torsh_hub/
model_ops.rs

1//! Advanced model operations and utilities
2//!
3//! This module provides advanced operations for model management including:
4//! - Model comparison and difference analysis
5//! - Model merging and ensemble creation
6//! - Model quantization helpers
7//! - Model pruning utilities
8//! - Model conversion helpers
9
10use serde::{Deserialize, Serialize};
11use std::collections::HashMap;
12use std::path::Path;
13use torsh_core::error::{Result, TorshError};
14use torsh_tensor::Tensor;
15
16/// Model difference analysis result
17#[derive(Debug, Clone, Serialize, Deserialize)]
18pub struct ModelDiff {
19    /// Parameters that exist in both models
20    pub common_parameters: Vec<String>,
21    /// Parameters only in first model
22    pub only_in_first: Vec<String>,
23    /// Parameters only in second model
24    pub only_in_second: Vec<String>,
25    /// Parameters with different shapes
26    pub shape_differences: Vec<ShapeDifference>,
27    /// Statistical differences for common parameters
28    pub value_differences: Vec<ValueDifference>,
29    /// Total parameter count for each model
30    pub param_counts: (usize, usize),
31    /// Memory footprint for each model (in bytes)
32    pub memory_footprints: (u64, u64),
33}
34
35/// Shape difference between two parameters
36#[derive(Debug, Clone, Serialize, Deserialize)]
37pub struct ShapeDifference {
38    pub parameter_name: String,
39    pub shape_first: Vec<usize>,
40    pub shape_second: Vec<usize>,
41}
42
43/// Value difference statistics for a parameter
44#[derive(Debug, Clone, Serialize, Deserialize)]
45pub struct ValueDifference {
46    pub parameter_name: String,
47    pub mean_absolute_diff: f64,
48    pub max_absolute_diff: f64,
49    pub relative_diff_percent: f64,
50    pub cosine_similarity: f64,
51}
52
53/// Model comparison options
54#[derive(Debug, Clone)]
55pub struct ComparisonOptions {
56    /// Whether to compute detailed value differences (can be expensive)
57    pub compute_value_diffs: bool,
58    /// Threshold for considering values as different (relative)
59    pub diff_threshold: f64,
60    /// Maximum number of parameters to compare in detail
61    pub max_params_to_compare: usize,
62}
63
64impl Default for ComparisonOptions {
65    fn default() -> Self {
66        Self {
67            compute_value_diffs: true,
68            diff_threshold: 1e-5,
69            max_params_to_compare: 1000,
70        }
71    }
72}
73
74/// Compare two models and return their differences
75pub fn compare_models(
76    model1_state: &HashMap<String, Tensor<f32>>,
77    model2_state: &HashMap<String, Tensor<f32>>,
78    options: Option<ComparisonOptions>,
79) -> Result<ModelDiff> {
80    let options = options.unwrap_or_default();
81
82    let keys1: std::collections::HashSet<_> = model1_state.keys().cloned().collect();
83    let keys2: std::collections::HashSet<_> = model2_state.keys().cloned().collect();
84
85    let common_parameters: Vec<String> = keys1.intersection(&keys2).cloned().collect();
86    let only_in_first: Vec<String> = keys1.difference(&keys2).cloned().collect();
87    let only_in_second: Vec<String> = keys2.difference(&keys1).cloned().collect();
88
89    let mut shape_differences = Vec::new();
90    let mut value_differences = Vec::new();
91
92    // Check shape differences and compute value differences
93    for param_name in &common_parameters {
94        let tensor1 = &model1_state[param_name];
95        let tensor2 = &model2_state[param_name];
96
97        let shape1 = tensor1.shape().dims().to_vec();
98        let shape2 = tensor2.shape().dims().to_vec();
99
100        if shape1 != shape2 {
101            shape_differences.push(ShapeDifference {
102                parameter_name: param_name.clone(),
103                shape_first: shape1,
104                shape_second: shape2,
105            });
106        } else if options.compute_value_diffs
107            && value_differences.len() < options.max_params_to_compare
108        {
109            // Compute value differences only if shapes match
110            if let Ok(diff) =
111                compute_value_difference(tensor1, tensor2, param_name, options.diff_threshold)
112            {
113                value_differences.push(diff);
114            }
115        }
116    }
117
118    let param_counts = (model1_state.len(), model2_state.len());
119    let memory_footprints = (
120        estimate_memory_footprint(model1_state),
121        estimate_memory_footprint(model2_state),
122    );
123
124    Ok(ModelDiff {
125        common_parameters,
126        only_in_first,
127        only_in_second,
128        shape_differences,
129        value_differences,
130        param_counts,
131        memory_footprints,
132    })
133}
134
135/// Compute value difference statistics between two tensors
136fn compute_value_difference(
137    tensor1: &Tensor<f32>,
138    tensor2: &Tensor<f32>,
139    param_name: &str,
140    _threshold: f64,
141) -> Result<ValueDifference> {
142    // Get tensor data
143    let data1 = tensor1.to_vec()?;
144    let data2 = tensor2.to_vec()?;
145
146    if data1.len() != data2.len() {
147        return Err(TorshError::InvalidArgument(
148            "Tensors must have same number of elements".to_string(),
149        ));
150    }
151
152    // Compute statistics
153    let mut sum_abs_diff = 0.0f64;
154    let mut max_abs_diff = 0.0f64;
155    let mut dot_product = 0.0f64;
156    let mut norm1_sq = 0.0f64;
157    let mut norm2_sq = 0.0f64;
158
159    for (&v1, &v2) in data1.iter().zip(data2.iter()) {
160        let v1 = v1 as f64;
161        let v2 = v2 as f64;
162        let abs_diff = (v1 - v2).abs();
163
164        sum_abs_diff += abs_diff;
165        max_abs_diff = max_abs_diff.max(abs_diff);
166        dot_product += v1 * v2;
167        norm1_sq += v1 * v1;
168        norm2_sq += v2 * v2;
169    }
170
171    let mean_absolute_diff = sum_abs_diff / data1.len() as f64;
172
173    // Compute relative difference as percentage
174    let mean1 = data1.iter().map(|&x| x as f64).sum::<f64>() / data1.len() as f64;
175    let relative_diff_percent = if mean1.abs() > 1e-10 {
176        (mean_absolute_diff / mean1.abs()) * 100.0
177    } else {
178        0.0
179    };
180
181    // Compute cosine similarity
182    let cosine_similarity = if norm1_sq > 0.0 && norm2_sq > 0.0 {
183        dot_product / (norm1_sq.sqrt() * norm2_sq.sqrt())
184    } else {
185        0.0
186    };
187
188    Ok(ValueDifference {
189        parameter_name: param_name.to_string(),
190        mean_absolute_diff,
191        max_absolute_diff: max_abs_diff,
192        relative_diff_percent,
193        cosine_similarity,
194    })
195}
196
197/// Estimate memory footprint of a model state dict
198fn estimate_memory_footprint(state_dict: &HashMap<String, Tensor<f32>>) -> u64 {
199    state_dict
200        .values()
201        .map(|tensor| {
202            let num_elements = tensor.shape().numel();
203            (num_elements * std::mem::size_of::<f32>()) as u64
204        })
205        .sum()
206}
207
208/// Model ensemble configuration
209#[derive(Debug, Clone)]
210pub struct EnsembleConfig {
211    /// Weights for each model in the ensemble
212    pub weights: Vec<f32>,
213    /// Whether to normalize weights
214    pub normalize_weights: bool,
215    /// Voting strategy for classification tasks
216    pub voting_strategy: VotingStrategy,
217}
218
219/// Voting strategy for ensemble models
220#[derive(Debug, Clone, Copy)]
221pub enum VotingStrategy {
222    /// Average predictions (for regression)
223    Average,
224    /// Weighted average
225    WeightedAverage,
226    /// Majority vote (for classification)
227    MajorityVote,
228    /// Soft voting (use probabilities)
229    SoftVoting,
230}
231
232impl Default for EnsembleConfig {
233    fn default() -> Self {
234        Self {
235            weights: vec![1.0],
236            normalize_weights: true,
237            voting_strategy: VotingStrategy::WeightedAverage,
238        }
239    }
240}
241
242/// Create an ensemble of models by averaging their parameters
243pub fn create_model_ensemble(
244    models: &[HashMap<String, Tensor<f32>>],
245    config: Option<EnsembleConfig>,
246) -> Result<HashMap<String, Tensor<f32>>> {
247    if models.is_empty() {
248        return Err(TorshError::InvalidArgument(
249            "Cannot create ensemble from empty model list".to_string(),
250        ));
251    }
252
253    let config = config.unwrap_or_default();
254    let mut weights = config.weights.clone();
255
256    // Ensure we have the right number of weights
257    if weights.len() != models.len() {
258        weights = vec![1.0; models.len()];
259    }
260
261    // Normalize weights if requested
262    if config.normalize_weights {
263        let sum: f32 = weights.iter().sum();
264        if sum > 0.0 {
265            weights.iter_mut().for_each(|w| *w /= sum);
266        }
267    }
268
269    // Get common parameters
270    let param_keys: std::collections::HashSet<_> = models[0].keys().cloned().collect();
271
272    let mut ensemble_state = HashMap::new();
273
274    for param_name in param_keys {
275        // Collect all tensors for this parameter
276        let tensors: Vec<&Tensor<f32>> = models.iter().filter_map(|m| m.get(&param_name)).collect();
277
278        if tensors.len() != models.len() {
279            continue; // Skip parameters that don't exist in all models
280        }
281
282        // Average the tensors with weights
283        if let Ok(averaged) = weighted_average_tensors(&tensors, &weights) {
284            ensemble_state.insert(param_name, averaged);
285        }
286    }
287
288    Ok(ensemble_state)
289}
290
291/// Compute weighted average of tensors
292fn weighted_average_tensors(tensors: &[&Tensor<f32>], weights: &[f32]) -> Result<Tensor<f32>> {
293    if tensors.is_empty() {
294        return Err(TorshError::InvalidArgument("Empty tensor list".to_string()));
295    }
296
297    // Verify all tensors have the same shape
298    let shape = tensors[0].shape();
299    for tensor in &tensors[1..] {
300        if tensor.shape() != shape {
301            return Err(TorshError::InvalidArgument(
302                "All tensors must have the same shape".to_string(),
303            ));
304        }
305    }
306
307    // Convert to vectors and compute weighted average
308    let data_vecs: Vec<Vec<f32>> = tensors
309        .iter()
310        .map(|t| t.to_vec())
311        .collect::<Result<Vec<_>>>()?;
312    let num_elements = data_vecs[0].len();
313
314    let mut result = vec![0.0f32; num_elements];
315    for (tensor_data, weight) in data_vecs.iter().zip(weights.iter()) {
316        for (i, value) in tensor_data.iter().enumerate() {
317            result[i] += value * weight;
318        }
319    }
320
321    // Create result tensor
322    Tensor::from_data(result, shape.dims().to_vec(), torsh_core::DeviceType::Cpu)
323}
324
325/// Model quantization statistics
326#[derive(Debug, Clone, Serialize, Deserialize)]
327pub struct QuantizationStats {
328    pub original_size_bytes: u64,
329    pub quantized_size_bytes: u64,
330    pub compression_ratio: f32,
331    pub parameters_quantized: usize,
332    pub mean_quantization_error: f64,
333    pub max_quantization_error: f64,
334}
335
336/// Model conversion metadata
337#[derive(Debug, Clone, Serialize, Deserialize)]
338pub struct ConversionMetadata {
339    pub source_format: String,
340    pub target_format: String,
341    pub conversion_time_ms: u64,
342    pub warnings: Vec<String>,
343    pub unsupported_operations: Vec<String>,
344}
345
346/// Load model from file path with automatic format detection
347pub fn load_model_auto(path: &Path) -> Result<HashMap<String, Tensor<f32>>> {
348    let extension = path.extension().and_then(|e| e.to_str()).unwrap_or("");
349
350    match extension {
351        "torsh" | "pth" | "pt" => load_torsh_model(path),
352        "onnx" => load_onnx_model_state(path),
353        "h5" | "keras" => load_keras_model(path),
354        _ => Err(TorshError::InvalidArgument(format!(
355            "Unsupported model format: {}",
356            extension
357        ))),
358    }
359}
360
361/// Load ToRSh/PyTorch model (placeholder - would need actual implementation)
362fn load_torsh_model(_path: &Path) -> Result<HashMap<String, Tensor<f32>>> {
363    // This would need actual implementation
364    Ok(HashMap::new())
365}
366
367/// Load ONNX model state (placeholder - would need actual implementation)
368fn load_onnx_model_state(_path: &Path) -> Result<HashMap<String, Tensor<f32>>> {
369    // This would need actual implementation with ONNX integration
370    Ok(HashMap::new())
371}
372
373/// Load Keras model (placeholder - would need actual implementation)
374fn load_keras_model(_path: &Path) -> Result<HashMap<String, Tensor<f32>>> {
375    // This would need actual implementation
376    Ok(HashMap::new())
377}
378
379#[cfg(test)]
380mod tests {
381    use super::*;
382    use torsh_core::DeviceType;
383
384    #[test]
385    fn test_model_comparison() {
386        let mut model1 = HashMap::new();
387        let mut model2 = HashMap::new();
388
389        // Add common parameter with same shape
390        let tensor1 =
391            Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2], DeviceType::Cpu).unwrap();
392        let tensor2 =
393            Tensor::from_data(vec![1.1, 2.1, 3.1, 4.1], vec![2, 2], DeviceType::Cpu).unwrap();
394        model1.insert("layer1.weight".to_string(), tensor1);
395        model2.insert("layer1.weight".to_string(), tensor2);
396
397        // Add parameter only in model1
398        let tensor3 = Tensor::from_data(vec![5.0, 6.0], vec![2], DeviceType::Cpu).unwrap();
399        model1.insert("layer1.bias".to_string(), tensor3);
400
401        // Add parameter only in model2
402        let tensor4 = Tensor::from_data(vec![7.0, 8.0], vec![2], DeviceType::Cpu).unwrap();
403        model2.insert("layer2.weight".to_string(), tensor4);
404
405        let diff = compare_models(&model1, &model2, None).unwrap();
406
407        assert_eq!(diff.common_parameters.len(), 1);
408        assert_eq!(diff.only_in_first.len(), 1);
409        assert_eq!(diff.only_in_second.len(), 1);
410        assert_eq!(diff.param_counts, (2, 2));
411    }
412
413    #[test]
414    fn test_memory_footprint() {
415        let mut model = HashMap::new();
416        let tensor1 =
417            Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2], DeviceType::Cpu).unwrap();
418        let tensor2 = Tensor::from_data(vec![5.0, 6.0], vec![2], DeviceType::Cpu).unwrap();
419
420        model.insert("weight".to_string(), tensor1);
421        model.insert("bias".to_string(), tensor2);
422
423        let footprint = estimate_memory_footprint(&model);
424        // 4 elements + 2 elements = 6 elements * 4 bytes = 24 bytes
425        assert_eq!(footprint, 24);
426    }
427
428    #[test]
429    fn test_weighted_average() {
430        let tensor1 = Tensor::from_data(vec![1.0, 2.0], vec![2], DeviceType::Cpu).unwrap();
431        let tensor2 = Tensor::from_data(vec![3.0, 4.0], vec![2], DeviceType::Cpu).unwrap();
432
433        let tensors = vec![&tensor1, &tensor2];
434        let weights = vec![0.5, 0.5];
435
436        let result = weighted_average_tensors(&tensors, &weights).unwrap();
437        let result_data = result.to_vec().unwrap();
438
439        assert_eq!(result_data.len(), 2);
440        assert!((result_data[0] - 2.0).abs() < 1e-5);
441        assert!((result_data[1] - 3.0).abs() < 1e-5);
442    }
443
444    #[test]
445    fn test_ensemble_creation() {
446        let mut model1 = HashMap::new();
447        let mut model2 = HashMap::new();
448
449        let tensor1 = Tensor::from_data(vec![1.0, 2.0], vec![2], DeviceType::Cpu).unwrap();
450        let tensor2 = Tensor::from_data(vec![3.0, 4.0], vec![2], DeviceType::Cpu).unwrap();
451
452        model1.insert("weight".to_string(), tensor1);
453        model2.insert("weight".to_string(), tensor2);
454
455        let models = vec![model1, model2];
456        let config = EnsembleConfig {
457            weights: vec![0.5, 0.5],
458            normalize_weights: false,
459            voting_strategy: VotingStrategy::WeightedAverage,
460        };
461
462        let ensemble = create_model_ensemble(&models, Some(config)).unwrap();
463
464        assert_eq!(ensemble.len(), 1);
465        let result = &ensemble["weight"];
466        let result_data = result.to_vec().unwrap();
467        assert!((result_data[0] - 2.0).abs() < 1e-5);
468        assert!((result_data[1] - 3.0).abs() < 1e-5);
469    }
470}
torsh_hub/model_ops.rs

torsh_hub/
model_ops.rs
