xref: /aosp_15_r20/external/pytorch/aten/src/ATen/core/CachingHostAllocator.h (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#include <c10/core/Allocator.h>
#include <c10/util/flat_hash_map.h>
#include <c10/util/llvmMathExtras.h>
#include <optional>

#include <deque>
#include <mutex>
#include <set>

C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-parameter")
namespace at {

/**
 * HostBlock is typically a fundamental memory block used in pinned memory. It
 * is likely related to Event and Stream of device runtime. It is probably a
 * base struct or interface that can be inherited and extended by each backend.
 */
template <typename S>
struct HostBlock {
  // constructor for search key
  HostBlock(size_t size) : size_(size) {}

  HostBlock(size_t size, void* ptr) : size_(size), ptr_(ptr) {}

  std::mutex mutex_;
  size_t size_{0}; // block size in bytes
  void* ptr_{nullptr}; // memory address
  bool allocated_{false}; // in-use flag
  size_t event_count_{0}; // number of related events
  ska::flat_hash_set<S> streams_; // streams on which the block was used
};

template <typename B>
struct alignas(64) FreeBlockList {
  std::mutex mutex_;
  std::deque<B*> list_;
};

namespace {
  // Max cached block sizes: (1 << MAX_SIZE_INDEX) bytes
  constexpr size_t MAX_SIZE_INDEX = 64;
}

/**
 * Note [HostAllocator design]
 * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 * We have three key data structures - the free list which stores blocks that
 * are not currently used, the block list which stores all blocks that have been
 * allocated, and the event queue which stores runtime events and their
 * corresponding blocks.
 *
 * Each of these are protected by a separate mutex. The key design principles
 * are to 1) only hold each mutex for the minimal amount of time possible, 2)
 * never do any possible expensive operations (such as CUDA runtime API calls)
 * while holding the lock.
 *
 * There are four public methods: allocate, free, record_event and empty_cache.
 *   1) In the allocate path, we first check to see if we can service our
 * request from this free list, and otherwise we create a new block with
 * allocate_host_memory.
 *   2) In the free path, we insert events (if required) into the event queue,
 * and if possible insert our block back into the free list. In allocate, we
 * first eagerly query events until we find one that is not ready, and insert
 * the corresponding block onto the free list if all the events recorded for a
 * block are ready.
 *   3) In the record_event path, we simply insert the given stream into the set
 * of streams tracked by the specified block. This set of streams is then
 * consumed in the free path.
 *   4) In the empty_cache path, we flush any available blocks into the free
 * list. Remove all element of free list, then remove them from block list and
 * release the associated pinned memory allocation via free_block.
 *
 * We generalize the caching host allocator into two parts: interface and
 * implementation. For any new backend looking to integrate with host allocator
 * and reuse caching mechanism, these two parts are necessary to be specialized.
 *
 * For the implementation, we provide a CachingHostAllocatorImpl struct
 * to abstract the caching mechanism. Any backend needs to provide a customized
 * implementation by specializing its own public functions and the related
 * runtime functions. Its template parameter S represents runtime Stream, E
 * denotes runtime Event, B indicates the fundamental memory block.
 *
 * For the interface, we provide a CachingHostAllocatorInterface struct as an
 * interface. Any backend needs to derive its own host allocator from this
 * interface. Its template parameter T refers to an implementation that
 * inherited from CachingHostAllocatorImpl.
 *
 * So this design can share the caching mechanism across each backend, and
 * provide flexibility to each backend. A backend can choose to follow this
 * implementation or reuse them by extending and overriding them as necessary.
 * Taking CUDA as an example, it specializes runtime related functions to reuse
 * the caching mechanism. Additionally, it extends the allocator's functionality
 * by adding the allocWithCudaHostRegister function to support page-locking the
 * memory range used by CUDA. Of course, you can also refer to
 * XPUCachingHostAllocator, which is a host caching allocator supported on XPU
 * backend, to implement a basic host caching allocator.
 *
 * Some of the invariants here are less strict than they could be - for example,
 * we do not enforce that free(Block* block) => block->event_count == 0. This is
 * for compatibility reasons, and we can explore enforcing these in subsequent
 * versions.
 *
 * Note that this caching host allocator does not split larger allocations into
 * smaller blocks, unlike the caching device allocator.
 */

template <
    typename S,
    typename E,
    typename B = HostBlock<S>>
struct CachingHostAllocatorImpl {
  virtual ~CachingHostAllocatorImpl() = default;

 public:
  // return data_ptr and block pair.
  virtual std::pair<void*, void*> allocate(size_t size) {
    if (size == 0) {
      return {nullptr, nullptr};
    }

    process_events();

    // First, try to allocate from the free list
    auto* block = get_free_block(size);
    if (block) {
      return {block->ptr_, reinterpret_cast<void*>(block)};
    }

    // Round up the allocation to the nearest power of two to improve reuse.
    // These power of two sizes are also used to index into the free list.
    size_t roundSize = c10::llvm::PowerOf2Ceil(size);
    void* ptr = nullptr;
    allocate_host_memory(roundSize, &ptr);

    // Then, create a new block.
    block = new B(roundSize, ptr);
    block->allocated_ = true;

    add_allocated_block(block);
    return {block->ptr_, reinterpret_cast<void*>(block)};
  }

  virtual void free(void* ctx) {
    if (!ctx) {
      return;
    }

    // Note: we can assume that free is correctly paired with alloc, and thus we
    // do not need to look up the ctx in blocks_.
    auto* block = reinterpret_cast<B*>(ctx);

    std::optional<std::vector<E>> events;
    {
      std::lock_guard<std::mutex> g(block->mutex_);
      block->allocated_ = false;
      if (block->streams_.empty()) {
        TORCH_INTERNAL_ASSERT(block->event_count_ == 0);
      } else {
        events = std::vector<E>();
        events->reserve(block->streams_.size());
        for (auto stream : block->streams_) {
          record_stream(events, stream);
        }
        block->event_count_ += events->size();
        block->streams_.clear();
      }
    }

    if (!events) {
      auto index = size_index(block->size_);
      std::lock_guard<std::mutex> g(free_list_[index].mutex_);
      free_list_[index].list_.push_back(block);
    } else {
      // restore these events that record by used streams.
      std::lock_guard<std::mutex> g(events_mutex_);
      for (auto&& event : *events) {
        events_.emplace_front(std::move(event), block);
      }
    }
  }

  virtual bool record_event(void* ptr, void* ctx, S stream) {
    auto* block = reinterpret_cast<B*>(ctx);

    // Note: we need to check if the passed-in `ctx` is valid. This is because
    // `record_event` (via `CachingHostAllocator_recordEvent`) can be invoked on
    // an arbitrary tensor, and is not guaranteed to correspond to a pinned
    // memory allocation. Therefore, we need to check that `ctx` is valid before
    // proceeding.
    {
      std::lock_guard<std::mutex> g(blocks_mutex_);
      if (blocks_.find(block) != blocks_.end()) {
        // Now we know this object is safe to access.
        std::lock_guard<std::mutex> gb(block->mutex_);
        TORCH_INTERNAL_ASSERT(block->allocated_);
        block->streams_.insert(stream);
        return true;
      }
      auto it = ptr_to_block_.find(ptr);
      if (it != ptr_to_block_.end()) {
        block = it->second;
        std::lock_guard<std::mutex> g(block->mutex_);
        TORCH_INTERNAL_ASSERT(block->allocated_);
        block->streams_.insert(stream);
        return true;
      }
    }

    return false;
  }

  virtual void empty_cache() {
    // Flush any available blocks into the free_list.
    process_events();

    // Remove all elements from the free list, remove them from the blocks
    // list, and free the associated pinned memory allocation. This requires
    // concurrently holding both the free list mutexes and the blocks mutex, and
    // is the only function that concurrently holds multiple mutexes.
    for (size_t i = 0; i < free_list_.size(); ++i) {
      std::lock(free_list_[i].mutex_, blocks_mutex_);
      std::lock_guard<std::mutex> gf(free_list_[i].mutex_, std::adopt_lock);
      std::lock_guard<std::mutex> gb(blocks_mutex_, std::adopt_lock);

      std::vector<B*> blocks_to_remove(free_list_[i].list_.begin(), free_list_[i].list_.end());
      free_list_[i].list_.clear();
      for (auto* block : blocks_to_remove) {
        blocks_.erase(block);
        ptr_to_block_.erase(block->ptr_);
        free_block(block);
        delete block;
      }
    }
  }

  inline size_t size_index(size_t size) {
    return c10::llvm::Log2_64_Ceil(size);
  }

  virtual void copy_data(void* dest [[maybe_unused]], const void* src [[maybe_unused]], std::size_t count [[maybe_unused]]) const {
    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for copy_data");
  }

 private:
  virtual void add_allocated_block(B* block) {
    std::lock_guard<std::mutex> g(blocks_mutex_);
    blocks_.insert(block);
    ptr_to_block_.insert({block->ptr_, block});
  }

  virtual B* get_free_block(size_t size) {
    auto index = size_index(size);
    std::lock_guard<std::mutex> g(free_list_[index].mutex_);
    if (free_list_[index].list_.size() > 0) {
      B* block = free_list_[index].list_.back();
      free_list_[index].list_.pop_back();
      block->allocated_ = true;
      return block;
    }
    return nullptr;
  }

  virtual void process_events() {
    while (true) {
      // Avoid calling cudaEventDestroy while holding a mutex, so move
      // intermediate events out of the lock into this object.
      // process the last event
      std::optional<std::pair<E, B*>> processed;
      {
        std::lock_guard<std::mutex> g(events_mutex_);
        if (!events_.empty()) {
          processed = std::move(events_.back());
          events_.pop_back();
        }
      }

      if (!processed) {
        return;
      }

      // otherwise, query the event
      {
        // now, see if we can handle this element
        auto& event = processed->first;
        if (!query_event(event)) {
          // push the event onto the back if it's not ready.
          {
            std::lock_guard<std::mutex> g(events_mutex_);
            events_.push_back(std::move(*processed));
          }
          return;
        }
      }

      // Process the events.
      TORCH_INTERNAL_ASSERT(processed);
      auto* block = processed->second;
      bool available = false;
      {
        std::lock_guard<std::mutex> g(block->mutex_);
        TORCH_INTERNAL_ASSERT(!block->allocated_)
        block->event_count_--;
        if (block->event_count_ == 0) {
          available = true;
        }
      }

      if (available) {
        auto index = size_index(block->size_);
        std::lock_guard<std::mutex> g(free_list_[index].mutex_);
        free_list_[index].list_.push_back(block);
      }
    }
  }

  /* These following functions are runtime-related. */

  // Allocate page-locked memory on the host.
  virtual void allocate_host_memory(size_t size, void** ptr) {
    TORCH_CHECK_NOT_IMPLEMENTED(
        false, "Not implemented for allocate_host_memory");
  }

  // Free block and release the pointer contained in block.
  virtual void free_block(B* block) {
    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for free_block");
  }

  // Record an event on stream and store event into events.
  virtual void record_stream(std::optional<std::vector<E>>& events, S stream) {
    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for record_stream");
  }

  // Query event if it is completed.
  virtual bool query_event(E& event) {
    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for query_event");
  }

  alignas(64) std::mutex blocks_mutex_;
  ska::flat_hash_set<B*> blocks_; // block list
  ska::flat_hash_map<void*, B*> ptr_to_block_;

  // We keep free list as a vector of free lists, one for each power of two
  // size. This allows us to quickly find a free block of the right size.
  // We use deque to store per size free list and guard the list with its own
  // mutex.
  alignas(64) std::vector<FreeBlockList<B>> free_list_ = std::vector<FreeBlockList<B>>(MAX_SIZE_INDEX);

  alignas(64) std::mutex events_mutex_;
  std::deque<std::pair<E, B*>> events_; // event queue paired with block
};

template <typename T>
struct CachingHostAllocatorInterface : public at::Allocator {
  CachingHostAllocatorInterface() : impl_(std::make_unique<T>()) {}

  at::DataPtr allocate(size_t size) override {
    TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for allocate");
  }

  void free(void* ctx) {
    impl_->free(ctx);
  }

  template <typename S>
  bool record_event(void* ptr, void* ctx, S stream) {
    return impl_->record_event(ptr, ctx, stream);
  }

  void empty_cache() {
    impl_->empty_cache();
  }

  void copy_data(void* dest, const void* src, std::size_t count)
      const override {
    impl_->copy_data(dest, src, count);
  }

  std::unique_ptr<T> impl_;
};

} // namespace at
C10_DIAGNOSTIC_POP()