trustformers-core 0.1.1

//! ROCm GPU backend for tensor operations
//!
//! This module provides ROCm GPU acceleration for tensor operations on AMD GPUs.
//! ROCm (Radeon Open Compute) is AMD's open-source GPU computing platform.
//!
//! Features:
//! - AMD GPU support via HIP (Heterogeneous-compute Interface for Portability)
//! - CUDA-compatible API
//! - Persistent buffer caching
//! - GPU-to-GPU operations
//!
//! Note: ROCm support is currently a placeholder. Full implementation requires:
//! - HIP runtime installation
//! - Rust HIP bindings (hip-rs or similar)
//! - AMD GPU hardware

#[allow(unused_imports)]
use crate::device::Device;
use crate::errors::Result;
use crate::tensor::Tensor;

#[cfg(feature = "rocm")]
use crate::errors::TrustformersError;
#[cfg(feature = "rocm")]
use std::collections::HashMap;
#[cfg(feature = "rocm")]
use std::sync::Arc;

/// Buffer ID for persistent GPU buffers
#[cfg(feature = "rocm")]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct BufferId(u64);

#[cfg(feature = "rocm")]
impl BufferId {
    pub fn new() -> Self {
        use std::sync::atomic::{AtomicU64, Ordering};
        static COUNTER: AtomicU64 = AtomicU64::new(0);
        BufferId(COUNTER.fetch_add(1, Ordering::SeqCst))
    }
}

#[cfg(feature = "rocm")]
impl Default for BufferId {
    fn default() -> Self {
        Self::new()
    }
}

/// ROCm GPU backend for matrix multiplication and element-wise operations
///
/// This is a placeholder implementation. Full ROCm support requires:
/// - HIP runtime libraries
/// - Rust HIP bindings
/// - AMD GPU hardware with ROCm drivers
#[cfg(feature = "rocm")]
pub struct RocmBackend {
    device_id: usize,
    // In a full implementation, this would contain:
    // - HIP device handle
    // - HIP stream handle
    // - Buffer cache
    // - Compiled kernels
}

#[cfg(feature = "rocm")]
impl RocmBackend {
    /// Create a new ROCm backend
    pub fn new(device_id: usize) -> Result<Self> {
        // Check if ROCm is available
        if !Self::is_rocm_available() {
            return Err(TrustformersError::hardware_error(
                "ROCm runtime not found. Please install ROCm toolkit.",
                "RocmBackend::new",
            ));
        }

        tracing::debug!("ROCm backend initialized on device {}", device_id);

        Ok(Self { device_id })
    }

    /// Check if ROCm is available on the system
    fn is_rocm_available() -> bool {
        // Check for ROCm installation
        std::path::Path::new("/opt/rocm").exists()
            || std::env::var("ROCM_PATH").is_ok()
            || std::env::var("HIP_PATH").is_ok()
    }

    /// Perform matrix multiplication on ROCm GPU
    ///
    /// Placeholder implementation - in a full version, this would:
    /// 1. Compile HIP kernel (similar to CUDA)
    /// 2. Allocate device memory
    /// 3. Launch HIP kernel
    /// 4. Copy result back to host
    pub fn matmul_f32(
        &self,
        a: &[f32],
        b: &[f32],
        m: usize,
        k: usize,
        n: usize,
    ) -> Result<Vec<f32>> {
        // HIP kernel source (CUDA-compatible)
        #[allow(dead_code)]
        const HIP_KERNEL_SRC: &str = r#"
extern "C" __global__ void matmul_kernel(
    const float* a,
    const float* b,
    float* c,
    unsigned int M,
    unsigned int N,
    unsigned int K
) {
    unsigned int row = blockIdx.y * blockDim.y + threadIdx.y;
    unsigned int col = blockIdx.x * blockDim.x + threadIdx.x;

    if (row >= M || col >= N) return;

    float sum = 0.0f;
    for (unsigned int i = 0; i < K; ++i) {
        sum += a[row * K + i] * b[i * N + col];
    }
    c[row * N + col] = sum;
}
"#;

        // Placeholder: Fallback to CPU implementation
        // TODO: Implement actual HIP kernel execution when HIP bindings are available
        tracing::debug!("ROCm GPU operations not yet implemented - using CPU fallback");

        // CPU fallback
        let mut result = vec![0.0f32; m * n];
        for i in 0..m {
            for j in 0..n {
                let mut sum = 0.0f32;
                for p in 0..k {
                    sum += a[i * k + p] * b[p * n + j];
                }
                result[i * n + j] = sum;
            }
        }

        Ok(result)
    }

    /// Execute GELU activation on GPU (placeholder)
    pub fn gelu_f32(&self, input: &[f32]) -> Result<Vec<f32>> {
        tracing::debug!("ROCm GPU operations not yet implemented - using CPU fallback");

        // CPU fallback GELU implementation
        let result: Vec<f32> = input
            .iter()
            .map(|&x| {
                if x > 10.0 {
                    x
                } else if x < -10.0 {
                    0.0
                } else {
                    let x_cubed = x * x * x;
                    let inner = 0.7978845608f32 * (x + 0.044715 * x_cubed);
                    let clamped = inner.clamp(-20.0, 20.0);
                    0.5 * x * (1.0 + clamped.tanh())
                }
            })
            .collect();

        Ok(result)
    }

    /// Execute LayerNorm on GPU (placeholder)
    pub fn layernorm_f32(
        &self,
        input: &[f32],
        weight: &[f32],
        bias: &[f32],
        seq_len: usize,
        hidden_size: usize,
        eps: f32,
    ) -> Result<Vec<f32>> {
        tracing::debug!("ROCm GPU operations not yet implemented - using CPU fallback");

        let total_size = seq_len * hidden_size;
        let mut result = vec![0.0f32; total_size];

        for pos in 0..seq_len {
            let offset = pos * hidden_size;

            // Compute mean
            let sum: f32 = input[offset..offset + hidden_size].iter().sum();
            let mean = sum / hidden_size as f32;

            // Compute variance
            let var_sum: f32 = input[offset..offset + hidden_size]
                .iter()
                .map(|&x| {
                    let diff = x - mean;
                    diff * diff
                })
                .sum();
            let variance = var_sum / hidden_size as f32;
            let std_dev = (variance + eps).sqrt();

            // Normalize and apply affine transform
            for i in 0..hidden_size {
                let normalized = (input[offset + i] - mean) / std_dev;
                result[offset + i] = normalized * weight[i] + bias[i];
            }
        }

        Ok(result)
    }

    /// Get device information
    pub fn device_info(&self) -> String {
        format!(
            "ROCm Device {} (placeholder - HIP bindings required)",
            self.device_id
        )
    }
}

/// Get or create ROCm backend instance
#[cfg(feature = "rocm")]
pub fn get_rocm_backend(device_id: usize) -> Result<Arc<RocmBackend>> {
    static ROCM_BACKENDS: once_cell::sync::Lazy<
        std::sync::Mutex<HashMap<usize, Arc<RocmBackend>>>,
    > = once_cell::sync::Lazy::new(|| std::sync::Mutex::new(HashMap::new()));

    let mut cache = ROCM_BACKENDS.lock().map_err(|_| {
        TrustformersError::hardware_error("Failed to lock ROCm backend cache", "get_rocm_backend")
    })?;

    if let std::collections::hash_map::Entry::Vacant(e) = cache.entry(device_id) {
        let backend = RocmBackend::new(device_id)?;
        e.insert(Arc::new(backend));
    }

    cache.get(&device_id).cloned().ok_or_else(|| {
        TrustformersError::hardware_error("ROCm backend not found", "get_rocm_backend")
    })
}

/// Dispatch matrix multiplication to ROCm backend
#[allow(unused_variables)]
pub fn dispatch_rocm_matmul(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
    #[cfg(feature = "rocm")]
    {
        match (a, b) {
            (Tensor::F32(a_arr), Tensor::F32(b_arr)) => {
                if a_arr.ndim() != 2 || b_arr.ndim() != 2 {
                    return Err(TrustformersError::shape_error(
                        "ROCm dispatch currently only supports 2D tensors".to_string(),
                    ));
                }

                let a_2d = a_arr
                    .clone()
                    .into_dimensionality::<scirs2_core::ndarray::Ix2>()
                    .map_err(|e| {
                        TrustformersError::shape_error(format!("Failed to convert to 2D: {}", e))
                    })?;
                let b_2d = b_arr
                    .clone()
                    .into_dimensionality::<scirs2_core::ndarray::Ix2>()
                    .map_err(|e| {
                        TrustformersError::shape_error(format!("Failed to convert to 2D: {}", e))
                    })?;

                let (m, k) = a_2d.dim();
                let (k2, n) = b_2d.dim();

                if k != k2 {
                    return Err(TrustformersError::shape_error(format!(
                        "Matrix dimension mismatch: {}×{} vs {}×{}",
                        m, k, k2, n
                    )));
                }

                let backend = get_rocm_backend(device_id)?;

                let a_data: Vec<f32> = a_2d.iter().copied().collect();
                let b_data: Vec<f32> = b_2d.iter().copied().collect();

                let result_data = backend.matmul_f32(&a_data, &b_data, m, k, n)?;

                let result_2d = scirs2_core::ndarray::Array2::from_shape_vec((m, n), result_data)
                    .map_err(|e| {
                    TrustformersError::shape_error(format!("Failed to reshape result: {}", e))
                })?;

                let result_dyn = result_2d.into_dyn();
                Ok(Tensor::F32(result_dyn))
            },
            _ => a.matmul(b),
        }
    }

    #[cfg(not(feature = "rocm"))]
    {
        // No ROCm support, fallback to CPU
        a.matmul(b)
    }
}

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

    #[test]
    #[cfg(feature = "rocm")]
    fn test_rocm_availability() {
        let available = RocmBackend::is_rocm_available();
        println!("ROCm available: {}", available);
    }

    #[test]
    #[cfg(feature = "rocm")]
    fn test_rocm_backend() -> Result<()> {
        match RocmBackend::new(0) {
            Ok(backend) => {
                println!("ROCm backend: {}", backend.device_info());
                Ok(())
            },
            Err(_) => {
                tracing::debug!("Skipping ROCm test: not available");
                Ok(())
            },
        }
    }

    #[test]
    #[cfg(feature = "rocm")]
    fn test_rocm_matmul_fallback() -> Result<()> {
        let backend = match RocmBackend::new(0) {
            Ok(b) => b,
            Err(_) => {
                tracing::debug!("Skipping ROCm test: not available");
                return Ok(());
            },
        };

        // Test CPU fallback
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![5.0, 6.0, 7.0, 8.0];

        let result = backend.matmul_f32(&a, &b, 2, 2, 2)?;

        // Expected: [[19, 22], [43, 50]]
        let expected = [19.0, 22.0, 43.0, 50.0];

        for (i, (&res, &exp)) in result.iter().zip(expected.iter()).enumerate() {
            assert!(
                (res - exp).abs() < 1e-4,
                "Mismatch at index {}: {} vs {}",
                i,
                res,
                exp
            );
        }

        Ok(())
    }
}