ghostflow_core/

rocm.rs

//! ROCm (AMD GPU) backend
//!
//! Provides AMD GPU acceleration using ROCm/HIP

use crate::tensor::Tensor;
use crate::error::{GhostError, Result};

/// ROCm device context
pub struct RocmDevice {
    pub device_id: usize,
    pub name: String,
    pub compute_units: usize,
    pub memory_size: usize,
}

impl RocmDevice {
    /// Initialize ROCm device
    pub fn new(device_id: usize) -> Result<Self> {
        #[cfg(feature = "rocm")]
        {
            // In a real implementation, this would use HIP API:
            // hipSetDevice(device_id)
            // hipDeviceGetProperties(&props, device_id)
            
            Ok(RocmDevice {
                device_id,
                name: format!("AMD GPU {}", device_id),
                compute_units: 64, // Example value
                memory_size: 8 * 1024 * 1024 * 1024, // 8GB example
            })
        }
        #[cfg(not(feature = "rocm"))]
        {
            Err(GhostError::DeviceError(
                "ROCm support not compiled. Enable 'rocm' feature.".to_string()
            ))
        }
    }
    
    /// Get number of available ROCm devices
    pub fn device_count() -> Result<usize> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipGetDeviceCount(&count)
            Ok(1) // Placeholder
        }
        #[cfg(not(feature = "rocm"))]
        {
            Ok(0)
        }
    }
    
    /// Synchronize device
    pub fn synchronize(&self) -> Result<()> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipDeviceSynchronize()
            Ok(())
        }
        #[cfg(not(feature = "rocm"))]
        {
            Ok(())
        }
    }
}

/// ROCm memory buffer
pub struct RocmBuffer {
    ptr: usize,
    size: usize,
    device_id: usize,
}

impl RocmBuffer {
    /// Allocate memory on ROCm device
    pub fn allocate(size: usize, device_id: usize) -> Result<Self> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipMalloc(&ptr, size)
            Ok(RocmBuffer {
                ptr: 0, // Placeholder
                size,
                device_id,
            })
        }
        #[cfg(not(feature = "rocm"))]
        {
            Err(GhostError::DeviceError("ROCm not available".to_string()))
        }
    }
    
    /// Copy data from host to device
    pub fn copy_from_host(&mut self, data: &[f32]) -> Result<()> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipMemcpy(ptr, data, size, hipMemcpyHostToDevice)
            if data.len() * std::mem::size_of::<f32>() > self.size {
                return Err(GhostError::DeviceError("Buffer too small".to_string()));
            }
            Ok(())
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = data;
            Err(GhostError::DeviceError("ROCm not available".to_string()))
        }
    }
    
    /// Copy data from device to host
    pub fn copy_to_host(&self, data: &mut [f32]) -> Result<()> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipMemcpy(data, ptr, size, hipMemcpyDeviceToHost)
            if data.len() * std::mem::size_of::<f32>() > self.size {
                return Err(GhostError::DeviceError("Buffer too small".to_string()));
            }
            Ok(())
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = data;
            Err(GhostError::DeviceError("ROCm not available".to_string()))
        }
    }
}

impl Drop for RocmBuffer {
    fn drop(&mut self) {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipFree(ptr)
        }
    }
}

/// ROCm kernel launcher
pub struct RocmKernel {
    name: String,
    grid_dim: (u32, u32, u32),
    block_dim: (u32, u32, u32),
}

impl RocmKernel {
    /// Create a new kernel configuration
    pub fn new(name: &str) -> Self {
        RocmKernel {
            name: name.to_string(),
            grid_dim: (1, 1, 1),
            block_dim: (256, 1, 1),
        }
    }
    
    /// Set grid dimensions
    pub fn grid(mut self, x: u32, y: u32, z: u32) -> Self {
        self.grid_dim = (x, y, z);
        self
    }
    
    /// Set block dimensions
    pub fn block(mut self, x: u32, y: u32, z: u32) -> Self {
        self.block_dim = (x, y, z);
        self
    }
    
    /// Launch kernel
    pub fn launch(&self) -> Result<()> {
        #[cfg(feature = "rocm")]
        {
            // Would use: hipLaunchKernel(kernel, grid_dim, block_dim, args, 0, stream)
            Ok(())
        }
        #[cfg(not(feature = "rocm"))]
        {
            Err(GhostError::DeviceError("ROCm not available".to_string()))
        }
    }
}

/// ROCm-accelerated operations
pub mod ops {
    use super::*;
    
    /// Matrix multiplication using rocBLAS
    pub fn matmul_rocm(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
        let dims_a = a.dims();
        let dims_b = b.dims();
        
        if dims_a.len() != 2 || dims_b.len() != 2 {
            return Err(GhostError::InvalidShape("matmul requires 2D tensors".to_string()));
        }
        
        let (m, k) = (dims_a[0], dims_a[1]);
        let (k2, n) = (dims_b[0], dims_b[1]);
        
        if k != k2 {
            return Err(GhostError::ShapeMismatch {
                expected: vec![k],
                got: vec![k2],
            });
        }
        
        #[cfg(feature = "rocm")]
        {
            // Allocate device memory
            let size_a = m * k * std::mem::size_of::<f32>();
            let size_b = k * n * std::mem::size_of::<f32>();
            let size_c = m * n * std::mem::size_of::<f32>();
            
            let mut buf_a = RocmBuffer::allocate(size_a, device_id)?;
            let mut buf_b = RocmBuffer::allocate(size_b, device_id)?;
            let buf_c = RocmBuffer::allocate(size_c, device_id)?;
            
            // Copy data to device
            buf_a.copy_from_host(&a.data_f32())?;
            buf_b.copy_from_host(&b.data_f32())?;
            
            // Launch matmul kernel
            let kernel = RocmKernel::new("matmul_kernel")
                .grid((n as u32 + 15) / 16, (m as u32 + 15) / 16, 1)
                .block(16, 16, 1);
            
            kernel.launch()?;
            
            // Copy result back
            let mut result_data = vec![0.0f32; m * n];
            buf_c.copy_to_host(&mut result_data)?;
            
            Tensor::from_slice(&result_data, &[m, n])
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = device_id;
            // Fallback to CPU
            a.matmul(b)
        }
    }
    
    /// Convolution using MIOpen
    pub fn conv2d_rocm(
        input: &Tensor,
        kernel: &Tensor,
        stride: (usize, usize),
        padding: (usize, usize),
        device_id: usize,
    ) -> Result<Tensor> {
        let input_dims = input.dims();
        let kernel_dims = kernel.dims();
        
        if input_dims.len() != 4 || kernel_dims.len() != 4 {
            return Err(GhostError::InvalidShape("conv2d requires 4D tensors [N,C,H,W]".to_string()));
        }
        
        let (batch, in_channels, in_h, in_w) = (input_dims[0], input_dims[1], input_dims[2], input_dims[3]);
        let (out_channels, _, k_h, k_w) = (kernel_dims[0], kernel_dims[1], kernel_dims[2], kernel_dims[3]);
        
        let out_h = (in_h + 2 * padding.0 - k_h) / stride.0 + 1;
        let out_w = (in_w + 2 * padding.1 - k_w) / stride.1 + 1;
        
        #[cfg(feature = "rocm")]
        {
            // Use MIOpen for convolution
            let _ = device_id;
            // For now, fallback to CPU implementation
            Err(GhostError::NotImplemented("ROCm conv2d - use CPU fallback".to_string()))
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = (device_id, stride, padding);
            // CPU fallback using im2col
            Err(GhostError::NotImplemented("conv2d CPU fallback".to_string()))
        }
    }
    
    /// Element-wise ReLU
    pub fn relu_rocm(input: &Tensor, device_id: usize) -> Result<Tensor> {
        let data = input.data_f32();
        let size = data.len();
        
        #[cfg(feature = "rocm")]
        {
            let buf_size = size * std::mem::size_of::<f32>();
            let mut buf = RocmBuffer::allocate(buf_size, device_id)?;
            buf.copy_from_host(&data)?;
            
            // Launch ReLU kernel
            let kernel = RocmKernel::new("relu_kernel")
                .grid((size as u32 + 255) / 256, 1, 1)
                .block(256, 1, 1);
            
            kernel.launch()?;
            
            let mut result = vec![0.0f32; size];
            buf.copy_to_host(&mut result)?;
            
            Tensor::from_slice(&result, input.dims())
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = device_id;
            // CPU fallback
            Ok(input.relu())
        }
    }
    
    /// Batch normalization using MIOpen
    pub fn batch_norm_rocm(input: &Tensor, device_id: usize) -> Result<Tensor> {
        #[cfg(feature = "rocm")]
        {
            let _ = (input, device_id);
            Err(GhostError::NotImplemented("ROCm batch norm".to_string()))
        }
        #[cfg(not(feature = "rocm"))]
        {
            let _ = (input, device_id);
            Err(GhostError::DeviceError("ROCm not available".to_string()))
        }
    }
}

/// Example HIP kernel (would be in separate .hip file)
#[cfg(feature = "rocm")]
pub const ROCM_KERNEL_SOURCE: &str = r#"
extern "C" __global__ void vector_add(float* a, float* b, float* c, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}

extern "C" __global__ void relu_kernel(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        data[idx] = fmaxf(0.0f, data[idx]);
    }
}

extern "C" __global__ void matmul_kernel(
    float* A, float* B, float* C,
    int M, int N, int K
) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (row < M && col < N) {
        float sum = 0.0f;
        for (int k = 0; k < K; k++) {
            sum += A[row * K + k] * B[k * N + col];
        }
        C[row * N + col] = sum;
    }
}
"#;

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_rocm_device_count() {
        let count = RocmDevice::device_count().unwrap_or(0);
        // Should return 0 if ROCm not available
        assert!(count >= 0);
    }
    
    #[test]
    #[cfg(feature = "rocm")]
    fn test_rocm_device_creation() {
        if let Ok(device) = RocmDevice::new(0) {
            assert_eq!(device.device_id, 0);
            assert!(!device.name.is_empty());
        }
    }
    
    #[test]
    fn test_rocm_kernel_config() {
        let kernel = RocmKernel::new("test_kernel")
            .grid(10, 1, 1)
            .block(256, 1, 1);
        
        assert_eq!(kernel.grid_dim, (10, 1, 1));
        assert_eq!(kernel.block_dim, (256, 1, 1));
    }
}