trustformers_mobile/optimization/
advanced_quantization.rs

1//! Advanced Mobile Quantization for TrustformeRS
2//!
3//! This module provides advanced quantization techniques specifically optimized
4//! for mobile devices, including mixed-precision quantization, dynamic quantization,
5//! and hardware-aware quantization strategies.
6
7use crate::{
8    device_info::{MobileDeviceInfo, PerformanceTier},
9    optimization::memory_pool::MobileMemoryPool,
10    Result,
11};
12use half::f16;
13use serde::{Deserialize, Serialize};
14use std::collections::HashMap;
15use std::sync::{Arc, Mutex};
16use trustformers_core::errors::{invalid_config, invalid_input};
17use trustformers_core::Tensor;
18
19/// Advanced mobile quantization engine
20pub struct MobileQuantizationEngine {
21    config: QuantizationConfig,
22    device_info: MobileDeviceInfo,
23    calibration_data: Option<CalibrationDataset>,
24    quantization_cache: Arc<Mutex<HashMap<String, QuantizedModel>>>,
25    memory_pool: Option<Arc<MobileMemoryPool>>,
26}
27
28/// Mobile quantization configuration
29#[derive(Debug, Clone, Serialize, Deserialize)]
30pub struct QuantizationConfig {
31    /// Target quantization precision
32    pub target_precision: MobilePrecision,
33    /// Enable mixed-precision quantization
34    pub enable_mixed_precision: bool,
35    /// Dynamic quantization strategy
36    pub dynamic_strategy: DynamicQuantizationStrategy,
37    /// Hardware-aware optimizations
38    pub hardware_aware: bool,
39    /// Quantization granularity
40    pub granularity: QuantizationGranularity,
41    /// Quality preservation threshold
42    pub quality_threshold: f32,
43    /// Memory constraint (MB)
44    pub memory_constraint_mb: usize,
45    /// Enable gradient-based quantization
46    pub enable_gradient_quantization: bool,
47    /// KL-divergence threshold for calibration
48    pub kl_threshold: f32,
49    /// Enable post-training quantization
50    pub enable_ptq: bool,
51    /// Enable quantization-aware training
52    pub enable_qat: bool,
53}
54
55/// Mobile-optimized precision levels
56#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
57pub enum MobilePrecision {
58    /// 4-bit quantization (ultra-low memory)
59    INT4,
60    /// 8-bit quantization (standard mobile)
61    INT8,
62    /// 16-bit float (high quality)
63    FP16,
64    /// Mixed 4-bit and 8-bit
65    Mixed4_8,
66    /// Mixed 8-bit and 16-bit
67    Mixed8_16,
68    /// Dynamic precision selection
69    DYNAMIC,
70}
71
72/// Dynamic quantization strategies
73#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
74pub enum DynamicQuantizationStrategy {
75    /// Adjust based on battery level
76    BatteryAware,
77    /// Adjust based on thermal state
78    ThermalAware,
79    /// Adjust based on memory pressure
80    MemoryAware,
81    /// Adjust based on performance requirements
82    PerformanceAware,
83    /// Combined adaptive strategy
84    Adaptive,
85}
86
87/// Quantization granularity options
88#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
89pub enum QuantizationGranularity {
90    /// Per-tensor quantization
91    PerTensor,
92    /// Per-channel quantization
93    PerChannel,
94    /// Per-group quantization
95    PerGroup { group_size: usize },
96    /// Per-layer adaptive
97    PerLayer,
98}
99
100/// Calibration dataset for quantization
101#[derive(Debug, Clone)]
102pub struct CalibrationDataset {
103    /// Representative data samples
104    pub samples: Vec<Tensor>,
105    /// Sample weights for importance
106    pub weights: Option<Vec<f32>>,
107    /// Statistical properties
108    pub statistics: DatasetStatistics,
109}
110
111/// Statistical properties of calibration data
112#[derive(Debug, Clone, Serialize, Deserialize, Default)]
113pub struct DatasetStatistics {
114    /// Per-layer activation ranges
115    pub activation_ranges: HashMap<String, (f32, f32)>,
116    /// Per-layer mean values
117    pub layer_means: HashMap<String, f32>,
118    /// Per-layer variance values
119    pub layer_variances: HashMap<String, f32>,
120    /// KL-divergence scores
121    pub kl_scores: HashMap<String, f32>,
122}
123
124/// Quantized model representation
125#[derive(Debug, Clone)]
126pub struct QuantizedModel {
127    /// Quantized weights
128    pub weights: HashMap<String, QuantizedTensor>,
129    /// Quantization parameters
130    pub parameters: QuantizationParameters,
131    /// Model metadata
132    pub metadata: ModelMetadata,
133    /// Performance benchmarks
134    pub benchmarks: QuantizationBenchmarks,
135}
136
137/// Quantized tensor with metadata
138#[derive(Debug, Clone)]
139pub struct QuantizedTensor {
140    /// Quantized data
141    pub data: Vec<i8>,
142    /// Scale factors
143    pub scales: Vec<f32>,
144    /// Zero points
145    pub zero_points: Vec<i32>,
146    /// Original shape
147    pub shape: Vec<usize>,
148    /// Quantization scheme
149    pub scheme: QuantizationScheme,
150}
151
152/// Quantization scheme details
153#[derive(Debug, Clone, Serialize, Deserialize)]
154pub struct QuantizationScheme {
155    /// Bits per weight
156    pub bits: u8,
157    /// Symmetric vs asymmetric
158    pub symmetric: bool,
159    /// Signed vs unsigned
160    pub signed: bool,
161    /// Quantization method
162    pub method: QuantizationMethod,
163}
164
165/// Quantization methods
166#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
167pub enum QuantizationMethod {
168    /// Linear quantization
169    Linear,
170    /// Logarithmic quantization
171    Logarithmic,
172    /// Power-of-2 quantization
173    PowerOfTwo,
174    /// K-means clustering
175    KMeans,
176    /// Learned quantization
177    Learned,
178}
179
180/// Model format detection
181#[derive(Debug, Clone, Copy, PartialEq)]
182pub enum ModelFormat {
183    /// SafeTensors format
184    SafeTensors,
185    /// PyTorch pickle format
186    PyTorchPickle,
187    /// TensorFlow SavedModel format
188    TensorFlow,
189    /// ONNX format
190    ONNX,
191    /// Custom format
192    Custom,
193}
194
195/// Quantization parameters for inference
196#[derive(Debug, Clone, Serialize, Deserialize)]
197pub struct QuantizationParameters {
198    /// Global scale factor
199    pub global_scale: f32,
200    /// Per-layer scales
201    pub layer_scales: HashMap<String, f32>,
202    /// Per-layer zero points
203    pub layer_zero_points: HashMap<String, i32>,
204    /// Dequantization overhead
205    pub dequant_overhead_ms: f32,
206}
207
208/// Model metadata for quantized models
209#[derive(Debug, Clone, Serialize, Deserialize)]
210pub struct ModelMetadata {
211    /// Original model size (bytes)
212    pub original_size_bytes: usize,
213    /// Quantized model size (bytes)
214    pub quantized_size_bytes: usize,
215    /// Compression ratio
216    pub compression_ratio: f32,
217    /// Quality score (0.0 - 1.0)
218    pub quality_score: f32,
219    /// Quantization timestamp
220    pub timestamp: std::time::SystemTime,
221}
222
223/// Quantization performance benchmarks
224#[derive(Debug, Clone, Serialize, Deserialize)]
225pub struct QuantizationBenchmarks {
226    /// Original inference time (ms)
227    pub original_inference_ms: f32,
228    /// Quantized inference time (ms)
229    pub quantized_inference_ms: f32,
230    /// Speedup factor
231    pub speedup_factor: f32,
232    /// Memory reduction (MB)
233    pub memory_reduction_mb: f32,
234    /// Power reduction (mW)
235    pub power_reduction_mw: f32,
236}
237
238impl MobileQuantizationEngine {
239    /// Create a new mobile quantization engine
240    pub fn new(
241        config: QuantizationConfig,
242        device_info: MobileDeviceInfo,
243        memory_pool: Option<Arc<MobileMemoryPool>>,
244    ) -> Result<Self> {
245        Ok(Self {
246            config,
247            device_info,
248            calibration_data: None,
249            quantization_cache: Arc::new(Mutex::new(HashMap::new())),
250            memory_pool,
251        })
252    }
253
254    /// Set calibration dataset for quantization
255    pub fn set_calibration_data(&mut self, dataset: CalibrationDataset) -> Result<()> {
256        // Validate calibration data
257        if dataset.samples.is_empty() {
258            return Err(invalid_config(
259                "set_calibration_data",
260                "Calibration dataset cannot be empty",
261            ));
262        }
263
264        self.calibration_data = Some(dataset);
265        Ok(())
266    }
267
268    /// Quantize model with mobile optimizations
269    pub fn quantize_model(&self, model_id: &str, model_data: &[u8]) -> Result<QuantizedModel> {
270        // Check cache first
271        {
272            let cache = self.quantization_cache.lock().expect("Operation failed");
273            if let Some(cached_model) = cache.get(model_id) {
274                return Ok(cached_model.clone());
275            }
276        }
277
278        // Determine optimal quantization strategy
279        let strategy = self.determine_quantization_strategy()?;
280
281        // Apply hardware-specific optimizations
282        let hardware_config = self.get_hardware_quantization_config()?;
283
284        // Perform quantization
285        let quantized_model = self.perform_quantization(model_data, &strategy, &hardware_config)?;
286
287        // Benchmark the quantized model
288        let benchmarks = self.benchmark_quantized_model(&quantized_model)?;
289
290        let final_model = QuantizedModel {
291            weights: quantized_model.weights,
292            parameters: quantized_model.parameters,
293            metadata: quantized_model.metadata,
294            benchmarks,
295        };
296
297        // Cache the result
298        {
299            let mut cache = self.quantization_cache.lock().expect("Operation failed");
300            cache.insert(model_id.to_string(), final_model.clone());
301        }
302
303        Ok(final_model)
304    }
305
306    /// Determine optimal quantization strategy based on device capabilities
307    fn determine_quantization_strategy(&self) -> Result<MobilePrecision> {
308        match (
309            &self.device_info.performance_scores.overall_tier,
310            &self.config.target_precision,
311        ) {
312            (PerformanceTier::High, MobilePrecision::DYNAMIC) => {
313                // High-end devices can handle mixed precision
314                Ok(MobilePrecision::Mixed8_16)
315            },
316            (PerformanceTier::Mid, MobilePrecision::DYNAMIC) => {
317                // Mid-range devices benefit from INT8
318                Ok(MobilePrecision::INT8)
319            },
320            (PerformanceTier::Budget, MobilePrecision::DYNAMIC) => {
321                // Low-end devices need aggressive quantization
322                Ok(MobilePrecision::Mixed4_8)
323            },
324            (_, precision) => Ok(*precision),
325        }
326    }
327
328    /// Get hardware-specific quantization configuration
329    fn get_hardware_quantization_config(&self) -> Result<HardwareQuantizationConfig> {
330        let mut config = HardwareQuantizationConfig::default();
331
332        // Configure for specific hardware
333        if self.device_info.npu_info.is_some() {
334            config.use_npu_kernels = true;
335            config.preferred_precision = MobilePrecision::INT8;
336        }
337
338        if self.device_info.gpu_info.is_some() {
339            config.use_gpu_kernels = true;
340            config.gpu_memory_optimization = true;
341        }
342
343        // ARM-specific optimizations
344        if self.device_info.cpu_info.architecture.contains("arm")
345            || self.device_info.cpu_info.architecture.contains("aarch64")
346        {
347            config.use_neon_instructions = true;
348            config.arm_specific_kernels = true;
349        }
350
351        Ok(config)
352    }
353
354    /// Perform the actual quantization
355    fn perform_quantization(
356        &self,
357        model_data: &[u8],
358        strategy: &MobilePrecision,
359        hardware_config: &HardwareQuantizationConfig,
360    ) -> Result<QuantizedModel> {
361        // Parse model weights (simplified)
362        let weights = self.parse_model_weights(model_data)?;
363
364        // Apply quantization based on strategy
365        let quantized_weights = match strategy {
366            MobilePrecision::INT4 => self.quantize_to_int4(&weights)?,
367            MobilePrecision::INT8 => self.quantize_to_int8(&weights)?,
368            MobilePrecision::FP16 => self.quantize_to_fp16(&weights)?,
369            MobilePrecision::Mixed4_8 => self.quantize_mixed_4_8(&weights)?,
370            MobilePrecision::Mixed8_16 => self.quantize_mixed_8_16(&weights)?,
371            MobilePrecision::DYNAMIC => self.quantize_dynamic(&weights)?,
372        };
373
374        // Calculate quantization parameters
375        let parameters = self.calculate_quantization_parameters(&quantized_weights)?;
376
377        // Generate metadata
378        let metadata = ModelMetadata {
379            original_size_bytes: model_data.len(),
380            quantized_size_bytes: self.calculate_quantized_size(&quantized_weights),
381            compression_ratio: model_data.len() as f32
382                / self.calculate_quantized_size(&quantized_weights) as f32,
383            quality_score: self.estimate_quality_score(&quantized_weights)?,
384            timestamp: std::time::SystemTime::now(),
385        };
386
387        Ok(QuantizedModel {
388            weights: quantized_weights,
389            parameters,
390            metadata,
391            benchmarks: QuantizationBenchmarks::default(), // Will be filled by benchmark_quantized_model
392        })
393    }
394
395    /// Quantize weights to 4-bit integers
396    fn quantize_to_int4(
397        &self,
398        weights: &HashMap<String, Tensor>,
399    ) -> Result<HashMap<String, QuantizedTensor>> {
400        let mut quantized = HashMap::new();
401
402        for (layer_name, tensor) in weights {
403            let tensor_data = tensor.data()?.to_vec();
404
405            // Calculate scale and zero point for 4-bit quantization
406            let min_val = tensor_data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
407            let max_val = tensor_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
408
409            let scale = (max_val - min_val) / 15.0; // 4-bit range: 0-15
410            let zero_point = (-min_val / scale).round() as i32;
411
412            // Quantize to 4-bit values (stored as i8)
413            let quantized_data: Vec<i8> = tensor_data
414                .iter()
415                .map(|&x| {
416                    let quantized = ((x / scale) + zero_point as f32).round();
417                    quantized.max(0.0).min(15.0) as i8
418                })
419                .collect();
420
421            let quantized_tensor = QuantizedTensor {
422                data: quantized_data,
423                scales: vec![scale],
424                zero_points: vec![zero_point],
425                shape: tensor.shape().to_vec(),
426                scheme: QuantizationScheme {
427                    bits: 4,
428                    symmetric: false,
429                    signed: false,
430                    method: QuantizationMethod::Linear,
431                },
432            };
433
434            quantized.insert(layer_name.clone(), quantized_tensor);
435        }
436
437        Ok(quantized)
438    }
439
440    /// Quantize weights to 8-bit integers
441    fn quantize_to_int8(
442        &self,
443        weights: &HashMap<String, Tensor>,
444    ) -> Result<HashMap<String, QuantizedTensor>> {
445        let mut quantized = HashMap::new();
446
447        for (layer_name, tensor) in weights {
448            let tensor_data = tensor.data()?.to_vec();
449
450            // Calculate scale and zero point for 8-bit quantization
451            let min_val = tensor_data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
452            let max_val = tensor_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
453
454            let scale = (max_val - min_val) / 255.0; // 8-bit range: 0-255
455            let zero_point = (-min_val / scale).round() as i32;
456
457            // Quantize to 8-bit values
458            let quantized_data: Vec<i8> = tensor_data
459                .iter()
460                .map(|&x| {
461                    let quantized = ((x / scale) + zero_point as f32).round();
462                    (quantized.max(0.0).min(255.0) as i32 - 128) as i8 // Convert to signed
463                })
464                .collect();
465
466            let quantized_tensor = QuantizedTensor {
467                data: quantized_data,
468                scales: vec![scale],
469                zero_points: vec![zero_point],
470                shape: tensor.shape().to_vec(),
471                scheme: QuantizationScheme {
472                    bits: 8,
473                    symmetric: false,
474                    signed: true,
475                    method: QuantizationMethod::Linear,
476                },
477            };
478
479            quantized.insert(layer_name.clone(), quantized_tensor);
480        }
481
482        Ok(quantized)
483    }
484
485    /// Quantize weights to FP16
486    fn quantize_to_fp16(
487        &self,
488        weights: &HashMap<String, Tensor>,
489    ) -> Result<HashMap<String, QuantizedTensor>> {
490        // For FP16, we just need to convert precision, no quantization parameters needed
491        let mut quantized = HashMap::new();
492
493        for (layer_name, tensor) in weights {
494            let tensor_data = tensor.data()?.to_vec();
495
496            // Convert to FP16 (stored as i8 pairs for compatibility)
497            let quantized_data: Vec<i8> = tensor_data
498                .iter()
499                .flat_map(|&x| {
500                    let fp16_bits = f16::from_f32(x).to_bits();
501                    [(fp16_bits & 0xFF) as i8, ((fp16_bits >> 8) & 0xFF) as i8]
502                })
503                .collect();
504
505            let quantized_tensor = QuantizedTensor {
506                data: quantized_data,
507                scales: vec![1.0], // No scaling needed for FP16
508                zero_points: vec![0],
509                shape: tensor.shape().to_vec(),
510                scheme: QuantizationScheme {
511                    bits: 16,
512                    symmetric: true,
513                    signed: true,
514                    method: QuantizationMethod::Linear,
515                },
516            };
517
518            quantized.insert(layer_name.clone(), quantized_tensor);
519        }
520
521        Ok(quantized)
522    }
523
524    /// Mixed 4-bit and 8-bit quantization
525    fn quantize_mixed_4_8(
526        &self,
527        weights: &HashMap<String, Tensor>,
528    ) -> Result<HashMap<String, QuantizedTensor>> {
529        let mut quantized = HashMap::new();
530
531        for (layer_name, tensor) in weights {
532            // Determine if this layer should use 4-bit or 8-bit
533            let use_4bit = self.should_use_4bit_for_layer(layer_name, tensor)?;
534
535            if use_4bit {
536                let quantized_4bit = self.quantize_to_int4(
537                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
538                )?;
539                quantized.extend(quantized_4bit);
540            } else {
541                let quantized_8bit = self.quantize_to_int8(
542                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
543                )?;
544                quantized.extend(quantized_8bit);
545            }
546        }
547
548        Ok(quantized)
549    }
550
551    /// Mixed 8-bit and 16-bit quantization
552    fn quantize_mixed_8_16(
553        &self,
554        weights: &HashMap<String, Tensor>,
555    ) -> Result<HashMap<String, QuantizedTensor>> {
556        let mut quantized = HashMap::new();
557
558        for (layer_name, tensor) in weights {
559            // Determine if this layer should use 8-bit or 16-bit
560            let use_8bit = self.should_use_8bit_for_layer(layer_name, tensor)?;
561
562            if use_8bit {
563                let quantized_8bit = self.quantize_to_int8(
564                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
565                )?;
566                quantized.extend(quantized_8bit);
567            } else {
568                let quantized_16bit = self.quantize_to_fp16(
569                    &[(layer_name.clone(), tensor.clone())].iter().cloned().collect(),
570                )?;
571                quantized.extend(quantized_16bit);
572            }
573        }
574
575        Ok(quantized)
576    }
577
578    /// Dynamic quantization based on runtime conditions
579    fn quantize_dynamic(
580        &self,
581        weights: &HashMap<String, Tensor>,
582    ) -> Result<HashMap<String, QuantizedTensor>> {
583        // Start with INT8 as baseline, can be adjusted at runtime
584        self.quantize_to_int8(weights)
585    }
586
587    /// Determine if a layer should use 4-bit quantization
588    fn should_use_4bit_for_layer(&self, layer_name: &str, tensor: &Tensor) -> Result<bool> {
589        // Use 4-bit for less critical layers (embeddings, some linear layers)
590        let is_embedding = layer_name.contains("embed") || layer_name.contains("token");
591        let is_output = layer_name.contains("output") || layer_name.contains("head");
592        let is_large = tensor.shape().iter().product::<usize>() > 1000000;
593
594        Ok(is_embedding || (is_large && !is_output))
595    }
596
597    /// Determine if a layer should use 8-bit quantization
598    fn should_use_8bit_for_layer(&self, layer_name: &str, tensor: &Tensor) -> Result<bool> {
599        // Use 8-bit for most layers, 16-bit for critical ones
600        let is_attention = layer_name.contains("attn") || layer_name.contains("attention");
601        let is_output = layer_name.contains("output") || layer_name.contains("head");
602        let is_norm = layer_name.contains("norm") || layer_name.contains("ln");
603
604        Ok(!(is_attention || is_output || is_norm))
605    }
606
607    /// Advanced model weight parsing with format detection
608    fn parse_model_weights(&self, model_data: &[u8]) -> Result<HashMap<String, Tensor>> {
609        // Enhanced model parsing with format detection and error handling
610        #[allow(dead_code)]
611        let mut weights = HashMap::new();
612
613        // Detect model format by magic bytes
614        let format = self.detect_model_format(model_data)?;
615
616        match format {
617            ModelFormat::SafeTensors => {
618                weights = self.parse_safetensors(model_data)?;
619            },
620            ModelFormat::PyTorchPickle => {
621                weights = self.parse_pytorch_pickle(model_data)?;
622            },
623            ModelFormat::TensorFlow => {
624                weights = self.parse_tensorflow(model_data)?;
625            },
626            ModelFormat::ONNX => {
627                weights = self.parse_onnx(model_data)?;
628            },
629            ModelFormat::Custom => {
630                weights = self.parse_custom_format(model_data)?;
631            },
632        }
633
634        // Validate parsed weights
635        self.validate_parsed_weights(&weights)?;
636
637        Ok(weights)
638    }
639
640    /// Calculate comprehensive quantization parameters
641    fn calculate_quantization_parameters(
642        &self,
643        weights: &HashMap<String, QuantizedTensor>,
644    ) -> Result<QuantizationParameters> {
645        let mut layer_scales = HashMap::new();
646        let mut layer_zero_points = HashMap::new();
647        let mut total_dequant_overhead = 0.0;
648
649        // Calculate per-layer parameters
650        for (layer_name, quantized_tensor) in weights {
651            // Get the primary scale and zero point
652            let scale = quantized_tensor.scales.first().copied().unwrap_or(1.0);
653            let zero_point = quantized_tensor.zero_points.first().copied().unwrap_or(0);
654
655            layer_scales.insert(layer_name.clone(), scale);
656            layer_zero_points.insert(layer_name.clone(), zero_point);
657
658            // Estimate dequantization overhead based on tensor size and quantization scheme
659            let tensor_size = quantized_tensor.data.len();
660            let overhead_factor = match quantized_tensor.scheme.bits {
661                4 => 0.05,  // 4-bit requires more work to unpack
662                8 => 0.03,  // 8-bit is straightforward
663                16 => 0.01, // 16-bit (FP16) is native on many devices
664                _ => 0.04,  // Default for other bit widths
665            };
666
667            total_dequant_overhead += (tensor_size as f32 * overhead_factor) / 1000.0;
668            // Convert to ms
669        }
670
671        // Calculate global scale as weighted average
672        let total_elements: f32 = weights.values().map(|t| t.data.len() as f32).sum();
673        let global_scale = if total_elements > 0.0 {
674            layer_scales.values().sum::<f32>() / layer_scales.len() as f32
675        } else {
676            1.0
677        };
678
679        Ok(QuantizationParameters {
680            global_scale,
681            layer_scales,
682            layer_zero_points,
683            dequant_overhead_ms: total_dequant_overhead,
684        })
685    }
686
687    /// Calculate precise quantized model size
688    fn calculate_quantized_size(&self, weights: &HashMap<String, QuantizedTensor>) -> usize {
689        let mut total_size = 0;
690
691        for (layer_name, quantized_tensor) in weights {
692            // Data size
693            let data_size = quantized_tensor.data.len();
694
695            // Metadata size (scales, zero points, shape info)
696            let metadata_size = quantized_tensor.scales.len() * 4 + // 4 bytes per f32 scale
697                               quantized_tensor.zero_points.len() * 4 + // 4 bytes per i32 zero point
698                               quantized_tensor.shape.len() * 8 + // 8 bytes per usize dimension
699                               layer_name.len() + 32; // Layer name + quantization scheme overhead
700
701            total_size += data_size + metadata_size;
702        }
703
704        total_size
705    }
706
707    /// Advanced quality score estimation using multiple metrics
708    fn estimate_quality_score(&self, weights: &HashMap<String, QuantizedTensor>) -> Result<f32> {
709        if weights.is_empty() {
710            return Ok(1.0);
711        }
712
713        let mut total_quality = 0.0;
714        let mut total_weight = 0.0;
715
716        for (layer_name, quantized_tensor) in weights {
717            let layer_weight = quantized_tensor.data.len() as f32;
718
719            // Quality estimation based on quantization scheme
720            let base_quality = match quantized_tensor.scheme.bits {
721                4 => 0.85,  // 4-bit typically has more quality loss
722                8 => 0.93,  // 8-bit is quite good
723                16 => 0.98, // 16-bit (FP16) is very close to original
724                _ => 0.90,  // Default for other bit widths
725            };
726
727            // Adjust quality based on layer type
728            let layer_quality_factor = self.get_layer_quality_factor(layer_name);
729            let adjusted_quality = base_quality * layer_quality_factor;
730
731            // Weight by tensor size (larger tensors have more impact)
732            total_quality += adjusted_quality * layer_weight;
733            total_weight += layer_weight;
734        }
735
736        let overall_quality = if total_weight > 0.0 { total_quality / total_weight } else { 1.0 };
737
738        // Apply calibration data influence if available
739        let calibration_factor = if let Some(ref cal_data) = self.calibration_data {
740            self.estimate_calibration_quality_impact(cal_data)?
741        } else {
742            0.95 // Slight penalty for no calibration
743        };
744
745        Ok((overall_quality * calibration_factor).min(1.0))
746    }
747
748    /// Comprehensive quantized model benchmarking
749    fn benchmark_quantized_model(&self, model: &QuantizedModel) -> Result<QuantizationBenchmarks> {
750        let mut benchmarks = QuantizationBenchmarks::default();
751
752        // Estimate original inference time based on model size
753        let original_params = model.metadata.original_size_bytes / 4; // Assume FP32
754        benchmarks.original_inference_ms =
755            self.estimate_inference_time(original_params, MobilePrecision::FP16)?;
756
757        // Estimate quantized inference time
758        let quantized_params = model.metadata.quantized_size_bytes;
759        let avg_precision = self.estimate_average_precision(&model.weights);
760        benchmarks.quantized_inference_ms =
761            self.estimate_inference_time(quantized_params, avg_precision)?;
762
763        // Calculate speedup factor
764        benchmarks.speedup_factor = if benchmarks.quantized_inference_ms > 0.0 {
765            benchmarks.original_inference_ms / benchmarks.quantized_inference_ms
766        } else {
767            1.0
768        };
769
770        // Calculate memory reduction
771        benchmarks.memory_reduction_mb = (model.metadata.original_size_bytes
772            - model.metadata.quantized_size_bytes) as f32
773            / (1024.0 * 1024.0);
774
775        // Estimate power reduction based on precision and device characteristics
776        benchmarks.power_reduction_mw = self.estimate_power_reduction(&model.weights)?;
777
778        Ok(benchmarks)
779    }
780
781    // Helper methods for enhanced functionality
782
783    /// Detect model format from magic bytes
784    fn detect_model_format(&self, data: &[u8]) -> Result<ModelFormat> {
785        if data.len() < 8 {
786            return Err(invalid_input("Model data too small to detect format"));
787        }
788
789        // Check for SafeTensors magic bytes
790        if data.starts_with(b"STFR") || data.starts_with(&[0x53, 0x54, 0x46, 0x52]) {
791            return Ok(ModelFormat::SafeTensors);
792        }
793
794        // Check for PyTorch pickle magic bytes
795        if data.starts_with(&[0x80, 0x02]) || data.starts_with(&[0x80, 0x03]) {
796            return Ok(ModelFormat::PyTorchPickle);
797        }
798
799        // Check for TensorFlow SavedModel
800        if data.starts_with(b"TF") {
801            return Ok(ModelFormat::TensorFlow);
802        }
803
804        // Check for ONNX
805        if data.starts_with(&[0x08, 0x01]) {
806            return Ok(ModelFormat::ONNX);
807        }
808
809        // Default to custom format
810        Ok(ModelFormat::Custom)
811    }
812
813    /// Parse SafeTensors format
814    fn parse_safetensors(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
815        // Simplified SafeTensors parsing
816        Ok(HashMap::new())
817    }
818
819    /// Parse PyTorch pickle format
820    fn parse_pytorch_pickle(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
821        // Simplified PyTorch pickle parsing
822        Ok(HashMap::new())
823    }
824
825    /// Parse TensorFlow format
826    fn parse_tensorflow(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
827        // Simplified TensorFlow parsing
828        Ok(HashMap::new())
829    }
830
831    /// Parse ONNX format
832    fn parse_onnx(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
833        // Simplified ONNX parsing
834        Ok(HashMap::new())
835    }
836
837    /// Parse custom format
838    fn parse_custom_format(&self, _data: &[u8]) -> Result<HashMap<String, Tensor>> {
839        // Simplified custom format parsing
840        Ok(HashMap::new())
841    }
842
843    /// Validate parsed weights
844    fn validate_parsed_weights(&self, weights: &HashMap<String, Tensor>) -> Result<()> {
845        if weights.is_empty() {
846            return Err(invalid_input("No weights found in model"));
847        }
848
849        for (layer_name, tensor) in weights {
850            // Check for valid tensor dimensions
851            if tensor.shape().is_empty() {
852                return Err(invalid_input(format!(
853                    "Invalid tensor shape for layer: {}",
854                    layer_name
855                )));
856            }
857
858            // Check for reasonable tensor sizes
859            let total_elements: usize = tensor.shape().iter().product();
860            if total_elements == 0 {
861                return Err(invalid_input(format!(
862                    "Empty tensor for layer: {}",
863                    layer_name
864                )));
865            }
866
867            // Check for extremely large tensors that might cause issues
868            if total_elements > 100_000_000 {
869                tracing::warn!(
870                    "Large tensor detected in layer {}: {} elements",
871                    layer_name,
872                    total_elements
873                );
874            }
875        }
876
877        Ok(())
878    }
879
880    /// Get quality factor based on layer type
881    fn get_layer_quality_factor(&self, layer_name: &str) -> f32 {
882        // Different layer types have different sensitivity to quantization
883        if layer_name.contains("output") || layer_name.contains("head") {
884            0.95 // Output layers are more sensitive
885        } else if layer_name.contains("attention") || layer_name.contains("attn") {
886            0.92 // Attention layers are quite sensitive
887        } else if layer_name.contains("norm") || layer_name.contains("ln") {
888            0.98 // Normalization layers are less sensitive
889        } else if layer_name.contains("embed") || layer_name.contains("token") {
890            0.90 // Embedding layers can tolerate more quantization
891        } else {
892            1.0 // Default for other layers
893        }
894    }
895
896    /// Estimate calibration quality impact
897    fn estimate_calibration_quality_impact(&self, cal_data: &CalibrationDataset) -> Result<f32> {
898        // More calibration samples generally lead to better quality
899        let sample_factor = (cal_data.samples.len() as f32 / 100.0).min(1.0);
900
901        // Check if we have good statistical coverage
902        let stats_quality =
903            if !cal_data.statistics.activation_ranges.is_empty() { 1.0 } else { 0.9 };
904
905        Ok(0.95 + 0.05 * sample_factor * stats_quality)
906    }
907
908    /// Estimate inference time based on parameters and precision
909    fn estimate_inference_time(&self, params: usize, precision: MobilePrecision) -> Result<f32> {
910        // Base computation time per parameter (in microseconds)
911        let base_time_per_param = match self.device_info.performance_scores.overall_tier {
912            PerformanceTier::VeryLow => 0.01,    // Very slow devices
913            PerformanceTier::Low => 0.008,       // Low-end devices
914            PerformanceTier::Budget => 0.005,    // Entry-level devices
915            PerformanceTier::Medium => 0.003,    // Medium-range devices
916            PerformanceTier::Mid => 0.002,       // Mid-range devices
917            PerformanceTier::High => 0.001,      // High-end devices
918            PerformanceTier::VeryHigh => 0.0007, // Very high-end devices
919            PerformanceTier::Flagship => 0.0005, // Premium flagship devices
920        };
921
922        // Precision multiplier
923        let precision_factor = match precision {
924            MobilePrecision::INT4 => 0.5,
925            MobilePrecision::INT8 => 0.7,
926            MobilePrecision::FP16 => 1.0,
927            MobilePrecision::Mixed4_8 => 0.6,
928            MobilePrecision::Mixed8_16 => 0.85,
929            MobilePrecision::DYNAMIC => 0.8,
930        };
931
932        // Hardware acceleration factor
933        let hw_factor = if self.device_info.npu_info.is_some() {
934            0.6
935        } else if self.device_info.gpu_info.is_some() {
936            0.8
937        } else {
938            1.0
939        };
940
941        let total_time = params as f32 * base_time_per_param * precision_factor * hw_factor;
942        Ok(total_time)
943    }
944
945    /// Estimate average precision from quantized weights
946    fn estimate_average_precision(
947        &self,
948        weights: &HashMap<String, QuantizedTensor>,
949    ) -> MobilePrecision {
950        if weights.is_empty() {
951            return MobilePrecision::FP16;
952        }
953
954        let mut total_bits = 0;
955        let mut total_tensors = 0;
956
957        for tensor in weights.values() {
958            total_bits += tensor.scheme.bits as u32;
959            total_tensors += 1;
960        }
961
962        let avg_bits = total_bits as f32 / total_tensors as f32;
963
964        match avg_bits.round() as u8 {
965            4 => MobilePrecision::INT4,
966            8 => MobilePrecision::INT8,
967            16 => MobilePrecision::FP16,
968            _ => MobilePrecision::INT8, // Default fallback
969        }
970    }
971
972    /// Estimate power reduction from quantization
973    fn estimate_power_reduction(&self, weights: &HashMap<String, QuantizedTensor>) -> Result<f32> {
974        let mut total_power_reduction = 0.0;
975
976        for tensor in weights.values() {
977            let tensor_size = tensor.data.len() as f32;
978
979            // Power reduction per operation based on bit width
980            let reduction_per_op = match tensor.scheme.bits {
981                4 => 0.08,  // 8mW reduction per 1000 operations
982                8 => 0.05,  // 5mW reduction per 1000 operations
983                16 => 0.02, // 2mW reduction per 1000 operations
984                _ => 0.04,  // Default
985            };
986
987            total_power_reduction += tensor_size * reduction_per_op / 1000.0;
988        }
989
990        Ok(total_power_reduction)
991    }
992}
993
994/// Hardware-specific quantization configuration
995#[derive(Debug, Clone)]
996struct HardwareQuantizationConfig {
997    use_npu_kernels: bool,
998    use_gpu_kernels: bool,
999    use_neon_instructions: bool,
1000    arm_specific_kernels: bool,
1001    gpu_memory_optimization: bool,
1002    preferred_precision: MobilePrecision,
1003}
1004
1005impl Default for HardwareQuantizationConfig {
1006    fn default() -> Self {
1007        Self {
1008            use_npu_kernels: false,
1009            use_gpu_kernels: false,
1010            use_neon_instructions: false,
1011            arm_specific_kernels: false,
1012            gpu_memory_optimization: false,
1013            preferred_precision: MobilePrecision::INT8,
1014        }
1015    }
1016}
1017
1018impl Default for QuantizationBenchmarks {
1019    fn default() -> Self {
1020        Self {
1021            original_inference_ms: 0.0,
1022            quantized_inference_ms: 0.0,
1023            speedup_factor: 1.0,
1024            memory_reduction_mb: 0.0,
1025            power_reduction_mw: 0.0,
1026        }
1027    }
1028}
1029
1030impl Default for QuantizationConfig {
1031    fn default() -> Self {
1032        Self {
1033            target_precision: MobilePrecision::INT8,
1034            enable_mixed_precision: true,
1035            dynamic_strategy: DynamicQuantizationStrategy::Adaptive,
1036            hardware_aware: true,
1037            granularity: QuantizationGranularity::PerChannel,
1038            quality_threshold: 0.9,
1039            memory_constraint_mb: 512,
1040            enable_gradient_quantization: false,
1041            kl_threshold: 0.01,
1042            enable_ptq: true,
1043            enable_qat: false,
1044        }
1045    }
1046}
1047
1048#[cfg(test)]
1049mod tests {
1050    use super::*;
1051    use trustformers_core::Tensor;
1052
1053    #[test]
1054    fn test_model_format_detection() {
1055        let engine = create_test_engine();
1056
1057        // Test SafeTensors format
1058        let safetensors_data = b"STFR\x00\x00\x00\x00test data";
1059        let format = engine.detect_model_format(safetensors_data).expect("Operation failed");
1060        assert_eq!(format, ModelFormat::SafeTensors);
1061
1062        // Test PyTorch pickle format
1063        let pytorch_data = b"\x80\x02test data";
1064        let format = engine.detect_model_format(pytorch_data).expect("Operation failed");
1065        assert_eq!(format, ModelFormat::PyTorchPickle);
1066
1067        // Test TensorFlow format
1068        let tf_data = b"TFtest data";
1069        let format = engine.detect_model_format(tf_data).expect("Operation failed");
1070        assert_eq!(format, ModelFormat::TensorFlow);
1071
1072        // Test ONNX format
1073        let onnx_data = b"\x08\x01test data";
1074        let format = engine.detect_model_format(onnx_data).expect("Operation failed");
1075        assert_eq!(format, ModelFormat::ONNX);
1076
1077        // Test custom format
1078        let custom_data = b"custom test data";
1079        let format = engine.detect_model_format(custom_data).expect("Operation failed");
1080        assert_eq!(format, ModelFormat::Custom);
1081    }
1082
1083    #[test]
1084    fn test_quantization_parameters_calculation() {
1085        let engine = create_test_engine();
1086        let weights = create_test_quantized_weights();
1087
1088        let params = engine.calculate_quantization_parameters(&weights).expect("Operation failed");
1089
1090        assert!(params.global_scale > 0.0);
1091        assert!(!params.layer_scales.is_empty());
1092        assert!(!params.layer_zero_points.is_empty());
1093        assert!(params.dequant_overhead_ms >= 0.0);
1094    }
1095
1096    #[test]
1097    fn test_quality_score_estimation() {
1098        let engine = create_test_engine();
1099        let weights = create_test_quantized_weights();
1100
1101        let quality = engine.estimate_quality_score(&weights).expect("Operation failed");
1102
1103        assert!((0.0..=1.0).contains(&quality));
1104    }
1105
1106    #[test]
1107    fn test_layer_quality_factors() {
1108        let engine = create_test_engine();
1109
1110        // Test different layer types
1111        assert_eq!(engine.get_layer_quality_factor("model.output.weight"), 0.95);
1112        assert_eq!(
1113            engine.get_layer_quality_factor("model.attention.weight"),
1114            0.92
1115        );
1116        assert_eq!(
1117            engine.get_layer_quality_factor("model.layer_norm.weight"),
1118            0.98
1119        );
1120        assert_eq!(
1121            engine.get_layer_quality_factor("model.embedding.weight"),
1122            0.90
1123        );
1124        assert_eq!(engine.get_layer_quality_factor("model.hidden.weight"), 1.0);
1125    }
1126
1127    #[test]
1128    fn test_inference_time_estimation() {
1129        let engine = create_test_engine();
1130
1131        let time = engine
1132            .estimate_inference_time(1000, MobilePrecision::INT8)
1133            .expect("Operation failed");
1134        assert!(time > 0.0);
1135
1136        let time_fp16 = engine
1137            .estimate_inference_time(1000, MobilePrecision::FP16)
1138            .expect("Operation failed");
1139        let time_int4 = engine
1140            .estimate_inference_time(1000, MobilePrecision::INT4)
1141            .expect("Operation failed");
1142
1143        // INT4 should be faster than FP16
1144        assert!(time_int4 < time_fp16);
1145    }
1146
1147    #[test]
1148    fn test_power_reduction_estimation() {
1149        let engine = create_test_engine();
1150        let weights = create_test_quantized_weights();
1151
1152        let power_reduction = engine.estimate_power_reduction(&weights).expect("Operation failed");
1153        assert!(power_reduction >= 0.0);
1154    }
1155
1156    #[test]
1157    fn test_quantized_size_calculation() {
1158        let engine = create_test_engine();
1159        let weights = create_test_quantized_weights();
1160
1161        let size = engine.calculate_quantized_size(&weights);
1162        assert!(size > 0);
1163    }
1164
1165    #[test]
1166    fn test_weight_validation() {
1167        let engine = create_test_engine();
1168
1169        // Test empty weights
1170        let empty_weights = HashMap::new();
1171        assert!(engine.validate_parsed_weights(&empty_weights).is_err());
1172
1173        // Test valid weights
1174        let valid_weights = create_test_weights();
1175        assert!(engine.validate_parsed_weights(&valid_weights).is_ok());
1176    }
1177
1178    #[test]
1179    fn test_calibration_data_validation() {
1180        let mut engine = create_test_engine();
1181
1182        // Test empty calibration data
1183        let empty_dataset = CalibrationDataset {
1184            samples: vec![],
1185            weights: None,
1186            statistics: DatasetStatistics::default(),
1187        };
1188        assert!(engine.set_calibration_data(empty_dataset).is_err());
1189
1190        // Test valid calibration data
1191        let valid_dataset = CalibrationDataset {
1192            samples: vec![Tensor::from_vec(vec![1.0, 2.0, 3.0], &[3]).expect("Operation failed")],
1193            weights: None,
1194            statistics: DatasetStatistics::default(),
1195        };
1196        assert!(engine.set_calibration_data(valid_dataset).is_ok());
1197    }
1198
1199    // Helper functions for tests
1200    fn create_test_engine() -> MobileQuantizationEngine {
1201        let config = QuantizationConfig::default();
1202        // Use the actual device detector for consistent structure
1203        let device_info = crate::device_info::MobileDeviceDetector::detect().unwrap_or_else(|_| {
1204            // Fallback test device info if detection fails
1205            use crate::device_info::*;
1206            MobileDeviceInfo {
1207                platform: crate::MobilePlatform::Generic,
1208                basic_info: BasicDeviceInfo {
1209                    platform: crate::MobilePlatform::Generic,
1210                    manufacturer: "Test".to_string(),
1211                    model: "Test Device".to_string(),
1212                    os_version: "1.0".to_string(),
1213                    hardware_id: "test".to_string(),
1214                    device_generation: Some(2023),
1215                },
1216                cpu_info: CpuInfo {
1217                    architecture: "aarch64".to_string(),
1218                    total_cores: 8,
1219                    core_count: 8,
1220                    performance_cores: 4,
1221                    efficiency_cores: 4,
1222                    max_frequency_mhz: Some(3000),
1223                    l1_cache_kb: Some(64),
1224                    l2_cache_kb: Some(512),
1225                    l3_cache_kb: Some(4096),
1226                    features: vec!["neon".to_string(), "fp16".to_string()],
1227                    simd_support: SimdSupport::Advanced,
1228                },
1229                memory_info: MemoryInfo {
1230                    total_mb: 8192,
1231                    available_mb: 6144,
1232                    total_memory: 8192,
1233                    available_memory: 6144,
1234                    bandwidth_mbps: Some(51200),
1235                    memory_type: "LPDDR5".to_string(),
1236                    frequency_mhz: Some(6400),
1237                    is_low_memory_device: false,
1238                },
1239                gpu_info: Some(GpuInfo {
1240                    vendor: "ARM".to_string(),
1241                    model: "Mali-G78".to_string(),
1242                    driver_version: "1.0".to_string(),
1243                    memory_mb: Some(2048),
1244                    compute_units: Some(14),
1245                    supported_apis: vec![GpuApi::OpenGLES3, GpuApi::Vulkan11],
1246                    performance_tier: GpuPerformanceTier::High,
1247                }),
1248                npu_info: None,
1249                thermal_info: ThermalInfo {
1250                    current_state: ThermalState::Nominal,
1251                    state: ThermalState::Nominal,
1252                    throttling_supported: true,
1253                    temperature_sensors: vec![],
1254                    thermal_zones: vec![],
1255                },
1256                power_info: PowerInfo {
1257                    battery_capacity_mah: Some(4000),
1258                    battery_level_percent: Some(80),
1259                    battery_level: Some(80),
1260                    battery_health_percent: Some(100),
1261                    charging_status: ChargingStatus::Discharging,
1262                    is_charging: false,
1263                    power_save_mode: false,
1264                    low_power_mode_available: true,
1265                },
1266                available_backends: vec![crate::MobileBackend::CPU, crate::MobileBackend::GPU],
1267                performance_scores: PerformanceScores {
1268                    cpu_single_core: Some(1200),
1269                    cpu_multi_core: Some(4800),
1270                    gpu_score: Some(2500),
1271                    memory_score: Some(1800),
1272                    overall_tier: PerformanceTier::Mid,
1273                    tier: PerformanceTier::Mid,
1274                },
1275            }
1276        });
1277
1278        MobileQuantizationEngine::new(config, device_info, None).expect("Operation failed")
1279    }
1280
1281    fn create_test_quantized_weights() -> HashMap<String, QuantizedTensor> {
1282        let mut weights = HashMap::new();
1283
1284        weights.insert(
1285            "layer1.weight".to_string(),
1286            QuantizedTensor {
1287                data: vec![1, 2, 3, 4, 5],
1288                scales: vec![0.1],
1289                zero_points: vec![0],
1290                shape: vec![5],
1291                scheme: QuantizationScheme {
1292                    bits: 8,
1293                    symmetric: false,
1294                    signed: true,
1295                    method: QuantizationMethod::Linear,
1296                },
1297            },
1298        );
1299
1300        weights.insert(
1301            "layer2.weight".to_string(),
1302            QuantizedTensor {
1303                data: vec![6, 7, 8, 9, 10],
1304                scales: vec![0.2],
1305                zero_points: vec![1],
1306                shape: vec![5],
1307                scheme: QuantizationScheme {
1308                    bits: 4,
1309                    symmetric: false,
1310                    signed: false,
1311                    method: QuantizationMethod::Linear,
1312                },
1313            },
1314        );
1315
1316        weights
1317    }
1318
1319    fn create_test_weights() -> HashMap<String, Tensor> {
1320        let mut weights = HashMap::new();
1321
1322        weights.insert(
1323            "layer1.weight".to_string(),
1324            Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], &[5]).expect("Operation failed"),
1325        );
1326        weights.insert(
1327            "layer2.weight".to_string(),
1328            Tensor::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], &[5]).expect("Operation failed"),
1329        );
1330
1331        weights
1332    }
1333}
trustformers_mobile/optimization/advanced_quantization.rs

trustformers_mobile/optimization/
advanced_quantization.rs
