voirs_recognizer/asr/
optimization_integration.rs

1//! Integration module for advanced model optimization
2//!
3//! This module provides high-level interfaces to apply various optimization
4//! techniques to ASR models, with automatic configuration and evaluation.
5
6use super::advanced_optimization::{
7    AdvancedOptimizationConfig, KnowledgeDistillationOptimizer, MixedPrecisionOptimizer,
8    PerformanceMeasurement, ProgressivePruningOptimizer,
9};
10use crate::RecognitionError;
11use candle_core::{Device, Tensor};
12use serde::{Deserialize, Serialize};
13use std::collections::HashMap;
14use std::time::Instant;
15use tracing::info;
16
17/// Comprehensive optimization pipeline
18#[derive(Debug)]
19pub struct OptimizationPipeline {
20    /// Advanced optimization configuration
21    config: AdvancedOptimizationConfig,
22    /// Knowledge distillation optimizer
23    kd_optimizer: Option<KnowledgeDistillationOptimizer>,
24    /// Progressive pruning optimizer
25    pruning_optimizer: Option<ProgressivePruningOptimizer>,
26    /// Mixed-precision optimizer
27    mp_optimizer: Option<MixedPrecisionOptimizer>,
28    /// Device
29    device: Device,
30    /// Optimization results
31    results: OptimizationResults,
32}
33
34/// Optimization pipeline results
35#[derive(Debug, Clone, Serialize, Deserialize, Default)]
36pub struct OptimizationResults {
37    /// Original model statistics
38    pub original_stats: ModelStats,
39    /// Optimized model statistics
40    pub optimized_stats: ModelStats,
41    /// Knowledge distillation results
42    pub distillation_results: Option<DistillationResults>,
43    /// Pruning results
44    pub pruning_results: Option<PruningResults>,
45    /// Mixed-precision results
46    pub mixed_precision_results: Option<MixedPrecisionResults>,
47    /// Overall optimization summary
48    pub summary: OptimizationSummary,
49}
50
51/// Model statistics
52#[derive(Debug, Clone, Serialize, Deserialize)]
53pub struct ModelStats {
54    /// Number of parameters
55    pub num_parameters: usize,
56    /// Model size in MB
57    pub model_size_mb: f32,
58    /// Inference time in milliseconds
59    pub inference_time_ms: f32,
60    /// Memory usage in MB
61    pub memory_usage_mb: f32,
62    /// Accuracy score
63    pub accuracy: f32,
64    /// Real-time factor
65    pub rtf: f32,
66}
67
68/// Knowledge distillation results
69#[derive(Debug, Clone, Serialize, Deserialize)]
70pub struct DistillationResults {
71    /// Final distillation loss
72    pub final_loss: f32,
73    /// Knowledge transfer efficiency
74    pub transfer_efficiency: f32,
75    /// Best temperature found
76    pub optimal_temperature: f32,
77    /// Accuracy retention
78    pub accuracy_retention: f32,
79}
80
81/// Pruning results
82#[derive(Debug, Clone, Serialize, Deserialize)]
83pub struct PruningResults {
84    /// Final sparsity achieved
85    pub final_sparsity: f32,
86    /// Model size reduction
87    pub size_reduction: f32,
88    /// Inference speedup
89    pub speedup: f32,
90    /// Accuracy retention
91    pub accuracy_retention: f32,
92    /// Number of pruning steps
93    pub pruning_steps: usize,
94}
95
96/// Mixed-precision results
97#[derive(Debug, Clone, Serialize, Deserialize)]
98pub struct MixedPrecisionResults {
99    /// Precision distribution
100    pub precision_distribution: HashMap<String, usize>, // DType name -> count
101    /// Estimated speedup
102    pub estimated_speedup: f32,
103    /// Memory reduction
104    pub memory_reduction: f32,
105    /// Accuracy retention
106    pub accuracy_retention: f32,
107}
108
109/// Overall optimization summary
110#[derive(Debug, Clone, Serialize, Deserialize)]
111pub struct OptimizationSummary {
112    /// Total optimization time in seconds
113    pub optimization_time_s: f32,
114    /// Overall speedup achieved
115    pub overall_speedup: f32,
116    /// Overall memory reduction
117    pub overall_memory_reduction: f32,
118    /// Overall model size reduction
119    pub overall_size_reduction: f32,
120    /// Final accuracy retention
121    pub final_accuracy_retention: f32,
122    /// Optimization techniques applied
123    pub techniques_applied: Vec<String>,
124    /// Whether optimization meets targets
125    pub meets_targets: bool,
126}
127
128impl OptimizationPipeline {
129    /// Create new optimization pipeline
130    #[must_use]
131    pub fn new(config: AdvancedOptimizationConfig, device: Device) -> Self {
132        Self {
133            kd_optimizer: if config.enable_knowledge_distillation {
134                Some(KnowledgeDistillationOptimizer::new(
135                    config.clone(),
136                    device.clone(),
137                ))
138            } else {
139                None
140            },
141            pruning_optimizer: if config.enable_progressive_pruning {
142                Some(ProgressivePruningOptimizer::new(
143                    config.clone(),
144                    device.clone(),
145                ))
146            } else {
147                None
148            },
149            mp_optimizer: if config.enable_mixed_precision {
150                Some(MixedPrecisionOptimizer::new(config.clone(), device.clone()))
151            } else {
152                None
153            },
154            config,
155            device,
156            results: OptimizationResults::default(),
157        }
158    }
159
160    /// Run comprehensive optimization pipeline
161    pub async fn optimize_model(
162        &mut self,
163        model_layers: &mut HashMap<String, Tensor>,
164        teacher_layers: Option<HashMap<String, Tensor>>,
165        validation_fn: impl Fn(&HashMap<String, Tensor>) -> Result<ModelStats, RecognitionError> + Copy,
166    ) -> Result<OptimizationResults, RecognitionError> {
167        let start_time = Instant::now();
168        info!("Starting comprehensive model optimization pipeline");
169
170        // Measure original model statistics
171        let original_stats = validation_fn(model_layers)?;
172        info!(
173            "Original model: {:.1}MB, {:.1}ms inference, {:.3} accuracy",
174            original_stats.model_size_mb, original_stats.inference_time_ms, original_stats.accuracy
175        );
176
177        let mut techniques_applied = Vec::new();
178
179        // Step 1: Knowledge Distillation (if enabled and teacher provided)
180        let distillation_results = if self.config.enable_knowledge_distillation {
181            if let (Some(teacher), Some(kd_optimizer)) =
182                (teacher_layers, self.kd_optimizer.as_mut())
183            {
184                info!("Applying knowledge distillation");
185                kd_optimizer.set_teacher_layers(teacher);
186                kd_optimizer.set_student_layers(model_layers.clone());
187
188                let results = Self::apply_knowledge_distillation_static(
189                    &self.device,
190                    kd_optimizer,
191                    model_layers,
192                    validation_fn,
193                )
194                .await?;
195                techniques_applied.push("Knowledge Distillation".to_string());
196                Some(results)
197            } else {
198                None
199            }
200        } else {
201            None
202        };
203
204        // Step 2: Progressive Pruning (if enabled)
205        let pruning_results = if self.config.enable_progressive_pruning {
206            if let Some(pruning_optimizer) = self.pruning_optimizer.as_mut() {
207                info!("Applying progressive pruning");
208                let results = Self::apply_progressive_pruning_static(
209                    &self.device,
210                    pruning_optimizer,
211                    model_layers,
212                    validation_fn,
213                )
214                .await?;
215                techniques_applied.push("Progressive Pruning".to_string());
216                Some(results)
217            } else {
218                None
219            }
220        } else {
221            None
222        };
223
224        // Step 3: Mixed-Precision Optimization (if enabled)
225        let mixed_precision_results = if self.config.enable_mixed_precision {
226            if let Some(mp_optimizer) = self.mp_optimizer.as_mut() {
227                info!("Applying mixed-precision optimization");
228                let results = Self::apply_mixed_precision_static(
229                    &self.device,
230                    mp_optimizer,
231                    model_layers,
232                    validation_fn,
233                )
234                .await?;
235                techniques_applied.push("Mixed-Precision".to_string());
236                Some(results)
237            } else {
238                None
239            }
240        } else {
241            None
242        };
243
244        // Measure final optimized model statistics
245        let optimized_stats = validation_fn(model_layers)?;
246        let optimization_time = start_time.elapsed().as_secs_f32();
247
248        // Compute overall metrics
249        let overall_speedup = original_stats.inference_time_ms / optimized_stats.inference_time_ms;
250        let overall_memory_reduction = (original_stats.memory_usage_mb
251            - optimized_stats.memory_usage_mb)
252            / original_stats.memory_usage_mb;
253        let overall_size_reduction = (original_stats.model_size_mb - optimized_stats.model_size_mb)
254            / original_stats.model_size_mb;
255        let final_accuracy_retention = optimized_stats.accuracy / original_stats.accuracy;
256
257        let meets_targets = optimized_stats.rtf <= self.config.performance_budget
258            && final_accuracy_retention >= self.config.accuracy_budget;
259
260        let summary = OptimizationSummary {
261            optimization_time_s: optimization_time,
262            overall_speedup,
263            overall_memory_reduction,
264            overall_size_reduction,
265            final_accuracy_retention,
266            techniques_applied,
267            meets_targets,
268        };
269
270        let results = OptimizationResults {
271            original_stats,
272            optimized_stats,
273            distillation_results,
274            pruning_results,
275            mixed_precision_results,
276            summary,
277        };
278
279        self.results = results.clone();
280
281        info!("Optimization completed in {:.1}s: {:.2}x speedup, {:.1}% memory reduction, {:.1}% accuracy retention",
282              optimization_time, overall_speedup, overall_memory_reduction * 100.0, final_accuracy_retention * 100.0);
283
284        Ok(results)
285    }
286
287    /// Apply knowledge distillation
288    async fn apply_knowledge_distillation_static(
289        device: &Device,
290        kd_optimizer: &mut KnowledgeDistillationOptimizer,
291        model_layers: &mut HashMap<String, Tensor>,
292        validation_fn: impl Fn(&HashMap<String, Tensor>) -> Result<ModelStats, RecognitionError>,
293    ) -> Result<DistillationResults, RecognitionError> {
294        // Analyze temperature sensitivity
295        let temperatures = vec![1.0, 2.0, 4.0, 8.0, 16.0];
296        let validation_data: Vec<Tensor> = vec![
297            Tensor::randn(0.0, 1.0, (1, 512), device)?,
298            Tensor::randn(0.0, 1.0, (1, 512), device)?,
299            Tensor::randn(0.0, 1.0, (1, 512), device)?,
300        ];
301
302        kd_optimizer
303            .analyze_temperature_sensitivity(temperatures, &validation_data)
304            .await?;
305
306        // Perform intermediate layer distillation
307        let layer_losses = kd_optimizer.distill_intermediate_layers()?;
308        let final_loss = layer_losses.values().sum::<f32>() / layer_losses.len() as f32;
309
310        // Get stats after distillation
311        let stats = kd_optimizer.get_stats();
312        let optimal_temperature = stats
313            .temperature_sensitivity
314            .iter()
315            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
316            .map_or(4.0, |&(temp, _)| temp);
317
318        // Measure final accuracy
319        let final_stats = validation_fn(model_layers)?;
320        let initial_accuracy = 1.0; // Assume teacher has 100% accuracy
321        let accuracy_retention = final_stats.accuracy / initial_accuracy;
322
323        let transfer_efficiency = stats.transfer_efficiency;
324
325        Ok(DistillationResults {
326            final_loss,
327            transfer_efficiency,
328            optimal_temperature,
329            accuracy_retention,
330        })
331    }
332
333    /// Apply progressive pruning
334    async fn apply_progressive_pruning_static(
335        _device: &Device,
336        pruning_optimizer: &mut ProgressivePruningOptimizer,
337        model_layers: &mut HashMap<String, Tensor>,
338        validation_fn: impl Fn(&HashMap<String, Tensor>) -> Result<ModelStats, RecognitionError>,
339    ) -> Result<PruningResults, RecognitionError> {
340        // Compute layer importance scores
341        pruning_optimizer.compute_layer_importance(model_layers)?;
342
343        let initial_stats = validation_fn(model_layers)?;
344        let mut all_step_results = Vec::new();
345
346        // Execute progressive pruning steps
347        let (current_step, total_steps) = pruning_optimizer.get_progress();
348        for _step in current_step..total_steps {
349            let step_result = pruning_optimizer.execute_pruning_step(model_layers, |layers| {
350                validation_fn(layers).map(|stats| stats.accuracy)
351            })?;
352            all_step_results.push(step_result);
353        }
354
355        let final_stats = validation_fn(model_layers)?;
356
357        // Compute overall results
358        let final_sparsity = if let Some(last_step) = all_step_results.last() {
359            last_step.pruning_ratio
360        } else {
361            0.0
362        };
363
364        let size_reduction =
365            (initial_stats.model_size_mb - final_stats.model_size_mb) / initial_stats.model_size_mb;
366        let speedup = initial_stats.inference_time_ms / final_stats.inference_time_ms;
367        let accuracy_retention = final_stats.accuracy / initial_stats.accuracy;
368
369        Ok(PruningResults {
370            final_sparsity,
371            size_reduction,
372            speedup,
373            accuracy_retention,
374            pruning_steps: all_step_results.len(),
375        })
376    }
377
378    /// Apply mixed-precision optimization
379    async fn apply_mixed_precision_static(
380        _device: &Device,
381        mp_optimizer: &mut MixedPrecisionOptimizer,
382        model_layers: &mut HashMap<String, Tensor>,
383        validation_fn: impl Fn(&HashMap<String, Tensor>) -> Result<ModelStats, RecognitionError>,
384    ) -> Result<MixedPrecisionResults, RecognitionError> {
385        let initial_stats = validation_fn(model_layers)?;
386
387        // Perform automatic precision selection
388        mp_optimizer.auto_select_precisions(model_layers, |layers| {
389            validation_fn(layers).map(|stats| PerformanceMeasurement {
390                inference_time_ms: stats.inference_time_ms,
391                memory_usage_mb: stats.memory_usage_mb,
392                accuracy: stats.accuracy,
393                model_size_mb: stats.model_size_mb,
394            })
395        })?;
396
397        // Apply mixed-precision configuration
398        let mp_stats = mp_optimizer.apply_mixed_precision(model_layers)?;
399
400        let final_stats = validation_fn(model_layers)?;
401
402        // Create precision distribution
403        let mut precision_distribution = HashMap::new();
404        precision_distribution.insert("FP32".to_string(), mp_stats.fp32_layers);
405        precision_distribution.insert("FP16".to_string(), mp_stats.fp16_layers);
406        precision_distribution.insert("INT8".to_string(), mp_stats.int8_layers);
407
408        let memory_reduction = (initial_stats.memory_usage_mb - final_stats.memory_usage_mb)
409            / initial_stats.memory_usage_mb;
410        let accuracy_retention = final_stats.accuracy / initial_stats.accuracy;
411
412        Ok(MixedPrecisionResults {
413            precision_distribution,
414            estimated_speedup: mp_stats.estimated_speedup,
415            memory_reduction,
416            accuracy_retention,
417        })
418    }
419
420    /// Generate optimization report
421    #[must_use]
422    pub fn generate_report(&self) -> String {
423        let results = &self.results;
424        let mut report = String::new();
425
426        report.push_str("# Model Optimization Report\n\n");
427
428        // Summary
429        report.push_str("## Summary\n");
430        report.push_str(&format!(
431            "- **Overall Speedup**: {:.2}x\n",
432            results.summary.overall_speedup
433        ));
434        report.push_str(&format!(
435            "- **Memory Reduction**: {:.1}%\n",
436            results.summary.overall_memory_reduction * 100.0
437        ));
438        report.push_str(&format!(
439            "- **Model Size Reduction**: {:.1}%\n",
440            results.summary.overall_size_reduction * 100.0
441        ));
442        report.push_str(&format!(
443            "- **Accuracy Retention**: {:.1}%\n",
444            results.summary.final_accuracy_retention * 100.0
445        ));
446        report.push_str(&format!(
447            "- **Optimization Time**: {:.1}s\n",
448            results.summary.optimization_time_s
449        ));
450        report.push_str(&format!(
451            "- **Meets Targets**: {}\n\n",
452            if results.summary.meets_targets {
453                "✅ Yes"
454            } else {
455                "❌ No"
456            }
457        ));
458
459        // Original vs Optimized
460        report.push_str("## Model Comparison\n");
461        report.push_str("| Metric | Original | Optimized | Improvement |\n");
462        report.push_str("|--------|----------|-----------|-------------|\n");
463
464        let size_improvement = (results.original_stats.model_size_mb
465            - results.optimized_stats.model_size_mb)
466            / results.original_stats.model_size_mb
467            * 100.0;
468        let speed_improvement = (results.original_stats.inference_time_ms
469            - results.optimized_stats.inference_time_ms)
470            / results.original_stats.inference_time_ms
471            * 100.0;
472        let memory_improvement = (results.original_stats.memory_usage_mb
473            - results.optimized_stats.memory_usage_mb)
474            / results.original_stats.memory_usage_mb
475            * 100.0;
476
477        report.push_str(&format!(
478            "| Model Size (MB) | {:.1} | {:.1} | {:.1}% |\n",
479            results.original_stats.model_size_mb,
480            results.optimized_stats.model_size_mb,
481            size_improvement
482        ));
483        report.push_str(&format!(
484            "| Inference Time (ms) | {:.1} | {:.1} | {:.1}% |\n",
485            results.original_stats.inference_time_ms,
486            results.optimized_stats.inference_time_ms,
487            speed_improvement
488        ));
489        report.push_str(&format!(
490            "| Memory Usage (MB) | {:.1} | {:.1} | {:.1}% |\n",
491            results.original_stats.memory_usage_mb,
492            results.optimized_stats.memory_usage_mb,
493            memory_improvement
494        ));
495        report.push_str(&format!(
496            "| Accuracy | {:.3} | {:.3} | {:.1}% |\n\n",
497            results.original_stats.accuracy,
498            results.optimized_stats.accuracy,
499            (results.optimized_stats.accuracy - results.original_stats.accuracy)
500                / results.original_stats.accuracy
501                * 100.0
502        ));
503
504        // Techniques Applied
505        report.push_str("## Optimization Techniques Applied\n");
506        for technique in &results.summary.techniques_applied {
507            report.push_str(&format!("- {technique}\n"));
508        }
509        report.push('\n');
510
511        // Detailed Results
512        if let Some(distillation) = &results.distillation_results {
513            report.push_str("### Knowledge Distillation Results\n");
514            report.push_str(&format!("- Final Loss: {:.6}\n", distillation.final_loss));
515            report.push_str(&format!(
516                "- Transfer Efficiency: {:.3}\n",
517                distillation.transfer_efficiency
518            ));
519            report.push_str(&format!(
520                "- Optimal Temperature: {:.1}\n",
521                distillation.optimal_temperature
522            ));
523            report.push_str(&format!(
524                "- Accuracy Retention: {:.1}%\n\n",
525                distillation.accuracy_retention * 100.0
526            ));
527        }
528
529        if let Some(pruning) = &results.pruning_results {
530            report.push_str("### Progressive Pruning Results\n");
531            report.push_str(&format!(
532                "- Final Sparsity: {:.1}%\n",
533                pruning.final_sparsity * 100.0
534            ));
535            report.push_str(&format!(
536                "- Size Reduction: {:.1}%\n",
537                pruning.size_reduction * 100.0
538            ));
539            report.push_str(&format!("- Speedup: {:.2}x\n", pruning.speedup));
540            report.push_str(&format!("- Pruning Steps: {}\n", pruning.pruning_steps));
541            report.push_str(&format!(
542                "- Accuracy Retention: {:.1}%\n\n",
543                pruning.accuracy_retention * 100.0
544            ));
545        }
546
547        if let Some(mixed_precision) = &results.mixed_precision_results {
548            report.push_str("### Mixed-Precision Results\n");
549            report.push_str(&format!(
550                "- Estimated Speedup: {:.2}x\n",
551                mixed_precision.estimated_speedup
552            ));
553            report.push_str(&format!(
554                "- Memory Reduction: {:.1}%\n",
555                mixed_precision.memory_reduction * 100.0
556            ));
557            report.push_str(&format!(
558                "- Accuracy Retention: {:.1}%\n",
559                mixed_precision.accuracy_retention * 100.0
560            ));
561            report.push_str("- Precision Distribution:\n");
562            for (precision, count) in &mixed_precision.precision_distribution {
563                report.push_str(&format!("  - {precision}: {count} layers\n"));
564            }
565            report.push('\n');
566        }
567
568        report
569    }
570
571    /// Get optimization results
572    #[must_use]
573    pub fn get_results(&self) -> &OptimizationResults {
574        &self.results
575    }
576}
577
578impl Default for ModelStats {
579    fn default() -> Self {
580        Self {
581            num_parameters: 0,
582            model_size_mb: 0.0,
583            inference_time_ms: 0.0,
584            memory_usage_mb: 0.0,
585            accuracy: 0.0,
586            rtf: 0.0,
587        }
588    }
589}
590
591impl Default for OptimizationSummary {
592    fn default() -> Self {
593        Self {
594            optimization_time_s: 0.0,
595            overall_speedup: 1.0,
596            overall_memory_reduction: 0.0,
597            overall_size_reduction: 0.0,
598            final_accuracy_retention: 1.0,
599            techniques_applied: Vec::new(),
600            meets_targets: false,
601        }
602    }
603}
604
605#[cfg(test)]
606mod tests {
607    use super::*;
608    use candle_core::Device;
609
610    #[tokio::test]
611    async fn test_optimization_pipeline_creation() {
612        let config = AdvancedOptimizationConfig::default();
613        let device = Device::Cpu;
614        let pipeline = OptimizationPipeline::new(config, device);
615
616        assert!(pipeline.mp_optimizer.is_some());
617        assert!(pipeline.kd_optimizer.is_none()); // Disabled by default
618        assert!(pipeline.pruning_optimizer.is_none()); // Disabled by default
619    }
620
621    #[test]
622    fn test_optimization_results_default() {
623        let results = OptimizationResults::default();
624        assert_eq!(results.original_stats.num_parameters, 0);
625        assert_eq!(results.optimized_stats.num_parameters, 0);
626        assert!(results.distillation_results.is_none());
627        assert!(results.pruning_results.is_none());
628        assert!(results.mixed_precision_results.is_none());
629    }
630
631    #[test]
632    fn test_report_generation() {
633        let mut results = OptimizationResults::default();
634        results.summary.overall_speedup = 1.5;
635        results.summary.overall_memory_reduction = 0.2;
636        results.summary.final_accuracy_retention = 0.98;
637        results.summary.techniques_applied = vec!["Mixed-Precision".to_string()];
638        results.summary.meets_targets = true;
639
640        let mut pipeline =
641            OptimizationPipeline::new(AdvancedOptimizationConfig::default(), Device::Cpu);
642        pipeline.results = results;
643
644        let report = pipeline.generate_report();
645        assert!(report.contains("# Model Optimization Report"));
646        assert!(report.contains("1.50x"));
647        assert!(report.contains("20.0%"));
648        assert!(report.contains("Mixed-Precision"));
649    }
650}
voirs_recognizer/asr/optimization_integration.rs

voirs_recognizer/asr/
optimization_integration.rs
