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88 lines
3.0 KiB
88 lines
3.0 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 <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 "paddle/operators/dropout_op.h"
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namespace paddle {
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namespace operators {
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template <typename T, typename AttrType>
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struct MaskGenerator {
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AttrType dropout_prob;
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int seed;
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__host__ __device__ MaskGenerator(AttrType dropout_prob, int seed)
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: dropout_prob(dropout_prob), seed(seed) {}
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inline __host__ __device__ T operator()(const unsigned int n) const {
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thrust::minstd_rand rng;
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rng.seed(seed);
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thrust::uniform_real_distribution<AttrType> dist(0, 1);
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rng.discard(n);
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if (dist(rng) < dropout_prob) {
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return static_cast<T>(0);
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}
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return static_cast<T>(1);
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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, typename AttrType>
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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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AttrType dropout_prob = context.Attr<AttrType>("dropout_prob");
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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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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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int size = framework::product(mask->dims());
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int seed = context.Attr<int>("seed");
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thrust::counting_iterator<unsigned int> index_sequence_begin(0);
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thrust::transform(index_sequence_begin, index_sequence_begin + size,
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thrust::device_ptr<T>(mask_data),
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MaskGenerator<T, AttrType>(dropout_prob, seed));
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auto M = EigenMatrix<T>::Reshape(*mask, 1);
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Y.device(place) = X * M;
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} else {
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Y.device(place) = X * (1.0f - dropout_prob);
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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(
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dropout,
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ops::GPUDropoutKernel<paddle::platform::CUDADeviceContext, float, float>);
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REGISTER_OP_CUDA_KERNEL(
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dropout_grad,
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ops::DropoutGradKernel<paddle::platform::CUDADeviceContext, float>);
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