wehub-resource-sync
112 lines
3.9 KiB
C++
112 lines
3.9 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, typename Context>
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void LUUnpackGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& pivots UNUSED,
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const DenseTensor& l UNUSED,
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const DenseTensor& u UNUSED,
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const DenseTensor& pmat UNUSED,
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const DenseTensor& l_grad,
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const DenseTensor& u_grad,
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bool unpack_ludata UNUSED,
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bool unpack_pivots UNUSED,
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DenseTensor* x_grad) {
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) return;
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DenseTensor dl_tril, du_triu;
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const auto ldims = l_grad.dims();
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dl_tril.Resize(ldims);
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auto H = ldims[ldims.size() - 2];
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auto W = ldims[ldims.size() - 1];
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dev_ctx.template Alloc<T>(&dl_tril);
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auto L_dataptr = dl_tril.data<T>();
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funcs::ForRange<Context> l_for_range(dev_ctx, l_grad.numel());
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funcs::TrilTriuCompute<T> tril_computer(
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l_grad.data<T>(), -1, true, H, W, L_dataptr);
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l_for_range(tril_computer);
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const auto udims = u_grad.dims();
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du_triu.Resize(udims);
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H = udims[udims.size() - 2];
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W = udims[udims.size() - 1];
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dev_ctx.template Alloc<T>(&du_triu);
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auto U_dataptr = du_triu.data<T>();
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funcs::ForRange<Context> u_for_range(dev_ctx, u_grad.numel());
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funcs::TrilTriuCompute<T> triu_computer(
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u_grad.data<T>(), 0, false, H, W, U_dataptr);
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u_for_range(triu_computer);
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auto xdims = x_grad->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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std::vector<int64_t> axes = {xrank - 2, xrank - 1};
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std::vector<int64_t> slice_starts(2, 0);
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std::vector<int64_t> slice_ends(2, 0);
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auto valuedims = vectorize(xdims);
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funcs::SetConstant<Context, T> setter;
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setter(dev_ctx, x_grad, static_cast<T>(0));
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if (m <= n) {
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slice_starts[0] = 0;
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slice_starts[1] = 0;
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slice_ends[0] = k;
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slice_ends[1] = k;
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valuedims[xrank - 2] = k;
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valuedims[xrank - 1] = k;
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SetValueCompute_dispatch<Context, T>(dev_ctx,
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x_grad,
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&dl_tril,
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x_grad,
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axes,
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&slice_starts,
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&slice_ends,
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valuedims,
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xrank);
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Tensor_Add<Context, T>(dev_ctx, *x_grad, du_triu, x_grad);
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} else {
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slice_starts[0] = 0;
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slice_starts[1] = 0;
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slice_ends[0] = k;
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slice_ends[1] = k;
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valuedims[xrank - 2] = k;
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valuedims[xrank - 1] = k;
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SetValueCompute_dispatch<Context, T>(dev_ctx,
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x_grad,
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&du_triu,
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x_grad,
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axes,
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&slice_starts,
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&slice_ends,
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valuedims,
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xrank);
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Tensor_Add<Context, T>(dev_ctx, *x_grad, dl_tril, x_grad);
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}
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}
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} // namespace phi
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