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expand_grad_kernel_impl.h
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expand_grad_kernel_impl.h
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/impl/expand_kernel_impl.h"
namespace phi {
template <typename Context, typename T, int Dims>
void ExpandBackward(const Context& ctx,
const DenseTensor& out_grad,
const std::vector<int>& reshape_dims_vec,
const std::vector<int>& reduce_dims_vec,
DenseTensor* in_grad) {
size_t reshape_size = reshape_dims_vec.size();
size_t reduce_size = reduce_dims_vec.size();
ctx.template Alloc<T>(in_grad);
in_grad->data<T>();
auto x_grad = EigenVector<T>::Flatten(*in_grad);
Eigen::DSizes<Eigen::DenseIndex, Dims * 2> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<Eigen::DenseIndex, Dims> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
auto out_grad0 = EigenVector<T>::Flatten(out_grad);
auto& place = *ctx.eigen_device();
phi::funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Dims>::Eval(
place, x_grad, out_grad0, reduce_dims, reshape_dims);
}
template <typename T, typename Context>
void ExpandGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const IntArray& shape,
DenseTensor* in_grad) {
auto expand_shape = shape.GetData();
auto x_dims = x.dims();
if (in_grad->dims() == out_grad.dims()) {
phi::Copy(ctx, out_grad, ctx.GetPlace(), false, in_grad);
return;
}
auto vec_in_dims = phi::vectorize<int>(x_dims);
auto diff = expand_shape.size() - vec_in_dims.size();
vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
// 1. reshape_dims_vec is the broadcast parameter.
// 2. reduce_dims_vec is the dimension parameter to compute gradients. For
// each dimension expanded, the gradients should be summed to original
// size.
std::vector<int> repeat_times(vec_in_dims.size());
for (size_t i = 0; i < vec_in_dims.size(); ++i) {
if (expand_shape[i] < 0) {
repeat_times[i] = 1;
} else {
repeat_times[i] = expand_shape[i] / vec_in_dims[i];
}
}
std::vector<int> reshape_dims_vec;
std::vector<int> reduce_dims_vec;
for (size_t i = 0; i < repeat_times.size(); ++i) {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(repeat_times[i]);
reshape_dims_vec.push_back(vec_in_dims[i]);
}
int dims = reduce_dims_vec.size();
PADDLE_ENFORCE_GE(
dims,
0,
phi::errors::InvalidArgument("The rank of the input 'Out@GRAD' for "
"expand_v2_grad op must be greater than or "
"equal to 0, but the value received is %d.",
dims));
PADDLE_ENFORCE_LE(dims,
MAX_RANK_SUPPORTED,
phi::errors::InvalidArgument(
"The rank of the input 'Out@GRAD' for "
"expand_v2_grad op must be less than or equal "
"to %d, but the value received is %d.",
MAX_RANK_SUPPORTED,
dims));
switch (dims) {
case 0:
ExpandBackward<Context, T, 1>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 1:
ExpandBackward<Context, T, 1>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 2:
ExpandBackward<Context, T, 2>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 3:
ExpandBackward<Context, T, 3>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 4:
ExpandBackward<Context, T, 4>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 5:
ExpandBackward<Context, T, 5>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
case 6:
ExpandBackward<Context, T, 6>(
ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
break;
default:
PADDLE_THROW(phi::errors::InvalidArgument(
"Only support tensor with rank being between 1 and 6. But "
"received tensor's rank = %d.",
dims));
}
}
} // namespace phi