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113 lines
4.5 KiB
113 lines
4.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/tensor.h"
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#include "paddle/fluid/operators/label_smooth_op.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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__global__ void LabelSmoothRunOriginKernel(const int N, const float epsilon,
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const int label_dim, const T* src,
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T* dst) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < N; idx += blockDim.x * gridDim.x) {
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dst[idx] = static_cast<T>(1 - epsilon) * src[idx] +
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static_cast<T>(epsilon / label_dim);
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}
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}
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template <typename T>
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__global__ void LabelSmoothRunDistKernel(const int N, const float epsilon,
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const int dist_numel, const T* src,
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const T* dist_data, T* dst) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < N; idx += blockDim.x * gridDim.x) {
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int dist_idx = idx % dist_numel;
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dst[idx] = static_cast<T>(1 - epsilon) * src[idx] +
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static_cast<T>(epsilon) * dist_data[dist_idx];
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}
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}
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template <typename T>
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__global__ void LabelSmoothGradRunKernel(const int N, const float epsilon,
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const T* src, T* dst) {
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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for (; idx < N; idx += blockDim.x * gridDim.x) {
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dst[idx] = static_cast<T>(1 - epsilon) * src[idx];
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}
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}
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template <typename DeviceContext, typename T>
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class LabelSmoothGPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out_t = ctx.Output<framework::LoDTensor>("Out");
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auto* in_t = ctx.Input<framework::LoDTensor>("X");
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auto* dist_t = ctx.Input<framework::Tensor>("PriorDist");
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auto label_dim = in_t->dims()[in_t->dims().size() - 1];
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auto epsilon = ctx.Attr<float>("epsilon");
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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auto size_prob = in_t->numel();
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const T* in_data = in_t->data<T>();
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T* out_data = out_t->mutable_data<T>(ctx.GetPlace());
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int threads = 512;
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int grid = (size_prob + threads - 1) / threads;
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auto stream = ctx.cuda_device_context().stream();
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if (dist_t) {
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auto dist_numel = dist_t->numel();
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const T* dist_data = dist_t->data<T>();
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LabelSmoothRunDistKernel<T><<<grid, threads, 0, stream>>>(
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size_prob, epsilon, dist_numel, in_data, dist_data, out_data);
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} else {
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LabelSmoothRunOriginKernel<T><<<grid, threads, 0, stream>>>(
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size_prob, epsilon, label_dim, in_data, out_data);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class LabelSmoothGradGPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_in_t = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_in_t->mutable_data<T>(ctx.GetPlace());
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auto epsilon = ctx.Attr<float>("epsilon");
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auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
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const T* in_data = d_out_t->data<T>();
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auto size_prob = d_out_t->numel();
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T* out_data = d_in_t->mutable_data<T>(ctx.GetPlace());
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int threads = 512;
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int grid = (size_prob + threads - 1) / threads;
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auto stream = ctx.cuda_device_context().stream();
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LabelSmoothGradRunKernel<T><<<grid, threads, 0, stream>>>(
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size_prob, epsilon, in_data, out_data);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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label_smooth,
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ops::LabelSmoothGPUKernel<paddle::platform::CUDADeviceContext, float>,
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ops::LabelSmoothGPUKernel<paddle::platform::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(
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label_smooth_grad,
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ops::LabelSmoothGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
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ops::LabelSmoothGradGPUKernel<paddle::platform::CUDADeviceContext, double>);
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