wehub-resource-sync
1006 lines
36 KiB
C++
1006 lines
36 KiB
C++
/* 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 <algorithm>
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#include <map>
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#include <set>
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#include <vector>
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/selected_rows_functor.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/core/mixed_vector.h"
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#ifdef PADDLE_WITH_XPU
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#endif
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#ifdef PADDLE_WITH_DNNL
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#include "paddle/phi/backends/onednn/axpy_handler.h"
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#endif
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#include "glog/logging.h"
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namespace phi::funcs {
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template <typename T>
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struct SelectedRowsAdd<CPUContext, T> {
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void operator()(const CPUContext& 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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auto& 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 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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out_value->numel() / out_rows.size()));
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auto in1_place = input1.place();
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PADDLE_ENFORCE_EQ(in1_place.GetType() == AllocationType::CPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the CPU place."));
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auto in2_place = input2.place();
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PADDLE_ENFORCE_EQ(in2_place.GetType() == AllocationType::CPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the CPU place."));
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auto out_place = dev_ctx.GetPlace();
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PADDLE_ENFORCE_EQ(out_place.GetType() == AllocationType::CPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the CPU place."));
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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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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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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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}
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};
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template struct PADDLE_API SelectedRowsAdd<CPUContext, float>;
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template struct PADDLE_API SelectedRowsAdd<CPUContext, double>;
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template <typename T>
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struct SelectedRowsAddTensor<CPUContext, T> {
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void operator()(const CPUContext& 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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const auto& in2_dims = input2.dims();
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const 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("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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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 =
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static_cast<int64_t>(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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phi::funcs::SetConstant<CPUContext, T> functor;
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functor(dev_ctx, output, static_cast<T>(0.0));
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auto* in1_data = in1_value.data<T>();
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auto* out_data = output->data<T>();
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for (size_t i = 0; i < in1_rows.size(); i++) {
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for (int64_t j = 0; j < in1_row_numel; j++) {
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out_data[in1_rows[i] * in1_row_numel + j] +=
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in1_data[i * in1_row_numel + j];
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}
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}
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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<CPUContext, float>;
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template struct PADDLE_API SelectedRowsAddTensor<CPUContext, double>;
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template <typename T>
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struct SelectedRowsAddTo<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx UNUSED,
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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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mixv_in2_rows.Extend(in1_rows.begin(), in1_rows.end());
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auto in1_place = input1.place();
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PADDLE_ENFORCE_EQ(in1_place.GetType() == AllocationType::CPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the CPU place."));
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auto in2_place = input2->place();
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PADDLE_ENFORCE_EQ(in2_place.GetType() == AllocationType::CPU,
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true,
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common::errors::InvalidArgument(
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"The running environment is not on the CPU 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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}
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};
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template struct PADDLE_API SelectedRowsAddTo<CPUContext, float>;
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template struct PADDLE_API SelectedRowsAddTo<CPUContext, double>;
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template struct PADDLE_API SelectedRowsAddTo<CPUContext, int>;
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template struct PADDLE_API SelectedRowsAddTo<CPUContext, int64_t>;
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template <typename T>
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struct SelectedRowsSumTo<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx,
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const std::vector<SelectedRows*>& input1,
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const std::vector<int64_t>& input2_offsets,
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SelectedRows* input2) {
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// Ensure all selected rows have the same height
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size_t size = 0u;
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for (auto item : input1) {
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auto& in_rows = item->rows();
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size += in_rows.end() - in_rows.begin();
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auto in1_height = item->height();
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PADDLE_ENFORCE_EQ(in1_height,
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input2->height(),
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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], second "
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"input height = [%d]",
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in1_height,
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input2->height()));
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}
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// concat rows
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std::vector<int64_t> in2_rows;
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in2_rows.reserve(in2_rows.size() + size);
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for (auto item : input1) {
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const Vector<int64_t>& in_rows = item->rows();
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in2_rows.insert(in2_rows.end(), in_rows.begin(), in_rows.end());
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}
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input2->set_rows(in2_rows);
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auto* in2_value = input2->mutable_value();
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auto* in2_data = in2_value->data<T>();
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auto blas = phi::funcs::GetBlas<CPUContext, T>(dev_ctx);
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size_t offset = 0u;
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for (size_t i = 0u; i != input1.size(); ++i) {
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auto& in_value = input1[i]->value();
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const auto* in_data = in_value.data<T>();
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offset += input2_offsets[i];
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blas.VCOPY(in_value.numel(), in_data, in2_data + offset);
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}
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}
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};
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template struct PADDLE_API SelectedRowsSumTo<CPUContext, float>;
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template struct PADDLE_API SelectedRowsSumTo<CPUContext, double>;
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template <typename T>
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struct SelectedRowsAddToTensor<CPUContext, T> {
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void operator()(const CPUContext& dev_ctx UNUSED,
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const SelectedRows& input1,
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DenseTensor* input2) {
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if (UNLIKELY(input1.rows().empty())) {
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LOG(WARNING) << "input selected rows is empty!";
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return;
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}
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auto in1_height = input1.height();
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const 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 =
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static_cast<int64_t>(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* input2_data = input2->data<T>();
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for (size_t i = 0; i < in1_rows.size(); i++) {
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for (int64_t j = 0; j < in1_row_numel; j++) {
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input2_data[in1_rows[i] * in1_row_numel + j] +=
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in1_data[i * in1_row_numel + j];
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}
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}
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}
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};
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#ifdef PADDLE_WITH_XPU
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template <typename T>
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struct SelectedRowsAddToTensor<XPUContext, T> {
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void operator()(const XPUContext& dev_ctx,
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const SelectedRows& input1,
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DenseTensor* input2) {
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if (UNLIKELY(input1.rows().size() == 0)) {
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LOG(WARNING) << "input selected rows is empty!";
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto in1_height = input1.height();
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const 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_rows_data = nullptr;
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xpu::VectorParam<int64_t> in1_rows_vec{
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in1_rows.data(), static_cast<int64_t>(in1_rows.size()), in1_rows_data};
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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* out_data = input2->data<T>();
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int64_t h = in1_rows.size();
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int64_t w = in1_row_numel;
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const std::vector<int64_t> xshape{h, w};
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int r = xpu::scatter<XPUType, int64_t>(
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dev_ctx.x_context(),
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nullptr,
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reinterpret_cast<const XPUType*>(in1_data),
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reinterpret_cast<XPUType*>(out_data),
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in1_rows_vec,
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xshape,
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0,
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false);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "scatter");
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}
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};
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#endif
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, float>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, double>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, int>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, int64_t>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, phi::float16>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, phi::bfloat16>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, phi::complex64>;
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template struct PADDLE_API SelectedRowsAddToTensor<CPUContext, phi::complex128>;
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#ifdef PADDLE_WITH_XPU
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template struct SelectedRowsAddToTensor<XPUContext, float>;
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#endif
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// This is a separated namespace for manipulate SelectedRows typed
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// data. Like merge duplicated rows, adding two SelectedRows etc.
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//
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// Another group of functors is called "scatter updates", which means
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// use SelectedRows to update a dense tensor with different Ops, like
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// add or mul.
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} // namespace phi::funcs
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namespace phi::funcs::scatter {
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template <typename T, typename DeviceContext>
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typename std::enable_if<!std::is_integral<T>::value>::type elementwise_add_to(
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phi::funcs::BlasT<DeviceContext, T>* blas,
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size_t data_len,
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const T* in,
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T* out) {
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blas->AXPY(data_len, T(1.f), in, out);
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}
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template <typename T, typename DeviceContext>
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typename std::enable_if<std::is_integral<T>::value>::type elementwise_add_to(
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phi::funcs::BlasT<DeviceContext, T>* blas UNUSED,
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size_t data_len,
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const T* in,
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T* out) {
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for (size_t i = 0; i < data_len; i++) {
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out[i] += in[i];
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}
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}
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template <typename T, typename DeviceContext>
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typename std::enable_if<std::is_same<T, phi::bfloat16>::value>::type
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add_sparse_inputs(const std::vector<const SelectedRows*>& inputs,
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const std::unordered_map<int64_t, size_t>& rows_to_id,
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int64_t input_width,
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const DeviceContext& dev_ctx,
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T* out_data) {
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#ifndef PADDLE_WITH_DNNL
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auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx);
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#endif
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for (auto* input : inputs) {
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if (input->rows().empty()) {
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continue;
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}
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auto* input_data = input->value().data<T>();
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auto& input_rows = input->rows();
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#ifdef PADDLE_WITH_DNNL
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OneDNNContext onednn_context(dev_ctx.GetPlace());
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funcs::OneDNNAXPYHandler<T> axpy_handler(
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input_width, T(1.f), onednn_context.GetEngine());
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for (size_t i = 0; i < input_rows.size(); i++) {
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size_t out_i = rows_to_id.at(input_rows[i]);
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axpy_handler(&input_data[i * input_width],
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&out_data[out_i * input_width]);
|
|
}
|
|
#else
|
|
for (size_t i = 0; i < input_rows.size(); i++) {
|
|
size_t out_i = rows_to_id.at(input_rows[i]);
|
|
elementwise_add_to<T, DeviceContext>(&blas,
|
|
static_cast<size_t>(input_width),
|
|
&input_data[i * input_width],
|
|
&out_data[out_i * input_width]);
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
|
|
template <typename T, typename DeviceContext>
|
|
typename std::enable_if<!std::is_same<T, phi::bfloat16>::value>::type
|
|
add_sparse_inputs(const std::vector<const SelectedRows*>& inputs,
|
|
const std::unordered_map<int64_t, size_t>& rows_to_id,
|
|
int64_t input_width,
|
|
const DeviceContext& dev_ctx,
|
|
T* out_data) {
|
|
VLOG(4) << "[CPU] add_sparse_inputs <" << typeid(T).name();
|
|
auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx);
|
|
for (auto* input : inputs) {
|
|
if (input->rows().empty()) {
|
|
continue;
|
|
}
|
|
auto* input_data = input->value().data<T>();
|
|
auto& input_rows = input->rows();
|
|
|
|
for (size_t i = 0; i < input_rows.size(); i++) {
|
|
size_t out_i = rows_to_id.at(input_rows[i]);
|
|
elementwise_add_to<T, DeviceContext>(&blas,
|
|
static_cast<size_t>(input_width),
|
|
&input_data[i * input_width],
|
|
&out_data[out_i * input_width]);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
struct MergeAddImpl {
|
|
SelectedRows operator()(const DeviceContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
const bool sorted_result = false) {
|
|
SelectedRows out;
|
|
(*this)(dev_ctx, input, &out, sorted_result);
|
|
return out;
|
|
}
|
|
|
|
void operator()(const DeviceContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
SelectedRows* output,
|
|
const bool sorted_result = false) {
|
|
std::vector<const SelectedRows*> inputs;
|
|
inputs.push_back(&input);
|
|
(*this)(dev_ctx, inputs, output, sorted_result);
|
|
}
|
|
|
|
void operator()(const DeviceContext& dev_ctx,
|
|
const std::vector<const SelectedRows*>& inputs,
|
|
SelectedRows* output,
|
|
const bool sorted_result = false) {
|
|
if (inputs.empty()) {
|
|
VLOG(3) << "no input! return";
|
|
return;
|
|
}
|
|
const SelectedRows* has_value_input = nullptr;
|
|
for (auto* in : inputs) {
|
|
if (!in->rows().empty()) {
|
|
has_value_input = in;
|
|
break;
|
|
}
|
|
}
|
|
if (has_value_input == nullptr) {
|
|
VLOG(3) << "no input has value! just return" << std::endl;
|
|
return;
|
|
}
|
|
auto input_width = has_value_input->value().dims()[1];
|
|
auto input_height = has_value_input->height();
|
|
SelectedRows& out = *output;
|
|
std::set<int64_t> merged_row_set;
|
|
size_t row_num = 0;
|
|
for (auto* input : inputs) {
|
|
if (input->rows().empty()) {
|
|
continue;
|
|
}
|
|
PADDLE_ENFORCE_EQ(input_width,
|
|
input->value().dims()[1],
|
|
common::errors::InvalidArgument(
|
|
"All inputs should have same "
|
|
"dimension except for the first one."));
|
|
PADDLE_ENFORCE_EQ(input_height,
|
|
input->height(),
|
|
common::errors::InvalidArgument(
|
|
"All inputs should have same height."));
|
|
row_num += input->rows().size();
|
|
merged_row_set.insert(input->rows().begin(), input->rows().end());
|
|
}
|
|
|
|
out.set_height(input_height);
|
|
DenseTensor* out_tensor = out.mutable_value();
|
|
out_tensor->Resize(
|
|
make_ddim({static_cast<int64_t>(merged_row_set.size()), input_width}));
|
|
auto* out_data = dev_ctx.template Alloc<T>(out_tensor);
|
|
|
|
if (merged_row_set.size() == row_num && !sorted_result) {
|
|
// no duplicated ids, just concat the result together
|
|
std::vector<int64_t> merge_rows;
|
|
merge_rows.reserve(row_num);
|
|
// concat rows
|
|
for (auto* in : inputs) {
|
|
merge_rows.insert(
|
|
merge_rows.end(), in->rows().begin(), in->rows().end());
|
|
}
|
|
out.set_rows(merge_rows);
|
|
auto in_place = inputs[0]->place();
|
|
auto out_place = out.place();
|
|
int64_t copied_numel = 0;
|
|
for (auto* in : inputs) {
|
|
auto* in_data = in->value().data<T>();
|
|
auto in_numel = in->rows().size() * input_width;
|
|
memory_utils::Copy(out_place,
|
|
out_data + copied_numel,
|
|
in_place,
|
|
in_data,
|
|
in_numel * sizeof(T));
|
|
copied_numel += static_cast<int64_t>(in_numel);
|
|
}
|
|
} else {
|
|
std::vector<int64_t> merge_rows(merged_row_set.begin(),
|
|
merged_row_set.end());
|
|
|
|
if (sorted_result) {
|
|
std::sort(merge_rows.begin(), merge_rows.end());
|
|
}
|
|
|
|
out.set_rows(merge_rows);
|
|
|
|
phi::funcs::SetConstant<DeviceContext, T> constant_functor;
|
|
constant_functor(dev_ctx, out.mutable_value(), static_cast<T>(0.f));
|
|
|
|
std::unordered_map<int64_t, size_t> rows_to_id;
|
|
for (size_t i = 0; i < merge_rows.size(); ++i) {
|
|
rows_to_id[merge_rows[i]] = i;
|
|
}
|
|
|
|
add_sparse_inputs<T, DeviceContext>(
|
|
inputs, rows_to_id, input_width, dev_ctx, out_data);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct MergeAdd<CPUContext, T> {
|
|
// unary functor, merge by adding duplicated rows in
|
|
// the input SelectedRows object.
|
|
SelectedRows operator()(const CPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
const bool sorted_result) {
|
|
return MergeAddImpl<CPUContext, T>()(dev_ctx, input, sorted_result);
|
|
}
|
|
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
SelectedRows* output,
|
|
const bool sorted_result) {
|
|
MergeAddImpl<CPUContext, T>()(dev_ctx, input, output, sorted_result);
|
|
}
|
|
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const std::vector<const SelectedRows*>& inputs,
|
|
SelectedRows* output,
|
|
const bool sorted_result) {
|
|
MergeAddImpl<CPUContext, T>()(dev_ctx, inputs, output, sorted_result);
|
|
}
|
|
};
|
|
|
|
#define TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(dtype) \
|
|
template struct MergeAddImpl<CPUContext, dtype>; \
|
|
template struct PADDLE_API MergeAdd<CPUContext, dtype>;
|
|
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(float)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(double)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(int)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(int64_t)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(phi::bfloat16)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(phi::complex64)
|
|
TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(phi::complex128)
|
|
|
|
#ifdef PADDLE_WITH_XPU
|
|
template <typename T>
|
|
struct MergeAdd<XPUContext, T> {
|
|
SelectedRows operator()(const XPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
const bool sorted_result = false) {
|
|
SelectedRows out;
|
|
(*this)(dev_ctx, input, &out, sorted_result);
|
|
return out;
|
|
}
|
|
|
|
void operator()(const XPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
SelectedRows* output,
|
|
const bool sorted_result = false) {
|
|
Vector<int64_t> input_rows(input.rows());
|
|
if (input_rows.size() == 0) {
|
|
return;
|
|
}
|
|
|
|
SelectedRows& out = *output;
|
|
std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
|
|
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
|
|
auto input_width = input.value().dims()[1];
|
|
|
|
out.set_rows(merge_rows);
|
|
out.set_height(input.height());
|
|
DenseTensor* out_tensor = out.mutable_value();
|
|
out_tensor->Resize(
|
|
make_ddim({static_cast<int64_t>(merge_rows.size()), input_width}));
|
|
dev_ctx.template Alloc<T>(out_tensor);
|
|
|
|
std::unordered_map<int64_t, size_t> rows_to_id;
|
|
for (size_t i = 0; i < merge_rows.size(); ++i) {
|
|
rows_to_id[merge_rows[i]] = i;
|
|
}
|
|
|
|
auto* y_data = out.mutable_value()->data<T>();
|
|
auto* x_data = input.value().data<T>();
|
|
int xm = input_rows.size();
|
|
int ym = merge_rows.size();
|
|
int n = input_width;
|
|
|
|
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
|
|
int64_t* x_rows_data = RAII_GUARD.alloc_l3_or_gm<int64_t>(xm);
|
|
int64_t* y_rows_data = RAII_GUARD.alloc_l3_or_gm<int64_t>(ym);
|
|
memory_utils::Copy(dev_ctx.GetPlace(),
|
|
y_rows_data,
|
|
CPUPlace(),
|
|
merge_rows.data(),
|
|
ym * sizeof(int64_t));
|
|
memory_utils::Copy(dev_ctx.GetPlace(),
|
|
x_rows_data,
|
|
CPUPlace(),
|
|
input_rows.data(),
|
|
xm * sizeof(int64_t));
|
|
int r = xpu::merge_dup_rows<T, int64_t>(dev_ctx.x_context(),
|
|
x_data,
|
|
y_data,
|
|
x_rows_data,
|
|
y_rows_data,
|
|
xm,
|
|
n,
|
|
ym);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merge_dup_rows");
|
|
}
|
|
|
|
void operator()(const XPUContext& dev_ctx,
|
|
const std::vector<const SelectedRows*>& inputs,
|
|
SelectedRows* output,
|
|
const bool sorted_result = false) {
|
|
if (inputs.size() == 0) {
|
|
VLOG(3) << "no input! return";
|
|
return;
|
|
}
|
|
const SelectedRows* has_value_input = nullptr;
|
|
for (auto* in : inputs) {
|
|
if (in->rows().size() > 0) {
|
|
has_value_input = in;
|
|
break;
|
|
}
|
|
}
|
|
if (has_value_input == nullptr) {
|
|
VLOG(3) << "no input has value! just return" << std::endl;
|
|
return;
|
|
}
|
|
auto input_width = has_value_input->value().dims()[1];
|
|
auto input_height = has_value_input->height();
|
|
SelectedRows& out = *output;
|
|
std::set<int64_t> merged_row_set;
|
|
size_t row_num = 0;
|
|
for (auto* input : inputs) {
|
|
if (input->rows().size() == 0) {
|
|
continue;
|
|
}
|
|
PADDLE_ENFORCE_EQ(input_width,
|
|
input->value().dims()[1],
|
|
common::errors::InvalidArgument(
|
|
"All inputs should have same "
|
|
"dimension except for the first one."));
|
|
PADDLE_ENFORCE_EQ(input_height,
|
|
input->height(),
|
|
common::errors::InvalidArgument(
|
|
"All inputs should have same height."));
|
|
row_num += input->rows().size();
|
|
merged_row_set.insert(input->rows().begin(), input->rows().end());
|
|
}
|
|
|
|
std::vector<int64_t> merge_rows(merged_row_set.begin(),
|
|
merged_row_set.end());
|
|
|
|
if (sorted_result) {
|
|
std::sort(merge_rows.begin(), merge_rows.end());
|
|
}
|
|
|
|
out.set_rows(merge_rows);
|
|
out.set_height(input_height);
|
|
|
|
DenseTensor* out_tensor = out.mutable_value();
|
|
out_tensor->Resize(
|
|
make_ddim({static_cast<int64_t>(merged_row_set.size()), input_width}));
|
|
dev_ctx.template Alloc<T>(out_tensor);
|
|
|
|
float* y_data = reinterpret_cast<float*>(out_tensor->data<T>());
|
|
|
|
std::unordered_map<int64_t, size_t> rows_to_id;
|
|
for (size_t i = 0; i < merge_rows.size(); ++i) {
|
|
rows_to_id[merge_rows[i]] = i;
|
|
}
|
|
|
|
for (auto* input : inputs) {
|
|
if (input->rows().size() == 0) {
|
|
continue;
|
|
}
|
|
auto& input_rows = input->rows();
|
|
|
|
auto* x_data = input->value().data<T>();
|
|
int xm = input_rows.size();
|
|
int ym = merge_rows.size();
|
|
int n = input_width;
|
|
|
|
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
|
|
int64_t* x_rows_data = RAII_GUARD.alloc_l3_or_gm<int64_t>(xm);
|
|
int64_t* y_rows_data = RAII_GUARD.alloc_l3_or_gm<int64_t>(ym);
|
|
memory_utils::Copy(dev_ctx.GetPlace(),
|
|
y_rows_data,
|
|
CPUPlace(),
|
|
merge_rows.data(),
|
|
ym * sizeof(int64_t));
|
|
memory_utils::Copy(dev_ctx.GetPlace(),
|
|
x_rows_data,
|
|
CPUPlace(),
|
|
input_rows.data(),
|
|
xm * sizeof(int64_t));
|
|
int r = xpu::merge_dup_rows<T, int64_t>(dev_ctx.x_context(),
|
|
x_data,
|
|
y_data,
|
|
x_rows_data,
|
|
y_rows_data,
|
|
xm,
|
|
n,
|
|
ym);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merge_dup_rows");
|
|
}
|
|
}
|
|
};
|
|
|
|
#endif
|
|
template <typename T>
|
|
struct MergeAverage<CPUContext, T> {
|
|
SelectedRows operator()(const CPUContext& dev_ctx,
|
|
const SelectedRows& input) {
|
|
SelectedRows out;
|
|
(*this)(dev_ctx, input, &out);
|
|
return out;
|
|
}
|
|
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const SelectedRows& input,
|
|
SelectedRows* output) {
|
|
std::vector<const SelectedRows*> inputs;
|
|
inputs.push_back(&input);
|
|
(*this)(dev_ctx, inputs, output);
|
|
}
|
|
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const std::vector<const SelectedRows*>& inputs,
|
|
SelectedRows* output) {
|
|
if (inputs.empty()) {
|
|
VLOG(3) << "no input! return";
|
|
return;
|
|
}
|
|
const SelectedRows* has_value_input = nullptr;
|
|
for (auto* in : inputs) {
|
|
if (!in->rows().empty()) {
|
|
has_value_input = in;
|
|
break;
|
|
}
|
|
}
|
|
if (has_value_input == nullptr) {
|
|
VLOG(3) << "no input has value! just return" << std::endl;
|
|
return;
|
|
}
|
|
auto input_width = has_value_input->value().dims()[1];
|
|
auto input_height = has_value_input->height();
|
|
SelectedRows& out = *output;
|
|
std::set<int64_t> merged_row_set;
|
|
for (auto* input : inputs) {
|
|
if (input->rows().empty()) {
|
|
continue;
|
|
}
|
|
PADDLE_ENFORCE_EQ(input_width,
|
|
input->value().dims()[1],
|
|
common::errors::InvalidArgument(
|
|
"All inputs should have same "
|
|
"dimension except for the first one."));
|
|
PADDLE_ENFORCE_EQ(input_height,
|
|
input->height(),
|
|
common::errors::InvalidArgument(
|
|
"All input should have same height."));
|
|
merged_row_set.insert(input->rows().begin(), input->rows().end());
|
|
}
|
|
|
|
out.set_height(input_height);
|
|
|
|
DenseTensor* out_tensor = out.mutable_value();
|
|
out_tensor->Resize(
|
|
make_ddim({static_cast<int64_t>(merged_row_set.size()), input_width}));
|
|
auto* out_data = dev_ctx.template Alloc<T>(out_tensor);
|
|
|
|
std::vector<int64_t> merge_rows(merged_row_set.begin(),
|
|
merged_row_set.end());
|
|
std::sort(merge_rows.begin(), merge_rows.end());
|
|
|
|
out.set_rows(merge_rows);
|
|
|
|
phi::funcs::SetConstant<CPUContext, T> constant_functor;
|
|
constant_functor(dev_ctx, out.mutable_value(), static_cast<T>(0.0));
|
|
|
|
std::unordered_map<int64_t, size_t> rows_to_id;
|
|
for (size_t i = 0; i < merge_rows.size(); ++i) {
|
|
rows_to_id[merge_rows[i]] = i;
|
|
}
|
|
|
|
auto blas = phi::funcs::GetBlas<CPUContext, T>(dev_ctx);
|
|
for (auto* input : inputs) {
|
|
if (input->rows().empty()) {
|
|
continue;
|
|
}
|
|
auto* input_data = input->value().data<T>();
|
|
auto& input_rows = input->rows();
|
|
|
|
for (size_t i = 0; i < input_rows.size(); i++) {
|
|
size_t out_i = rows_to_id[input_rows[i]];
|
|
elementwise_add_to<T>(&blas,
|
|
static_cast<size_t>(input_width),
|
|
&input_data[i * input_width],
|
|
&out_data[out_i * input_width]);
|
|
}
|
|
}
|
|
size_t input_width_cast = static_cast<size_t>(input_width);
|
|
T count = static_cast<T>(inputs.size());
|
|
for (size_t i = 0; i < merge_rows.size(); i++) {
|
|
for (size_t j = 0; j < input_width_cast; j++) {
|
|
out_data[i * input_width + j] = out_data[i * input_width + j] / count;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
#ifdef PADDLE_WITH_XPU
|
|
template struct MergeAdd<XPUContext, float>;
|
|
#endif
|
|
|
|
template struct PADDLE_API MergeAverage<CPUContext, int>;
|
|
template struct PADDLE_API MergeAverage<CPUContext, int64_t>;
|
|
template struct PADDLE_API MergeAverage<CPUContext, float>;
|
|
template struct PADDLE_API MergeAverage<CPUContext, double>;
|
|
|
|
template <typename T>
|
|
struct UpdateToTensor<CPUContext, T> {
|
|
void operator()(const CPUContext& dev_ctx,
|
|
const ScatterOps& op,
|
|
const SelectedRows& input1,
|
|
DenseTensor* input2) {
|
|
auto in1_height = input1.height();
|
|
const 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 = input1.value();
|
|
auto& in1_rows = input1.rows();
|
|
|
|
int64_t in1_row_numel =
|
|
static_cast<int64_t>(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.data<T>();
|
|
auto* input2_data = input2->data<T>();
|
|
|
|
// FIXME(typhoonzero): use macro fix the below messy code.
|
|
switch (op) {
|
|
case ScatterOps::ASSIGN:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] =
|
|
in1_data[i * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::ADD:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] +=
|
|
in1_data[i * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::SUB:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] -=
|
|
in1_data[i * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::SUBBY:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] =
|
|
in1_data[i * in1_row_numel + j] -
|
|
input2_data[in1_rows[i] * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::MUL:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] *=
|
|
in1_data[i * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::DIV:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] /=
|
|
in1_data[i * in1_row_numel + j];
|
|
break;
|
|
case ScatterOps::DIVBY:
|
|
INLINE_FOR2(in1_rows.size(), in1_row_numel)
|
|
input2_data[in1_rows[i] * in1_row_numel + j] =
|
|
in1_data[i * in1_row_numel + j] /
|
|
input2_data[in1_rows[i] * in1_row_numel + j];
|
|
break;
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace phi::funcs::scatter
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