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paddlepaddle--paddle/paddle/phi/kernels/sparse/gpu/reshape_kernel.cu
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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/backends/gpu/gpu_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
#include "paddle/phi/kernels/sparse/sparse_utils_kernel.h"
namespace phi {
namespace sparse {
template <typename IntT>
__global__ void ReshapeCooCudaKernel(const IntT* x_indices_data,
const int num_x_sparse_part_dims,
const int num_out_sparse_part_dims,
const int64_t x_nnz,
const int64_t* x_sparse_part_strides,
const int64_t* out_sparse_part_strides,
IntT* out_indices_data) {
CUDA_KERNEL_LOOP_TYPE(j, x_nnz, int64_t) {
IntT location = 0;
for (int i = 0; i < num_x_sparse_part_dims; ++i) {
location += x_indices_data[i * x_nnz + j] *
static_cast<IntT>(x_sparse_part_strides[i]);
}
for (int i = 0; i < num_out_sparse_part_dims; ++i) {
out_indices_data[i * x_nnz + j] =
location / static_cast<IntT>(out_sparse_part_strides[i]);
location %= static_cast<IntT>(out_sparse_part_strides[i]);
}
}
}
template <typename T, typename IntT, typename Context>
void ReshapeCooGPUKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const phi::IntArray& shape,
SparseCooTensor* out) {
int64_t x_nnz = x.nnz();
std::vector<int> new_shape(shape.GetData().begin(), shape.GetData().end());
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<IntT, 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 out indices
const auto* x_indices_data = x.indices().data<IntT>();
auto* out_indices_data = out_indices.data<IntT>();
const DDim& x_sparse_part_strides =
common::stride(make_ddim(x_sparse_part_dims));
const DDim& out_sparse_part_strides =
common::stride(make_ddim(out_sparse_part_dims));
int64_t *destination_x_sparse_part_strides,
*destination_out_sparse_part_strides;
auto destination_x_sparse_part_strides_tensor = memory_utils::Alloc(
dev_ctx.GetPlace(),
sizeof(int64_t) * x_sparse_part_strides.size(),
phi::Stream(reinterpret_cast<phi::StreamId>(dev_ctx.stream())));
destination_x_sparse_part_strides = reinterpret_cast<int64_t*>(
destination_x_sparse_part_strides_tensor->ptr());
memory_utils::Copy(dev_ctx.GetPlace(),
reinterpret_cast<void*>(destination_x_sparse_part_strides),
phi::CPUPlace(),
x_sparse_part_strides.Get(),
sizeof(int64_t) * x_sparse_part_strides.size(),
dev_ctx.stream());
auto destination_out_sparse_part_strides_tensor = memory_utils::Alloc(
dev_ctx.GetPlace(),
sizeof(int64_t) * out_sparse_part_strides.size(),
phi::Stream(reinterpret_cast<phi::StreamId>(dev_ctx.stream())));
destination_out_sparse_part_strides = reinterpret_cast<int64_t*>(
destination_out_sparse_part_strides_tensor->ptr());
memory_utils::Copy(dev_ctx.GetPlace(),
destination_out_sparse_part_strides,
phi::CPUPlace(),
out_sparse_part_strides.Get(),
sizeof(int64_t) * out_sparse_part_strides.size(),
dev_ctx.stream());
auto config = backends::gpu::GetGpuLaunchConfig1D(dev_ctx, x_nnz, 1);
ReshapeCooCudaKernel<<<config.block_per_grid.x,
config.thread_per_block.x,
0,
dev_ctx.stream()>>>(
x_indices_data,
x_sparse_part_dims.size(),
out_sparse_part_dims.size(),
x_nnz,
destination_x_sparse_part_strides,
destination_out_sparse_part_strides,
out_indices_data);
}
template <typename T, typename Context>
void ReshapeCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const phi::IntArray& shape,
SparseCooTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.indices().dtype(), "ReshapeCooGPUKernel", ([&] {
ReshapeCooGPUKernel<T, data_t, Context>(dev_ctx, x, shape, out);
}));
}
// just copy from paddle\phi\kernels\sparse\cpu\reshape_kernel.cc
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,
GPU,
ALL_LAYOUT,
phi::sparse::ReshapeCooKernel,
phi::float16,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(reshape_csr,
GPU,
ALL_LAYOUT,
phi::sparse::ReshapeCsrKernel,
phi::float16,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}