wehub-resource-sync
86 lines
2.7 KiB
C++
86 lines
2.7 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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// CUDA and HIP use same api
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#include <algorithm>
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#include <cmath>
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#include <numeric>
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#include <set>
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#include <vector>
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#include "paddle/phi/core/visit_type.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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namespace phi {
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template <typename Functor>
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void ReduceGrad(const GPUContext& dev_ctx,
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DenseTensor* d_out,
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DenseTensor* d_x,
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DataType out_dtype,
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Functor functor) {
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std::vector<const DenseTensor*> inputs = {d_out};
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std::vector<DenseTensor*> outputs = {d_x};
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PD_VISIT_ALL_TYPES(out_dtype, "BroadcastKernel", ([&] {
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funcs::BroadcastKernel<data_t>(
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dev_ctx, inputs, &outputs, functor, 0);
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}));
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}
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template <typename OutT, typename Context, typename Functor>
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void ReduceGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* x_grad,
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Functor functor) {
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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auto* in_x = &x;
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auto* d_out = &out_grad;
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auto* d_x = x_grad;
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// get reduce_dim and reduce_num for reduce_mean_grad
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int dim_size = in_x->dims().size();
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std::vector<int> reduce_dims =
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funcs::details::GetReduceDim(dims, dim_size, reduce_all);
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auto update_dims = vectorize(d_x->dims());
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int64_t reduce_num = 1;
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for (auto i : reduce_dims) {
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reduce_num *= (in_x->dims())[i];
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update_dims[i] = 1;
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}
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// make new tensor
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DenseTensor new_d_out(d_out->dtype());
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new_d_out.ShareDataWith(*d_out);
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new_d_out.Resize(update_dims);
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dev_ctx.Alloc(d_x, x.dtype());
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auto pt_d_out = new_d_out;
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auto pt_d_x = *d_x;
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std::vector<const DenseTensor*> inputs = {&pt_d_out};
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std::vector<DenseTensor*> outputs = {&pt_d_x};
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funcs::BroadcastKernel<OutT>(dev_ctx, inputs, &outputs, functor, 0);
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}
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} // namespace phi
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#endif
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