/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. 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/operators/sequence_erase_op.h" #include "paddle/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 int in_len, const T* tokens, const int tokens_len, int* num_erased) { int index = blockIdx.x * blockDim.x + threadIdx.x; if (index < in_len) { int erased = 0; for (int i = 0; i < tokens_len; ++i) { if (in_dat[index] == tokens[i]) { erased = 1; } } num_erased[index + 1] = erased; if (index == 0) { num_erased[0] = 0; } } } template __global__ void GetOutLod(const T* num_erased, const int* in_lod, const int lod_len, int* 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 int in_len, const int* 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 tokens_len = tokens.size(); auto in_len = in->numel(); auto in_dat = in->data(); auto lod0 = lod[0]; thrust::host_vector host_tokens(tokens_len); for (size_t i = 0; i < tokens.size(); ++i) { host_tokens[i] = tokens[i]; } thrust::device_vector dev_tokens = host_tokens; thrust::device_vector num_erased(in_len + 1); T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data()); int* 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_len, num_erased_ptr); thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(), num_erased.begin() + 1); // Calc LoD auto lod_len = lod0.size(); thrust::host_vector host_lod(lod_len); for (size_t i = 0; i < lod_len; ++i) { host_lod[i] = lod0[i]; } thrust::device_vector dev_in_lod = host_lod; thrust::device_vector dev_out_lod(lod_len); int* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data()); int* 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); thrust::host_vector host_out_lod = dev_out_lod; std::vector out_lod0(lod_len, 0); for (size_t i = 0; i < lod_len; i++) { out_lod0[i] = host_out_lod[i]; } framework::LoD out_lod; out_lod.push_back(out_lod0); out->set_lod(out_lod); // Set output out->Resize({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);