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reshape_kernel.cc
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/
reshape_kernel.cc
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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.
#include "paddle/phi/kernels/sparse/unary_kernel.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/kernels/sparse/sparse_utils_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
namespace phi {
namespace sparse {
template <typename T, typename Context>
void ReshapeCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const phi::IntArray& shape,
SparseCooTensor* out) {
// TODO(OccupyMars2025): Currently, reshape is only applicable to sparse dims
int64_t x_nnz = x.nnz();
// Use DDim::reshape to handle -1 and 0 in the argument "shape"
std::vector<int> new_shape(shape.GetData().begin(), shape.GetData().end());
phi::DDim out_dims = x.dims().reshape(new_shape);
// get sparse part dimensions of x and out
std::vector<int64_t> x_sparse_part_dims;
std::vector<int64_t> out_sparse_part_dims;
for (int i = 0; i < x.sparse_dim(); ++i) {
x_sparse_part_dims.push_back(x.dims()[i]);
}
for (int i = 0; i < out_dims.size() - x.dense_dim(); ++i) {
out_sparse_part_dims.push_back(out_dims[i]);
}
DenseTensor out_indices = Empty<int64_t, Context>(
dev_ctx, {static_cast<int64_t>(out_sparse_part_dims.size()), x_nnz});
DenseTensor out_values(x.values());
out->SetMember(out_indices, out_values, out_dims, x.coalesced());
// compute values of indices
const DenseTensor& x_indices = x.indices();
const auto* x_indices_data = x_indices.data<int64_t>();
auto* out_indices_data = out_indices.data<int64_t>();
const phi::DDim& x_sparse_part_strides =
phi::stride(phi::make_ddim(x_sparse_part_dims));
const phi::DDim& out_sparse_part_strides =
phi::stride(phi::make_ddim(out_sparse_part_dims));
int64_t location = 0;
for (int64_t j = 0; j < x_nnz; ++j) {
location = 0;
for (int i = 0; i < x.sparse_dim(); ++i) {
location += x_indices_data[i * x_nnz + j] * x_sparse_part_strides[i];
}
for (size_t i = 0; i < out_sparse_part_dims.size(); ++i) {
out_indices_data[i * x_nnz + j] = location / out_sparse_part_strides[i];
location %= out_sparse_part_strides[i];
}
}
}
template <typename T, typename Context>
void ReshapeCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
const phi::IntArray& shape,
SparseCsrTensor* out) {
// transform csr format to coo format, and then use coo kernel
const SparseCooTensor x_coo = CsrToCoo<T, Context>(dev_ctx, x);
SparseCooTensor out_coo;
ReshapeCooKernel<T, Context>(dev_ctx, x_coo, shape, &out_coo);
CooToCsrKernel<T, Context>(dev_ctx, out_coo, out);
}
} // namespace sparse
} // namespace phi
PD_REGISTER_KERNEL(reshape_coo,
CPU,
ALL_LAYOUT,
phi::sparse::ReshapeCooKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(reshape_csr,
CPU,
ALL_LAYOUT,
phi::sparse::ReshapeCsrKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}