-
Notifications
You must be signed in to change notification settings - Fork 68
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add sparse index support to knowhere with linscan and wand algorithm;…
… added related unit test Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
- Loading branch information
1 parent
042d20d
commit c0f90d3
Showing
12 changed files
with
1,416 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,261 @@ | ||
| // 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. | ||
|
|
||
| #pragma once | ||
|
|
||
| #include <algorithm> | ||
| #include <cstddef> | ||
| #include <cstdint> | ||
| #include <cstring> | ||
| #include <type_traits> | ||
| #include <vector> | ||
|
|
||
| #include "knowhere/operands.h" | ||
|
|
||
| namespace knowhere::sparse { | ||
|
|
||
| // 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; | ||
|
|
||
| template <typename T> | ||
| struct IdVal { | ||
| table_t id; | ||
| T val; | ||
|
|
||
| IdVal() = default; | ||
| IdVal(table_t id, T val) : id(id), val(val) { | ||
| } | ||
|
|
||
| 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); | ||
| } | ||
|
|
||
| inline friend bool | ||
| operator==(const IdVal& lhs, const IdVal& rhs) { | ||
| return lhs.id == rhs.id && lhs.val == rhs.val; | ||
| } | ||
| }; | ||
|
|
||
| template <typename T> | ||
| class SparseRow { | ||
| static_assert(std::is_same_v<T, fp32>, "SparseRow supports float only"); | ||
|
|
||
| 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) { | ||
| } | ||
|
|
||
| SparseRow(size_t count, uint8_t* data, bool own_data) : data_(data), count_(count), own_data_(own_data) { | ||
| } | ||
|
|
||
| // 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_); | ||
| } | ||
|
|
||
| SparseRow(SparseRow<T>&& other) noexcept : SparseRow() { | ||
| swap(*this, other); | ||
| } | ||
|
|
||
| SparseRow& | ||
| operator=(const SparseRow<T>& other) { | ||
| if (this != &other) { | ||
| SparseRow<T> tmp(other); | ||
| swap(*this, tmp); | ||
| } | ||
| return *this; | ||
| } | ||
|
|
||
| SparseRow& | ||
| operator=(SparseRow<T>&& other) noexcept { | ||
| swap(*this, other); | ||
| return *this; | ||
| } | ||
|
|
||
| ~SparseRow() { | ||
| if (own_data_ && data_ != nullptr) { | ||
| delete[] data_; | ||
| data_ = nullptr; | ||
| } | ||
| } | ||
|
|
||
| size_t | ||
| size() const { | ||
| return count_; | ||
| } | ||
|
|
||
| size_t | ||
| memory_usage() const { | ||
| return count_ * element_size() + sizeof(*this); | ||
| } | ||
|
|
||
| void* | ||
| data() { | ||
| return data_; | ||
| } | ||
|
|
||
| const void* | ||
| data() const { | ||
| return data_; | ||
| } | ||
|
|
||
| // 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; | ||
| } | ||
|
|
||
| IdVal<T> | ||
| operator[](size_t i) const { | ||
| auto* elem = reinterpret_cast<const ElementProxy*>(data_) + i; | ||
| return {elem->index, elem->value}; | ||
| } | ||
|
|
||
| void | ||
| set_at(size_t i, table_t index, T value) { | ||
| auto* elem = reinterpret_cast<ElementProxy*>(data_) + i; | ||
| elem->index = index; | ||
| elem->value = value; | ||
| } | ||
|
|
||
| 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; | ||
|
|
||
| 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; | ||
| } | ||
|
|
||
| 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_); | ||
| } | ||
|
|
||
| static inline size_t | ||
| element_size() { | ||
| return sizeof(table_t) + sizeof(T); | ||
| } | ||
|
|
||
| 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_; | ||
| }; | ||
|
|
||
| // 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_; | ||
| } | ||
|
|
||
| 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}; | ||
| } | ||
|
|
||
| size_t size_ = 0, capacity_; | ||
| std::vector<IdVal<T>> pool_; | ||
| }; // class MaxMinHeap | ||
|
|
||
| } // namespace knowhere::sparse |
Oops, something went wrong.