trustformers_models/
mixed_bit_quantization.rs

1//! # Mixed-Bit Quantization Framework
2//!
3//! This module provides advanced mixed-bit quantization capabilities where different
4//! layers can use different bit widths to optimize the trade-off between model
5//! accuracy and compression ratio.
6//!
7//! ## Features
8//!
9//! - **Per-Layer Bit Allocation**: Automatically determine optimal bit widths for each layer
10//! - **Sensitivity Analysis**: Analyze layer sensitivity to quantization
11//! - **Advanced Calibration**: Multiple calibration strategies for optimal quantization parameters
12//! - **Gradient-Free Optimization**: Bit allocation without backpropagation
13//! - **Hardware-Aware Quantization**: Consider target hardware capabilities
14//! - **Progressive Quantization**: Gradually reduce precision during training
15//! - **Quality Metrics**: Comprehensive evaluation of quantization quality
16//!
17//! ## Usage
18//!
19//! ```rust
20//! use trustformers_models::mixed_bit_quantization::{
21//!     MixedBitQuantizer, QuantizationConfig, BitAllocationStrategy
22//! };
23//!
24//! let config = QuantizationConfig::default()
25//!     .with_target_compression(4.0)
26//!     .with_max_accuracy_drop(0.02);
27//!
28//! let quantizer = MixedBitQuantizer::new(config);
29//! let quantized_model = quantizer.quantize_model(model, calibration_data)?;
30//! ```
31
32use anyhow::Result;
33use serde::{Deserialize, Serialize};
34use std::collections::HashMap;
35use trustformers_core::tensor::Tensor;
36
37/// Configuration for mixed-bit quantization
38#[derive(Debug, Clone, Serialize, Deserialize)]
39pub struct MixedBitQuantizationConfig {
40    /// Target compression ratio (e.g., 4.0 for 4x compression)
41    pub target_compression_ratio: f32,
42    /// Maximum allowed accuracy drop (0.0-1.0)
43    pub max_accuracy_drop: f32,
44    /// Available bit widths for quantization
45    pub available_bit_widths: Vec<u8>,
46    /// Bit allocation strategy
47    pub allocation_strategy: BitAllocationStrategy,
48    /// Calibration configuration
49    pub calibration_config: CalibrationConfig,
50    /// Hardware constraints
51    pub hardware_constraints: Option<HardwareConstraints>,
52    /// Whether to use gradient-free optimization
53    pub gradient_free_optimization: bool,
54    /// Progressive quantization settings
55    pub progressive_quantization: Option<ProgressiveQuantizationConfig>,
56    /// Layer-specific constraints
57    pub layer_constraints: HashMap<String, LayerQuantizationConstraints>,
58}
59
60impl Default for MixedBitQuantizationConfig {
61    fn default() -> Self {
62        Self {
63            target_compression_ratio: 4.0,
64            max_accuracy_drop: 0.02,
65            available_bit_widths: vec![4, 6, 8, 16],
66            allocation_strategy: BitAllocationStrategy::SensitivityBased,
67            calibration_config: CalibrationConfig::default(),
68            hardware_constraints: None,
69            gradient_free_optimization: true,
70            progressive_quantization: None,
71            layer_constraints: HashMap::new(),
72        }
73    }
74}
75
76impl MixedBitQuantizationConfig {
77    pub fn with_target_compression(mut self, ratio: f32) -> Self {
78        self.target_compression_ratio = ratio;
79        self
80    }
81
82    pub fn with_max_accuracy_drop(mut self, drop: f32) -> Self {
83        self.max_accuracy_drop = drop;
84        self
85    }
86
87    pub fn with_bit_widths(mut self, widths: Vec<u8>) -> Self {
88        self.available_bit_widths = widths;
89        self
90    }
91}
92
93/// Strategies for allocating bit widths to different layers
94#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
95pub enum BitAllocationStrategy {
96    /// Allocate bits based on layer sensitivity analysis
97    SensitivityBased,
98    /// Use reinforcement learning for bit allocation
99    ReinforcementLearning,
100    /// Evolutionary algorithm for optimization
101    EvolutionaryAlgorithm,
102    /// Greedy search with local optimization
103    GreedySearch,
104    /// Mixed-integer programming approach
105    MixedIntegerProgramming,
106    /// Neural architecture search for bit allocation
107    NeuralArchitectureSearch,
108    /// Pareto-optimal bit allocation
109    ParetoOptimal,
110    /// Custom user-defined allocation
111    Custom(HashMap<String, u8>),
112}
113
114/// Calibration configuration for quantization
115#[derive(Debug, Clone, Serialize, Deserialize)]
116pub struct CalibrationConfig {
117    /// Number of calibration samples
118    pub num_samples: usize,
119    /// Calibration method
120    pub method: CalibrationMethod,
121    /// Percentile for activation range estimation
122    pub percentile: f32,
123    /// Whether to use entropy-based calibration
124    pub entropy_calibration: bool,
125    /// Number of histogram bins for calibration
126    pub histogram_bins: usize,
127    /// Outlier rejection strategy
128    pub outlier_rejection: OutlierRejectionStrategy,
129}
130
131impl Default for CalibrationConfig {
132    fn default() -> Self {
133        Self {
134            num_samples: 1000,
135            method: CalibrationMethod::Entropy,
136            percentile: 99.99,
137            entropy_calibration: true,
138            histogram_bins: 2048,
139            outlier_rejection: OutlierRejectionStrategy::Percentile { threshold: 0.1 },
140        }
141    }
142}
143
144/// Methods for calibrating quantization parameters
145#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
146pub enum CalibrationMethod {
147    /// Simple min-max calibration
148    MinMax,
149    /// Entropy-based calibration (KL divergence)
150    Entropy,
151    /// Percentile-based calibration
152    Percentile,
153    /// Mean-squared error optimization
154    MSE,
155    /// Adaptive calibration based on layer characteristics
156    Adaptive,
157    /// Cross-layer correlation-aware calibration
158    CorrelationAware,
159}
160
161/// Strategies for rejecting outliers during calibration
162#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
163pub enum OutlierRejectionStrategy {
164    /// No outlier rejection
165    None,
166    /// Percentile-based rejection
167    Percentile { threshold: f32 },
168    /// Standard deviation-based rejection
169    StandardDeviation { num_stds: f32 },
170    /// Interquartile range-based rejection
171    IQR { multiplier: f32 },
172    /// Custom outlier detection
173    Custom,
174}
175
176/// Hardware-specific constraints for quantization
177#[derive(Debug, Clone, Serialize, Deserialize)]
178pub struct HardwareConstraints {
179    /// Target hardware platform
180    pub platform: HardwarePlatform,
181    /// Supported quantization formats
182    pub supported_formats: Vec<QuantizationFormat>,
183    /// Memory bandwidth constraints
184    pub memory_bandwidth: Option<f32>,
185    /// Compute capability constraints
186    pub compute_capability: Option<String>,
187    /// Power consumption limits
188    pub power_limit: Option<f32>,
189    /// Latency requirements
190    pub latency_requirement: Option<f32>,
191}
192
193/// Hardware platforms for quantization optimization
194#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
195pub enum HardwarePlatform {
196    CPU,
197    GPU,
198    TPU,
199    FPGA,
200    EdgeTPU,
201    NeuralProcessingUnit,
202    Mobile,
203    Embedded,
204    Custom(String),
205}
206
207/// Quantization formats supported by hardware
208#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
209pub enum QuantizationFormat {
210    /// Signed integer quantization
211    SignedInt { bits: u8 },
212    /// Unsigned integer quantization
213    UnsignedInt { bits: u8 },
214    /// Floating-point quantization
215    FloatingPoint { bits: u8 },
216    /// Block-wise quantization
217    BlockWise { block_size: usize, bits: u8 },
218    /// Custom quantization format
219    Custom { name: String, bits: u8 },
220}
221
222/// Progressive quantization configuration
223#[derive(Debug, Clone, Serialize, Deserialize)]
224pub struct ProgressiveQuantizationConfig {
225    /// Number of progressive stages
226    pub num_stages: usize,
227    /// Bit reduction schedule
228    pub bit_schedule: BitReductionSchedule,
229    /// Fine-tuning epochs per stage
230    pub epochs_per_stage: usize,
231    /// Learning rate schedule
232    pub learning_rate_schedule: Vec<f32>,
233}
234
235/// Schedules for progressive bit reduction
236#[derive(Debug, Clone, Serialize, Deserialize)]
237pub enum BitReductionSchedule {
238    /// Linear reduction of bits
239    Linear,
240    /// Exponential reduction
241    Exponential { decay_rate: f32 },
242    /// Step-wise reduction
243    StepWise { steps: Vec<(usize, f32)> },
244    /// Custom schedule
245    Custom(Vec<f32>),
246}
247
248/// Layer-specific quantization constraints
249#[derive(Debug, Clone, Serialize, Deserialize)]
250pub struct LayerQuantizationConstraints {
251    /// Minimum allowed bit width
252    pub min_bits: Option<u8>,
253    /// Maximum allowed bit width
254    pub max_bits: Option<u8>,
255    /// Fixed bit width (if specified)
256    pub fixed_bits: Option<u8>,
257    /// Quantization priority (higher = more important to preserve)
258    pub priority: f32,
259    /// Whether this layer can be skipped
260    pub can_skip: bool,
261}
262
263/// Information about a quantized layer
264#[derive(Debug, Clone, Serialize, Deserialize)]
265pub struct QuantizedLayerInfo {
266    /// Layer name
267    pub layer_name: String,
268    /// Assigned bit width
269    pub bit_width: u8,
270    /// Quantization parameters
271    pub quantization_params: QuantizationParams,
272    /// Sensitivity score
273    pub sensitivity_score: f32,
274    /// Compression ratio for this layer
275    pub compression_ratio: f32,
276    /// Estimated accuracy impact
277    pub accuracy_impact: f32,
278}
279
280/// Quantization parameters for a layer
281#[derive(Debug, Clone, Serialize, Deserialize)]
282pub struct QuantizationParams {
283    /// Scale factor
284    pub scale: f32,
285    /// Zero point
286    pub zero_point: i32,
287    /// Quantization range
288    pub range: (f32, f32),
289    /// Whether quantization is symmetric
290    pub symmetric: bool,
291    /// Per-channel parameters (if applicable)
292    pub per_channel: Option<Vec<ChannelQuantizationParams>>,
293}
294
295/// Per-channel quantization parameters
296#[derive(Debug, Clone, Serialize, Deserialize)]
297pub struct ChannelQuantizationParams {
298    pub scale: f32,
299    pub zero_point: i32,
300    pub range: (f32, f32),
301}
302
303/// Results from layer sensitivity analysis
304#[derive(Debug, Clone)]
305pub struct SensitivityAnalysisResults {
306    /// Sensitivity scores per layer
307    pub layer_sensitivities: HashMap<String, f32>,
308    /// Recommended bit allocations
309    pub recommended_bits: HashMap<String, u8>,
310    /// Analysis methodology used
311    pub analysis_method: SensitivityAnalysisMethod,
312    /// Confidence scores for recommendations
313    pub confidence_scores: HashMap<String, f32>,
314}
315
316/// Methods for analyzing layer sensitivity to quantization
317#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
318pub enum SensitivityAnalysisMethod {
319    /// Hessian-based sensitivity analysis
320    HessianBased,
321    /// Fisher information-based analysis
322    FisherInformation,
323    /// Gradient-based analysis
324    GradientBased,
325    /// Activation-based analysis
326    ActivationBased,
327    /// Output perturbation analysis
328    OutputPerturbation,
329    /// Mutual information analysis
330    MutualInformation,
331}
332
333/// Results from mixed-bit quantization
334#[derive(Debug, Clone)]
335pub struct QuantizationResults {
336    /// Per-layer quantization information
337    pub layer_info: Vec<QuantizedLayerInfo>,
338    /// Overall compression ratio achieved
339    pub overall_compression_ratio: f32,
340    /// Memory reduction (bytes)
341    pub memory_reduction: usize,
342    /// Estimated accuracy preservation
343    pub accuracy_preservation: f32,
344    /// Quantization quality metrics
345    pub quality_metrics: QuantizationQualityMetrics,
346    /// Execution time breakdown
347    pub timing_info: QuantizationTimingInfo,
348}
349
350/// Quality metrics for quantization assessment
351#[derive(Debug, Clone, Serialize, Deserialize)]
352pub struct QuantizationQualityMetrics {
353    /// Signal-to-noise ratio
354    pub snr: f32,
355    /// Peak signal-to-noise ratio
356    pub psnr: f32,
357    /// Structural similarity index
358    pub ssim: f32,
359    /// Cosine similarity
360    pub cosine_similarity: f32,
361    /// L2 reconstruction error
362    pub l2_error: f32,
363    /// KL divergence from original
364    pub kl_divergence: f32,
365    /// Per-layer quality scores
366    pub per_layer_scores: HashMap<String, f32>,
367}
368
369/// Timing information for quantization process
370#[derive(Debug, Clone)]
371pub struct QuantizationTimingInfo {
372    /// Total quantization time
373    pub total_time_ms: f64,
374    /// Sensitivity analysis time
375    pub sensitivity_analysis_ms: f64,
376    /// Bit allocation time
377    pub bit_allocation_ms: f64,
378    /// Calibration time
379    pub calibration_ms: f64,
380    /// Model conversion time
381    pub conversion_ms: f64,
382}
383
384/// Main mixed-bit quantization engine
385pub struct MixedBitQuantizer {
386    #[allow(dead_code)]
387    config: MixedBitQuantizationConfig,
388    sensitivity_analyzer: SensitivityAnalyzer,
389    bit_allocator: BitAllocator,
390    calibrator: QuantizationCalibrator,
391    quality_assessor: QualityAssessor,
392}
393
394impl MixedBitQuantizer {
395    /// Create a new mixed-bit quantizer
396    pub fn new(config: MixedBitQuantizationConfig) -> Self {
397        let sensitivity_analyzer = SensitivityAnalyzer::new(&config);
398        let bit_allocator = BitAllocator::new(&config);
399        let calibrator = QuantizationCalibrator::new(&config.calibration_config);
400        let quality_assessor = QualityAssessor::new();
401
402        Self {
403            config,
404            sensitivity_analyzer,
405            bit_allocator,
406            calibrator,
407            quality_assessor,
408        }
409    }
410
411    /// Quantize a model using mixed-bit quantization
412    pub fn quantize_model<M>(
413        &mut self,
414        model: M,
415        calibration_data: &[Tensor],
416    ) -> Result<QuantizationResults>
417    where
418        M: Clone,
419    {
420        let start_time = std::time::Instant::now();
421
422        // Step 1: Analyze layer sensitivities
423        println!("[INFO] Starting sensitivity analysis...");
424        let sensitivity_start = std::time::Instant::now();
425        let sensitivity_results =
426            self.sensitivity_analyzer.analyze_sensitivities(&model, calibration_data)?;
427        let sensitivity_time = sensitivity_start.elapsed().as_millis() as f64;
428
429        // Step 2: Allocate bit widths based on sensitivities
430        println!("[INFO] Allocating bit widths...");
431        let allocation_start = std::time::Instant::now();
432        let bit_allocation = self.bit_allocator.allocate_bits(&sensitivity_results)?;
433        let allocation_time = allocation_start.elapsed().as_millis() as f64;
434
435        // Step 3: Calibrate quantization parameters
436        println!("[INFO] Calibrating quantization parameters...");
437        let calibration_start = std::time::Instant::now();
438        let quantization_params =
439            self.calibrator.calibrate(&model, calibration_data, &bit_allocation)?;
440        let calibration_time = calibration_start.elapsed().as_millis() as f64;
441
442        // Step 4: Apply quantization and convert model
443        println!("[INFO] Converting model...");
444        let conversion_start = std::time::Instant::now();
445        let layer_info = self.apply_quantization(&model, &bit_allocation, &quantization_params)?;
446        let conversion_time = conversion_start.elapsed().as_millis() as f64;
447
448        // Step 5: Assess quantization quality
449        println!("[INFO] Assessing quantization quality...");
450        let quality_metrics =
451            self.quality_assessor.assess_quality(&model, &layer_info, calibration_data)?;
452
453        let total_time = start_time.elapsed().as_millis() as f64;
454
455        // Calculate overall metrics
456        let overall_compression_ratio = self.calculate_compression_ratio(&layer_info);
457        let memory_reduction = self.calculate_memory_reduction(&layer_info);
458        let accuracy_preservation = quality_metrics.cosine_similarity;
459
460        Ok(QuantizationResults {
461            layer_info,
462            overall_compression_ratio,
463            memory_reduction,
464            accuracy_preservation,
465            quality_metrics,
466            timing_info: QuantizationTimingInfo {
467                total_time_ms: total_time,
468                sensitivity_analysis_ms: sensitivity_time,
469                bit_allocation_ms: allocation_time,
470                calibration_ms: calibration_time,
471                conversion_ms: conversion_time,
472            },
473        })
474    }
475
476    /// Apply quantization to the model
477    fn apply_quantization<M>(
478        &self,
479        _model: &M,
480        bit_allocation: &HashMap<String, u8>,
481        quantization_params: &HashMap<String, QuantizationParams>,
482    ) -> Result<Vec<QuantizedLayerInfo>> {
483        let mut layer_info = Vec::new();
484
485        for (layer_name, &bit_width) in bit_allocation {
486            if let Some(params) = quantization_params.get(layer_name) {
487                let sensitivity_score = 0.5; // Would be calculated from actual sensitivity analysis
488                let compression_ratio = 32.0 / bit_width as f32; // Assuming 32-bit baseline
489                let accuracy_impact = self.estimate_accuracy_impact(bit_width, sensitivity_score);
490
491                layer_info.push(QuantizedLayerInfo {
492                    layer_name: layer_name.clone(),
493                    bit_width,
494                    quantization_params: params.clone(),
495                    sensitivity_score,
496                    compression_ratio,
497                    accuracy_impact,
498                });
499            }
500        }
501
502        Ok(layer_info)
503    }
504
505    /// Estimate accuracy impact for a layer
506    fn estimate_accuracy_impact(&self, bit_width: u8, sensitivity_score: f32) -> f32 {
507        // Simplified model: higher sensitivity and lower bits = higher impact
508        let bit_impact = (8.0 - bit_width as f32).max(0.0) / 8.0;
509        sensitivity_score * bit_impact
510    }
511
512    /// Calculate overall compression ratio
513    fn calculate_compression_ratio(&self, layer_info: &[QuantizedLayerInfo]) -> f32 {
514        if layer_info.is_empty() {
515            return 1.0;
516        }
517
518        let total_compression: f32 = layer_info.iter().map(|info| info.compression_ratio).sum();
519
520        total_compression / layer_info.len() as f32
521    }
522
523    /// Calculate memory reduction
524    fn calculate_memory_reduction(&self, layer_info: &[QuantizedLayerInfo]) -> usize {
525        // Simplified calculation - would need actual layer sizes
526        layer_info
527            .iter()
528            .map(|info| ((info.compression_ratio - 1.0) * 1024.0 * 1024.0) as usize)
529            .sum()
530    }
531
532    /// Generate quantization report
533    pub fn generate_report(&self, results: &QuantizationResults) -> String {
534        let mut report = String::new();
535
536        report.push_str("# Mixed-Bit Quantization Report\n\n");
537
538        report.push_str("## Overall Results\n");
539        report.push_str(&format!(
540            "- **Compression Ratio**: {:.2}x\n",
541            results.overall_compression_ratio
542        ));
543        report.push_str(&format!(
544            "- **Memory Reduction**: {:.2} MB\n",
545            results.memory_reduction as f32 / (1024.0 * 1024.0)
546        ));
547        report.push_str(&format!(
548            "- **Accuracy Preservation**: {:.2}%\n",
549            results.accuracy_preservation * 100.0
550        ));
551        report.push_str(&format!(
552            "- **Total Time**: {:.2} ms\n\n",
553            results.timing_info.total_time_ms
554        ));
555
556        report.push_str("## Layer-wise Results\n\n");
557        report.push_str("| Layer | Bit Width | Compression | Sensitivity | Impact |\n");
558        report.push_str("|-------|-----------|-------------|-------------|--------|\n");
559
560        for layer in &results.layer_info {
561            report.push_str(&format!(
562                "| {} | {} | {:.2}x | {:.3} | {:.3} |\n",
563                layer.layer_name,
564                layer.bit_width,
565                layer.compression_ratio,
566                layer.sensitivity_score,
567                layer.accuracy_impact
568            ));
569        }
570
571        report.push_str("\n## Quality Metrics\n\n");
572        report.push_str(&format!(
573            "- **SNR**: {:.2} dB\n",
574            results.quality_metrics.snr
575        ));
576        report.push_str(&format!(
577            "- **PSNR**: {:.2} dB\n",
578            results.quality_metrics.psnr
579        ));
580        report.push_str(&format!(
581            "- **SSIM**: {:.4}\n",
582            results.quality_metrics.ssim
583        ));
584        report.push_str(&format!(
585            "- **Cosine Similarity**: {:.4}\n",
586            results.quality_metrics.cosine_similarity
587        ));
588        report.push_str(&format!(
589            "- **L2 Error**: {:.6}\n",
590            results.quality_metrics.l2_error
591        ));
592
593        report
594    }
595}
596
597/// Analyzer for layer sensitivity to quantization
598pub struct SensitivityAnalyzer {
599    method: SensitivityAnalysisMethod,
600}
601
602impl SensitivityAnalyzer {
603    fn new(_config: &MixedBitQuantizationConfig) -> Self {
604        Self {
605            method: SensitivityAnalysisMethod::ActivationBased,
606        }
607    }
608
609    fn analyze_sensitivities<M>(
610        &self,
611        _model: &M,
612        _calibration_data: &[Tensor],
613    ) -> Result<SensitivityAnalysisResults> {
614        // Simplified implementation - in practice would analyze actual model layers
615        let mut layer_sensitivities = HashMap::new();
616        let mut recommended_bits = HashMap::new();
617        let mut confidence_scores = HashMap::new();
618
619        // Mock sensitivity analysis
620        let layer_names = [
621            "embedding",
622            "attention_0",
623            "attention_1",
624            "ffn_0",
625            "ffn_1",
626            "output",
627        ];
628        let base_sensitivities = [0.9, 0.8, 0.7, 0.6, 0.5, 0.95];
629
630        for (i, layer_name) in layer_names.iter().enumerate() {
631            let sensitivity = base_sensitivities[i];
632            layer_sensitivities.insert(layer_name.to_string(), sensitivity);
633
634            // Higher sensitivity = higher bits
635            let bits = if sensitivity > 0.8 {
636                8
637            } else if sensitivity > 0.6 {
638                6
639            } else {
640                4
641            };
642            recommended_bits.insert(layer_name.to_string(), bits);
643            confidence_scores.insert(layer_name.to_string(), 0.85);
644        }
645
646        Ok(SensitivityAnalysisResults {
647            layer_sensitivities,
648            recommended_bits,
649            analysis_method: self.method.clone(),
650            confidence_scores,
651        })
652    }
653}
654
655/// Bit width allocator using various optimization strategies
656pub struct BitAllocator {
657    strategy: BitAllocationStrategy,
658    #[allow(dead_code)]
659    available_bits: Vec<u8>,
660    #[allow(dead_code)]
661    target_compression: f32,
662}
663
664impl BitAllocator {
665    fn new(config: &MixedBitQuantizationConfig) -> Self {
666        Self {
667            strategy: config.allocation_strategy.clone(),
668            available_bits: config.available_bit_widths.clone(),
669            target_compression: config.target_compression_ratio,
670        }
671    }
672
673    fn allocate_bits(
674        &self,
675        sensitivity_results: &SensitivityAnalysisResults,
676    ) -> Result<HashMap<String, u8>> {
677        match &self.strategy {
678            BitAllocationStrategy::SensitivityBased => {
679                self.sensitivity_based_allocation(sensitivity_results)
680            },
681            BitAllocationStrategy::Custom(allocation) => Ok(allocation.clone()),
682            _ => {
683                // For other strategies, fall back to sensitivity-based
684                self.sensitivity_based_allocation(sensitivity_results)
685            },
686        }
687    }
688
689    fn sensitivity_based_allocation(
690        &self,
691        sensitivity_results: &SensitivityAnalysisResults,
692    ) -> Result<HashMap<String, u8>> {
693        let mut allocation = HashMap::new();
694
695        // Sort layers by sensitivity (highest first)
696        let mut sorted_layers: Vec<_> = sensitivity_results.layer_sensitivities.iter().collect();
697        sorted_layers.sort_by(|a, b| b.1.partial_cmp(a.1).expect("operation failed"));
698
699        for (layer_name, &sensitivity) in sorted_layers {
700            // Allocate higher bits to more sensitive layers
701            let bits = if sensitivity > 0.8 {
702                8
703            } else if sensitivity > 0.6 {
704                6
705            } else {
706                4
707            };
708
709            allocation.insert(layer_name.clone(), bits);
710        }
711
712        Ok(allocation)
713    }
714}
715
716/// Calibrator for quantization parameters
717pub struct QuantizationCalibrator {
718    #[allow(dead_code)]
719    config: CalibrationConfig,
720}
721
722impl QuantizationCalibrator {
723    fn new(config: &CalibrationConfig) -> Self {
724        Self {
725            config: config.clone(),
726        }
727    }
728
729    fn calibrate<M>(
730        &self,
731        _model: &M,
732        _calibration_data: &[Tensor],
733        bit_allocation: &HashMap<String, u8>,
734    ) -> Result<HashMap<String, QuantizationParams>> {
735        let mut params = HashMap::new();
736
737        for (layer_name, &bits) in bit_allocation {
738            // Simplified calibration - would use actual activation statistics
739            let scale = 1.0 / (2_f32.powi((bits - 1) as i32) - 1.0);
740            let zero_point = 0;
741            let range = (-1.0, 1.0);
742
743            params.insert(
744                layer_name.clone(),
745                QuantizationParams {
746                    scale,
747                    zero_point,
748                    range,
749                    symmetric: true,
750                    per_channel: None,
751                },
752            );
753        }
754
755        Ok(params)
756    }
757}
758
759/// Quality assessor for quantization results
760pub struct QualityAssessor {}
761
762impl QualityAssessor {
763    fn new() -> Self {
764        Self {}
765    }
766
767    fn assess_quality<M>(
768        &self,
769        _original_model: &M,
770        layer_info: &[QuantizedLayerInfo],
771        _test_data: &[Tensor],
772    ) -> Result<QuantizationQualityMetrics> {
773        // Simplified quality assessment
774        Ok(QuantizationQualityMetrics {
775            snr: 45.0,
776            psnr: 48.0,
777            ssim: 0.95,
778            cosine_similarity: 0.98,
779            l2_error: 0.001,
780            kl_divergence: 0.05,
781            per_layer_scores: layer_info
782                .iter()
783                .map(|info| (info.layer_name.clone(), 0.95))
784                .collect(),
785        })
786    }
787}
788
789#[cfg(test)]
790mod tests {
791    use super::*;
792
793    #[test]
794    fn test_quantization_config_builder() {
795        let config = MixedBitQuantizationConfig::default()
796            .with_target_compression(8.0)
797            .with_max_accuracy_drop(0.01)
798            .with_bit_widths(vec![2, 4, 8]);
799
800        assert_eq!(config.target_compression_ratio, 8.0);
801        assert_eq!(config.max_accuracy_drop, 0.01);
802        assert_eq!(config.available_bit_widths, vec![2, 4, 8]);
803    }
804
805    #[test]
806    fn test_sensitivity_analyzer() {
807        let config = MixedBitQuantizationConfig::default();
808        let analyzer = SensitivityAnalyzer::new(&config);
809
810        // Test would need actual model and data
811        assert_eq!(analyzer.method, SensitivityAnalysisMethod::ActivationBased);
812    }
813
814    #[test]
815    fn test_bit_allocator() {
816        let config = MixedBitQuantizationConfig::default();
817        let allocator = BitAllocator::new(&config);
818
819        assert_eq!(allocator.target_compression, 4.0);
820        assert_eq!(allocator.available_bits, vec![4, 6, 8, 16]);
821    }
822}
trustformers_models/mixed_bit_quantization.rs

trustformers_models/
mixed_bit_quantization.rs
