torsh_sparse/

scirs2_sparse_integration.rs

//! Comprehensive scirs2-sparse integration for advanced sparse matrix operations
//!
//! This module provides integration with scirs2-sparse's high-performance sparse matrix
//! algorithms and data structures while maintaining PyTorch compatibility.
//!
//! # Features
//!
//! - **Advanced Sparse Formats**: COO, CSR, CSC, BSR, DIA, ELL with GPU support
//! - **High-Performance Operations**: SpMV, SpMM, SpGEMM with SIMD/GPU acceleration
//! - **Sparse Linear Algebra**: Direct and iterative solvers for sparse systems
//! - **Neural Network Support**: Sparse layers, pruning, and optimization
//! - **Memory Optimization**: Adaptive compression and memory pooling
//! - **Pattern Analysis**: Sparsity pattern detection and optimization

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::TorshResult;
use std::collections::HashMap;
use torsh_core::{DType, DeviceType, TorshError};
use torsh_tensor::Tensor;

// SciRS2 imports following the policy
use scirs2_core as _; // Always available
#[cfg(feature = "scirs2-integration")]
use scirs2_sparse as _; // Available with scirs2-integration feature

/// Advanced sparse matrix processor using scirs2-sparse capabilities
pub struct SciRS2SparseProcessor {
    config: SparseConfig,
    format_cache: HashMap<String, SparseFormat>,
    optimization_stats: OptimizationStats,
}

/// Configuration for sparse matrix operations
#[derive(Debug, Clone)]
pub struct SparseConfig {
    /// Default sparse format for operations
    pub default_format: SparseFormat,
    /// Device type for computations
    pub device: DeviceType,
    /// Data type for sparse values
    pub dtype: DType,
    /// Enable automatic format conversion
    pub auto_format_conversion: bool,
    /// Memory optimization level (0 = none, 3 = aggressive)
    pub memory_optimization: u8,
    /// Use GPU acceleration when available
    pub use_gpu: bool,
    /// SIMD optimization level
    pub simd_level: SIMDLevel,
    /// Sparsity threshold for conversion decisions
    pub sparsity_threshold: f64,
}

/// Sparse matrix formats supported by the processor
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SparseFormat {
    /// Coordinate format (COO) - best for construction
    Coo,
    /// Compressed Sparse Row (CSR) - best for row operations
    Csr,
    /// Compressed Sparse Column (CSC) - best for column operations
    Csc,
    /// Block Sparse Row (BSR) - best for block-structured matrices
    Bsr,
    /// Diagonal format (DIA) - best for diagonal-dominant matrices
    Dia,
    /// ELLPACK format (ELL) - best for GPU operations
    Ell,
    /// Diagonal Sparse Row (DSR) - hybrid format
    Dsr,
    /// Run-Length Encoding (RLE) - best for specific patterns
    Rle,
}

/// SIMD optimization levels
#[derive(Debug, Clone, Copy)]
pub enum SIMDLevel {
    None,
    Basic,
    Advanced,
    Maximum,
}

/// Sparse operation types for performance optimization
#[derive(Debug, Clone, Copy)]
pub enum SparseOperation {
    /// Sparse matrix-vector multiplication
    SpMV,
    /// Sparse matrix-matrix multiplication
    SpMM,
    /// Sparse general matrix multiplication
    SpGEMM,
    /// Matrix transpose
    Transpose,
    /// Format conversion
    Conversion,
    /// Factorization
    Factorization,
}

/// Optimization statistics for sparse operations
#[derive(Debug, Clone, Default)]
pub struct OptimizationStats {
    pub operations_performed: u64,
    pub format_conversions: u64,
    pub memory_saved: u64,
    pub gpu_accelerated_ops: u64,
    pub simd_accelerated_ops: u64,
}

/// Sparse matrix metadata for optimization decisions
#[derive(Debug, Clone)]
pub struct SparseMatrixInfo {
    pub rows: usize,
    pub cols: usize,
    pub nnz: usize,
    pub sparsity: f64,
    pub format: SparseFormat,
    pub has_diagonal_structure: bool,
    pub has_block_structure: bool,
    pub optimal_format: SparseFormat,
}

impl Default for SparseConfig {
    fn default() -> Self {
        Self {
            default_format: SparseFormat::Csr,
            device: DeviceType::Cpu,
            dtype: DType::F32,
            auto_format_conversion: true,
            memory_optimization: 2,
            use_gpu: false,
            simd_level: SIMDLevel::Advanced,
            sparsity_threshold: 0.1,
        }
    }
}

impl SciRS2SparseProcessor {
    pub fn new(config: SparseConfig) -> Self {
        Self {
            config,
            format_cache: HashMap::new(),
            optimization_stats: OptimizationStats::default(),
        }
    }

    /// Create with default configuration
    pub fn default() -> Self {
        Self::new(SparseConfig::default())
    }

    /// Create processor optimized for GPU acceleration
    pub fn gpu_optimized() -> Self {
        Self::new(SparseConfig {
            default_format: SparseFormat::Ell,
            device: DeviceType::Cuda(0),
            dtype: DType::F32,
            auto_format_conversion: true,
            memory_optimization: 3,
            use_gpu: true,
            simd_level: SIMDLevel::Maximum,
            sparsity_threshold: 0.05,
        })
    }

    /// Create processor optimized for neural networks
    pub fn neural_network_optimized() -> Self {
        Self::new(SparseConfig {
            default_format: SparseFormat::Csr,
            device: DeviceType::Cpu,
            dtype: DType::F32,
            auto_format_conversion: true,
            memory_optimization: 2,
            use_gpu: false,
            simd_level: SIMDLevel::Advanced,
            sparsity_threshold: 0.9, // High sparsity for NN pruning
        })
    }

    /// Analyze sparse matrix and recommend optimal format
    pub fn analyze_matrix(&mut self, matrix: &Tensor) -> TorshResult<SparseMatrixInfo> {
        let shape = matrix.shape();
        if shape.ndim() != 2 {
            return Err(TorshError::InvalidArgument(
                "Matrix analysis requires 2D tensor".to_string(),
            ));
        }

        let (rows, cols) = (shape.dims()[0], shape.dims()[1]);

        // Calculate sparsity (placeholder implementation)
        let total_elements = rows * cols;
        let nnz = self.count_nonzeros(matrix)?;
        let sparsity = 1.0 - (nnz as f64 / total_elements as f64);

        // Analyze patterns
        let has_diagonal_structure = self.has_diagonal_pattern(matrix)?;
        let has_block_structure = self.has_block_pattern(matrix)?;

        // Recommend optimal format based on analysis
        let optimal_format = self.recommend_format(
            rows,
            cols,
            nnz,
            sparsity,
            has_diagonal_structure,
            has_block_structure,
        );

        Ok(SparseMatrixInfo {
            rows,
            cols,
            nnz,
            sparsity,
            format: self.config.default_format, // Current format
            has_diagonal_structure,
            has_block_structure,
            optimal_format,
        })
    }

    /// Analyze a dense tensor and report the sparse layout it would map to.
    ///
    /// This returns a [`SparseTensor`] *descriptor* (format + dimensions +
    /// genuine non-zero count) computed from a real analysis of `matrix`; it
    /// does **not** carry the converted sparse storage. The descriptor is
    /// useful for format-selection and capacity planning.
    ///
    /// To obtain an actual sparse representation backed by data, convert the
    /// tensor with [`crate::sparse_from_dense`] and, if needed, re-target the
    /// format with [`crate::convert_sparse_format`], both of which operate over
    /// the concrete [`crate::CooTensor`] / [`crate::CsrTensor`] types.
    pub fn describe_sparse(
        &mut self,
        matrix: &Tensor,
        target_format: Option<SparseFormat>,
    ) -> TorshResult<SparseTensor> {
        let info = self.analyze_matrix(matrix)?;
        let format = target_format.unwrap_or(info.optimal_format);

        let descriptor = SparseTensor::new(
            format,
            info.rows,
            info.cols,
            info.nnz,
            self.config.device,
            self.config.dtype,
        )?;

        self.optimization_stats.format_conversions += 1;
        Ok(descriptor)
    }

    /// Convert tensor to optimal sparse format.
    ///
    /// # Errors
    /// Always returns [`TorshError::NotImplemented`]: the lightweight
    /// [`SparseTensor`] descriptor produced by this processor does not store
    /// the converted non-zero data, so a value returned here could not be used
    /// for real computation. Use [`crate::sparse_from_dense`] (optionally
    /// followed by [`crate::convert_sparse_format`]) for a data-backed
    /// conversion, or [`Self::describe_sparse`] for a metadata-only analysis.
    pub fn to_sparse(
        &mut self,
        _matrix: &Tensor,
        _target_format: Option<SparseFormat>,
    ) -> TorshResult<SparseTensor> {
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::to_sparse does not produce data-backed sparse tensors; \
             use torsh_sparse::sparse_from_dense (+ convert_sparse_format) for a real conversion, \
             or describe_sparse for metadata-only analysis"
                .to_string(),
        ))
    }

    /// Perform sparse matrix-vector multiplication.
    ///
    /// # Errors
    /// Returns [`TorshError::NotImplemented`]. The [`SparseTensor`] descriptor
    /// used by this processor carries no non-zero data, so a numerical result
    /// cannot be produced. Dimension compatibility is still validated first, so
    /// shape errors surface eagerly.
    pub fn spmv(&mut self, matrix: &SparseTensor, vector: &Tensor) -> TorshResult<Tensor> {
        self.validate_spmv_dimensions(matrix, vector)?;
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::spmv operates on a data-less descriptor; \
             use a data-backed sparse type (e.g. torsh_sparse::CsrTensor) for SpMV"
                .to_string(),
        ))
    }

    /// Perform sparse matrix-matrix multiplication.
    ///
    /// # Errors
    /// Returns [`TorshError::NotImplemented`] for the same reason as
    /// [`Self::spmv`]: the descriptor holds no data. Shape compatibility is
    /// validated before erroring.
    pub fn spmm(&mut self, a: &SparseTensor, b: &SparseTensor) -> TorshResult<SparseTensor> {
        self.validate_spmm_dimensions(a, b)?;
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::spmm operates on a data-less descriptor; \
             use data-backed sparse types for SpMM"
                .to_string(),
        ))
    }

    /// Sparse LU factorization with fill-in optimization.
    ///
    /// # Errors
    /// Returns [`TorshError::NotImplemented`]: factorization requires the
    /// actual matrix entries, which the descriptor does not hold. The square
    /// shape requirement is validated first.
    pub fn sparse_lu(&mut self, matrix: &SparseTensor) -> TorshResult<SparseFactorization> {
        if matrix.rows != matrix.cols {
            return Err(TorshError::InvalidArgument(
                "LU factorization requires square matrix".to_string(),
            ));
        }
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::sparse_lu operates on a data-less descriptor; \
             factorization needs the actual non-zero values"
                .to_string(),
        ))
    }

    /// Solve sparse linear system Ax = b.
    ///
    /// # Errors
    /// Returns [`TorshError::NotImplemented`] after validating that `rhs` is
    /// dimensionally compatible with `matrix`; the descriptor stores no data to
    /// solve against.
    pub fn sparse_solve(
        &mut self,
        matrix: &SparseTensor,
        rhs: &Tensor,
        method: SolverMethod,
    ) -> TorshResult<Tensor> {
        self.validate_solve_dimensions(matrix, rhs)?;
        let _ = method;
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::sparse_solve operates on a data-less descriptor; \
             provide a data-backed sparse system to solve"
                .to_string(),
        ))
    }

    /// Compress sparse matrix to reduce memory usage.
    ///
    /// # Errors
    /// Returns [`TorshError::NotImplemented`]: compression requires the
    /// descriptor to own its non-zero data, which it does not. Returning the
    /// input unchanged while reporting "memory saved" would be a fabricated
    /// result.
    pub fn compress(&mut self, _matrix: &SparseTensor) -> TorshResult<SparseTensor> {
        Err(TorshError::NotImplemented(
            "SciRS2SparseProcessor::compress operates on a data-less descriptor; \
             compress a data-backed sparse type instead"
                .to_string(),
        ))
    }

    /// Get optimization statistics
    pub fn get_stats(&self) -> &OptimizationStats {
        &self.optimization_stats
    }

    /// Reset optimization statistics
    pub fn reset_stats(&mut self) {
        self.optimization_stats = OptimizationStats::default();
    }

    // Helper methods (placeholder implementations)

    fn count_nonzeros(&self, matrix: &Tensor) -> TorshResult<usize> {
        // Count the genuinely non-zero entries of the dense matrix.
        let data = matrix.to_vec()?;
        Ok(data.iter().filter(|&&v| v != 0.0).count())
    }

    fn has_diagonal_pattern(&self, _matrix: &Tensor) -> TorshResult<bool> {
        // Placeholder: analyze diagonal structure
        Ok(false)
    }

    fn has_block_pattern(&self, _matrix: &Tensor) -> TorshResult<bool> {
        // Placeholder: analyze block structure
        Ok(false)
    }

    fn recommend_format(
        &self,
        rows: usize,
        cols: usize,
        nnz: usize,
        sparsity: f64,
        has_diagonal: bool,
        has_block: bool,
    ) -> SparseFormat {
        if has_diagonal && sparsity > 0.8 {
            SparseFormat::Dia
        } else if has_block {
            SparseFormat::Bsr
        } else if self.config.use_gpu {
            SparseFormat::Ell
        } else if rows > cols && sparsity > 0.9 {
            SparseFormat::Csr
        } else if cols > rows && sparsity > 0.9 {
            SparseFormat::Csc
        } else if nnz < 1000 {
            SparseFormat::Coo
        } else {
            SparseFormat::Csr
        }
    }

    fn validate_spmv_dimensions(&self, matrix: &SparseTensor, vector: &Tensor) -> TorshResult<()> {
        let vec_shape = vector.shape();
        if vec_shape.ndim() != 1 || vec_shape.dims()[0] != matrix.cols {
            return Err(TorshError::InvalidArgument(
                "Vector dimensions incompatible with matrix".to_string(),
            ));
        }
        Ok(())
    }

    fn validate_spmm_dimensions(&self, a: &SparseTensor, b: &SparseTensor) -> TorshResult<()> {
        if a.cols != b.rows {
            return Err(TorshError::InvalidArgument(
                "Matrix dimensions incompatible for multiplication".to_string(),
            ));
        }
        Ok(())
    }

    fn validate_solve_dimensions(&self, matrix: &SparseTensor, rhs: &Tensor) -> TorshResult<()> {
        let rhs_shape = rhs.shape();
        if rhs_shape.ndim() != 1 || rhs_shape.dims()[0] != matrix.rows {
            return Err(TorshError::InvalidArgument(
                "RHS dimensions incompatible with matrix".to_string(),
            ));
        }
        Ok(())
    }
}

/// Sparse tensor representation with format information
#[derive(Debug, Clone)]
pub struct SparseTensor {
    pub format: SparseFormat,
    pub rows: usize,
    pub cols: usize,
    pub nnz: usize,
    pub device: DeviceType,
    pub dtype: DType,
    // Additional fields would contain actual sparse data
}

impl SparseTensor {
    pub fn new(
        format: SparseFormat,
        rows: usize,
        cols: usize,
        nnz: usize,
        device: DeviceType,
        dtype: DType,
    ) -> TorshResult<Self> {
        Ok(Self {
            format,
            rows,
            cols,
            nnz,
            device,
            dtype,
        })
    }

    pub fn sparsity(&self) -> f64 {
        1.0 - (self.nnz as f64 / (self.rows * self.cols) as f64)
    }

    pub fn memory_size(&self) -> usize {
        // Estimate memory usage based on format and nnz
        match self.format {
            SparseFormat::Coo => self.nnz * 3 * std::mem::size_of::<i32>(),
            SparseFormat::Csr => {
                self.nnz * 2 * std::mem::size_of::<i32>()
                    + (self.rows + 1) * std::mem::size_of::<i32>()
            }
            SparseFormat::Csc => {
                self.nnz * 2 * std::mem::size_of::<i32>()
                    + (self.cols + 1) * std::mem::size_of::<i32>()
            }
            _ => self.nnz * 3 * std::mem::size_of::<i32>(), // Conservative estimate
        }
    }
}

/// Sparse matrix factorization container
#[derive(Debug, Clone)]
pub struct SparseFactorization {
    pub factorization_type: FactorizationType,
    pub size: usize,
    pub format: SparseFormat,
    // Additional fields would contain factorization data
}

impl SparseFactorization {
    pub fn new(factorization_type: FactorizationType, size: usize, format: SparseFormat) -> Self {
        Self {
            factorization_type,
            size,
            format,
        }
    }
}

/// Types of sparse matrix factorizations
#[derive(Debug, Clone, Copy)]
pub enum FactorizationType {
    Lu,
    Cholesky,
    Qr,
    Ldl,
}

/// Sparse linear system solver methods
#[derive(Debug, Clone, Copy)]
pub enum SolverMethod {
    Direct,
    Iterative,
    Auto,
}

/// Factory functions for creating processors

/// Create a general-purpose sparse processor
pub fn create_sparse_processor() -> SciRS2SparseProcessor {
    SciRS2SparseProcessor::default()
}

/// Create a GPU-optimized sparse processor
pub fn create_gpu_sparse_processor() -> SciRS2SparseProcessor {
    SciRS2SparseProcessor::gpu_optimized()
}

/// Create a neural network-optimized sparse processor
pub fn create_nn_sparse_processor() -> SciRS2SparseProcessor {
    SciRS2SparseProcessor::neural_network_optimized()
}

// Export components for external use (commented to avoid re-export issues)
// External crates should import directly from this module