// SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0

//! # CUDA Storage Support
//!
//! This module provides CUDA-specific storage implementations for the block manager.
//! It is conditionally compiled based on the `cuda` feature flag.
//!
//! ## Features
//!
//! The following types are available when the `cuda` feature is enabled:
//! - [`PinnedStorage`] - Page-locked host memory for efficient GPU transfers
//! - [`DeviceStorage`] - Direct GPU memory allocation
//!
//! ## Storage Allocators
//!
//! The module provides allocators for each storage type:
//! - [`PinnedAllocator`] - Creates pinned host memory allocations
//! - [`DeviceAllocator`] - Creates device memory allocations
//!
//! ## CUDA Context Management
//!
//! The module provides a singleton [`Cuda`] type for managing CUDA contexts:
//! - Thread-safe context management
//! - Lazy initialization of device contexts
//! - Automatic cleanup of resources
//!
//! ## Usage
//!
//! ### Using Allocators
//! ```rust,ignore
//! use dynamo_llm::block_manager::storage::{DeviceAllocator, PinnedAllocator, StorageAllocator};
//!
//! // Create a pinned memory allocator
//! let pinned_allocator = PinnedAllocator::default();
//! let pinned_storage = pinned_allocator.allocate(1024).unwrap();
//!
//! // Create a device memory allocator for a specific device
//! let device_allocator = DeviceAllocator::new(1).unwrap();  // Use device 1
//! let device_storage = device_allocator.allocate(1024).unwrap();
//! ```
//!
//! ### Memory Operations
//! ```rust,ignore
//! use dynamo_llm::block_manager::storage::{
//!     PinnedAllocator, StorageAllocator, Storage, StorageMemset
//! };
//!
//! // Initialize memory
//! let mut storage = PinnedAllocator::default().allocate(1024).unwrap();
//!
//! // Initialize memory
//! storage.memset(0, 0, 1024).unwrap();
//!
//! // Access memory through raw pointers (requires unsafe)
//! unsafe {
//!     let ptr = storage.as_mut_ptr();
//!     // Use the pointer...
//! }
//! ```
//!
//! ## Safety
//!
//! All CUDA operations are wrapped in safe Rust interfaces that ensure:
//! - Proper resource cleanup
//! - Thread safety
//! - Memory alignment requirements
//! - Error handling for CUDA operations

use super::*;

use std::{
    collections::HashMap,
    sync::{Arc, Mutex, OnceLock},
};

use cudarc::driver::{CudaContext, sys};

use crate::block_manager::numa_allocator;

/// Allocates pinned host memory, preferring write-combined if supported.
///
/// Write-combined (WC) memory is optimal for PCIe DMA transfers but may not be
/// supported on systems with cache-coherent CPU-GPU interconnects (e.g., Grace
/// Hopper/Blackwell with NVLink-C2C). This function tries WC first and falls
/// back to regular pinned memory if not supported.
///
/// # Safety
///
/// Caller must ensure a valid CUDA context is bound to the current thread.
unsafe fn malloc_host_prefer_writecombined(size: usize) -> Result<*mut u8, StorageError> {
    // First, try write-combined allocation (optimal for PCIe systems)
    // SAFETY: Caller guarantees a valid CUDA context is bound to the current thread
    match unsafe { cudarc::driver::result::malloc_host(size, sys::CU_MEMHOSTALLOC_WRITECOMBINED) } {
        Ok(ptr) => Ok(ptr as *mut u8),
        Err(_) => {
            // Write-combined not supported (e.g., Grace Hopper/Blackwell),
            // fall back to regular pinned memory
            tracing::debug!("Write-combined memory not supported, using regular pinned memory");
            // SAFETY: Same as above - caller guarantees valid CUDA context
            unsafe { cudarc::driver::result::malloc_host(size, 0) }
                .map(|ptr| ptr as *mut u8)
                .map_err(StorageError::Cuda)
        }
    }
}

/// Trait for [Storage] types that can be accessed by CUDA
pub trait CudaAccessible: Storage {}

/// Trait for types that can provide a CUDA context.
pub trait CudaContextProivder {
    /// Get a referene to the [`CudaContext`].
    fn cuda_context(&self) -> &Arc<CudaContext>;
}

/// Singleton for managing CUDA contexts.
pub struct Cuda {
    contexts: HashMap<usize, Arc<CudaContext>>,
}

impl Cuda {
    // Private constructor
    fn new() -> Self {
        Self {
            contexts: HashMap::new(),
        }
    }

    /// Get a CUDA context for a specific device_id.
    /// If the context does not exist, it will return None.
    ///
    /// This will not lazily instantiate a context for a device. Use
    /// [Cuda::device_or_create]
    pub fn device(device_id: usize) -> Option<Arc<CudaContext>> {
        Cuda::instance()
            .lock()
            .unwrap()
            .get_existing_context(device_id)
    }

    /// Get or initialize a CUDA context for a specific device_id.
    /// If the context does not exist, it will be created or fail.
    ///
    /// This will lazily instantiate a context for a device. Use
    /// [CudaContextManager::device] to get an existing context.
    pub fn device_or_create(device_id: usize) -> Result<Arc<CudaContext>, StorageError> {
        Cuda::instance().lock().unwrap().get_context(device_id)
    }

    /// Check if a CUDA context exists for a specific device_id.
    pub fn is_initialized(device_id: usize) -> bool {
        Cuda::instance().lock().unwrap().has_context(device_id)
    }

    // Get the singleton instance
    fn instance() -> &'static Mutex<Cuda> {
        static INSTANCE: OnceLock<Mutex<Cuda>> = OnceLock::new();
        INSTANCE.get_or_init(|| Mutex::new(Cuda::new()))
    }

    // Get or create a CUDA context for a specific device
    fn get_context(&mut self, device_id: usize) -> Result<Arc<CudaContext>, StorageError> {
        // Check if we already have a context for this device
        if let Some(ctx) = self.contexts.get(&device_id) {
            return Ok(ctx.clone());
        }

        // Create a new context for this device
        let ctx = CudaContext::new(device_id)?;

        // Store the context
        self.contexts.insert(device_id, ctx.clone());

        Ok(ctx)
    }

    // Get a context if it exists, but don't create one
    pub fn get_existing_context(&self, device_id: usize) -> Option<Arc<CudaContext>> {
        self.contexts.get(&device_id).cloned()
    }

    // Check if a context exists for a device
    pub fn has_context(&self, device_id: usize) -> bool {
        self.contexts.contains_key(&device_id)
    }
}

/// Pinned host memory storage using CUDA page-locked memory
#[derive(Debug)]
pub struct PinnedStorage {
    ptr: u64,
    size: usize,
    handles: RegistrationHandles,
    ctx: Arc<CudaContext>,
}

impl Local for PinnedStorage {}
impl SystemAccessible for PinnedStorage {}
impl CudaAccessible for PinnedStorage {}

impl PinnedStorage {
    /// Create a new pinned storage with the given size
    pub fn new(ctx: &Arc<CudaContext>, size: usize) -> Result<Self, StorageError> {
        unsafe {
            ctx.bind_to_thread().map_err(StorageError::Cuda)?;

            // Try NUMA-aware allocation if enabled, otherwise use direct allocation
            let ptr = if numa_allocator::is_numa_enabled() {
                let device_id = ctx.cu_device() as u32;
                match numa_allocator::worker_pool::NumaWorkerPool::global()
                    .allocate_pinned_for_gpu(size, device_id)
                {
                    Ok(ptr) => ptr,
                    Err(e) => {
                        tracing::warn!("NUMA allocation failed: {}, using direct allocation", e);
                        malloc_host_prefer_writecombined(size)?
                    }
                }
            } else {
                malloc_host_prefer_writecombined(size)?
            };

            assert!(!ptr.is_null(), "Failed to allocate pinned memory");
            assert!(ptr.is_aligned(), "Pinned memory is not aligned");
            assert!(size < isize::MAX as usize);

            let ptr = ptr as u64;
            Ok(Self {
                ptr,
                size,
                handles: RegistrationHandles::new(),
                ctx: ctx.clone(),
            })
        }
    }
}

impl Drop for PinnedStorage {
    fn drop(&mut self) {
        self.handles.release();
        unsafe {
            if let Err(e) = cudarc::driver::result::free_host(self.ptr as _) {
                tracing::error!(
                    "Failed to free pinned storage at 0x{:x} (size={}): {}",
                    self.ptr,
                    self.size,
                    e
                );
            }
        }
    }
}

impl Storage for PinnedStorage {
    fn storage_type(&self) -> StorageType {
        StorageType::Pinned
    }

    fn addr(&self) -> u64 {
        self.ptr
    }

    fn size(&self) -> usize {
        self.size
    }

    unsafe fn as_ptr(&self) -> *const u8 {
        self.ptr as *const u8
    }

    unsafe fn as_mut_ptr(&mut self) -> *mut u8 {
        self.ptr as *mut u8
    }
}

impl CudaContextProivder for PinnedStorage {
    fn cuda_context(&self) -> &Arc<CudaContext> {
        &self.ctx
    }
}

impl RegisterableStorage for PinnedStorage {
    fn register(
        &mut self,
        key: &str,
        handle: Box<dyn RegistationHandle>,
    ) -> Result<(), StorageError> {
        self.handles.register(key, handle)
    }

    fn is_registered(&self, key: &str) -> bool {
        self.handles.is_registered(key)
    }

    fn registration_handle(&self, key: &str) -> Option<&dyn RegistationHandle> {
        self.handles.registration_handle(key)
    }
}

impl StorageMemset for PinnedStorage {
    fn memset(&mut self, value: u8, offset: usize, size: usize) -> Result<(), StorageError> {
        if offset + size > self.size {
            return Err(StorageError::OperationFailed(
                "memset: offset + size > storage size".into(),
            ));
        }
        unsafe {
            let ptr = (self.ptr as *mut u8).add(offset);
            std::ptr::write_bytes(ptr, value, size);
        }
        Ok(())
    }
}

/// Allocator for PinnedStorage
pub struct PinnedAllocator {
    ctx: Arc<CudaContext>,
}

impl Default for PinnedAllocator {
    fn default() -> Self {
        Self {
            ctx: Cuda::device_or_create(0).expect("Failed to create CUDA context"),
        }
    }
}

impl PinnedAllocator {
    /// Create a new pinned allocator
    pub fn new() -> Result<Self, StorageError> {
        Ok(Self {
            ctx: Cuda::device_or_create(0)?,
        })
    }
}

impl StorageAllocator<PinnedStorage> for PinnedAllocator {
    fn allocate(&self, size: usize) -> Result<PinnedStorage, StorageError> {
        PinnedStorage::new(&self.ctx, size)
    }
}

/// An enum indicating the type of device storage.
/// This is needed to ensure ownership of memory is correctly handled.
/// When building a [`DeviceStorage`] from a torch tensor, we need to ensure that
/// the torch tensor is not GCed until the [`DeviceStorage`] is dropped.
/// Because of this, we need to store a reference to the torch tensor in the [`DeviceStorage`]
#[derive(Debug)]
enum DeviceStorageType {
    Owned,                                   // Memory that we allocated ourselves.
    Torch { _tensor: Arc<dyn TorchTensor> }, // Memory that came from a torch tensor.
}

/// CUDA device memory storage
#[derive(Debug)]
pub struct DeviceStorage {
    ptr: u64,
    size: usize,
    ctx: Arc<CudaContext>,
    handles: RegistrationHandles,
    _storage_type: DeviceStorageType,
}

impl Local for DeviceStorage {}
impl CudaAccessible for DeviceStorage {}

impl DeviceStorage {
    /// Create a new device storage with the given size
    pub fn new(ctx: &Arc<CudaContext>, size: usize) -> Result<Self, StorageError> {
        ctx.bind_to_thread().map_err(StorageError::Cuda)?;
        let ptr = unsafe { cudarc::driver::result::malloc_sync(size).map_err(StorageError::Cuda)? };

        Ok(Self {
            ptr,
            size,
            ctx: ctx.clone(),
            handles: RegistrationHandles::new(),
            _storage_type: DeviceStorageType::Owned,
        })
    }

    pub fn new_from_torch(
        ctx: &Arc<CudaContext>,
        tensor: Arc<dyn TorchTensor>,
    ) -> Result<Self, StorageError> {
        let device = tensor.device();

        let TorchDevice::Cuda(device_id) = device else {
            return Err(StorageError::InvalidConfig("Tensor is not CUDA!".into()));
        };

        if device_id != ctx.cu_device() as usize {
            return Err(StorageError::InvalidConfig(
                "Tensor is not on the same device as the context!".into(),
            ));
        }

        let data_ptr = tensor.data_ptr();
        let size = tensor.size_bytes();

        Ok(Self {
            ptr: data_ptr,
            size,
            ctx: ctx.clone(),
            handles: RegistrationHandles::new(),
            _storage_type: DeviceStorageType::Torch { _tensor: tensor },
        })
    }

    /// Get the CUDA context
    pub fn context(&self) -> &Arc<CudaContext> {
        &self.ctx
    }
}

impl Storage for DeviceStorage {
    fn storage_type(&self) -> StorageType {
        StorageType::Device(self.ctx.cu_device() as u32)
    }

    fn addr(&self) -> u64 {
        self.ptr
    }

    fn size(&self) -> usize {
        self.size
    }

    unsafe fn as_ptr(&self) -> *const u8 {
        self.ptr as *const u8
    }

    unsafe fn as_mut_ptr(&mut self) -> *mut u8 {
        self.ptr as *mut u8
    }
}

impl CudaContextProivder for DeviceStorage {
    fn cuda_context(&self) -> &Arc<CudaContext> {
        &self.ctx
    }
}

impl Drop for DeviceStorage {
    fn drop(&mut self) {
        self.handles.release();
        match &self._storage_type {
            DeviceStorageType::Owned => {
                unsafe { cudarc::driver::result::free_sync(self.ptr as _) }.unwrap()
            }
            DeviceStorageType::Torch { _tensor } => {
                // Do nothing. The torch storage is resposible for cleaning up itself.
            }
        }
    }
}

impl RegisterableStorage for DeviceStorage {
    fn register(
        &mut self,
        key: &str,
        handle: Box<dyn RegistationHandle>,
    ) -> Result<(), StorageError> {
        self.handles.register(key, handle)
    }

    fn is_registered(&self, key: &str) -> bool {
        self.handles.is_registered(key)
    }

    fn registration_handle(&self, key: &str) -> Option<&dyn RegistationHandle> {
        self.handles.registration_handle(key)
    }
}

/// Allocator for DeviceStorage
pub struct DeviceAllocator {
    ctx: Arc<CudaContext>,
}

impl Default for DeviceAllocator {
    fn default() -> Self {
        Self {
            ctx: CudaContext::new(0).expect("Failed to create CUDA context"),
        }
    }
}

impl DeviceAllocator {
    /// Create a new device allocator
    pub fn new(device_id: usize) -> Result<Self, StorageError> {
        Ok(Self {
            ctx: Cuda::device_or_create(device_id)?,
        })
    }

    pub fn ctx(&self) -> &Arc<CudaContext> {
        &self.ctx
    }
}

impl StorageAllocator<DeviceStorage> for DeviceAllocator {
    fn allocate(&self, size: usize) -> Result<DeviceStorage, StorageError> {
        DeviceStorage::new(&self.ctx, size)
    }
}

#[cfg(all(test, feature = "testing-cuda"))]
mod tests {
    use super::*;

    #[derive(Debug, Clone)]
    struct MockTensor {
        device: TorchDevice,
        data_ptr: u64,
        size_bytes: usize,
    }

    impl MockTensor {
        pub fn new(device: TorchDevice, data_ptr: u64, size_bytes: usize) -> Self {
            Self {
                device,
                data_ptr,
                size_bytes,
            }
        }
    }

    impl TorchTensor for MockTensor {
        fn device(&self) -> TorchDevice {
            self.device.clone()
        }

        fn data_ptr(&self) -> u64 {
            self.data_ptr
        }

        fn size_bytes(&self) -> usize {
            self.size_bytes
        }

        fn shape(&self) -> Vec<usize> {
            vec![self.size_bytes]
        }

        fn stride(&self) -> Vec<usize> {
            vec![1]
        }
    }

    #[test]
    fn test_device_storage_from_torch_valid_tensor() {
        let ctx = Cuda::device_or_create(0).expect("Failed to create CUDA context");
        let size_bytes = 1024;

        let actual_storage =
            std::mem::ManuallyDrop::new(DeviceStorage::new(&ctx, size_bytes).unwrap());

        let tensor = MockTensor::new(TorchDevice::Cuda(0), actual_storage.addr(), size_bytes);

        let storage = DeviceStorage::new_from_torch(&ctx, Arc::new(tensor)).unwrap();

        assert_eq!(storage.size(), size_bytes);
        assert_eq!(storage.storage_type(), StorageType::Device(0));
        assert_eq!(storage.addr(), actual_storage.addr());
    }

    #[test]
    fn test_device_storage_from_torch_cpu_tensor_fails() {
        let ctx = Cuda::device_or_create(0).expect("Failed to create CUDA context");
        let size_bytes = 1024;

        let actual_storage = DeviceStorage::new(&ctx, size_bytes).unwrap();

        let tensor = MockTensor::new(
            TorchDevice::Other("cpu".to_string()),
            actual_storage.addr(),
            size_bytes,
        );

        let result = DeviceStorage::new_from_torch(&ctx, Arc::new(tensor));
        assert!(result.is_err());

        if let Err(StorageError::InvalidConfig(msg)) = result {
            assert!(msg.contains("Tensor is not CUDA"));
        } else {
            panic!("Expected InvalidConfig error for CPU tensor");
        }
    }

    #[test]
    fn test_device_storage_wrong_device() {
        let ctx = Cuda::device_or_create(0).expect("Failed to create CUDA context");
        let size_bytes = 1024;

        let actual_storage = DeviceStorage::new(&ctx, size_bytes).unwrap();

        let tensor = MockTensor::new(TorchDevice::Cuda(1), actual_storage.addr(), size_bytes);

        let result = DeviceStorage::new_from_torch(&ctx, Arc::new(tensor));
        assert!(result.is_err());
    }

    #[test]
    fn test_malloc_host_prefer_writecombined_allocates_memory() {
        let ctx = Cuda::device_or_create(0).expect("Failed to create CUDA context");
        let size = 4096; // One page

        unsafe {
            ctx.bind_to_thread().expect("Failed to bind CUDA context");

            // Test allocation succeeds (either write-combined or fallback)
            let ptr = malloc_host_prefer_writecombined(size)
                .expect("malloc_host_prefer_writecombined should succeed");

            // Verify pointer is valid and non-null
            assert!(!ptr.is_null(), "Allocated pointer should not be null");

            // Verify memory is accessible by writing and reading
            std::ptr::write_volatile(ptr, 0xAB);
            let val = std::ptr::read_volatile(ptr);
            assert_eq!(val, 0xAB, "Should be able to write and read pinned memory");

            // Clean up
            cudarc::driver::result::free_host(ptr as _).expect("Failed to free pinned memory");
        }
    }

    /// Test PinnedStorage::new with NUMA disabled (the direct allocation path)
    ///
    /// This test confirms that when `DYN_KVBM_ENABLE_NUMA` is not set,
    /// PinnedStorage::new uses the direct malloc_host_prefer_writecombined path
    /// (lines 222-224 in the source).
    #[test]
    // `remove_var` is not thread-safe, so we need to run this test in a serial context
    // #[serial]
    fn test_pinned_storage_new_without_numa() {
        // Verify NUMA is actually disabled for this test
        assert!(
            !numa_allocator::is_numa_enabled(),
            "NUMA should be disabled for this test"
        );

        let ctx = Cuda::device_or_create(0).expect("Failed to create CUDA context");
        let size = 8192;

        // Create PinnedStorage - this should take the non-NUMA path
        let mut storage =
            PinnedStorage::new(&ctx, size).expect("PinnedStorage::new should succeed");

        // Verify storage properties
        assert_eq!(storage.size(), size);
        assert_eq!(storage.storage_type(), StorageType::Pinned);
        assert_ne!(storage.addr(), 0, "Address should be non-zero");

        // Verify memory is accessible
        unsafe {
            let ptr = storage.as_mut_ptr();
            assert!(!ptr.is_null(), "Pointer should not be null");

            // Write a pattern to verify memory is usable
            for i in 0..size {
                std::ptr::write_volatile(ptr.add(i), (i & 0xFF) as u8);
            }

            // Read back and verify
            for i in 0..size {
                let val = std::ptr::read_volatile(ptr.add(i));
                assert_eq!(
                    val,
                    (i & 0xFF) as u8,
                    "Memory content mismatch at offset {}",
                    i
                );
            }
        }
    }
}
