scirs2_autograd/optimization/
expression_simplification.rs

1//! Expression simplification optimization
2//!
3//! This module implements algebraic simplifications for computation graphs,
4//! such as x + 0 -> x, x * 1 -> x, x - x -> 0, etc.
5
6use super::{OptimizationError, SimplificationPattern};
7use crate::graph::{Graph, TensorID};
8use crate::tensor::TensorInternal;
9use crate::Float;
10use std::collections::HashMap;
11
12/// Type alias for the transform function used in simplification rules.
13type TransformFn = Box<dyn Fn(&[TensorID]) -> Result<TensorID, OptimizationError>>;
14
15/// Expression simplifier
16pub struct ExpressionSimplifier<F: Float> {
17    /// Rules for simplification
18    rules: Vec<SimplificationRule<F>>,
19    /// Cache of simplified expressions
20    cache: HashMap<String, TensorID>,
21}
22
23impl<F: Float> ExpressionSimplifier<F> {
24    /// Create a new expression simplifier with default rules
25    pub fn new() -> Self {
26        let mut simplifier = Self {
27            rules: Vec::new(),
28            cache: HashMap::new(),
29        };
30        simplifier.load_default_rules();
31        simplifier
32    }
33
34    /// Load default simplification rules
35    fn load_default_rules(&mut self) {
36        // Identity rules
37        self.add_rule(SimplificationRule::new(
38            "add_zero",
39            SimplificationPattern::AddZero,
40            create_identity_replacement,
41        ));
42
43        self.add_rule(SimplificationRule::new(
44            "sub_zero",
45            SimplificationPattern::SubZero,
46            create_identity_replacement,
47        ));
48
49        self.add_rule(SimplificationRule::new(
50            "mul_one",
51            SimplificationPattern::MulOne,
52            create_identity_replacement,
53        ));
54
55        self.add_rule(SimplificationRule::new(
56            "div_one",
57            SimplificationPattern::DivOne,
58            create_identity_replacement,
59        ));
60
61        // Zero rules
62        self.add_rule(SimplificationRule::new(
63            "mul_zero",
64            SimplificationPattern::MulZero,
65            |_inputs| create_zero_replacement(),
66        ));
67
68        // Self-operation rules
69        self.add_rule(SimplificationRule::new(
70            "sub_self",
71            SimplificationPattern::SubSelf,
72            |_inputs| create_zero_replacement(),
73        ));
74
75        self.add_rule(SimplificationRule::new(
76            "div_self",
77            SimplificationPattern::DivSelf,
78            |_inputs| create_one_replacement(),
79        ));
80
81        // Composite function rules
82        self.add_rule(SimplificationRule::new(
83            "log_exp",
84            SimplificationPattern::LogExp,
85            create_inner_replacement,
86        ));
87
88        self.add_rule(SimplificationRule::new(
89            "exp_log",
90            SimplificationPattern::ExpLog,
91            create_inner_replacement,
92        ));
93
94        // Power rules
95        self.add_rule(SimplificationRule::new(
96            "pow_one",
97            SimplificationPattern::PowOne,
98            create_identity_replacement,
99        ));
100
101        self.add_rule(SimplificationRule::new(
102            "pow_zero",
103            SimplificationPattern::PowZero,
104            |_inputs| create_one_replacement(),
105        ));
106    }
107
108    /// Add a simplification rule
109    pub fn add_rule(&mut self, rule: SimplificationRule<F>) {
110        self.rules.push(rule);
111    }
112
113    /// Apply expression simplification to a graph
114    pub fn simplify_expressions(
115        &mut self,
116        _graph: &mut Graph<F>,
117    ) -> Result<usize, OptimizationError> {
118        let simplified_count = 0;
119
120        // Implementation would:
121        // 1. Traverse all nodes in the graph
122        // 2. For each node, check if it matches any simplification pattern
123        // 3. Apply the corresponding rule to create a simplified version
124        // 4. Replace the original node with the simplified version
125        // 5. Update all references in the graph
126
127        Ok(simplified_count)
128    }
129
130    /// Check if a tensor matches any simplification pattern
131    pub(crate) fn find_applicable_rule(
132        &self,
133        _tensor_internal: &TensorInternal<F>,
134    ) -> Option<&SimplificationRule<F>> {
135        // Check each rule to see if it applies to this tensor
136        self.rules
137            .iter()
138            .find(|&rule| rule.matches(_tensor_internal))
139            .map(|v| v as _)
140    }
141
142    /// Apply a specific rule to simplify a tensor
143    pub(crate) fn apply_rule(
144        &self,
145        _rule: &SimplificationRule<F>,
146        _tensor_internal: &TensorInternal<F>,
147        _graph: &mut Graph<F>,
148    ) -> Result<TensorID, OptimizationError> {
149        // Apply the rule's transformation to create a new simplified tensor
150        Err(OptimizationError::InvalidOperation(
151            "Rule application not implemented".to_string(),
152        ))
153    }
154
155    /// Clear the simplification cache
156    pub fn clear_cache(&mut self) {
157        self.cache.clear();
158    }
159}
160
161/// Create an identity replacement (return the first input tensor)
162fn create_identity_replacement(inputs: &[TensorID]) -> Result<TensorID, OptimizationError> {
163    inputs.first().copied().ok_or_else(|| {
164        OptimizationError::InvalidOperation(
165            "Identity replacement requires at least one input".to_string(),
166        )
167    })
168}
169
170/// Create a zero replacement tensor
171fn create_zero_replacement() -> Result<TensorID, OptimizationError> {
172    // Create a constant zero tensor
173    Err(OptimizationError::InvalidOperation(
174        "Zero replacement not implemented".to_string(),
175    ))
176}
177
178/// Create a one replacement tensor
179fn create_one_replacement() -> Result<TensorID, OptimizationError> {
180    // Create a constant one tensor
181    Err(OptimizationError::InvalidOperation(
182        "One replacement not implemented".to_string(),
183    ))
184}
185
186/// Create an inner replacement (for patterns like log(exp(x)), return x)
187fn create_inner_replacement(inputs: &[TensorID]) -> Result<TensorID, OptimizationError> {
188    // For patterns like log(exp(x)), return the inner argument x
189    inputs.first().copied().ok_or_else(|| {
190        OptimizationError::InvalidOperation(
191            "Inner replacement requires at least one input".to_string(),
192        )
193    })
194}
195
196impl<F: Float> Default for ExpressionSimplifier<F> {
197    fn default() -> Self {
198        Self::new()
199    }
200}
201
202/// A simplification rule that can be applied to nodes
203pub struct SimplificationRule<F: Float> {
204    /// Name of this rule
205    name: String,
206    /// Pattern this rule matches
207    pattern: SimplificationPattern,
208    /// Function to apply the transformation
209    transform: TransformFn,
210    /// Phantom data for the Float type parameter
211    _phantom: std::marker::PhantomData<F>,
212}
213
214impl<F: Float> SimplificationRule<F> {
215    /// Create a new simplification rule
216    pub fn new<Transform>(name: &str, pattern: SimplificationPattern, transform: Transform) -> Self
217    where
218        Transform: Fn(&[TensorID]) -> Result<TensorID, OptimizationError> + 'static,
219    {
220        Self {
221            name: name.to_string(),
222            pattern,
223            transform: Box::new(transform),
224            _phantom: std::marker::PhantomData,
225        }
226    }
227
228    /// Get the name of this rule
229    pub fn name(&self) -> &str {
230        &self.name
231    }
232
233    /// Get the pattern this rule matches
234    pub fn pattern(&self) -> SimplificationPattern {
235        self.pattern
236    }
237
238    /// Check if this rule matches a tensor internal node
239    pub(crate) fn matches(&self, _tensor_internal: &TensorInternal<F>) -> bool {
240        // Check if the tensor internal's operation and structure matches this rule's pattern
241        match self.pattern {
242            SimplificationPattern::AddZero => self.matches_add_zero(_tensor_internal),
243            SimplificationPattern::SubZero => self.matches_sub_zero(_tensor_internal),
244            SimplificationPattern::MulOne => self.matches_mul_one(_tensor_internal),
245            SimplificationPattern::DivOne => self.matches_div_one(_tensor_internal),
246            SimplificationPattern::MulZero => self.matches_mul_zero(_tensor_internal),
247            SimplificationPattern::SubSelf => self.matches_sub_self(_tensor_internal),
248            SimplificationPattern::DivSelf => self.matches_div_self(_tensor_internal),
249            SimplificationPattern::LogExp => self.matches_log_exp(_tensor_internal),
250            SimplificationPattern::ExpLog => self.matches_exp_log(_tensor_internal),
251            SimplificationPattern::SqrtSquare => self.matches_sqrt_square(_tensor_internal),
252            SimplificationPattern::PowOne => self.matches_pow_one(_tensor_internal),
253            SimplificationPattern::PowZero => self.matches_pow_zero(_tensor_internal),
254        }
255    }
256
257    /// Apply this rule to create a simplified tensor
258    pub fn apply(&self, inputs: &[TensorID]) -> Result<TensorID, OptimizationError> {
259        (self.transform)(inputs)
260    }
261
262    // Pattern matching methods
263    fn matches_add_zero(&self, _tensor_internal: &TensorInternal<F>) -> bool {
264        // Check if this is an Add operation with one operand being zero
265        false
266    }
267
268    fn matches_sub_zero(&self, _tensor_internal: &TensorInternal<F>) -> bool {
269        // Check if this is a Sub operation with the second operand being zero
270        false
271    }
272
273    fn matches_mul_one(&self, _tensor_internal: &TensorInternal<F>) -> bool {
274        // Check if this is a Mul operation with one operand being one
275        false
276    }
277
278    fn matches_div_one(&self, _tensor_internal: &TensorInternal<F>) -> bool {
279        // Check if this is a Div operation with the second operand being one
280        false
281    }
282
283    fn matches_mul_zero(&self, _tensor_internal: &TensorInternal<F>) -> bool {
284        // Check if this is a Mul operation with one operand being zero
285        false
286    }
287
288    fn matches_sub_self(&self, _tensor_internal: &TensorInternal<F>) -> bool {
289        // Check if this is a Sub operation with both operands being the same
290        false
291    }
292
293    fn matches_div_self(&self, _tensor_internal: &TensorInternal<F>) -> bool {
294        // Check if this is a Div operation with both operands being the same
295        false
296    }
297
298    fn matches_log_exp(&self, _tensor_internal: &TensorInternal<F>) -> bool {
299        // Check if this is a Log operation applied to an Exp operation
300        false
301    }
302
303    fn matches_exp_log(&self, _tensor_internal: &TensorInternal<F>) -> bool {
304        // Check if this is an Exp operation applied to a Log operation
305        false
306    }
307
308    fn matches_sqrt_square(&self, _tensor_internal: &TensorInternal<F>) -> bool {
309        // Check if this is a Sqrt operation applied to a Square operation
310        false
311    }
312
313    fn matches_pow_one(&self, _tensor_internal: &TensorInternal<F>) -> bool {
314        // Check if this is a Pow operation with exponent one
315        false
316    }
317
318    fn matches_pow_zero(&self, _tensor_internal: &TensorInternal<F>) -> bool {
319        // Check if this is a Pow operation with exponent zero
320        false
321    }
322}
323
324/// Algebraic expression analyzer
325pub struct AlgebraicAnalyzer<F: Float> {
326    _phantom: std::marker::PhantomData<F>,
327}
328
329impl<F: Float> AlgebraicAnalyzer<F> {
330    /// Create a new algebraic analyzer
331    pub fn new() -> Self {
332        Self {
333            _phantom: std::marker::PhantomData,
334        }
335    }
336
337    /// Analyze an expression for simplification opportunities
338    pub(crate) fn analyze(
339        &self,
340        _tensor_internal: &TensorInternal<F>,
341    ) -> Vec<SimplificationOpportunity> {
342        let opportunities = Vec::new();
343
344        // Analyze the tensor and its subgraph for various patterns:
345        // - Identity operations (x + 0, x * 1, etc.)
346        // - Redundant operations (x - x, x / x, etc.)
347        // - Composite functions that can be simplified
348        // - Commutative/associative rearrangements
349
350        opportunities
351    }
352
353    /// Check for associative rearrangement opportunities
354    pub(crate) fn find_associative_opportunities(
355        &self,
356        _tensor_internal: &TensorInternal<F>,
357    ) -> Vec<AssociativityPattern> {
358        // Look for patterns like (a + b) + c that can be rearranged
359        // for better constant folding or other optimizations
360        Vec::new()
361    }
362
363    /// Check for commutative rearrangement opportunities
364    pub(crate) fn find_commutative_opportunities(
365        &self,
366        _tensor_internal: &TensorInternal<F>,
367    ) -> Vec<CommutativityPattern> {
368        // Look for patterns where operands can be reordered
369        // to enable other optimizations
370        Vec::new()
371    }
372
373    /// Check for distributive law opportunities
374    pub(crate) fn find_distributive_opportunities(
375        &self,
376        _tensor_internal: &TensorInternal<F>,
377    ) -> Vec<DistributivityPattern> {
378        // Look for patterns like a * (b + c) that can be expanded
379        // or patterns like a*b + a*c that can be factored
380        Vec::new()
381    }
382}
383
384impl<F: Float> Default for AlgebraicAnalyzer<F> {
385    fn default() -> Self {
386        Self::new()
387    }
388}
389
390/// Types of simplification opportunities
391#[derive(Debug, Clone)]
392pub struct SimplificationOpportunity {
393    /// The pattern that was found
394    pub pattern: SimplificationPattern,
395    /// Description of the opportunity
396    pub description: String,
397    /// Estimated benefit (higher is better)
398    pub benefit: f32,
399}
400
401/// Patterns for associative operations
402#[derive(Debug, Clone)]
403pub struct AssociativityPattern {
404    /// The operation that can be rearranged
405    pub operation: String,
406    /// Description of the rearrangement
407    pub description: String,
408}
409
410/// Patterns for commutative operations
411#[derive(Debug, Clone)]
412pub struct CommutativityPattern {
413    /// The operation that can have operands reordered
414    pub operation: String,
415    /// Description of the reordering
416    pub description: String,
417}
418
419/// Patterns for distributive operations
420#[derive(Debug, Clone)]
421pub struct DistributivityPattern {
422    /// Type of distributive transformation
423    pub transformation_type: DistributiveType,
424    /// Description of the transformation
425    pub description: String,
426}
427
428/// Types of distributive transformations
429#[derive(Debug, Clone, Copy)]
430pub enum DistributiveType {
431    /// Factor out common terms: a*b + a*c -> a*(b + c)
432    Factor,
433    /// Expand: a*(b + c) -> a*b + a*c
434    Expand,
435}
436
437/// Canonical form converter
438pub struct CanonicalFormConverter<F: Float> {
439    _phantom: std::marker::PhantomData<F>,
440}
441
442impl<F: Float> CanonicalFormConverter<F> {
443    /// Create a new canonical form converter
444    pub fn new() -> Self {
445        Self {
446            _phantom: std::marker::PhantomData,
447        }
448    }
449
450    /// Convert an expression to canonical form
451    pub(crate) fn canonicalize(
452        &self,
453        _tensor_internal: &TensorInternal<F>,
454    ) -> Result<TensorID, OptimizationError> {
455        // Convert expressions to a standard canonical form:
456        // - Sort operands in a consistent order
457        // - Normalize associative operations
458        // - Apply standard algebraic transformations
459
460        Err(OptimizationError::InvalidOperation(
461            "Canonicalization not implemented".to_string(),
462        ))
463    }
464
465    /// Check if two expressions are equivalent in canonical form
466    pub(crate) fn are_equivalent(
467        &self,
468        _node1: &TensorInternal<F>,
469        _node2: &TensorInternal<F>,
470    ) -> bool {
471        // Compare the canonical forms of two expressions
472        false
473    }
474}
475
476impl<F: Float> Default for CanonicalFormConverter<F> {
477    fn default() -> Self {
478        Self::new()
479    }
480}
481
482/// Utility functions for expression simplification
483///
484/// Create common simplification patterns
485#[allow(dead_code)]
486pub fn create_standard_rules<F: Float>() -> Vec<SimplificationRule<F>> {
487    // This would create the standard set of simplification rules
488    // that most users would want
489    Vec::new()
490}
491
492/// Check if an operation is commutative
493#[allow(dead_code)]
494pub fn is_commutative(op_name: &str) -> bool {
495    matches!(op_name, "Add" | "Mul" | "Min" | "Max")
496}
497
498/// Check if an operation is associative
499#[allow(dead_code)]
500pub fn is_associative(op_name: &str) -> bool {
501    matches!(op_name, "Add" | "Mul" | "Min" | "Max")
502}
503
504/// Check if an operation has an identity element
505#[allow(dead_code)]
506pub fn has_identity(op_name: &str) -> bool {
507    matches!(op_name, "Add" | "Mul")
508}
509
510/// Get the identity element for an operation
511#[allow(dead_code)]
512pub fn get_identity<F: Float>(op_name: &str) -> Option<F> {
513    match op_name {
514        "Add" => Some(F::zero()),
515        "Mul" => Some(F::one()),
516        _ => None,
517    }
518}
519
520#[cfg(test)]
521mod tests {
522    use super::*;
523
524    #[test]
525    fn test_expression_simplifier_creation() {
526        let _simplifier = ExpressionSimplifier::<f32>::new();
527    }
528
529    #[test]
530    fn test_algebraic_analyzer_creation() {
531        let _analyzer = AlgebraicAnalyzer::<f32>::new();
532    }
533
534    #[test]
535    fn test_canonical_form_converter_creation() {
536        let _converter = CanonicalFormConverter::<f32>::new();
537    }
538
539    #[test]
540    fn test_operation_properties() {
541        assert!(is_commutative("Add"));
542        assert!(is_commutative("Mul"));
543        assert!(!is_commutative("Sub"));
544        assert!(!is_commutative("Div"));
545
546        assert!(is_associative("Add"));
547        assert!(is_associative("Mul"));
548        assert!(!is_associative("Sub"));
549        assert!(!is_associative("Div"));
550
551        assert!(has_identity("Add"));
552        assert!(has_identity("Mul"));
553        assert!(!has_identity("Sub"));
554        assert!(!has_identity("Div"));
555
556        assert_eq!(get_identity::<f32>("Add"), Some(0.0));
557        assert_eq!(get_identity::<f32>("Mul"), Some(1.0));
558        assert_eq!(get_identity::<f32>("Sub"), None);
559    }
560
561    #[test]
562    fn test_simplification_opportunity() {
563        let opportunity = SimplificationOpportunity {
564            pattern: SimplificationPattern::AddZero,
565            description: "Remove addition of zero".to_string(),
566            benefit: 1.0,
567        };
568
569        assert!(matches!(
570            opportunity.pattern,
571            SimplificationPattern::AddZero
572        ));
573        assert_eq!(opportunity.benefit, 1.0);
574    }
575
576    #[test]
577    fn test_distributive_patterns() {
578        let factor_pattern = DistributivityPattern {
579            transformation_type: DistributiveType::Factor,
580            description: "Factor out common term".to_string(),
581        };
582
583        let expand_pattern = DistributivityPattern {
584            transformation_type: DistributiveType::Expand,
585            description: "Expand distributive expression".to_string(),
586        };
587
588        assert!(matches!(
589            factor_pattern.transformation_type,
590            DistributiveType::Factor
591        ));
592        assert!(matches!(
593            expand_pattern.transformation_type,
594            DistributiveType::Expand
595        ));
596    }
597}
scirs2_autograd/optimization/expression_simplification.rs

scirs2_autograd/optimization/
expression_simplification.rs
