wehub-resource-sync
97 lines
2.9 KiB
C++
97 lines
2.9 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 "paddle/phi/kernels/impl/lu_kernel_impl.h"
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namespace phi {
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template <typename T>
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struct LuUnpackEyeFunctor {
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LuUnpackEyeFunctor(int64_t num_columns, T* output)
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: num_columns_(num_columns), output_(output) {}
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HOSTDEVICE void operator()(size_t idx) const {
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output_[idx * num_columns_ + idx % num_columns_] = static_cast<T>(1);
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}
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int64_t num_columns_;
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T* output_;
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};
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template <typename T, typename Context>
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void LUUnpackKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& pivots,
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bool unpack_ludata,
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bool unpack_pivots,
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DenseTensor* pmat,
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DenseTensor* l,
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DenseTensor* u) {
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auto xdims = x.dims();
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int xrank = xdims.size();
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int64_t m = xdims[xrank - 2];
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int64_t n = xdims[xrank - 1];
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int64_t k = std::min(m, n);
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if (unpack_ludata) {
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dev_ctx.template Alloc<T>(l);
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dev_ctx.template Alloc<T>(u);
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if (x.numel() != 0) {
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DenseTensor L, U;
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LU_Unpack<Context, T>(dev_ctx, &x, &L, &U);
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if (m >= n) {
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Copy(dev_ctx, L, dev_ctx.GetPlace(), false, l);
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Tensor_narrow<Context, T>(dev_ctx, &U, u, 0, k, 0, k);
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} else {
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Copy(dev_ctx, U, dev_ctx.GetPlace(), false, u);
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Tensor_narrow<Context, T>(dev_ctx, &L, l, 0, k, 0, k);
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}
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}
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}
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if (unpack_pivots) {
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dev_ctx.template Alloc<T>(pmat);
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if (x.numel() == 0 || pivots.numel() == 0) {
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// columns is the last dim.
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auto pmat_dims = pmat->dims();
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int64_t columns = pmat_dims[pmat_dims.size() - 1];
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if (columns == 0) return;
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T* pmat_data = pmat->data<T>();
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funcs::SetConstant<Context, T> set_zero;
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set_zero(dev_ctx, pmat, static_cast<T>(0));
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int64_t rows = pmat->numel() / columns;
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funcs::ForRange<Context> for_range(dev_ctx, rows);
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LuUnpackEyeFunctor<T> functor(columns, pmat_data);
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for_range(functor);
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return;
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}
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PADDLE_ENFORCE_EQ(
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pivots.dtype(),
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DataType::INT32,
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common::errors::InvalidArgument(
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"The pivots of lu_unpack must be of type int32, but received [%s].",
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pivots.dtype()));
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Unpack_Pivot<Context, T>(dev_ctx, pivots, pmat, m, k);
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}
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}
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} // namespace phi
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