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108 lines
3.4 KiB
108 lines
3.4 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/operators/math/cross_entropy.h"
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namespace paddle {
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namespace operators {
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namespace math {
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namespace {
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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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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] = -math::TolerableValue<T>()(log(X[i * D + label[i]]));
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}
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}
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template <typename T>
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__device__ __forceinline__ T sum_single_warp(T val) {
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val += __shfl_down(val, 16);
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val += __shfl_down(val, 8);
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val += __shfl_down(val, 4);
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val += __shfl_down(val, 2);
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val += __shfl_down(val, 1);
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return val;
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}
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template <typename T>
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__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
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const int class_num) {
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int tid = threadIdx.x;
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extern __shared__ T d_sum[];
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d_sum[tid] = 0;
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int cur_idx = tid;
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int next_idx = blockIdx.x * class_num + tid;
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while (cur_idx < class_num) {
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d_sum[tid] +=
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math::TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
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next_idx += blockDim.x;
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cur_idx += blockDim.x;
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}
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__syncthreads();
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for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
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if (tid < stride) d_sum[tid] += d_sum[tid + stride];
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__syncthreads();
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}
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T val = d_sum[tid];
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val = sum_single_warp<T>(val);
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if (tid == 0) Y[blockIdx.x] = -val;
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}
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} // namespace
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using Tensor = framework::Tensor;
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template <typename T>
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class CrossEntropyFunctor<platform::GPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& ctx, framework::Tensor* out,
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const framework::Tensor* prob,
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const framework::Tensor* labels, bool softLabel) {
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const T* prob_data = prob->data<T>();
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T* loss_data = out->mutable_data<T>(ctx.GetPlace());
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int batch_size = prob->dims()[0];
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int class_num = prob->dims()[1];
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if (softLabel) {
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const T* label_data = labels->data<T>();
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int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
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SoftCrossEntropyKernel<T><<<
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batch_size, block, block * sizeof(T),
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream()>>>(
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loss_data, prob_data, label_data, class_num);
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} else {
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const int* label_data = labels->data<int>();
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int block = 512;
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int grid = (batch_size + block - 1) / block;
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CrossEntropyKernel<T><<<
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grid, block, 0,
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reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream()>>>(
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loss_data, prob_data, label_data, batch_size, class_num);
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}
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}
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};
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template class CrossEntropyFunctor<platform::GPUPlace, float>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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