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120 lines
4.4 KiB
120 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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#define EIGEN_USE_GPU
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#include "paddle/operators/adagrad_op.h"
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/math/selected_rows_functor.h"
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#include "paddle/platform/cuda_helper.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, int block_size>
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__global__ void MergeGradKernel(const T* grad, const int64_t* grad_rows,
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T* grad_merge, const int64_t* grad_merge_rows,
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size_t grad_merge_rows_size,
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int64_t row_numel) {
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const int ty = blockIdx.y;
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int tid = threadIdx.x;
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__shared__ size_t grad_merge_idx;
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if (tid == 0) {
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for (size_t i = 0; i < grad_merge_rows_size; i++) {
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if (grad_rows[ty] == grad_merge_rows[i]) {
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grad_merge_idx = i;
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}
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}
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}
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__syncthreads();
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grad += ty * row_numel;
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grad_merge += grad_merge_idx * row_numel;
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for (int index = tid; index < row_numel; index += block_size) {
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paddle::platform::CudaAtomicAdd(grad_merge + index, grad[index]);
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}
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}
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template <typename T, int block_size>
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__global__ void SparseAdagradFunctorKernel(const T* grad, const int64_t* rows,
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const T* learning_rate, T* param,
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T* moment, int64_t row_numel,
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T epsilon) {
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const int ty = blockIdx.y;
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int tid = threadIdx.x;
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grad += ty * row_numel;
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param += rows[ty] * row_numel;
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moment += rows[ty] * row_numel;
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for (int index = tid; index < row_numel; index += block_size) {
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// Since index in rows of SelectedRows can be duplicate, we have to use
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// Atomic Operation to avoid concurrent write error.
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paddle::platform::CudaAtomicAdd(param + index,
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-1.0 * learning_rate[0] * grad[index] /
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(sqrt(moment[index]) + epsilon));
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}
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}
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} // namespace
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template <typename T>
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struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
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void operator()(const platform::CUDADeviceContext& context,
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const framework::SelectedRows& grad,
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const framework::Tensor& learning_rate, T epsilon,
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framework::Tensor* moment, framework::Tensor* param) {
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// 1. g_m.rows = set(g.rows)
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auto grad_width = grad.value().dims()[1];
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math::scatter::MergeAdd<platform::CUDADeviceContext, T> merge_func;
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auto grad_merge = merge_func(context, grad);
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auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
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auto& merge_rows = grad_merge.rows();
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// 2. m += g_m * g_m
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math::scatter::Mul<platform::CUDADeviceContext, T> sqare_func;
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auto grad_square = sqare_func(context, grad_merge, grad_merge);
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math::SelectedRowsAddToTensor<platform::CUDADeviceContext, T> functor;
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functor(context, grad_square, moment);
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// 3. update parameter
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auto* lr = learning_rate.data<T>();
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auto* param_data = param->data<T>();
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auto* moment_data = moment->data<T>();
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const int block_size = 256;
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dim3 threads(block_size, 1);
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dim3 grid2(1, merge_rows.size());
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SparseAdagradFunctorKernel<
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T, 256><<<grid2, threads, 0,
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.stream()>>>(grad_merge_data, grad_merge.rows().data(),
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lr, param_data, moment_data, grad_width,
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epsilon);
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}
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};
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template struct SparseAdagradFunctor<platform::CUDADeviceContext, float>;
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template struct SparseAdagradFunctor<platform::CUDADeviceContext, double>;
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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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adagrad, ops::AdagradOpKernel<paddle::platform::CUDADeviceContext, float>,
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ops::AdagradOpKernel<paddle::platform::CUDADeviceContext, double>);
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