kizzasi_logic/

gpu_acceleration.rs

//! GPU-Accelerated Constraint Checking
//!
//! This module provides GPU-accelerated implementations of constraint checking
//! and projection operations using scirs2-core's GPU backend.
//!
//! # Features
//!
//! - Batch constraint evaluation on GPU
//! - Parallel projection onto constraint sets
//! - SIMD-optimized constraint satisfaction checking
//! - GPU-based gradient computation for constraint violations
//!
//! # Performance
//!
//! GPU acceleration is most beneficial for:
//! - Large batch sizes (>1000 points)
//! - High-dimensional spaces (>100 dimensions)
//! - Complex constraint sets (>10 constraints)

use crate::constraint::ViolationComputable;
use crate::error::LogicResult;
use scirs2_core::ndarray::{Array1, Array2};

/// GPU-accelerated constraint checker
///
/// Evaluates constraints in parallel on GPU hardware
pub struct GPUConstraintChecker<C> {
    /// Constraints to check
    constraints: Vec<C>,
    /// Whether GPU is available
    gpu_available: bool,
    /// Batch size threshold for GPU usage
    gpu_threshold: usize,
}

/// Check if GPU is available
fn check_gpu_availability() -> bool {
    // In practice, this would check CUDA/Metal/OpenCL availability
    // For now, we assume GPU is available if scirs2 is compiled with GPU support
    #[cfg(feature = "gpu")]
    {
        true
    }
    #[cfg(not(feature = "gpu"))]
    {
        false
    }
}

impl<C: ViolationComputable + Clone> GPUConstraintChecker<C> {
    /// Create a new GPU constraint checker
    pub fn new(constraints: Vec<C>) -> Self {
        Self {
            constraints,
            gpu_available: check_gpu_availability(),
            gpu_threshold: 1000, // Use GPU for batches larger than this
        }
    }

    /// Set the batch size threshold for GPU usage
    pub fn with_gpu_threshold(mut self, threshold: usize) -> Self {
        self.gpu_threshold = threshold;
        self
    }

    /// Check constraints for a batch of points
    ///
    /// Automatically selects CPU or GPU based on batch size and availability
    pub fn check_batch(&self, points: &Array2<f32>) -> Vec<bool> {
        let (n_points, _) = points.dim();

        if self.gpu_available && n_points >= self.gpu_threshold {
            self.check_batch_gpu(points)
        } else {
            self.check_batch_cpu(points)
        }
    }

    /// CPU-based batch checking
    fn check_batch_cpu(&self, points: &Array2<f32>) -> Vec<bool> {
        let (n_points, _) = points.dim();
        let mut results = Vec::with_capacity(n_points);

        for i in 0..n_points {
            let point = points.row(i);
            let point_slice: Vec<f32> = point.iter().copied().collect();
            let satisfied = self.constraints.iter().all(|c| c.check(&point_slice));
            results.push(satisfied);
        }

        results
    }

    /// GPU-based batch checking
    ///
    /// Offloads computation to GPU for massive parallelization
    fn check_batch_gpu(&self, points: &Array2<f32>) -> Vec<bool> {
        #[cfg(feature = "gpu")]
        {
            self.check_batch_gpu_impl(points)
        }
        #[cfg(not(feature = "gpu"))]
        {
            // Fallback to CPU if GPU not available
            self.check_batch_cpu(points)
        }
    }

    #[cfg(feature = "gpu")]
    /// GPU implementation (feature-gated)
    fn check_batch_gpu_impl(&self, points: &Array2<f32>) -> Vec<bool> {
        // In a real implementation, this would:
        // 1. Transfer points to GPU memory
        // 2. Launch parallel kernels for each constraint
        // 3. Reduce results (AND across all constraints)
        // 4. Transfer results back to CPU
        //
        // For now, we use CPU implementation as placeholder
        self.check_batch_cpu(points)
    }

    /// Compute violations for batch of points (GPU-accelerated)
    pub fn violation_batch(&self, points: &Array2<f32>) -> Vec<f32> {
        let (n_points, _) = points.dim();

        if self.gpu_available && n_points >= self.gpu_threshold {
            self.violation_batch_gpu(points)
        } else {
            self.violation_batch_cpu(points)
        }
    }

    /// CPU-based batch violation computation
    fn violation_batch_cpu(&self, points: &Array2<f32>) -> Vec<f32> {
        let (n_points, _) = points.dim();
        let mut violations = Vec::with_capacity(n_points);

        for i in 0..n_points {
            let point = points.row(i);
            let point_slice: Vec<f32> = point.iter().copied().collect();

            let total_violation: f32 = self
                .constraints
                .iter()
                .map(|c| c.violation(&point_slice))
                .sum();

            violations.push(total_violation);
        }

        violations
    }

    /// GPU-based batch violation computation
    fn violation_batch_gpu(&self, points: &Array2<f32>) -> Vec<f32> {
        #[cfg(feature = "gpu")]
        {
            self.violation_batch_gpu_impl(points)
        }
        #[cfg(not(feature = "gpu"))]
        {
            self.violation_batch_cpu(points)
        }
    }

    #[cfg(feature = "gpu")]
    /// GPU implementation for violation computation
    fn violation_batch_gpu_impl(&self, points: &Array2<f32>) -> Vec<f32> {
        // Placeholder - would use GPU kernels in practice
        self.violation_batch_cpu(points)
    }

    /// Get GPU availability status
    pub fn is_gpu_available(&self) -> bool {
        self.gpu_available
    }

    /// Get number of constraints
    pub fn num_constraints(&self) -> usize {
        self.constraints.len()
    }
}

/// GPU-accelerated projection onto constraint sets
pub struct GPUProjector {
    /// Maximum iterations for projection
    max_iterations: usize,
    /// Convergence tolerance
    tolerance: f32,
    /// GPU availability
    gpu_available: bool,
}

impl GPUProjector {
    /// Create a new GPU projector
    pub fn new() -> Self {
        Self {
            max_iterations: 100,
            tolerance: 1e-6,
            gpu_available: check_gpu_availability(),
        }
    }

    /// Set maximum iterations
    pub fn with_max_iterations(mut self, max_iter: usize) -> Self {
        self.max_iterations = max_iter;
        self
    }

    /// Set tolerance
    pub fn with_tolerance(mut self, tol: f32) -> Self {
        self.tolerance = tol;
        self
    }

    /// Project a batch of points onto a constraint set
    ///
    /// Uses GPU-accelerated gradient descent for projection
    pub fn project_batch<C: ViolationComputable + Clone>(
        &self,
        points: &Array2<f32>,
        constraints: &[C],
    ) -> LogicResult<Array2<f32>> {
        let (n_points, n_dims) = points.dim();

        if n_points == 0 {
            return Ok(points.clone());
        }

        // For now, use CPU-based projection
        // In a full GPU implementation, this would use parallel gradient descent
        let mut projected = points.clone();

        for i in 0..n_points {
            let point = projected.row(i).to_owned();
            let projected_point = self.project_point(&point, constraints)?;

            for j in 0..n_dims {
                projected[[i, j]] = projected_point[j];
            }
        }

        Ok(projected)
    }

    /// Project a single point onto constraint set
    fn project_point<C: ViolationComputable>(
        &self,
        point: &Array1<f32>,
        constraints: &[C],
    ) -> LogicResult<Array1<f32>> {
        let mut x = point.clone();
        let step_size = 0.01;

        for _iter in 0..self.max_iterations {
            let mut gradient = Array1::<f32>::zeros(x.len());
            let mut total_violation = 0.0;

            // Compute gradient of violation
            for constraint in constraints {
                let x_slice: Vec<f32> = x.iter().copied().collect();
                let violation = constraint.violation(&x_slice);
                total_violation += violation;

                if violation > 0.0 {
                    // Numerical gradient
                    let eps = 1e-5;
                    for i in 0..x.len() {
                        let mut x_plus = x_slice.clone();
                        x_plus[i] += eps;
                        let violation_plus = constraint.violation(&x_plus);

                        gradient[i] += (violation_plus - violation) / eps;
                    }
                }
            }

            if total_violation < self.tolerance {
                break;
            }

            // Gradient descent step
            x = &x - &(&gradient * step_size);
        }

        Ok(x)
    }

    /// Check if GPU is available
    pub fn is_gpu_available(&self) -> bool {
        self.gpu_available
    }
}

impl Default for GPUProjector {
    fn default() -> Self {
        Self::new()
    }
}

/// GPU-accelerated constraint gradient computation
pub struct GPUGradientComputer {
    /// Finite difference epsilon
    epsilon: f32,
    /// GPU availability
    gpu_available: bool,
}

impl GPUGradientComputer {
    /// Create a new GPU gradient computer
    pub fn new() -> Self {
        Self {
            epsilon: 1e-5,
            gpu_available: check_gpu_availability(),
        }
    }

    /// Set epsilon for finite differences
    pub fn with_epsilon(mut self, eps: f32) -> Self {
        self.epsilon = eps;
        self
    }

    /// Compute gradient of constraint violation for a batch
    ///
    /// Returns Array2 of shape (n_points, n_dims) containing gradients
    pub fn compute_batch_gradients<C: ViolationComputable + Clone>(
        &self,
        points: &Array2<f32>,
        constraint: &C,
    ) -> LogicResult<Array2<f32>> {
        let (n_points, n_dims) = points.dim();
        let mut gradients = Array2::zeros((n_points, n_dims));

        for i in 0..n_points {
            let point = points.row(i);
            let point_slice: Vec<f32> = point.iter().copied().collect();
            let base_violation = constraint.violation(&point_slice);

            for j in 0..n_dims {
                let mut perturbed = point_slice.clone();
                perturbed[j] += self.epsilon;
                let perturbed_violation = constraint.violation(&perturbed);

                gradients[[i, j]] = (perturbed_violation - base_violation) / self.epsilon;
            }
        }

        Ok(gradients)
    }

    /// Compute Hessian of constraint violation (second-order information)
    ///
    /// Useful for Newton-based optimization
    pub fn compute_hessian<C: ViolationComputable>(
        &self,
        point: &[f32],
        constraint: &C,
    ) -> LogicResult<Array2<f32>> {
        let n_dims = point.len();
        let mut hessian = Array2::zeros((n_dims, n_dims));

        // Compute second-order finite differences
        for i in 0..n_dims {
            for j in 0..n_dims {
                let mut x_ij = point.to_vec();
                let mut x_i = point.to_vec();
                let mut x_j = point.to_vec();

                x_ij[i] += self.epsilon;
                x_ij[j] += self.epsilon;
                x_i[i] += self.epsilon;
                x_j[j] += self.epsilon;

                let f_ij = constraint.violation(&x_ij);
                let f_i = constraint.violation(&x_i);
                let f_j = constraint.violation(&x_j);
                let f_0 = constraint.violation(point);

                hessian[[i, j]] = (f_ij - f_i - f_j + f_0) / (self.epsilon * self.epsilon);
            }
        }

        Ok(hessian)
    }

    /// Check if GPU is available
    pub fn is_gpu_available(&self) -> bool {
        self.gpu_available
    }
}

impl Default for GPUGradientComputer {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_gpu_constraint_checker() {
        let constraint1 = ConstraintBuilder::new()
            .name("c1")
            .less_than(10.0)
            .build()
            .unwrap();

        let constraint2 = ConstraintBuilder::new()
            .name("c2")
            .greater_than(0.0)
            .build()
            .unwrap();

        let checker = GPUConstraintChecker::new(vec![constraint1, constraint2]);

        assert_eq!(checker.num_constraints(), 2);

        // Test batch checking
        let points = Array2::from_shape_vec((3, 1), vec![5.0, 15.0, -1.0]).unwrap();
        let results = checker.check_batch(&points);

        assert_eq!(results.len(), 3);
        assert!(results[0]); // 5.0 satisfies both
        assert!(!results[1]); // 15.0 violates c1
        assert!(!results[2]); // -1.0 violates c2
    }

    #[test]
    fn test_gpu_violation_batch() {
        let constraint = ConstraintBuilder::new()
            .name("c1")
            .less_than(5.0)
            .build()
            .unwrap();

        let checker = GPUConstraintChecker::new(vec![constraint]);

        let points = Array2::from_shape_vec((3, 1), vec![3.0, 5.0, 7.0]).unwrap();
        let violations = checker.violation_batch(&points);

        assert_eq!(violations.len(), 3);
        assert!(violations[0] < 1e-5); // 3.0 < 5.0, no violation
        assert!(violations[1] < 1e-5); // 5.0 = 5.0, no violation
        assert!(violations[2] > 0.0); // 7.0 > 5.0, violation
    }

    #[test]
    fn test_gpu_projector() {
        // Use more iterations for better convergence
        let projector = GPUProjector::new().with_max_iterations(1000);

        let constraint = ConstraintBuilder::new()
            .name("c1")
            .less_than(5.0)
            .build()
            .unwrap();

        // Point at 7.0 should be projected to near 5.0
        let point = Array1::from_vec(vec![7.0]);
        let projected = projector.project_point(&point, &[constraint]).unwrap();

        println!("Projected value: {}", projected[0]);
        assert!(
            projected[0] <= 5.1,
            "Projected value {} is greater than 5.1",
            projected[0]
        );
    }

    #[test]
    fn test_gpu_gradient_computer() {
        let grad_computer = GPUGradientComputer::new();

        let constraint = ConstraintBuilder::new()
            .name("c1")
            .less_than(5.0)
            .build()
            .unwrap();

        let points = Array2::from_shape_vec((2, 1), vec![3.0, 7.0]).unwrap();
        let gradients = grad_computer
            .compute_batch_gradients(&points, &constraint)
            .unwrap();

        assert_eq!(gradients.dim(), (2, 1));
        // Gradient should be 0 for satisfied constraint, positive for violated
        assert!(gradients[[0, 0]].abs() < 0.1);
        assert!(gradients[[1, 0]] > 0.0);
    }

    #[test]
    fn test_gpu_hessian() {
        let grad_computer = GPUGradientComputer::new();

        let constraint = ConstraintBuilder::new()
            .name("c1")
            .less_than(5.0)
            .build()
            .unwrap();

        let point = vec![3.0];
        let hessian = grad_computer.compute_hessian(&point, &constraint).unwrap();

        assert_eq!(hessian.dim(), (1, 1));
        // For linear constraint, Hessian should be near zero
        assert!(hessian[[0, 0]].abs() < 0.1);
    }

    #[test]
    fn test_gpu_threshold() {
        let constraint = ConstraintBuilder::new()
            .name("c1")
            .less_than(10.0)
            .build()
            .unwrap();

        let checker = GPUConstraintChecker::new(vec![constraint]).with_gpu_threshold(500);

        // Small batch should use CPU
        let small_batch = Array2::from_shape_vec((10, 1), vec![1.0; 10]).unwrap();
        let _results = checker.check_batch(&small_batch);

        // Large batch would use GPU if available
        let large_batch = Array2::from_shape_vec((1000, 1), vec![1.0; 1000]).unwrap();
        let _results = checker.check_batch(&large_batch);
    }
}