wehub-resource-sync
67 lines
2.4 KiB
C++
67 lines
2.4 KiB
C++
// Copyright (c) 2024 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/core/dense_tensor.h"
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#include "paddle/phi/infermeta/unary.h"
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#include "paddle/phi/kernels/activation_kernel.h"
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#include "paddle/phi/kernels/diag_kernel.h"
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#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
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#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/impl/diag_embed_impl.h"
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#include "paddle/phi/kernels/matmul_kernel.h"
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#include "paddle/phi/kernels/slice_kernel.h"
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#include "paddle/phi/kernels/svd_kernel.h"
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#include "paddle/phi/kernels/transpose_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void SvdvalsGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& s_grad,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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auto x_dims = x.dims();
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int64_t rows = x_dims[x_dims.size() - 2];
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int64_t cols = x_dims[x_dims.size() - 1];
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int64_t batches = x.numel() / (rows * cols);
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DenseTensor dX_term;
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if (batches == 1) {
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dX_term = Diag<T, Context>(dev_ctx, s_grad, 0, 0);
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} else {
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MetaTensor meta_dX(&dX_term);
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DiagEmbedInferMeta(s_grad, 0, -1, -2, &meta_dX);
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DiagEmbedKernel<T, Context>(dev_ctx, s_grad, 0, -1, -2, &dX_term);
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}
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DenseTensor U, VH, S_recomputed;
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MetaTensor meta_u(&U), meta_s(&S_recomputed), meta_vh(&VH);
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SvdInferMeta(x, false, &meta_u, &meta_s, &meta_vh);
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SvdKernel<T, Context>(dev_ctx,
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x,
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false,
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&U,
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&S_recomputed,
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&VH); // Crucial: recomputing SVD
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*x_grad =
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Matmul<T, Context>(dev_ctx, Matmul<T, Context>(dev_ctx, U, dX_term), VH);
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}
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} // namespace phi
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