wehub-resource-sync
101 lines
3.6 KiB
C++
101 lines
3.6 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/kernel_registry.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/funcs/broadcast_function.h"
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#include "paddle/phi/kernels/funcs/compare_functors.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/funcs/elementwise_functor.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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#include "paddle/phi/kernels/reduce_sum_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void ReduceCudaAMaxAMinGrad(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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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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bool NanEqual = false) {
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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* out_y = &out;
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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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auto reduce_dims = 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 reduce_out
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DenseTensor new_y(out_y->type());
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new_y.ShareDataWith(*out_y);
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new_y.Resize(update_dims);
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// make new tensor d_out
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DenseTensor new_dout(d_out->type());
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new_dout.ShareDataWith(*d_out);
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new_dout.Resize(update_dims);
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dev_ctx.Alloc(d_x, d_out->dtype());
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DenseTensor new_in_tensor(*in_x);
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DenseTensor new_dx(*d_x);
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// make equal_out
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DenseTensor equal_out;
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equal_out.Resize(in_x->dims());
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dev_ctx.template Alloc<T>(&equal_out);
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// make new tensor equal_count
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DenseTensor equal_count;
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equal_count.Resize(update_dims);
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dev_ctx.template Alloc<T>(&equal_count);
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// compute
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// 1. equal_out = Equal(x, y)
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std::vector<const DenseTensor*> equal_inputs = {&new_y, &new_in_tensor};
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std::vector<DenseTensor*> equal_outputs = {&equal_out};
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if (NanEqual)
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funcs::BroadcastKernel<T>(
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dev_ctx, equal_inputs, &equal_outputs, funcs::NanEqualFunctor<T>(), 0);
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else
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funcs::BroadcastKernel<T>(
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dev_ctx, equal_inputs, &equal_outputs, funcs::EqualFunctor<T>(), 0);
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// 2. equal_count = reduceSum(equal_out)
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SumKernel<T, Context>(dev_ctx,
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equal_out,
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reduce_dims,
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equal_out.dtype(),
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keep_dim,
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&equal_count);
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// 3. dx = dout * 1
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MultiplyKernel<T, Context>(dev_ctx, new_dout, equal_out, &equal_out);
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// 4. dx = Div(dx, equal_out)
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DivideKernel<T, Context>(dev_ctx, equal_out, equal_count, &new_dx);
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}
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} // namespace phi
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