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119 lines
4.3 KiB
119 lines
4.3 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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#define EIGEN_USE_GPU
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#include "paddle/fluid/operators/sgd_op.h"
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#include "paddle/fluid/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>
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__global__ void SGDKernel(const T* g, const T* p, const T* learning_rate,
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const int num, T* p_out) {
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T lr = learning_rate[0];
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int grid_size = blockDim.x * gridDim.x;
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num; i += grid_size) {
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T g_data = g[i];
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T p_data = p[i];
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p_out[i] = p_data - lr * g_data;
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}
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}
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template <typename T, int block_size>
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__global__ void SparseSGDFunctorKernel(const T* selected_rows,
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const int64_t* rows,
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const T* learning_rate, T* tensor_out,
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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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selected_rows += ty * row_numel;
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tensor_out += 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(
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tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
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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 SGDOpCUDAKernel : 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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auto* param = ctx.Input<framework::Tensor>("Param");
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auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
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auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
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auto* grad_var = ctx.InputVar("Grad");
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// Actually, all tensors are LoDTensor except SelectedRows.
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if (grad_var->IsType<framework::LoDTensor>()) {
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param_out->mutable_data<T>(ctx.GetPlace());
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auto* grad = ctx.Input<framework::Tensor>("Grad");
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auto* grad_data = grad->data<T>();
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auto* param_data = param->data<T>();
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auto* param_out_data = param_out->data<T>();
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int block = 512;
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int grid = (param->numel() + block - 1) / block;
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SGDKernel<T><<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
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grad_data, param_data, learning_rate->data<T>(), param->numel(),
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param_out_data);
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} else if (grad_var->IsType<framework::SelectedRows>()) {
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// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
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// This manual optimization brings difficulty to track data dependency.
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// It's better to find a more elegant solution.
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PADDLE_ENFORCE_EQ(param, param_out);
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auto* grad = ctx.Input<framework::SelectedRows>("Grad");
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auto in_height = grad->height();
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auto out_dims = param_out->dims();
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PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
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auto& in_value = grad->value();
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framework::Vector<int64_t> in_rows(grad->rows());
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int64_t in_row_numel = in_value.numel() / in_rows.size();
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PADDLE_ENFORCE_EQ(in_row_numel, param_out->numel() / in_height);
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auto* in_data = in_value.data<T>();
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auto* out_data = param_out->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 grid(1, in_rows.size());
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SparseSGDFunctorKernel<
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T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
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in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
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out_data, in_row_numel);
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} else {
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PADDLE_THROW("Unsupported Variable Type of Grad");
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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_CUDA_KERNEL(sgd, ops::SGDOpCUDAKernel<float>,
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ops::SGDOpCUDAKernel<double>);
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