rustorch 0.6.29

//! GPU-accelerated sparse operations
//! GPU加速スパース演算

use super::{SparseFormat, SparseOps, SparseTensor};
use crate::error::{RusTorchError, RusTorchResult};
use ndarray::{Array1, Array2, ArrayD};
use num_traits::{Float, FromPrimitive, One, Zero};

/// GPU-accelerated sparse operations using CUDA
/// CUDAを使用したGPU加速スパース演算
#[cfg(feature = "cuda")]
pub struct CudaSparseOps;

#[cfg(feature = "cuda")]
impl CudaSparseOps {
    /// Initialize cuSPARSE library context
    /// cuSPARSEライブラリコンテキストを初期化
    pub fn init() -> RusTorchResult<Self> {
        // In a full implementation, would initialize cuSPARSE context
        // 完全実装では、cuSPARSEコンテキストを初期化
        Ok(CudaSparseOps)
    }

    /// GPU-accelerated sparse matrix-vector multiplication
    /// GPU加速スパース行列ベクトル乗算
    pub fn spmv<T: Float + Copy + std::ops::AddAssign>(
        &self,
        sparse_matrix: &SparseTensor<T>,
        vector: &Array1<T>,
    ) -> RusTorchResult<Array1<T>> {
        if sparse_matrix.format != SparseFormat::CSR {
            return Err(RusTorchError::InvalidOperation {
                operation: "cuda_spmv".to_string(),
                message: "CUDA SpMV requires CSR format".to_string(),
            });
        }

        // In a full implementation, would use cuSPARSE csrmv
        // 完全実装では、cuSPARSE csrmvを使用

        // For now, fall back to CPU implementation using direct computation
        if sparse_matrix.format != SparseFormat::CSR {
            return Err(RusTorchError::InvalidOperation {
                operation: "spmv_gpu".to_string(),
                message: "Only CSR format supported".to_string(),
            });
        }

        // For CSR format: indices[0] = row_ptr, indices[1] = col_indices
        if sparse_matrix.indices.len() < 2 {
            return Err(RusTorchError::InvalidOperation {
                operation: "spmv_gpu".to_string(),
                message: "CSR format requires row_ptr and col_indices".to_string(),
            });
        }

        let row_ptr = &sparse_matrix.indices[0];
        let col_indices = &sparse_matrix.indices[1];
        let mut result = Array1::zeros(sparse_matrix.shape[0]);

        for i in 0..sparse_matrix.shape[0] {
            let start = row_ptr[i];
            let end = row_ptr[i + 1];
            for j in start..end {
                result[i] += sparse_matrix.values[j] * vector[col_indices[j]];
            }
        }
        Ok(result)
    }

    /// GPU-accelerated sparse matrix-matrix multiplication
    /// GPU加速スパース行列行列乗算
    pub fn spmm<T: Float + Copy + std::ops::AddAssign>(
        &self,
        sparse_a: &SparseTensor<T>,
        dense_b: &Array2<T>,
    ) -> RusTorchResult<Array2<T>> {
        if sparse_a.format != SparseFormat::CSR {
            return Err(RusTorchError::InvalidOperation {
                operation: "cuda_spmm".to_string(),
                message: "CUDA SpMM requires CSR format".to_string(),
            });
        }

        // In a full implementation, would use cuSPARSE csrmm
        // 完全実装では、cuSPARSE csrmmを使用

        // For now, fall back to CPU implementation - simplified matrix multiplication
        if sparse_a.format != SparseFormat::CSR {
            return Err(RusTorchError::InvalidOperation {
                operation: "spmm_gpu".to_string(),
                message: "Only CSR format supported".to_string(),
            });
        }

        // For CSR format: indices[0] = row_ptr, indices[1] = col_indices
        if sparse_a.indices.len() < 2 {
            return Err(RusTorchError::InvalidOperation {
                operation: "spmm_gpu".to_string(),
                message: "CSR format requires row_ptr and col_indices".to_string(),
            });
        }

        let row_ptr = &sparse_a.indices[0];
        let col_indices = &sparse_a.indices[1];
        let mut result = Array2::zeros((sparse_a.shape[0], dense_b.ncols()));

        for i in 0..sparse_a.shape[0] {
            let start = row_ptr[i];
            let end = row_ptr[i + 1];
            for j in start..end {
                let col_idx = col_indices[j];
                let sparse_val = sparse_a.values[j];
                for k in 0..dense_b.ncols() {
                    result[[i, k]] += sparse_val * dense_b[[col_idx, k]];
                }
            }
        }
        Ok(result)
    }

    /// GPU-accelerated sparse-sparse addition
    /// GPU加速スパース-スパース加算
    pub fn sparse_add<T: Float + Copy + std::ops::AddAssign>(
        &self,
        a: &SparseTensor<T>,
        b: &SparseTensor<T>,
    ) -> RusTorchResult<SparseTensor<T>> {
        // In a full implementation, would use cuSPARSE csrgemm
        // 完全実装では、cuSPARSE csrgemmを使用

        // For now, fall back to CPU implementation - simplified for same format
        if a.format != b.format {
            return Err(RusTorchError::InvalidOperation {
                operation: "sparse_add_gpu".to_string(),
                message: "Both tensors must have same format".to_string(),
            });
        }

        // Simple implementation: create result with union of indices
        let mut result = a.clone();

        // For CSR format, this is simplified - would need proper merge in full implementation
        for (i, &val) in b.values.iter().enumerate() {
            if i < result.values.len() {
                result.values[i] += val;
            }
        }

        Ok(result)
    }

    /// Sparse tensor format conversion on GPU
    /// GPU上でのスパーステンソル形式変換
    pub fn convert_format<T: Float + Copy>(
        &self,
        tensor: &SparseTensor<T>,
        target_format: SparseFormat,
    ) -> RusTorchResult<SparseTensor<T>> {
        match (tensor.format, target_format) {
            (SparseFormat::COO, SparseFormat::CSR) => {
                // Would use cuSPARSE XcooSortByRow + XcoocheckSorted + Xcoo2csr
                // For now, return error - conversion not implemented
                Err(RusTorchError::NotImplemented {
                    feature: "COO to CSR conversion".to_string(),
                })
            }
            (SparseFormat::CSR, SparseFormat::COO) => {
                // Would use cuSPARSE Xcsr2coo
                // For now, return error - conversion not implemented
                Err(RusTorchError::NotImplemented {
                    feature: "CSR to COO conversion".to_string(),
                })
            }
            _ => Err(RusTorchError::NotImplemented {
                feature: format!(
                    "GPU conversion from {:?} to {:?}",
                    tensor.format, target_format
                ),
            }),
        }
    }
}

/// Metal-accelerated sparse operations for Apple Silicon
/// Apple Silicon用Metal加速スパース演算
#[cfg(feature = "metal")]
pub struct MetalSparseOps {
    /// Metal device for computation
    /// 計算用Metalデバイス
    pub device: metal::Device,
    /// Command queue for GPU operations
    /// GPU演算用コマンドキュー
    pub command_queue: metal::CommandQueue,
}

#[cfg(feature = "metal")]
impl MetalSparseOps {
    /// Initialize Metal sparse operations
    /// Metalスパース演算を初期化
    pub fn new() -> RusTorchResult<Self> {
        let device =
            metal::Device::system_default().ok_or_else(|| RusTorchError::BackendUnavailable {
                backend: "Metal".to_string(),
            })?;

        let command_queue = device.new_command_queue();

        Ok(Self {
            device,
            command_queue,
        })
    }

    /// Metal-accelerated sparse matrix operations
    /// Metal加速スパース行列演算
    pub fn spmv<T: Float + Copy + std::ops::AddAssign>(
        &self,
        sparse_matrix: &SparseTensor<T>,
        vector: &Array1<T>,
    ) -> RusTorchResult<Array1<T>> {
        // In a full implementation, would use Metal Performance Shaders
        // 完全実装では、Metal Performance Shadersを使用

        // For now, fall back to CPU implementation using direct computation
        if sparse_matrix.format != SparseFormat::CSR {
            return Err(RusTorchError::InvalidOperation {
                operation: "metal_spmv".to_string(),
                message: "Only CSR format supported".to_string(),
            });
        }

        // For CSR format: indices[0] = row_ptr, indices[1] = col_indices
        if sparse_matrix.indices.len() < 2 {
            return Err(RusTorchError::InvalidOperation {
                operation: "spmv_gpu".to_string(),
                message: "CSR format requires row_ptr and col_indices".to_string(),
            });
        }

        let row_ptr = &sparse_matrix.indices[0];
        let col_indices = &sparse_matrix.indices[1];
        let mut result = Array1::zeros(sparse_matrix.shape[0]);

        for i in 0..sparse_matrix.shape[0] {
            let start = row_ptr[i];
            let end = row_ptr[i + 1];
            for j in start..end {
                result[i] += sparse_matrix.values[j] * vector[col_indices[j]];
            }
        }
        Ok(result)
    }
}

/// Optimized sparse tensor memory layout for GPU computation
/// GPU計算用最適化スパーステンソルメモリレイアウト
#[derive(Debug, Clone)]
pub struct GpuSparseLayout<T: Float> {
    /// Coalesced memory layout for GPU efficiency
    /// GPU効率のための結合メモリレイアウト
    pub values: Vec<T>,
    /// Optimized index layout
    /// 最適化インデックスレイアウト
    pub indices: Vec<Vec<u32>>, // Use u32 for GPU compatibility
    /// Memory alignment for SIMD operations
    /// SIMD演算用メモリアライメント
    pub alignment: usize,
}

impl<T: Float + Copy> GpuSparseLayout<T> {
    /// Convert sparse tensor to GPU-optimized layout
    /// スパーステンソルをGPU最適化レイアウトに変換
    pub fn from_sparse_tensor(tensor: &SparseTensor<T>) -> Self {
        let values = tensor.values.to_vec();
        let indices: Vec<Vec<u32>> = tensor
            .indices
            .iter()
            .map(|arr| arr.iter().map(|&x| x as u32).collect())
            .collect();

        Self {
            values,
            indices,
            alignment: 16, // 128-bit alignment for SIMD
        }
    }

    /// Get memory usage in bytes with alignment padding
    /// アライメントパディング付きメモリ使用量（バイト）を取得
    pub fn memory_usage(&self) -> usize {
        let value_bytes = self.values.len() * std::mem::size_of::<T>();
        let index_bytes: usize = self
            .indices
            .iter()
            .map(|arr| arr.len() * std::mem::size_of::<u32>())
            .sum();

        // Add alignment padding
        let total_unaligned = value_bytes + index_bytes;
        (total_unaligned + self.alignment - 1) & !(self.alignment - 1)
    }

    /// Validate GPU memory constraints
    /// GPUメモリ制約を検証
    pub fn validate_gpu_memory(&self, available_memory: usize) -> RusTorchResult<()> {
        let required_memory = self.memory_usage();

        if required_memory > available_memory {
            return Err(RusTorchError::OutOfMemory {
                requested: required_memory,
                available: available_memory,
            });
        }

        Ok(())
    }
}

/// Sparse operation batching for GPU efficiency
/// GPU効率のためのスパース演算バッチング
pub struct SparseBatchProcessor<T: Float> {
    /// Maximum batch size for GPU memory
    /// GPUメモリの最大バッチサイズ
    pub max_batch_size: usize,
    /// Current batch of sparse operations
    /// 現在のスパース演算バッチ
    pub batch: Vec<SparseTensor<T>>,
}

impl<T: Float + Copy + Zero + One + std::ops::AddAssign + PartialOrd + FromPrimitive>
    SparseBatchProcessor<T>
where
    T: Zero + One + std::ops::AddAssign + FromPrimitive,
{
    /// Create sparse batch processor
    /// スパースバッチプロセッサを作成
    pub fn new(max_batch_size: usize) -> Self {
        Self {
            max_batch_size,
            batch: Vec::new(),
        }
    }

    /// Add sparse tensor to batch
    /// スパーステンソルをバッチに追加
    pub fn add_to_batch(&mut self, tensor: SparseTensor<T>) -> RusTorchResult<()> {
        if self.batch.len() >= self.max_batch_size {
            return Err(RusTorchError::InvalidOperation {
                operation: "sparse_batch_add".to_string(),
                message: "Batch is full".to_string(),
            });
        }

        self.batch.push(tensor);
        Ok(())
    }

    /// Process entire batch with GPU operations
    /// GPU演算でバッチ全体を処理
    pub fn process_batch(&mut self) -> RusTorchResult<Vec<Array1<T>>> {
        let mut results = Vec::new();

        for sparse_tensor in &self.batch {
            // Placeholder for actual GPU batch processing
            // 実際のGPUバッチ処理のプレースホルダー
            let dummy_vector = Array1::ones(sparse_tensor.shape[1]);
            let result = sparse_tensor.spmv(&dummy_vector)?;
            results.push(result);
        }

        self.batch.clear();
        Ok(results)
    }

    /// Get current batch utilization
    /// 現在のバッチ利用率を取得
    pub fn batch_utilization(&self) -> f32 {
        self.batch.len() as f32 / self.max_batch_size as f32
    }
}

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

    #[test]
    fn test_gpu_layout_conversion() {
        let indices = vec![
            Array1::from_vec(vec![0, 1, 2]),
            Array1::from_vec(vec![1, 2, 0]),
        ];
        let values = Array1::from_vec(vec![1.0f32, 2.0, 3.0]);
        let shape = vec![3, 3];

        let sparse_tensor = SparseTensor::from_coo(indices, values, shape).unwrap();
        let gpu_layout = GpuSparseLayout::from_sparse_tensor(&sparse_tensor);

        assert_eq!(gpu_layout.values.len(), 3);
        assert_eq!(gpu_layout.indices.len(), 2);
        assert!(gpu_layout.memory_usage() > 0);
    }

    #[test]
    fn test_sparse_batch_processor() {
        let mut processor = SparseBatchProcessor::new(2);

        let sparse1 = SparseTensor::from_coo(
            vec![Array1::from_vec(vec![0]), Array1::from_vec(vec![0])],
            Array1::from_vec(vec![1.0f32]),
            vec![2, 2],
        )
        .unwrap();

        let sparse2 = SparseTensor::from_coo(
            vec![Array1::from_vec(vec![1]), Array1::from_vec(vec![1])],
            Array1::from_vec(vec![2.0f32]),
            vec![2, 2],
        )
        .unwrap();

        processor.add_to_batch(sparse1).unwrap();
        assert_eq!(processor.batch_utilization(), 0.5);

        processor.add_to_batch(sparse2).unwrap();
        assert_eq!(processor.batch_utilization(), 1.0);

        let results = processor.process_batch().unwrap();
        assert_eq!(results.len(), 2);
        assert_eq!(processor.batch_utilization(), 0.0);
    }

    #[cfg(feature = "cuda")]
    #[test]
    fn test_cuda_sparse_ops() {
        let cuda_ops = CudaSparseOps::init().unwrap();

        let sparse_matrix = SparseTensor::from_coo(
            vec![Array1::from_vec(vec![0, 1]), Array1::from_vec(vec![0, 1])],
            Array1::from_vec(vec![1.0f32, 2.0]),
            vec![2, 2],
        )
        .unwrap()
        .to_csr()
        .unwrap();

        let vector = Array1::from_vec(vec![1.0, 2.0]);
        let result = cuda_ops.spmv(&sparse_matrix, &vector).unwrap();

        assert_eq!(result.len(), 2);
    }
}