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118 lines
4.0 KiB
118 lines
4.0 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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#include <thrust/device_ptr.h>
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#include <thrust/iterator/counting_iterator.h>
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#include <thrust/random.h>
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#include <thrust/transform.h>
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#include <string>
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#include "paddle/fluid/operators/dropout_op.h"
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#include "paddle/fluid/platform/float16.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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__global__ void RandomGenerator(const size_t n, const int seed,
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const float dropout_prob, const T* src,
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T* mask_data, T* dst,
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bool is_upscale_in_train) {
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thrust::minstd_rand rng;
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rng.seed(seed);
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thrust::uniform_real_distribution<float> dist(0, 1);
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int idx = blockDim.x * blockIdx.x + threadIdx.x;
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int step_size = 0;
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T mask;
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T dest;
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for (; idx < n; idx += blockDim.x * gridDim.x) {
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T s = src[idx];
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if (step_size == 0) {
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rng.discard(idx);
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step_size = blockDim.x * gridDim.x;
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} else {
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rng.discard(step_size);
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}
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if (dist(rng) < dropout_prob) {
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mask = static_cast<T>(0);
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} else {
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if (is_upscale_in_train) {
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mask = static_cast<T>(1.0f / (1.0f - dropout_prob));
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} else {
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mask = static_cast<T>(1);
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}
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}
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dest = s * mask;
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mask_data[idx] = mask;
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dst[idx] = dest;
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}
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}
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// It seems that Eigen::Tensor::setRandom in GPU will SEGFAULT.
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// Use std::random and thrust::random(thrust is a std library in CUDA) to
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// implement uniform random.
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template <typename Place, typename T>
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class GPUDropoutKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* x = context.Input<Tensor>("X");
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auto* y = context.Output<Tensor>("Out");
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y->mutable_data<T>(context.GetPlace());
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float dropout_prob = context.Attr<float>("dropout_prob");
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auto dropout_implementation =
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context.Attr<std::string>("dropout_implementation");
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auto& place = *context.template device_context<Place>().eigen_device();
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if (!context.Attr<bool>("is_test")) {
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auto* mask = context.Output<Tensor>("Mask");
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auto* mask_data = mask->mutable_data<T>(context.GetPlace());
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size_t size = framework::product(mask->dims());
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auto* x_data = x->data<T>();
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auto* y_data = y->mutable_data<T>(context.GetPlace());
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std::random_device rnd;
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int seed =
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context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
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int threads = 512;
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int grid = (x->numel() + threads - 1) / threads;
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RandomGenerator<
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T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
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size, seed, dropout_prob, x_data, mask_data, y_data,
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(dropout_implementation == "upscale_in_train"));
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} else {
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auto X = EigenMatrix<T>::Reshape(*x, 1);
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auto Y = EigenMatrix<T>::Reshape(*y, 1);
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if (dropout_implementation == "upscale_in_train") {
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Y.device(place) = X;
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} else {
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Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
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}
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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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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(
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dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
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ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>,
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ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(
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dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
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ops::DropoutGradKernel<plat::CUDADeviceContext, double>);
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