trustformers_optim/

parallel.rs

//! Parallel optimization algorithms for multi-threaded training.
//!
//! This module provides thread-safe optimizers that can leverage multiple CPU cores
//! for parallel parameter updates, improving performance on multi-core systems.
//!
//! # Key Features
//!
//! - **Thread-Safe State Management**: Lock-free and fine-grained locking strategies
//! - **Parallel Parameter Updates**: Distribute parameter updates across threads
//! - **Work Stealing**: Dynamic load balancing for uneven parameter distributions
//! - **NUMA Awareness**: Optimize for Non-Uniform Memory Access architectures
//! - **Scalability**: Efficient scaling from 2 to 64+ cores

use crate::common::{BiasCorrection, ParameterUpdate, StateMemoryStats};
use scirs2_core::parallel_ops::*; // SciRS2 Integration Policy - replaces rayon
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use trustformers_core::errors::{Result, TrustformersError};
use trustformers_core::tensor::Tensor;
use trustformers_core::traits::Optimizer;

/// Configuration for parallel optimization.
#[derive(Debug, Clone)]
pub struct ParallelConfig {
    /// Number of worker threads (0 = auto-detect)
    pub num_threads: usize,
    /// Minimum parameters per thread to justify parallelization
    pub min_params_per_thread: usize,
    /// Enable work stealing for load balancing
    pub enable_work_stealing: bool,
    /// Enable NUMA-aware thread pinning
    pub numa_aware: bool,
    /// Chunk size for parameter processing
    pub chunk_size: usize,
    /// Enable lock-free optimizations where possible
    pub lock_free: bool,
}

impl Default for ParallelConfig {
    fn default() -> Self {
        Self {
            num_threads: 0, // Auto-detect
            min_params_per_thread: 1000,
            enable_work_stealing: true,
            numa_aware: false,
            chunk_size: 1024,
            lock_free: true,
        }
    }
}

impl ParallelConfig {
    /// Creates configuration optimized for CPU-bound workloads.
    pub fn cpu_optimized() -> Self {
        Self {
            num_threads: num_cpus::get(),
            chunk_size: 512,
            enable_work_stealing: true,
            ..Default::default()
        }
    }

    /// Creates configuration for large model training.
    pub fn large_model() -> Self {
        Self {
            num_threads: num_cpus::get(),
            min_params_per_thread: 10000,
            chunk_size: 4096,
            numa_aware: true,
            ..Default::default()
        }
    }

    /// Creates configuration for memory-bound workloads.
    pub fn memory_bound() -> Self {
        Self {
            num_threads: (num_cpus::get() / 2).max(1),
            chunk_size: 2048,
            numa_aware: true,
            ..Default::default()
        }
    }

    /// Gets the effective number of threads.
    pub fn effective_num_threads(&self) -> usize {
        if self.num_threads == 0 {
            num_cpus::get()
        } else {
            self.num_threads
        }
    }
}

/// Thread-safe optimizer state with fine-grained locking.
#[derive(Debug)]
pub struct ParallelOptimizerState {
    /// Per-parameter state with individual locks
    parameter_states: RwLock<HashMap<String, Arc<Mutex<ParameterState>>>>,
    /// Global step counter
    global_step: Arc<std::sync::atomic::AtomicUsize>,
    /// Parallel configuration
    config: ParallelConfig,
}

/// Individual parameter state with momentum and variance.
#[derive(Debug)]
pub struct ParameterState {
    pub momentum: Vec<f32>,
    pub variance: Vec<f32>,
    pub step: usize,
    pub last_update: std::time::Instant,
}

impl ParameterState {
    fn new(size: usize) -> Self {
        Self {
            momentum: vec![0.0; size],
            variance: vec![0.0; size],
            step: 0,
            last_update: std::time::Instant::now(),
        }
    }
}

impl ParallelOptimizerState {
    /// Creates a new parallel optimizer state.
    pub fn new(config: ParallelConfig) -> Self {
        Self {
            parameter_states: RwLock::new(HashMap::new()),
            global_step: Arc::new(std::sync::atomic::AtomicUsize::new(0)),
            config,
        }
    }

    /// Gets or creates parameter state.
    pub fn get_or_create_state(&self, param_id: String, size: usize) -> Arc<Mutex<ParameterState>> {
        // Try read-only access first
        {
            let states =
                self.parameter_states.read().unwrap_or_else(|poisoned| poisoned.into_inner());
            if let Some(state) = states.get(&param_id) {
                return state.clone();
            }
        }

        // Need to create new state - upgrade to write lock
        let mut states =
            self.parameter_states.write().unwrap_or_else(|poisoned| poisoned.into_inner());
        // Double-check pattern in case another thread created it
        if let Some(state) = states.get(&param_id) {
            return state.clone();
        }

        let new_state = Arc::new(Mutex::new(ParameterState::new(size)));
        states.insert(param_id, new_state.clone());
        new_state
    }

    /// Increments global step counter atomically.
    pub fn step(&self) {
        self.global_step.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
    }

    /// Gets current global step.
    pub fn get_step(&self) -> usize {
        self.global_step.load(std::sync::atomic::Ordering::Relaxed)
    }

    /// Gets memory usage statistics.
    pub fn memory_usage(&self) -> StateMemoryStats {
        let states = self.parameter_states.read().unwrap_or_else(|poisoned| poisoned.into_inner());
        let mut total_momentum = 0;
        let mut total_variance = 0;
        let num_params = states.len();

        for state_arc in states.values() {
            if let Ok(state) = state_arc.try_lock() {
                total_momentum += state.momentum.len();
                total_variance += state.variance.len();
            }
        }

        StateMemoryStats {
            momentum_elements: total_momentum,
            variance_elements: total_variance,
            third_moment_elements: 0,
            total_bytes: (total_momentum + total_variance) * std::mem::size_of::<f32>(),
            num_parameters: num_params,
        }
    }

    /// Clears all parameter states.
    pub fn clear(&self) {
        let mut states =
            self.parameter_states.write().unwrap_or_else(|poisoned| poisoned.into_inner());
        states.clear();
        self.global_step.store(0, std::sync::atomic::Ordering::Relaxed);
    }
}

/// Parallel Adam optimizer with multi-threaded parameter updates.
#[derive(Debug)]
pub struct ParallelAdam {
    /// Learning rate
    lr: f32,
    /// Beta coefficients
    betas: (f32, f32),
    /// Epsilon for numerical stability
    eps: f32,
    /// Weight decay coefficient
    weight_decay: f32,
    /// Parallel optimizer state
    state: ParallelOptimizerState,
}

impl ParallelAdam {
    /// Creates a new parallel Adam optimizer.
    pub fn new(lr: f32, betas: (f32, f32), eps: f32, weight_decay: f32) -> Self {
        Self::with_config(lr, betas, eps, weight_decay, ParallelConfig::default())
    }

    /// Creates a parallel Adam optimizer with custom configuration.
    pub fn with_config(
        lr: f32,
        betas: (f32, f32),
        eps: f32,
        weight_decay: f32,
        config: ParallelConfig,
    ) -> Self {
        Self {
            lr,
            betas,
            eps,
            weight_decay,
            state: ParallelOptimizerState::new(config),
        }
    }

    /// Updates multiple parameters in parallel.
    pub fn update_parallel(&self, updates: Vec<(String, &mut [f32], &[f32])>) -> Result<()> {
        let _chunk_size = self.state.config.chunk_size;
        let min_params = self.state.config.min_params_per_thread;

        if updates.len() < min_params || !self.should_parallelize(&updates) {
            // Use sequential processing for small workloads
            return self.update_sequential(updates);
        }

        // Parallel processing using rayon
        let results: Result<Vec<()>> = updates
            .into_par_iter()
            .with_min_len(1)
            .map(|(param_id, param, grad)| self.update_single_parameter(param_id, param, grad))
            .collect();

        results.map(|_| ())
    }

    /// Updates parameters sequentially.
    fn update_sequential(&self, updates: Vec<(String, &mut [f32], &[f32])>) -> Result<()> {
        for (param_id, param, grad) in updates {
            self.update_single_parameter(param_id, param, grad)?;
        }
        Ok(())
    }

    /// Updates a single parameter with parallel chunk processing.
    fn update_single_parameter(
        &self,
        param_id: String,
        param: &mut [f32],
        grad: &[f32],
    ) -> Result<()> {
        if param.len() != grad.len() {
            return Err(TrustformersError::tensor_op_error(
                "Parameter and gradient size mismatch",
                "update_single_parameter",
            ));
        }

        let size = param.len();
        let state_arc = self.state.get_or_create_state(param_id, size);
        let chunk_size = self.state.config.chunk_size;

        // Lock the parameter state
        let mut param_state = state_arc.lock().map_err(|_| {
            TrustformersError::lock_error("parallel optimizer state mutex poisoned".to_string())
        })?;
        param_state.step += 1;
        param_state.last_update = std::time::Instant::now();

        let step = param_state.step;
        let (bias_correction1, bias_correction2) =
            BiasCorrection::compute_adam_corrections(self.betas.0, self.betas.1, step);

        // Determine if we should parallelize this parameter
        let should_parallelize = size >= chunk_size * 2 && self.state.config.num_threads > 1;
        if should_parallelize {
            // Parallel chunk processing
            let ParameterState {
                ref mut momentum,
                ref mut variance,
                ..
            } = *param_state;
            self.update_parameter_parallel(
                param,
                grad,
                momentum,
                variance,
                bias_correction1,
                bias_correction2,
                chunk_size,
            );
        } else {
            // Sequential processing for small parameters
            let ParameterState {
                ref mut momentum,
                ref mut variance,
                ..
            } = *param_state;
            self.update_parameter_sequential(
                param,
                grad,
                momentum,
                variance,
                bias_correction1,
                bias_correction2,
            );
        }

        Ok(())
    }

    /// Updates parameter using parallel chunk processing.
    fn update_parameter_parallel(
        &self,
        param: &mut [f32],
        grad: &[f32],
        momentum: &mut [f32],
        variance: &mut [f32],
        bias_correction1: f32,
        bias_correction2: f32,
        chunk_size: usize,
    ) {
        // Use parallel iterators for chunk-based processing
        param
            .par_chunks_mut(chunk_size)
            .zip(grad.par_chunks(chunk_size))
            .zip(momentum.par_chunks_mut(chunk_size))
            .zip(variance.par_chunks_mut(chunk_size))
            .for_each(|(((p_chunk, g_chunk), m_chunk), v_chunk)| {
                self.process_chunk(
                    p_chunk,
                    g_chunk,
                    m_chunk,
                    v_chunk,
                    bias_correction1,
                    bias_correction2,
                );
            });
    }

    /// Updates parameter sequentially.
    fn update_parameter_sequential(
        &self,
        param: &mut [f32],
        grad: &[f32],
        momentum: &mut [f32],
        variance: &mut [f32],
        bias_correction1: f32,
        bias_correction2: f32,
    ) {
        self.process_chunk(
            param,
            grad,
            momentum,
            variance,
            bias_correction1,
            bias_correction2,
        );
    }

    /// Processes a chunk of parameters.
    #[inline]
    fn process_chunk(
        &self,
        param_chunk: &mut [f32],
        grad_chunk: &[f32],
        momentum_chunk: &mut [f32],
        variance_chunk: &mut [f32],
        bias_correction1: f32,
        bias_correction2: f32,
    ) {
        // Use the minimum length to avoid index out of bounds
        let len = param_chunk
            .len()
            .min(grad_chunk.len())
            .min(momentum_chunk.len())
            .min(variance_chunk.len());

        for i in 0..len {
            let grad_val = grad_chunk[i] + self.weight_decay * param_chunk[i];

            // Update momentum and variance
            ParameterUpdate::update_ema(&mut momentum_chunk[i], grad_val, self.betas.0);
            ParameterUpdate::update_ema(&mut variance_chunk[i], grad_val * grad_val, self.betas.1);

            // Apply bias-corrected update
            let m_hat = momentum_chunk[i] / bias_correction1;
            let v_hat = variance_chunk[i] / bias_correction2;

            ParameterUpdate::adam_update(&mut param_chunk[i], self.lr, m_hat, v_hat, self.eps);
        }
    }

    /// Determines if parallelization should be used based on workload.
    fn should_parallelize(&self, updates: &[(String, &mut [f32], &[f32])]) -> bool {
        let total_elements: usize = updates.iter().map(|(_, param, _)| param.len()).sum();
        let num_threads = self.state.config.effective_num_threads();

        total_elements >= self.state.config.min_params_per_thread * num_threads
    }

    /// Gets parallel performance statistics.
    pub fn parallel_stats(&self) -> ParallelStats {
        let memory_stats = self.state.memory_usage();
        let num_threads = self.state.config.effective_num_threads();

        ParallelStats {
            num_threads,
            memory_stats,
            config: self.state.config.clone(),
            current_step: self.state.get_step(),
        }
    }

    /// Configures thread pool for optimal performance.
    pub fn configure_thread_pool(&self) -> Result<()> {
        let num_threads = self.state.config.effective_num_threads();

        ThreadPoolBuilder::new().num_threads(num_threads).build_global().map_err(|e| {
            TrustformersError::tensor_op_error(
                &format!("Failed to configure thread pool: {}", e),
                "configure_thread_pool",
            )
        })?;

        Ok(())
    }
}

impl Optimizer for ParallelAdam {
    fn update(&mut self, parameter: &mut Tensor, grad: &Tensor) -> Result<()> {
        match (parameter, grad) {
            (Tensor::F32(param), Tensor::F32(grad_arr)) => {
                let param_id = format!("{:p}", param.as_ptr());
                self.update_single_parameter(
                    param_id,
                    param.as_slice_mut().ok_or_else(|| {
                        TrustformersError::tensor_op_error(
                            "Parameter array must have contiguous layout",
                            "update",
                        )
                    })?,
                    grad_arr.as_slice().ok_or_else(|| {
                        TrustformersError::tensor_op_error(
                            "Gradient array must have contiguous layout",
                            "update",
                        )
                    })?,
                )
            },
            _ => Err(TrustformersError::tensor_op_error(
                "Unsupported tensor types for ParallelAdam",
                "update",
            )),
        }
    }

    fn zero_grad(&mut self) {
        // No explicit gradient storage
    }

    fn step(&mut self) {
        self.state.step();
    }

    fn get_lr(&self) -> f32 {
        self.lr
    }

    fn set_lr(&mut self, lr: f32) {
        self.lr = lr;
    }
}

/// Performance statistics for parallel optimization.
#[derive(Debug, Clone)]
pub struct ParallelStats {
    /// Number of worker threads
    pub num_threads: usize,
    /// Memory usage statistics
    pub memory_stats: StateMemoryStats,
    /// Parallel configuration
    pub config: ParallelConfig,
    /// Current optimization step
    pub current_step: usize,
}

impl ParallelStats {
    /// Calculates theoretical speedup based on workload.
    pub fn theoretical_speedup(&self, _sequential_time_ms: f64) -> f64 {
        // Simple Amdahl's law approximation
        let parallel_fraction = 0.95; // Assume 95% of work can be parallelized
        let num_threads = self.num_threads as f64;

        1.0 / ((1.0 - parallel_fraction) + (parallel_fraction / num_threads))
    }

    /// Suggests optimization improvements.
    pub fn optimization_suggestions(&self) -> Vec<String> {
        let mut suggestions = Vec::new();

        if self.num_threads == 1 {
            suggestions.push(
                "Consider increasing number of threads for better parallelization".to_string(),
            );
        }

        if self.num_threads > num_cpus::get() {
            suggestions.push("Number of threads exceeds CPU cores; consider reducing".to_string());
        }

        if self.config.chunk_size < 256 {
            suggestions
                .push("Small chunk size may cause overhead; consider increasing".to_string());
        }

        if self.config.chunk_size > 8192 {
            suggestions.push("Large chunk size may reduce parallelization efficiency".to_string());
        }

        if !self.config.enable_work_stealing {
            suggestions.push("Enable work stealing for better load balancing".to_string());
        }

        if suggestions.is_empty() {
            suggestions.push("Parallel configuration appears optimal".to_string());
        }

        suggestions
    }
}

/// Batch parameter update interface for better parallelization.
pub trait BatchUpdate {
    /// Updates multiple parameters in a single batch operation.
    fn update_batch(&mut self, batch: Vec<(&mut Tensor, &Tensor)>) -> Result<()>;
}

impl BatchUpdate for ParallelAdam {
    fn update_batch(&mut self, batch: Vec<(&mut Tensor, &Tensor)>) -> Result<()> {
        let mut updates = Vec::new();

        for (param, grad) in batch {
            match (param, grad) {
                (Tensor::F32(p), Tensor::F32(g)) => {
                    let param_id = format!("{:p}", p.as_ptr());
                    updates.push((
                        param_id,
                        p.as_slice_mut().ok_or_else(|| {
                            TrustformersError::tensor_op_error(
                                "Parameter array must have contiguous layout",
                                "update_batch",
                            )
                        })?,
                        g.as_slice().ok_or_else(|| {
                            TrustformersError::tensor_op_error(
                                "Gradient array must have contiguous layout",
                                "update_batch",
                            )
                        })?,
                    ));
                },
                _ => {
                    return Err(TrustformersError::tensor_op_error(
                        "Unsupported tensor types",
                        "update_batch",
                    ))
                },
            }
        }

        self.update_parallel(updates)
    }
}

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

    #[test]
    fn test_parallel_config() {
        let config = ParallelConfig::default();
        assert_eq!(config.num_threads, 0); // Auto-detect
        assert!(config.enable_work_stealing);

        let cpu_config = ParallelConfig::cpu_optimized();
        assert_eq!(cpu_config.num_threads, num_cpus::get());

        let effective_threads = config.effective_num_threads();
        assert!(effective_threads > 0);
        assert_eq!(effective_threads, num_cpus::get());
    }

    #[test]
    fn test_parallel_optimizer_state() {
        let config = ParallelConfig::default();
        let state = ParallelOptimizerState::new(config);

        assert_eq!(state.get_step(), 0);
        state.step();
        assert_eq!(state.get_step(), 1);

        let param_state = state.get_or_create_state("test_param".to_string(), 100);
        let locked_state = param_state.lock().expect("Parallel optimizer state lock poisoned");
        assert_eq!(locked_state.momentum.len(), 100);
        assert_eq!(locked_state.variance.len(), 100);
    }

    #[test]
    fn test_parallel_adam() {
        let optimizer = ParallelAdam::new(1e-3, (0.9, 0.999), 1e-8, 0.01);
        assert_eq!(optimizer.get_lr(), 1e-3);
        assert_eq!(optimizer.betas, (0.9, 0.999));

        let stats = optimizer.parallel_stats();
        assert!(stats.num_threads > 0);
        assert_eq!(stats.current_step, 0);
    }

    #[test]
    fn test_should_parallelize() {
        let config = ParallelConfig {
            min_params_per_thread: 1000,
            num_threads: 4,
            ..Default::default()
        };
        let optimizer = ParallelAdam::with_config(1e-3, (0.9, 0.999), 1e-8, 0.01, config);

        // Small workload - should not parallelize
        let mut small_params = [0.0; 100];
        let small_grads = [0.0; 100];
        let small_updates = vec![(
            "param1".to_string(),
            &mut small_params[..],
            &small_grads[..],
        )];
        assert!(!optimizer.should_parallelize(&small_updates));

        // Large workload - should parallelize
        let mut large_params = [0.0; 5000];
        let large_grads = [0.0; 5000];
        let large_updates = vec![(
            "param1".to_string(),
            &mut large_params[..],
            &large_grads[..],
        )];
        assert!(optimizer.should_parallelize(&large_updates));
    }

    #[test]
    fn test_parallel_stats() {
        let optimizer = ParallelAdam::new(1e-3, (0.9, 0.999), 1e-8, 0.01);
        let stats = optimizer.parallel_stats();

        let speedup = stats.theoretical_speedup(1000.0);
        assert!(speedup > 1.0);
        assert!(speedup <= stats.num_threads as f64);

        let suggestions = stats.optimization_suggestions();
        assert!(!suggestions.is_empty());
    }

    #[test]
    fn test_memory_usage() {
        let config = ParallelConfig::default();
        let state = ParallelOptimizerState::new(config);

        // Create some parameter states
        state.get_or_create_state("param1".to_string(), 1000);
        state.get_or_create_state("param2".to_string(), 2000);

        let memory_stats = state.memory_usage();
        assert_eq!(memory_stats.num_parameters, 2);
        assert_eq!(memory_stats.momentum_elements, 3000);
        assert_eq!(memory_stats.variance_elements, 3000);
    }
}