],

tensorflow/python/keras/distribute/keras_premade_models_test.py

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