trustformers_wasm/optimization/quantization/
quantizer.rs

1//! Web-optimized quantizer with runtime adaptation
2
3use crate::optimization::quantization::algorithms::*;
4use crate::optimization::quantization::config::*;
5use serde::{Deserialize, Serialize};
6use std::vec::Vec;
7use wasm_bindgen::prelude::*;
8
9/// Result of quantization operation
10#[wasm_bindgen]
11#[derive(Debug, Clone)]
12pub struct QuantizationResult {
13    data: Vec<u8>,
14    stats: QuantizationStats,
15}
16
17#[wasm_bindgen]
18impl QuantizationResult {
19    /// Get the quantized data
20    pub fn data(&self) -> Vec<u8> {
21        self.data.clone()
22    }
23
24    /// Get a summary of the quantization
25    pub fn summary(&self) -> String {
26        format!(
27            "Quantization completed: {:.1}x compression, {:.1}% size reduction, {:.1}x estimated speedup",
28            self.stats.compression_ratio(),
29            self.stats.size_reduction_percent(),
30            self.stats.estimated_speedup()
31        )
32    }
33
34    /// Get detailed statistics
35    pub fn stats(&self) -> QuantizationStats {
36        self.stats.clone()
37    }
38}
39
40/// Web-optimized quantizer with runtime adaptation
41#[wasm_bindgen]
42pub struct WebQuantizer {
43    config: QuantizationConfig,
44    #[allow(dead_code)]
45    device_capabilities: DeviceCapabilities,
46    #[allow(dead_code)]
47    runtime_monitor: RuntimeMonitor,
48    adaptive_state: AdaptiveQuantizationState,
49}
50
51#[wasm_bindgen]
52impl WebQuantizer {
53    /// Create a new web quantizer
54    #[wasm_bindgen(constructor)]
55    pub fn new(config: QuantizationConfig) -> Self {
56        let device_capabilities = DeviceCapabilities {
57            supports_int8: true,
58            supports_int4: true,
59            supports_fp16: true,
60            memory_bandwidth_gb_s: 100.0,
61            compute_capability: ComputeCapability::Medium,
62        };
63
64        let runtime_monitor = RuntimeMonitor {
65            inference_times: Vec::new(),
66            memory_usage: Vec::new(),
67            accuracy_scores: Vec::new(),
68            thermal_state: ThermalState::Nominal,
69            adaptation_history: Vec::new(),
70        };
71
72        let adaptive_state = AdaptiveQuantizationState {
73            current_strategy: config.strategy(),
74            current_precision: config.precision(),
75            adaptation_rate: 0.1,
76            performance_target: config.performance_threshold(),
77            accuracy_target: config.accuracy_threshold(),
78            last_adaptation: 0.0,
79            confidence_score: 0.8,
80        };
81
82        Self {
83            config,
84            device_capabilities,
85            runtime_monitor,
86            adaptive_state,
87        }
88    }
89
90    /// Quantize tensor data using the configured strategy
91    pub fn quantize(&self, data: &[f32]) -> Result<Vec<f32>, JsValue> {
92        match self.adaptive_state.current_strategy {
93            QuantizationStrategy::None => Ok(data.to_vec()),
94            QuantizationStrategy::Dynamic => {
95                apply_dynamic_quantization(data, self.adaptive_state.current_precision)
96            },
97            QuantizationStrategy::Static => {
98                apply_static_quantization(data, self.adaptive_state.current_precision)
99            },
100            QuantizationStrategy::PostTraining => {
101                apply_post_training_quantization(data, self.adaptive_state.current_precision)
102            },
103            QuantizationStrategy::AWQ => {
104                apply_awq_quantization(data, self.adaptive_state.current_precision)
105            },
106            QuantizationStrategy::GPTQ => {
107                apply_gptq_quantization(data, self.adaptive_state.current_precision)
108            },
109            QuantizationStrategy::SmoothQuant => {
110                apply_smoothquant_quantization(data, self.adaptive_state.current_precision)
111            },
112            QuantizationStrategy::LLMInt8 => {
113                apply_llm_int8_quantization(data, self.adaptive_state.current_precision)
114            },
115            QuantizationStrategy::QLoRA => {
116                apply_qlora_quantization(data, self.adaptive_state.current_precision)
117            },
118            QuantizationStrategy::GGML => {
119                apply_ggml_quantization(data, self.adaptive_state.current_precision)
120            },
121            QuantizationStrategy::AdaptiveBitwidth => {
122                apply_adaptive_bitwidth_quantization(data, self.adaptive_state.current_precision)
123            },
124            QuantizationStrategy::OutlierAware => {
125                apply_outlier_aware_quantization(data, self.adaptive_state.current_precision)
126            },
127            QuantizationStrategy::HQQ => {
128                apply_hqq_quantization(data, self.adaptive_state.current_precision)
129            },
130            QuantizationStrategy::SpQR => {
131                apply_spqr_quantization(data, self.adaptive_state.current_precision)
132            },
133            QuantizationStrategy::AQLM => {
134                apply_aqlm_quantization(data, self.adaptive_state.current_precision)
135            },
136            _ => Err(JsValue::from_str("Unsupported quantization strategy")),
137        }
138    }
139
140    /// Get quantization statistics
141    pub fn get_stats(&self, original_data: &[f32], quantized_data: &[f32]) -> QuantizationStats {
142        let original_size = original_data.len() * 4; // 4 bytes per f32
143        let quantized_size = quantized_data.len() * 4; // Simplified for placeholder
144        let compression_ratio = original_size as f32 / quantized_size as f32;
145        let size_reduction = (1.0 - quantized_size as f32 / original_size as f32) * 100.0;
146
147        QuantizationStats::new(
148            original_size,
149            quantized_size,
150            compression_ratio,
151            size_reduction,
152            compression_ratio * 0.8, // Simplified estimation
153            self.adaptive_state.current_strategy,
154            self.adaptive_state.current_precision,
155        )
156    }
157
158    /// Check if a model should be quantized based on size
159    pub fn should_quantize(&self, model_size_bytes: usize) -> bool {
160        let model_size_mb = model_size_bytes as f32 / (1024.0 * 1024.0);
161        model_size_mb > self.config.target_size_mb()
162    }
163
164    /// Quantize model data
165    pub fn quantize_model(&self, model_data: &[u8]) -> Result<QuantizationResult, JsValue> {
166        // Convert bytes to f32 for processing
167        let float_data: Vec<f32> = model_data
168            .chunks_exact(4)
169            .map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
170            .collect();
171
172        // Apply quantization
173        let quantized_floats = self.quantize(&float_data)?;
174
175        // Get stats before converting to bytes
176        let stats = self.get_stats(&float_data, &quantized_floats);
177
178        // Convert back to bytes
179        let quantized_bytes: Vec<u8> =
180            quantized_floats.into_iter().flat_map(|f| f.to_le_bytes()).collect();
181
182        Ok(QuantizationResult {
183            data: quantized_bytes,
184            stats,
185        })
186    }
187
188    /// Get recommended quantization settings for a given model size
189    pub fn get_recommended_settings(&self, model_size_bytes: usize) -> QuantizationConfig {
190        let model_size_mb = model_size_bytes as f32 / (1024.0 * 1024.0);
191
192        if model_size_mb < 10.0 {
193            QuantizationConfig::new(QuantizationStrategy::None, QuantizationPrecision::FP16)
194        } else if model_size_mb < 50.0 {
195            QuantizationConfig::new(QuantizationStrategy::Dynamic, QuantizationPrecision::FP16)
196        } else if model_size_mb < 200.0 {
197            QuantizationConfig::new(
198                QuantizationStrategy::PostTraining,
199                QuantizationPrecision::INT8,
200            )
201        } else {
202            QuantizationConfig::new(QuantizationStrategy::AWQ, QuantizationPrecision::INT4)
203        }
204    }
205}
206
207/// Quantized model data
208#[derive(Debug, Clone, Serialize, Deserialize)]
209pub struct QuantizedModelData {
210    pub quantized_weights: Vec<Vec<f32>>,
211    pub scale_factors: Vec<f32>,
212    pub zero_points: Vec<f32>,
213    pub metadata: QuantizationMetadata,
214}
215
216/// Quantization metadata
217#[derive(Debug, Clone, Serialize, Deserialize)]
218pub struct QuantizationMetadata {
219    pub strategy: QuantizationStrategy,
220    pub precision: QuantizationPrecision,
221    pub compression_ratio: f32,
222    pub accuracy_retention: f32,
223}
224
225impl QuantizedModelData {
226    pub fn new(strategy: QuantizationStrategy, precision: QuantizationPrecision) -> Self {
227        Self {
228            quantized_weights: Vec::new(),
229            scale_factors: Vec::new(),
230            zero_points: Vec::new(),
231            metadata: QuantizationMetadata {
232                strategy,
233                precision,
234                compression_ratio: 1.0,
235                accuracy_retention: 1.0,
236            },
237        }
238    }
239}
240
241#[cfg(test)]
242mod tests {
243    use super::*;
244
245    #[test]
246    fn test_quantizer_creation() {
247        let config = QuantizationConfig::auto();
248        let quantizer = WebQuantizer::new(config);
249        assert_eq!(
250            quantizer.adaptive_state.current_strategy,
251            QuantizationStrategy::Dynamic
252        );
253    }
254
255    #[test]
256    fn test_basic_quantization() {
257        let config =
258            QuantizationConfig::new(QuantizationStrategy::Dynamic, QuantizationPrecision::INT8);
259        let quantizer = WebQuantizer::new(config);
260        let data = vec![1.0, 2.0, 3.0, 4.0];
261        let result = quantizer.quantize(&data);
262        assert!(result.is_ok());
263    }
264
265    #[test]
266    fn test_quantization_stats() {
267        let config = QuantizationConfig::auto();
268        let quantizer = WebQuantizer::new(config);
269        let original = vec![1.0, 2.0, 3.0, 4.0];
270        let quantized = vec![0.5, 1.0, 1.5, 2.0];
271        let stats = quantizer.get_stats(&original, &quantized);
272        assert!(stats.compression_ratio() >= 1.0);
273    }
274
275    #[test]
276    fn test_quantized_model_data() {
277        let data = QuantizedModelData::new(QuantizationStrategy::AWQ, QuantizationPrecision::INT8);
278        assert_eq!(data.metadata.strategy, QuantizationStrategy::AWQ);
279        assert_eq!(data.metadata.precision, QuantizationPrecision::INT8);
280    }
281}
trustformers_wasm/optimization/quantization/quantizer.rs

trustformers_wasm/optimization/quantization/
quantizer.rs
