scirs2_io/ml_framework/
optimization.rs

1//! Model optimization features
2
3use crate::error::Result;
4use crate::ml_framework::MLModel;
5
6/// Model optimization techniques
7#[derive(Debug, Clone)]
8pub enum OptimizationTechnique {
9    /// Remove unnecessary operations
10    Pruning { sparsity: f32 },
11    /// Fuse operations
12    OperatorFusion,
13    /// Constant folding
14    ConstantFolding,
15    /// Graph optimization
16    GraphOptimization,
17    /// Knowledge distillation
18    Distillation,
19}
20
21/// Model optimizer
22pub struct ModelOptimizer {
23    techniques: Vec<OptimizationTechnique>,
24}
25
26impl Default for ModelOptimizer {
27    fn default() -> Self {
28        Self::new()
29    }
30}
31
32impl ModelOptimizer {
33    pub fn new() -> Self {
34        Self {
35            techniques: Vec::new(),
36        }
37    }
38
39    pub fn add_technique(mut self, technique: OptimizationTechnique) -> Self {
40        self.techniques.push(technique);
41        self
42    }
43
44    /// Optimize model
45    pub fn optimize(&self, model: &MLModel) -> Result<MLModel> {
46        let mut optimized = model.clone();
47
48        for technique in &self.techniques {
49            match technique {
50                OptimizationTechnique::Pruning { sparsity } => {
51                    optimized = self.apply_pruning(optimized, *sparsity)?;
52                }
53                OptimizationTechnique::OperatorFusion => {
54                    // Implement operator fusion
55                }
56                _ => {}
57            }
58        }
59
60        Ok(optimized)
61    }
62
63    fn apply_pruning(&self, mut model: MLModel, sparsity: f32) -> Result<MLModel> {
64        for (_, tensor) in model.weights.iter_mut() {
65            let data = tensor.data.as_slice_mut().unwrap();
66            let threshold = self.compute_pruning_threshold(data, sparsity);
67
68            for val in data.iter_mut() {
69                if val.abs() < threshold {
70                    *val = 0.0;
71                }
72            }
73        }
74
75        Ok(model)
76    }
77
78    fn compute_pruning_threshold(&self, data: &[f32], sparsity: f32) -> f32 {
79        let mut sorted: Vec<f32> = data.iter().map(|x| x.abs()).collect();
80        sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
81        let idx = (sorted.len() as f32 * sparsity) as usize;
82        sorted.get(idx).copied().unwrap_or(0.0)
83    }
84}
scirs2_io/ml_framework/optimization.rs

scirs2_io/ml_framework/
optimization.rs
