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263 lines
8.8 KiB
263 lines
8.8 KiB
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#pragma once
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#include <algorithm>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/platform/for_range.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct DiagonalFunctor {
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DiagonalFunctor(const T* input, const int64_t* diag_stride,
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const int64_t* ret_strides, int64_t pos, int64_t dim_size,
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T* diag)
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: input_(input),
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diag_stride_(diag_stride),
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ret_strides_(ret_strides),
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pos_(pos),
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dim_size_(dim_size),
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diag_(diag) {}
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HOSTDEVICE void operator()(size_t idx) const {
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int64_t position = pos_;
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int64_t num = idx;
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for (int64_t i = 0; i < dim_size_; i++) {
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position += num / diag_stride_[i] * ret_strides_[i];
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num = num % diag_stride_[i];
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}
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diag_[idx] = input_[position];
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}
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const T* input_;
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const int64_t* diag_stride_;
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const int64_t* ret_strides_;
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int64_t pos_;
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int64_t dim_size_;
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T* diag_;
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};
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template <typename T>
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struct TraceGradFunctor {
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TraceGradFunctor(const T* d_out, const int64_t* out_stride,
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const int64_t* x_strides, int64_t pos, int64_t dim_size,
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int64_t dim1, int64_t dim2, int64_t diag_size, T* d_x)
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: d_out_(d_out),
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out_stride_(out_stride),
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x_strides_(x_strides),
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pos_(pos),
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dim_size_(dim_size),
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dim1_(dim1),
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dim2_(dim2),
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diag_size_(diag_size),
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d_x_(d_x) {}
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HOSTDEVICE void operator()(size_t idx) const {
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int64_t num = idx - pos_;
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int64_t position = 0;
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if (num >= 0) {
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int64_t dim1 = 0;
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int64_t dim2 = 0;
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int64_t out_idx = 0;
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for (int64_t i = 0; i < dim_size_; i++) {
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if (i != dim1_ && i != dim2_) {
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position += num / x_strides_[i] * out_stride_[out_idx++];
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} else if (i == dim1_) {
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dim1 = num / x_strides_[i];
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} else {
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dim2 = num / x_strides_[i];
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}
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num = num % x_strides_[i];
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}
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if (dim1 == dim2 && dim1 < diag_size_) {
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d_x_[idx] = d_out_[position];
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}
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}
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}
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const T* d_out_;
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const int64_t* out_stride_;
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const int64_t* x_strides_;
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int64_t pos_;
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int64_t dim_size_;
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int64_t dim1_;
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int64_t dim2_;
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int64_t diag_size_;
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T* d_x_;
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};
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template <typename DeviceContext, typename T>
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framework::Tensor Diagonal(const framework::ExecutionContext& context,
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const framework::Tensor* input, int64_t offset,
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int64_t dim1, int64_t dim2) {
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auto* input_data = input->data<T>();
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auto input_dims = input->dims();
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auto input_stride = framework::stride(input_dims);
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auto dim1_ = dim1 < 0 ? input_dims.size() + dim1 : dim1;
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auto dim2_ = dim2 < 0 ? input_dims.size() + dim2 : dim2;
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auto len1 = input_dims[std::min(dim1_, dim2_)];
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auto len2 = input_dims[std::max(dim1_, dim2_)];
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auto stride1 = input_stride[std::min(dim1_, dim2_)];
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auto stride2 = input_stride[std::max(dim1_, dim2_)];
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int offset_stride = 0;
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if (offset >= 0) {
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offset_stride = stride2;
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len2 -= offset;
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} else {
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offset_stride = stride1;
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len1 += offset;
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}
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int diag_size = len2 < len1 ? len2 : len1;
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if (diag_size > 0) {
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auto ret_strides = vectorize(input_stride);
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auto ret_dims = vectorize(input_dims);
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ret_strides.erase(ret_strides.begin() + std::max(dim1_, dim2_));
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ret_strides.erase(ret_strides.begin() + std::min(dim1_, dim2_));
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ret_dims.erase(ret_dims.begin() + std::max(dim1_, dim2_));
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ret_dims.erase(ret_dims.begin() + std::min(dim1_, dim2_));
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if (ret_strides.empty()) {
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ret_strides.push_back(1);
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ret_dims.push_back(1);
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}
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ret_strides.push_back(stride1 + stride2);
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ret_dims.push_back(diag_size);
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framework::Tensor diag;
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framework::DDim diag_dims = framework::make_ddim(ret_dims);
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auto dig_stride = framework::stride(diag_dims);
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auto diag_data = diag.mutable_data<T>(diag_dims, context.GetPlace());
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int64_t pos = std::abs(offset) * offset_stride;
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int64_t dim_size = ret_strides.size();
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#if defined(__NVCC__) || defined(__HIPCC__)
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thrust::device_vector<int64_t> diag_vec(vectorize(dig_stride));
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const int64_t* diag_arr = thrust::raw_pointer_cast(diag_vec.data());
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thrust::device_vector<int64_t> ret_vec(ret_strides);
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const int64_t* ret_arr = thrust::raw_pointer_cast(ret_vec.data());
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#else
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auto* diag_arr = dig_stride.Get();
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const auto* ret_arr = ret_strides.data();
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#endif
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auto& dev_ctx = context.template device_context<DeviceContext>();
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platform::ForRange<DeviceContext> for_range(dev_ctx, diag.numel());
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DiagonalFunctor<T> functor(input_data, diag_arr, ret_arr, pos, dim_size,
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diag_data);
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for_range(functor);
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return diag;
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} else {
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return {};
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}
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}
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template <typename DeviceContext, typename T>
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class TraceKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input = context.Input<framework::Tensor>("Input");
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auto* out = context.Output<framework::Tensor>("Out");
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const int64_t offset = context.Attr<int>("offset");
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const int64_t dim1 = context.Attr<int>("axis1");
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const int64_t dim2 = context.Attr<int>("axis2");
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auto output_dims = out->dims();
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out->mutable_data<T>(context.GetPlace());
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const framework::Tensor diag =
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Diagonal<DeviceContext, T>(context, input, offset, dim1, dim2);
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if (diag.numel() > 0) {
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auto x = framework::EigenMatrix<T>::Reshape(diag, diag.dims().size() - 1);
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auto output = framework::EigenVector<T>::Flatten(*out);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto reduce_dim = Eigen::array<int, 1>({1});
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output.device(place) = x.sum(reduce_dim);
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out->Resize(output_dims);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class TraceGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const auto* d_out =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_x =
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context.Output<framework::Tensor>(framework::GradVarName("Input"));
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int64_t offset = context.Attr<int>("offset");
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int64_t dim1 = context.Attr<int>("axis1");
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int64_t dim2 = context.Attr<int>("axis2");
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auto input_dims = d_x->dims();
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auto input_stride = framework::stride(input_dims);
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auto output_dims = d_out->dims();
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auto output_stride = framework::stride(output_dims);
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auto* out_data = d_out->data<T>();
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T* x_data = d_x->mutable_data<T>(context.GetPlace());
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math::SetConstant<DeviceContext, T> set_zero;
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auto& dev_ctx = context.template device_context<DeviceContext>();
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set_zero(dev_ctx, d_x, static_cast<T>(0.0));
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auto dim1_ = dim1 < 0 ? input_dims.size() + dim1 : dim1;
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auto dim2_ = dim2 < 0 ? input_dims.size() + dim2 : dim2;
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auto len1 = input_dims[std::min(dim1_, dim2_)];
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auto len2 = input_dims[std::max(dim1_, dim2_)];
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auto stride1 = input_stride[std::min(dim1_, dim2_)];
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auto stride2 = input_stride[std::max(dim1_, dim2_)];
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int offset_stride = 0;
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if (offset >= 0) {
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offset_stride = stride2;
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len2 -= offset;
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} else {
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offset_stride = stride1;
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len1 += offset;
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}
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int64_t diag_size = len2 < len1 ? len2 : len1;
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int64_t pos = std::abs(offset) * offset_stride;
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if (diag_size > 0) {
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#if defined(__NVCC__) || defined(__HIPCC__)
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thrust::device_vector<int64_t> output_vec(vectorize(output_stride));
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const int64_t* output_arr = thrust::raw_pointer_cast(output_vec.data());
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thrust::device_vector<int64_t> input_vec(vectorize(input_stride));
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const int64_t* input_arr = thrust::raw_pointer_cast(input_vec.data());
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#else
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const auto* output_arr = output_stride.Get();
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const auto* input_arr = input_stride.Get();
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#endif
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platform::ForRange<DeviceContext> for_range(dev_ctx, d_x->numel());
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TraceGradFunctor<T> functor(out_data, output_arr, input_arr, pos,
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input_dims.size(), dim1_, dim2_, diag_size,
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x_data);
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for_range(functor);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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