wehub-resource-sync
74 lines
2.8 KiB
C++
74 lines
2.8 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/core/dense_tensor.h"
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#include "paddle/phi/kernels/complex_kernel.h"
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#include "paddle/phi/kernels/elementwise_divide_kernel.h"
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#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
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#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
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#include "paddle/phi/kernels/funcs/diag_functor.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/unsqueeze.h"
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#include "paddle/phi/kernels/matmul_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 EighGradKernel(const Context& dev_ctx,
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const DenseTensor& out_w,
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const DenseTensor& out_v,
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const DenseTensor& dout_w,
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const DenseTensor& dout_v,
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DenseTensor* dx) {
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dev_ctx.template Alloc<T>(dx);
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if (out_v.numel() == 0) {
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return;
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}
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auto& dims = out_v.dims();
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const int m = dims[dims.size() - 1];
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DenseTensor tV = TransposeLast2Dim<T>(dev_ctx, Conj<T>(dev_ctx, out_v));
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DenseTensor W = Subtract<dtype::Real<T>>(
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dev_ctx, funcs::Unsqueeze(out_w, -2), funcs::Unsqueeze(out_w, -1));
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DenseTensor result = Matmul<T>(dev_ctx, tV, dout_v);
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result.Resize(dims);
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dev_ctx.template Alloc<T>(&result);
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std::vector<int> out_shape = vectorize<int>(dims);
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DenseTensor constant;
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constant.Resize(out_shape);
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dev_ctx.template Alloc<T>(&constant);
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funcs::SetConstant<Context, T>()(dev_ctx, &constant, T(0.5));
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result = Subtract<T>(
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dev_ctx, result, Conj<T>(dev_ctx, TransposeLast2Dim<T>(dev_ctx, result)));
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result = Multiply<T>(dev_ctx, result, constant);
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if (result.type() != W.type()) {
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auto x_vector = EigenVector<T>::Flatten(result);
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auto y_vector = EigenVector<dtype::Real<T>>::Flatten(W);
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auto out_vector = EigenVector<T>::Flatten(result);
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auto& place = *dev_ctx.eigen_device();
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out_vector.device(place) = x_vector / y_vector;
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} else {
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result = Divide<T>(dev_ctx, result, W);
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}
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result =
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funcs::DiagFill<T, dtype::Real<T>>(dev_ctx, m, m, m, 0, dout_w, result);
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*dx = Matmul<T>(dev_ctx, out_v, Matmul<T>(dev_ctx, result, tV));
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}
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} // namespace phi
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