text: """
  Copyright 2020 The OneFlow 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.
  """
  
  import unittest
  
  import numpy as np
  
  import oneflow.compatible.single_client.unittest
  from oneflow.compatible import single_client as flow
  
  config = flow.function_config()
  
  
  def make_job(x_shape, b_shape, dtype=flow.float32):
      config.use_xla_jit(False)
      config.use_tensorrt(False)
  
      @flow.global_function(config)
      def bias_add_job(
          x=flow.FixedTensorDef(x_shape, dtype=dtype),
          bias=flow.FixedTensorDef(b_shape, dtype=dtype),
      ):
          return flow.nn.bias_add(x, bias)
  
      return bias_add_job
  
  
  def make_xla_job(x_shape, b_shape, dtype=flow.float32):
      config.use_xla_jit(True)
      config.use_tensorrt(False)
  
      @flow.global_function(config)
      def xla_bias_add_job(
          x=flow.FixedTensorDef(x_shape, dtype=dtype),
          bias=flow.FixedTensorDef(b_shape, dtype=dtype),
      ):
          return flow.nn.bias_add(x, bias)
  
      return xla_bias_add_job
  
  
  def make_trt_job(x_shape, b_shape, dtype=flow.float32):
      config.use_xla_jit(False)
      config.use_tensorrt(True)
  
      @flow.global_function(config)
      def trt_bias_add_job(
          x=flow.FixedTensorDef(x_shape, dtype=dtype),
          bias=flow.FixedTensorDef(b_shape, dtype=dtype),
      ):
          return flow.nn.bias_add(x, bias)
  
      return trt_bias_add_job
  
  
  class TestBiasAdd(unittest.TestCase):
      def _test_body(self, x, bias, dtype=np.float32):
          f1 = make_job(x.shape, bias.shape, dtype=flow.float32)
          f2 = make_xla_job(x.shape, bias.shape, dtype=flow.float32)
          a = f1(x, bias).get()
          b = f2(x, bias).get()
          print("without xla: ", a)
          print("with xla: ", b)
          self.assertTrue(np.allclose(a.numpy(), b.numpy(), rtol=0.001, atol=1e-05))
          flow.clear_default_session()
          f3 = make_trt_job(x.shape, bias.shape, dtype=flow.float32)
          c = f3(x, bias).get()
          print("with tensorrt: ", c)
          self.assertTrue(np.allclose(a.numpy(), c.numpy(), rtol=0.001, atol=1e-05))
          flow.clear_default_session()
  
      def _test_ones_body(self, x_shape, bias_shape, dtype=np.float32):
          x = np.ones(x_shape, dtype=dtype)
          b = np.ones(bias_shape, dtype=dtype)
          self._test_body(x, b, dtype=dtype)
  
      def _test_random_body(self, x_shape, bias_shape, dtype=np.float32):
          x = np.random.random(x_shape).astype(dtype)
          b = np.random.random(bias_shape).astype(dtype)
          self._test_body(x, b, dtype=dtype)
  
      def test_ones_input(self):
          self._test_ones_body((1, 10), 10)
          self._test_ones_body((2, 10, 2), 10)
          self._test_ones_body((2, 5, 2, 2), 5)
  
      def test_random_input(self):
          self._test_random_body((1, 10), 10)
          self._test_random_body((2, 10, 2), 10)
          self._test_random_body((2, 5, 2, 2), 5)
  
  
  if __name__ == "__main__":
      unittest.main()
  