• Y
    Implement the GPU kernel of fc operator (#19687) · a65c728e
    Yiqun Liu 提交于
    * Refine the codes related to fc op.
    
    * Add GPU implementation for fc functor.
    
    * Apply fc_fuse_pass in GPU inference.
    test=develop
    
    * Change the cmake for fc op.
    
    * Change PADDLE_ENFORCE to PADDLE_ENFORCE_EQ.
    
    * Add an attribute to set the activation type in fc_op.
    
    * Enhance the unittest of fc_op.
    test=develop
    
    * Remove the declaration of FCOpGrad back to the header file.
    test=develop
    
    * Set default value for newly added arguments in test_fc_op.
    test=develop
    未验证
    a65c728e
fc.cu 2.5 KB
/* Copyright (c) 2016 PaddlePaddle 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. */

#include <algorithm>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/fc.h"

namespace paddle {
namespace operators {
namespace math {

template <typename T, bool DoRelu>
__global__ void InplaceAddReluKernel(const T* bias, T* data, int M, int N) {
  for (int i = blockIdx.x; i < M; i += gridDim.x) {
    int index = i * N + threadIdx.x;
    for (int j = threadIdx.x; j < N; j += blockDim.x) {
      T tmp = data[index] + bias[j];
      if (DoRelu) {
        data[index] = (tmp > 0) ? tmp : 0;
      } else {
        data[index] = tmp;
      }
      index += blockDim.x;
    }
  }
}

template <typename T>
class FCFunctor<platform::CUDADeviceContext, T> {
 public:
  void operator()(const platform::CUDADeviceContext& context, const int M,
                  const int N, const int K, const T* X, const T* W, T* Y,
                  const T* B = nullptr, bool relu = false) {
    auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context);
    blas.GEMM(false, false, M, N, K, static_cast<T>(1.0), X, K, W, N,
              static_cast<T>(0.0), Y, N);
    if (B == NULL) {
      return;
    }

    const int kThreadsPerBlock = 1024;
    int max_threads = context.GetMaxPhysicalThreadCount();
    int num_threads = std::min(kThreadsPerBlock, (((N + 31) >> 5) << 5));
    int num_blocks = std::max(max_threads / num_threads, 1);
    if (relu) {
      InplaceAddReluKernel<
          T, true><<<num_blocks, num_threads, 0, context.stream()>>>(B, Y, M,
                                                                     N);
    } else {
      InplaceAddReluKernel<
          T, false><<<num_blocks, num_threads, 0, context.stream()>>>(B, Y, M,
                                                                      N);
    }
  }
};

template class FCFunctor<platform::CUDADeviceContext, float>;
template class FCFunctor<platform::CUDADeviceContext, double>;

}  // namespace math
}  // namespace operators
}  // namespace paddle
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