wehub-resource-sync
92 lines
3.1 KiB
Plaintext
92 lines
3.1 KiB
Plaintext
// 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/fill_diagonal_kernel.h"
|
|
|
|
#include <algorithm>
|
|
#include <vector>
|
|
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/funcs/common_shape.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T>
|
|
__global__ void fill_constant_kernel(const int64_t featuresize,
|
|
T* in_data,
|
|
int64_t strides,
|
|
int offset,
|
|
T fillvar,
|
|
int dims) {
|
|
for (int64_t idx = static_cast<int64_t>(blockIdx.x) * featuresize +
|
|
static_cast<int64_t>(threadIdx.x);
|
|
idx * strides + offset < (blockIdx.x + 1) * featuresize;
|
|
idx += blockDim.x) {
|
|
// to check if the new position with offset is still in the same line;
|
|
// this modify should not affect across lines.
|
|
// out_dims[1] is also work for tensor with dim>2, for which the dims must
|
|
// be the same number
|
|
if ((idx * strides) % dims + offset < dims &&
|
|
(idx * strides) % dims + offset >= 0) {
|
|
in_data[idx * strides + offset] = fillvar;
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void FillDiagonalKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
float value,
|
|
int offset,
|
|
bool wrap,
|
|
DenseTensor* out) {
|
|
if (out && out->numel() == 0) {
|
|
dev_ctx.template Alloc<T>(out);
|
|
return;
|
|
}
|
|
const int64_t kMaxBlockDim = 512;
|
|
Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
|
|
|
|
T* out_data = dev_ctx.template Alloc<T>(out);
|
|
auto fill_val = static_cast<T>(value);
|
|
T temp_var = static_cast<T>(fill_val);
|
|
|
|
auto size = out->numel();
|
|
auto out_dims = out->dims();
|
|
auto strides = funcs::CalStride(out_dims);
|
|
|
|
// The wrap mode supported only the dims equals to 2; In wrap mode, the
|
|
// value will be filled in cycles
|
|
if (!wrap) {
|
|
size = std::min(size, out_dims[1] * out_dims[1]);
|
|
}
|
|
|
|
int64_t kBlockDim = std::min(int64_t(size / strides), kMaxBlockDim);
|
|
fill_constant_kernel<T><<<1, kBlockDim, 0>>>(
|
|
size, out_data, strides, offset, temp_var, out_dims[1]);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(fill_diagonal,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::FillDiagonalKernel,
|
|
float,
|
|
double,
|
|
int64_t,
|
|
int,
|
|
phi::float16,
|
|
bool) {}
|