Files
mlx/mlx/backend/cuda/lru_cache.h
Cheng 6f5874a2f2 [CUDA] Initial implementation of Convolution with cuDNN (#2385)
* Link with cuDNN

* Initial implementation

* Remove backend apis

* Fix recording cudnn conv

* More unused backend apis

* Fix C++ conv tests

* include cudnn as python dep

* Install libcudnn9-dev-cuda-12 in CI

* cudnn only accepts contiguous inputs

* Switch to backend apis

* Plan needs to be kept alive

* Turn off tf32

* Add cache

* Test the native cuda graph api

* Set cudnn stream before execution

* Make LRUCache more like a normal container

* Do error check for cublas handle

* Zero-initilizing array

* Use tf32 for conv

* Skip TestConv.test_torch_conv_2D test

---------

Co-authored-by: Awni Hannun <awni@apple.com>
2025-07-25 08:12:10 +09:00

147 lines
3.2 KiB
C++

// Copyright © 2025 Apple Inc.
#pragma once
#include <list>
#include <unordered_map>
#include <utility>
namespace mlx::core {
template <
typename K,
typename V,
template <typename...> typename M = std::unordered_map>
class LRUCache {
public:
using value_type = std::pair<K, V>;
using list_type = std::list<value_type>;
using iterator = typename list_type::iterator;
using const_iterator = typename list_type::const_iterator;
using map_type = M<K, iterator>;
explicit LRUCache(size_t capacity) : capacity_(capacity) {}
size_t size() const {
return map_.size();
}
size_t capacity() const {
return capacity_;
}
bool empty() const {
return vlist_.empty();
}
void resize(size_t new_capacity) {
capacity_ = new_capacity;
trim();
}
iterator begin() {
return vlist_.begin();
}
const_iterator begin() const {
return vlist_.begin();
}
iterator end() {
return vlist_.end();
}
const_iterator end() const {
return vlist_.end();
}
void clear() {
map_.clear();
vlist_.clear();
}
iterator find(const K& key) {
auto it = map_.find(key);
if (it == map_.end())
return end();
vlist_.splice(vlist_.begin(), vlist_, it->second);
return it->second;
}
template <typename U>
std::pair<iterator, bool> emplace(const K& key, U&& value) {
auto it = map_.find(key);
if (it != map_.end()) {
vlist_.splice(vlist_.begin(), vlist_, it->second);
return {it->second, false};
}
vlist_.emplace_front(key, std::forward<U>(value));
map_[key] = vlist_.begin();
trim();
return {vlist_.begin(), true};
}
iterator erase(iterator pos) {
map_.erase(pos->first);
return vlist_.erase(pos);
}
private:
void trim() {
while (map_.size() > capacity_) {
auto last = std::prev(vlist_.end());
map_.erase(last->first);
vlist_.pop_back();
}
}
list_type vlist_;
map_type map_;
size_t capacity_;
};
// Turn a POD struct into a container key by doing bytes compare.
template <typename T>
struct BytesKey {
T pod;
static_assert(std::is_standard_layout_v<T>, "T is not POD");
BytesKey(T pod) : pod(std::move(pod)) {}
BytesKey(const BytesKey& other) {
memcpy(&pod, &other.pod, sizeof(T));
}
BytesKey(BytesKey&& other) {
memcpy(&pod, &other.pod, sizeof(T));
}
bool operator==(const BytesKey& other) const {
auto* ptr1 = reinterpret_cast<const uint8_t*>(&pod);
auto* ptr2 = reinterpret_cast<const uint8_t*>(&other.pod);
return memcmp(ptr1, ptr2, sizeof(T)) == 0;
}
};
// Compute hash according to the bytes value of T.
template <typename T>
struct BytesHash {
static_assert(std::is_standard_layout_v<T>, "T is not POD");
size_t operator()(const T& pod) const {
auto* ptr = reinterpret_cast<const uint8_t*>(&pod);
uint32_t value = 0x811C9DC5;
for (int i = 0; i < sizeof(T); ++i) {
value ^= ptr[i];
value *= 0x01000193;
}
return value;
}
};
template <typename K, typename V>
using BytesKeyHashMap = std::unordered_map<K, V, BytesHash<K>>;
template <typename K, typename V>
using LRUBytesKeyCache = LRUCache<BytesKey<K>, V, BytesKeyHashMap>;
} // namespace mlx::core