// Copyright © 2025 Apple Inc.

#include "mlx/backend/cuda/allocator.h"
#include "mlx/backend/cuda/device.h"
#include "mlx/backend/cuda/utils.h"
#include "mlx/backend/gpu/device_info.h"
#include "mlx/memory.h"
#include "mlx/scheduler.h"
#include "mlx/utils.h"

#include <cuda_runtime.h>
#include <fmt/format.h>

#include <cassert>

namespace mlx::core {

namespace cu {

constexpr int page_size = 16384;

// Any allocations smaller than this will try to use the small pool
constexpr int small_block_size = 8;

#if CUDART_VERSION >= 13000
inline cudaMemLocation cuda_mem_loc(int i) {
  cudaMemLocation loc;
  loc.type = cudaMemLocationTypeDevice;
  loc.id = i;
  return loc;
}
#else
inline int cuda_mem_loc(int i) {
  return i;
}
#endif // CUDART_VERSION >= 13000

// The small pool size in bytes. This should be a multiple of the host page
// size and small_block_size.
constexpr int small_pool_size = 4 * page_size;

SmallSizePool::SmallSizePool() {
  auto num_blocks = small_pool_size / small_block_size;
  buffer_ = new Block[num_blocks];

  next_free_ = buffer_;

  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));

  int device_count = gpu::device_count();
  for (int i = 0; i < device_count; ++i) {
    if (cu::device(i).concurrent_managed_access()) {
      auto loc = cuda_mem_loc(i);
      CHECK_CUDA_ERROR(cudaMemAdvise(
          data_, small_pool_size, cudaMemAdviseSetAccessedBy, loc));
    }
  }

  auto curr = next_free_;
  for (size_t i = 1; i < num_blocks; ++i) {
    curr->next = buffer_ + i;
    curr = curr->next;
  }
  curr->next = nullptr;
}

SmallSizePool::~SmallSizePool() {
  CHECK_CUDA_ERROR(cudaFree(data_));
  delete[] buffer_;
}

CudaBuffer* SmallSizePool::malloc() {
  if (next_free_ == nullptr) {
    return nullptr;
  }
  Block* b = next_free_;
  uint64_t i = next_free_ - buffer_;
  next_free_ = next_free_->next;
  b->buf.data = static_cast<char*>(data_) + i * small_block_size;
  b->buf.size = small_block_size;
  b->buf.device = -1;
  return &b->buf;
}

void SmallSizePool::free(CudaBuffer* buf) {
  auto b = reinterpret_cast<Block*>(buf);
  b->next = next_free_;
  next_free_ = b;
}

bool SmallSizePool::in_pool(CudaBuffer* buf) {
  constexpr int num_blocks = (small_pool_size / small_block_size);
  auto b = reinterpret_cast<Block*>(buf);
  int64_t block_num = b - buffer_;
  return block_num >= 0 && block_num < num_blocks;
}

CudaAllocator::CudaAllocator()
    : buffer_cache_(
          page_size,
          [](CudaBuffer* buf) { return buf->size; },
          [this](CudaBuffer* buf) { cuda_free(buf); }) {
  size_t free;
  CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total_memory_));
  memory_limit_ = total_memory_ * 0.95;
  free_limit_ = total_memory_ - memory_limit_;
  max_pool_size_ = memory_limit_;

  int device_count = 0;
  CHECK_CUDA_ERROR(cudaGetDeviceCount(&device_count));
  int curr;
  CHECK_CUDA_ERROR(cudaGetDevice(&curr));
  for (int i = 0; i < device_count; ++i) {
    CHECK_CUDA_ERROR(cudaSetDevice(i));
    cudaStream_t s;
    CHECK_CUDA_ERROR(cudaStreamCreateWithFlags(&s, cudaStreamNonBlocking));
    free_streams_.push_back(s);

    cudaMemPool_t mem_pool;
    CHECK_CUDA_ERROR(cudaDeviceGetDefaultMemPool(&mem_pool, i));
    mem_pools_.push_back(mem_pool);
  }
  CHECK_CUDA_ERROR(cudaSetDevice(curr));
}

void copy_to_managed(CudaBuffer& buf) {
  // TODO maybe make this async on a i/o stream to avoid synchronizing the
  // device on malloc/and free
  void* new_data;
  CHECK_CUDA_ERROR(cudaMallocManaged(&new_data, buf.size));
  buf.device = -1;
  CHECK_CUDA_ERROR(cudaMemcpy(new_data, buf.data, buf.size, cudaMemcpyDefault));
  CHECK_CUDA_ERROR(cudaFree(buf.data));
  buf.data = new_data;
}

Buffer
CudaAllocator::malloc_async(size_t size, int device, cudaStream_t stream) {
  if (size == 0) {
    return Buffer{new CudaBuffer{nullptr, 0, -1}};
  }

  // Find available buffer from cache.
  std::unique_lock lock(mutex_);
  if (size <= small_block_size) {
    size = 8;
  } else if (size < page_size) {
    size = next_power_of_2(size);
  } else {
    size = page_size * ((size + page_size - 1) / page_size);
  }

  if (size <= small_block_size || stream == nullptr) {
    device = -1;
  }

  CudaBuffer* buf = buffer_cache_.reuse_from_cache(size);
  if (!buf) {
    // If we have a lot of memory pressure try to reclaim memory from the cache.
    int64_t mem_to_free =
        get_active_memory() + get_cache_memory() + size - memory_limit_;
    if (mem_to_free > 0) {
      buffer_cache_.release_cached_buffers(mem_to_free);
    }

    // Try the scalar pool first
    if (size <= small_block_size) {
      buf = scalar_pool_.malloc();
    }
    lock.unlock();
    if (!buf) {
      void* data = nullptr;
      if (device == -1) {
        CHECK_CUDA_ERROR(cudaMallocManaged(&data, size));
      } else {
        CHECK_CUDA_ERROR(cudaMallocAsync(&data, size, stream));
      }
      if (!data) {
        std::ostringstream msg;
        msg << "[malloc] Unable to allocate " << size << " bytes.";
        throw std::runtime_error(msg.str());
      }
      buf = new CudaBuffer{data, size, device};
    }
    lock.lock();

    // If any cuda memory pool has too much reserved memory, clear some
    // memory from the cache. This prevents graph / kernel execution failing
    // from OOM
    if (get_cache_memory() > 0) {
      for (auto p : mem_pools_) {
        size_t used = 0;
        CHECK_CUDA_ERROR(cudaMemPoolGetAttribute(
            p, cudaMemPoolAttrReservedMemCurrent, &used));
        if (used > (total_memory_ - free_limit_)) {
          buffer_cache_.release_cached_buffers(free_limit_);
          break;
        }
      }
    }
  }
  active_memory_ += buf->size;
  peak_memory_ = std::max(active_memory_, peak_memory_);

  // Maintain the cache below the requested limit.
  if (get_cache_memory() > max_pool_size_) {
    buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
  }
  // Copy to managed here if the buffer is not on the right device
  if (buf->device >= 0 && buf->device != device) {
    copy_to_managed(*buf);
  }
  return Buffer{buf};
}

Buffer CudaAllocator::malloc(size_t size) {
  return malloc_async(size, -1, nullptr);
}

void CudaAllocator::free(Buffer buffer) {
  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
  if (!buf) {
    return;
  }
  if (buf->size == 0) {
    delete buf;
    return;
  }

  std::unique_lock lock(mutex_);
  active_memory_ -= buf->size;
  if (get_cache_memory() < max_pool_size_) {
    buffer_cache_.recycle_to_cache(buf);
  } else {
    cuda_free(buf);
  }
}

size_t CudaAllocator::size(Buffer buffer) const {
  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
  if (!buf) {
    return 0;
  }
  return buf->size;
}

// This must be called with mutex_ aquired
void CudaAllocator::cuda_free(CudaBuffer* buf) {
  if (scalar_pool_.in_pool(buf)) {
    scalar_pool_.free(buf);
  } else {
    if (buf->device >= 0) {
      CHECK_CUDA_ERROR(cudaFreeAsync(buf->data, free_streams_[buf->device]));
    } else {
      CHECK_CUDA_ERROR(cudaFree(buf->data));
    }
    delete buf;
  }
}

size_t CudaAllocator::get_active_memory() const {
  return active_memory_;
}

size_t CudaAllocator::get_peak_memory() const {
  return peak_memory_;
}

void CudaAllocator::reset_peak_memory() {
  std::lock_guard lock(mutex_);
  peak_memory_ = 0;
}

size_t CudaAllocator::get_memory_limit() {
  return memory_limit_;
}

size_t CudaAllocator::set_memory_limit(size_t limit) {
  std::lock_guard lock(mutex_);
  std::swap(limit, memory_limit_);
  return limit;
}

size_t CudaAllocator::get_cache_memory() const {
  return buffer_cache_.cache_size();
}

size_t CudaAllocator::set_cache_limit(size_t limit) {
  std::lock_guard lk(mutex_);
  std::swap(limit, max_pool_size_);
  return limit;
}

void CudaAllocator::clear_cache() {
  std::lock_guard lk(mutex_);
  buffer_cache_.clear();
}

CudaAllocator& allocator() {
  static auto* allocator_ = []() {
    // Ensure scheduler is created before allocator.
    scheduler::scheduler();
    // By creating the |allocator_| on heap, the destructor of CudaAllocator
    // will not be called on exit and buffers in the cache will be leaked. This
    // can save some time at program exit.
    return new CudaAllocator();
  }();
  return *allocator_;
}

Buffer malloc_async(size_t size, CommandEncoder& encoder) {
  auto buffer = allocator().malloc_async(
      size, encoder.device().cuda_device(), encoder.stream());
  if (size && !buffer.ptr()) {
    std::ostringstream msg;
    msg << "[malloc_async] Unable to allocate " << size << " bytes.";
    throw std::runtime_error(msg.str());
  }
  return buffer;
}

} // namespace cu

namespace allocator {

Allocator& allocator() {
  return cu::allocator();
}

void* Buffer::raw_ptr() {
  if (!ptr_) {
    return nullptr;
  }
  auto& cbuf = *static_cast<cu::CudaBuffer*>(ptr_);
  if (cbuf.device != -1) {
    copy_to_managed(cbuf);
  }
  return cbuf.data;
}

} // namespace allocator

size_t get_active_memory() {
  return cu::allocator().get_active_memory();
}
size_t get_peak_memory() {
  return cu::allocator().get_peak_memory();
}
void reset_peak_memory() {
  return cu::allocator().reset_peak_memory();
}
size_t set_memory_limit(size_t limit) {
  return cu::allocator().set_memory_limit(limit);
}
size_t get_memory_limit() {
  return cu::allocator().get_memory_limit();
}
size_t get_cache_memory() {
  return cu::allocator().get_cache_memory();
}
size_t set_cache_limit(size_t limit) {
  return cu::allocator().set_cache_limit(limit);
}
void clear_cache() {
  cu::allocator().clear_cache();
}

// Not supported in CUDA.
size_t set_wired_limit(size_t) {
  return 0;
}

} // namespace mlx::core
