torsh-sparse 0.1.2

//! GPU support for sparse tensors
//!
//! This module provides CUDA sparse tensor operations using cuSPARSE.

#[allow(dead_code)]
use crate::{CooTensor, CsrTensor, SparseFormat, SparseTensor, TorshResult};
use std::sync::Arc;
use torsh_core::{device::DeviceType, DType, TorshError};
use torsh_tensor::Tensor;

/// CUDA sparse tensor wrapper
#[derive(Debug, Clone)]
pub struct CudaSparseTensor {
    /// The sparse format used on GPU
    pub format: SparseFormat,
    /// Device index
    pub device_id: i32,
    /// Data type
    pub dtype: DType,
    /// Shape of the tensor
    pub shape: [usize; 2],
    /// Number of non-zero elements
    pub nnz: usize,
    /// Raw data pointers (placeholder for actual CUDA implementation)
    data_ptr: Option<Arc<CudaHandle>>,
}

/// Handle for CUDA sparse tensor data
#[derive(Debug)]
struct CudaHandle {
    /// Placeholder for actual CUDA context and data
    _context: (),
}

impl CudaSparseTensor {
    /// Create a new CUDA sparse tensor from COO format
    pub fn from_coo(coo: &CooTensor, device_id: i32) -> TorshResult<Self> {
        if !Self::is_cuda_available() {
            return Err(TorshError::InvalidArgument(
                "CUDA is not available".to_string(),
            ));
        }

        Ok(Self {
            format: SparseFormat::Coo,
            device_id,
            dtype: coo.dtype(),
            shape: [coo.shape().dims()[0], coo.shape().dims()[1]],
            nnz: coo.nnz(),
            data_ptr: Some(Arc::new(CudaHandle { _context: () })),
        })
    }

    /// Create a new CUDA sparse tensor from CSR format
    pub fn from_csr(csr: &CsrTensor, device_id: i32) -> TorshResult<Self> {
        if !Self::is_cuda_available() {
            return Err(TorshError::InvalidArgument(
                "CUDA is not available".to_string(),
            ));
        }

        Ok(Self {
            format: SparseFormat::Csr,
            device_id,
            dtype: csr.dtype(),
            shape: [csr.shape().dims()[0], csr.shape().dims()[1]],
            nnz: csr.nnz(),
            data_ptr: Some(Arc::new(CudaHandle { _context: () })),
        })
    }

    /// Convert to COO format on GPU
    pub fn to_coo_gpu(&self) -> TorshResult<CudaSparseTensor> {
        match self.format {
            SparseFormat::Coo => Ok(self.clone()),
            SparseFormat::Csr => {
                // Placeholder: Convert CSR to COO on GPU using cuSPARSE
                Ok(CudaSparseTensor {
                    format: SparseFormat::Coo,
                    device_id: self.device_id,
                    dtype: self.dtype,
                    shape: self.shape,
                    nnz: self.nnz,
                    data_ptr: self.data_ptr.clone(),
                })
            }
            _ => Err(TorshError::ComputeError(format!(
                "Conversion from {:?} to COO not implemented on GPU",
                self.format
            ))),
        }
    }

    /// Convert to CSR format on GPU
    pub fn to_csr_gpu(&self) -> TorshResult<CudaSparseTensor> {
        match self.format {
            SparseFormat::Csr => Ok(self.clone()),
            SparseFormat::Coo => {
                // Placeholder: Convert COO to CSR on GPU using cuSPARSE
                Ok(CudaSparseTensor {
                    format: SparseFormat::Csr,
                    device_id: self.device_id,
                    dtype: self.dtype,
                    shape: self.shape,
                    nnz: self.nnz,
                    data_ptr: self.data_ptr.clone(),
                })
            }
            _ => Err(TorshError::ComputeError(format!(
                "Conversion from {:?} to CSR not implemented on GPU",
                self.format
            ))),
        }
    }

    /// Sparse matrix multiplication on GPU
    pub fn spmm(&self, dense: &Tensor) -> TorshResult<Tensor> {
        if !matches!(dense.device(), DeviceType::Cuda(_)) {
            return Err(TorshError::InvalidArgument(
                "Dense tensor must be on CUDA device for GPU SPMM".to_string(),
            ));
        }

        // Placeholder: Implement cuSPARSE SPMM
        Err(TorshError::ComputeError(
            "GPU sparse matrix multiplication not yet implemented".to_string(),
        ))
    }

    /// Sparse matrix-sparse matrix multiplication on GPU
    pub fn spgemm(&self, other: &CudaSparseTensor) -> TorshResult<CudaSparseTensor> {
        if self.device_id != other.device_id {
            return Err(TorshError::InvalidArgument(
                "Both tensors must be on the same CUDA device".to_string(),
            ));
        }

        // Placeholder: Implement cuSPARSE SpGEMM
        Err(TorshError::ComputeError(
            "GPU sparse-sparse matrix multiplication not yet implemented".to_string(),
        ))
    }

    /// Copy tensor to CPU
    pub fn to_cpu(&self) -> TorshResult<CooTensor> {
        // Placeholder: Copy data from GPU to CPU and create COO tensor
        Err(TorshError::ComputeError(
            "GPU to CPU copy not yet implemented".to_string(),
        ))
    }

    /// Check if CUDA is available
    pub fn is_cuda_available() -> bool {
        // Placeholder: Check for CUDA runtime and cuSPARSE
        false
    }

    /// Get device memory usage
    pub fn memory_usage(&self) -> usize {
        match self.dtype {
            DType::F32 => self.nnz * (4 + 4 + 4), // values + row_indices + col_indices for COO
            DType::F64 => self.nnz * (8 + 4 + 4), // values + row_indices + col_indices for COO
            DType::I32 => self.nnz * (4 + 4 + 4),
            DType::I64 => self.nnz * (8 + 4 + 4),
            _ => 0,
        }
    }

    /// Get number of non-zero elements
    pub fn nnz(&self) -> usize {
        self.nnz
    }

    /// Get tensor shape
    pub fn shape(&self) -> [usize; 2] {
        self.shape
    }

    /// Get data type
    pub fn dtype(&self) -> DType {
        self.dtype
    }

    /// Get device ID
    pub fn device_id(&self) -> i32 {
        self.device_id
    }

    /// Get sparse format
    pub fn format(&self) -> SparseFormat {
        self.format
    }
}

/// CUDA sparse tensor operations
pub struct CudaSparseOps;

impl CudaSparseOps {
    /// Batched sparse matrix multiplication
    pub fn batched_spmm(
        sparse_tensors: &[CudaSparseTensor],
        _dense_tensor: &Tensor,
    ) -> TorshResult<Vec<Tensor>> {
        if sparse_tensors.is_empty() {
            return Ok(Vec::new());
        }

        // Check all tensors are on the same device
        let device_id = sparse_tensors[0].device_id;
        for tensor in sparse_tensors {
            if tensor.device_id != device_id {
                return Err(TorshError::InvalidArgument(
                    "All sparse tensors must be on the same CUDA device".to_string(),
                ));
            }
        }

        // Placeholder: Implement batched cuSPARSE operations
        Err(TorshError::ComputeError(
            "Batched GPU sparse operations not yet implemented".to_string(),
        ))
    }

    /// Mixed precision sparse operations
    pub fn mixed_precision_spmm(
        _sparse: &CudaSparseTensor,
        _dense: &Tensor,
        _output_dtype: DType,
    ) -> TorshResult<Tensor> {
        // Placeholder: Implement mixed precision computation
        Err(TorshError::ComputeError(
            "Mixed precision sparse operations not yet implemented".to_string(),
        ))
    }

    /// Memory-optimized sparse operations
    pub fn memory_efficient_spgemm(
        _a: &CudaSparseTensor,
        _b: &CudaSparseTensor,
        _memory_limit: usize,
    ) -> TorshResult<CudaSparseTensor> {
        // Placeholder: Implement memory-conscious SpGEMM
        Err(TorshError::ComputeError(
            "Memory-efficient sparse operations not yet implemented".to_string(),
        ))
    }
}

/// CUDA sparse tensor factory
pub struct CudaSparseTensorFactory;

impl CudaSparseTensorFactory {
    /// Create sparse tensor from dense on GPU
    pub fn from_dense(dense: &Tensor, _threshold: f64) -> TorshResult<CudaSparseTensor> {
        if !matches!(dense.device(), DeviceType::Cuda(_)) {
            return Err(TorshError::InvalidArgument(
                "Input tensor must be on CUDA device".to_string(),
            ));
        }

        // Placeholder: Convert dense to sparse on GPU
        Err(TorshError::ComputeError(
            "Dense to sparse conversion on GPU not yet implemented".to_string(),
        ))
    }

    /// Create random sparse tensor on GPU
    pub fn random_sparse(
        shape: [usize; 2],
        density: f64,
        dtype: DType,
        device_id: i32,
    ) -> TorshResult<CudaSparseTensor> {
        if !CudaSparseTensor::is_cuda_available() {
            return Err(TorshError::InvalidArgument(
                "CUDA is not available".to_string(),
            ));
        }

        let nnz = (shape[0] as f64 * shape[1] as f64 * density) as usize;

        // Placeholder: Generate random sparse tensor on GPU
        Ok(CudaSparseTensor {
            format: SparseFormat::Coo,
            device_id,
            dtype,
            shape,
            nnz,
            data_ptr: Some(Arc::new(CudaHandle { _context: () })),
        })
    }

    /// Create identity sparse matrix on GPU
    pub fn identity(size: usize, dtype: DType, device_id: i32) -> TorshResult<CudaSparseTensor> {
        if !CudaSparseTensor::is_cuda_available() {
            return Err(TorshError::InvalidArgument(
                "CUDA is not available".to_string(),
            ));
        }

        Ok(CudaSparseTensor {
            format: SparseFormat::Csr,
            device_id,
            dtype,
            shape: [size, size],
            nnz: size,
            data_ptr: Some(Arc::new(CudaHandle { _context: () })),
        })
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::CooTensor;
    use torsh_core::Shape;

    #[test]
    fn test_cuda_availability() {
        // This will return false in most test environments
        let _available = CudaSparseTensor::is_cuda_available();
        // Test passes regardless of CUDA availability
        // This test just ensures the function doesn't panic
    }

    #[test]
    fn test_cuda_sparse_tensor_creation() {
        // Create a simple COO tensor for testing
        let coo = CooTensor::new(
            vec![0, 1, 2],
            vec![0, 1, 2],
            vec![1.0, 2.0, 3.0],
            Shape::new(vec![3, 3]),
        )
        .unwrap();

        // Attempt to create CUDA tensor (will fail if CUDA not available)
        match CudaSparseTensor::from_coo(&coo, 0) {
            Ok(cuda_tensor) => {
                assert_eq!(cuda_tensor.format(), SparseFormat::Coo);
                assert_eq!(cuda_tensor.dtype(), DType::F32);
                assert_eq!(cuda_tensor.shape(), [3, 3]);
                assert_eq!(cuda_tensor.nnz(), 3);
            }
            Err(_) => {
                // Expected when CUDA is not available
            }
        }
    }

    #[test]
    fn test_memory_usage_calculation() {
        let cuda_tensor = CudaSparseTensor {
            format: SparseFormat::Coo,
            device_id: 0,
            dtype: DType::F32,
            shape: [100, 100],
            nnz: 1000,
            data_ptr: None,
        };

        // For F32 COO: 1000 * (4 + 4 + 4) = 12000 bytes
        assert_eq!(cuda_tensor.memory_usage(), 12000);
    }
}