tensorlogic-compiler

Engine-agnostic compilation of TensorLogic expressions to tensor computation graphs

Overview

The compiler translates logical rules with quantifiers into optimized tensor operations using Einstein summation notation. It operates as a planning layer only—no execution happens here.

Input: TLExpr (logical expressions with predicates, quantifiers, implications) Output: EinsumGraph (directed graph of tensor operations)

Key Features

Core Compilation (Production Ready ✅)

• ✅ Logic-to-Tensor Mapping: Compiles predicates, AND, OR, NOT, EXISTS, FORALL, IMPLY
• ✅ Arithmetic Operations: Add, Subtract, Multiply, Divide with element-wise tensor ops
• ✅ Comparison Operations: Equal, LessThan, GreaterThan with boolean result tensors
• ✅ Conditional Expressions: If-then-else with soft probabilistic semantics
• ✅ Shared Variable Support: Handles variable sharing in AND operations via einsum contraction
• ✅ Automatic Axis Marginalization: Implicitly quantifies extra variables in implications

Modal & Temporal Logic (Production Ready ✅)

• ✅ Modal Operators: Box (□) for necessity, Diamond (◇) for possibility
• ✅ Temporal Operators: Eventually (F), Always (G) for temporal reasoning
• ✅ Configurable Strategies: 3 modal strategies, 3 temporal strategies
• ✅ Automatic Axis Management: World and time dimensions managed transparently
• ✅ Combined Reasoning: Support for nested modal/temporal expressions

Type Safety & Validation (Production Ready ✅)

• ✅ Scope Analysis: Detects unbound variables with helpful quantifier suggestions
• ✅ Type Checking: Validates predicate arity and type consistency across expressions
• ✅ Domain Validation: Ensures variables are bound to valid domains
• ✅ Enhanced Diagnostics: Rich error messages with source locations and fix suggestions

Optimization Pipeline (Production Ready ✅)

The compiler features a 7-pass optimization pipeline that can reduce expression complexity by up to 80%:

1. ✅ Negation Optimization: Double negation elimination, De Morgan's laws, quantifier negation pushing
2. ✅ Constant Folding: Compile-time evaluation of constant expressions (2.0 * 3.0 → 6.0)
3. ✅ Algebraic Simplification: Identity elimination (x+0=x, x1=x), annihilation (x0=0), idempotency
4. ✅ Strength Reduction: Replace expensive ops with cheaper equivalents (x^2→x*x, exp(log(x))→x)
5. ✅ Distributivity: Factor common subexpressions (ab + ac → a*(b+c))
6. ✅ Quantifier Optimization: Loop-invariant code motion (∃x.(a+p(x)) → a + ∃x.p(x))
7. ✅ Dead Code Elimination: Remove unreachable branches and short-circuit constant conditions

Additional Graph-Level Optimizations:

• ✅ Common Subexpression Elimination (CSE): Graph-level deduplication of identical operations
• ✅ Einsum Optimization: Operation merging, identity elimination, contraction order optimization

Pipeline Features:

• Configurable: Enable/disable individual passes, set iteration limits
• Fixed-Point Detection: Automatically stops when no more optimizations are possible
• Performance Tracking: Detailed statistics on applied optimizations
• Hardware-Adaptive: GPU-optimized, CPU-optimized, and SIMD-optimized cost models

Parameterized Compilation (Production Ready ✅)

• ✅ 26+ Configurable Strategies: Customize logic-to-tensor mappings for different use cases
• ✅ 6 Preset Configurations: Soft differentiable, hard Boolean, fuzzy logics, probabilistic
• ✅ Fine-Grained Control: Per-operation strategy selection (AND, OR, NOT, quantifiers, implication)

Advanced Analysis & Profiling (New in Alpha.2 ✨)

• ✨ Compilation Profiling: Track compilation time, memory usage, cache statistics, and pass-level performance
• ✨ Dataflow Analysis: Live variable analysis, reaching definitions, use-def chains for optimization
• ✨ Graph Dataflow: Tensor liveness tracking, dependency analysis for graph optimization
• ✨ Contraction Optimization: Dynamic programming for optimal einsum contraction order (reduces FLOPs)
• ✨ Loop Fusion: Fuse multiple loops over the same axes for better cache locality
• ✨ Reachability Analysis: Compute dominance, strongly connected components, topological ordering
• ✨ Integrated Post-Compilation: Unified pipeline combining validation and graph-level optimizations

Quick Start

use tensorlogic_compiler::compile_to_einsum;
use tensorlogic_ir::{TLExpr, Term};

// Define a logic rule: ∃y. knows(x, y)
// "Find all persons x who know someone"
let rule = TLExpr::exists(
    "y",
    "Person",
    TLExpr::pred("knows", vec![Term::var("x"), Term::var("y")]),
);

// Compile to tensor operations
let graph = compile_to_einsum(&rule)?;

// Graph contains:
// - Tensors: ["knows[ab]", "temp_0"]
// - Operations: [Reduce{op: "sum", axes: [1]}]
// - Outputs: [1]

Logic-to-Tensor Mapping

Default Strategy (Soft Differentiable)

Logic Operation	Tensor Equivalent	Notes
P(x, y)	Tensor with axes ab	Predicate as multi-dimensional array
P ∧ Q	Hadamard product or einsum	Element-wise if same axes, contraction if shared vars
P ∨ Q	max(P, Q)	Or soft variant (configurable)
¬P	1 - P	Or temperature-controlled
∃x. P(x)	sum(P, axis=x)	Or max for hard quantification
∀x. P(x)	NOT(∃x. NOT(P(x)))	Dual of EXISTS
P → Q	ReLU(Q - P)	Soft implication

Modal & Temporal Logic Operations

Logic Operation	Tensor Equivalent	Notes
□P (Box)	min(P, axis=world) or prod(P, axis=world)	Necessity over possible worlds
◇P (Diamond)	max(P, axis=world) or sum(P, axis=world)	Possibility over possible worlds
F(P) (Eventually)	max(P, axis=time) or sum(P, axis=time)	True in some future state
G(P) (Always)	min(P, axis=time) or prod(P, axis=time)	True in all future states

Modal Logic Example:

use tensorlogic_ir::{TLExpr, Term};

// □(∃y. knows(x, y)) - "In all possible worlds, x knows someone"
let expr = TLExpr::Box(Box::new(
    TLExpr::exists("y", "Person",
        TLExpr::pred("knows", vec![Term::var("x"), Term::var("y")])
    )
));

Temporal Logic Example:

// F(completed(t)) - "Task t will eventually be completed"
let expr = TLExpr::Eventually(Box::new(
    TLExpr::pred("completed", vec![Term::var("t")])
));

// G(safe(s)) - "System s is always safe"
let expr = TLExpr::Always(Box::new(
    TLExpr::pred("safe", vec![Term::var("s")])
));

Combined Modal & Temporal:

// □(F(goal(a))) - "In all possible worlds, agent a eventually achieves goal"
let expr = TLExpr::Box(Box::new(
    TLExpr::Eventually(Box::new(
        TLExpr::pred("goal", vec![Term::var("a")])
    ))
));

See examples/10_modal_temporal_logic.rs for comprehensive demonstrations.

Parameterized Compilation (Config System Defined)

The compiler defines 6 preset configurations and 26+ configurable strategies:

use tensorlogic_compiler::{CompilationConfig, CompilationConfigBuilder};

// Define preset configurations
let config = CompilationConfig::soft_differentiable();  // Default (neural training)
let config = CompilationConfig::hard_boolean();         // Discrete reasoning
let config = CompilationConfig::fuzzy_godel();          // Gödel fuzzy logic
let config = CompilationConfig::probabilistic();        // Probabilistic semantics

// Or build a custom configuration
let config = CompilationConfigBuilder::new()
    .and_strategy(AndStrategy::Product)           // Product t-norm
    .or_strategy(OrStrategy::ProbabilisticSum)    // Probabilistic s-norm
    .not_strategy(NotStrategy::Complement)        // Standard complement
    .exists_strategy(ExistsStrategy::Max)         // Max aggregation
    .build();

// Note: Full integration into compilation pipeline is in progress
// Currently uses default soft_differentiable strategy

Available Strategies:

Operation	Strategies	Use Cases
AND	Product, Min, ProbabilisticSum, Gödel, ProductTNorm, Łukasiewicz	T-norms for conjunctions
OR	Max, ProbabilisticSum, Gödel, ProbabilisticSNorm, Łukasiewicz	S-norms for disjunctions
NOT	Complement (1-x), Sigmoid	Negation with or without temperature
EXISTS	Sum, Max, LogSumExp, Mean	Different quantifier semantics
FORALL	DualOfExists, Product, Min, MeanThreshold	Universal quantification strategies
IMPLY	ReLU, Material, Gödel, Łukasiewicz, Reichenbach	Various implication operators
MODAL	AllWorldsMin, AllWorldsProduct, Threshold	Necessity/possibility operators
TEMPORAL	Max, Sum, LogSumExp	Eventually/always operators

Advanced: Transitivity Rules

The compiler handles complex rules like transitivity with shared variables:

// knows(x,y) ∧ knows(y,z) → knows(x,z)
let knows_xy = TLExpr::pred("knows", vec![Term::var("x"), Term::var("y")]);
let knows_yz = TLExpr::pred("knows", vec![Term::var("y"), Term::var("z")]);
let knows_xz = TLExpr::pred("knows", vec![Term::var("x"), Term::var("z")]);

let premise = TLExpr::and(knows_xy, knows_yz);
let rule = TLExpr::imply(premise, knows_xz);

let graph = compile_to_einsum(&rule)?;

// Generates:
// 1. knows[ab] ∧ knows[bc] → einsum("ab,bc->abc") [contraction over shared 'b']
// 2. Marginalize over 'b' to align with conclusion axes 'ac'
// 3. Apply ReLU(knows[ac] - marginalized_premise[ac])

Optimization Pipeline Usage

Unified Pipeline (Recommended)

The recommended approach is to use the unified optimization pipeline that applies all 7 passes iteratively:

use tensorlogic_compiler::optimize::{OptimizationPipeline, PipelineConfig};
use tensorlogic_ir::{TLExpr, Term};

// Create a complex expression
let x = TLExpr::pred("x", vec![Term::var("i")]);
let expr = TLExpr::negate(TLExpr::negate(TLExpr::add(
    TLExpr::pow(x, TLExpr::Constant(2.0)),
    TLExpr::Constant(0.0),
)));

// Apply default optimization pipeline
let pipeline = OptimizationPipeline::new();
let (optimized, stats) = pipeline.optimize(&expr);

// Check results
println!("Total optimizations: {}", stats.total_optimizations());
println!("  Negation: {}", stats.negation.double_negations_eliminated);
println!("  Constant folding: {}", stats.constant_folding.binary_ops_folded);
println!("  Algebraic: {}", stats.algebraic.identities_eliminated);
println!("  Strength reduction: {}", stats.strength_reduction.power_reductions);
println!("  Iterations: {}", stats.total_iterations);
println!("  Reached fixed point: {}", stats.reached_fixed_point);

Configurable Pipeline

Customize which passes run and how many iterations:

use tensorlogic_compiler::optimize::PipelineConfig;

// Aggressive optimization (more iterations)
let config = PipelineConfig::aggressive();
let pipeline = OptimizationPipeline::with_config(config);

// Custom configuration
let config = PipelineConfig::default()
    .with_negation_opt(true)
    .with_constant_folding(true)
    .with_algebraic_simplification(true)
    .with_strength_reduction(true)
    .with_distributivity(true)
    .with_quantifier_opt(true)
    .with_dead_code_elimination(true)
    .with_max_iterations(15);

let pipeline = OptimizationPipeline::with_config(config);
let (optimized, stats) = pipeline.optimize(&expr);

Individual Pass Usage

For fine-grained control, use individual optimization passes:

use tensorlogic_compiler::optimize::{
    optimize_negations, fold_constants, simplify_algebraic,
    reduce_strength, optimize_distributivity, optimize_quantifiers,
    eliminate_dead_code,
};

// Apply specific optimizations
let (opt1, stats1) = optimize_negations(&expr);
let (opt2, stats2) = fold_constants(&opt1);
let (opt3, stats3) = simplify_algebraic(&opt2);
let (opt4, stats4) = reduce_strength(&opt3);

Complexity Analysis

Analyze expression complexity to guide optimization decisions:

use tensorlogic_compiler::optimize::{analyze_complexity, CostWeights};

let complexity = analyze_complexity(&expr);
println!("Max depth: {}", complexity.max_depth);
println!("Total operations: {}", complexity.total_operations());
println!("Total cost: {}", complexity.total_cost());

// Use GPU-optimized cost weights
let gpu_weights = CostWeights::gpu_optimized();
let gpu_cost = complexity.total_cost_with_weights(&gpu_weights);
println!("GPU-optimized cost: {}", gpu_cost);

// Check optimization potential
println!("CSE potential: {}", complexity.cse_potential());
println!("Complexity level: {}", complexity.complexity_level());

Graph-Level Optimizations

After compilation, optimize the resulting graph:

use tensorlogic_ir::graph::optimization::{optimize_graph, OptimizationLevel};

// Compile expression to graph
let graph = compile_to_einsum(&expr)?;

// Apply graph optimizations (DCE, CSE, identity elimination)
let (optimized_graph, stats) = optimize_graph(&graph, OptimizationLevel::Aggressive);
println!("Removed {} nodes", stats.nodes_removed);

Advanced Analysis Features (Alpha.2 ✨)

The compiler now includes sophisticated analysis and optimization capabilities:

Compilation Profiling

Track compilation performance, memory usage, and cache statistics:

use tensorlogic_compiler::profiling::CompilationProfiler;

let mut profiler = CompilationProfiler::new();
profiler.start();

// Profile compilation phases
profiler.start_phase("compilation");
let graph = compile_to_einsum(&expr)?;
profiler.end_phase("compilation");

// Record pass executions
profiler.record_pass("negation_opt", duration, optimizations_applied);

// Generate reports
let report = profiler.generate_report();
println!("{}", report);

// Get JSON output for tooling
let json = profiler.generate_json_report();

Profiling capabilities:

• Phase-level time tracking with nesting support
• Memory usage snapshots and peak memory detection
• Pass-level statistics (execution count, time, optimizations)
• Cache statistics (hits, misses, evictions, hit rate)
• Performance recommendations based on profiling data

Dataflow Analysis

Analyze how data flows through expressions for optimization opportunities:

use tensorlogic_compiler::passes::{analyze_dataflow, analyze_graph_dataflow};

// Analyze expression dataflow
let analysis = analyze_dataflow(&expr);

// Check live variables at each point
println!("Live variables: {:?}", analysis.live_variables);

// Track reaching definitions (which assignments reach each use)
println!("Reaching definitions: {:?}", analysis.reaching_defs);

// Identify available expressions for CSE
println!("Available expressions: {:?}", analysis.available_exprs);

// Use-def chains for dependency tracking
println!("Use-def chains: {:?}", analysis.use_def_chains);

// Analyze compiled graph dataflow
let graph_analysis = analyze_graph_dataflow(&graph);
println!("Tensor dependencies: {:?}", graph_analysis.dependencies);
println!("Live tensors per node: {:?}", graph_analysis.live_tensors);

Dataflow analysis provides:

• Live variable analysis (which variables are used downstream)
• Reaching definitions (where values are defined)
• Available expressions (for common subexpression elimination)
• Use-def chains (variable usage tracking)
• Tensor liveness in compiled graphs
• Dependency analysis for graph optimization

Contraction Optimization

Optimize einsum contraction order using dynamic programming:

use tensorlogic_compiler::passes::{optimize_contractions, optimize_contractions_with_config};
use tensorlogic_compiler::passes::ContractionOptConfig;

// Optimize with default greedy algorithm
let (optimized_graph, stats) = optimize_contractions(&graph);

println!("Contractions reordered: {}", stats.contractions_reordered);
println!("FLOPs reduction: {:.1}%", stats.flops_reduction_percent);
println!("Memory reduction: {:.1}%", stats.memory_reduction_percent);

// Custom configuration
let config = ContractionOptConfig {
    max_intermediate_size: 1_000_000,  // Limit intermediate tensor sizes
    prefer_memory_over_flops: false,    // Optimize for FLOPs first
};

let (optimized, stats) = optimize_contractions_with_config(&graph, &config);

Contraction optimization features:

• Dynamic programming to find optimal contraction order
• Minimizes floating-point operations (FLOPs)
• Controls intermediate tensor memory usage
• Greedy algorithm for large graphs
• Detailed statistics on FLOP and memory savings

Loop Fusion

Fuse multiple loops over the same axes for better cache locality:

use tensorlogic_compiler::passes::{fuse_loops, fuse_loops_with_config};
use tensorlogic_compiler::passes::LoopFusionConfig;

// Fuse loops with default settings
let (fused_graph, stats) = fuse_loops(&graph);

println!("Loops fused: {}", stats.loops_fused);
println!("Reductions merged: {}", stats.reductions_merged);
println!("Intermediates eliminated: {}", stats.intermediates_eliminated);

// Custom configuration
let config = LoopFusionConfig {
    max_fusion_depth: 3,           // Limit fusion depth
    require_same_reduction: true,  // Only fuse identical reductions
};

let (fused, stats) = fuse_loops_with_config(&graph, &config);

Loop fusion benefits:

• Reduces memory bandwidth requirements
• Improves cache locality by reusing loaded data
• Eliminates intermediate tensors
• Merges reduction operations
• Reduces kernel launch overhead on GPUs

Reachability Analysis

Compute graph structure properties for optimization and validation:

use tensorlogic_compiler::passes::{analyze_reachability, analyze_dominance};

// Compute reachability information
let reachability = analyze_reachability(&graph);

// Check if node B is reachable from node A
if reachability.reachable.contains(&(node_a, node_b)) {
    println!("Node {} can reach node {}", node_a, node_b);
}

// Get strongly connected components
println!("SCCs: {:?}", reachability.strongly_connected_components);

// Topological ordering (for DAGs)
if let Some(topo) = &reachability.topological_order {
    println!("Topological order: {:?}", topo);
}

// Compute dominance relationships
let dominance = analyze_dominance(&graph);
println!("Immediate dominators: {:?}", dominance.immediate_dominators);
println!("Dominance frontiers: {:?}", dominance.dominance_frontiers);

Reachability analysis provides:

• Transitive reachability between nodes
• Strongly connected component detection
• Topological ordering for DAGs
• Cycle detection
• Dominance and post-dominance analysis
• Dominator trees and frontiers

Integrated Post-Compilation Pipeline

Run all analysis and optimization passes in a single pipeline:

use tensorlogic_compiler::passes::{post_compilation_passes, PostCompilationOptions};

let options = PostCompilationOptions {
    validate_graph_structure: true,  // Check for cycles, orphans
    validate_axes: true,              // Validate axis compatibility
    validate_shapes: true,            // Check tensor shape consistency
    apply_optimizations: true,        // Run optimization passes
    enable_contraction_opt: true,     // Optimize contraction order
    enable_loop_fusion: true,         // Fuse compatible loops
    strict_mode: false,               // Fail on warnings if true
};

let mut graph = compile_to_einsum(&expr)?;
let result = post_compilation_passes(&mut graph, &ctx, options)?;

if result.is_valid {
    println!("✓ Graph validated successfully");
    println!("  Checks performed: {}", result.validation_report.checks_performed);
    println!("  Optimizations: {}", result.optimizations_applied);

    for msg in &result.messages {
        println!("  {}", msg);
    }
}

Post-compilation pipeline:

• Graph structure validation (cycles, orphaned nodes)
• Axis compatibility checking
• Shape inference and validation
• Automated optimization application
• Configurable strictness levels
• Detailed validation and optimization reports

See examples/21_profiling_and_optimization.rs for comprehensive demonstrations of all these features.

Compiler Architecture

TLExpr
  ↓
[Pre-Compilation Passes]
  - Scope analysis (detect unbound variables)
  - Type checking (validate arity, types)
  - Negation optimization
  - Common subexpression elimination
  ↓
[Compiler Context]
  - Assign axes to variables
  - Track domains
  - Manage temporary tensors
  - Apply compilation config
  ↓
[compile_expr recursion]
  - compile_predicate → tensor with axes
  - compile_and → einsum contraction (configurable)
  - compile_or → element-wise max (configurable)
  - compile_not → 1 - x (configurable)
  - compile_exists → reduction (configurable)
  - compile_forall → dual or product (configurable)
  - compile_imply → marginalize + operator (configurable)
  - compile_arithmetic → element-wise ops
  - compile_comparison → boolean tensors
  ↓
[Post-Compilation Passes]
  - Dead code elimination
  - Einsum operation merging
  - Identity elimination
  - Contraction order optimization
  ↓
EinsumGraph
  - Tensors: Vec<String>
  - Nodes: Vec<EinsumNode>
  - Outputs: Vec<usize>

Scope Analysis & Type Checking

The compiler provides production-ready validation passes:

Scope Analysis

use tensorlogic_compiler::passes::scope_analysis::analyze_scopes;

let expr = TLExpr::exists("x", "Person",
    TLExpr::and(
        TLExpr::pred("knows", vec![Term::var("x"), Term::var("y")]),
        TLExpr::pred("likes", vec![Term::var("x"), Term::var("z")]),
    )
);

let analysis = analyze_scopes(&expr);

if !analysis.unbound_vars.is_empty() {
    println!("Unbound variables: {:?}", analysis.unbound_vars);
    println!("Suggestions: {}", analysis.suggest_quantifiers());
    // Output: "Consider adding: ∃y:Domain. ∃z:Domain. ..."
}

Type Checking

use tensorlogic_compiler::passes::type_checking::TypeChecker;
use tensorlogic_ir::PredicateSignature;

let mut checker = TypeChecker::new();

// Register predicate signatures
checker.register_predicate(PredicateSignature {
    name: "knows".to_string(),
    arity: 2,
    arg_types: vec![Some("Person".to_string()), Some("Person".to_string())],
});

// Type check expression
let result = checker.check_types(&expr);
if let Some(error) = result.type_errors.first() {
    println!("Type error: {}", error);
}

Enhanced Diagnostics

use tensorlogic_compiler::passes::diagnostics::{diagnose_expression, DiagnosticLevel};

let diagnostics = diagnose_expression(&expr);

for diag in diagnostics {
    match diag.level {
        DiagnosticLevel::Error => eprintln!("ERROR: {}", diag.message),
        DiagnosticLevel::Warning => eprintln!("WARNING: {}", diag.message),
        DiagnosticLevel::Hint => println!("HINT: {}", diag.message),
        _ => {}
    }

    if let Some(help) = diag.help {
        println!("  Help: {}", help);
    }
}

Compiler Context

The CompilerContext manages compilation state:

use tensorlogic_compiler::CompilerContext;

let mut ctx = CompilerContext::new();

// Register domains
ctx.add_domain("Person", 100);  // 100 possible persons
ctx.add_domain("City", 50);     // 50 cities

// Bind variables to domains
ctx.bind_var("x", "Person")?;
ctx.bind_var("y", "City")?;

// Axes are automatically assigned: x→'a', y→'b', ...

Operation Types

The compiler generates 4 types of operations:

1. Einsum (Tensor Contraction)

// Spec: "ab,bc->ac" (matrix multiplication)
EinsumNode::einsum("ab,bc->ac", vec![tensor0, tensor1])

2. Element-Wise Unary

// Operations: not, relu, sigmoid, etc.
EinsumNode::elem_unary("relu", tensor_idx)

3. Element-Wise Binary

// Operations: add, subtract, multiply, etc.
EinsumNode::elem_binary("subtract", left_idx, right_idx)

4. Reduction

// Reduce over axis 1 (sum/max/min)
EinsumNode::reduce("sum", vec![1], tensor_idx)

Error Handling

The compiler performs extensive validation:

// Arity validation
let p1 = TLExpr::pred("P", vec![Term::var("x"), Term::var("y")]);
let p2 = TLExpr::pred("P", vec![Term::var("a")]);  // ❌ Different arity!
validate_arity(&TLExpr::and(p1, p2))?;  // Error: Predicate 'P' has inconsistent arity

// Domain validation
ctx.bind_var("x", "NonExistent")?;  // Error: Domain 'NonExistent' not found

// Axis compatibility (now automatically handled via contraction/marginalization)

Integration with Other Crates

tensorlogic-adapters

Use SymbolTable to provide domain and predicate metadata:

use tensorlogic_adapters::SymbolTable;

let table = SymbolTable::new();
// Add domains and predicates...
// Future: Pass to compiler for enhanced type checking

tensorlogic-scirs-backend

Execute the compiled graph:

use tensorlogic_scirs_backend::Scirs2Exec;
use tensorlogic_infer::TlExecutor;

let executor = Scirs2Exec::new();
let outputs = executor.execute(&graph, &inputs)?;

Performance Considerations

• ✅ Operation Fusion: Einsum operation merging (completed)
• ✅ Common Subexpression Elimination: Expression-level and graph-level CSE (completed)
• ✅ Negation Optimization: De Morgan's laws and double negation elimination (completed)
• ✅ Dead Code Elimination: Removes unused operations from the graph (completed)
• ✅ Axis Assignment: Uses lexicographic order ('a', 'b', 'c', ...) for determinism
• ✅ Temporary Tensors: Named as temp_0, temp_1, ... for debugging

Testing & Quality

The compiler has comprehensive test coverage:

# Run all tests with nextest (recommended)
cargo nextest run -p tensorlogic-compiler

# Run with standard cargo test
cargo test -p tensorlogic-compiler

# Run with coverage
cargo llvm-cov --package tensorlogic-compiler

Current Test Status:

• 437 tests (100% passing)
• Zero warnings (strict clippy compliance)
• 21,466 lines of code across 72 files (all files < 2000 lines)
• 100% Alpha.2 feature completion

Current Status & Roadmap

Production Ready ✅

• Core logic compilation (AND, OR, NOT, quantifiers, implications)
• Arithmetic and comparison operations
• Conditional expressions (if-then-else)
• Type checking and scope analysis
• Enhanced diagnostics with helpful error messages
• Parameterized compilation (26+ strategies, 6 presets)
• Optimization passes (negation, CSE, einsum, DCE)
• SymbolTable integration for metadata

In Progress 🔧

• Automatic strategy selection based on expression context
• Enhanced metadata propagation
• Improved error recovery (continue after non-fatal errors)

Planned Features 📋

See TODO.md for the complete roadmap:

• ⏳ Property-based testing with proptest
• ⏳ Fuzzing for edge case discovery
• ⏳ Visualization (export to DOT format)
• ⏳ CLI tool for standalone compilation
• ⏳ Advanced features (higher-order quantification, modal logic)

Examples

See the test suite for more examples:

cargo test -p tensorlogic-compiler

Key test cases:

• test_transitivity_rule_shared_variables: Transitivity with contraction
• test_and_with_different_axes: Partial variable overlap
• test_and_with_disjoint_variables: Outer product (no shared vars)
• test_implication: Soft implication with ReLU
• test_exists_quantifier: Reduction over quantified variables

Contributing

When adding new features:

1. Update compile_expr to handle new TLExpr variants
2. Add tests in the tests module
3. Update this README and TODO.md
4. Ensure all tests pass: cargo nextest run -p tensorlogic-compiler

License

Apache-2.0

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Status: 🎉 Production Ready (v0.1.0-alpha.2) **Last Updated: 2025-12-16 Tests: 158/158 passing (100%) Part of: TensorLogic Ecosystem