ghostflow_ml/

gpu.rs

//! GPU Acceleration - CUDA/OpenCL Support
//!
//! This module provides GPU acceleration utilities for tensor operations.
//! Note: This is a foundational implementation. Full GPU support requires
//! external libraries like wgpu, vulkan, or CUDA bindings.

use ghostflow_core::Tensor;
use std::sync::{Arc, Mutex};

/// GPU device type
#[derive(Clone, Copy, Debug, PartialEq)]
pub enum DeviceType {
    CPU,
    CUDA,
    OpenCL,
    Metal,
    Vulkan,
}

/// GPU device information
#[derive(Clone, Debug)]
pub struct DeviceInfo {
    pub device_type: DeviceType,
    pub device_id: usize,
    pub name: String,
    pub compute_capability: (u32, u32),
    pub total_memory: usize,
    pub available_memory: usize,
}

impl DeviceInfo {
    pub fn cpu() -> Self {
        DeviceInfo {
            device_type: DeviceType::CPU,
            device_id: 0,
            name: "CPU".to_string(),
            compute_capability: (0, 0),
            total_memory: 0,
            available_memory: 0,
        }
    }

    pub fn cuda(device_id: usize) -> Self {
        DeviceInfo {
            device_type: DeviceType::CUDA,
            device_id,
            name: format!("CUDA Device {}", device_id),
            compute_capability: (7, 5), // Example: RTX 2080
            total_memory: 8 * 1024 * 1024 * 1024, // 8GB
            available_memory: 7 * 1024 * 1024 * 1024,
        }
    }
}

/// GPU context manager
pub struct GPUContext {
    device: DeviceInfo,
    #[allow(dead_code)]
    stream: Option<usize>,
    #[allow(dead_code)]
    memory_pool: Arc<Mutex<Vec<Vec<f32>>>>,
}

impl GPUContext {
    pub fn new(device: DeviceInfo) -> Self {
        GPUContext {
            device,
            stream: None,
            memory_pool: Arc::new(Mutex::new(Vec::new())),
        }
    }

    pub fn cpu() -> Self {
        Self::new(DeviceInfo::cpu())
    }

    pub fn cuda(device_id: usize) -> Result<Self, String> {
        // In a real implementation, this would initialize CUDA
        if Self::is_cuda_available() {
            Ok(Self::new(DeviceInfo::cuda(device_id)))
        } else {
            Err("CUDA not available".to_string())
        }
    }

    pub fn is_cuda_available() -> bool {
        // Placeholder - would check for CUDA runtime
        false
    }

    pub fn is_opencl_available() -> bool {
        // Placeholder - would check for OpenCL runtime
        false
    }

    pub fn device_count() -> usize {
        // Placeholder - would query available devices
        if Self::is_cuda_available() { 1 } else { 0 }
    }

    pub fn device_info(&self) -> &DeviceInfo {
        &self.device
    }

    pub fn synchronize(&self) {
        // Placeholder - would synchronize GPU operations
    }

    pub fn allocate(&self, size: usize) -> Vec<f32> {
        // Simplified memory allocation
        vec![0.0f32; size]
    }

    pub fn deallocate(&self, _buffer: Vec<f32>) {
        // Placeholder - would free GPU memory
    }
}

/// GPU tensor wrapper
pub struct GPUTensor {
    data: Vec<f32>,
    dims: Vec<usize>,
    device: DeviceType,
    context: Arc<GPUContext>,
}

impl GPUTensor {
    pub fn new(data: Vec<f32>, dims: Vec<usize>, context: Arc<GPUContext>) -> Self {
        GPUTensor {
            data,
            dims,
            device: context.device.device_type,
            context,
        }
    }

    pub fn from_tensor(tensor: &Tensor, context: Arc<GPUContext>) -> Self {
        GPUTensor::new(
            tensor.data_f32().to_vec(),
            tensor.dims().to_vec(),
            context,
        )
    }

    pub fn to_tensor(&self) -> Tensor {
        Tensor::from_slice(&self.data, &self.dims).unwrap()
    }

    pub fn to_device(&mut self, device: DeviceType) {
        if self.device == device {
            return;
        }

        // Placeholder - would transfer data between devices
        self.device = device;
    }

    pub fn dims(&self) -> &[usize] {
        &self.dims
    }

    pub fn device(&self) -> DeviceType {
        self.device
    }

    /// Matrix multiplication on GPU
    pub fn matmul(&self, other: &GPUTensor) -> GPUTensor {
        assert_eq!(self.dims.len(), 2);
        assert_eq!(other.dims.len(), 2);
        assert_eq!(self.dims[1], other.dims[0]);

        let m = self.dims[0];
        let k = self.dims[1];
        let n = other.dims[1];

        let mut result = vec![0.0f32; m * n];

        // CPU fallback (GPU implementation would use cuBLAS or similar)
        for i in 0..m {
            for j in 0..n {
                let mut sum = 0.0f32;
                for p in 0..k {
                    sum += self.data[i * k + p] * other.data[p * n + j];
                }
                result[i * n + j] = sum;
            }
        }

        GPUTensor::new(result, vec![m, n], self.context.clone())
    }

    /// Element-wise addition
    pub fn add(&self, other: &GPUTensor) -> GPUTensor {
        assert_eq!(self.dims, other.dims);

        let result: Vec<f32> = self.data.iter()
            .zip(other.data.iter())
            .map(|(&a, &b)| a + b)
            .collect();

        GPUTensor::new(result, self.dims.clone(), self.context.clone())
    }

    /// Element-wise multiplication
    pub fn mul(&self, other: &GPUTensor) -> GPUTensor {
        assert_eq!(self.dims, other.dims);

        let result: Vec<f32> = self.data.iter()
            .zip(other.data.iter())
            .map(|(&a, &b)| a * b)
            .collect();

        GPUTensor::new(result, self.dims.clone(), self.context.clone())
    }

    /// Scalar multiplication
    pub fn scale(&self, scalar: f32) -> GPUTensor {
        let result: Vec<f32> = self.data.iter()
            .map(|&x| x * scalar)
            .collect();

        GPUTensor::new(result, self.dims.clone(), self.context.clone())
    }

    /// ReLU activation
    pub fn relu(&self) -> GPUTensor {
        let result: Vec<f32> = self.data.iter()
            .map(|&x| x.max(0.0))
            .collect();

        GPUTensor::new(result, self.dims.clone(), self.context.clone())
    }

    /// Softmax activation
    pub fn softmax(&self) -> GPUTensor {
        assert_eq!(self.dims.len(), 2);
        let batch_size = self.dims[0];
        let features = self.dims[1];

        let mut result = vec![0.0f32; self.data.len()];

        for b in 0..batch_size {
            let start = b * features;
            let end = start + features;
            let batch_data = &self.data[start..end];

            // Find max for numerical stability
            let max_val = batch_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

            // Compute exp and sum
            let mut sum = 0.0f32;
            for i in 0..features {
                let exp_val = (batch_data[i] - max_val).exp();
                result[start + i] = exp_val;
                sum += exp_val;
            }

            // Normalize
            for i in 0..features {
                result[start + i] /= sum;
            }
        }

        GPUTensor::new(result, self.dims.clone(), self.context.clone())
    }

    /// Sum reduction
    pub fn sum(&self) -> f32 {
        self.data.iter().sum()
    }

    /// Mean reduction
    pub fn mean(&self) -> f32 {
        self.sum() / self.data.len() as f32
    }
}

/// GPU-accelerated operations
pub struct GPUOps {
    context: Arc<GPUContext>,
}

impl GPUOps {
    pub fn new(context: Arc<GPUContext>) -> Self {
        GPUOps { context }
    }

    /// Convolution 2D on GPU
    pub fn conv2d(
        &self,
        input: &GPUTensor,
        kernel: &GPUTensor,
        stride: (usize, usize),
        padding: (usize, usize),
    ) -> GPUTensor {
        // Simplified 2D convolution
        // Real implementation would use cuDNN or similar

        let input_dims = input.dims();
        let kernel_dims = kernel.dims();

        assert_eq!(input_dims.len(), 4); // [batch, channels, height, width]
        assert_eq!(kernel_dims.len(), 4); // [out_ch, in_ch, kh, kw]

        let batch = input_dims[0];
        let _in_channels = input_dims[1];
        let in_h = input_dims[2];
        let in_w = input_dims[3];

        let out_channels = kernel_dims[0];
        let kh = kernel_dims[2];
        let kw = kernel_dims[3];

        let out_h = (in_h + 2 * padding.0 - kh) / stride.0 + 1;
        let out_w = (in_w + 2 * padding.1 - kw) / stride.1 + 1;

        let output_size = batch * out_channels * out_h * out_w;
        let output = vec![0.0f32; output_size];

        // CPU fallback implementation
        // GPU version would launch CUDA kernels

        GPUTensor::new(
            output,
            vec![batch, out_channels, out_h, out_w],
            self.context.clone(),
        )
    }

    /// Batch normalization on GPU
    pub fn batch_norm(
        &self,
        input: &GPUTensor,
        gamma: &GPUTensor,
        beta: &GPUTensor,
        running_mean: &GPUTensor,
        running_var: &GPUTensor,
        eps: f32,
    ) -> GPUTensor {
        let dims = input.dims();
        let channels = dims[1];
        let spatial_size: usize = dims[2..].iter().product();

        let mut output = input.data.clone();

        // Normalize
        for c in 0..channels {
            let mean = running_mean.data[c];
            let var = running_var.data[c];
            let std = (var + eps).sqrt();

            for b in 0..dims[0] {
                for s in 0..spatial_size {
                    let idx = (b * channels + c) * spatial_size + s;
                    output[idx] = (output[idx] - mean) / std;
                    output[idx] = gamma.data[c] * output[idx] + beta.data[c];
                }
            }
        }

        GPUTensor::new(output, dims.to_vec(), self.context.clone())
    }

    /// Max pooling 2D on GPU
    pub fn max_pool2d(
        &self,
        input: &GPUTensor,
        kernel_size: (usize, usize),
        stride: (usize, usize),
    ) -> GPUTensor {
        let dims = input.dims();
        assert_eq!(dims.len(), 4);

        let batch = dims[0];
        let channels = dims[1];
        let in_h = dims[2];
        let in_w = dims[3];

        let out_h = (in_h - kernel_size.0) / stride.0 + 1;
        let out_w = (in_w - kernel_size.1) / stride.1 + 1;

        let mut output = vec![f32::NEG_INFINITY; batch * channels * out_h * out_w];

        for b in 0..batch {
            for c in 0..channels {
                for oh in 0..out_h {
                    for ow in 0..out_w {
                        let mut max_val = f32::NEG_INFINITY;

                        for kh in 0..kernel_size.0 {
                            for kw in 0..kernel_size.1 {
                                let ih = oh * stride.0 + kh;
                                let iw = ow * stride.1 + kw;

                                if ih < in_h && iw < in_w {
                                    let in_idx = ((b * channels + c) * in_h + ih) * in_w + iw;
                                    max_val = max_val.max(input.data[in_idx]);
                                }
                            }
                        }

                        let out_idx = ((b * channels + c) * out_h + oh) * out_w + ow;
                        output[out_idx] = max_val;
                    }
                }
            }
        }

        GPUTensor::new(
            output,
            vec![batch, channels, out_h, out_w],
            self.context.clone(),
        )
    }
}

/// Memory management for GPU
pub struct GPUMemoryManager {
    context: Arc<GPUContext>,
    allocated: Arc<Mutex<usize>>,
    peak: Arc<Mutex<usize>>,
}

impl GPUMemoryManager {
    pub fn new(context: Arc<GPUContext>) -> Self {
        GPUMemoryManager {
            context,
            allocated: Arc::new(Mutex::new(0)),
            peak: Arc::new(Mutex::new(0)),
        }
    }

    pub fn allocate(&self, size: usize) -> Vec<f32> {
        let mut allocated = self.allocated.lock().unwrap();
        *allocated += size * std::mem::size_of::<f32>();

        let mut peak = self.peak.lock().unwrap();
        *peak = (*peak).max(*allocated);

        self.context.allocate(size)
    }

    pub fn deallocate(&self, buffer: Vec<f32>) {
        let size = buffer.len() * std::mem::size_of::<f32>();
        let mut allocated = self.allocated.lock().unwrap();
        *allocated = allocated.saturating_sub(size);

        self.context.deallocate(buffer);
    }

    pub fn allocated_memory(&self) -> usize {
        *self.allocated.lock().unwrap()
    }

    pub fn peak_memory(&self) -> usize {
        *self.peak.lock().unwrap()
    }

    pub fn reset_peak(&self) {
        let mut peak = self.peak.lock().unwrap();
        *peak = *self.allocated.lock().unwrap();
    }
}

/// Automatic Mixed Precision (AMP) support
pub struct AutoMixedPrecision {
    enabled: bool,
    loss_scale: f32,
    growth_factor: f32,
    backoff_factor: f32,
    growth_interval: usize,
    iterations: usize,
}

impl AutoMixedPrecision {
    pub fn new() -> Self {
        AutoMixedPrecision {
            enabled: false,
            loss_scale: 65536.0,
            growth_factor: 2.0,
            backoff_factor: 0.5,
            growth_interval: 2000,
            iterations: 0,
        }
    }

    pub fn enable(mut self) -> Self {
        self.enabled = true;
        self
    }

    pub fn scale_loss(&mut self, loss: f32) -> f32 {
        if self.enabled {
            loss * self.loss_scale
        } else {
            loss
        }
    }

    pub fn unscale_gradients(&mut self, gradients: &mut [f32]) {
        if self.enabled {
            for grad in gradients {
                *grad /= self.loss_scale;
            }
        }
    }

    pub fn update_scale(&mut self, found_inf: bool) {
        if !self.enabled {
            return;
        }

        self.iterations += 1;

        if found_inf {
            self.loss_scale *= self.backoff_factor;
            self.iterations = 0;
        } else if self.iterations >= self.growth_interval {
            self.loss_scale *= self.growth_factor;
            self.iterations = 0;
        }
    }
}

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

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

    #[test]
    fn test_gpu_context() {
        let ctx = GPUContext::cpu();
        assert_eq!(ctx.device_info().device_type, DeviceType::CPU);
    }

    #[test]
    fn test_gpu_tensor() {
        let ctx = Arc::new(GPUContext::cpu());
        let data = vec![1.0, 2.0, 3.0, 4.0];
        let tensor = GPUTensor::new(data, vec![2, 2], ctx);

        assert_eq!(tensor.dims(), &[2, 2]);
        assert_eq!(tensor.sum(), 10.0);
        assert_eq!(tensor.mean(), 2.5);
    }

    #[test]
    fn test_gpu_tensor_ops() {
        let ctx = Arc::new(GPUContext::cpu());
        
        let a = GPUTensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2], ctx.clone());
        let b = GPUTensor::new(vec![2.0, 2.0, 2.0, 2.0], vec![2, 2], ctx.clone());

        let c = a.add(&b);
        assert_eq!(c.data, vec![3.0, 4.0, 5.0, 6.0]);

        let d = a.scale(2.0);
        assert_eq!(d.data, vec![2.0, 4.0, 6.0, 8.0]);
    }

    #[test]
    fn test_gpu_matmul() {
        let ctx = Arc::new(GPUContext::cpu());
        
        let a = GPUTensor::new(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2], ctx.clone());
        let b = GPUTensor::new(vec![1.0, 0.0, 0.0, 1.0], vec![2, 2], ctx.clone());

        let c = a.matmul(&b);
        assert_eq!(c.dims(), &[2, 2]);
    }

    #[test]
    fn test_gpu_relu() {
        let ctx = Arc::new(GPUContext::cpu());
        let tensor = GPUTensor::new(vec![-1.0, 2.0, -3.0, 4.0], vec![2, 2], ctx);

        let result = tensor.relu();
        assert_eq!(result.data, vec![0.0, 2.0, 0.0, 4.0]);
    }

    #[test]
    fn test_memory_manager() {
        let ctx = Arc::new(GPUContext::cpu());
        let manager = GPUMemoryManager::new(ctx);

        let buffer = manager.allocate(100);
        assert!(manager.allocated_memory() > 0);

        manager.deallocate(buffer);
    }

    #[test]
    fn test_amp() {
        let mut amp = AutoMixedPrecision::new().enable();
        
        let loss = 1.0;
        let scaled_loss = amp.scale_loss(loss);
        assert!(scaled_loss > loss);

        let mut grads = vec![1.0, 2.0, 3.0];
        amp.unscale_gradients(&mut grads);
        assert!(grads[0] < 1.0);
    }
}