label_smooth_op.cu 4.5 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Y
Yibing Liu 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14

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. */

15
#include "paddle/fluid/framework/tensor.h"
Y
Yi Wang 已提交
16
#include "paddle/fluid/operators/label_smooth_op.h"
17 18
namespace paddle {
namespace operators {
Y
Yibing Liu 已提交
19

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
template <typename T>
__global__ void LabelSmoothRunOriginKernel(const int N, const float epsilon,
                                           const int label_dim, const T* src,
                                           T* dst) {
  int idx = blockDim.x * blockIdx.x + threadIdx.x;
  for (; idx < N; idx += blockDim.x * gridDim.x) {
    dst[idx] = static_cast<T>(1 - epsilon) * src[idx] +
               static_cast<T>(epsilon / label_dim);
  }
}

template <typename T>
__global__ void LabelSmoothRunDistKernel(const int N, const float epsilon,
                                         const int dist_numel, const T* src,
                                         const T* dist_data, T* dst) {
  int idx = blockDim.x * blockIdx.x + threadIdx.x;
  for (; idx < N; idx += blockDim.x * gridDim.x) {
37
    int dist_idx = idx % dist_numel;
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
    dst[idx] = static_cast<T>(1 - epsilon) * src[idx] +
               static_cast<T>(epsilon) * dist_data[dist_idx];
  }
}

template <typename T>
__global__ void LabelSmoothGradRunKernel(const int N, const float epsilon,
                                         const T* src, T* dst) {
  int idx = blockDim.x * blockIdx.x + threadIdx.x;
  for (; idx < N; idx += blockDim.x * gridDim.x) {
    dst[idx] = static_cast<T>(1 - epsilon) * src[idx];
  }
}

template <typename DeviceContext, typename T>
class LabelSmoothGPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* out_t = ctx.Output<framework::LoDTensor>("Out");
    auto* in_t = ctx.Input<framework::LoDTensor>("X");
    auto* dist_t = ctx.Input<framework::Tensor>("PriorDist");
59
    auto label_dim = in_t->dims()[in_t->dims().size() - 1];
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
    auto epsilon = ctx.Attr<float>("epsilon");
    auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
    auto size_prob = in_t->numel();
    const T* in_data = in_t->data<T>();
    T* out_data = out_t->mutable_data<T>(ctx.GetPlace());
    int threads = 512;
    int grid = (size_prob + threads - 1) / threads;
    auto stream = ctx.cuda_device_context().stream();
    if (dist_t) {
      auto dist_numel = dist_t->numel();
      const T* dist_data = dist_t->data<T>();
      LabelSmoothRunDistKernel<T><<<grid, threads, 0, stream>>>(
          size_prob, epsilon, dist_numel, in_data, dist_data, out_data);

    } else {
      LabelSmoothRunOriginKernel<T><<<grid, threads, 0, stream>>>(
          size_prob, epsilon, label_dim, in_data, out_data);
    }
  }
};

template <typename DeviceContext, typename T>
class LabelSmoothGradGPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
    auto* d_in_t = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
    d_in_t->mutable_data<T>(ctx.GetPlace());

    auto epsilon = ctx.Attr<float>("epsilon");
    auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
    const T* in_data = d_out_t->data<T>();
    auto size_prob = d_out_t->numel();
    T* out_data = d_in_t->mutable_data<T>(ctx.GetPlace());
    int threads = 512;
    int grid = (size_prob + threads - 1) / threads;
    auto stream = ctx.cuda_device_context().stream();
    LabelSmoothGradRunKernel<T><<<grid, threads, 0, stream>>>(
        size_prob, epsilon, in_data, out_data);
  }
};
}  // namespace operators
}  // namespace paddle
Y
Yibing Liu 已提交
103 104 105 106
namespace ops = paddle::operators;

REGISTER_OP_CUDA_KERNEL(
    label_smooth,
107 108
    ops::LabelSmoothGPUKernel<paddle::platform::CUDADeviceContext, float>,
    ops::LabelSmoothGPUKernel<paddle::platform::CUDADeviceContext, double>);
Y
Yibing Liu 已提交
109 110
REGISTER_OP_CUDA_KERNEL(
    label_smooth_grad,
111 112
    ops::LabelSmoothGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
    ops::LabelSmoothGradGPUKernel<paddle::platform::CUDADeviceContext, double>);