/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include #include #include "paddle/fluid/operators/sequence_erase_op.h" #include "paddle/fluid/platform/cuda_helper.h" namespace paddle { namespace operators { using platform::PADDLE_CUDA_NUM_THREADS; using LoDTensor = framework::LoDTensor; template __global__ void LabelErasedIdx(const T* in_dat, const int64_t in_len, const int* tokens, const size_t tokens_len, size_t* num_erased) { int index = blockIdx.x * blockDim.x + threadIdx.x; if (index < in_len) { for (size_t i = 0; i < tokens_len; ++i) { if (in_dat[index] == tokens[i]) { num_erased[index + 1] = 1; break; } } } } __global__ void GetOutLod(const size_t* num_erased, const size_t* in_lod, const size_t lod_len, size_t* out_lod0) { int index = blockIdx.x * blockDim.x + threadIdx.x; if (index < lod_len) { out_lod0[index] = in_lod[index] - num_erased[in_lod[index]]; } } template __global__ void SetOutput(const T* in_dat, const int64_t in_len, const size_t* num_erased, T* out_dat) { int index = blockIdx.x * blockDim.x + threadIdx.x; if (index < in_len) { if (num_erased[index] == num_erased[index + 1]) { out_dat[index - num_erased[index]] = in_dat[index]; } } } template class SequenceEraseOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* in = ctx.Input("X"); auto* out = ctx.Output("Out"); auto lod = in->lod(); PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now."); PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(), "The actual size mismatches with the LoD information."); auto tokens = ctx.Attr>("tokens"); auto in_len = in->numel(); auto in_dat = in->data(); // Copy tokens to GPU thrust::device_vector dev_tokens(tokens.begin(), tokens.end()); int* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data()); // Count number of elements to be erased thrust::device_vector num_erased(in_len + 1, 0); size_t* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data()); auto stream = ctx.cuda_device_context().stream(); LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>( in_dat, in_len, dev_tokens_ptr, tokens.size(), num_erased_ptr); thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(), num_erased.begin() + 1); // Copy LoD to GPU auto lod0 = lod[0]; auto lod_len = lod0.size(); const size_t* dev_in_lod_ptr = lod0.CUDAData(ctx.GetPlace()); // Calc output LoD thrust::device_vector dev_out_lod(lod_len); size_t* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data()); GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>( num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr); // Set LoD for output std::vector out_lod0(dev_out_lod.begin(), dev_out_lod.end()); framework::LoD out_lod; out_lod.push_back(out_lod0); out->set_lod(out_lod); // Set output out->Resize({static_cast(out_lod0.back()), 1}); auto out_dat = out->mutable_data(ctx.GetPlace()); SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len, num_erased_ptr, out_dat); } }; } // namespace operators } // namespace paddle REGISTER_OP_CUDA_KERNEL(sequence_erase, paddle::operators::SequenceEraseOpCUDAKernel, paddle::operators::SequenceEraseOpCUDAKernel);