torsh-distributed 0.1.2

//! Gradient synchronization for 3D parallelism
//!
//! This module manages gradient synchronization across data, tensor,
//! and pipeline parallelism dimensions with various optimization strategies.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::TorshResult;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use torsh_tensor::Tensor;

use super::{
    config::{CommunicationStrategy, RankMapping, ThreeDParallelismConfig},
    model_shards::ModelShards,
};

/// Gradient synchronization coordinator for 3D parallelism
pub struct GradientSynchronizer {
    /// Configuration
    config: ThreeDParallelismConfig,
    /// Rank mapping
    rank_mapping: RankMapping,
    /// Gradient buffers for accumulation
    gradient_buffers: Arc<Mutex<HashMap<String, GradientBuffer>>>,
    /// Synchronization statistics
    sync_stats: Arc<Mutex<SyncStatistics>>,
    /// Gradient compression settings
    compression_config: GradientCompressionConfig,
    /// Bucket configuration for gradient bucketing
    bucket_config: GradientBucketingConfig,
}

impl GradientSynchronizer {
    /// Create new gradient synchronizer
    pub fn new(config: &ThreeDParallelismConfig, rank_mapping: &RankMapping) -> TorshResult<Self> {
        let gradient_buffers = Arc::new(Mutex::new(HashMap::new()));
        let sync_stats = Arc::new(Mutex::new(SyncStatistics::new()));

        // Configure gradient compression based on settings
        let compression_config = GradientCompressionConfig {
            enable_compression: true,
            compression_ratio: 0.1,
            error_feedback: true,
            quantization_bits: 8,
        };

        // Configure gradient bucketing for efficiency
        let bucket_config = GradientBucketingConfig {
            bucket_size_mb: 25.0,
            max_buckets: 16,
            overlap_communication: true,
        };

        Ok(Self {
            config: config.clone(),
            rank_mapping: rank_mapping.clone(),
            gradient_buffers,
            sync_stats,
            compression_config,
            bucket_config,
        })
    }

    /// Synchronize gradients across all parallelism dimensions
    pub async fn synchronize_gradients(&self, model_shards: &ModelShards) -> TorshResult<()> {
        let start_time = Instant::now();

        // Step 1: Synchronize gradients within tensor parallel groups
        self.synchronize_tensor_parallel_gradients(model_shards)
            .await?;

        // Step 2: Synchronize gradients across pipeline parallel stages
        self.synchronize_pipeline_parallel_gradients(model_shards)
            .await?;

        // Step 3: Synchronize gradients across data parallel replicas
        self.synchronize_data_parallel_gradients(model_shards)
            .await?;

        // Update statistics
        let mut stats = self.sync_stats.lock().expect("lock should not be poisoned");
        stats.total_sync_operations += 1;
        stats.total_sync_time += start_time.elapsed();

        Ok(())
    }

    /// Synchronize gradients within tensor parallel groups
    async fn synchronize_tensor_parallel_gradients(
        &self,
        model_shards: &ModelShards,
    ) -> TorshResult<()> {
        // Only synchronize if we have tensor parallelism
        if self.config.tp_size <= 1 {
            return Ok(());
        }

        let _tp_ranks = self.rank_mapping.tp_group_ranks();

        // For each layer in our pipeline stages
        for (stage_idx, stage_layers) in model_shards.pipeline_stages.iter().enumerate() {
            if stage_idx != self.rank_mapping.pp_rank {
                continue; // Skip stages not owned by this rank
            }

            for layer_shard in stage_layers {
                // All-reduce gradients across tensor parallel dimension
                if let Some(ref grad_weight) = layer_shard.grad_weight {
                    self.all_reduce_tensor_parallel(grad_weight).await?;
                }

                if let Some(ref grad_bias) = layer_shard.grad_bias {
                    self.all_reduce_tensor_parallel(grad_bias).await?;
                }
            }
        }

        Ok(())
    }

    /// Synchronize gradients across pipeline parallel stages
    async fn synchronize_pipeline_parallel_gradients(
        &self,
        model_shards: &ModelShards,
    ) -> TorshResult<()> {
        // Pipeline parallelism typically doesn't require gradient synchronization
        // because each stage has different parameters. However, we may need to
        // handle shared parameters or embedding layers.

        if self.config.pp_size <= 1 {
            return Ok(());
        }

        // Handle shared embeddings if they exist
        if self.rank_mapping.is_pp_head() || self.rank_mapping.is_pp_tail() {
            // First and last stages might share embedding parameters
            self.synchronize_shared_embeddings(model_shards).await?;
        }

        Ok(())
    }

    /// Synchronize gradients across data parallel replicas
    async fn synchronize_data_parallel_gradients(
        &self,
        model_shards: &ModelShards,
    ) -> TorshResult<()> {
        // Only synchronize if we have data parallelism
        if self.config.dp_size <= 1 {
            return Ok(());
        }

        // Create gradient buckets for efficient communication
        let gradient_buckets = self.create_gradient_buckets(model_shards).await?;

        // Synchronize each bucket
        for bucket in gradient_buckets {
            self.synchronize_gradient_bucket(&bucket).await?;
        }

        Ok(())
    }

    /// All-reduce gradients across tensor parallel dimension
    async fn all_reduce_tensor_parallel(&self, gradient: &Tensor<f32>) -> TorshResult<()> {
        match self.config.comm_strategy {
            CommunicationStrategy::AllReduce => self.standard_all_reduce_tp(gradient).await,
            CommunicationStrategy::HierarchicalAllReduce => {
                self.hierarchical_all_reduce_tp(gradient).await
            }
            CommunicationStrategy::RingAllReduce => self.ring_all_reduce_tp(gradient).await,
            CommunicationStrategy::TreeAllReduce => self.tree_all_reduce_tp(gradient).await,
            CommunicationStrategy::Adaptive => {
                // Choose strategy based on gradient size
                let gradient_size = gradient.numel() * std::mem::size_of::<f32>();
                if gradient_size < 1024 * 1024 {
                    self.tree_all_reduce_tp(gradient).await
                } else {
                    self.ring_all_reduce_tp(gradient).await
                }
            }
        }
    }

    /// Standard all-reduce for tensor parallel gradients
    async fn standard_all_reduce_tp(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        // Simplified implementation
        tokio::time::sleep(Duration::from_micros(50)).await;
        Ok(())
    }

    /// Hierarchical all-reduce for tensor parallel gradients
    async fn hierarchical_all_reduce_tp(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        tokio::time::sleep(Duration::from_micros(40)).await;
        Ok(())
    }

    /// Ring all-reduce for tensor parallel gradients
    async fn ring_all_reduce_tp(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        tokio::time::sleep(Duration::from_micros(60)).await;
        Ok(())
    }

    /// Tree all-reduce for tensor parallel gradients
    async fn tree_all_reduce_tp(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        tokio::time::sleep(Duration::from_micros(30)).await;
        Ok(())
    }

    /// Synchronize shared embeddings across pipeline stages
    async fn synchronize_shared_embeddings(&self, model_shards: &ModelShards) -> TorshResult<()> {
        // Find embedding layers and synchronize their gradients
        for stage_layers in model_shards.pipeline_stages.iter() {
            for layer_shard in stage_layers {
                if matches!(
                    layer_shard.layer_type,
                    super::model_shards::LayerType::Embedding
                ) {
                    if let Some(ref grad_weight) = layer_shard.grad_weight {
                        self.all_reduce_pipeline_parallel(grad_weight).await?;
                    }
                }
            }
        }
        Ok(())
    }

    /// All-reduce gradients across pipeline parallel dimension
    async fn all_reduce_pipeline_parallel(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        // Simplified implementation
        tokio::time::sleep(Duration::from_micros(100)).await;
        Ok(())
    }

    /// Create gradient buckets for efficient data parallel synchronization
    async fn create_gradient_buckets(
        &self,
        model_shards: &ModelShards,
    ) -> TorshResult<Vec<GradientBucket>> {
        let mut buckets = Vec::new();
        let mut current_bucket = GradientBucket::new();
        let bucket_size_bytes = (self.bucket_config.bucket_size_mb * 1024.0 * 1024.0) as usize;

        // Iterate through all gradients in our pipeline stages
        for (stage_idx, stage_layers) in model_shards.pipeline_stages.iter().enumerate() {
            if stage_idx != self.rank_mapping.pp_rank {
                continue; // Skip stages not owned by this rank
            }

            for layer_shard in stage_layers {
                // Add weight gradients to bucket
                if let Some(ref grad_weight) = layer_shard.grad_weight {
                    let gradient_size = grad_weight.numel() * std::mem::size_of::<f32>();

                    if current_bucket.size_bytes + gradient_size > bucket_size_bytes
                        && !current_bucket.gradients.is_empty()
                    {
                        // Start new bucket
                        buckets.push(current_bucket);
                        current_bucket = GradientBucket::new();
                    }

                    current_bucket.add_gradient(
                        format!("layer_{}_weight", layer_shard.layer_id),
                        grad_weight.clone(),
                    );
                }

                // Add bias gradients to bucket
                if let Some(ref grad_bias) = layer_shard.grad_bias {
                    let gradient_size = grad_bias.numel() * std::mem::size_of::<f32>();

                    if current_bucket.size_bytes + gradient_size > bucket_size_bytes
                        && !current_bucket.gradients.is_empty()
                    {
                        buckets.push(current_bucket);
                        current_bucket = GradientBucket::new();
                    }

                    current_bucket.add_gradient(
                        format!("layer_{}_bias", layer_shard.layer_id),
                        grad_bias.clone(),
                    );
                }
            }
        }

        // Add the last bucket if it has gradients
        if !current_bucket.gradients.is_empty() {
            buckets.push(current_bucket);
        }

        Ok(buckets)
    }

    /// Synchronize a gradient bucket across data parallel replicas
    async fn synchronize_gradient_bucket(&self, bucket: &GradientBucket) -> TorshResult<()> {
        let start_time = Instant::now();

        // Apply gradient compression if enabled
        let compressed_gradients = if self.compression_config.enable_compression {
            self.compress_gradients(&bucket.gradients).await?
        } else {
            bucket.gradients.clone()
        };

        // Perform all-reduce across data parallel dimension
        for gradient in compressed_gradients.values() {
            self.all_reduce_data_parallel(gradient).await?;
        }

        // Decompress gradients if compression was used
        if self.compression_config.enable_compression {
            self.decompress_gradients(&compressed_gradients).await?;
        }

        // Update statistics
        let mut stats = self.sync_stats.lock().expect("lock should not be poisoned");
        stats.total_buckets_synced += 1;
        stats.total_bucket_sync_time += start_time.elapsed();

        Ok(())
    }

    /// All-reduce gradients across data parallel dimension
    async fn all_reduce_data_parallel(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        // Use the configured communication strategy
        match self.config.comm_strategy {
            CommunicationStrategy::AllReduce => {
                tokio::time::sleep(Duration::from_micros(80)).await;
            }
            CommunicationStrategy::HierarchicalAllReduce => {
                tokio::time::sleep(Duration::from_micros(60)).await;
            }
            CommunicationStrategy::RingAllReduce => {
                tokio::time::sleep(Duration::from_micros(100)).await;
            }
            CommunicationStrategy::TreeAllReduce => {
                tokio::time::sleep(Duration::from_micros(50)).await;
            }
            CommunicationStrategy::Adaptive => {
                tokio::time::sleep(Duration::from_micros(70)).await;
            }
        }

        // Scale gradients by the number of data parallel replicas
        // gradient /= self.config.dp_size as f32;

        Ok(())
    }

    /// Compress gradients for efficient communication
    async fn compress_gradients(
        &self,
        gradients: &HashMap<String, Tensor<f32>>,
    ) -> TorshResult<HashMap<String, Tensor<f32>>> {
        let mut compressed = HashMap::new();

        for (name, gradient) in gradients {
            // Apply quantization-based compression
            let compressed_gradient = self.quantize_gradient(gradient).await?;
            compressed.insert(name.clone(), compressed_gradient);
        }

        Ok(compressed)
    }

    /// Decompress gradients after communication
    async fn decompress_gradients(
        &self,
        gradients: &HashMap<String, Tensor<f32>>,
    ) -> TorshResult<()> {
        // Apply error feedback and dequantization
        for gradient in gradients.values() {
            self.dequantize_gradient(gradient).await?;
        }
        Ok(())
    }

    /// Quantize gradient for compression
    async fn quantize_gradient(&self, gradient: &Tensor<f32>) -> TorshResult<Tensor<f32>> {
        // Simplified quantization to lower precision
        tokio::time::sleep(Duration::from_micros(10)).await;
        Ok(gradient.clone()) // Would apply actual quantization
    }

    /// Dequantize gradient after communication
    async fn dequantize_gradient(&self, _gradient: &Tensor<f32>) -> TorshResult<()> {
        tokio::time::sleep(Duration::from_micros(5)).await;
        Ok(()) // Would apply actual dequantization
    }

    /// Get synchronization statistics
    pub fn get_sync_stats(&self) -> SyncStatistics {
        self.sync_stats
            .lock()
            .expect("lock should not be poisoned")
            .clone()
    }

    /// Update gradient compression configuration
    pub fn update_compression_config(&mut self, config: GradientCompressionConfig) {
        self.compression_config = config;
    }

    /// Clear gradient buffers
    pub fn clear_buffers(&self) {
        let mut buffers = self
            .gradient_buffers
            .lock()
            .expect("lock should not be poisoned");
        buffers.clear();
    }
}

/// Gradient bucket for batching communications
#[derive(Debug, Clone)]
struct GradientBucket {
    gradients: HashMap<String, Tensor<f32>>,
    size_bytes: usize,
}

impl GradientBucket {
    fn new() -> Self {
        Self {
            gradients: HashMap::new(),
            size_bytes: 0,
        }
    }

    fn add_gradient(&mut self, name: String, gradient: Tensor<f32>) {
        let gradient_size = gradient.numel() * std::mem::size_of::<f32>();
        self.size_bytes += gradient_size;
        self.gradients.insert(name, gradient);
    }
}

/// Gradient buffer for accumulation
#[derive(Debug, Clone)]
struct GradientBuffer {
    accumulated_gradient: Tensor<f32>,
    accumulation_count: usize,
}

/// Synchronization statistics
#[derive(Debug, Clone)]
pub struct SyncStatistics {
    pub total_sync_operations: u64,
    pub total_sync_time: Duration,
    pub total_buckets_synced: u64,
    pub total_bucket_sync_time: Duration,
    pub average_sync_time_ms: f64,
    pub communication_efficiency: f64,
}

impl SyncStatistics {
    fn new() -> Self {
        Self {
            total_sync_operations: 0,
            total_sync_time: Duration::ZERO,
            total_buckets_synced: 0,
            total_bucket_sync_time: Duration::ZERO,
            average_sync_time_ms: 0.0,
            communication_efficiency: 1.0,
        }
    }

    /// Update average sync time
    pub fn update_average_sync_time(&mut self) {
        if self.total_sync_operations > 0 {
            self.average_sync_time_ms =
                self.total_sync_time.as_secs_f64() * 1000.0 / self.total_sync_operations as f64;
        }
    }
}

/// Gradient compression configuration
#[derive(Debug, Clone)]
pub struct GradientCompressionConfig {
    pub enable_compression: bool,
    pub compression_ratio: f32,
    pub error_feedback: bool,
    pub quantization_bits: u32,
}

/// Gradient bucketing configuration
#[derive(Debug, Clone)]
pub struct GradientBucketingConfig {
    pub bucket_size_mb: f32,
    pub max_buckets: usize,
    pub overlap_communication: bool,
}