wehub-resource-sync
128 lines
4.3 KiB
C++
128 lines
4.3 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/common/macros.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/reduce_grad_functions.h"
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namespace phi {
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template <typename Context,
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typename T,
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typename Functor,
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bool kNoNeedBufferX = false,
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bool kNoNeedBufferY = false>
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void ComputeFromInput(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad UNUSED,
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const optional<DenseTensor>& out,
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const DenseTensor& input2,
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const std::vector<int64_t>& dims,
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bool keep_dim UNUSED,
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bool reduce_all,
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DenseTensor* x_grad) {
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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auto* input0 = &x;
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auto* input1 = out.get_ptr();
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auto* output = x_grad;
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dev_ctx.template Alloc<T>(output);
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// The dims has full dim, set the reduce_all is True
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const auto& input_dim_size = x.dims().size();
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std::set<int> dims_set(dims.begin(), dims.end());
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bool full_dim = true;
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for (auto i = 0; i < input_dim_size; i++) {
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if (dims_set.find(i) == dims_set.end()) {
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full_dim = false;
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break;
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}
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}
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reduce_all = (reduce_all || full_dim);
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// NOTE: EigenTensor::From() uses tensor->data()
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// if op has NoNeedBufferVarsInferer, the corresponding kNoNeedBufferX or
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// kNoNeedBufferY should set true
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// and use fake var that has same dims.
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if (kNoNeedBufferX) {
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input0 = output;
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}
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if (kNoNeedBufferY) {
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input1 = &input2;
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}
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const std::vector<int> const_dims{dims.begin(), dims.end()};
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// NOTE(dengkaipeng): Out is unnecessary in some reduce kernel and
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// not be set as Input in grad Maker, use Out_grad to replace here
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if (!input1) input1 = &input2;
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Functor functor;
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funcs::LaunchReduceGradKernel<Context, T, Functor>(dev_ctx,
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input0,
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input1,
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&input2,
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output,
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functor,
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const_dims,
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reduce_all);
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}
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template <typename Context,
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typename T,
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typename Functor,
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bool kNoNeedBufferX = false,
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bool kNoNeedBufferY = false>
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void ReduceGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<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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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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if (x.dtype() != out_grad.dtype()) {
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DenseTensorMeta x_grad_meta(
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out_grad.dtype(), x_grad->dims(), x_grad->layout());
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DenseTensor x_grad_tmp = Empty<Context>(dev_ctx, std::move(x_grad_meta));
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ComputeFromInput<Context, T, Functor, kNoNeedBufferX, kNoNeedBufferY>(
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dev_ctx,
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x,
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out_grad,
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out,
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out_grad,
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dims,
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keep_dim,
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reduce_all,
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&x_grad_tmp);
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CastKernel<T>(dev_ctx, x_grad_tmp, x.dtype(), x_grad);
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} else {
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ComputeFromInput<Context, T, Functor, kNoNeedBufferX, kNoNeedBufferY>(
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dev_ctx,
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x,
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out_grad,
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out,
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out_grad,
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dims,
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keep_dim,
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reduce_all,
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x_grad);
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}
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}
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} // namespace phi
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