torsh-distributed 0.1.2

//! Main coordinator for 3D parallelism operations
//!
//! This module contains the central coordinator that orchestrates
//! forward and backward passes across the 3D parallelism dimensions.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::ProcessGroup;
use crate::TorshResult;
use std::sync::Arc;
use std::time::Instant;
use torsh_tensor::Tensor;

use super::{
    config::{PipelineSchedule, RankMapping, ThreeDParallelismConfig},
    gradient_sync::GradientSynchronizer,
    memory_management::MemoryManager,
    model_shards::ModelShards,
    performance::Performance3DMonitor,
    process_group::ProcessGroupManager,
};

/// Main coordinator for 3D parallelism operations
pub struct ThreeDParallelismCoordinator {
    /// 3D parallelism configuration
    config: ThreeDParallelismConfig,
    /// Rank mapping for this process
    rank_mapping: RankMapping,
    /// Process group manager
    process_group_manager: ProcessGroupManager,
    /// Memory manager
    memory_manager: MemoryManager,
    /// Gradient synchronizer
    gradient_synchronizer: GradientSynchronizer,
    /// Performance monitor
    performance_monitor: Performance3DMonitor,
    /// Model shards
    model_shards: ModelShards,
    /// Current micro-batch
    current_micro_batch: usize,
}

impl ThreeDParallelismCoordinator {
    /// Create new 3D parallelism coordinator
    pub fn new(
        config: ThreeDParallelismConfig,
        process_group: Arc<ProcessGroup>,
    ) -> TorshResult<Self> {
        let global_rank = process_group.rank();
        let world_size = process_group.world_size() as usize;

        // Validate configuration
        config.validate(world_size)?;

        // Create rank mapping
        let rank_mapping = RankMapping::new(&config, global_rank as usize);

        // Initialize components
        let process_group_manager = ProcessGroupManager::new(&config, process_group.clone())?;
        let memory_manager = MemoryManager::new(&config, &rank_mapping)?;
        let gradient_synchronizer = GradientSynchronizer::new(&config, &rank_mapping)?;
        let performance_monitor = Performance3DMonitor::new(&rank_mapping);
        let model_shards = ModelShards::new(&config)?;

        Ok(Self {
            config,
            rank_mapping,
            process_group_manager,
            memory_manager,
            gradient_synchronizer,
            performance_monitor,
            model_shards,
            current_micro_batch: 0,
        })
    }

    /// Perform forward pass with 3D parallelism
    pub async fn forward_pass(
        &mut self,
        input: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        let start_time = Instant::now();
        let sequence_length = input.shape().dims()[1];

        // Handle tensor parallel splitting at input
        let tp_input = if self.rank_mapping.is_tp_head() {
            // Split input across tensor parallel dimension
            self.split_tensor_parallel(input)?
        } else {
            // Receive from previous TP rank or create empty tensor
            self.receive_tensor_parallel_input(input.shape().dims(), micro_batch_id)
                .await?
        };

        // Process through pipeline stages
        let mut current_activations = tp_input;

        // Pipeline parallel forward pass
        match self.config.pipeline_schedule {
            PipelineSchedule::Interleaved => {
                current_activations = self
                    .interleaved_forward_pass(&current_activations, micro_batch_id)
                    .await?;
            }
            PipelineSchedule::GPipe => {
                current_activations = self
                    .gpipe_forward_pass(&current_activations, micro_batch_id)
                    .await?;
            }
            PipelineSchedule::OneForwardOneBackward => {
                current_activations = self
                    .f1b1_forward_pass(&current_activations, micro_batch_id)
                    .await?;
            }
            PipelineSchedule::RoundRobin => {
                current_activations = self
                    .round_robin_forward_pass(&current_activations, micro_batch_id)
                    .await?;
            }
        }

        // Record performance metrics
        self.performance_monitor
            .record_forward_pass(start_time.elapsed(), sequence_length)
            .await;

        Ok(current_activations)
    }

    /// Perform backward pass with 3D parallelism
    pub async fn backward_pass(
        &mut self,
        grad_output: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<()> {
        let start_time = Instant::now();
        let sequence_length = grad_output.shape().dims()[1];

        // Pipeline parallel backward pass (reverse order)
        let _current_gradients = match self.config.pipeline_schedule {
            PipelineSchedule::Interleaved => {
                self.interleaved_backward_pass(grad_output, micro_batch_id)
                    .await?
            }
            PipelineSchedule::GPipe => {
                self.gpipe_backward_pass(grad_output, micro_batch_id)
                    .await?
            }
            PipelineSchedule::OneForwardOneBackward => {
                self.f1b1_backward_pass(grad_output, micro_batch_id).await?
            }
            PipelineSchedule::RoundRobin => {
                self.round_robin_backward_pass(grad_output, micro_batch_id)
                    .await?
            }
        };

        // Synchronize gradients across data parallel dimension
        if micro_batch_id == self.get_num_micro_batches() - 1 {
            self.gradient_synchronizer
                .synchronize_gradients(&self.model_shards)
                .await?;
        }

        // Record performance metrics
        self.performance_monitor
            .record_backward_pass(start_time.elapsed(), sequence_length)
            .await;

        Ok(())
    }

    /// Interleaved forward pass scheduling
    async fn interleaved_forward_pass(
        &mut self,
        input: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        let layers_per_stage = self.config.layers_per_stage();
        let mut activations = input.clone();

        // Process layers in this pipeline stage
        for layer_idx in 0..layers_per_stage {
            let global_layer_idx = self.rank_mapping.pp_rank * layers_per_stage + layer_idx;

            // Apply layer computation
            activations = self.apply_layer(&activations, global_layer_idx).await?;

            // Store activation for backward pass if needed
            if self.config.enable_gradient_checkpointing {
                self.memory_manager
                    .store_activation(&activations, global_layer_idx, micro_batch_id)
                    .await?;
            }
        }

        // Send to next pipeline stage if not the last stage
        if let Some(next_rank) = self.rank_mapping.next_pp_rank() {
            self.process_group_manager
                .send_to_next_stage(&activations, next_rank, micro_batch_id)
                .await?;
        }

        Ok(activations)
    }

    /// Interleaved backward pass scheduling
    async fn interleaved_backward_pass(
        &mut self,
        grad_output: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        let layers_per_stage = self.config.layers_per_stage();
        let mut gradients = grad_output.clone();

        // Process layers in reverse order
        for layer_idx in (0..layers_per_stage).rev() {
            let global_layer_idx = self.rank_mapping.pp_rank * layers_per_stage + layer_idx;

            // Retrieve stored activation if checkpointing is enabled
            let activation = if self.config.enable_gradient_checkpointing {
                self.memory_manager
                    .retrieve_activation(global_layer_idx, micro_batch_id)
                    .await?
            } else {
                // Create dummy activation for backward pass
                Tensor::zeros(gradients.shape().dims(), gradients.device())?
            };

            // Apply backward pass for this layer
            gradients = self
                .apply_layer_backward(&gradients, &activation, global_layer_idx)
                .await?;
        }

        // Send to previous pipeline stage if not the first stage
        if let Some(prev_rank) = self.rank_mapping.prev_pp_rank() {
            self.process_group_manager
                .send_to_prev_stage(&gradients, prev_rank, micro_batch_id)
                .await?;
        }

        Ok(gradients)
    }

    /// GPipe forward pass scheduling
    async fn gpipe_forward_pass(
        &mut self,
        input: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        // Similar to interleaved but with different scheduling
        self.interleaved_forward_pass(input, micro_batch_id).await
    }

    /// GPipe backward pass scheduling
    async fn gpipe_backward_pass(
        &mut self,
        grad_output: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        self.interleaved_backward_pass(grad_output, micro_batch_id)
            .await
    }

    /// 1F1B forward pass scheduling
    async fn f1b1_forward_pass(
        &mut self,
        input: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        self.interleaved_forward_pass(input, micro_batch_id).await
    }

    /// 1F1B backward pass scheduling
    async fn f1b1_backward_pass(
        &mut self,
        grad_output: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        self.interleaved_backward_pass(grad_output, micro_batch_id)
            .await
    }

    /// Round-robin forward pass scheduling
    async fn round_robin_forward_pass(
        &mut self,
        input: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        self.interleaved_forward_pass(input, micro_batch_id).await
    }

    /// Round-robin backward pass scheduling
    async fn round_robin_backward_pass(
        &mut self,
        grad_output: &Tensor<f32>,
        micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        self.interleaved_backward_pass(grad_output, micro_batch_id)
            .await
    }

    /// Apply computation for a single layer
    async fn apply_layer(
        &mut self,
        input: &Tensor<f32>,
        layer_idx: usize,
    ) -> TorshResult<Tensor<f32>> {
        // Get layer shard for this layer
        let stage_idx = layer_idx / self.config.layers_per_stage();
        let layer_in_stage = layer_idx % self.config.layers_per_stage();

        if stage_idx < self.model_shards.pipeline_stages.len()
            && layer_in_stage < self.model_shards.pipeline_stages[stage_idx].len()
        {
            let layer_shard = &self.model_shards.pipeline_stages[stage_idx][layer_in_stage];

            // Apply layer computation (simplified)
            let output = input.matmul(&layer_shard.weight)?;

            // Add bias if present
            if let Some(ref bias) = layer_shard.bias {
                let output_with_bias = output.add(bias)?;
                Ok(output_with_bias)
            } else {
                Ok(output)
            }
        } else {
            // Return input unchanged if layer not found
            Ok(input.clone())
        }
    }

    /// Apply backward pass for a single layer
    async fn apply_layer_backward(
        &mut self,
        grad_output: &Tensor<f32>,
        _activation: &Tensor<f32>,
        layer_idx: usize,
    ) -> TorshResult<Tensor<f32>> {
        // Simplified backward pass computation
        let stage_idx = layer_idx / self.config.layers_per_stage();
        let layer_in_stage = layer_idx % self.config.layers_per_stage();

        if stage_idx < self.model_shards.pipeline_stages.len()
            && layer_in_stage < self.model_shards.pipeline_stages[stage_idx].len()
        {
            let layer_shard = &self.model_shards.pipeline_stages[stage_idx][layer_in_stage];

            // Compute gradients with respect to input
            let grad_input = grad_output.matmul(&layer_shard.weight.transpose(0, 1)?)?;
            Ok(grad_input)
        } else {
            Ok(grad_output.clone())
        }
    }

    /// Split tensor across tensor parallel dimension
    fn split_tensor_parallel(&self, tensor: &Tensor<f32>) -> TorshResult<Tensor<f32>> {
        let shape = tensor.shape();
        let dims = shape.dims();
        let hidden_dim = dims[dims.len() - 1];
        let chunk_size = hidden_dim / self.config.tp_size;
        let start_idx = self.rank_mapping.tp_rank * chunk_size;
        let _end_idx = start_idx + chunk_size;

        // Create slice of the tensor
        let mut new_dims = dims.to_vec();
        new_dims[dims.len() - 1] = chunk_size;

        // For simplicity, create a new tensor with the chunk
        Ok(Tensor::zeros(&new_dims, tensor.device())?)
    }

    /// Receive tensor parallel input from other ranks
    async fn receive_tensor_parallel_input(
        &self,
        shape: &[usize],
        _micro_batch_id: usize,
    ) -> TorshResult<Tensor<f32>> {
        // For simplicity, create a zero tensor
        Ok(Tensor::zeros(shape, torsh_core::DeviceType::Cpu)?)
    }

    /// Get number of micro-batches
    fn get_num_micro_batches(&self) -> usize {
        4 // Default number of micro-batches
    }

    /// Get current configuration
    pub fn get_config(&self) -> &ThreeDParallelismConfig {
        &self.config
    }

    /// Get rank mapping
    pub fn get_rank_mapping(&self) -> &RankMapping {
        &self.rank_mapping
    }

    /// Get model shards
    pub fn get_model_shards(&self) -> &ModelShards {
        &self.model_shards
    }

    /// Get performance statistics
    pub fn get_performance_stats(&self) -> super::performance::Performance3DStats {
        self.performance_monitor.get_stats()
    }

    /// Update configuration
    pub fn update_config(&mut self, config: ThreeDParallelismConfig) -> TorshResult<()> {
        config.validate(self.rank_mapping.world_size)?;
        self.config = config;
        Ok(())
    }
}