wehub-resource-sync
126 lines
4.0 KiB
C++
126 lines
4.0 KiB
C++
// Copyright (c) 2022 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 <type_traits>
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#include <vector>
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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#include "paddle/phi/kernels/logsumexp_kernel.h"
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namespace phi {
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#define HANDLE_DIM(NDIM, RDIM) \
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if (ndim == NDIM && rdim == RDIM) { \
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funcs::ReduceFunctor<Context, T, NDIM, RDIM, LogsumexpFunctor<T>>( \
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dev_ctx, x, out, axis, keepdim); \
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}
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template <typename T>
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struct LogsumexpFunctor {
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template <typename Context, typename X, typename Y, typename Dim>
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void operator()(const Context& place, X* x, Y* y, const Dim& dim) {
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using MT = typename MPTypeTrait<T>::Type;
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auto x_dim = x->dimensions();
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auto t_dim = x_dim;
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for (int i = 0; i < static_cast<int>(dim.size()); i++) {
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t_dim[dim[i]] = 1;
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}
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auto r_dim = x_dim;
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for (int i = 0; i < static_cast<int>(r_dim.size()); i++) {
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r_dim[i] = 1;
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}
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for (int i = 0; i < static_cast<int>(dim.size()); i++) {
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r_dim[dim[i]] = x_dim[dim[i]];
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}
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auto x_mt = (*x).template cast<MT>();
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auto y_dim = y->dimensions();
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auto x_max = x_mt.maximum(dim).eval();
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y->device(place) =
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(x_max +
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(x_mt - x_max.reshape(t_dim).broadcast(r_dim)).exp().sum(dim).log())
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.reshape(y_dim)
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.template cast<T>();
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}
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};
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template <typename T, typename Context>
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void LogsumexpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int>& axis_in,
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bool keepdim,
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bool reduce_all,
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DenseTensor* out) {
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), -INFINITY, out);
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return;
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}
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std::vector<int64_t> axis;
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axis.reserve(axis_in.size());
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std::for_each(axis_in.begin(), axis_in.end(), [&axis](const int& t) {
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axis.push_back(static_cast<int64_t>(t));
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});
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dev_ctx.template Alloc<T>(out);
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reduce_all = recompute_reduce_all(x, axis, reduce_all);
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auto x_dim = x.dims();
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for (int i = 0; i < x_dim.size(); i++) {
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PADDLE_ENFORCE_LT(0,
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x_dim[i],
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errors::InvalidArgument(
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"The dims of Input(X) should be greater than 0."));
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}
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if (reduce_all) {
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// Flatten and reduce 1-D tensor
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auto input = EigenVector<T>::Flatten(x);
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auto output = EigenScalar<T>::From(*out);
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auto& place = *dev_ctx.eigen_device();
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auto reduce_dim = Eigen::array<int, 1>({{0}});
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LogsumexpFunctor<T>()(place, &input, &output, reduce_dim);
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} else {
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int ndim = x.dims().size();
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int rdim = axis.size();
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if (ndim > 4) {
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PADDLE_THROW(common::errors::Unimplemented(
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"Unsupported dimensions, please keep maximum dimensions of input "
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"data less than 4."));
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}
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// comments for accelerating compiling temporarily.
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// HANDLE_DIM(6, 5);
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// HANDLE_DIM(6, 4);
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// HANDLE_DIM(6, 3);
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// HANDLE_DIM(6, 2);
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// HANDLE_DIM(6, 1);
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// HANDLE_DIM(5, 4);
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// HANDLE_DIM(5, 3);
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// HANDLE_DIM(5, 2);
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// HANDLE_DIM(5, 1);
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HANDLE_DIM(4, 3);
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HANDLE_DIM(4, 2);
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HANDLE_DIM(4, 1);
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HANDLE_DIM(3, 2);
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HANDLE_DIM(3, 1);
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HANDLE_DIM(2, 1);
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}
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}
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} // namespace phi
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