trustformers_debug/
computation_graph.rs

1//! Computation graph analysis tools for debugging deep learning models.
2//!
3//! This module provides comprehensive analysis tools for computation graphs,
4//! including node analysis, dependency tracking, optimization opportunities,
5//! bottleneck detection, and graph visualization capabilities.
6
7use anyhow::Result;
8use serde::{Deserialize, Serialize};
9use std::collections::{HashMap, HashSet, VecDeque};
10use std::fmt;
11use uuid::Uuid;
12
13/// Represents a computation graph for analysis
14#[derive(Debug, Clone, Serialize, Deserialize)]
15pub struct ComputationGraph {
16    /// Unique identifier for this graph
17    pub id: Uuid,
18    /// Map of node ID to node information
19    pub nodes: HashMap<String, GraphNode>,
20    /// Adjacency list representing edges (dependencies)
21    pub edges: HashMap<String, Vec<String>>,
22    /// Root nodes (inputs to the computation)
23    pub root_nodes: HashSet<String>,
24    /// Leaf nodes (outputs of the computation)
25    pub leaf_nodes: HashSet<String>,
26    /// Metadata about the graph
27    pub metadata: GraphMetadata,
28}
29
30/// Metadata about the computation graph
31#[derive(Debug, Clone, Serialize, Deserialize)]
32pub struct GraphMetadata {
33    /// Name of the model/graph
34    pub name: String,
35    /// Total number of nodes
36    pub node_count: usize,
37    /// Total number of edges
38    pub edge_count: usize,
39    /// Maximum depth of the graph
40    pub max_depth: usize,
41    /// Memory usage estimate in bytes
42    pub estimated_memory_usage: u64,
43    /// FLOP count estimate
44    pub estimated_flops: u64,
45    /// Timestamp when graph was created
46    pub created_at: chrono::DateTime<chrono::Utc>,
47}
48
49/// Represents a single node in the computation graph
50#[derive(Debug, Clone, Serialize, Deserialize)]
51pub struct GraphNode {
52    /// Unique identifier for this node
53    pub id: String,
54    /// Human-readable name
55    pub name: String,
56    /// Type of operation (e.g., "MatMul", "Add", "ReLU")
57    pub operation_type: OperationType,
58    /// Input tensor shapes
59    pub input_shapes: Vec<Vec<usize>>,
60    /// Output tensor shapes
61    pub output_shapes: Vec<Vec<usize>>,
62    /// Computational complexity (FLOPs)
63    pub flop_count: u64,
64    /// Memory usage estimate in bytes
65    pub memory_usage: u64,
66    /// Execution time in microseconds (if profiled)
67    pub execution_time_us: Option<u64>,
68    /// Number of parameters (for parameterized operations)
69    pub parameter_count: Option<u64>,
70    /// Position in topological ordering
71    pub topo_order: Option<usize>,
72    /// Depth in the graph (distance from inputs)
73    pub depth: usize,
74    /// Additional metadata
75    pub metadata: HashMap<String, String>,
76}
77
78/// Types of operations in the computation graph
79#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
80pub enum OperationType {
81    // Arithmetic operations
82    Add,
83    Subtract,
84    Multiply,
85    Divide,
86    MatMul,
87    Dot,
88
89    // Activation functions
90    ReLU,
91    Sigmoid,
92    Tanh,
93    GELU,
94    Softmax,
95
96    // Normalization
97    LayerNorm,
98    BatchNorm,
99    RMSNorm,
100
101    // Convolution operations
102    Conv1D,
103    Conv2D,
104    Conv3D,
105    ConvTranspose,
106
107    // Pooling operations
108    MaxPool,
109    AvgPool,
110    AdaptivePool,
111
112    // Tensor operations
113    Reshape,
114    Transpose,
115    Concat,
116    Split,
117    Slice,
118    Gather,
119    Scatter,
120
121    // Reduction operations
122    Sum,
123    Mean,
124    Max,
125    Min,
126
127    // Attention operations
128    Attention,
129    MultiHeadAttention,
130    SelfAttention,
131    CrossAttention,
132
133    // Embedding operations
134    Embedding,
135    PositionalEmbedding,
136
137    // Loss functions
138    CrossEntropyLoss,
139    MSELoss,
140    L1Loss,
141
142    // Control flow
143    If,
144    While,
145    Loop,
146
147    // Custom operations
148    Custom(String),
149}
150
151/// Configuration for computation graph analysis
152#[derive(Debug, Clone, Serialize, Deserialize)]
153pub struct GraphAnalysisConfig {
154    /// Whether to perform memory analysis
155    pub enable_memory_analysis: bool,
156    /// Whether to perform FLOP analysis
157    pub enable_flop_analysis: bool,
158    /// Whether to detect optimization opportunities
159    pub enable_optimization_analysis: bool,
160    /// Whether to perform bottleneck detection
161    pub enable_bottleneck_detection: bool,
162    /// Whether to analyze data flow patterns
163    pub enable_dataflow_analysis: bool,
164    /// Threshold for considering a node a bottleneck (microseconds)
165    pub bottleneck_threshold_us: u64,
166    /// Memory threshold for large operations (bytes)
167    pub large_memory_threshold: u64,
168}
169
170impl Default for GraphAnalysisConfig {
171    fn default() -> Self {
172        Self {
173            enable_memory_analysis: true,
174            enable_flop_analysis: true,
175            enable_optimization_analysis: true,
176            enable_bottleneck_detection: true,
177            enable_dataflow_analysis: true,
178            bottleneck_threshold_us: 1000,             // 1ms
179            large_memory_threshold: 1024 * 1024 * 100, // 100MB
180        }
181    }
182}
183
184/// Main computation graph analyzer
185#[derive(Debug)]
186pub struct ComputationGraphAnalyzer {
187    config: GraphAnalysisConfig,
188    graphs: HashMap<Uuid, ComputationGraph>,
189    analysis_results: HashMap<Uuid, GraphAnalysisResult>,
190}
191
192/// Comprehensive analysis result for a computation graph
193#[derive(Debug, Clone, Serialize, Deserialize)]
194pub struct GraphAnalysisResult {
195    /// Graph being analyzed
196    pub graph_id: Uuid,
197    /// Memory analysis results
198    pub memory_analysis: Option<MemoryAnalysis>,
199    /// FLOP analysis results
200    pub flop_analysis: Option<FlopAnalysis>,
201    /// Optimization opportunities
202    pub optimization_opportunities: Vec<OptimizationOpportunity>,
203    /// Bottleneck analysis
204    pub bottleneck_analysis: Option<BottleneckAnalysis>,
205    /// Data flow analysis
206    pub dataflow_analysis: Option<DataFlowAnalysis>,
207    /// Critical path analysis
208    pub critical_path: Vec<String>,
209    /// Graph statistics
210    pub statistics: GraphStatistics,
211    /// Recommendations for improvement
212    pub recommendations: Vec<String>,
213}
214
215/// Memory usage analysis
216#[derive(Debug, Clone, Serialize, Deserialize)]
217pub struct MemoryAnalysis {
218    /// Total memory usage in bytes
219    pub total_memory_usage: u64,
220    /// Peak memory usage in bytes
221    pub peak_memory_usage: u64,
222    /// Memory usage by operation type
223    pub memory_by_operation: HashMap<OperationType, u64>,
224    /// Nodes with highest memory usage
225    pub memory_hotspots: Vec<(String, u64)>,
226    /// Memory fragmentation estimate
227    pub fragmentation_ratio: f64,
228    /// Suggested memory optimizations
229    pub optimization_suggestions: Vec<String>,
230}
231
232/// FLOP (Floating Point Operations) analysis
233#[derive(Debug, Clone, Serialize, Deserialize)]
234pub struct FlopAnalysis {
235    /// Total FLOP count
236    pub total_flops: u64,
237    /// FLOP count by operation type
238    pub flops_by_operation: HashMap<OperationType, u64>,
239    /// Nodes with highest FLOP count
240    pub compute_hotspots: Vec<(String, u64)>,
241    /// Arithmetic intensity (FLOPs per byte)
242    pub arithmetic_intensity: f64,
243    /// Computational complexity analysis
244    pub complexity_analysis: ComplexityAnalysis,
245}
246
247/// Complexity analysis of the computation
248#[derive(Debug, Clone, Serialize, Deserialize)]
249pub struct ComplexityAnalysis {
250    /// Time complexity estimate
251    pub time_complexity: String,
252    /// Space complexity estimate
253    pub space_complexity: String,
254    /// Parallelization potential (0.0 to 1.0)
255    pub parallelization_potential: f64,
256    /// Sequential dependencies
257    pub sequential_dependencies: usize,
258}
259
260/// Optimization opportunity detection
261#[derive(Debug, Clone, Serialize, Deserialize)]
262pub struct OptimizationOpportunity {
263    /// Type of optimization
264    pub optimization_type: OptimizationType,
265    /// Description of the opportunity
266    pub description: String,
267    /// Nodes involved in this optimization
268    pub affected_nodes: Vec<String>,
269    /// Estimated performance improvement
270    pub estimated_improvement: EstimatedImprovement,
271    /// Implementation difficulty (1-5)
272    pub implementation_difficulty: u8,
273    /// Priority level
274    pub priority: OptimizationPriority,
275}
276
277/// Types of optimizations that can be applied
278#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
279pub enum OptimizationType {
280    /// Fuse multiple operations into one
281    OperationFusion,
282    /// Eliminate redundant computations
283    RedundancyElimination,
284    /// Optimize memory layout
285    MemoryLayoutOptimization,
286    /// Use more efficient algorithms
287    AlgorithmicOptimization,
288    /// Parallelize sequential operations
289    Parallelization,
290    /// Optimize data access patterns
291    DataAccessOptimization,
292    /// Reduce precision where safe
293    PrecisionOptimization,
294    /// Cache intermediate results
295    Memoization,
296    /// Optimize control flow
297    ControlFlowOptimization,
298}
299
300/// Priority levels for optimizations
301#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
302pub enum OptimizationPriority {
303    Low,
304    Medium,
305    High,
306    Critical,
307}
308
309/// Estimated improvement from an optimization
310#[derive(Debug, Clone, Serialize, Deserialize)]
311pub struct EstimatedImprovement {
312    /// Estimated speedup (multiplicative factor)
313    pub speedup_factor: f64,
314    /// Estimated memory reduction in bytes
315    pub memory_reduction: u64,
316    /// Estimated energy savings (0.0 to 1.0)
317    pub energy_savings: f64,
318}
319
320/// Bottleneck analysis results
321#[derive(Debug, Clone, Serialize, Deserialize)]
322pub struct BottleneckAnalysis {
323    /// Nodes that are bottlenecks
324    pub bottleneck_nodes: Vec<String>,
325    /// Critical path through the graph
326    pub critical_path_nodes: Vec<String>,
327    /// Total critical path time
328    pub critical_path_time_us: u64,
329    /// Nodes that could benefit from parallelization
330    pub parallelizable_nodes: Vec<String>,
331    /// Scheduling suggestions
332    pub scheduling_suggestions: Vec<String>,
333}
334
335/// Data flow analysis results
336#[derive(Debug, Clone, Serialize, Deserialize)]
337pub struct DataFlowAnalysis {
338    /// Data dependencies between nodes
339    pub data_dependencies: HashMap<String, Vec<String>>,
340    /// Live variables at each node
341    pub live_variables: HashMap<String, HashSet<String>>,
342    /// Variable lifetime analysis
343    pub variable_lifetimes: HashMap<String, VariableLifetime>,
344    /// Memory reuse opportunities
345    pub memory_reuse_opportunities: Vec<MemoryReuseOpportunity>,
346}
347
348/// Lifetime information for a variable
349#[derive(Debug, Clone, Serialize, Deserialize)]
350pub struct VariableLifetime {
351    /// Node where variable is created
352    pub birth_node: String,
353    /// Node where variable is last used
354    pub death_node: String,
355    /// All nodes that use this variable
356    pub usage_nodes: Vec<String>,
357    /// Memory footprint in bytes
358    pub memory_footprint: u64,
359}
360
361/// Memory reuse opportunity
362#[derive(Debug, Clone, Serialize, Deserialize)]
363pub struct MemoryReuseOpportunity {
364    /// Variables that can share memory
365    pub reusable_variables: Vec<String>,
366    /// Memory that can be saved
367    pub memory_savings: u64,
368    /// Implementation complexity
369    pub complexity: u8,
370}
371
372/// Graph statistics
373#[derive(Debug, Clone, Serialize, Deserialize)]
374pub struct GraphStatistics {
375    /// Number of nodes by operation type
376    pub nodes_by_type: HashMap<OperationType, usize>,
377    /// Average node fan-in
378    pub average_fan_in: f64,
379    /// Average node fan-out
380    pub average_fan_out: f64,
381    /// Graph diameter (longest shortest path)
382    pub diameter: usize,
383    /// Clustering coefficient
384    pub clustering_coefficient: f64,
385    /// Number of strongly connected components
386    pub strongly_connected_components: usize,
387}
388
389impl ComputationGraphAnalyzer {
390    /// Create a new computation graph analyzer
391    pub fn new(config: GraphAnalysisConfig) -> Self {
392        Self {
393            config,
394            graphs: HashMap::new(),
395            analysis_results: HashMap::new(),
396        }
397    }
398
399    /// Add a computation graph for analysis
400    pub fn add_graph(&mut self, graph: ComputationGraph) -> Result<()> {
401        let graph_id = graph.id;
402        self.graphs.insert(graph_id, graph);
403        Ok(())
404    }
405
406    /// Create a computation graph from operations
407    pub fn create_graph(
408        &mut self,
409        name: String,
410        operations: Vec<(String, OperationType, Vec<String>)>, // (node_id, op_type, dependencies)
411    ) -> Result<Uuid> {
412        let graph_id = Uuid::new_v4();
413        let mut nodes = HashMap::new();
414        let mut edges = HashMap::new();
415        let mut root_nodes = HashSet::new();
416        let mut leaf_nodes = HashSet::new();
417
418        // Create nodes
419        for (node_id, op_type, dependencies) in &operations {
420            let node = GraphNode {
421                id: node_id.clone(),
422                name: node_id.clone(),
423                operation_type: op_type.clone(),
424                input_shapes: vec![],
425                output_shapes: vec![],
426                flop_count: self.estimate_flops(op_type, &[]),
427                memory_usage: self.estimate_memory(op_type, &[]),
428                execution_time_us: None,
429                parameter_count: self.estimate_parameters(op_type),
430                topo_order: None,
431                depth: 0,
432                metadata: HashMap::new(),
433            };
434            nodes.insert(node_id.clone(), node);
435
436            // Track dependencies
437            if dependencies.is_empty() {
438                root_nodes.insert(node_id.clone());
439            }
440            edges.insert(node_id.clone(), dependencies.clone());
441        }
442
443        // Identify leaf nodes
444        let all_dependencies: HashSet<String> = edges.values().flatten().cloned().collect();
445        for node_id in nodes.keys() {
446            if !all_dependencies.contains(node_id) {
447                leaf_nodes.insert(node_id.clone());
448            }
449        }
450
451        // Calculate depth and topological order
452        self.calculate_depth_and_topo_order(&mut nodes, &edges)?;
453
454        let metadata = GraphMetadata {
455            name,
456            node_count: nodes.len(),
457            edge_count: edges.values().map(|deps| deps.len()).sum(),
458            max_depth: nodes.values().map(|n| n.depth).max().unwrap_or(0),
459            estimated_memory_usage: nodes.values().map(|n| n.memory_usage).sum(),
460            estimated_flops: nodes.values().map(|n| n.flop_count).sum(),
461            created_at: chrono::Utc::now(),
462        };
463
464        let graph = ComputationGraph {
465            id: graph_id,
466            nodes,
467            edges,
468            root_nodes,
469            leaf_nodes,
470            metadata,
471        };
472
473        self.graphs.insert(graph_id, graph);
474        Ok(graph_id)
475    }
476
477    /// Analyze a computation graph
478    pub fn analyze_graph(&mut self, graph_id: Uuid) -> Result<GraphAnalysisResult> {
479        let graph = self
480            .graphs
481            .get(&graph_id)
482            .ok_or_else(|| anyhow::anyhow!("Graph not found: {}", graph_id))?;
483
484        let mut result = GraphAnalysisResult {
485            graph_id,
486            memory_analysis: None,
487            flop_analysis: None,
488            optimization_opportunities: Vec::new(),
489            bottleneck_analysis: None,
490            dataflow_analysis: None,
491            critical_path: Vec::new(),
492            statistics: self.calculate_statistics(graph)?,
493            recommendations: Vec::new(),
494        };
495
496        // Perform different types of analysis based on configuration
497        if self.config.enable_memory_analysis {
498            result.memory_analysis = Some(self.analyze_memory_usage(graph)?);
499        }
500
501        if self.config.enable_flop_analysis {
502            result.flop_analysis = Some(self.analyze_flop_usage(graph)?);
503        }
504
505        if self.config.enable_optimization_analysis {
506            result.optimization_opportunities = self.detect_optimization_opportunities(graph)?;
507        }
508
509        if self.config.enable_bottleneck_detection {
510            result.bottleneck_analysis = Some(self.analyze_bottlenecks(graph)?);
511        }
512
513        if self.config.enable_dataflow_analysis {
514            result.dataflow_analysis = Some(self.analyze_dataflow(graph)?);
515        }
516
517        result.critical_path = self.find_critical_path(graph)?;
518        result.recommendations = self.generate_recommendations(&result)?;
519
520        self.analysis_results.insert(graph_id, result.clone());
521        Ok(result)
522    }
523
524    /// Get analysis results for a graph
525    pub fn get_analysis_result(&self, graph_id: Uuid) -> Option<&GraphAnalysisResult> {
526        self.analysis_results.get(&graph_id)
527    }
528
529    /// Export graph analysis to DOT format for visualization
530    pub fn export_to_dot(&self, graph_id: Uuid) -> Result<String> {
531        let graph = self
532            .graphs
533            .get(&graph_id)
534            .ok_or_else(|| anyhow::anyhow!("Graph not found: {}", graph_id))?;
535
536        let mut dot = String::new();
537        dot.push_str(&format!("digraph \"{}\" {{\n", graph.metadata.name));
538        dot.push_str("  rankdir=TB;\n");
539        dot.push_str("  node [shape=box, style=filled];\n\n");
540
541        // Add nodes with styling based on operation type
542        for node in graph.nodes.values() {
543            let color = self.get_node_color(&node.operation_type);
544            let label = format!(
545                "{}\\n{}\\n{:.1} GFLOP\\n{:.1} MB",
546                node.name,
547                format!("{:?}", node.operation_type),
548                node.flop_count as f64 / 1e9,
549                node.memory_usage as f64 / (1024.0 * 1024.0)
550            );
551
552            dot.push_str(&format!(
553                "  \"{}\" [label=\"{}\", fillcolor=\"{}\"];\n",
554                node.id, label, color
555            ));
556        }
557
558        dot.push('\n');
559
560        // Add edges
561        for (node_id, dependencies) in &graph.edges {
562            for dep in dependencies {
563                dot.push_str(&format!("  \"{}\" -> \"{}\";\n", dep, node_id));
564            }
565        }
566
567        dot.push_str("}\n");
568        Ok(dot)
569    }
570
571    // Private helper methods
572
573    fn calculate_depth_and_topo_order(
574        &self,
575        nodes: &mut HashMap<String, GraphNode>,
576        edges: &HashMap<String, Vec<String>>,
577    ) -> Result<()> {
578        // Topological sort and depth calculation
579        let mut in_degree: HashMap<String, usize> = HashMap::new();
580        let mut adj_list: HashMap<String, Vec<String>> = HashMap::new();
581
582        // Initialize in-degrees and adjacency list
583        for node_id in nodes.keys() {
584            in_degree.insert(node_id.clone(), 0);
585            adj_list.insert(node_id.clone(), Vec::new());
586        }
587
588        for (node_id, dependencies) in edges {
589            in_degree.insert(node_id.clone(), dependencies.len());
590            for dep in dependencies {
591                adj_list.get_mut(dep).unwrap().push(node_id.clone());
592            }
593        }
594
595        // Kahn's algorithm for topological sorting and depth calculation
596        let mut queue = VecDeque::new();
597        let mut topo_order = 0;
598
599        // Find all nodes with no incoming edges
600        for (node_id, &degree) in &in_degree {
601            if degree == 0 {
602                queue.push_back((node_id.clone(), 0)); // (node_id, depth)
603            }
604        }
605
606        while let Some((node_id, depth)) = queue.pop_front() {
607            // Update node
608            if let Some(node) = nodes.get_mut(&node_id) {
609                node.depth = depth;
610                node.topo_order = Some(topo_order);
611                topo_order += 1;
612            }
613
614            // Process neighbors
615            if let Some(neighbors) = adj_list.get(&node_id) {
616                for neighbor in neighbors {
617                    *in_degree.get_mut(neighbor).unwrap() -= 1;
618                    if in_degree[neighbor] == 0 {
619                        queue.push_back((neighbor.clone(), depth + 1));
620                    }
621                }
622            }
623        }
624
625        Ok(())
626    }
627
628    fn estimate_flops(&self, op_type: &OperationType, shapes: &[Vec<usize>]) -> u64 {
629        // Simplified FLOP estimation
630        match op_type {
631            OperationType::MatMul => {
632                if shapes.len() >= 2 {
633                    let a_shape = &shapes[0];
634                    let b_shape = &shapes[1];
635                    if a_shape.len() >= 2 && b_shape.len() >= 2 {
636                        let m = a_shape[a_shape.len() - 2];
637                        let k = a_shape[a_shape.len() - 1];
638                        let n = b_shape[b_shape.len() - 1];
639                        return (2 * m * k * n) as u64;
640                    }
641                }
642                1000000 // Default estimate
643            },
644            OperationType::Add | OperationType::Subtract | OperationType::Multiply => {
645                shapes.first().map(|s| s.iter().product::<usize>() as u64).unwrap_or(1000)
646            },
647            OperationType::ReLU | OperationType::Sigmoid | OperationType::Tanh => {
648                shapes.first().map(|s| s.iter().product::<usize>() as u64).unwrap_or(1000)
649            },
650            OperationType::LayerNorm | OperationType::BatchNorm => {
651                shapes.first().map(|s| (s.iter().product::<usize>() * 5) as u64).unwrap_or(5000)
652            },
653            _ => 1000, // Default estimate
654        }
655    }
656
657    fn estimate_memory(&self, op_type: &OperationType, shapes: &[Vec<usize>]) -> u64 {
658        // Simplified memory estimation (assuming float32 = 4 bytes)
659        let element_size = 4u64;
660        match op_type {
661            OperationType::MatMul => {
662                shapes
663                    .iter()
664                    .map(|s| s.iter().product::<usize>() as u64 * element_size)
665                    .sum::<u64>()
666                    .max(1024) // Minimum 1KB
667            },
668            _ => shapes
669                .first()
670                .map(|s| s.iter().product::<usize>() as u64 * element_size)
671                .unwrap_or(1024),
672        }
673    }
674
675    fn estimate_parameters(&self, op_type: &OperationType) -> Option<u64> {
676        match op_type {
677            OperationType::MatMul => Some(1000000), // Example: 1M parameters
678            OperationType::Conv2D => Some(500000),
679            OperationType::Embedding => Some(2000000),
680            OperationType::LayerNorm => Some(1000),
681            _ => None,
682        }
683    }
684
685    fn analyze_memory_usage(&self, graph: &ComputationGraph) -> Result<MemoryAnalysis> {
686        let total_memory_usage = graph.nodes.values().map(|n| n.memory_usage).sum();
687
688        let mut memory_by_operation: HashMap<OperationType, u64> = HashMap::new();
689        for node in graph.nodes.values() {
690            *memory_by_operation.entry(node.operation_type.clone()).or_insert(0) +=
691                node.memory_usage;
692        }
693
694        let mut memory_hotspots: Vec<(String, u64)> =
695            graph.nodes.values().map(|n| (n.id.clone(), n.memory_usage)).collect();
696        memory_hotspots.sort_by(|a, b| b.1.cmp(&a.1));
697        memory_hotspots.truncate(10); // Top 10
698
699        let peak_memory_usage = total_memory_usage; // Simplified
700        let fragmentation_ratio = 0.1; // Simplified estimate
701
702        let optimization_suggestions = vec![
703            "Consider memory pooling for frequently allocated tensors".to_string(),
704            "Implement in-place operations where possible".to_string(),
705            "Use gradient checkpointing for memory-intensive layers".to_string(),
706        ];
707
708        Ok(MemoryAnalysis {
709            total_memory_usage,
710            peak_memory_usage,
711            memory_by_operation,
712            memory_hotspots,
713            fragmentation_ratio,
714            optimization_suggestions,
715        })
716    }
717
718    fn analyze_flop_usage(&self, graph: &ComputationGraph) -> Result<FlopAnalysis> {
719        let total_flops = graph.nodes.values().map(|n| n.flop_count).sum();
720
721        let mut flops_by_operation: HashMap<OperationType, u64> = HashMap::new();
722        for node in graph.nodes.values() {
723            *flops_by_operation.entry(node.operation_type.clone()).or_insert(0) += node.flop_count;
724        }
725
726        let mut compute_hotspots: Vec<(String, u64)> =
727            graph.nodes.values().map(|n| (n.id.clone(), n.flop_count)).collect();
728        compute_hotspots.sort_by(|a, b| b.1.cmp(&a.1));
729        compute_hotspots.truncate(10); // Top 10
730
731        let total_memory = graph.nodes.values().map(|n| n.memory_usage).sum::<u64>();
732        let arithmetic_intensity =
733            if total_memory > 0 { total_flops as f64 / total_memory as f64 } else { 0.0 };
734
735        let complexity_analysis = ComplexityAnalysis {
736            time_complexity: "O(n)".to_string(),  // Simplified
737            space_complexity: "O(n)".to_string(), // Simplified
738            parallelization_potential: 0.7,       // Simplified estimate
739            sequential_dependencies: graph.metadata.max_depth,
740        };
741
742        Ok(FlopAnalysis {
743            total_flops,
744            flops_by_operation,
745            compute_hotspots,
746            arithmetic_intensity,
747            complexity_analysis,
748        })
749    }
750
751    fn detect_optimization_opportunities(
752        &self,
753        graph: &ComputationGraph,
754    ) -> Result<Vec<OptimizationOpportunity>> {
755        let mut opportunities = Vec::new();
756
757        // Look for operation fusion opportunities
758        opportunities.extend(self.detect_fusion_opportunities(graph)?);
759
760        // Look for redundant operations
761        opportunities.extend(self.detect_redundancy_opportunities(graph)?);
762
763        // Look for memory optimization opportunities
764        opportunities.extend(self.detect_memory_optimizations(graph)?);
765
766        Ok(opportunities)
767    }
768
769    fn detect_fusion_opportunities(
770        &self,
771        graph: &ComputationGraph,
772    ) -> Result<Vec<OptimizationOpportunity>> {
773        let mut opportunities = Vec::new();
774
775        // Look for patterns like MatMul + Add (bias addition)
776        for node in graph.nodes.values() {
777            if let OperationType::Add = node.operation_type {
778                let empty_deps = vec![];
779                let dependencies = graph.edges.get(&node.id).unwrap_or(&empty_deps);
780                for dep in dependencies {
781                    if let Some(dep_node) = graph.nodes.get(dep) {
782                        if let OperationType::MatMul = dep_node.operation_type {
783                            opportunities.push(OptimizationOpportunity {
784                                optimization_type: OptimizationType::OperationFusion,
785                                description:
786                                    "Fuse MatMul and Add operations into a single GEMM operation"
787                                        .to_string(),
788                                affected_nodes: vec![dep.clone(), node.id.clone()],
789                                estimated_improvement: EstimatedImprovement {
790                                    speedup_factor: 1.2,
791                                    memory_reduction: 1024 * 1024, // 1MB
792                                    energy_savings: 0.1,
793                                },
794                                implementation_difficulty: 2,
795                                priority: OptimizationPriority::Medium,
796                            });
797                        }
798                    }
799                }
800            }
801        }
802
803        Ok(opportunities)
804    }
805
806    fn detect_redundancy_opportunities(
807        &self,
808        _graph: &ComputationGraph,
809    ) -> Result<Vec<OptimizationOpportunity>> {
810        // Simplified - in real implementation would detect common subexpressions
811        Ok(vec![])
812    }
813
814    fn detect_memory_optimizations(
815        &self,
816        graph: &ComputationGraph,
817    ) -> Result<Vec<OptimizationOpportunity>> {
818        let mut opportunities = Vec::new();
819
820        // Look for large memory operations
821        for node in graph.nodes.values() {
822            if node.memory_usage > self.config.large_memory_threshold {
823                opportunities.push(OptimizationOpportunity {
824                    optimization_type: OptimizationType::MemoryLayoutOptimization,
825                    description: format!(
826                        "Optimize memory layout for large operation: {}",
827                        node.name
828                    ),
829                    affected_nodes: vec![node.id.clone()],
830                    estimated_improvement: EstimatedImprovement {
831                        speedup_factor: 1.1,
832                        memory_reduction: node.memory_usage / 4, // 25% reduction
833                        energy_savings: 0.05,
834                    },
835                    implementation_difficulty: 3,
836                    priority: OptimizationPriority::Medium,
837                });
838            }
839        }
840
841        Ok(opportunities)
842    }
843
844    fn analyze_bottlenecks(&self, graph: &ComputationGraph) -> Result<BottleneckAnalysis> {
845        let mut bottleneck_nodes = Vec::new();
846        let mut parallelizable_nodes = Vec::new();
847
848        for node in graph.nodes.values() {
849            if let Some(exec_time) = node.execution_time_us {
850                if exec_time > self.config.bottleneck_threshold_us {
851                    bottleneck_nodes.push(node.id.clone());
852                }
853            }
854
855            // Check if node can be parallelized (simplified heuristic)
856            match node.operation_type {
857                OperationType::MatMul | OperationType::Conv2D | OperationType::Add => {
858                    parallelizable_nodes.push(node.id.clone());
859                },
860                _ => {},
861            }
862        }
863
864        let critical_path_nodes = self.find_critical_path(graph)?;
865        let critical_path_time_us = critical_path_nodes
866            .iter()
867            .filter_map(|id| graph.nodes.get(id))
868            .filter_map(|node| node.execution_time_us)
869            .sum();
870
871        let scheduling_suggestions = vec![
872            "Consider parallel execution of independent operations".to_string(),
873            "Use asynchronous execution for I/O operations".to_string(),
874            "Implement pipeline parallelism for sequential operations".to_string(),
875        ];
876
877        Ok(BottleneckAnalysis {
878            bottleneck_nodes,
879            critical_path_nodes,
880            critical_path_time_us,
881            parallelizable_nodes,
882            scheduling_suggestions,
883        })
884    }
885
886    fn analyze_dataflow(&self, graph: &ComputationGraph) -> Result<DataFlowAnalysis> {
887        let mut data_dependencies = HashMap::new();
888        let mut live_variables = HashMap::new();
889        let mut variable_lifetimes = HashMap::new();
890
891        // Simplified dataflow analysis
892        for (node_id, dependencies) in &graph.edges {
893            data_dependencies.insert(node_id.clone(), dependencies.clone());
894            live_variables.insert(node_id.clone(), dependencies.iter().cloned().collect());
895
896            // Create variable lifetimes for dependencies
897            for dep in dependencies {
898                if !variable_lifetimes.contains_key(dep) {
899                    variable_lifetimes.insert(
900                        dep.clone(),
901                        VariableLifetime {
902                            birth_node: dep.clone(),
903                            death_node: node_id.clone(),
904                            usage_nodes: vec![node_id.clone()],
905                            memory_footprint: graph
906                                .nodes
907                                .get(dep)
908                                .map(|n| n.memory_usage)
909                                .unwrap_or(0),
910                        },
911                    );
912                } else {
913                    let lifetime = variable_lifetimes.get_mut(dep).unwrap();
914                    lifetime.death_node = node_id.clone();
915                    lifetime.usage_nodes.push(node_id.clone());
916                }
917            }
918        }
919
920        let memory_reuse_opportunities = vec![MemoryReuseOpportunity {
921            reusable_variables: vec!["var1".to_string(), "var2".to_string()],
922            memory_savings: 1024 * 1024, // 1MB
923            complexity: 2,
924        }];
925
926        Ok(DataFlowAnalysis {
927            data_dependencies,
928            live_variables,
929            variable_lifetimes,
930            memory_reuse_opportunities,
931        })
932    }
933
934    fn find_critical_path(&self, graph: &ComputationGraph) -> Result<Vec<String>> {
935        // Simplified critical path finding - uses depth as proxy
936        let mut path = Vec::new();
937        let mut current_depth = graph.metadata.max_depth;
938
939        while current_depth > 0 {
940            // Find a node at the current depth
941            for node in graph.nodes.values() {
942                if node.depth == current_depth {
943                    path.push(node.id.clone());
944                    current_depth -= 1;
945                    break;
946                }
947            }
948            current_depth = current_depth.saturating_sub(1);
949        }
950
951        path.reverse();
952        Ok(path)
953    }
954
955    fn calculate_statistics(&self, graph: &ComputationGraph) -> Result<GraphStatistics> {
956        let mut nodes_by_type: HashMap<OperationType, usize> = HashMap::new();
957        for node in graph.nodes.values() {
958            *nodes_by_type.entry(node.operation_type.clone()).or_insert(0) += 1;
959        }
960
961        let total_fan_in: usize = graph.edges.values().map(|deps| deps.len()).sum();
962        let total_fan_out = total_fan_in; // In a DAG, total fan-in equals total fan-out
963        let average_fan_in = total_fan_in as f64 / graph.nodes.len() as f64;
964        let average_fan_out = total_fan_out as f64 / graph.nodes.len() as f64;
965
966        Ok(GraphStatistics {
967            nodes_by_type,
968            average_fan_in,
969            average_fan_out,
970            diameter: graph.metadata.max_depth,
971            clustering_coefficient: 0.0, // Simplified - DAGs have clustering coefficient of 0
972            strongly_connected_components: graph.nodes.len(), // Each node is its own SCC in a DAG
973        })
974    }
975
976    fn generate_recommendations(&self, analysis: &GraphAnalysisResult) -> Result<Vec<String>> {
977        let mut recommendations = Vec::new();
978
979        // Memory-based recommendations
980        if let Some(ref memory_analysis) = analysis.memory_analysis {
981            if memory_analysis.total_memory_usage > 1024 * 1024 * 1024 {
982                // > 1GB
983                recommendations.push(
984                    "Consider using gradient checkpointing to reduce memory usage".to_string(),
985                );
986            }
987            if memory_analysis.fragmentation_ratio > 0.2 {
988                recommendations
989                    .push("Implement memory pooling to reduce fragmentation".to_string());
990            }
991        }
992
993        // FLOP-based recommendations
994        if let Some(ref flop_analysis) = analysis.flop_analysis {
995            if flop_analysis.arithmetic_intensity < 1.0 {
996                recommendations
997                    .push("Consider kernel fusion to improve arithmetic intensity".to_string());
998            }
999            if flop_analysis.complexity_analysis.parallelization_potential > 0.5 {
1000                recommendations.push(
1001                    "Explore parallelization opportunities for compute-intensive operations"
1002                        .to_string(),
1003                );
1004            }
1005        }
1006
1007        // Optimization opportunities
1008        if analysis.optimization_opportunities.len() > 3 {
1009            recommendations.push(
1010                "Multiple optimization opportunities detected - prioritize by estimated impact"
1011                    .to_string(),
1012            );
1013        }
1014
1015        // Bottleneck recommendations
1016        if let Some(ref bottleneck_analysis) = analysis.bottleneck_analysis {
1017            if !bottleneck_analysis.bottleneck_nodes.is_empty() {
1018                recommendations.push(
1019                    "Address bottleneck operations through optimization or parallelization"
1020                        .to_string(),
1021                );
1022            }
1023        }
1024
1025        Ok(recommendations)
1026    }
1027
1028    fn get_node_color(&self, op_type: &OperationType) -> &'static str {
1029        match op_type {
1030            OperationType::MatMul | OperationType::Dot => "lightblue",
1031            OperationType::Add
1032            | OperationType::Subtract
1033            | OperationType::Multiply
1034            | OperationType::Divide => "lightgreen",
1035            OperationType::ReLU
1036            | OperationType::Sigmoid
1037            | OperationType::Tanh
1038            | OperationType::GELU => "orange",
1039            OperationType::LayerNorm | OperationType::BatchNorm | OperationType::RMSNorm => {
1040                "yellow"
1041            },
1042            OperationType::Conv1D | OperationType::Conv2D | OperationType::Conv3D => "lightcoral",
1043            OperationType::Attention | OperationType::MultiHeadAttention => "purple",
1044            OperationType::Embedding | OperationType::PositionalEmbedding => "pink",
1045            _ => "lightgray",
1046        }
1047    }
1048}
1049
1050impl fmt::Display for OperationType {
1051    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
1052        match self {
1053            OperationType::Custom(name) => write!(f, "Custom({})", name),
1054            _ => write!(f, "{:?}", self),
1055        }
1056    }
1057}
1058
1059impl Default for ComputationGraphAnalyzer {
1060    fn default() -> Self {
1061        Self::new(GraphAnalysisConfig::default())
1062    }
1063}
1064
1065#[cfg(test)]
1066mod tests {
1067    use super::*;
1068
1069    #[test]
1070    fn test_computation_graph_creation() {
1071        let mut analyzer = ComputationGraphAnalyzer::default();
1072
1073        let operations = vec![
1074            (
1075                "input".to_string(),
1076                OperationType::Custom("Input".to_string()),
1077                vec![],
1078            ),
1079            (
1080                "linear1".to_string(),
1081                OperationType::MatMul,
1082                vec!["input".to_string()],
1083            ),
1084            (
1085                "relu1".to_string(),
1086                OperationType::ReLU,
1087                vec!["linear1".to_string()],
1088            ),
1089            (
1090                "linear2".to_string(),
1091                OperationType::MatMul,
1092                vec!["relu1".to_string()],
1093            ),
1094            (
1095                "output".to_string(),
1096                OperationType::Custom("Output".to_string()),
1097                vec!["linear2".to_string()],
1098            ),
1099        ];
1100
1101        let graph_id = analyzer.create_graph("test_model".to_string(), operations).unwrap();
1102        let analysis = analyzer.analyze_graph(graph_id).unwrap();
1103
1104        assert_eq!(analysis.statistics.nodes_by_type.len(), 4); // MatMul, ReLU, Custom("Input"), Custom("Output")
1105        assert!(analysis.critical_path.len() > 0);
1106    }
1107
1108    #[test]
1109    fn test_optimization_detection() {
1110        let mut analyzer = ComputationGraphAnalyzer::default();
1111
1112        let operations = vec![
1113            (
1114                "input".to_string(),
1115                OperationType::Custom("Input".to_string()),
1116                vec![],
1117            ),
1118            (
1119                "matmul".to_string(),
1120                OperationType::MatMul,
1121                vec!["input".to_string()],
1122            ),
1123            (
1124                "add".to_string(),
1125                OperationType::Add,
1126                vec!["matmul".to_string()],
1127            ),
1128        ];
1129
1130        let graph_id = analyzer.create_graph("fusion_test".to_string(), operations).unwrap();
1131        let analysis = analyzer.analyze_graph(graph_id).unwrap();
1132
1133        assert!(analysis
1134            .optimization_opportunities
1135            .iter()
1136            .any(|op| op.optimization_type == OptimizationType::OperationFusion));
1137    }
1138
1139    #[test]
1140    fn test_dot_export() {
1141        let mut analyzer = ComputationGraphAnalyzer::default();
1142
1143        let operations = vec![
1144            ("a".to_string(), OperationType::MatMul, vec![]),
1145            ("b".to_string(), OperationType::ReLU, vec!["a".to_string()]),
1146        ];
1147
1148        let graph_id = analyzer.create_graph("simple".to_string(), operations).unwrap();
1149        let dot = analyzer.export_to_dot(graph_id).unwrap();
1150
1151        assert!(dot.contains("digraph"));
1152        assert!(dot.contains("MatMul"));
1153        assert!(dot.contains("ReLU"));
1154    }
1155}
trustformers_debug/computation_graph.rs

trustformers_debug/
computation_graph.rs
