paddlepaddle--paddle
wehub-resource-sync
116 行
3.8 KiB
C++
116 行
3.8 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.
|
|
|
|
#pragma once
|
|
#include "paddle/phi/core/utils/data_type.h"
|
|
#include "paddle/phi/kernels/funcs/fft.h"
|
|
|
|
namespace phi {
|
|
namespace funcs {
|
|
namespace detail {
|
|
|
|
const int64_t kMaxFFTNdim = 3;
|
|
const int64_t kMaxDataNdim = kMaxFFTNdim + 1;
|
|
|
|
struct FFTConfigKey {
|
|
int signal_ndim_; // 1 <= signal_ndim <= kMaxFFTNdim
|
|
// These include additional batch dimension as well.
|
|
int64_t sizes_[kMaxDataNdim];
|
|
int64_t input_shape_[kMaxDataNdim];
|
|
int64_t output_shape_[kMaxDataNdim];
|
|
FFTTransformType fft_type_;
|
|
DataType value_type_;
|
|
|
|
using shape_t = std::vector<int64_t>;
|
|
FFTConfigKey() = default;
|
|
|
|
FFTConfigKey(const shape_t& in_shape,
|
|
const shape_t& out_shape,
|
|
const shape_t& signal_size,
|
|
FFTTransformType fft_type,
|
|
DataType value_type) {
|
|
// Padding bits must be zeroed for hashing
|
|
memset(this, 0, sizeof(*this));
|
|
signal_ndim_ = signal_size.size() - 1;
|
|
fft_type_ = fft_type;
|
|
value_type_ = value_type;
|
|
std::copy(signal_size.cbegin(), signal_size.cend(), sizes_);
|
|
std::copy(in_shape.cbegin(), in_shape.cend(), input_shape_);
|
|
std::copy(out_shape.cbegin(), out_shape.cend(), output_shape_);
|
|
}
|
|
};
|
|
|
|
// Hashing machinery for Key
|
|
// Fowler–Noll–Vo hash function
|
|
// see
|
|
// https://en.wikipedia.org/wiki/Fowler%E2%80%93Noll%E2%80%93Vo_hash_function
|
|
template <typename Key>
|
|
struct KeyHash {
|
|
// Key must be a POD because we read out its memory
|
|
// contents as char* when hashing
|
|
static_assert(std::is_pod<Key>::value, "Key must be plain old data type");
|
|
|
|
size_t operator()(const Key& params) const {
|
|
auto ptr = reinterpret_cast<const uint8_t*>(¶ms);
|
|
uint32_t value = 0x811C9DC5;
|
|
for (int i = 0; i < static_cast<int>(sizeof(Key)); ++i) {
|
|
value ^= ptr[i];
|
|
value *= 0x01000193;
|
|
}
|
|
return static_cast<size_t>(value);
|
|
}
|
|
};
|
|
|
|
template <typename Key>
|
|
struct KeyEqual {
|
|
// Key must be a POD because we read out its memory
|
|
// contents as char* when comparing
|
|
static_assert(std::is_pod<Key>::value, "Key must be plain old data type");
|
|
|
|
bool operator()(const Key& a, const Key& b) const {
|
|
auto ptr1 = reinterpret_cast<const uint8_t*>(&a);
|
|
auto ptr2 = reinterpret_cast<const uint8_t*>(&b);
|
|
return memcmp(ptr1, ptr2, sizeof(Key)) == 0;
|
|
}
|
|
};
|
|
|
|
static FFTConfigKey create_fft_configkey(const DenseTensor& input,
|
|
const DenseTensor& output,
|
|
int signal_ndim) {
|
|
// Create the transform plan (either from cache or locally)
|
|
DataType input_dtype = input.dtype();
|
|
const auto value_type =
|
|
IsComplexType(input_dtype) ? ToRealType(input_dtype) : input_dtype;
|
|
const auto fft_type = GetFFTTransformType(input.dtype(), output.dtype());
|
|
// signal sizes
|
|
std::vector<int64_t> signal_size(signal_ndim + 1);
|
|
|
|
signal_size[0] = input.dims()[0];
|
|
for (int64_t i = 1; i <= signal_ndim; ++i) {
|
|
auto in_size = input.dims()[i];
|
|
auto out_size = output.dims()[i];
|
|
signal_size[i] = std::max(in_size, out_size);
|
|
}
|
|
FFTConfigKey key(vectorize(input.dims()),
|
|
vectorize(output.dims()),
|
|
signal_size,
|
|
fft_type,
|
|
value_type);
|
|
return key;
|
|
}
|
|
|
|
} // namespace detail
|
|
} // namespace funcs
|
|
} // namespace phi
|