text: # Copyright 2020 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.
  # ==============================================================================
  """Benchmarks on Keras layers."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import six
  
  import tensorflow as tf
  from tensorflow.python.keras.benchmarks.layer_benchmarks import layer_benchmarks_test_base
  from tensorflow.python.platform import benchmark
  
  
  def _layer_call_backward(layer, x):
    with tf.GradientTape() as tape:
      y = layer(x)
      loss = tf.reduce_mean(y**2)
  
    _ = tape.gradient(loss, layer.trainable_variables)
  
  
  class KerasLayerBenchmarks(six.with_metaclass(
      benchmark.ParameterizedBenchmark,
      layer_benchmarks_test_base.LayerBenchmarksBase)):
  
    _benchmark_parameters = [
        ("Conv2D_small_shape", tf.keras.layers.Conv2D,
         {"filters": 1, "kernel_size": 1, "activation": "relu"},
         (1, 1, 1, 1), 10000),
        ("Conv2D_normal_shape", tf.keras.layers.Conv2D,
         {"filters": 1, "kernel_size": 1, "activation": "relu"},
         (64, 28, 28, 3), 10000),
        ("LSTM_small_shape", tf.keras.layers.LSTM,
         {"units": 1}, (1, 1, 1), 10000),
        ("LSTM_normal_shape", tf.keras.layers.LSTM,
         {"units": 4}, (32, 10, 8), 10000),
    ]
  
    def benchmark_layer_call(self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
  
      fn = functools.partial(layer, x)
      self.run_report(fn, num_iters)
  
    def benchmark_layer_call_with_function(
        self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
      layer.call = tf.function(layer.call)
  
      fn = functools.partial(layer, x)
      self.run_report(fn, num_iters)
  
    def benchmark_layer_call_with_xla(
        self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
      layer.call = tf.function(
          layer.call, experimental_compile=True)
  
      fn = functools.partial(layer, x)
      self.run_report(fn, num_iters)
  
    def benchmark_layer_call_backward(
        self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
  
      fn = functools.partial(_layer_call_backward, layer, x)
      self.run_report(fn, num_iters)
  
    def benchmark_layer_call_backward_with_function(
        self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
      layer.call = tf.function(layer.call)
  
      fn = functools.partial(_layer_call_backward, layer, x)
      self.run_report(fn, num_iters)
  
  
  class KerasLayerBenchmarksBackwardXLA(six.with_metaclass(
      benchmark.ParameterizedBenchmark,
      layer_benchmarks_test_base.LayerBenchmarksBase)):
  
    _benchmark_parameters = [
        ("Conv2D_small_shape", tf.keras.layers.Conv2D,
         {"filters": 1, "kernel_size": 1, "activation": "relu"},
         (1, 1, 1, 1), 10000),
        ("Conv2D_normal_shape", tf.keras.layers.Conv2D,
         {"filters": 1, "kernel_size": 1, "activation": "relu"},
         (64, 28, 28, 3), 10000),
        # TODO(b/153480400)
        # ("LSTM_small_shape", tf.keras.layers.LSTM,
        #  {"units": 1}, (1, 1, 1), 10000),
        # ("LSTM_normal_shape", tf.keras.layers.LSTM,
        #  {"units": 4}, (32, 10, 8), 10000),
    ]
  
    def benchmark_layer_call_backward_with_xla(
        self, layer_cls, layer_args, input_shape, num_iters):
      layer = layer_cls(**layer_args)
      x = tf.ones(input_shape)
      layer.call = tf.function(
          layer.call, experimental_compile=True)
  
      fn = functools.partial(_layer_call_backward, layer, x)
      self.run_report(fn, num_iters)
  
  
  if __name__ == "__main__":
    tf.test.main()
  