text: # Copyright 2021 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 Tensorflow -> CPURT compilation."""
  
  import numpy as np
  
  from tensorflow.compiler.mlir.tfrt.jit.python_binding import tf_cpurt
  from tensorflow.python.platform import test
  
  cpurt = tf_cpurt.TfCpurtExecutor()
  
  
  def softmax(x):
    z = x - np.max(x, axis=-1, keepdims=True)
    numerator = np.exp(z)
    denominator = np.sum(numerator, axis=-1, keepdims=True)
    result = numerator / denominator
    return result
  
  
  class TfSoftmaxTest(test.TestCase):
  
    def test_dynamic_softmax(self):
      mlir_function = """
          func @test(%input: tensor<?x?xf32>) -> tensor<?x?xf32> {
            %0 = "tf.Softmax"(%input) : (tensor<?x?xf32>) -> tensor<?x?xf32>
            return %0 : tensor<?x?xf32>
        }"""
  
      compiled = cpurt.compile(mlir_function, 'test', vectorize=True)
  
      arg0 = np.random.uniform(1, 5, size=(8, 8)).astype(np.float32)
  
      [res] = cpurt.execute(compiled, [arg0])
      np.testing.assert_allclose(res, softmax(arg0), atol=0.00001)
  
    def test_static_softmax(self):
      mlir_function = """
          func @test(%input: tensor<10x8xf32>) -> tensor<10x8xf32> {
            %0 = "tf.Softmax"(%input) : (tensor<10x8xf32>) -> tensor<10x8xf32>
            return %0 : tensor<10x8xf32>
        }"""
  
      compiled = cpurt.compile(mlir_function, 'test', vectorize=True)
  
      arg0 = np.random.uniform(1, 5, size=(10, 8)).astype(np.float32)
  
      [res] = cpurt.execute(compiled, [arg0])
      np.testing.assert_allclose(res, softmax(arg0), atol=0.00001)
  
  
  if __name__ == '__main__':
    test.main()
  