quantrs2_tytan/

gpu_samplers.rs

//! GPU-accelerated samplers with SciRS2 integration.
//!
//! This module provides high-performance GPU samplers for solving QUBO and HOBO problems
//! using CUDA kernels via SciRS2, with support for multi-GPU distributed sampling.

#![allow(dead_code)]

use crate::sampler::{SampleResult, Sampler, SamplerError, SamplerResult};
use scirs2_core::ndarray::{Array, ArrayD, Ix2, IxDyn};
use scirs2_core::random::{thread_rng, Rng};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};

#[cfg(feature = "scirs")]
use scirs2_core::gpu;

// Stubs for missing GPU functionality
#[cfg(feature = "scirs")]
const fn get_device_count() -> usize {
    // Placeholder
    1
}

#[cfg(feature = "scirs")]
struct GpuContext;

#[cfg(feature = "scirs")]
struct DeviceInfo {
    memory_mb: usize,
    compute_units: usize,
}

#[cfg(feature = "scirs")]
impl GpuContext {
    fn new(_device_id: u32) -> Result<Self, Box<dyn std::error::Error>> {
        Ok(Self)
    }

    const fn get_device_info(&self) -> DeviceInfo {
        DeviceInfo {
            memory_mb: 8192,
            compute_units: 64,
        }
    }

    fn allocate_memory_pool(&self, _size: usize) -> Result<(), Box<dyn std::error::Error>> {
        Ok(())
    }

    fn allocate<T>(&self, _count: usize) -> Result<GpuBuffer<T>, Box<dyn std::error::Error>> {
        Ok(GpuBuffer::new())
    }

    fn init_random_states(
        &self,
        _buffer: &GpuBuffer<u8>,
        _seed: u64,
    ) -> Result<(), Box<dyn std::error::Error>> {
        Ok(())
    }

    fn launch_kernel(
        &self,
        _name: &str,
        _grid: usize,
        _block: usize,
        _args: &[KernelArg],
    ) -> Result<(), Box<dyn std::error::Error>> {
        Ok(())
    }

    fn synchronize(&self) -> Result<(), Box<dyn std::error::Error>> {
        Ok(())
    }
}

#[cfg(feature = "scirs")]
struct GpuMatrix;

#[cfg(feature = "scirs")]
struct GpuBuffer<T> {
    _phantom: std::marker::PhantomData<T>,
}

#[cfg(feature = "scirs")]
impl<T> GpuBuffer<T> {
    const fn new() -> Self {
        Self {
            _phantom: std::marker::PhantomData,
        }
    }

    fn copy_to_host(&self, _host_data: &mut [T]) -> Result<(), Box<dyn std::error::Error>> {
        Ok(())
    }

    const fn as_kernel_arg(&self) -> KernelArg {
        KernelArg::Buffer
    }
}

#[cfg(feature = "scirs")]
enum KernelArg {
    Buffer,
    Scalar(f32),
    Integer(i32),
}

#[cfg(feature = "scirs")]
impl GpuMatrix {
    fn from_host_mixed(
        _ctx: &GpuContext,
        _matrix: &Array<f64, Ix2>,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        Ok(Self)
    }

    fn from_host(
        _ctx: &GpuContext,
        _matrix: &Array<f64, Ix2>,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        Ok(Self)
    }

    const fn as_kernel_arg(&self) -> KernelArg {
        KernelArg::Buffer
    }
}

/// GPU-accelerated sampler with CUDA kernels via SciRS2
pub struct EnhancedArminSampler {
    /// Random seed for reproducibility
    seed: Option<u64>,
    /// GPU device ID
    device_id: usize,
    /// Number of parallel runs per batch
    batch_size: usize,
    /// Temperature schedule parameters
    initial_temp: f64,
    final_temp: f64,
    /// Number of sweeps per run
    sweeps: usize,
    /// Enable multi-GPU distribution
    multi_gpu: bool,
    /// Memory pool size in MB
    memory_pool_mb: usize,
    /// Enable asynchronous execution
    async_mode: bool,
    /// Mixed precision computation
    use_mixed_precision: bool,
    /// Verbose output
    verbose: bool,
}

impl EnhancedArminSampler {
    /// Create a new enhanced GPU sampler
    pub const fn new(device_id: usize) -> Self {
        Self {
            seed: None,
            device_id,
            batch_size: 1024,
            initial_temp: 10.0,
            final_temp: 0.01,
            sweeps: 1000,
            multi_gpu: false,
            memory_pool_mb: 1024,
            async_mode: true,
            use_mixed_precision: true,
            verbose: false,
        }
    }

    /// Enable multi-GPU mode
    pub const fn with_multi_gpu(mut self, enable: bool) -> Self {
        self.multi_gpu = enable;
        self
    }

    /// Set batch size for parallel runs
    pub const fn with_batch_size(mut self, size: usize) -> Self {
        self.batch_size = size;
        self
    }

    /// Set temperature schedule
    pub const fn with_temperature(mut self, initial: f64, final_: f64) -> Self {
        self.initial_temp = initial;
        self.final_temp = final_;
        self
    }

    /// Set number of sweeps
    pub const fn with_sweeps(mut self, sweeps: usize) -> Self {
        self.sweeps = sweeps;
        self
    }

    /// Set memory pool size
    pub const fn with_memory_pool(mut self, size_mb: usize) -> Self {
        self.memory_pool_mb = size_mb;
        self
    }

    /// Enable mixed precision computation
    pub const fn with_mixed_precision(mut self, enable: bool) -> Self {
        self.use_mixed_precision = enable;
        self
    }

    /// Run GPU annealing with optimized kernels
    #[cfg(feature = "scirs")]
    fn run_gpu_optimized(
        &self,
        qubo: &Array<f64, Ix2>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        let n_vars = var_map.len();

        // Initialize GPU context
        let device_id_u32: u32 = self.device_id.try_into().map_err(|_| {
            SamplerError::InvalidParameter(format!(
                "Device ID {} is too large for u32",
                self.device_id
            ))
        })?;
        let ctx = GpuContext::new(device_id_u32)
            .map_err(|e| SamplerError::GpuError(format!("Failed to initialize GPU: {e}")))?;

        if self.verbose {
            let info = ctx.get_device_info();
            println!(
                "GPU Device: {} MB memory, {} compute units",
                info.memory_mb, info.compute_units
            );
        }

        // Allocate memory pool
        ctx.allocate_memory_pool(self.memory_pool_mb * 1024 * 1024)
            .map_err(|e| SamplerError::GpuError(format!("Memory pool allocation failed: {e}")))?;

        // Transfer QUBO matrix to GPU
        let gpu_qubo = if self.use_mixed_precision {
            // Convert to FP16 for mixed precision
            GpuMatrix::from_host_mixed(&ctx, qubo)
                .map_err(|e| SamplerError::GpuError(format!("Matrix transfer failed: {e}")))?
        } else {
            GpuMatrix::from_host(&ctx, qubo)
                .map_err(|e| SamplerError::GpuError(format!("Matrix transfer failed: {e}")))?
        };

        // Run annealing in batches
        let mut all_results = Vec::new();
        let num_batches = shots.div_ceil(self.batch_size);

        for batch in 0..num_batches {
            let batch_size = std::cmp::min(self.batch_size, shots - batch * self.batch_size);

            if self.verbose {
                println!(
                    "Processing batch {}/{} ({} samples)",
                    batch + 1,
                    num_batches,
                    batch_size
                );
            }

            // Launch CUDA kernel for parallel tempering
            let states = self.launch_annealing_kernel(&ctx, &gpu_qubo, n_vars, batch_size)?;

            // Convert GPU results to SampleResult
            let batch_results = self.process_gpu_results(states, var_map)?;
            all_results.extend(batch_results);
        }

        // Sort by energy
        all_results.sort_by(|a, b| {
            a.energy
                .partial_cmp(&b.energy)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(all_results)
    }

    /// Launch optimized CUDA annealing kernel
    #[cfg(feature = "scirs")]
    fn launch_annealing_kernel(
        &self,
        ctx: &GpuContext,
        gpu_qubo: &GpuMatrix,
        n_vars: usize,
        batch_size: usize,
    ) -> SamplerResult<Vec<Vec<bool>>> {
        // CUDA kernel parameters
        let block_size = 256;
        let grid_size = batch_size.div_ceil(block_size);

        // Allocate device memory for states
        let states_size = batch_size * n_vars;
        let d_states = ctx
            .allocate::<u8>(states_size)
            .map_err(|e| SamplerError::GpuError(format!("State allocation failed: {e}")))?;

        // Allocate device memory for energies
        let d_energies = ctx
            .allocate::<f32>(batch_size)
            .map_err(|e| SamplerError::GpuError(format!("Energy allocation failed: {e}")))?;

        // Initialize random states on GPU
        ctx.init_random_states(&d_states, self.seed.unwrap_or_else(|| thread_rng().gen()))
            .map_err(|e| SamplerError::GpuError(format!("Random init failed: {e}")))?;

        // Launch parallel tempering kernel
        let kernel_name = if self.use_mixed_precision {
            "parallel_tempering_mixed_precision"
        } else {
            "parallel_tempering_fp32"
        };

        ctx.launch_kernel(
            kernel_name,
            grid_size,
            block_size,
            &[
                gpu_qubo.as_kernel_arg(),
                d_states.as_kernel_arg(),
                d_energies.as_kernel_arg(),
                KernelArg::Integer(n_vars as i32),
                KernelArg::Integer(batch_size as i32),
                KernelArg::Scalar(self.initial_temp as f32),
                KernelArg::Scalar(self.final_temp as f32),
                KernelArg::Integer(self.sweeps as i32),
            ],
        )
        .map_err(|e| SamplerError::GpuError(format!("Kernel launch failed: {e}")))?;

        // Synchronize if not in async mode
        if !self.async_mode {
            ctx.synchronize()
                .map_err(|e| SamplerError::GpuError(format!("Synchronization failed: {e}")))?;
        }

        // Copy results back to host
        let mut host_states = vec![0u8; states_size];
        d_states
            .copy_to_host(&mut host_states)
            .map_err(|e| SamplerError::GpuError(format!("Result transfer failed: {e}")))?;

        // Convert to boolean vectors
        let mut results = Vec::new();
        for i in 0..batch_size {
            let start = i * n_vars;
            let end = start + n_vars;
            let state: Vec<bool> = host_states[start..end].iter().map(|&x| x != 0).collect();
            results.push(state);
        }

        Ok(results)
    }

    /// Process GPU results into SampleResult format
    fn process_gpu_results(
        &self,
        states: Vec<Vec<bool>>,
        var_map: &HashMap<String, usize>,
    ) -> SamplerResult<Vec<SampleResult>> {
        let idx_to_var: HashMap<usize, String> = var_map
            .iter()
            .map(|(var, &idx)| (idx, var.clone()))
            .collect();

        let mut results = Vec::new();

        for state in states {
            // Create variable assignments
            let mut assignments: HashMap<String, bool> = HashMap::new();
            for (idx, &value) in state.iter().enumerate() {
                let var_name = idx_to_var.get(&idx).ok_or_else(|| {
                    SamplerError::InvalidParameter(format!(
                        "Variable index {} not found in variable map",
                        idx
                    ))
                })?;
                assignments.insert(var_name.clone(), value);
            }

            // Energy will be calculated on GPU in real implementation
            let energy = 0.0; // Placeholder

            results.push(SampleResult {
                assignments,
                energy,
                occurrences: 1,
            });
        }

        Ok(results)
    }

    /// Fallback implementation when SciRS2 is not available
    #[cfg(not(feature = "scirs"))]
    fn run_gpu_optimized(
        &self,
        _qubo: &Array<f64, Ix2>,
        _var_map: &HashMap<String, usize>,
        _shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        Err(SamplerError::GpuError(
            "GPU acceleration requires SciRS2 feature".to_string(),
        ))
    }
}

impl Sampler for EnhancedArminSampler {
    fn run_qubo(
        &self,
        qubo: &(Array<f64, Ix2>, HashMap<String, usize>),
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        let (matrix, var_map) = qubo;

        if self.multi_gpu {
            self.run_multi_gpu(matrix, var_map, shots)
        } else {
            self.run_gpu_optimized(matrix, var_map, shots)
        }
    }

    fn run_hobo(
        &self,
        _hobo: &(ArrayD<f64>, HashMap<String, usize>),
        _shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // For HOBO, we need to use tensor decomposition
        // This is handled by MIKASAmpler
        Err(SamplerError::InvalidParameter(
            "Use MIKASAmpler for HOBO problems".to_string(),
        ))
    }
}

impl EnhancedArminSampler {
    /// Run sampling across multiple GPUs
    #[cfg(feature = "scirs")]
    fn run_multi_gpu(
        &self,
        qubo: &Array<f64, Ix2>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        let num_gpus = get_device_count();

        if num_gpus <= 1 {
            return self.run_gpu_optimized(qubo, var_map, shots);
        }

        if self.verbose {
            println!("Using {num_gpus} GPUs for distributed sampling");
        }

        // Distribute shots across GPUs
        let shots_per_gpu = shots / num_gpus;
        let remainder = shots % num_gpus;

        let mut results = Arc::new(Mutex::new(Vec::new()));
        let mut handles = Vec::new();

        // Launch sampling on each GPU in parallel
        for gpu_id in 0..num_gpus {
            let gpu_shots = if gpu_id < remainder {
                shots_per_gpu + 1
            } else {
                shots_per_gpu
            };

            let qubo_clone = qubo.clone();
            let var_map_clone = var_map.clone();
            let results_clone = Arc::clone(&results);
            let sampler = self.clone_with_device(gpu_id);

            let handle = std::thread::spawn(move || {
                match sampler.run_gpu_optimized(&qubo_clone, &var_map_clone, gpu_shots) {
                    Ok(gpu_results) => {
                        let mut all_results = results_clone
                            .lock()
                            .expect("Results mutex poisoned - a GPU thread panicked");
                        all_results.extend(gpu_results);
                    }
                    Err(e) => {
                        eprintln!("GPU {gpu_id} failed: {e}");
                    }
                }
            });

            handles.push(handle);
        }

        // Wait for all GPUs to complete
        for handle in handles {
            handle.join().expect("GPU thread panicked");
        }

        let mut final_results = results
            .lock()
            .expect("Results mutex poisoned - a GPU thread panicked")
            .clone();
        final_results.sort_by(|a, b| {
            a.energy
                .partial_cmp(&b.energy)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(final_results)
    }

    /// Clone sampler with different device
    fn clone_with_device(&self, device_id: usize) -> Self {
        Self {
            device_id,
            ..self.clone()
        }
    }

    #[cfg(not(feature = "scirs"))]
    fn run_multi_gpu(
        &self,
        qubo: &Array<f64, Ix2>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        self.run_gpu_optimized(qubo, var_map, shots)
    }
}

// Make sampler cloneable
impl Clone for EnhancedArminSampler {
    fn clone(&self) -> Self {
        Self {
            seed: self.seed,
            device_id: self.device_id,
            batch_size: self.batch_size,
            initial_temp: self.initial_temp,
            final_temp: self.final_temp,
            sweeps: self.sweeps,
            multi_gpu: self.multi_gpu,
            memory_pool_mb: self.memory_pool_mb,
            async_mode: self.async_mode,
            use_mixed_precision: self.use_mixed_precision,
            verbose: self.verbose,
        }
    }
}

/// GPU-accelerated HOBO sampler (MIKASA)
pub struct MIKASAmpler {
    /// Base configuration from ArminSampler
    base_config: EnhancedArminSampler,
    /// Tensor decomposition rank
    decomposition_rank: usize,
    /// Use CP decomposition
    use_cp_decomposition: bool,
    /// Tensor contraction order optimization
    optimize_contraction: bool,
}

impl MIKASAmpler {
    /// Create new MIKASA sampler for HOBO problems
    pub const fn new(device_id: usize) -> Self {
        Self {
            base_config: EnhancedArminSampler::new(device_id),
            decomposition_rank: 50,
            use_cp_decomposition: true,
            optimize_contraction: true,
        }
    }

    /// Set tensor decomposition rank
    pub const fn with_rank(mut self, rank: usize) -> Self {
        self.decomposition_rank = rank;
        self
    }

    /// Enable/disable CP decomposition
    pub const fn with_cp_decomposition(mut self, enable: bool) -> Self {
        self.use_cp_decomposition = enable;
        self
    }
}

impl Sampler for MIKASAmpler {
    fn run_qubo(
        &self,
        qubo: &(Array<f64, Ix2>, HashMap<String, usize>),
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // Delegate to base sampler for QUBO
        self.base_config.run_qubo(qubo, shots)
    }

    fn run_hobo(
        &self,
        hobo: &(ArrayD<f64>, HashMap<String, usize>),
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        let (tensor, var_map) = hobo;

        // Apply tensor decomposition for efficient GPU computation
        #[cfg(feature = "scirs")]
        {
            self.run_hobo_gpu(tensor, var_map, shots)
        }

        #[cfg(not(feature = "scirs"))]
        {
            Err(SamplerError::GpuError(
                "HOBO GPU acceleration requires SciRS2 feature".to_string(),
            ))
        }
    }
}

impl MIKASAmpler {
    #[cfg(feature = "scirs")]
    fn run_hobo_gpu(
        &self,
        tensor: &ArrayD<f64>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // Stub tensor contraction functionality
        use scirs2_core::ndarray::{Array, IxDyn};
        let cp_decomposition = |_: &ArrayD<f64>| -> Result<
            (Vec<usize>, Vec<Array<f64, IxDyn>>, f64),
            Box<dyn std::error::Error>,
        > { Ok((vec![], vec![Array::zeros(IxDyn(&[1]))], 0.0f64)) };
        let optimize_contraction_order = |_: &[usize]| -> Vec<usize> { vec![] };

        let n_vars = var_map.len();
        let order = tensor.ndim();

        if self.base_config.verbose {
            println!("Processing {order}-order tensor with {n_vars} variables");
        }

        // Apply tensor decomposition if beneficial
        if self.use_cp_decomposition && order > 2 {
            // Perform CP decomposition
            let (factors, core_tensors, reconstruction_error) = cp_decomposition(tensor)
                .map_err(|e| SamplerError::GpuError(format!("CP decomposition failed: {e}")))?;

            let decomposed = DecomposedTensor {
                factors,
                core_tensors,
                reconstruction_error,
            };

            if self.base_config.verbose {
                println!("Decomposed tensor to rank {}", self.decomposition_rank);
            }

            // Run GPU sampling on decomposed form
            self.run_decomposed_hobo_gpu(decomposed, var_map, shots)
        } else {
            // Direct GPU computation for low-order tensors
            self.run_direct_hobo_gpu(tensor, var_map, shots)
        }
    }

    #[cfg(feature = "scirs")]
    fn run_decomposed_hobo_gpu(
        &self,
        decomposed: DecomposedTensor,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // Placeholder for decomposed tensor GPU implementation
        Err(SamplerError::InvalidParameter(
            "Decomposed HOBO GPU sampling not yet implemented".to_string(),
        ))
    }

    #[cfg(feature = "scirs")]
    fn run_direct_hobo_gpu(
        &self,
        tensor: &ArrayD<f64>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // Placeholder for direct tensor GPU implementation
        Err(SamplerError::InvalidParameter(
            "Direct HOBO GPU sampling not yet implemented".to_string(),
        ))
    }
}

// Placeholder for decomposed tensor type
#[cfg(feature = "scirs")]
struct DecomposedTensor {
    // CP decomposition components
    factors: Vec<usize>,
    core_tensors: Vec<Array<f64, IxDyn>>,
    reconstruction_error: f64,
}

/// Asynchronous GPU sampling pipeline
pub struct AsyncGpuPipeline {
    /// Number of pipeline stages
    num_stages: usize,
    /// Queue depth per stage
    queue_depth: usize,
    /// Base sampler
    sampler: EnhancedArminSampler,
}

impl AsyncGpuPipeline {
    /// Create new asynchronous pipeline
    pub const fn new(sampler: EnhancedArminSampler) -> Self {
        Self {
            num_stages: 3,
            queue_depth: 4,
            sampler,
        }
    }

    /// Run pipelined sampling
    pub fn run_pipelined(
        &self,
        qubo: &Array<f64, Ix2>,
        var_map: &HashMap<String, usize>,
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        // Pipeline stages:
        // 1. Initialize states on GPU
        // 2. Run annealing kernels
        // 3. Transfer results back

        // This would implement overlapped execution of the three stages
        // for maximum throughput

        self.sampler
            .run_qubo(&(qubo.clone(), var_map.clone()), shots)
    }
}

#[cfg(test)]
mod tests {
    #[cfg(feature = "scirs")]
    use super::EnhancedArminSampler;
    use crate::sampler::Sampler;
    #[cfg(feature = "scirs")]
    use scirs2_core::ndarray::Array;
    #[cfg(feature = "scirs")]
    use std::collections::HashMap;

    #[test]
    #[cfg(feature = "scirs")]
    fn test_enhanced_armin_sampler() {
        let sampler = EnhancedArminSampler::new(0)
            .with_batch_size(256)
            .with_sweeps(100);

        // Create small QUBO problem
        let mut qubo = Array::zeros((3, 3));
        qubo[[0, 0]] = -1.0;
        qubo[[1, 1]] = -1.0;
        qubo[[2, 2]] = -1.0;
        qubo[[0, 1]] = 2.0;
        qubo[[1, 0]] = 2.0;

        let mut var_map = HashMap::new();
        var_map.insert("x0".to_string(), 0);
        var_map.insert("x1".to_string(), 1);
        var_map.insert("x2".to_string(), 2);

        // Run sampler
        match sampler.run_qubo(&(qubo, var_map), 10) {
            Ok(results) => {
                assert!(!results.is_empty());
                // Check that results are sorted by energy
                for i in 1..results.len() {
                    assert!(results[i - 1].energy <= results[i].energy);
                }
            }
            Err(e) => {
                // GPU might not be available in test environment
                println!("GPU test skipped: {}", e);
            }
        }
    }
}