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119 lines
4.5 KiB
119 lines
4.5 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_vector.h>
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#include <thrust/host_vector.h>
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#include "paddle/fluid/operators/sequence_erase_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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using platform::PADDLE_CUDA_NUM_THREADS;
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using LoDTensor = framework::LoDTensor;
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template <typename T>
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__global__ void LabelErasedIdx(const T* in_dat, const int64_t in_len,
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const int* tokens, const size_t tokens_len,
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size_t* num_erased) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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if (index < in_len) {
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for (size_t i = 0; i < tokens_len; ++i) {
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if (in_dat[index] == tokens[i]) {
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num_erased[index + 1] = 1;
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break;
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}
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}
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}
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}
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__global__ void GetOutLod(const size_t* num_erased, const size_t* in_lod,
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const size_t lod_len, size_t* out_lod0) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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if (index < lod_len) {
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out_lod0[index] = in_lod[index] - num_erased[in_lod[index]];
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}
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}
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template <typename T>
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__global__ void SetOutput(const T* in_dat, const int64_t in_len,
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const size_t* num_erased, T* out_dat) {
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int index = blockIdx.x * blockDim.x + threadIdx.x;
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if (index < in_len) {
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if (num_erased[index] == num_erased[index + 1]) {
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out_dat[index - num_erased[index]] = in_dat[index];
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}
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}
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}
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template <typename T>
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class SequenceEraseOpCUDAKernel : 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* in = ctx.Input<LoDTensor>("X");
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auto* out = ctx.Output<LoDTensor>("Out");
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auto lod = in->lod();
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PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
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PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(),
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"The actual size mismatches with the LoD information.");
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auto tokens = ctx.Attr<std::vector<int>>("tokens");
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auto in_len = in->numel();
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auto in_dat = in->data<T>();
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// Copy tokens to GPU
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thrust::device_vector<int> dev_tokens(tokens.begin(), tokens.end());
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int* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
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// Count number of elements to be erased
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thrust::device_vector<size_t> num_erased(in_len + 1, 0);
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size_t* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data());
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auto stream = ctx.cuda_device_context().stream();
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LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
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PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
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in_dat, in_len, dev_tokens_ptr, tokens.size(), num_erased_ptr);
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thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
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num_erased.begin() + 1);
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// Copy LoD to GPU
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auto lod0 = lod[0];
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auto lod_len = lod0.size();
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const size_t* dev_in_lod_ptr = lod0.CUDAData(ctx.GetPlace());
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// Calc output LoD
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thrust::device_vector<size_t> dev_out_lod(lod_len);
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size_t* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
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GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
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PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
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num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr);
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// Set LoD for output
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std::vector<size_t> out_lod0(dev_out_lod.begin(), dev_out_lod.end());
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framework::LoD out_lod;
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out_lod.push_back(out_lod0);
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out->set_lod(out_lod);
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// Set output
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out->Resize({static_cast<int64_t>(out_lod0.back()), 1});
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auto out_dat = out->mutable_data<T>(ctx.GetPlace());
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SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
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PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len,
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num_erased_ptr, out_dat);
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP_CUDA_KERNEL(sequence_erase,
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paddle::operators::SequenceEraseOpCUDAKernel<int32_t>,
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paddle::operators::SequenceEraseOpCUDAKernel<int64_t>);
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