wehub-resource-sync
116 lines
3.5 KiB
C++
116 lines
3.5 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/cumprod_kernel.h"
|
|
|
|
#include <cstdint>
|
|
#include <type_traits>
|
|
|
|
#include "paddle/phi/backends/cpu/cpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/funcs/complex_functors.h"
|
|
#include "paddle/phi/kernels/funcs/cumprod.h"
|
|
|
|
namespace phi {
|
|
template <typename T, typename Context>
|
|
void CumprodKernel(const Context& dev_ctx,
|
|
const DenseTensor& input,
|
|
int dim,
|
|
bool exclusive,
|
|
bool reverse,
|
|
DenseTensor* out) {
|
|
const DenseTensor* x = &input;
|
|
auto* x_data = x->data<T>();
|
|
auto* out_ptr = dev_ctx.template Alloc<T>(out);
|
|
DDim shape = x->dims();
|
|
DenseTensor out_tmp;
|
|
T* out_data = nullptr;
|
|
if (x_data == out_ptr) {
|
|
out_tmp.Resize(shape);
|
|
out_data = dev_ctx.template Alloc<T>(&out_tmp);
|
|
} else {
|
|
out_data = out_ptr;
|
|
}
|
|
|
|
size_t outer_dim = 1;
|
|
size_t mid_dim = 1;
|
|
size_t inner_dim = 1;
|
|
GetCumprodDimInfo(shape, dim, &outer_dim, &mid_dim, &inner_dim);
|
|
if (shape.size() == 0) {
|
|
Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, out);
|
|
return;
|
|
}
|
|
if (reverse == false) {
|
|
for (size_t i = 0; i < outer_dim; i++) {
|
|
for (size_t j = 0; j < mid_dim; j++) {
|
|
for (size_t k = 0; k < inner_dim; k++) {
|
|
size_t pos = i * mid_dim * inner_dim + j * inner_dim + k;
|
|
if (j == 0) {
|
|
if (exclusive) {
|
|
out_data[pos] = static_cast<T>(1.0);
|
|
} else {
|
|
out_data[pos] = x_data[pos];
|
|
}
|
|
} else {
|
|
if (exclusive) {
|
|
out_data[pos] =
|
|
out_data[pos - inner_dim] * x_data[pos - inner_dim];
|
|
} else {
|
|
out_data[pos] = out_data[pos - inner_dim] * x_data[pos];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
for (size_t i = 0; i < outer_dim; i++) {
|
|
for (size_t j = mid_dim; j > 0; j--) {
|
|
for (size_t k = 0; k < inner_dim; k++) {
|
|
size_t pos = i * mid_dim * inner_dim + (j - 1) * inner_dim + k;
|
|
if (j == mid_dim) {
|
|
if (exclusive) {
|
|
out_data[pos] = static_cast<T>(1.0);
|
|
} else {
|
|
out_data[pos] = x_data[pos];
|
|
}
|
|
} else {
|
|
if (exclusive) {
|
|
out_data[pos] =
|
|
out_data[pos + inner_dim] * x_data[pos + inner_dim];
|
|
} else {
|
|
out_data[pos] = out_data[pos + inner_dim] * x_data[pos];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if (x_data == out_ptr) {
|
|
memcpy(out_ptr, out_data, out->numel() * sizeof(T));
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(cumprod,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::CumprodKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::complex64,
|
|
phi::complex128) {}
|