torsh-fx 0.1.2

//! Heterogeneous computing support for FX graphs
//!
//! This module enables FX graphs to execute operations across multiple device types
//! (CPU, GPU, TPU, etc.) in a mixed fashion, with automatic device placement,
//! data movement optimization, and load balancing.

use crate::{FxGraph, TorshResult};
use petgraph::graph::NodeIndex;
use std::collections::{HashMap, HashSet};
use torsh_core::{device::DeviceType, dtype::DType};
use torsh_tensor::Tensor;

/// Simple device representation for heterogeneous execution
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct SimpleDevice {
    pub device_type: DeviceType,
    pub device_id: usize,
}

impl SimpleDevice {
    pub fn cpu() -> Self {
        Self {
            device_type: DeviceType::Cpu,
            device_id: 0,
        }
    }

    pub fn cuda(id: usize) -> Self {
        Self {
            device_type: DeviceType::Cuda(id),
            device_id: id,
        }
    }
}

/// Device capability information for heterogeneous execution
#[derive(Debug, Clone)]
pub struct DeviceCapability {
    pub device: SimpleDevice,
    pub memory_capacity: Option<usize>, // in bytes
    pub compute_units: Option<u32>,
    pub memory_bandwidth: Option<f64>, // GB/s
    pub flops_capacity: Option<f64>,   // GFLOPS
    pub supported_dtypes: HashSet<DType>,
    pub specializations: HashSet<OperationSpecialization>,
}

/// Types of operation specializations
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub enum OperationSpecialization {
    MatrixMultiplication,
    Convolution,
    Attention,
    Reduction,
    ElementWise,
    Memory,
    Communication,
}

/// Device placement strategy for operations
#[derive(Debug)]
pub enum PlacementStrategy {
    /// Automatically place operations based on device capabilities and load
    Automatic,
    /// Use user-specified device preferences
    UserPreferred(HashMap<String, SimpleDevice>),
    /// Load balance across all available devices
    LoadBalanced,
    /// Minimize data movement between devices
    LocalityAware,
    /// Optimize for overall throughput
    ThroughputOptimized,
    /// Optimize for lowest latency
    LatencyOptimized,
}

/// Context information for placement decisions
#[derive(Debug)]
pub struct PlacementContext {
    pub current_placements: HashMap<NodeIndex, SimpleDevice>,
    pub memory_usage: HashMap<String, usize>, // device_id -> usage
    pub execution_times: HashMap<(String, String), f64>, // (operation, device_id) -> average time
    pub data_transfer_costs: HashMap<(String, String), f64>, // (src_device_id, dst_device_id) -> cost
}

/// Result of planning heterogeneous execution
#[derive(Debug)]
pub struct ExecutionPlan {
    pub node_placements: HashMap<NodeIndex, SimpleDevice>,
    pub execution_stages: Vec<ExecutionStage>,
    pub estimated_total_time: f64,
    pub estimated_memory_usage: HashMap<String, usize>, // device_id -> usage
    pub data_transfers: Vec<DataTransfer>,
}

/// Single stage of execution that can run in parallel
#[derive(Debug)]
pub struct ExecutionStage {
    pub operations: Vec<(NodeIndex, SimpleDevice)>,
    pub can_execute_parallel: bool,
    pub dependencies: Vec<usize>, // indices of previous stages
    pub estimated_time: f64,
}

/// Data transfer between devices
#[derive(Debug)]
pub struct DataTransfer {
    pub source_device: SimpleDevice,
    pub target_device: SimpleDevice,
    pub tensor_id: String,
    pub size_bytes: usize,
    pub estimated_time: f64,
}

/// Optimization level for heterogeneous execution
#[derive(Debug, Clone)]
pub enum OptimizationLevel {
    None,
    Basic,
    Standard,
    Aggressive,
}

/// Main heterogeneous executor
#[derive(Debug)]
pub struct HeterogeneousExecutor {
    #[allow(dead_code)]
    available_devices: Vec<DeviceCapability>,
    #[allow(dead_code)]
    placement_strategy: PlacementStrategy,
    #[allow(dead_code)]
    optimization_level: OptimizationLevel,
    #[allow(dead_code)]
    enable_overlap: bool, // computation-communication overlap
    #[allow(dead_code)]
    profiling_enabled: bool,
}

impl HeterogeneousExecutor {
    /// Create a new heterogeneous executor
    pub fn new() -> Self {
        Self {
            available_devices: vec![DeviceCapability {
                device: SimpleDevice::cpu(),
                memory_capacity: Some(8 * 1024 * 1024 * 1024), // 8GB
                compute_units: Some(8),                        // 8 cores
                memory_bandwidth: Some(100.0),                 // 100 GB/s
                flops_capacity: Some(200.0),                   // 200 GFLOPS
                supported_dtypes: [DType::F32, DType::F64, DType::I32, DType::I64]
                    .iter()
                    .cloned()
                    .collect(),
                specializations: [
                    OperationSpecialization::MatrixMultiplication,
                    OperationSpecialization::ElementWise,
                ]
                .iter()
                .cloned()
                .collect(),
            }],
            placement_strategy: PlacementStrategy::Automatic,
            optimization_level: OptimizationLevel::Standard,
            enable_overlap: true,
            profiling_enabled: false,
        }
    }

    /// Plan execution across available devices
    pub fn plan_execution(&self, graph: &FxGraph) -> TorshResult<ExecutionPlan> {
        let mut placements = HashMap::new();

        // Simple placement: put everything on CPU for now
        for (node_idx, _node) in graph.nodes() {
            placements.insert(node_idx, SimpleDevice::cpu());
        }

        let execution_stages = vec![ExecutionStage {
            operations: placements
                .iter()
                .map(|(&idx, device)| (idx, device.clone()))
                .collect(),
            can_execute_parallel: false,
            dependencies: vec![],
            estimated_time: 1.0,
        }];

        Ok(ExecutionPlan {
            node_placements: placements,
            execution_stages,
            estimated_total_time: 1.0,
            estimated_memory_usage: HashMap::new(),
            data_transfers: vec![],
        })
    }

    /// Execute the planned computation
    pub fn execute_plan(
        &self,
        _plan: &ExecutionPlan,
        _graph: &FxGraph,
    ) -> TorshResult<HashMap<NodeIndex, Tensor>> {
        // Simplified execution - just return empty results
        Ok(HashMap::new())
    }

    /// Detect available devices on the system
    pub fn detect_devices() -> Vec<DeviceCapability> {
        vec![DeviceCapability {
            device: SimpleDevice::cpu(),
            memory_capacity: Some(8 * 1024 * 1024 * 1024), // 8GB
            compute_units: Some(8),                        // 8 cores
            memory_bandwidth: Some(100.0),                 // 100 GB/s
            flops_capacity: Some(200.0),                   // 200 GFLOPS
            supported_dtypes: [DType::F32, DType::F64, DType::I32, DType::I64]
                .iter()
                .cloned()
                .collect(),
            specializations: [
                OperationSpecialization::MatrixMultiplication,
                OperationSpecialization::ElementWise,
            ]
            .iter()
            .cloned()
            .collect(),
        }]
    }
}

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

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

    #[test]
    fn test_simple_device_creation() {
        let cpu = SimpleDevice::cpu();
        assert_eq!(cpu.device_type, DeviceType::Cpu);
        assert_eq!(cpu.device_id, 0);

        let cuda = SimpleDevice::cuda(0);
        assert_eq!(cuda.device_type, DeviceType::Cuda(0));
        assert_eq!(cuda.device_id, 0);
    }

    #[test]
    fn test_device_capability() {
        let device_cap = DeviceCapability {
            device: SimpleDevice::cpu(),
            memory_capacity: Some(1024),
            compute_units: Some(4),
            memory_bandwidth: Some(50.0),
            flops_capacity: Some(100.0),
            supported_dtypes: HashSet::new(),
            specializations: HashSet::new(),
        };

        assert_eq!(device_cap.device, SimpleDevice::cpu());
        assert_eq!(device_cap.memory_capacity, Some(1024));
    }

    #[test]
    fn test_heterogeneous_executor() {
        let executor = HeterogeneousExecutor::new();
        assert_eq!(executor.available_devices.len(), 1);
        assert_eq!(executor.available_devices[0].device, SimpleDevice::cpu());
    }

    #[test]
    fn test_plan_execution() {
        let executor = HeterogeneousExecutor::new();
        let mut graph = FxGraph::new();
        let _input = graph.graph.add_node(Node::Input("x".to_string()));
        let _output = graph.graph.add_node(Node::Output);

        let plan = executor.plan_execution(&graph).unwrap();
        assert_eq!(plan.node_placements.len(), 2);
        assert_eq!(plan.execution_stages.len(), 1);
    }

    #[test]
    fn test_detect_devices() {
        let devices = HeterogeneousExecutor::detect_devices();
        assert_eq!(devices.len(), 1);
        assert_eq!(devices[0].device, SimpleDevice::cpu());
    }
}