wehub-resource-sync
641 lines
23 KiB
Plaintext
641 lines
23 KiB
Plaintext
/* 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 <set>
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
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namespace phi {
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namespace funcs {
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template <typename T>
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struct SelectedRowsAdd<GPUContext, T> {
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void operator()(const GPUContext& dev_ctx,
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const SelectedRows& input1,
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const SelectedRows& input2,
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SelectedRows* output) {
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auto in1_height = input1.height();
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PADDLE_ENFORCE_EQ(
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in1_height,
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input2.height(),
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common::errors::InvalidArgument("The two inputs height must be equal."
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"But received first input height = "
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"[%d], second input height = [%d]",
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in1_height,
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input2.height()));
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output->set_height(in1_height);
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Vector<int64_t> in1_rows(input1.rows());
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auto& in2_rows = input2.rows();
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std::vector<int64_t> out_rows;
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out_rows.reserve(in1_rows.size() + in2_rows.size());
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// concat rows
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out_rows.insert(out_rows.end(), in1_rows.begin(), in1_rows.end());
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out_rows.insert(out_rows.end(), in2_rows.begin(), in2_rows.end());
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output->set_rows(out_rows);
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auto* out_value = output->mutable_value();
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auto& in1_value = input1.value();
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auto& in2_value = input2.value();
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auto in1_row_numel = in1_value.numel() / in1_rows.size();
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PADDLE_ENFORCE_EQ(
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in1_row_numel,
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in2_value.numel() / in2_rows.size(),
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common::errors::InvalidArgument(
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"The two inputs width must be equal."
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"But received first input width = [%d], second input width = [%d]",
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in1_row_numel,
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in2_value.numel() / in2_rows.size()));
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PADDLE_ENFORCE_EQ(
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in1_row_numel,
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out_value->numel() / out_rows.size(),
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common::errors::InvalidArgument(
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"The input and oupput width must be equal."
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"But received input width = [%d], output width = [%d]",
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in1_row_numel,
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out_value->numel() / out_rows.size()));
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auto* out_data = out_value->data<T>();
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auto* in1_data = in1_value.data<T>();
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auto in1_place = input1.place();
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PADDLE_ENFORCE_EQ(in1_place.GetType() == AllocationType::GPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the GPU place."));
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auto in2_place = input2.place();
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PADDLE_ENFORCE_EQ(in2_place.GetType() == AllocationType::GPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the GPU place."));
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auto out_place = dev_ctx.GetPlace();
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PADDLE_ENFORCE_EQ(out_place.GetType() == AllocationType::GPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the GPU place."));
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memory_utils::Copy(out_place,
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out_data,
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in1_place,
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in1_data,
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in1_value.numel() * sizeof(T),
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dev_ctx.stream());
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auto* in2_data = in2_value.data<T>();
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memory_utils::Copy(out_place,
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out_data + in1_value.numel(),
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in2_place,
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in2_data,
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in2_value.numel() * sizeof(T),
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dev_ctx.stream());
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}
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};
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template struct PADDLE_API SelectedRowsAdd<GPUContext, float>;
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template struct SelectedRowsAdd<GPUContext, double>;
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namespace {
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template <typename T, int block_size>
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__global__ void SelectedRowsAddTensorKernel(const T* selected_rows,
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const int64_t* rows,
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T* tensor_out,
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int64_t row_numel) {
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const int ty = blockIdx.x;
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int tid = threadIdx.x;
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selected_rows += ty * row_numel;
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tensor_out += rows[ty] * row_numel;
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for (int64_t index = tid; index < row_numel; index += block_size) {
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// Since index in rows of SelectedRows can be duplicate, we can not use
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// tensor_out[index] += selected_rows[index]; Instead, we have to use
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// AtomicAdd to avoid concurrent write error.
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CudaAtomicAdd(tensor_out + index, selected_rows[index]);
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}
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}
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} // namespace
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template <typename T>
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struct SelectedRowsAddTensor<GPUContext, T> {
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void operator()(const GPUContext& dev_ctx,
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const SelectedRows& input1,
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const DenseTensor& input2,
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DenseTensor* output) {
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auto in1_height = input1.height();
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auto in2_dims = input2.dims();
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auto out_dims = output->dims();
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PADDLE_ENFORCE_EQ(
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in1_height,
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in2_dims[0],
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common::errors::InvalidArgument(
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"The two inputs height must be equal."
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"But received first input height = [%d], first input height = [%d]",
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in1_height,
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in2_dims[0]));
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PADDLE_ENFORCE_EQ(
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in1_height,
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out_dims[0],
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common::errors::InvalidArgument(
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"The input and output height must be equal."
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"But received input height = [%d], output height = [%d]",
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in1_height,
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out_dims[0]));
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auto& in1_value = input1.value();
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auto& in1_rows = input1.rows();
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int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
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PADDLE_ENFORCE_EQ(
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in1_row_numel,
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input2.numel() / in1_height,
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common::errors::InvalidArgument(
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"The two inputs width must be equal."
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"But received first input width = [%d], second input width = [%d]",
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in1_row_numel,
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input2.numel() / in1_height));
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PADDLE_ENFORCE_EQ(
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in1_row_numel,
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output->numel() / in1_height,
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common::errors::InvalidArgument(
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"The input and output width must be equal."
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"But received input width = [%d], output width = [%d]",
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in1_row_numel,
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output->numel() / in1_height));
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auto* in1_data = in1_value.data<T>();
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auto* in2_data = input2.data<T>();
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auto* out_data = output->data<T>();
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funcs::SetConstant<GPUContext, T> functor;
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functor(dev_ctx, output, static_cast<T>(0));
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const int block_size = 256;
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dim3 threads(block_size, 1);
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dim3 grid(in1_rows.size(), 1);
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phi::MixVector<int64_t> mixv_in1_rows(&in1_rows);
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SelectedRowsAddTensorKernel<T, block_size>
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<<<grid, threads, 0, dev_ctx.stream()>>>(
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in1_data,
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mixv_in1_rows.CUDAData(dev_ctx.GetPlace()),
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out_data,
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in1_row_numel);
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auto out_eigen = EigenVector<T>::Flatten(*output);
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auto in2_eigen = EigenVector<T>::Flatten(input2);
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out_eigen.device(*dev_ctx.eigen_device()) = out_eigen + in2_eigen;
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}
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};
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template struct PADDLE_API SelectedRowsAddTensor<GPUContext, float>;
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template struct PADDLE_API SelectedRowsAddTensor<GPUContext, double>;
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template struct SelectedRowsAdd<GPUContext, phi::float16>;
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template struct SelectedRowsAddTensor<GPUContext, phi::float16>;
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template <typename T>
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struct SelectedRowsAddTo<GPUContext, T> {
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void operator()(const GPUContext& dev_ctx,
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const SelectedRows& input1,
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const int64_t input2_offset,
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SelectedRows* input2) {
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auto in1_height = input1.height();
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PADDLE_ENFORCE_EQ(
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in1_height,
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input2->height(),
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common::errors::InvalidArgument("The two inputs height must be equal."
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"But received first input height = "
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"[%d], second input height = [%d]",
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in1_height,
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input2->height()));
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auto& in1_rows = input1.rows();
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auto& in2_rows = *(input2->mutable_rows());
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auto& in1_value = input1.value();
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auto* in2_value = input2->mutable_value();
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// concat rows
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phi::MixVector<int64_t> mixv_in2_rows(&in2_rows);
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if (in1_rows.size()) {
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mixv_in2_rows.Extend(in1_rows.begin(), in1_rows.end());
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}
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auto in1_place = input1.place();
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PADDLE_ENFORCE_EQ(in1_place.GetType() == AllocationType::GPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the GPU place."));
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auto in2_place = input2->place();
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PADDLE_ENFORCE_EQ(in1_place.GetType() == AllocationType::GPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the GPU place."));
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auto* in1_data = in1_value.data<T>();
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auto* in2_data = in2_value->data<T>();
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memory_utils::Copy(in2_place,
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in2_data + input2_offset,
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in1_place,
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in1_data,
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in1_value.numel() * sizeof(T),
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dev_ctx.stream());
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}
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};
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template struct PADDLE_API SelectedRowsAddTo<GPUContext, float>;
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template struct SelectedRowsAddTo<GPUContext, double>;
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template struct SelectedRowsAddTo<GPUContext, int>;
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template struct SelectedRowsAddTo<GPUContext, int64_t>;
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template struct SelectedRowsAddTo<GPUContext, phi::float16>;
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namespace {
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template <typename T, int block_size>
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__global__ void SelectedRowsAddToTensorKernel(const T* selected_rows,
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const int64_t* rows,
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T* tensor_out,
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int64_t row_numel) {
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const int ty = blockIdx.x;
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int tid = threadIdx.x;
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selected_rows += ty * row_numel;
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tensor_out += rows[ty] * row_numel;
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for (int64_t index = tid; index < row_numel; index += block_size) {
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// Since index in rows of SelectedRows can be duplicate, we have to use
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// Atomic Operation to avoid concurrent write error.
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CudaAtomicAdd(tensor_out + index, selected_rows[index]);
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}
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}
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} // namespace
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template <typename T>
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struct SelectedRowsAddToTensor<GPUContext, T> {
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void operator()(const GPUContext& dev_ctx,
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const SelectedRows& input1,
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DenseTensor* input2) {
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auto in1_height = input1.height();
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auto in2_dims = input2->dims();
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PADDLE_ENFORCE_EQ(
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in1_height,
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in2_dims[0],
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common::errors::InvalidArgument("The two inputs height must be equal."
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"But received first input height = "
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"[%d], second input height = [%d]",
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in1_height,
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in2_dims[0]));
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auto& in1_value = input1.value();
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auto& in1_rows = input1.rows();
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int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
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PADDLE_ENFORCE_EQ(
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in1_row_numel,
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input2->numel() / in1_height,
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common::errors::InvalidArgument(
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"The two inputs width must be equal."
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"But received first input width = [%d], second input width = [%d]",
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in1_row_numel,
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input2->numel() / in1_height));
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auto* in1_data = in1_value.data<T>();
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auto* in2_data = input2->data<T>();
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const int block_size = 256;
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dim3 threads(block_size, 1);
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dim3 grid(in1_rows.size(), 1);
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phi::MixVector<int64_t> mixv_in1_rows(&in1_rows);
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SelectedRowsAddToTensorKernel<T, block_size>
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<<<grid, threads, 0, dev_ctx.stream()>>>(
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in1_data,
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mixv_in1_rows.CUDAData(dev_ctx.GetPlace()),
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in2_data,
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in1_row_numel);
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}
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};
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template struct PADDLE_API SelectedRowsAddToTensor<GPUContext, float>;
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template struct PADDLE_API SelectedRowsAddToTensor<GPUContext, double>;
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template struct SelectedRowsAddToTensor<GPUContext, int>;
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template struct SelectedRowsAddToTensor<GPUContext, int64_t>;
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template struct SelectedRowsAddToTensor<GPUContext, phi::float16>;
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template struct SelectedRowsAddToTensor<GPUContext, phi::complex64>;
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template struct SelectedRowsAddToTensor<GPUContext, phi::complex128>;
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namespace scatter {
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template <typename T, int block_size>
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__global__ void MergeAddKernel(const T* input,
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const int64_t* input_rows,
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T* out,
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const int64_t* out_rows,
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size_t out_rows_size,
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int64_t row_numel) {
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const int ty = blockIdx.x;
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int tid = threadIdx.x;
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__shared__ size_t out_idx;
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if (tid == 0) {
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for (size_t i = 0; i < out_rows_size; i++) {
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if (input_rows[ty] == out_rows[i]) {
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out_idx = i;
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}
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}
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}
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__syncthreads();
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input += ty * row_numel;
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out += out_idx * row_numel;
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for (int64_t index = tid; index < row_numel; index += block_size) {
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CudaAtomicAdd(out + index, input[index]);
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}
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}
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template <typename DeviceContext, typename T>
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struct MergeAddImpl {
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SelectedRows operator()(const DeviceContext& dev_ctx,
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const SelectedRows& input,
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const bool sorted_result = false) {
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SelectedRows out;
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(*this)(dev_ctx, input, &out);
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return out;
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}
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void operator()(const DeviceContext& dev_ctx,
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const SelectedRows& input,
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SelectedRows* output,
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const bool sorted_result = false) {
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Vector<int64_t> input_rows(input.rows());
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if (input_rows.size() == 0) {
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return;
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}
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SelectedRows& out = *output;
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std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
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std::vector<int64_t> merge_rows_cpu(row_set.begin(), row_set.end());
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Vector<int64_t> merge_rows(merge_rows_cpu);
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auto input_width = input.value().dims()[1];
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out.set_rows(merge_rows);
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out.set_height(input.height());
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DenseTensor* out_tensor = out.mutable_value();
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out_tensor->Resize(
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make_ddim({static_cast<int64_t>(merge_rows.size()), input_width}));
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dev_ctx.template Alloc<T>(out_tensor);
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funcs::SetConstant<DeviceContext, T> constant_functor;
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constant_functor(dev_ctx, out.mutable_value(), static_cast<T>(0));
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auto* out_data = out.mutable_value()->data<T>();
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auto* input_data = input.value().data<T>();
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const int block_size = 256;
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dim3 threads(block_size, 1);
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dim3 grid1(input_rows.size(), 1);
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phi::MixVector<int64_t> mix_vector_input(&input_rows);
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phi::MixVector<int64_t> mix_vector_out(out.mutable_rows());
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MergeAddKernel<T, 256><<<grid1, threads, 0, dev_ctx.stream()>>>(
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input_data,
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mix_vector_input.CUDAData(dev_ctx.GetPlace()),
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out_data,
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mix_vector_out.CUDAMutableData(dev_ctx.GetPlace()),
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out.rows().size(),
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input_width);
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mix_vector_out.CopyToCPU();
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}
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void operator()(const DeviceContext& dev_ctx,
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const std::vector<const SelectedRows*>& inputs,
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SelectedRows* output,
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const bool sorted_result = false) {
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if (inputs.size() == 0) {
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VLOG(3) << "no input! return";
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return;
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}
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const SelectedRows* has_value_input = nullptr;
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for (auto* in : inputs) {
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if (in->rows().size() > 0) {
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has_value_input = in;
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break;
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}
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}
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if (has_value_input == nullptr) {
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VLOG(3) << "no input has value! just return" << std::endl;
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return;
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}
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auto input_width = has_value_input->value().dims()[1];
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auto input_height = has_value_input->height();
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SelectedRows& out = *output;
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std::set<int64_t> merged_row_set;
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for (auto* input : inputs) {
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if (input->rows().size() == 0) {
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continue;
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}
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PADDLE_ENFORCE_EQ(input_width,
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input->value().dims()[1],
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common::errors::InvalidArgument(
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"All input should have same "
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"dimension except for the first one."));
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PADDLE_ENFORCE_EQ(input_height,
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input->height(),
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common::errors::InvalidArgument(
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"All input should have same height."));
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merged_row_set.insert(input->rows().begin(), input->rows().end());
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}
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std::vector<int64_t> merge_rows_cpu(merged_row_set.begin(),
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merged_row_set.end());
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Vector<int64_t> merge_rows(merge_rows_cpu);
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out.set_rows(merge_rows);
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out.set_height(input_height);
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DenseTensor* out_tensor = out.mutable_value();
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out_tensor->Resize(
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make_ddim({static_cast<int64_t>(merge_rows.size()), input_width}));
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dev_ctx.template Alloc<T>(out_tensor);
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funcs::SetConstant<DeviceContext, T> constant_functor;
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constant_functor(dev_ctx, out.mutable_value(), static_cast<T>(0));
|
|
|
|
auto* out_data = out.mutable_value()->data<T>();
|
|
|
|
const int block_size = 256;
|
|
dim3 threads(block_size, 1);
|
|
|
|
for (auto* input : inputs) {
|
|
if (input->rows().size() == 0) {
|
|
continue;
|
|
}
|
|
auto* input_data = input->value().data<T>();
|
|
auto& input_rows = input->rows();
|
|
dim3 grid1(input_rows.size(), 1);
|
|
|
|
phi::MixVector<int64_t> mix_vector_input(&input_rows);
|
|
phi::MixVector<int64_t> mix_vector_out(out.mutable_rows());
|
|
MergeAddKernel<T, 256><<<grid1, threads, 0, dev_ctx.stream()>>>(
|
|
input_data,
|
|
mix_vector_input.CUDAData(dev_ctx.GetPlace()),
|
|
out_data,
|
|
mix_vector_out.CUDAMutableData(dev_ctx.GetPlace()),
|
|
out.rows().size(),
|
|
input_width);
|
|
mix_vector_out.CopyToCPU();
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct MergeAdd<GPUContext, T> {
|
|
// unary functor, merge by adding duplicated rows in
|
|
// the input SelectedRows object.
|
|
SelectedRows operator()(const GPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
const bool sorted_result) {
|
|
return MergeAddImpl<GPUContext, T>()(dev_ctx, input, sorted_result);
|
|
}
|
|
|
|
void operator()(const GPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
SelectedRows* output,
|
|
const bool sorted_result) {
|
|
MergeAddImpl<GPUContext, T>()(dev_ctx, input, output, sorted_result);
|
|
}
|
|
|
|
void operator()(const GPUContext& dev_ctx,
|
|
const std::vector<const SelectedRows*>& inputs,
|
|
SelectedRows* output,
|
|
const bool sorted_result) {
|
|
MergeAddImpl<GPUContext, T>()(dev_ctx, inputs, output, sorted_result);
|
|
}
|
|
};
|
|
|
|
#define TEMPLATE_SPECIALIZED_FOR_MERGEADD(dtype) \
|
|
template struct MergeAddImpl<GPUContext, dtype>; \
|
|
template struct PADDLE_API MergeAdd<GPUContext, dtype>;
|
|
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(float)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(double)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(int)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(int64_t)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(phi::float16)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(phi::bfloat16)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(phi::complex64)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD(phi::complex128)
|
|
|
|
template <typename T, int block_size>
|
|
__global__ void UpdateToTensorKernel(const T* selected_rows,
|
|
const int64_t* rows,
|
|
const ScatterOps& op,
|
|
T* tensor_out,
|
|
int64_t row_numel) {
|
|
const int ty = blockIdx.x;
|
|
int tid = threadIdx.x;
|
|
|
|
selected_rows += ty * row_numel;
|
|
tensor_out += rows[ty] * row_numel;
|
|
// FIXME(typhoonzero): use macro fix the below messy code.
|
|
switch (op) {
|
|
case ScatterOps::ASSIGN:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] = selected_rows[index];
|
|
}
|
|
break;
|
|
case ScatterOps::ADD:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] += selected_rows[index];
|
|
}
|
|
break;
|
|
case ScatterOps::SUB:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] -= selected_rows[index];
|
|
}
|
|
break;
|
|
case ScatterOps::SUBBY:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] = selected_rows[index] - tensor_out[index];
|
|
}
|
|
break;
|
|
case ScatterOps::MUL:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] *= selected_rows[index];
|
|
}
|
|
break;
|
|
case ScatterOps::DIV:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] /= selected_rows[index];
|
|
}
|
|
break;
|
|
case ScatterOps::DIVBY:
|
|
for (int64_t index = tid; index < row_numel; index += block_size) {
|
|
tensor_out[index] = selected_rows[index] / tensor_out[index];
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
struct UpdateToTensor<GPUContext, T> {
|
|
void operator()(const GPUContext& dev_ctx,
|
|
const ScatterOps& op,
|
|
const SelectedRows& input1,
|
|
DenseTensor* input2) {
|
|
// NOTE: Use SelectedRowsAddToTensor for better performance
|
|
// no additional MergeAdd called.
|
|
MergeAdd<GPUContext, T> merge_func;
|
|
auto merged_in1 = merge_func(dev_ctx, input1);
|
|
|
|
auto in1_height = merged_in1.height();
|
|
auto in2_dims = input2->dims();
|
|
PADDLE_ENFORCE_EQ(
|
|
in1_height,
|
|
in2_dims[0],
|
|
common::errors::InvalidArgument("The two inputs height must be equal."
|
|
"But received first input height = "
|
|
"[%d], second input height = [%d]",
|
|
in1_height,
|
|
in2_dims[0]));
|
|
|
|
auto& in1_value = merged_in1.value();
|
|
auto& in1_rows = merged_in1.rows();
|
|
|
|
int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
|
|
PADDLE_ENFORCE_EQ(
|
|
in1_row_numel,
|
|
input2->numel() / in1_height,
|
|
common::errors::InvalidArgument(
|
|
"The two inputs width must be equal."
|
|
"But received first input width = [%d], second input width = [%d]",
|
|
in1_row_numel,
|
|
input2->numel() / in1_height));
|
|
|
|
auto* in1_data = in1_value.template data<T>();
|
|
auto* in2_data = input2->data<T>();
|
|
|
|
dim3 threads(phi::PADDLE_CUDA_NUM_THREADS, 1);
|
|
dim3 grid(in1_rows.size(), 1);
|
|
UpdateToTensorKernel<T, phi::PADDLE_CUDA_NUM_THREADS>
|
|
<<<grid, threads, 0, dev_ctx.stream()>>>(
|
|
in1_data, in1_rows.cuda_data(), op, in2_data, in1_row_numel);
|
|
}
|
|
};
|
|
} // namespace scatter
|
|
} // namespace funcs
|
|
} // namespace phi
|