wehub-resource-sync
116 lines
3.8 KiB
C++
116 lines
3.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/fft.h"
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namespace phi {
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namespace funcs {
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namespace detail {
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const int64_t kMaxFFTNdim = 3;
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const int64_t kMaxDataNdim = kMaxFFTNdim + 1;
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struct FFTConfigKey {
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int signal_ndim_; // 1 <= signal_ndim <= kMaxFFTNdim
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// These include additional batch dimension as well.
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int64_t sizes_[kMaxDataNdim];
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int64_t input_shape_[kMaxDataNdim];
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int64_t output_shape_[kMaxDataNdim];
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FFTTransformType fft_type_;
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DataType value_type_;
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using shape_t = std::vector<int64_t>;
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FFTConfigKey() = default;
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FFTConfigKey(const shape_t& in_shape,
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const shape_t& out_shape,
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const shape_t& signal_size,
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FFTTransformType fft_type,
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DataType value_type) {
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// Padding bits must be zeroed for hashing
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memset(this, 0, sizeof(*this));
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signal_ndim_ = signal_size.size() - 1;
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fft_type_ = fft_type;
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value_type_ = value_type;
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std::copy(signal_size.cbegin(), signal_size.cend(), sizes_);
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std::copy(in_shape.cbegin(), in_shape.cend(), input_shape_);
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std::copy(out_shape.cbegin(), out_shape.cend(), output_shape_);
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}
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};
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// Hashing machinery for Key
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// Fowler–Noll–Vo hash function
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// see
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// https://en.wikipedia.org/wiki/Fowler%E2%80%93Noll%E2%80%93Vo_hash_function
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template <typename Key>
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struct KeyHash {
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// Key must be a POD because we read out its memory
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// contents as char* when hashing
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static_assert(std::is_pod<Key>::value, "Key must be plain old data type");
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size_t operator()(const Key& params) const {
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auto ptr = reinterpret_cast<const uint8_t*>(¶ms);
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uint32_t value = 0x811C9DC5;
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for (int i = 0; i < static_cast<int>(sizeof(Key)); ++i) {
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value ^= ptr[i];
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value *= 0x01000193;
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}
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return static_cast<size_t>(value);
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}
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};
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template <typename Key>
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struct KeyEqual {
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// Key must be a POD because we read out its memory
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// contents as char* when comparing
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static_assert(std::is_pod<Key>::value, "Key must be plain old data type");
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bool operator()(const Key& a, const Key& b) const {
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auto ptr1 = reinterpret_cast<const uint8_t*>(&a);
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auto ptr2 = reinterpret_cast<const uint8_t*>(&b);
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return memcmp(ptr1, ptr2, sizeof(Key)) == 0;
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}
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};
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static FFTConfigKey create_fft_configkey(const DenseTensor& input,
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const DenseTensor& output,
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int signal_ndim) {
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// Create the transform plan (either from cache or locally)
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DataType input_dtype = input.dtype();
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const auto value_type =
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IsComplexType(input_dtype) ? ToRealType(input_dtype) : input_dtype;
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const auto fft_type = GetFFTTransformType(input.dtype(), output.dtype());
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// signal sizes
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std::vector<int64_t> signal_size(signal_ndim + 1);
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signal_size[0] = input.dims()[0];
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for (int64_t i = 1; i <= signal_ndim; ++i) {
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auto in_size = input.dims()[i];
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auto out_size = output.dims()[i];
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signal_size[i] = std::max(in_size, out_size);
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}
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FFTConfigKey key(vectorize(input.dims()),
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vectorize(output.dims()),
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signal_size,
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fft_type,
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value_type);
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return key;
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}
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} // namespace detail
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} // namespace funcs
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} // namespace phi
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