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Add sparse index support to knowhere with linscan and wand algorithm;…
… added related unit test (#341) Signed-off-by: Buqian Zheng <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| // Copyright (C) 2019-2023 Zilliz. 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 | ||
| // valributed under the License is valributed 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. | ||
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| #pragma once | ||
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| #include <algorithm> | ||
| #include <cstddef> | ||
| #include <cstdint> | ||
| #include <cstring> | ||
| #include <type_traits> | ||
| #include <vector> | ||
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| #include "knowhere/operands.h" | ||
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| namespace knowhere::sparse { | ||
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| // integer type in SparseRow | ||
| using table_t = uint32_t; | ||
| // type used to represent the id of a vector in the index interface. | ||
| // this is same as other index types. | ||
| using label_t = int64_t; | ||
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| template <typename T> | ||
| struct IdVal { | ||
| table_t id; | ||
| T val; | ||
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| IdVal() = default; | ||
| IdVal(table_t id, T val) : id(id), val(val) { | ||
| } | ||
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| inline friend bool | ||
| operator<(const IdVal& lhs, const IdVal& rhs) { | ||
| return lhs.val < rhs.val || (lhs.val == rhs.val && lhs.id < rhs.id); | ||
| } | ||
| inline friend bool | ||
| operator>(const IdVal& lhs, const IdVal& rhs) { | ||
| return !(lhs < rhs); | ||
| } | ||
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| inline friend bool | ||
| operator==(const IdVal& lhs, const IdVal& rhs) { | ||
| return lhs.id == rhs.id && lhs.val == rhs.val; | ||
| } | ||
| }; | ||
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| template <typename T> | ||
| class SparseRow { | ||
| static_assert(std::is_same_v<T, fp32>, "SparseRow supports float only"); | ||
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| public: | ||
| // construct an SparseRow with memory allocated to hold `count` elements. | ||
| SparseRow(size_t count = 0) | ||
| : data_(count ? new uint8_t[count * element_size()] : nullptr), count_(count), own_data_(true) { | ||
| } | ||
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| SparseRow(size_t count, uint8_t* data, bool own_data) : data_(data), count_(count), own_data_(own_data) { | ||
| } | ||
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| // copy constructor and copy assignment operator perform deep copy | ||
| SparseRow(const SparseRow<T>& other) : SparseRow(other.count_) { | ||
| std::copy(other.data_, other.data_ + count_ * element_size(), data_); | ||
| } | ||
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| SparseRow(SparseRow<T>&& other) noexcept : SparseRow() { | ||
| swap(*this, other); | ||
| } | ||
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| SparseRow& | ||
| operator=(const SparseRow<T>& other) { | ||
| if (this != &other) { | ||
| SparseRow<T> tmp(other); | ||
| swap(*this, tmp); | ||
| } | ||
| return *this; | ||
| } | ||
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| SparseRow& | ||
| operator=(SparseRow<T>&& other) noexcept { | ||
| swap(*this, other); | ||
| return *this; | ||
| } | ||
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| ~SparseRow() { | ||
| if (own_data_ && data_ != nullptr) { | ||
| delete[] data_; | ||
| data_ = nullptr; | ||
| } | ||
| } | ||
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| size_t | ||
| size() const { | ||
| return count_; | ||
| } | ||
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| size_t | ||
| memory_usage() const { | ||
| return count_ * element_size() + sizeof(*this); | ||
| } | ||
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| void* | ||
| data() { | ||
| return data_; | ||
| } | ||
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| const void* | ||
| data() const { | ||
| return data_; | ||
| } | ||
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| // dim of a sparse vector is the max index + 1, or 0 for an empty vector. | ||
| int64_t | ||
| dim() const { | ||
| if (count_ == 0) { | ||
| return 0; | ||
| } | ||
| auto* elem = reinterpret_cast<const ElementProxy*>(data_) + count_ - 1; | ||
| return elem->index + 1; | ||
| } | ||
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| IdVal<T> | ||
| operator[](size_t i) const { | ||
| auto* elem = reinterpret_cast<const ElementProxy*>(data_) + i; | ||
| return {elem->index, elem->value}; | ||
| } | ||
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| void | ||
| set_at(size_t i, table_t index, T value) { | ||
| auto* elem = reinterpret_cast<ElementProxy*>(data_) + i; | ||
| elem->index = index; | ||
| elem->value = value; | ||
| } | ||
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| float | ||
| dot(const SparseRow<T>& other) const { | ||
| float product_sum = 0.0f; | ||
| size_t i = 0, j = 0; | ||
| while (i < count_ && j < other.count_) { | ||
| auto* left = reinterpret_cast<const ElementProxy*>(data_) + i; | ||
| auto* right = reinterpret_cast<const ElementProxy*>(other.data_) + j; | ||
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| if (left->index < right->index) { | ||
| ++i; | ||
| } else if (left->index > right->index) { | ||
| ++j; | ||
| } else { | ||
| product_sum += left->value * right->value; | ||
| ++i; | ||
| ++j; | ||
| } | ||
| } | ||
| return product_sum; | ||
| } | ||
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| friend void | ||
| swap(SparseRow<T>& left, SparseRow<T>& right) { | ||
| using std::swap; | ||
| swap(left.count_, right.count_); | ||
| swap(left.data_, right.data_); | ||
| swap(left.own_data_, right.own_data_); | ||
| } | ||
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| static inline size_t | ||
| element_size() { | ||
| return sizeof(table_t) + sizeof(T); | ||
| } | ||
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| private: | ||
| // ElementProxy is used to access elements in the data_ array and should | ||
| // never be actually constructed. | ||
| struct __attribute__((packed)) ElementProxy { | ||
| table_t index; | ||
| T value; | ||
| ElementProxy() = delete; | ||
| ElementProxy(const ElementProxy&) = delete; | ||
| }; | ||
| // data_ must be sorted by column id. use raw pointer for easy mmap and zero | ||
| // copy. | ||
| uint8_t* data_; | ||
| size_t count_; | ||
| bool own_data_; | ||
| }; | ||
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| // When pushing new elements into a MaxMinHeap, only `capacity` elements with the | ||
| // largest val are kept. pop()/top() returns the smallest element out of them. | ||
| template <typename T> | ||
| class MaxMinHeap { | ||
| public: | ||
| explicit MaxMinHeap(int capacity) : capacity_(capacity), pool_(capacity) { | ||
| } | ||
| void | ||
| push(table_t id, T val) { | ||
| if (size_ < capacity_) { | ||
| pool_[size_] = {id, val}; | ||
| size_ += 1; | ||
| std::push_heap(pool_.begin(), pool_.begin() + size_, std::greater<IdVal<T>>()); | ||
| } else if (val > pool_[0].val) { | ||
| sift_down(id, val); | ||
| } | ||
| } | ||
| table_t | ||
| pop() { | ||
| std::pop_heap(pool_.begin(), pool_.begin() + size_, std::greater<IdVal<T>>()); | ||
| size_ -= 1; | ||
| return pool_[size_].id; | ||
| } | ||
| [[nodiscard]] size_t | ||
| size() const { | ||
| return size_; | ||
| } | ||
| [[nodiscard]] bool | ||
| empty() const { | ||
| return size() == 0; | ||
| } | ||
| IdVal<T> | ||
| top() const { | ||
| return pool_[0]; | ||
| } | ||
| [[nodiscard]] bool | ||
| full() const { | ||
| return size_ == capacity_; | ||
| } | ||
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| private: | ||
| void | ||
| sift_down(table_t id, T val) { | ||
| size_t i = 0; | ||
| for (; 2 * i + 1 < size_;) { | ||
| size_t j = i; | ||
| size_t l = 2 * i + 1, r = 2 * i + 2; | ||
| if (pool_[l].val < val) { | ||
| j = l; | ||
| } | ||
| if (r < size_ && pool_[r].val < std::min(pool_[l].val, val)) { | ||
| j = r; | ||
| } | ||
| if (i == j) { | ||
| break; | ||
| } | ||
| pool_[i] = pool_[j]; | ||
| i = j; | ||
| } | ||
| pool_[i] = {id, val}; | ||
| } | ||
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| size_t size_ = 0, capacity_; | ||
| std::vector<IdVal<T>> pool_; | ||
| }; // class MaxMinHeap | ||
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| } // namespace knowhere::sparse |
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