wehub-resource-sync
107 lines
3.6 KiB
C++
107 lines
3.6 KiB
C++
// 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/split_kernel.h"
|
|
|
|
#include "paddle/phi/backends/xpu/enforce_xpu.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void SplitKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const IntArray& sections,
|
|
const Scalar& axis_scalar,
|
|
std::vector<DenseTensor*> outs) {
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
int axis = axis_scalar.to<int>();
|
|
auto in_dims = x.dims();
|
|
auto input_shape = vectorize<int64_t>(in_dims);
|
|
std::vector<XPUType*> out_ptrs;
|
|
|
|
// Vectors to keep track of zero-sized and non-zero-sized outputs
|
|
std::vector<XPUType*> non_zero_out_ptrs;
|
|
std::vector<int64_t> non_zero_split_lists;
|
|
|
|
for (size_t j = 0; j < outs.size(); ++j) {
|
|
dev_ctx.template Alloc<T>(outs[j]);
|
|
out_ptrs.push_back(reinterpret_cast<XPUType*>(outs[j]->data<T>()));
|
|
int64_t section_size =
|
|
axis < outs[j]->dims().size() ? outs[j]->dims()[axis] : 1;
|
|
|
|
if (section_size > 0) {
|
|
non_zero_out_ptrs.push_back(
|
|
reinterpret_cast<XPUType*>(outs[j]->data<T>()));
|
|
non_zero_split_lists.push_back(section_size);
|
|
} else {
|
|
auto zero_dims = in_dims;
|
|
zero_dims[axis] = 0;
|
|
outs[j]->Resize(zero_dims);
|
|
}
|
|
}
|
|
|
|
if (x.numel() == 0) {
|
|
return;
|
|
}
|
|
|
|
// Perform the split operation only on non-zero sections
|
|
if (!non_zero_split_lists.empty()) {
|
|
int r = xpu::split<XPUType>(dev_ctx.x_context(),
|
|
reinterpret_cast<const XPUType*>(x.data<T>()),
|
|
non_zero_out_ptrs,
|
|
input_shape,
|
|
non_zero_split_lists,
|
|
axis);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "split");
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void SplitWithNumKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
int num,
|
|
const Scalar& axis_scalar,
|
|
std::vector<DenseTensor*> outs) {
|
|
int axis_value = axis_scalar.to<int>();
|
|
int64_t input_axis_dim = x.dims().at(axis_value);
|
|
std::vector<int64_t> sections_vec;
|
|
for (int i = 0; i < num; ++i) {
|
|
sections_vec.push_back(input_axis_dim / static_cast<int64_t>(num));
|
|
}
|
|
IntArray sections(sections_vec);
|
|
SplitKernel<T, Context>(dev_ctx, x, sections, axis_scalar, outs);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(split,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::SplitKernel,
|
|
float,
|
|
int64_t,
|
|
int,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|
|
PD_REGISTER_KERNEL(split_with_num,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::SplitWithNumKernel,
|
|
float,
|
|
int64_t,
|
|
int,
|
|
phi::float16,
|
|
phi::bfloat16) {}
|