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                if let Some(adj) = adj_list.get_mut(dep) {
592                    adj.push(node_id.clone());
593                }
594            }
595        }
596
597        // Kahn's algorithm for topological sorting and depth calculation
598        let mut queue = VecDeque::new();
599        let mut topo_order = 0;
600
601        // Find all nodes with no incoming edges
602        for (node_id, &degree) in &in_degree {
603            if degree == 0 {
604                queue.push_back((node_id.clone(), 0)); // (node_id, depth)
605            }
606        }
607
608        while let Some((node_id, depth)) = queue.pop_front() {
609            // Update node
610            if let Some(node) = nodes.get_mut(&node_id) {
611                node.depth = depth;
612                node.topo_order = Some(topo_order);
613                topo_order += 1;
614            }
615
616            // Process neighbors
617            if let Some(neighbors) = adj_list.get(&node_id) {
618                for neighbor in neighbors {
619                    if let Some(degree) = in_degree.get_mut(neighbor) {
620                        *degree -= 1;
621                        if *degree == 0 {
622                            queue.push_back((neighbor.clone(), depth + 1));
623                        }
624                    }
625                }
626            }
627        }
628
629        Ok(())
630    }
631
632    fn estimate_flops(&self, op_type: &OperationType, shapes: &[Vec<usize>]) -> u64 {
633        // Simplified FLOP estimation
634        match op_type {
635            OperationType::MatMul => {
636                if shapes.len() >= 2 {
637                    let a_shape = &shapes[0];
638                    let b_shape = &shapes[1];
639                    if a_shape.len() >= 2 && b_shape.len() >= 2 {
640                        let m = a_shape[a_shape.len() - 2];
641                        let k = a_shape[a_shape.len() - 1];
642                        let n = b_shape[b_shape.len() - 1];
643                        return (2 * m * k * n) as u64;
644                    }
645                }
646                1000000 // Default estimate
647            },
648            OperationType::Add | OperationType::Subtract | OperationType::Multiply => {
649                shapes.first().map(|s| s.iter().product::<usize>() as u64).unwrap_or(1000)
650            },
651            OperationType::ReLU | OperationType::Sigmoid | OperationType::Tanh => {
652                shapes.first().map(|s| s.iter().product::<usize>() as u64).unwrap_or(1000)
653            },
654            OperationType::LayerNorm | OperationType::BatchNorm => {
655                shapes.first().map(|s| (s.iter().product::<usize>() * 5) as u64).unwrap_or(5000)
656            },
657            _ => 1000, // Default estimate
658        }
659    }
660
661    fn estimate_memory(&self, op_type: &OperationType, shapes: &[Vec<usize>]) -> u64 {
662        // Simplified memory estimation (assuming float32 = 4 bytes)
663        let element_size = 4u64;
664        match op_type {
665            OperationType::MatMul => {
666                shapes
667                    .iter()
668                    .map(|s| s.iter().product::<usize>() as u64 * element_size)
669                    .sum::<u64>()
670                    .max(1024) // Minimum 1KB
671            },
672            _ => shapes
673                .first()
674                .map(|s| s.iter().product::<usize>() as u64 * element_size)
675                .unwrap_or(1024),
676        }
677    }
678
679    fn estimate_parameters(&self, op_type: &OperationType) -> Option<u64> {
680        match op_type {
681            OperationType::MatMul => Some(1000000), // Example: 1M parameters
682            OperationType::Conv2D => Some(500000),
683            OperationType::Embedding => Some(2000000),
684            OperationType::LayerNorm => Some(1000),
685            _ => None,
686        }
687    }
688
689    fn analyze_memory_usage(&self, graph: &ComputationGraph) -> Result<MemoryAnalysis> {
690        let total_memory_usage = graph.nodes.values().map(|n| n.memory_usage).sum();
691
692        let mut memory_by_operation: HashMap<OperationType, u64> = HashMap::new();
693        for node in graph.nodes.values() {
694            *memory_by_operation.entry(node.operation_type.clone()).or_insert(0) +=
695                node.memory_usage;
696        }
697
698        let mut memory_hotspots: Vec<(String, u64)> =
699            graph.nodes.values().map(|n| (n.id.clone(), n.memory_usage)).collect();
700        memory_hotspots.sort_by_key(|item| std::cmp::Reverse(item.1));
701        memory_hotspots.truncate(10); // Top 10
702
703        let peak_memory_usage = total_memory_usage; // Simplified
704        let fragmentation_ratio = 0.1; // Simplified estimate
705
706        let optimization_suggestions = vec![
707            "Consider memory pooling for frequently allocated tensors".to_string(),
708            "Implement in-place operations where possible".to_string(),
709            "Use gradient checkpointing for memory-intensive layers".to_string(),
710        ];
711
712        Ok(MemoryAnalysis {
713            total_memory_usage,
714            peak_memory_usage,
715            memory_by_operation,
716            memory_hotspots,
717            fragmentation_ratio,
718            optimization_suggestions,
719        })
720    }
721
722    fn analyze_flop_usage(&self, graph: &ComputationGraph) -> Result<FlopAnalysis> {
723        let total_flops = graph.nodes.values().map(|n| n.flop_count).sum();
724
725        let mut flops_by_operation: HashMap<OperationType, u64> = HashMap::new();
726        for node in graph.nodes.values() {
727            *flops_by_operation.entry(node.operation_type.clone()).or_insert(0) += node.flop_count;
728        }
729
730        let mut compute_hotspots: Vec<(String, u64)> =
731            graph.nodes.values().map(|n| (n.id.clone(), n.flop_count)).collect();
732        compute_hotspots.sort_by_key(|item| std::cmp::Reverse(item.1));
733        compute_hotspots.truncate(10); // Top 10
734
735        let total_memory = graph.nodes.values().map(|n| n.memory_usage).sum::<u64>();
736        let arithmetic_intensity =
737            if total_memory > 0 { total_flops as f64 / total_memory as f64 } else { 0.0 };
738
739        let complexity_analysis = ComplexityAnalysis {
740            time_complexity: "O(n)".to_string(),  // Simplified
741            space_complexity: "O(n)".to_string(), // Simplified
742            parallelization_potential: 0.7,       // Simplified estimate
743            sequential_dependencies: graph.metadata.max_depth,
744        };
745
746        Ok(FlopAnalysis {
747            total_flops,
748            flops_by_operation,
749            compute_hotspots,
750            arithmetic_intensity,
751            complexity_analysis,
752        })
753    }
754
755    fn detect_optimization_opportunities(
756        &self,
757        graph: &ComputationGraph,
758    ) -> Result<Vec<OptimizationOpportunity>> {
759        let mut opportunities = Vec::new();
760
761        // Look for operation fusion opportunities
762        opportunities.extend(self.detect_fusion_opportunities(graph)?);
763
764        // Look for redundant operations
765        opportunities.extend(self.detect_redundancy_opportunities(graph)?);
766
767        // Look for memory optimization opportunities
768        opportunities.extend(self.detect_memory_optimizations(graph)?);
769
770        Ok(opportunities)
771    }
772
773    fn detect_fusion_opportunities(
774        &self,
775        graph: &ComputationGraph,
776    ) -> Result<Vec<OptimizationOpportunity>> {
777        let mut opportunities = Vec::new();
778
779        // Look for patterns like MatMul + Add (bias addition)
780        for node in graph.nodes.values() {
781            if let OperationType::Add = node.operation_type {
782                let empty_deps = vec![];
783                let dependencies = graph.edges.get(&node.id).unwrap_or(&empty_deps);
784                for dep in dependencies {
785                    if let Some(dep_node) = graph.nodes.get(dep) {
786                        if let OperationType::MatMul = dep_node.operation_type {
787                            opportunities.push(OptimizationOpportunity {
788                                optimization_type: OptimizationType::OperationFusion,
789                                description:
790                                    "Fuse MatMul and Add operations into a single GEMM operation"
791                                        .to_string(),
792                                affected_nodes: vec![dep.clone(), node.id.clone()],
793                                estimated_improvement: EstimatedImprovement {
794                                    speedup_factor: 1.2,
795                                    memory_reduction: 1024 * 1024, // 1MB
796                                    energy_savings: 0.1,
797                                },
798                                implementation_difficulty: 2,
799                                priority: OptimizationPriority::Medium,
800                            });
801                        }
802                    }
803                }
804            }
805        }
806
807        Ok(opportunities)
808    }
809
810    fn detect_redundancy_opportunities(
811        &self,
812        _graph: &ComputationGraph,
813    ) -> Result<Vec<OptimizationOpportunity>> {
814        // Simplified - in real implementation would detect common subexpressions
815        Ok(vec![])
816    }
817
818    fn detect_memory_optimizations(
819        &self,
820        graph: &ComputationGraph,
821    ) -> Result<Vec<OptimizationOpportunity>> {
822        let mut opportunities = Vec::new();
823
824        // Look for large memory operations
825        for node in graph.nodes.values() {
826            if node.memory_usage > self.config.large_memory_threshold {
827                opportunities.push(OptimizationOpportunity {
828                    optimization_type: OptimizationType::MemoryLayoutOptimization,
829                    description: format!(
830                        "Optimize memory layout for large operation: {}",
831                        node.name
832                    ),
833                    affected_nodes: vec![node.id.clone()],
834                    estimated_improvement: EstimatedImprovement {
835                        speedup_factor: 1.1,
836                        memory_reduction: node.memory_usage / 4, // 25% reduction
837                        energy_savings: 0.05,
838                    },
839                    implementation_difficulty: 3,
840                    priority: OptimizationPriority::Medium,
841                });
842            }
843        }
844
845        Ok(opportunities)
846    }
847
848    fn analyze_bottlenecks(&self, graph: &ComputationGraph) -> Result<BottleneckAnalysis> {
849        let mut bottleneck_nodes = Vec::new();
850        let mut parallelizable_nodes = Vec::new();
851
852        for node in graph.nodes.values() {
853            if let Some(exec_time) = node.execution_time_us {
854                if exec_time > self.config.bottleneck_threshold_us {
855                    bottleneck_nodes.push(node.id.clone());
856                }
857            }
858
859            // Check if node can be parallelized (simplified heuristic)
860            match node.operation_type {
861                OperationType::MatMul | OperationType::Conv2D | OperationType::Add => {
862                    parallelizable_nodes.push(node.id.clone());
863                },
864                _ => {},
865            }
866        }
867
868        let critical_path_nodes = self.find_critical_path(graph)?;
869        let critical_path_time_us = critical_path_nodes
870            .iter()
871            .filter_map(|id| graph.nodes.get(id))
872            .filter_map(|node| node.execution_time_us)
873            .sum();
874
875        let scheduling_suggestions = vec![
876            "Consider parallel execution of independent operations".to_string(),
877            "Use asynchronous execution for I/O operations".to_string(),
878            "Implement pipeline parallelism for sequential operations".to_string(),
879        ];
880
881        Ok(BottleneckAnalysis {
882            bottleneck_nodes,
883            critical_path_nodes,
884            critical_path_time_us,
885            parallelizable_nodes,
886            scheduling_suggestions,
887        })
888    }
889
890    fn analyze_dataflow(&self, graph: &ComputationGraph) -> Result<DataFlowAnalysis> {
891        let mut data_dependencies = HashMap::new();
892        let mut live_variables = HashMap::new();
893        let mut variable_lifetimes = HashMap::new();
894
895        // Simplified dataflow analysis
896        for (node_id, dependencies) in &graph.edges {
897            data_dependencies.insert(node_id.clone(), dependencies.clone());
898            live_variables.insert(node_id.clone(), dependencies.iter().cloned().collect());
899
900            // Create variable lifetimes for dependencies
901            for dep in dependencies {
902                if !variable_lifetimes.contains_key(dep) {
903                    variable_lifetimes.insert(
904                        dep.clone(),
905                        VariableLifetime {
906                            birth_node: dep.clone(),
907                            death_node: node_id.clone(),
908                            usage_nodes: vec![node_id.clone()],
909                            memory_footprint: graph
910                                .nodes
911                                .get(dep)
912                                .map(|n| n.memory_usage)
913                                .unwrap_or(0),
914                        },
915                    );
916                } else {
917                    let lifetime = variable_lifetimes
918                        .get_mut(dep)
919                        .expect("variable lifetime should exist for previously seen dependency");
920                    lifetime.death_node = node_id.clone();
921                    lifetime.usage_nodes.push(node_id.clone());
922                }
923            }
924        }
925
926        let memory_reuse_opportunities = vec![MemoryReuseOpportunity {
927            reusable_variables: vec!["var1".to_string(), "var2".to_string()],
928            memory_savings: 1024 * 1024, // 1MB
929            complexity: 2,
930        }];
931
932        Ok(DataFlowAnalysis {
933            data_dependencies,
934            live_variables,
935            variable_lifetimes,
936            memory_reuse_opportunities,
937        })
938    }
939
940    fn find_critical_path(&self, graph: &ComputationGraph) -> Result<Vec<String>> {
941        // Simplified critical path finding - uses depth as proxy
942        let mut path = Vec::new();
943        let mut current_depth = graph.metadata.max_depth;
944
945        while current_depth > 0 {
946            // Find a node at the current depth
947            for node in graph.nodes.values() {
948                if node.depth == current_depth {
949                    path.push(node.id.clone());
950                    current_depth -= 1;
951                    break;
952                }
953            }
954            current_depth = current_depth.saturating_sub(1);
955        }
956
957        path.reverse();
958        Ok(path)
959    }
960
961    fn calculate_statistics(&self, graph: &ComputationGraph) -> Result<GraphStatistics> {
962        let mut nodes_by_type: HashMap<OperationType, usize> = HashMap::new();
963        for node in graph.nodes.values() {
964            *nodes_by_type.entry(node.operation_type.clone()).or_insert(0) += 1;
965        }
966
967        let total_fan_in: usize = graph.edges.values().map(|deps| deps.len()).sum();
968        let total_fan_out = total_fan_in; // In a DAG, total fan-in equals total fan-out
969        let average_fan_in = total_fan_in as f64 / graph.nodes.len() as f64;
970        let average_fan_out = total_fan_out as f64 / graph.nodes.len() as f64;
971
972        Ok(GraphStatistics {
973            nodes_by_type,
974            average_fan_in,
975            average_fan_out,
976            diameter: graph.metadata.max_depth,
977            clustering_coefficient: 0.0, // Simplified - DAGs have clustering coefficient of 0
978            strongly_connected_components: graph.nodes.len(), // Each node is its own SCC in a DAG
979        })
980    }
981
982    fn generate_recommendations(&self, analysis: &GraphAnalysisResult) -> Result<Vec<String>> {
983        let mut recommendations = Vec::new();
984
985        // Memory-based recommendations
986        if let Some(ref memory_analysis) = analysis.memory_analysis {
987            if memory_analysis.total_memory_usage > 1024 * 1024 * 1024 {
988                // > 1GB
989                recommendations.push(
990                    "Consider using gradient checkpointing to reduce memory usage".to_string(),
991                );
992            }
993            if memory_analysis.fragmentation_ratio > 0.2 {
994                recommendations
995                    .push("Implement memory pooling to reduce fragmentation".to_string());
996            }
997        }
998
999        // FLOP-based recommendations
1000        if let Some(ref flop_analysis) = analysis.flop_analysis {
1001            if flop_analysis.arithmetic_intensity < 1.0 {
1002                recommendations
1003                    .push("Consider kernel fusion to improve arithmetic intensity".to_string());
1004            }
1005            if flop_analysis.complexity_analysis.parallelization_potential > 0.5 {
1006                recommendations.push(
1007                    "Explore parallelization opportunities for compute-intensive operations"
1008                        .to_string(),
1009                );
1010            }
1011        }
1012
1013        // Optimization opportunities
1014        if analysis.optimization_opportunities.len() > 3 {
1015            recommendations.push(
1016                "Multiple optimization opportunities detected - prioritize by estimated impact"
1017                    .to_string(),
1018            );
1019        }
1020
1021        // Bottleneck recommendations
1022        if let Some(ref bottleneck_analysis) = analysis.bottleneck_analysis {
1023            if !bottleneck_analysis.bottleneck_nodes.is_empty() {
1024                recommendations.push(
1025                    "Address bottleneck operations through optimization or parallelization"
1026                        .to_string(),
1027                );
1028            }
1029        }
1030
1031        Ok(recommendations)
1032    }
1033
1034    fn get_node_color(&self, op_type: &OperationType) -> &'static str {
1035        match op_type {
1036            OperationType::MatMul | OperationType::Dot => "lightblue",
1037            OperationType::Add
1038            | OperationType::Subtract
1039            | OperationType::Multiply
1040            | OperationType::Divide => "lightgreen",
1041            OperationType::ReLU
1042            | OperationType::Sigmoid
1043            | OperationType::Tanh
1044            | OperationType::GELU => "orange",
1045            OperationType::LayerNorm | OperationType::BatchNorm | OperationType::RMSNorm => {
1046                "yellow"
1047            },
1048            OperationType::Conv1D | OperationType::Conv2D | OperationType::Conv3D => "lightcoral",
1049            OperationType::Attention | OperationType::MultiHeadAttention => "purple",
1050            OperationType::Embedding | OperationType::PositionalEmbedding => "pink",
1051            _ => "lightgray",
1052        }
1053    }
1054}
1055
1056impl fmt::Display for OperationType {
1057    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
1058        match self {
1059            OperationType::Custom(name) => write!(f, "Custom({})", name),
1060            _ => write!(f, "{:?}", self),
1061        }
1062    }
1063}
1064
1065impl Default for ComputationGraphAnalyzer {
1066    fn default() -> Self {
1067        Self::new(GraphAnalysisConfig::default())
1068    }
1069}
1070
1071#[cfg(test)]
1072mod tests {
1073    use super::*;
1074
1075    #[test]
1076    fn test_computation_graph_creation() {
1077        let mut analyzer = ComputationGraphAnalyzer::default();
1078
1079        let operations = vec![
1080            (
1081                "input".to_string(),
1082                OperationType::Custom("Input".to_string()),
1083                vec![],
1084            ),
1085            (
1086                "linear1".to_string(),
1087                OperationType::MatMul,
1088                vec!["input".to_string()],
1089            ),
1090            (
1091                "relu1".to_string(),
1092                OperationType::ReLU,
1093                vec!["linear1".to_string()],
1094            ),
1095            (
1096                "linear2".to_string(),
1097                OperationType::MatMul,
1098                vec!["relu1".to_string()],
1099            ),
1100            (
1101                "output".to_string(),
1102                OperationType::Custom("Output".to_string()),
1103                vec!["linear2".to_string()],
1104            ),
1105        ];
1106
1107        let graph_id = analyzer
1108            .create_graph("test_model".to_string(), operations)
1109            .expect("operation failed in test");
1110        let analysis = analyzer.analyze_graph(graph_id).expect("operation failed in test");
1111
1112        assert_eq!(analysis.statistics.nodes_by_type.len(), 4); // MatMul, ReLU, Custom("Input"), Custom("Output")
1113        assert!(!analysis.critical_path.is_empty());
1114    }
1115
1116    #[test]
1117    fn test_optimization_detection() {
1118        let mut analyzer = ComputationGraphAnalyzer::default();
1119
1120        let operations = vec![
1121            (
1122                "input".to_string(),
1123                OperationType::Custom("Input".to_string()),
1124                vec![],
1125            ),
1126            (
1127                "matmul".to_string(),
1128                OperationType::MatMul,
1129                vec!["input".to_string()],
1130            ),
1131            (
1132                "add".to_string(),
1133                OperationType::Add,
1134                vec!["matmul".to_string()],
1135            ),
1136        ];
1137
1138        let graph_id = analyzer
1139            .create_graph("fusion_test".to_string(), operations)
1140            .expect("operation failed in test");
1141        let analysis = analyzer.analyze_graph(graph_id).expect("operation failed in test");
1142
1143        assert!(analysis
1144            .optimization_opportunities
1145            .iter()
1146            .any(|op| op.optimization_type == OptimizationType::OperationFusion));
1147    }
1148
1149    #[test]
1150    fn test_dot_export() {
1151        let mut analyzer = ComputationGraphAnalyzer::default();
1152
1153        let operations = vec![
1154            ("a".to_string(), OperationType::MatMul, vec![]),
1155            ("b".to_string(), OperationType::ReLU, vec!["a".to_string()]),
1156        ];
1157
1158        let graph_id = analyzer
1159            .create_graph("simple".to_string(), operations)
1160            .expect("operation failed in test");
1161        let dot = analyzer.export_to_dot(graph_id).expect("operation failed in test");
1162
1163        assert!(dot.contains("digraph"));
1164        assert!(dot.contains("MatMul"));
1165        assert!(dot.contains("ReLU"));
1166    }
1167}
trustformers_debug/computation_graph.rs

trustformers_debug/
computation_graph.rs
