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122 lines
4.1 KiB
122 lines
4.1 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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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/platform/assert.h"
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#include "paddle/platform/cuda_helper.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
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__global__ void LookupTable(T* output, const T* table, const int32_t* ids,
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const int N, const int K, const int D) {
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int idx = threadIdx.x;
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int idy = blockIdx.x + threadIdx.y * GridDimX;
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while (idy < K) {
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int id = ids[idy];
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PADDLE_ASSERT(id >= 0);
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PADDLE_ASSERT(id < N);
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T* out = output + idy * D;
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const T* tab = table + id * D;
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for (int i = idx; i < D; i += BlockDimX) {
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out[i] = tab[i];
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}
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idy += BlockDimY * GridDimX;
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}
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}
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template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
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__global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids,
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const int N, const int K, const int D) {
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int idx = threadIdx.x;
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int idy = blockIdx.x + threadIdx.y * GridDimX;
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while (idy < K) {
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int id = ids[idy];
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PADDLE_ASSERT(id >= 0);
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PADDLE_ASSERT(id < N);
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const T* out = output + idy * D;
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T* tab = table + id * D;
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for (int i = idx; i < D; i += BlockDimX) {
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paddle::platform::CudaAtomicAdd(&tab[i], out[i]);
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}
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idy += BlockDimY * GridDimX;
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}
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}
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template <typename T>
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class LookupTableCUDAKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto table_t = context.Input<Tensor>("W");
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auto ids_t = context.Input<Tensor>("Ids");
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auto output_t = context.Output<Tensor>("Out");
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size_t N = table_t->dims()[0];
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size_t D = table_t->dims()[1];
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size_t K = ids_t->numel();
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auto ids = ids_t->data<int32_t>();
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auto table = table_t->data<T>();
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auto output = output_t->mutable_data<T>(context.GetPlace());
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dim3 threads(128, 8);
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dim3 grids(8, 1);
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LookupTable<T, 128, 8, 8><<<
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grids, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
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context.device_context())
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.stream()>>>(output, table, ids, N, K, D);
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}
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};
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template <typename T>
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class LookupTableGradCUDAKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto ids_t = context.Input<Tensor>("Ids");
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auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
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auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
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int N = d_table_t->dims()[0];
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int D = d_table_t->dims()[1];
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int K = ids_t->numel();
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const int32_t* ids = ids_t->data<int32_t>();
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const T* d_output = d_output_t->data<T>();
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T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
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auto t = framework::EigenVector<T>::Flatten(*d_table_t);
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t.device(context.GetEigenDevice<platform::GPUPlace>()) =
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t.constant(static_cast<T>(0));
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dim3 threads(128, 8);
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dim3 grids(8, 1);
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LookupTableGrad<T, 128, 8, 8><<<
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grids, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
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context.device_context())
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.stream()>>>(d_table, d_output, ids, N, K, D);
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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_GPU_KERNEL(lookup_table, ops::LookupTableCUDAKernel<float>);
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REGISTER_OP_GPU_KERNEL(lookup_table_grad,
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ops::LookupTableGradCUDAKernel<float>);
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