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113 lines
4.2 KiB
113 lines
4.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/cross_entropy_op.h"
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namespace paddle {
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namespace operators {
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namespace {
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template <typename T>
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__global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
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const int* label, const int N,
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const int D) {
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// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
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// CUDA_1D_KERNEL_LOOP(i, N) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
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i += blockDim.x * gridDim.x) {
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int idx = i * D + label[i];
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dX[idx] = -dY[i] / X[idx];
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}
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}
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template <typename T>
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__global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
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const T* label, const int N,
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const int D) {
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int ids = blockIdx.x * blockDim.x + threadIdx.x;
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if (ids < N * D) {
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int row_ids = ids / D;
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dX[ids] = -label[ids] * dY[row_ids] / X[ids];
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}
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}
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} // namespace
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template <typename T>
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class CrossEntropyOpCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
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"This kernel only runs on GPU device.");
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const Tensor* x = ctx.Input<Tensor>("X");
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const Tensor* label = ctx.Input<Tensor>("Label");
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Tensor* y = ctx.Output<Tensor>("Y");
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y->mutable_data<T>(ctx.GetPlace());
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math::CrossEntropyFunctor<platform::GPUPlace, T>()(
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ctx.device_context(), y, x, label, ctx.Attr<bool>("softLabel"));
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}
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};
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template <typename T>
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class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
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"This kernel only runs on GPU device.");
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const Tensor* x = ctx.Input<Tensor>("X");
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const Tensor* label = ctx.Input<Tensor>("Label");
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Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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dx->mutable_data<T>(ctx.GetPlace());
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const T* dy_data =
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ctx.Input<Tensor>(framework::GradVarName("Y"))->data<T>();
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T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
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const T* x_data = x->data<T>();
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int batch_size = x->dims()[0];
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int class_num = x->dims()[1];
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int block = 512;
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int grid = (batch_size * class_num + block - 1) / block;
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if (ctx.Attr<bool>("softLabel")) {
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auto* label_data = label->data<T>();
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SoftCrossEntropyGradientKernel<T><<<
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grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
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ctx.device_context())
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.stream()>>>(dx_data, dy_data, x_data, label_data,
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batch_size, class_num);
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} else {
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math::SetConstant<platform::GPUPlace, T>(ctx.device_context(), dx, 0);
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auto* label_data = label->data<int>();
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grid = (batch_size + block - 1) / block;
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CrossEntropyGradientKernel<T><<<
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grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
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ctx.device_context())
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.stream()>>>(dx_data, dy_data, x_data, label_data,
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batch_size, class_num);
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}
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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_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>);
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REGISTER_OP_GPU_KERNEL(cross_entropy_grad,
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ops::CrossEntropyGradientOpCUDAKernel<float>);
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