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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/roll_kernel.h"
#include "paddle/common/array.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/roll_kernel_impl.h"
namespace phi {
template <typename T, typename Context>
void RollKernel(const Context& dev_ctx,
const DenseTensor& x,
const IntArray& shifts,
const std::vector<int64_t>& axis,
DenseTensor* out) {
if (x.numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
auto* in_data = x.data<T>();
T* out_data = dev_ctx.template Alloc<T>(out);
auto shifts_data = shifts.GetData();
int rank = shifts_data.size();
int64_t numel = x.numel();
auto input_dim = x.dims();
auto stride_dim = common::stride(input_dim);
std::vector<int64_t> strides(rank), sizes(rank);
if (axis.size() == 0) {
strides[0] = 1;
sizes[0] = numel;
shifts_data[0] = (shifts_data[0] % numel + numel) % numel;
} else {
for (int i = 0; i < rank; i++) {
int dim = axis[i] >= 0 ? axis[i] : axis[i] + input_dim.size();
int64_t size = input_dim[dim];
if (size != 0) {
shifts_data[i] = (shifts_data[i] % size + size) % size;
strides[i] = stride_dim[dim];
sizes[i] = size;
}
}
}
LaunchRollKernel<T, Context>(
dev_ctx, in_data, out_data, rank, numel, shifts_data, strides, sizes);
}
} // namespace phi
PD_REGISTER_KERNEL(roll,
GPU,
ALL_LAYOUT,
phi::RollKernel,
phi::float16,
phi::bfloat16,
bool,
float,
double,
int,
int64_t,
phi::complex64,
phi::complex128) {}