torsh_hub/

profiling.rs

//! # Model Profiling and Performance Analysis
//!
//! This module provides comprehensive profiling and debugging capabilities for deep learning models,
//! enabling detailed performance analysis, memory usage tracking, and optimization recommendations.
//!
//! ## Features
//!
//! ### Performance Profiling
//! - **Layer-wise Timing**: Measure execution time for each layer
//! - **Operation Profiling**: Track time spent in individual operations
//! - **Throughput Analysis**: Measure samples/second and FLOPs
//! - **Bottleneck Detection**: Automatically identify performance bottlenecks
//!
//! ### Memory Analysis
//! - **Memory Tracking**: Monitor memory usage over time
//! - **Peak Memory Detection**: Identify peak memory usage points
//! - **Allocation Tracking**: Track tensor allocations and deallocations
//! - **Memory Leaks**: Detect potential memory leaks
//!
//! ### Advanced Monitoring
//! - **CPU Monitoring**: Track CPU usage, context switches, and frequency
//! - **GPU Monitoring**: Monitor GPU utilization, memory, temperature, and power
//! - **I/O Monitoring**: Track disk and network I/O statistics
//! - **Real-time Metrics**: Live performance counters and metrics
//!
//! ### Optimization
//! - **Optimization Recommendations**: Automatic suggestions for improvement
//! - **Comparative Analysis**: Compare performance across runs
//! - **Export Options**: Export results to JSON, CSV, or binary formats
//!
//! ## Quick Start
//!
//! ```no_run
//! use torsh_hub::profiling::{ModelProfiler, ProfilerConfig};
//! use std::time::Duration;
//!
//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // Create a profiler with custom config
//! let config = ProfilerConfig {
//!     enable_memory_profiling: true,
//!     enable_layer_timing: true,
//!     enable_shape_tracking: true,
//!     enable_gradient_tracking: false,
//!     memory_sample_interval: Duration::from_millis(100),
//!     max_profile_history: 100,
//!     profile_dir: std::path::PathBuf::from("./profiles"),
//!     enable_call_stack: true,
//!     enable_op_profiling: true,
//! };
//!
//! let mut profiler = ModelProfiler::new(config)?;
//!
//! // Start profiling (returns session_id)
//! let session_id = profiler.start_profiling("model_id")?;
//!
//! // ... run your model ...
//!
//! // Stop profiling and get results
//! let result = profiler.stop_profiling(&session_id)?;
//!
//! // Analyze results
//! println!("Total time: {:?}", result.performance_summary.total_time);
//! println!("Bottlenecks: {:?}", result.bottlenecks);
//! # Ok(())
//! # }
//! ```
//!
//! ## Advanced Usage
//!
//! ### Layer Profiling
//!
//! ```no_run
//! # use torsh_hub::profiling::{ModelProfiler, LayerMemoryUsage};
//! # fn example(profiler: &mut ModelProfiler) -> Result<(), Box<dyn std::error::Error>> {
//! // Profile individual layers
//! let memory_usage = LayerMemoryUsage {
//!     peak_forward_memory: 1024 * 1024,
//!     peak_backward_memory: 512 * 1024,
//!     parameter_memory: 256 * 1024,
//!     activation_memory: 512 * 1024,
//!     gradient_memory: 256 * 1024,
//! };
//! profiler.record_layer_execution(
//!     "session1",
//!     "conv1",
//!     "Conv2d",
//!     std::time::Duration::from_millis(5),
//!     memory_usage.clone(),
//!     vec![vec![1, 3, 224, 224]],
//!     vec![vec![1, 64, 112, 112]]
//! )?;
//! # Ok(())
//! # }
//! ```
//!
//! ### Memory Tracking
//!
//! ```no_run
//! # use torsh_hub::profiling::{ModelProfiler, MemorySnapshot, MemoryPoolStats};
//! # use std::collections::HashMap;
//! # fn example(profiler: &mut ModelProfiler) -> Result<(), Box<dyn std::error::Error>> {
//! // Create and record memory snapshots
//! let snapshot = MemorySnapshot {
//!     timestamp: std::time::SystemTime::now(),
//!     total_allocated: 1024 * 1024 * 1024,
//!     peak_memory: 2048 * 1024 * 1024,
//!     active_memory: 512 * 1024 * 1024,
//!     fragmentation_ratio: 0.1,
//!     device_memory: HashMap::new(),
//!     pool_stats: MemoryPoolStats {
//!         active_allocations: 10,
//!         cached_blocks: 5,
//!         pool_size: 1024 * 1024 * 1024,
//!         allocation_requests: 100,
//!         cache_hits: 50,
//!     },
//! };
//! profiler.record_memory_snapshot("session1", snapshot)?;
//! # Ok(())
//! # }
//! ```

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::{Duration, Instant, SystemTime};
use torsh_core::error::{Result, TorshError};

/// Comprehensive model profiler for performance analysis
pub struct ModelProfiler {
    /// Active profiling sessions
    active_sessions: HashMap<String, ProfilingSession>,
    /// Completed profiling results
    completed_profiles: HashMap<String, ProfilingResult>,
    /// Profiler configuration
    config: ProfilerConfig,
    /// System resource monitor
    resource_monitor: ResourceMonitor,
}

/// Configuration for model profiling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilerConfig {
    /// Enable memory profiling
    pub enable_memory_profiling: bool,
    /// Enable layer-wise timing
    pub enable_layer_timing: bool,
    /// Enable tensor shape tracking
    pub enable_shape_tracking: bool,
    /// Enable gradient tracking
    pub enable_gradient_tracking: bool,
    /// Memory sampling interval
    pub memory_sample_interval: Duration,
    /// Maximum profile history to keep
    pub max_profile_history: usize,
    /// Profile data directory
    pub profile_dir: PathBuf,
    /// Enable detailed call stack tracking
    pub enable_call_stack: bool,
    /// Enable operation-level profiling
    pub enable_op_profiling: bool,
}

/// Active profiling session
#[derive(Debug)]
pub struct ProfilingSession {
    /// Session identifier
    pub session_id: String,
    /// Model being profiled
    pub model_id: String,
    /// Session start time
    pub start_time: Instant,
    /// Layer performance data
    pub layer_profiles: HashMap<String, LayerProfile>,
    /// Memory snapshots
    pub memory_snapshots: Vec<MemorySnapshot>,
    /// Operation traces
    pub operation_traces: Vec<OperationTrace>,
    /// Current execution context
    pub execution_context: ExecutionContext,
    /// Performance counters
    pub counters: PerformanceCounters,
}

/// Layer-specific profiling information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerProfile {
    /// Layer name/identifier
    pub layer_name: String,
    /// Layer type (e.g., "Linear", "Conv2d", "BatchNorm")
    pub layer_type: String,
    /// Forward pass timings
    pub forward_times: Vec<Duration>,
    /// Backward pass timings
    pub backward_times: Vec<Duration>,
    /// Memory usage for this layer
    pub memory_usage: LayerMemoryUsage,
    /// Input tensor shapes
    pub input_shapes: Vec<Vec<usize>>,
    /// Output tensor shapes
    pub output_shapes: Vec<Vec<usize>>,
    /// Parameter count
    pub parameter_count: usize,
    /// Gradient statistics
    pub gradient_stats: Option<GradientStatistics>,
    /// Layer utilization metrics
    pub utilization: LayerUtilization,
}

/// Memory usage for a specific layer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerMemoryUsage {
    /// Peak memory usage during forward pass
    pub peak_forward_memory: u64,
    /// Peak memory usage during backward pass
    pub peak_backward_memory: u64,
    /// Memory allocated for parameters
    pub parameter_memory: u64,
    /// Memory allocated for activations
    pub activation_memory: u64,
    /// Memory allocated for gradients
    pub gradient_memory: u64,
}

/// Memory snapshot at a point in time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemorySnapshot {
    /// Timestamp of snapshot
    pub timestamp: SystemTime,
    /// Total allocated memory
    pub total_allocated: u64,
    /// Peak memory usage
    pub peak_memory: u64,
    /// Currently active memory
    pub active_memory: u64,
    /// Memory fragmentation ratio
    pub fragmentation_ratio: f32,
    /// Per-device memory breakdown
    pub device_memory: HashMap<String, DeviceMemoryInfo>,
    /// Memory pool statistics
    pub pool_stats: MemoryPoolStats,
}

/// Device-specific memory information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceMemoryInfo {
    /// Device identifier
    pub device_id: String,
    /// Total memory capacity
    pub total_capacity: u64,
    /// Currently allocated memory
    pub allocated: u64,
    /// Free memory available
    pub free: u64,
    /// Memory utilization percentage
    pub utilization: f32,
}

/// Memory pool statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPoolStats {
    /// Number of active allocations
    pub active_allocations: usize,
    /// Number of cached blocks
    pub cached_blocks: usize,
    /// Total pool size
    pub pool_size: u64,
    /// Number of allocation requests
    pub allocation_requests: usize,
    /// Number of cache hits
    pub cache_hits: usize,
}

/// Operation trace for detailed execution analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationTrace {
    /// Operation identifier
    pub op_id: String,
    /// Operation type
    pub op_type: String,
    /// Start timestamp
    pub start_time: SystemTime,
    /// End timestamp
    pub end_time: SystemTime,
    /// Input tensor information
    pub inputs: Vec<TensorInfo>,
    /// Output tensor information
    pub outputs: Vec<TensorInfo>,
    /// Operation parameters
    pub parameters: HashMap<String, String>,
    /// Execution device
    pub device: String,
    /// Memory allocated during operation
    pub memory_delta: i64,
    /// Call stack information
    pub call_stack: Option<Vec<String>>,
}

/// Tensor information for profiling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorInfo {
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Data type
    pub dtype: String,
    /// Device location
    pub device: String,
    /// Memory size in bytes
    pub memory_size: u64,
    /// Whether tensor requires gradients
    pub requires_grad: bool,
}

/// Execution context tracking
#[derive(Debug, Clone)]
pub struct ExecutionContext {
    /// Current execution mode (training/evaluation)
    pub mode: ExecutionMode,
    /// Active gradient context
    pub grad_enabled: bool,
    /// Current batch size
    pub batch_size: Option<usize>,
    /// Execution stack depth
    pub stack_depth: usize,
    /// Current operation being executed
    pub current_operation: Option<String>,
}

/// Model execution modes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExecutionMode {
    Training,
    Evaluation,
    Inference,
}

/// Performance counters for detailed metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct PerformanceCounters {
    /// Total forward passes
    pub forward_passes: u64,
    /// Total backward passes
    pub backward_passes: u64,
    /// Total operations executed
    pub operations_executed: u64,
    /// Total memory allocations
    pub memory_allocations: u64,
    /// Total memory deallocations
    pub memory_deallocations: u64,
    /// Cache hits
    pub cache_hits: u64,
    /// Cache misses
    pub cache_misses: u64,
    /// Gradient computations
    pub gradient_computations: u64,
}

/// Gradient statistics for debugging
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientStatistics {
    /// Mean gradient magnitude
    pub mean_magnitude: f32,
    /// Standard deviation of gradients
    pub std_deviation: f32,
    /// Maximum gradient value
    pub max_gradient: f32,
    /// Minimum gradient value
    pub min_gradient: f32,
    /// Gradient norm (L2)
    pub gradient_norm: f32,
    /// Number of zero gradients
    pub zero_gradients: usize,
    /// Number of NaN gradients
    pub nan_gradients: usize,
    /// Number of infinite gradients
    pub inf_gradients: usize,
}

/// Layer utilization metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerUtilization {
    /// Compute utilization percentage
    pub compute_utilization: f32,
    /// Memory utilization percentage
    pub memory_utilization: f32,
    /// Parameter utilization (active parameters)
    pub parameter_utilization: f32,
    /// Activation sparsity
    pub activation_sparsity: f32,
    /// Gradient sparsity
    pub gradient_sparsity: f32,
}

/// Complete profiling result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilingResult {
    /// Session metadata
    pub session_info: SessionInfo,
    /// Overall performance summary
    pub performance_summary: PerformanceSummary,
    /// Layer-wise analysis
    pub layer_analysis: HashMap<String, LayerProfile>,
    /// Memory analysis
    pub memory_analysis: MemoryAnalysis,
    /// Operation analysis
    pub operation_analysis: OperationAnalysis,
    /// Bottleneck analysis
    pub bottlenecks: Vec<PerformanceBottleneck>,
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
    /// Resource utilization
    pub resource_utilization: ResourceUtilizationSummary,
}

/// Session information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SessionInfo {
    /// Session ID
    pub session_id: String,
    /// Model ID
    pub model_id: String,
    /// Start time
    pub start_time: SystemTime,
    /// End time
    pub end_time: SystemTime,
    /// Total duration
    pub duration: Duration,
    /// Profile configuration used
    pub config: ProfilerConfig,
}

/// Performance summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceSummary {
    /// Total execution time
    pub total_time: Duration,
    /// Average forward pass time
    pub avg_forward_time: Duration,
    /// Average backward pass time
    pub avg_backward_time: Duration,
    /// Throughput (samples per second)
    pub throughput: f32,
    /// Memory efficiency ratio
    pub memory_efficiency: f32,
    /// Compute efficiency ratio
    pub compute_efficiency: f32,
    /// Overall performance score
    pub performance_score: f32,
}

/// Memory analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAnalysis {
    /// Peak memory usage
    pub peak_memory: u64,
    /// Average memory usage
    pub avg_memory: u64,
    /// Memory fragmentation analysis
    pub fragmentation_analysis: FragmentationAnalysis,
    /// Memory leak detection
    pub leak_detection: LeakDetection,
    /// Memory timeline
    pub memory_timeline: Vec<MemorySnapshot>,
}

/// Operation analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationAnalysis {
    /// Most expensive operations
    pub expensive_ops: Vec<OperationCost>,
    /// Operation frequency analysis
    pub operation_frequency: HashMap<String, usize>,
    /// Critical path analysis
    pub critical_path: Vec<String>,
    /// Operation dependency graph
    pub dependency_graph: OperationDependencyGraph,
}

/// System resource monitor
pub struct ResourceMonitor {
    /// CPU usage tracking
    cpu_monitor: CpuMonitor,
    /// Memory usage tracking
    memory_monitor: MemoryMonitor,
    /// GPU usage tracking (if available)
    gpu_monitor: Option<GpuMonitor>,
    /// I/O monitoring
    io_monitor: IoMonitor,
}

impl ResourceMonitor {
    /// Get CPU monitor
    pub fn cpu_monitor(&self) -> &CpuMonitor {
        &self.cpu_monitor
    }

    /// Get memory monitor
    pub fn memory_monitor(&self) -> &MemoryMonitor {
        &self.memory_monitor
    }

    /// Get GPU monitor
    pub fn gpu_monitor(&self) -> Option<&GpuMonitor> {
        self.gpu_monitor.as_ref()
    }

    /// Get I/O monitor
    pub fn io_monitor(&self) -> &IoMonitor {
        &self.io_monitor
    }
}

/// CPU monitoring
pub struct CpuMonitor {
    /// CPU usage history
    cpu_usage_history: Vec<f32>,
    /// Per-core usage
    per_core_usage: Vec<f32>,
    /// Context switches
    context_switches: u64,
    /// CPU frequency
    cpu_frequency: f32,
}

impl CpuMonitor {
    /// Get CPU usage history
    pub fn cpu_usage_history(&self) -> &[f32] {
        &self.cpu_usage_history
    }

    /// Get per-core usage
    pub fn per_core_usage(&self) -> &[f32] {
        &self.per_core_usage
    }

    /// Get context switches count
    pub fn context_switches(&self) -> u64 {
        self.context_switches
    }

    /// Get CPU frequency
    pub fn cpu_frequency(&self) -> f32 {
        self.cpu_frequency
    }
}

/// Memory monitoring
pub struct MemoryMonitor {
    /// Memory usage timeline
    memory_timeline: Vec<MemorySnapshot>,
    /// Allocation tracking
    allocation_tracker: AllocationTracker,
    /// Garbage collection monitoring
    gc_monitor: GcMonitor,
}

impl MemoryMonitor {
    /// Get memory usage timeline
    pub fn memory_timeline(&self) -> &[MemorySnapshot] {
        &self.memory_timeline
    }

    /// Get allocation tracker
    pub fn allocation_tracker(&self) -> &AllocationTracker {
        &self.allocation_tracker
    }

    /// Get garbage collection monitor
    pub fn gc_monitor(&self) -> &GcMonitor {
        &self.gc_monitor
    }
}

/// GPU monitoring
pub struct GpuMonitor {
    /// GPU utilization
    gpu_utilization: Vec<f32>,
    /// GPU memory usage
    gpu_memory_usage: Vec<u64>,
    /// GPU temperature
    gpu_temperature: Vec<f32>,
    /// GPU power consumption
    gpu_power: Vec<f32>,
}

impl GpuMonitor {
    /// Get GPU utilization history
    pub fn gpu_utilization(&self) -> &[f32] {
        &self.gpu_utilization
    }

    /// Get GPU memory usage history
    pub fn gpu_memory_usage(&self) -> &[u64] {
        &self.gpu_memory_usage
    }

    /// Get GPU temperature history
    pub fn gpu_temperature(&self) -> &[f32] {
        &self.gpu_temperature
    }

    /// Get GPU power consumption history
    pub fn gpu_power(&self) -> &[f32] {
        &self.gpu_power
    }
}

/// I/O monitoring
pub struct IoMonitor {
    /// Disk read/write statistics
    disk_stats: DiskStats,
    /// Network I/O statistics
    network_stats: NetworkStats,
}

impl IoMonitor {
    /// Get disk statistics
    pub fn disk_stats(&self) -> &DiskStats {
        &self.disk_stats
    }

    /// Get network statistics
    pub fn network_stats(&self) -> &NetworkStats {
        &self.network_stats
    }
}

// Additional analysis structures
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FragmentationAnalysis {
    pub fragmentation_ratio: f32,
    pub largest_free_block: u64,
    pub allocation_patterns: Vec<AllocationPattern>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LeakDetection {
    pub potential_leaks: Vec<MemoryLeak>,
    pub leak_score: f32,
    pub recommendations: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBottleneck {
    pub bottleneck_type: BottleneckType,
    pub location: String,
    pub severity: f32,
    pub impact: f32,
    pub description: String,
    pub suggestions: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BottleneckType {
    Compute,
    Memory,
    IO,
    Communication,
    Synchronization,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub recommendation_type: OptimizationType,
    pub priority: Priority,
    pub expected_improvement: f32,
    pub implementation_effort: ImplementationEffort,
    pub description: String,
    pub code_examples: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    ModelArchitecture,
    MemoryOptimization,
    ComputeOptimization,
    DataLoading,
    Parallelization,
    Quantization,
    Pruning,
    Caching,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Priority {
    Critical,
    High,
    Medium,
    Low,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImplementationEffort {
    Trivial,
    Easy,
    Medium,
    Hard,
    Expert,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilizationSummary {
    pub cpu_utilization: CpuUtilizationSummary,
    pub memory_utilization: MemoryUtilizationSummary,
    pub gpu_utilization: Option<GpuUtilizationSummary>,
    pub io_utilization: IoUtilizationSummary,
}

// Additional supporting structures
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationCost {
    pub operation: String,
    pub total_time: Duration,
    pub call_count: usize,
    pub avg_time: Duration,
    pub memory_cost: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationDependencyGraph {
    pub nodes: Vec<String>,
    pub edges: Vec<(String, String)>,
    pub critical_path: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationPattern {
    pub size: u64,
    pub frequency: usize,
    pub lifetime: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryLeak {
    pub allocation_site: String,
    pub size: u64,
    pub age: Duration,
    pub confidence: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct AllocationTracker {
    pub total_allocations: usize,
    pub total_deallocations: usize,
    pub current_allocations: usize,
    pub peak_allocations: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GcMonitor {
    pub gc_count: usize,
    pub total_gc_time: Duration,
    pub avg_gc_time: Duration,
    pub memory_reclaimed: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DiskStats {
    pub bytes_read: u64,
    pub bytes_written: u64,
    pub read_operations: u64,
    pub write_operations: u64,
    pub avg_read_latency: Duration,
    pub avg_write_latency: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct NetworkStats {
    pub bytes_sent: u64,
    pub bytes_received: u64,
    pub packets_sent: u64,
    pub packets_received: u64,
    pub connection_count: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuUtilizationSummary {
    pub avg_utilization: f32,
    pub peak_utilization: f32,
    pub per_core_avg: Vec<f32>,
    pub context_switches: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryUtilizationSummary {
    pub avg_utilization: f32,
    pub peak_utilization: f32,
    pub fragmentation_score: f32,
    pub allocation_efficiency: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuUtilizationSummary {
    pub avg_utilization: f32,
    pub peak_utilization: f32,
    pub memory_utilization: f32,
    pub temperature: f32,
    pub power_consumption: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoUtilizationSummary {
    pub disk_utilization: f32,
    pub network_utilization: f32,
    pub io_wait_time: Duration,
    pub bandwidth_efficiency: f32,
}

impl Default for ProfilerConfig {
    fn default() -> Self {
        Self {
            enable_memory_profiling: true,
            enable_layer_timing: true,
            enable_shape_tracking: true,
            enable_gradient_tracking: false,
            memory_sample_interval: Duration::from_millis(100),
            max_profile_history: 100,
            profile_dir: PathBuf::from("./profiles"),
            enable_call_stack: false,
            enable_op_profiling: true,
        }
    }
}

impl ModelProfiler {
    /// Create a new model profiler
    pub fn new(config: ProfilerConfig) -> Result<Self> {
        std::fs::create_dir_all(&config.profile_dir)?;

        Ok(Self {
            active_sessions: HashMap::new(),
            completed_profiles: HashMap::new(),
            config,
            resource_monitor: ResourceMonitor::new()?,
        })
    }

    /// Start profiling a model
    pub fn start_profiling(&mut self, model_id: &str) -> Result<String> {
        let session_id = format!(
            "session_{}_{}",
            model_id,
            SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("system time should be after UNIX epoch")
                .as_secs()
        );

        let session = ProfilingSession {
            session_id: session_id.clone(),
            model_id: model_id.to_string(),
            start_time: Instant::now(),
            layer_profiles: HashMap::new(),
            memory_snapshots: Vec::new(),
            operation_traces: Vec::new(),
            execution_context: ExecutionContext {
                mode: ExecutionMode::Training,
                grad_enabled: true,
                batch_size: None,
                stack_depth: 0,
                current_operation: None,
            },
            counters: PerformanceCounters::default(),
        };

        self.active_sessions.insert(session_id.clone(), session);

        // Start resource monitoring
        self.resource_monitor.start_monitoring(&session_id)?;

        println!("Started profiling session: {}", session_id);
        Ok(session_id)
    }

    /// Stop profiling and generate results
    pub fn stop_profiling(&mut self, session_id: &str) -> Result<ProfilingResult> {
        let session = self.active_sessions.remove(session_id).ok_or_else(|| {
            TorshError::InvalidArgument(format!("Unknown session: {}", session_id))
        })?;

        // Stop resource monitoring
        self.resource_monitor.stop_monitoring(session_id)?;

        // Analyze the collected data
        let result = self.analyze_session(session)?;

        // Store the result
        self.completed_profiles
            .insert(session_id.to_string(), result.clone());

        // Save to disk
        self.save_profile_result(session_id, &result)?;

        println!("Completed profiling session: {}", session_id);
        Ok(result)
    }

    /// Record a layer execution
    pub fn record_layer_execution(
        &mut self,
        session_id: &str,
        layer_name: &str,
        layer_type: &str,
        forward_time: Duration,
        memory_usage: LayerMemoryUsage,
        input_shapes: Vec<Vec<usize>>,
        output_shapes: Vec<Vec<usize>>,
    ) -> Result<()> {
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            let layer_profile = session
                .layer_profiles
                .entry(layer_name.to_string())
                .or_insert_with(|| LayerProfile {
                    layer_name: layer_name.to_string(),
                    layer_type: layer_type.to_string(),
                    forward_times: Vec::new(),
                    backward_times: Vec::new(),
                    memory_usage: memory_usage.clone(),
                    input_shapes: Vec::new(),
                    output_shapes: Vec::new(),
                    parameter_count: 0,
                    gradient_stats: None,
                    utilization: LayerUtilization::default(),
                });

            layer_profile.forward_times.push(forward_time);
            layer_profile.memory_usage = memory_usage;
            layer_profile.input_shapes.extend(input_shapes);
            layer_profile.output_shapes.extend(output_shapes);

            session.counters.forward_passes += 1;
        }

        Ok(())
    }

    /// Record a memory snapshot
    pub fn record_memory_snapshot(
        &mut self,
        session_id: &str,
        snapshot: MemorySnapshot,
    ) -> Result<()> {
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session.memory_snapshots.push(snapshot);
        }
        Ok(())
    }

    /// Record an operation trace
    pub fn record_operation(&mut self, session_id: &str, trace: OperationTrace) -> Result<()> {
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session.operation_traces.push(trace);
            session.counters.operations_executed += 1;
        }
        Ok(())
    }

    /// Get active profiling sessions
    pub fn get_active_sessions(&self) -> Vec<String> {
        self.active_sessions.keys().cloned().collect()
    }

    /// Get completed profiling results
    pub fn get_completed_profiles(&self) -> &HashMap<String, ProfilingResult> {
        &self.completed_profiles
    }

    /// Analyze a completed session
    fn analyze_session(&self, session: ProfilingSession) -> Result<ProfilingResult> {
        let duration = session.start_time.elapsed();

        // Calculate performance summary
        let performance_summary = self.calculate_performance_summary(&session, duration);

        // Analyze memory usage
        let memory_analysis = self.analyze_memory_usage(&session);

        // Analyze operations
        let operation_analysis = self.analyze_operations(&session);

        // Identify bottlenecks
        let bottlenecks = self.identify_bottlenecks(&session);

        // Generate recommendations
        let recommendations = self.generate_recommendations(&session, &bottlenecks);

        // Summarize resource utilization
        let resource_utilization = self.summarize_resource_utilization(&session);

        Ok(ProfilingResult {
            session_info: SessionInfo {
                session_id: session.session_id.clone(),
                model_id: session.model_id.clone(),
                start_time: SystemTime::now() - duration,
                end_time: SystemTime::now(),
                duration,
                config: self.config.clone(),
            },
            performance_summary,
            layer_analysis: session.layer_profiles,
            memory_analysis,
            operation_analysis,
            bottlenecks,
            recommendations,
            resource_utilization,
        })
    }

    fn calculate_performance_summary(
        &self,
        session: &ProfilingSession,
        duration: Duration,
    ) -> PerformanceSummary {
        let total_forward_time: Duration = session
            .layer_profiles
            .values()
            .flat_map(|layer| &layer.forward_times)
            .sum();

        let total_backward_time: Duration = session
            .layer_profiles
            .values()
            .flat_map(|layer| &layer.backward_times)
            .sum();

        let forward_count = session.counters.forward_passes;
        let avg_forward_time = if forward_count > 0 {
            total_forward_time / forward_count as u32
        } else {
            Duration::from_secs(0)
        };

        let backward_count = session.counters.backward_passes;
        let avg_backward_time = if backward_count > 0 {
            total_backward_time / backward_count as u32
        } else {
            Duration::from_secs(0)
        };

        PerformanceSummary {
            total_time: duration,
            avg_forward_time,
            avg_backward_time,
            throughput: forward_count as f32 / duration.as_secs_f32(),
            memory_efficiency: 0.85,  // Placeholder
            compute_efficiency: 0.78, // Placeholder
            performance_score: 0.82,  // Placeholder
        }
    }

    fn analyze_memory_usage(&self, session: &ProfilingSession) -> MemoryAnalysis {
        let peak_memory = session
            .memory_snapshots
            .iter()
            .map(|snapshot| snapshot.peak_memory)
            .max()
            .unwrap_or(0);

        let avg_memory = if !session.memory_snapshots.is_empty() {
            session
                .memory_snapshots
                .iter()
                .map(|snapshot| snapshot.active_memory)
                .sum::<u64>()
                / session.memory_snapshots.len() as u64
        } else {
            0
        };

        MemoryAnalysis {
            peak_memory,
            avg_memory,
            fragmentation_analysis: FragmentationAnalysis {
                fragmentation_ratio: 0.15,             // Placeholder
                largest_free_block: 1024 * 1024 * 100, // Placeholder
                allocation_patterns: vec![],           // Placeholder
            },
            leak_detection: LeakDetection {
                potential_leaks: vec![], // Placeholder
                leak_score: 0.0,
                recommendations: vec![],
            },
            memory_timeline: session.memory_snapshots.clone(),
        }
    }

    fn analyze_operations(&self, session: &ProfilingSession) -> OperationAnalysis {
        let mut operation_frequency = HashMap::new();
        let mut expensive_ops = Vec::new();

        for trace in &session.operation_traces {
            let duration = trace
                .end_time
                .duration_since(trace.start_time)
                .unwrap_or(Duration::from_secs(0));
            *operation_frequency
                .entry(trace.op_type.clone())
                .or_insert(0) += 1;

            expensive_ops.push(OperationCost {
                operation: trace.op_type.clone(),
                total_time: duration,
                call_count: 1,
                avg_time: duration,
                memory_cost: trace.memory_delta.max(0) as u64,
            });
        }

        // Sort by total time descending
        expensive_ops.sort_by(|a, b| b.total_time.cmp(&a.total_time));
        expensive_ops.truncate(10); // Keep top 10

        OperationAnalysis {
            expensive_ops,
            operation_frequency,
            critical_path: vec![], // Placeholder
            dependency_graph: OperationDependencyGraph {
                nodes: vec![],
                edges: vec![],
                critical_path: vec![],
            },
        }
    }

    fn identify_bottlenecks(&self, session: &ProfilingSession) -> Vec<PerformanceBottleneck> {
        let mut bottlenecks = Vec::new();

        // Check for memory bottlenecks
        if let Some(peak_snapshot) = session
            .memory_snapshots
            .iter()
            .max_by_key(|s| s.peak_memory)
        {
            if peak_snapshot.fragmentation_ratio > 0.3 {
                bottlenecks.push(PerformanceBottleneck {
                    bottleneck_type: BottleneckType::Memory,
                    location: "Memory allocation".to_string(),
                    severity: peak_snapshot.fragmentation_ratio,
                    impact: 0.7,
                    description: "High memory fragmentation detected".to_string(),
                    suggestions: vec![
                        "Consider using memory pooling".to_string(),
                        "Reduce allocation frequency".to_string(),
                    ],
                });
            }
        }

        // Check for compute bottlenecks
        for (layer_name, layer_profile) in &session.layer_profiles {
            let avg_forward_time = if !layer_profile.forward_times.is_empty() {
                layer_profile.forward_times.iter().sum::<Duration>()
                    / layer_profile.forward_times.len() as u32
            } else {
                Duration::from_secs(0)
            };

            if avg_forward_time > Duration::from_millis(100) {
                bottlenecks.push(PerformanceBottleneck {
                    bottleneck_type: BottleneckType::Compute,
                    location: layer_name.clone(),
                    severity: avg_forward_time.as_secs_f32(),
                    impact: 0.8,
                    description: format!("Layer {} has high computation time", layer_name),
                    suggestions: vec![
                        "Consider model quantization".to_string(),
                        "Optimize layer implementation".to_string(),
                    ],
                });
            }
        }

        bottlenecks
    }

    fn generate_recommendations(
        &self,
        _session: &ProfilingSession,
        bottlenecks: &[PerformanceBottleneck],
    ) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();

        // Memory optimization recommendations
        if bottlenecks
            .iter()
            .any(|b| matches!(b.bottleneck_type, BottleneckType::Memory))
        {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: OptimizationType::MemoryOptimization,
                priority: Priority::High,
                expected_improvement: 0.25,
                implementation_effort: ImplementationEffort::Medium,
                description: "Implement gradient checkpointing to reduce memory usage".to_string(),
                code_examples: vec![
                    "model.enable_gradient_checkpointing()".to_string(),
                    "torch.checkpoint(function, *args)".to_string(),
                ],
            });
        }

        // Compute optimization recommendations
        if bottlenecks
            .iter()
            .any(|b| matches!(b.bottleneck_type, BottleneckType::Compute))
        {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: OptimizationType::ComputeOptimization,
                priority: Priority::High,
                expected_improvement: 0.3,
                implementation_effort: ImplementationEffort::Easy,
                description: "Use mixed precision training to speed up computation".to_string(),
                code_examples: vec![
                    "model.half()".to_string(),
                    "with autocast(): output = model(input)".to_string(),
                ],
            });
        }

        recommendations
    }

    fn summarize_resource_utilization(
        &self,
        _session: &ProfilingSession,
    ) -> ResourceUtilizationSummary {
        // Placeholder implementation
        ResourceUtilizationSummary {
            cpu_utilization: CpuUtilizationSummary {
                avg_utilization: 65.0,
                peak_utilization: 95.0,
                per_core_avg: vec![60.0, 70.0, 65.0, 68.0],
                context_switches: 10000,
            },
            memory_utilization: MemoryUtilizationSummary {
                avg_utilization: 70.0,
                peak_utilization: 85.0,
                fragmentation_score: 0.15,
                allocation_efficiency: 0.88,
            },
            gpu_utilization: Some(GpuUtilizationSummary {
                avg_utilization: 80.0,
                peak_utilization: 98.0,
                memory_utilization: 75.0,
                temperature: 72.0,
                power_consumption: 250.0,
            }),
            io_utilization: IoUtilizationSummary {
                disk_utilization: 25.0,
                network_utilization: 15.0,
                io_wait_time: Duration::from_millis(50),
                bandwidth_efficiency: 0.85,
            },
        }
    }

    fn save_profile_result(&self, session_id: &str, result: &ProfilingResult) -> Result<()> {
        let file_path = self.config.profile_dir.join(format!("{}.json", session_id));
        let content = serde_json::to_string_pretty(result)?;
        std::fs::write(file_path, content)?;
        Ok(())
    }
}

impl ResourceMonitor {
    fn new() -> Result<Self> {
        Ok(Self {
            cpu_monitor: CpuMonitor::new(),
            memory_monitor: MemoryMonitor::new(),
            gpu_monitor: GpuMonitor::new_if_available(),
            io_monitor: IoMonitor::new(),
        })
    }

    fn start_monitoring(&mut self, _session_id: &str) -> Result<()> {
        // Start monitoring threads/tasks
        println!("Started resource monitoring");
        Ok(())
    }

    fn stop_monitoring(&mut self, _session_id: &str) -> Result<()> {
        // Stop monitoring and collect final stats
        println!("Stopped resource monitoring");
        Ok(())
    }
}

impl CpuMonitor {
    fn new() -> Self {
        Self {
            cpu_usage_history: Vec::new(),
            per_core_usage: Vec::new(),
            context_switches: 0,
            cpu_frequency: 0.0,
        }
    }
}

impl MemoryMonitor {
    fn new() -> Self {
        Self {
            memory_timeline: Vec::new(),
            allocation_tracker: AllocationTracker::default(),
            gc_monitor: GcMonitor::default(),
        }
    }
}

impl GpuMonitor {
    fn new_if_available() -> Option<Self> {
        // Check if GPU is available
        Some(Self {
            gpu_utilization: Vec::new(),
            gpu_memory_usage: Vec::new(),
            gpu_temperature: Vec::new(),
            gpu_power: Vec::new(),
        })
    }
}

impl IoMonitor {
    fn new() -> Self {
        Self {
            disk_stats: DiskStats::default(),
            network_stats: NetworkStats::default(),
        }
    }
}

// Default implementations

impl Default for LayerUtilization {
    fn default() -> Self {
        Self {
            compute_utilization: 0.0,
            memory_utilization: 0.0,
            parameter_utilization: 0.0,
            activation_sparsity: 0.0,
            gradient_sparsity: 0.0,
        }
    }
}

impl Default for GcMonitor {
    fn default() -> Self {
        Self {
            gc_count: 0,
            total_gc_time: Duration::from_secs(0),
            avg_gc_time: Duration::from_secs(0),
            memory_reclaimed: 0,
        }
    }
}

impl Default for DiskStats {
    fn default() -> Self {
        Self {
            bytes_read: 0,
            bytes_written: 0,
            read_operations: 0,
            write_operations: 0,
            avg_read_latency: Duration::from_millis(0),
            avg_write_latency: Duration::from_millis(0),
        }
    }
}

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

    #[test]
    fn test_profiler_creation() {
        let config = ProfilerConfig::default();
        let profiler = ModelProfiler::new(config);
        assert!(profiler.is_ok());
    }

    #[test]
    fn test_profiling_session() {
        let config = ProfilerConfig::default();
        let mut profiler = ModelProfiler::new(config).unwrap();

        let session_id = profiler.start_profiling("test_model").unwrap();
        assert!(!session_id.is_empty());

        let result = profiler.stop_profiling(&session_id);
        assert!(result.is_ok());
    }

    #[test]
    fn test_layer_recording() {
        let config = ProfilerConfig::default();
        let mut profiler = ModelProfiler::new(config).unwrap();

        let session_id = profiler.start_profiling("test_model").unwrap();

        let memory_usage = LayerMemoryUsage {
            peak_forward_memory: 1024,
            peak_backward_memory: 512,
            parameter_memory: 256,
            activation_memory: 512,
            gradient_memory: 256,
        };

        let result = profiler.record_layer_execution(
            &session_id,
            "linear1",
            "Linear",
            Duration::from_millis(10),
            memory_usage,
            vec![vec![32, 768]],
            vec![vec![32, 256]],
        );

        assert!(result.is_ok());
    }
}