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134 lines
4.4 KiB
134 lines
4.4 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/framework/op_registry.h"
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#include "paddle/platform/assert.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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__host__ __device__ T clipping_log(const T x) {
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PADDLE_ASSERT(std::is_floating_point<T>::value);
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const T kApproInf = 1e20;
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T v = log(x);
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if (v == INFINITY) {
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return kApproInf;
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}
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if (v == -INFINITY) {
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return -kApproInf;
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}
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return v;
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}
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template <typename T>
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__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
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const int N, 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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PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
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Y[i] = -clipping_log(X[i * D + label[i]]);
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}
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}
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// TODO(qingqing): make zero setting an common function.
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template <typename T>
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__global__ void zero(T* X, const int 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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X[i] = 0.0;
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}
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}
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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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class OnehotCrossEntropyOpCUDAKernel : public framework::OpKernel {
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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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"It must use GPUPlace.");
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auto X = ctx.Input<Tensor>("X");
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const T* Xdata = X->data<T>();
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const int* label_data = ctx.Input<Tensor>("label")->data<int>();
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auto Y = ctx.Output<Tensor>("Y");
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Y->mutable_data<T>(ctx.GetPlace());
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T* Ydata = Y->data<T>();
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int N = X->dims()[0];
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int D = X->dims()[1];
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int block = 512;
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int grid = (N + block - 1) / block;
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// TODO(qingqing) launch kernel on specified stream
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// base on ExecutionContext.
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CrossEntropyKernel<T><<<grid, block>>>(Ydata, Xdata, label_data, N, D);
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}
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};
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template <typename T>
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class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
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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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"It must use GPUPlace.");
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auto X = ctx.Input<Tensor>("X");
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auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
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auto label = ctx.Input<Tensor>("label");
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auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
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auto* dYdata = dY->template data<T>();
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auto* Xdata = X->template data<T>();
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auto* label_data = label->data<int>();
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int N = X->dims()[0];
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int D = X->dims()[1];
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int block = 512;
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int grid = (N * D + block - 1) / block;
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zero<T><<<grid, block>>>(dXdata, N * D);
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grid = (N + block - 1) / block;
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// TODO(qingqing): launch kernel on specified stream
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// base on ExecutionContext.
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CrossEntropyGradientKernel<T><<<grid, block>>>(dXdata, dYdata, Xdata,
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label_data, N, D);
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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(onehot_cross_entropy,
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ops::OnehotCrossEntropyOpCUDAKernel<float>);
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REGISTER_OP_GPU_KERNEL(onehot_cross_entropy_grad,
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ops::OnehotCrossEntropyGradientOpCUDAKernel<float>);
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