text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  # Copyright (c) 2022 NVIDIA Corporation. 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.
  
  from __future__ import print_function
  
  import unittest
  import numpy as np
  import paddle
  import paddle.fluid.core as core
  from op_test import OpTest, skip_check_grad_ci
  
  
  def get_outputs(DOut, X, Y):
      DX = np.dot(DOut, Y.T)
      DY = np.dot(X.T, DOut)
      DBias = np.sum(DOut, axis=0)
  
      return DX, DY, DBias
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYBiasFP16(OpTest):
      def setUp(self):
          self.op_type = "fused_gemm_epilogue_grad"
          self.place = core.CUDAPlace(0)
          self.init_dtype_type()
  
          self.inputs = {
              'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
              'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
              'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
          }
  
          self.attrs = {"activation": 'none'}
  
          DX, DY, DBias = get_outputs(self.inputs['DOut'], self.inputs['X'],
                                      self.inputs['Y'])
          self.outputs = {'DX': DX, 'DY': DY, 'DBias': DBias}
  
      def init_dtype_type(self):
          self.dtype = np.float16
          self.atol = 1e-3
  
      def test_check_output(self):
          if self.dtype == np.float16 and not core.is_float16_supported(
                  self.place):
              return
          self.check_output_with_place(self.place, atol=self.atol)
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYBiasFP32(
          TestFuseGemmEpilogueGradOpDXYBiasFP16):
      def init_dtype_type(self):
          self.dtype = np.single
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYBiasFP64(
          TestFuseGemmEpilogueGradOpDXYBiasFP16):
      def init_dtype_type(self):
          self.dtype = np.double
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYBiasFP16(OpTest):
      def setUp(self):
          self.op_type = "fused_gemm_epilogue_grad"
          self.place = core.CUDAPlace(0)
          self.init_dtype_type()
  
          self.inputs = {
              'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
              'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
              'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
          }
  
          self.attrs = {"activation": 'none'}
  
          _, DY, DBias = get_outputs(self.inputs['DOut'], self.inputs['X'],
                                     self.inputs['Y'])
          self.outputs = {'DY': DY, 'DBias': DBias}
  
      def init_dtype_type(self):
          self.dtype = np.float16
          self.atol = 1e-3
  
      def test_check_output(self):
          if self.dtype == np.float16 and not core.is_float16_supported(
                  self.place):
              return
          self.check_output_with_place(self.place, atol=self.atol)
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYBiasFP32(
          TestFuseGemmEpilogueGradOpDYBiasFP16):
      def init_dtype_type(self):
          self.dtype = np.single
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYBiasFP64(
          TestFuseGemmEpilogueGradOpDYBiasFP16):
      def init_dtype_type(self):
          self.dtype = np.double
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYFP16(OpTest):
      def setUp(self):
          self.op_type = "fused_gemm_epilogue_grad"
          self.place = core.CUDAPlace(0)
          self.init_dtype_type()
  
          self.inputs = {
              'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
              'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
              'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
          }
  
          self.attrs = {"activation": 'none'}
  
          _, DY, _ = get_outputs(self.inputs['DOut'], self.inputs['X'],
                                 self.inputs['Y'])
          self.outputs = {'DY': DY}
  
      def init_dtype_type(self):
          self.dtype = np.float16
          self.atol = 1e-3
  
      def test_check_output(self):
          if self.dtype == np.float16 and not core.is_float16_supported(
                  self.place):
              return
          self.check_output_with_place(self.place, atol=self.atol)
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYFP32(TestFuseGemmEpilogueGradOpDYFP16):
      def init_dtype_type(self):
          self.dtype = np.single
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDYFP64(TestFuseGemmEpilogueGradOpDYFP16):
      def init_dtype_type(self):
          self.dtype = np.double
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYFP16(OpTest):
      def setUp(self):
          self.op_type = "fused_gemm_epilogue_grad"
          self.place = core.CUDAPlace(0)
          self.init_dtype_type()
  
          self.inputs = {
              'DOut': np.random.random((8, 128)).astype(self.dtype) - 0.5,
              'X': np.random.random((8, 4)).astype(self.dtype) - 0.5,
              'Y': np.random.random((4, 128)).astype(self.dtype) - 0.5
          }
  
          self.attrs = {"activation": 'none'}
  
          DX, DY, _ = get_outputs(self.inputs['DOut'], self.inputs['X'],
                                  self.inputs['Y'])
          self.outputs = {'DX': DX, 'DY': DY}
  
      def init_dtype_type(self):
          self.dtype = np.float16
          self.atol = 1e-3
  
      def test_check_output(self):
          if self.dtype == np.float16 and not core.is_float16_supported(
                  self.place):
              return
          self.check_output_with_place(self.place, atol=self.atol)
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYFP32(TestFuseGemmEpilogueGradOpDXYFP16):
      def init_dtype_type(self):
          self.dtype = np.single
          self.atol = 1e-6
  
  
  @skip_check_grad_ci(reason="no grap op")
  @unittest.skipIf(not core.is_compiled_with_cuda(),
                   "core is not compiled with CUDA")
  class TestFuseGemmEpilogueGradOpDXYFP64(TestFuseGemmEpilogueGradOpDXYFP16):
      def init_dtype_type(self):
          self.dtype = np.double
          self.atol = 1e-6
  
  
  if __name__ == "__main__":
      np.random.seed(0)
      unittest.main()
  