ipfrs_tensorlogic/

gpu.rs

//! GPU Execution Backend (Stub for Future Integration)
//!
//! This module provides a framework for GPU-accelerated computation graph execution.
//! Currently implements stubs that will be filled in when CUDA/OpenCL/Vulkan
//! integration is added.
//!
//! ## Future Integration Points
//!
//! - CUDA support via cuda-sys or cudarc
//! - OpenCL support via opencl3
//! - Vulkan compute support via vulkano or ash
//! - Metal support for Apple Silicon
//! - ROCm support for AMD GPUs

use std::collections::HashMap;
use thiserror::Error;

/// Errors that can occur during GPU operations
#[derive(Debug, Error)]
pub enum GpuError {
    #[error("GPU not available")]
    NotAvailable,

    #[error("Unsupported GPU operation: {0}")]
    UnsupportedOperation(String),

    #[error("GPU memory allocation failed: {0}")]
    AllocationFailed(String),

    #[error("Kernel compilation failed: {0}")]
    CompilationFailed(String),

    #[error("Kernel execution failed: {0}")]
    ExecutionFailed(String),

    #[error("Data transfer failed: {0}")]
    TransferFailed(String),
}

/// GPU device types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GpuBackend {
    /// NVIDIA CUDA
    Cuda,
    /// OpenCL (cross-platform)
    OpenCL,
    /// Vulkan Compute
    Vulkan,
    /// Apple Metal
    Metal,
    /// AMD ROCm
    Rocm,
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GpuDevice {
    /// Device ID
    pub id: usize,
    /// Device name
    pub name: String,
    /// Backend type
    pub backend: GpuBackend,
    /// Total memory in bytes
    pub total_memory: usize,
    /// Available memory in bytes
    pub available_memory: usize,
    /// Compute capability (backend-specific)
    pub compute_capability: String,
    /// Number of compute units
    pub compute_units: usize,
}

impl GpuDevice {
    /// Create a new GPU device descriptor
    pub fn new(id: usize, name: String, backend: GpuBackend) -> Self {
        Self {
            id,
            name,
            backend,
            total_memory: 0,
            available_memory: 0,
            compute_capability: "unknown".to_string(),
            compute_units: 0,
        }
    }

    /// Check if device has sufficient memory
    pub fn has_memory(&self, required: usize) -> bool {
        self.available_memory >= required
    }

    /// Get memory utilization as a percentage
    pub fn memory_utilization(&self) -> f32 {
        if self.total_memory == 0 {
            return 0.0;
        }
        let used = self.total_memory - self.available_memory;
        (used as f32 / self.total_memory as f32) * 100.0
    }
}

/// GPU buffer for storing tensor data
#[derive(Debug)]
pub struct GpuBuffer {
    /// Buffer ID
    #[allow(dead_code)]
    id: usize,
    /// Size in bytes
    size: usize,
    /// Backend-specific handle (opaque)
    #[allow(dead_code)]
    handle: Option<u64>,
}

impl GpuBuffer {
    /// Create a new GPU buffer (stub)
    pub fn new(size: usize) -> Result<Self, GpuError> {
        // Stub: Would allocate GPU memory here
        Ok(Self {
            id: 0,
            size,
            handle: None,
        })
    }

    /// Get buffer size
    pub fn size(&self) -> usize {
        self.size
    }

    /// Upload data to GPU (stub)
    pub fn upload(&mut self, _data: &[f32]) -> Result<(), GpuError> {
        // Stub: Would transfer data to GPU here
        Err(GpuError::NotAvailable)
    }

    /// Download data from GPU (stub)
    pub fn download(&self, _data: &mut [f32]) -> Result<(), GpuError> {
        // Stub: Would transfer data from GPU here
        Err(GpuError::NotAvailable)
    }
}

/// GPU kernel for executing operations
#[derive(Debug)]
pub struct GpuKernel {
    /// Kernel name
    name: String,
    /// Compiled kernel handle
    #[allow(dead_code)]
    handle: Option<u64>,
}

impl GpuKernel {
    /// Compile a kernel from source (stub)
    pub fn compile(_name: &str, _source: &str) -> Result<Self, GpuError> {
        // Stub: Would compile kernel source here
        Err(GpuError::NotAvailable)
    }

    /// Execute the kernel (stub)
    pub fn execute(
        &self,
        _inputs: &[&GpuBuffer],
        _outputs: &mut [&mut GpuBuffer],
        _workgroup_size: (usize, usize, usize),
    ) -> Result<(), GpuError> {
        // Stub: Would launch kernel here
        Err(GpuError::NotAvailable)
    }

    /// Get kernel name
    pub fn name(&self) -> &str {
        &self.name
    }
}

/// GPU executor for computation graphs
pub struct GpuExecutor {
    /// Selected device
    device: Option<GpuDevice>,
    /// Compiled kernels
    kernels: HashMap<String, GpuKernel>,
    /// Buffer pool
    buffers: Vec<GpuBuffer>,
}

impl GpuExecutor {
    /// Create a new GPU executor
    pub fn new() -> Self {
        Self {
            device: None,
            kernels: HashMap::new(),
            buffers: Vec::new(),
        }
    }

    /// Select a GPU device
    pub fn select_device(&mut self, device: GpuDevice) {
        self.device = Some(device);
    }

    /// List available GPU devices (stub)
    pub fn list_devices() -> Result<Vec<GpuDevice>, GpuError> {
        // Stub: Would enumerate GPUs here
        Ok(Vec::new())
    }

    /// Check if GPU is available
    pub fn is_available(&self) -> bool {
        self.device.is_some()
    }

    /// Get current device
    pub fn device(&self) -> Option<&GpuDevice> {
        self.device.as_ref()
    }

    /// Allocate a buffer on GPU
    pub fn allocate_buffer(&mut self, size: usize) -> Result<usize, GpuError> {
        let buffer = GpuBuffer::new(size)?;
        let id = self.buffers.len();
        self.buffers.push(buffer);
        Ok(id)
    }

    /// Free a buffer
    pub fn free_buffer(&mut self, id: usize) {
        if id < self.buffers.len() {
            // Stub: Would free GPU memory here
            self.buffers.remove(id);
        }
    }

    /// Compile and cache a kernel
    pub fn compile_kernel(&mut self, name: &str, source: &str) -> Result<(), GpuError> {
        let kernel = GpuKernel::compile(name, source)?;
        self.kernels.insert(name.to_string(), kernel);
        Ok(())
    }

    /// Execute a computation graph on GPU (stub)
    pub fn execute_graph(
        &mut self,
        _graph: &crate::ComputationGraph,
    ) -> Result<HashMap<String, Vec<f32>>, GpuError> {
        // Stub: Would execute graph on GPU here
        Err(GpuError::NotAvailable)
    }

    /// Get number of compiled kernels
    pub fn kernel_count(&self) -> usize {
        self.kernels.len()
    }

    /// Get number of allocated buffers
    pub fn buffer_count(&self) -> usize {
        self.buffers.len()
    }
}

impl Default for GpuExecutor {
    fn default() -> Self {
        Self::new()
    }
}

/// GPU memory manager for optimal allocation
pub struct GpuMemoryManager {
    /// Total memory budget
    total_memory: usize,
    /// Currently allocated memory
    allocated: usize,
    /// Allocation tracking
    allocations: HashMap<usize, usize>,
}

impl GpuMemoryManager {
    /// Create a new memory manager
    pub fn new(total_memory: usize) -> Self {
        Self {
            total_memory,
            allocated: 0,
            allocations: HashMap::new(),
        }
    }

    /// Allocate memory
    pub fn allocate(&mut self, size: usize) -> Result<usize, GpuError> {
        if self.allocated + size > self.total_memory {
            return Err(GpuError::AllocationFailed(
                "Insufficient GPU memory".to_string(),
            ));
        }

        let id = self.allocations.len();
        self.allocations.insert(id, size);
        self.allocated += size;
        Ok(id)
    }

    /// Free memory
    pub fn free(&mut self, id: usize) {
        if let Some(size) = self.allocations.remove(&id) {
            self.allocated -= size;
        }
    }

    /// Get available memory
    pub fn available(&self) -> usize {
        self.total_memory - self.allocated
    }

    /// Get memory utilization
    pub fn utilization(&self) -> f32 {
        (self.allocated as f32 / self.total_memory as f32) * 100.0
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gpu_device() {
        let device = GpuDevice::new(0, "Test GPU".to_string(), GpuBackend::Cuda);
        assert_eq!(device.id, 0);
        assert_eq!(device.name, "Test GPU");
        assert_eq!(device.backend, GpuBackend::Cuda);
    }

    #[test]
    fn test_gpu_device_memory() {
        let mut device = GpuDevice::new(0, "Test".to_string(), GpuBackend::Cuda);
        device.total_memory = 1000;
        device.available_memory = 600;

        assert!(device.has_memory(500));
        assert!(!device.has_memory(700));
        assert_eq!(device.memory_utilization(), 40.0);
    }

    #[test]
    fn test_gpu_executor() {
        let executor = GpuExecutor::new();
        assert!(!executor.is_available());
        assert_eq!(executor.kernel_count(), 0);
        assert_eq!(executor.buffer_count(), 0);
    }

    #[test]
    fn test_gpu_executor_with_device() {
        let mut executor = GpuExecutor::new();
        let device = GpuDevice::new(0, "Test".to_string(), GpuBackend::Cuda);

        executor.select_device(device);
        assert!(executor.is_available());
        assert_eq!(executor.device().unwrap().id, 0);
    }

    #[test]
    fn test_gpu_buffer_creation() {
        // This will fail since GPU is not actually available
        let result = GpuBuffer::new(1024);
        // In stub mode, buffer creation succeeds but operations fail
        assert!(result.is_ok());
    }

    #[test]
    fn test_memory_manager() {
        let mut manager = GpuMemoryManager::new(1000);

        let id1 = manager.allocate(400).unwrap();
        assert_eq!(manager.available(), 600);
        assert_eq!(manager.utilization(), 40.0);

        let id2 = manager.allocate(300).unwrap();
        assert_eq!(manager.available(), 300);

        // Should fail - not enough memory
        assert!(manager.allocate(400).is_err());

        manager.free(id1);
        assert_eq!(manager.available(), 700);

        manager.free(id2);
        assert_eq!(manager.available(), 1000);
    }

    #[test]
    fn test_list_devices() {
        // In stub mode, no devices are available
        let devices = GpuExecutor::list_devices().unwrap();
        assert_eq!(devices.len(), 0);
    }
}