wehub-resource-sync
136 lines
4.2 KiB
C++
136 lines
4.2 KiB
C++
/* Copyright (c) 2025 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. */
|
|
|
|
#pragma once
|
|
|
|
#include <array>
|
|
#include <cstdint>
|
|
#include <type_traits>
|
|
#include <vector>
|
|
|
|
#include "paddle/phi/kernels/funcs/dense_tensor_iterator.h"
|
|
#include "paddle/phi/kernels/funcs/index_elementwise_utils.h"
|
|
|
|
namespace phi {
|
|
namespace funcs {
|
|
|
|
template <typename Value>
|
|
struct CPUDivMod {
|
|
Value div, mod;
|
|
|
|
CPUDivMod(Value div, Value mod) : div(div), mod(mod) {}
|
|
};
|
|
|
|
template <typename Value>
|
|
struct CPUIntDivider {
|
|
CPUIntDivider() = default;
|
|
explicit CPUIntDivider(Value d) : divisor(d) {}
|
|
|
|
inline CPUDivMod<Value> cpu_divmod(Value n) const {
|
|
return CPUDivMod<Value>(n / divisor, n % divisor);
|
|
}
|
|
|
|
Value divisor;
|
|
};
|
|
|
|
template <int NARGS, typename index_t = uint32_t, bool signed_strides = false>
|
|
struct CPUOffsetCalculator {
|
|
using stride_t =
|
|
std::conditional_t<signed_strides, std::make_signed_t<index_t>, index_t>;
|
|
using offset_type = std::array<stride_t, std::max<int>(NARGS, 1)>;
|
|
|
|
CPUOffsetCalculator(int dims,
|
|
const int64_t* sizes,
|
|
const int64_t* const* strides,
|
|
const int64_t* element_sizes = nullptr)
|
|
: dims(dims) {
|
|
PADDLE_ENFORCE_LE(
|
|
dims,
|
|
MAX_DIMS,
|
|
common::errors::InvalidArgument(
|
|
"Tensor has too many dims. Maximum dim is %d.", MAX_DIMS));
|
|
for (int i = 0; i < dims; i++) {
|
|
sizes_[i] = CPUIntDivider<index_t>(sizes[i]);
|
|
for (int arg = 0; arg < NARGS; arg++) {
|
|
int64_t element_size =
|
|
(element_sizes == nullptr ? 1LL : element_sizes[arg]);
|
|
strides_[i][arg] = strides[arg][i] / element_size;
|
|
}
|
|
}
|
|
}
|
|
|
|
offset_type cpu_get(index_t linear_idx) const {
|
|
offset_type offsets;
|
|
for (int arg = 0; arg < NARGS; arg++) {
|
|
offsets[arg] = 0;
|
|
}
|
|
for (int dim = 0; dim < MAX_DIMS; ++dim) {
|
|
if (dim == dims) {
|
|
break;
|
|
}
|
|
auto divmod = sizes_[dim].cpu_divmod(linear_idx);
|
|
linear_idx = divmod.div;
|
|
|
|
for (int arg = 0; arg < NARGS; arg++) {
|
|
offsets[arg] += divmod.mod * strides_[dim][arg];
|
|
}
|
|
}
|
|
return offsets;
|
|
}
|
|
|
|
int dims;
|
|
CPUIntDivider<index_t> sizes_[MAX_DIMS];
|
|
stride_t strides_[MAX_DIMS][std::max<int>(NARGS, 1)];
|
|
};
|
|
|
|
template <int N, bool signed_strides = false>
|
|
static CPUOffsetCalculator<N, uint32_t, signed_strides>
|
|
CPUmake_offset_calculator_put(std::vector<int64_t> desired_shape,
|
|
std::array<int64_t*, N> strides_array) {
|
|
return CPUOffsetCalculator<N, uint32_t, signed_strides>(
|
|
desired_shape.size(), desired_shape.data(), strides_array.data());
|
|
}
|
|
|
|
template <int N, bool signed_strides = false>
|
|
static CPUOffsetCalculator<N, uint32_t, signed_strides>
|
|
CPUmake_offset_calculator(int ndim,
|
|
const int64_t* shape,
|
|
const std::vector<std::vector<int64_t>>& strides) {
|
|
std::array<const int64_t*, N> strides_array;
|
|
for (int i = 0; i < N; ++i) {
|
|
strides_array[i] = strides[i].data();
|
|
}
|
|
|
|
return CPUOffsetCalculator<N, uint32_t, signed_strides>(
|
|
ndim, shape, strides_array.data());
|
|
}
|
|
|
|
template <int N, bool signed_strides = false>
|
|
static CPUOffsetCalculator<N, uint32_t, signed_strides>
|
|
CPUmake_offset_calculator(const DenseTensorIteratorBase& iter) {
|
|
PADDLE_ENFORCE_LE(N,
|
|
iter.ntensors(),
|
|
::common::errors::InvalidArgument(
|
|
"Tensor Numel must less or equal than Args"));
|
|
std::array<const int64_t*, N> strides;
|
|
for (int i = 0; i < N; i++) {
|
|
strides[i] = iter.operands_[i].stride_bytes.data();
|
|
}
|
|
return CPUOffsetCalculator<N, uint32_t, signed_strides>(
|
|
iter.ndim(), iter.shape().data(), strides.data());
|
|
}
|
|
|
|
} // namespace funcs
|
|
} // namespace phi
|