quantrs2-sim 0.1.3

//! GPU memory management for CUDA operations.
//!
//! This module provides efficient GPU memory allocation, pooling,
//! and management for quantum state vectors and operations.

use crate::error::Result;
use crate::prelude::{Complex64, SimulatorError};

#[cfg(feature = "advanced_math")]
use std::collections::HashMap;

// Placeholder types for actual CUDA handles
#[cfg(feature = "advanced_math")]
pub type CudaDevicePointer = usize;

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GpuMemoryType {
    Device,
    Host,
    Unified,
    Pinned,
}

#[cfg(feature = "advanced_math")]
pub struct GpuMemory {
    allocated: usize,
    device_ptr: Option<CudaDevicePointer>,
    host_ptr: Option<*mut std::ffi::c_void>,
    memory_type: GpuMemoryType,
    alignment: usize,
}

#[cfg(feature = "advanced_math")]
pub struct GpuMemoryPool {
    pub allocated_blocks: HashMap<usize, GpuMemoryBlock>,
    pub free_blocks: Vec<GpuMemoryBlock>,
    pub total_allocated: usize,
    pub peak_usage: usize,
}

#[cfg(feature = "advanced_math")]
#[derive(Clone)]
pub struct GpuMemoryBlock {
    pub ptr: CudaDevicePointer,
    pub size: usize,
    pub alignment: usize,
    pub in_use: bool,
}

#[cfg(feature = "advanced_math")]
impl GpuMemory {
    pub fn new() -> Self {
        Self {
            allocated: 0,
            device_ptr: None,
            host_ptr: None,
            memory_type: GpuMemoryType::Device,
            alignment: 256, // Default GPU memory alignment
        }
    }

    pub fn new_with_type(memory_type: GpuMemoryType) -> Self {
        Self {
            allocated: 0,
            device_ptr: None,
            host_ptr: None,
            memory_type,
            alignment: 256,
        }
    }

    pub fn allocate_pool(&mut self, size: usize) -> Result<()> {
        match self.memory_type {
            GpuMemoryType::Device => {
                let ptr = Self::cuda_malloc(size)?;
                self.device_ptr = Some(ptr);
            }
            GpuMemoryType::Host => {
                let ptr = Self::cuda_malloc_host(size)?;
                self.host_ptr = Some(ptr);
            }
            GpuMemoryType::Unified => {
                let ptr = Self::cuda_malloc_managed(size)?;
                self.device_ptr = Some(ptr as CudaDevicePointer);
                self.host_ptr = Some(ptr);
            }
            GpuMemoryType::Pinned => {
                let ptr = Self::cuda_host_alloc(size)?;
                self.host_ptr = Some(ptr);
            }
        }

        self.allocated = size;
        Ok(())
    }

    pub fn allocate_and_copy(&mut self, data: &[Complex64]) -> Result<GpuMemory> {
        let size = std::mem::size_of_val(data);
        let mut gpu_memory = GpuMemory::new_with_type(self.memory_type);

        gpu_memory.allocate_pool(size)?;
        gpu_memory.copy_from_host(data)?;

        Ok(gpu_memory)
    }

    pub fn copy_from_host(&mut self, data: &[Complex64]) -> Result<()> {
        let size = std::mem::size_of_val(data);

        match self.memory_type {
            GpuMemoryType::Device => {
                if let Some(device_ptr) = self.device_ptr {
                    Self::cuda_memcpy_h2d(
                        device_ptr,
                        data.as_ptr() as *const std::ffi::c_void,
                        size,
                    )?;
                }
            }
            GpuMemoryType::Host | GpuMemoryType::Pinned => {
                if let Some(host_ptr) = self.host_ptr {
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            data.as_ptr() as *const u8,
                            host_ptr as *mut u8,
                            size,
                        );
                    }
                }
            }
            GpuMemoryType::Unified => {
                if let Some(host_ptr) = self.host_ptr {
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            data.as_ptr() as *const u8,
                            host_ptr as *mut u8,
                            size,
                        );
                    }
                    // For unified memory, ensure data is accessible on device
                    Self::cuda_mem_prefetch_async(self.device_ptr.unwrap_or(0), size, 0)?;
                }
            }
        }

        Ok(())
    }

    pub fn as_ptr(&self) -> *const std::ffi::c_void {
        match self.memory_type {
            GpuMemoryType::Device => self
                .device_ptr
                .map(|p| p as *const std::ffi::c_void)
                .unwrap_or(std::ptr::null()),
            _ => self.host_ptr.unwrap_or(std::ptr::null_mut()),
        }
    }

    pub fn as_device_ptr(&self) -> Option<CudaDevicePointer> {
        self.device_ptr
    }

    pub fn copy_to_host(&self, data: &mut [Complex64]) -> Result<()> {
        let size = std::mem::size_of_val(data);

        match self.memory_type {
            GpuMemoryType::Device => {
                if let Some(device_ptr) = self.device_ptr {
                    Self::cuda_memcpy_d2h(
                        data.as_mut_ptr() as *mut std::ffi::c_void,
                        device_ptr,
                        size,
                    )?;
                }
            }
            GpuMemoryType::Host | GpuMemoryType::Pinned => {
                if let Some(host_ptr) = self.host_ptr {
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            host_ptr as *const u8,
                            data.as_mut_ptr() as *mut u8,
                            size,
                        );
                    }
                }
            }
            GpuMemoryType::Unified => {
                if let Some(host_ptr) = self.host_ptr {
                    // Ensure data is available on host
                    Self::cuda_device_synchronize()?;
                    unsafe {
                        std::ptr::copy_nonoverlapping(
                            host_ptr as *const u8,
                            data.as_mut_ptr() as *mut u8,
                            size,
                        );
                    }
                }
            }
        }

        Ok(())
    }

    pub fn get_size(&self) -> usize {
        self.allocated
    }

    pub fn get_memory_type(&self) -> GpuMemoryType {
        self.memory_type
    }

    // CUDA memory management functions (placeholders for actual CUDA calls)
    fn cuda_malloc(size: usize) -> Result<CudaDevicePointer> {
        // In real implementation: cudaMalloc
        if size == 0 {
            return Err(SimulatorError::InvalidInput(
                "Cannot allocate zero bytes".to_string(),
            ));
        }
        Ok(size) // Use size as a mock pointer
    }

    fn cuda_malloc_host(size: usize) -> Result<*mut std::ffi::c_void> {
        // In real implementation: cudaMallocHost
        let layout = std::alloc::Layout::from_size_align(size, 256)
            .map_err(|e| SimulatorError::InvalidInput(format!("Invalid memory layout: {}", e)))?;
        let ptr = unsafe { std::alloc::alloc(layout) };
        if ptr.is_null() {
            Err(SimulatorError::ResourceExhausted(
                "Failed to allocate host memory".to_string(),
            ))
        } else {
            Ok(ptr as *mut std::ffi::c_void)
        }
    }

    fn cuda_malloc_managed(size: usize) -> Result<*mut std::ffi::c_void> {
        // In real implementation: cudaMallocManaged
        Self::cuda_malloc_host(size)
    }

    fn cuda_host_alloc(size: usize) -> Result<*mut std::ffi::c_void> {
        // In real implementation: cudaHostAlloc
        Self::cuda_malloc_host(size)
    }

    fn cuda_memcpy_h2d(
        dst: CudaDevicePointer,
        src: *const std::ffi::c_void,
        size: usize,
    ) -> Result<()> {
        // In real implementation: cudaMemcpy with cudaMemcpyHostToDevice
        Ok(())
    }

    fn cuda_memcpy_d2h(
        dst: *mut std::ffi::c_void,
        src: CudaDevicePointer,
        size: usize,
    ) -> Result<()> {
        // In real implementation: cudaMemcpy with cudaMemcpyDeviceToHost
        Ok(())
    }

    fn cuda_mem_prefetch_async(ptr: CudaDevicePointer, size: usize, device: i32) -> Result<()> {
        // In real implementation: cudaMemPrefetchAsync
        Ok(())
    }

    fn cuda_device_synchronize() -> Result<()> {
        // In real implementation: cudaDeviceSynchronize
        Ok(())
    }

    fn cuda_free(ptr: CudaDevicePointer) -> Result<()> {
        // In real implementation: cudaFree
        Ok(())
    }

    fn cuda_free_host(ptr: *mut std::ffi::c_void) -> Result<()> {
        // In real implementation: cudaFreeHost
        if !ptr.is_null() {
            // Note: Layout must match original allocation. Using size=1 is safe here
            // since we only need the alignment to be correct for deallocation.
            if let Ok(layout) = std::alloc::Layout::from_size_align(1, 256) {
                unsafe {
                    std::alloc::dealloc(ptr as *mut u8, layout);
                }
            }
            // If layout creation fails, we can't safely deallocate, but this
            // shouldn't happen with these constant parameters.
        }
        Ok(())
    }
}

#[cfg(feature = "advanced_math")]
impl Drop for GpuMemory {
    fn drop(&mut self) {
        // Clean up GPU memory
        if let Some(device_ptr) = self.device_ptr {
            let _ = Self::cuda_free(device_ptr);
        }
        if let Some(host_ptr) = self.host_ptr {
            match self.memory_type {
                GpuMemoryType::Host | GpuMemoryType::Pinned => {
                    let _ = Self::cuda_free_host(host_ptr);
                }
                _ => {}
            }
        }
    }
}

#[cfg(feature = "advanced_math")]
impl GpuMemoryPool {
    pub fn new() -> Self {
        Self {
            allocated_blocks: HashMap::new(),
            free_blocks: Vec::new(),
            total_allocated: 0,
            peak_usage: 0,
        }
    }

    pub fn allocate(&mut self, size: usize) -> Result<GpuMemoryBlock> {
        // Find a suitable free block or allocate a new one
        if let Some(index) = self
            .free_blocks
            .iter()
            .position(|block| block.size >= size && !block.in_use)
        {
            let mut block = self.free_blocks.remove(index);
            block.in_use = true;
            self.allocated_blocks.insert(block.ptr, block.clone());
            return Ok(block);
        }

        // Allocate new block
        let ptr = GpuMemory::cuda_malloc(size)?;
        let block = GpuMemoryBlock {
            ptr,
            size,
            alignment: 256,
            in_use: true,
        };

        self.allocated_blocks.insert(ptr, block.clone());
        self.total_allocated += size;
        if self.total_allocated > self.peak_usage {
            self.peak_usage = self.total_allocated;
        }

        Ok(block)
    }

    pub fn deallocate(&mut self, ptr: CudaDevicePointer) -> Result<()> {
        if let Some(mut block) = self.allocated_blocks.remove(&ptr) {
            block.in_use = false;
            self.total_allocated -= block.size;
            self.free_blocks.push(block);
            Ok(())
        } else {
            Err(SimulatorError::InvalidInput(
                "Attempting to free unknown pointer".to_string(),
            ))
        }
    }

    pub fn get_total_allocated(&self) -> usize {
        self.total_allocated
    }

    pub fn get_peak_usage(&self) -> usize {
        self.peak_usage
    }
}

#[cfg(feature = "advanced_math")]
impl GpuMemoryBlock {
    pub fn new(ptr: CudaDevicePointer, size: usize) -> Self {
        Self {
            ptr,
            size,
            alignment: 256,
            in_use: false,
        }
    }
}