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#pragma once

#include <functional>

#include <mutex>

#include <thread>

#include <unordered_map>

#include "message_queue.h"

#include "triton/backend/backend_common.h"

#include "triton/backend/backend_memory.h"

#include "triton/core/tritonserver.h"

#ifdef TRITON_ENABLE_GPU

#include <cuda_runtime_api.h>

#endif // TRITON_ENABLE_GPU

namespace triton { namespace backend { namespace python {

class MemoryRecord {

public:

virtual const std::function<void(void*)>& ReleaseCallback() = 0;

virtual void* MemoryId() = 0;

virtual ~MemoryRecord() = default;

};

#ifdef TRITON_ENABLE_GPU

class BackendMemoryRecord : public MemoryRecord {

public:

BackendMemoryRecord(std::unique_ptr<BackendMemory> backend_memory);

const std::function<void(void*)>& ReleaseCallback() override;

void* MemoryId() override;

~BackendMemoryRecord() { backend_memory_.reset(); }

private:

std::unique_ptr<BackendMemory> backend_memory_;

std::function<void(void*)> release_callback_;

};

#endif

/// Memory manager class is used primarily for managing the lifetime of GPU

/// tensors in BLS. It mainly consists of a background thread that monitors a

/// message queue in shared memory. Whenever a GPU tensor is created, it will

/// be pushed to the memory manager. The stub process must send a message to the

/// message queue asking the memory manager to deallocate the GPU tensor.

class MemoryManager {

public:

MemoryManager(std::unique_ptr<MessageQueue<intptr_t>>&& memory_message_queue);

intptr_t AddRecord(std::unique_ptr<MemoryRecord>&& memory_record);

TRITONSERVER_Error* ResetCounter();

~MemoryManager();

private:

std::thread thread_;

std::unordered_map<intptr_t, std::unique_ptr<MemoryRecord>> records_;

std::unique_ptr<MessageQueue<intptr_t>> message_queue_;

void QueueMonitorThread();

std::mutex mu_;

};

}}}; // namespace triton::backend::python