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RC.1 Release Status

Version: 0.1.0-rc.1 Release Date: 2026-03-06 Status: Production Ready

This crate is part of the TensorLogic v0.1.0-rc.1 release with:

  • Zero compiler warnings
  • 300+ tests passing
  • Complete documentation
  • Production-ready quality

See main TODO.md for overall project status.

pytensorlogic TODO

Completed

Infrastructure

  • Basic PyO3 structure
  • abi3-py39 configuration
  • NumPy integration
  • Module organization (types, compiler, executor, numpy_conversion)
  • Maturin build with zero warnings

Core Types Binding - COMPLETE

  • PyTerm - Expose Term to Python with repr and str
  • PyTLExpr - Expose TLExpr with all logical operations
  • PyEinsumGraph - Expose compiled graphs with stats
  • Helper functions: var(), const(), pred(), and_(), or_(), not_(), exists(), forall(), imply(), constant()

Compilation API - COMPLETE

  • py_compile() - Main compilation function
  • PyCompilationConfig - Configuration wrapper with 6 presets
    • soft_differentiable() - Default for neural training
    • hard_boolean() - Discrete Boolean logic
    • fuzzy_godel() - Godel fuzzy logic
    • fuzzy_product() - Product fuzzy logic
    • fuzzy_lukasiewicz() - Lukasiewicz fuzzy logic
    • probabilistic() - Probabilistic interpretation
  • py_compile_with_config() - Compilation with custom config
  • py_compile_with_context() - Compilation with CompilerContext
  • Error handling with PyRuntimeError

Execution API - COMPLETE

  • py_execute() - Execute graphs with NumPy inputs
  • Dynamic tensor shape handling (ArrayD)
  • Input/output via Python dictionaries
  • Integration with Scirs2Exec backend
  • Proper error propagation to Python

NumPy Integration - COMPLETE

  • NumPy interop module (numpy_conversion.rs)
    • numpy_to_array2() - Convert 2D arrays
    • array2_to_numpy() - Export 2D arrays
    • numpy_to_arrayd() - Convert dynamic arrays
    • arrayd_to_numpy() - Export dynamic arrays
  • Proper lifetime management with PyReadonlyArray
  • Safe memory handling with readwrite() slices
  • Zero-copy optimization (requires unsafe improvements) - FUTURE
  • PyTorch interop - FUTURE

Python-Friendly API - COMPLETE

  • Pythonic naming (snake_case for all functions)
  • Comprehensive docstrings with Args/Returns/Example
  • repr implementations for all types
  • str implementations using pretty-printing
  • Proper error messages
  • Type hints (.pyi stub files) (1100+ lines)
  • Context managers
    • ExecutionContext - Managed graph execution
    • CompilationContext - Managed compilation

Arithmetic Operations - COMPLETE

  • add() - Addition operation (left + right)
  • sub() - Subtraction operation (left - right)
  • mul() - Multiplication operation (left * right)
  • div() - Division operation (left / right)
  • Full integration with compilation and execution
  • Comprehensive examples (examples/arithmetic_operations.py)
  • Full test coverage (tests/test_types.py, tests/test_execution.py)

Comparison Operations - COMPLETE

  • eq() - Equality comparison (left == right)
  • lt() - Less than comparison (left < right)
  • gt() - Greater than comparison (left > right)
  • lte() - Less than or equal (left <= right)
  • gte() - Greater than or equal (left >= right)
  • Full integration with compilation and execution
  • Comprehensive examples (examples/comparison_conditionals.py)
  • Full test coverage (tests/test_types.py, tests/test_execution.py)

Conditional Operations - COMPLETE

  • if_then_else() - Conditional expression (ternary operator)
  • Support for nested conditionals
  • Comprehensive examples (examples/comparison_conditionals.py)
  • Full test coverage (tests/test_types.py, tests/test_execution.py)

Development Infrastructure - COMPLETE

  • pytensorlogic.pyi - Complete type stubs for IDE support (1100+ lines)
  • pytest test suite - 300+ tests covering all operations
    • test_types.py - Type creation and operation tests
    • test_execution.py - End-to-end execution tests
    • test_backend.py - Backend selection tests
    • test_provenance.py - Provenance tracking tests (40+ tests)
    • test_training.py - Training API tests (40+ tests)
    • test_persistence.py - Model persistence tests (20+ tests)
    • test_dsl.py - Rule Builder DSL tests (100+ tests)
  • pytest.ini - Test configuration
  • requirements-dev.txt - Development dependencies
  • Python examples - 12 runnable demonstration scripts
    • arithmetic_operations.py - All arithmetic operations
    • comparison_conditionals.py - All comparisons and conditionals
    • basic_usage.py - Comprehensive usage guide
    • backend_selection.py - Backend selection
    • provenance_tracking.py - Provenance tracking (450+ lines)
    • training_workflow.py - Training API (450+ lines, 10 scenarios)
    • model_persistence.py - Model persistence (600+ lines, 10 scenarios)
    • rule_builder_dsl.py - Rule Builder DSL (550+ lines, 10 examples)
    • async_execution_demo.py - Async execution (300+ lines)
    • performance_benchmark.py - Performance benchmarks
    • memory_profiling.py - Memory profiling and streaming
    • advanced_symbol_table.py - SymbolTable and CompilerContext

Advanced Domain Management - COMPLETE

  • DomainInfo - Domain representation with metadata
    • name, cardinality properties
    • description, elements support
    • set_description(), set_elements() methods
  • PredicateInfo - Predicate representation
    • name, arity, arg_domains properties
    • description support
  • SymbolTable - Complete symbol table management
    • add_domain(), add_predicate()
    • bind_variable(), get_domain(), get_predicate()
    • get_variable_domain(), list_domains(), list_predicates()
    • infer_from_expr() - Automatic schema inference
    • get_variable_bindings() - Query all bindings
    • to_json() / from_json() - JSON serialization
  • CompilerContext - Low-level compilation control
    • add_domain(), bind_var()
    • assign_axis() - Einsum axis assignment
    • fresh_temp() - Temporary tensor names
    • get_domains(), get_variable_bindings(), get_axis_assignments()
    • get_variable_domain(), get_variable_axis()
  • Comprehensive example (examples/advanced_symbol_table.py)

Backend Selection API - COMPLETE

  • PyBackend - Backend enumeration (AUTO, SCIRS2_CPU, SCIRS2_SIMD, SCIRS2_GPU)
  • PyBackendCapabilities - Backend capability information
    • name, version, devices, dtypes, features, max_dims properties
    • supports_device(), supports_dtype(), supports_feature() methods
    • summary(), to_dict() methods
  • get_backend_capabilities() - Query backend capabilities
  • list_available_backends() - List all backends with availability
  • get_default_backend() - Get default backend
  • get_system_info() - System and backend information
  • Backend parameter in py_execute()

Provenance Tracking - COMPLETE

  • SourceLocation - Source code location tracking
    • file, line, column properties
    • String representation
  • SourceSpan - Source code span representation
    • start, end locations
    • Span formatting
  • Provenance - Provenance metadata for IR nodes
    • rule_id, source_file, span properties
    • Custom attributes (add_attribute, get_attribute, get_attributes)
    • Full Python bindings
  • ProvenanceTracker - RDF and tensor computation mappings
    • track_entity() - Entity to tensor mappings
    • track_shape() - SHACL shape to rule mappings
    • track_inferred_triple() - RDF* triple tracking
    • get_entity(), get_tensor() - Bidirectional lookups
    • get_entity_mappings(), get_shape_mappings()
    • get_high_confidence_inferences() - Confidence filtering
    • to_rdf_star(), to_rdfstar_turtle() - RDF* export
    • to_json(), from_json() - JSON serialization
    • RDF* support with enable_rdfstar flag
  • Graph Provenance Functions
    • get_provenance() - Extract provenance from graphs
    • get_metadata() - Extract metadata from graphs
    • provenance_tracker() - Helper function
  • Type stubs - pytensorlogic.pyi updated
  • Test suite - test_provenance.py (300+ lines, 40+ tests)
  • Example - provenance_tracking.py (450+ lines, 10 scenarios)

Training API - COMPLETE

  • Loss Functions - Multiple loss function implementations
    • mse_loss() - Mean Squared Error for regression
    • bce_loss() - Binary Cross-Entropy for binary classification
    • cross_entropy_loss() - Cross-Entropy for multi-class classification
    • LossFunction class with call method
  • Optimizers - Optimizer implementations for parameter updates
    • sgd() - Stochastic Gradient Descent with momentum
    • adam() - Adam optimizer with beta1, beta2, epsilon
    • rmsprop() - RMSprop optimizer with alpha, epsilon
    • Learning rate adjustment support
  • Callbacks - Training monitoring and control
    • early_stopping() - Early stopping with patience and min_delta
    • model_checkpoint() - Model checkpointing during training
    • logger() - Training progress logging with verbosity control
  • Trainer Class - High-level training interface
    • fit() method with epochs, validation_data, verbose
    • evaluate() method for model evaluation
    • predict() method for inference
    • TrainingHistory tracking with metrics
  • Convenience Functions
    • fit() function for quick training without explicit Trainer
  • Type stubs - pytensorlogic.pyi updated with training types
  • Test suite - test_training.py (370+ lines, 40+ tests)
  • Example - training_workflow.py (450+ lines, 10 scenarios)
  • Code quality - Zero clippy warnings, SCIRS2 compliant

Model Persistence - COMPLETE

  • ModelPackage - Complete model serialization container
    • graph, config, symbol_table, parameters, metadata properties
    • add_metadata(), get_metadata() - Metadata management
    • save_json(), load_json() - JSON format (human-readable)
    • save_binary(), load_binary() - Binary format (compact)
    • to_json(), from_json() - JSON string conversion
    • to_bytes(), from_bytes() - Binary conversion
    • getstate, setstate - Pickle support
  • Persistence Functions
    • save_model() - Save compiled graphs
    • load_model() - Load compiled graphs
    • save_full_model() - Save with config and metadata
    • load_full_model() - Load complete models
    • model_package() - Helper function
  • Format Support
    • JSON format (human-readable, cross-platform)
    • Binary format (compact, efficient)
    • Auto format detection from file extension
    • Pickle support for Python workflows
  • Type stubs - pytensorlogic.pyi updated (200+ lines)
  • Test suite - test_persistence.py (400+ lines, 20+ tests)
  • Example - model_persistence.py (600+ lines, 10 scenarios)
  • ONNX export - FUTURE

Rule Builder DSL - COMPLETE

  • Var class with domain bindings
  • PredicateBuilder for callable predicates with arity/domain validation
  • Operator overloading (&, |, ~, >>)
  • RuleBuilder context manager
  • Symbol table integration
  • Multiple compilation strategies
  • Comprehensive examples and tests
  • Full type stubs

Jupyter Integration - COMPLETE

  • Rich HTML Display - _repr_html_() methods for all major types
    • EinsumGraph - Node statistics and type breakdown
    • SymbolTable - Domains, predicates, variables in tables
    • CompilationConfig - Configuration semantics
    • ModelPackage - Component checklist and metadata
    • TrainingHistory - Epoch-by-epoch loss tables
    • Provenance - Rule origin and attributes
  • HTML Generation Module (jupyter.rs)
    • HTML table generator
    • Card/badge components
    • Key-value list formatter
    • Specialized visualizers for each type
  • Visualization widgets - FUTURE
  • Interactive debugging - FUTURE
  • Progress bars - FUTURE

Performance Monitoring - COMPLETE

  • GIL Release - Release GIL during CPU-bound tensor operations
  • Parallel execution - BatchExecutor and execute_parallel
  • Async support - AsyncResult, execute_async
  • Memory profiling - Complete performance monitoring
    • MemorySnapshot class
    • Profiler class with timing statistics
    • Timer context manager
    • memory_snapshot(), get_memory_info(), reset_memory_tracking()

Streaming Execution - COMPLETE

  • StreamingExecutor - Process large datasets in chunks
    • Configurable chunk_size and overlap
    • execute_streaming() method
  • DataGenerator - Memory-efficient data loading
  • ResultAccumulator - Accumulate streaming results
    • add(), combine(), stats()
  • process_stream() - Process iterator through graph

Async Cancellation - COMPLETE

  • CancellationToken - Cancel async operations
    • cancel(), is_cancelled(), reset()
  • AsyncResult cancellation support
    • cancel(), is_cancelled(), get_cancellation_token()

Utility Functions & Context Managers - COMPLETE

  • Custom Exceptions - Better error handling
    • CompilationError - Compilation failures
    • ExecutionError - Execution failures
    • ValidationError - Input validation failures
    • BackendError - Backend operations failures
    • ConfigurationError - Invalid configuration
  • ExecutionContext - Context manager for execution
    • execute(), get_results(), execution_count(), clear_results()
  • CompilationContext - Context manager for compilation
    • compile(), get_graphs(), get_graph(), graph_count()
  • Utility Functions
    • quick_execute() - One-liner compile + execute
    • validate_inputs() - Input validation
    • batch_compile() - Compile multiple expressions
    • batch_predict() - Predict on multiple inputs

Documentation - COMPLETE

  • Comprehensive README.md
  • QUICKSTART.md (Quick start guide)
  • examples/README.md (Example navigation)
  • Complete API reference (in README.md)
  • COMPLIANCE_REPORT.md - Quality validation report
  • PACKAGING.md - Complete packaging guide
  • pytensorlogic.pyi - Complete with all types (1100+ lines)
  • Sphinx documentation - FUTURE
  • Tutorial Jupyter notebooks - FUTURE

Future Enhancements

Integrations

  • PyTorch tensor integration
  • GPU backend support
  • ONNX export

Packaging

  • PyPI release
  • maturin build and wheel distribution
  • Platform-specific builds (Linux x86_64, macOS arm64/x86_64, Windows)

Testing

  • Coverage reporting in CI (pytest-cov configured, needs CI pipeline)
  • Benchmark suite in CI (pytest-benchmark configured)

Advanced Features

  • Visualization widgets for Jupyter
  • Interactive debugging
  • Progress bars for long operations
  • mypy strict mode validation

Total Items: 120+ tasks Completion: 100% of core + medium + performance + utility features Release: v0.1.0-rc.1 (2026-03-06)

Completion Summary

  • Phase 1 Complete: Core types binding (PyTerm, PyTLExpr, PyEinsumGraph)
  • Phase 2 Complete: Compilation API (compile, config presets)
  • Phase 3 Complete: Execution API (execute with NumPy)
  • Phase 4 Complete: NumPy interop (bidirectional conversion)
  • Phase 5 Complete: Python-friendly API (docstrings, repr, error handling)
  • Phase 6 Complete: Arithmetic operations (add, sub, mul, div)
  • Phase 7 Complete: Comparison operations (eq, lt, gt, lte, gte)
  • Phase 8 Complete: Conditional operations (if_then_else)
  • Phase 9 Complete: Type stubs (.pyi) and testing infrastructure
  • Phase 10 Complete: SymbolTable and domain management
  • Phase 11 Complete: CompilerContext for advanced compilation
  • Phase 12 Complete: Backend selection API (CPU/SIMD/GPU)
  • Phase 13 Complete: Provenance tracking (full RDF* support)
  • Phase 14 Complete: Training API (loss functions, optimizers, callbacks)
  • Phase 15 Complete: Model Persistence (save/load, multiple formats, pickle)
  • Phase 16 Complete: Jupyter Integration (rich HTML display for all types)
  • Phase 17 Complete: Rule Builder DSL (Python-native syntax, operator overloading)
  • Phase 18 Complete: Performance Monitoring (GIL release, profiler, memory tracking)
  • Phase 19 Complete: Streaming Execution (StreamingExecutor, ResultAccumulator)
  • Phase 20 Complete: Async Cancellation (CancellationToken, cancel support)
  • Phase 21 Complete: Utility Functions (context managers, custom exceptions, helpers)

Build Status

  • Maturin build succeeds with zero warnings
  • Release build optimized and ready
  • All dependencies resolved
  • Zero clippy warnings

Test & Example Status

  • 300+ pytest tests across 7 test files
  • 12 comprehensive examples
  • Type stub file (pytensorlogic.pyi) with full API coverage (1100+ lines)
  • pytest.ini configuration
  • requirements-dev.txt with all dependencies

API Surface

  • Core: var(), const(), pred(), and_(), or_(), not_(), exists(), forall(), imply(), constant(), if_then_else()
  • Arithmetic: add(), sub(), mul(), div()
  • Comparisons: eq(), lt(), gt(), lte(), gte()
  • Compilation: compile(), compile_with_config(), compile_with_context()
  • Execution: execute(), execute_async(), execute_parallel()
  • Async: AsyncResult, BatchExecutor, CancellationToken, cancellation_token()
  • Adapters: DomainInfo, PredicateInfo, SymbolTable, CompilerContext
  • Backend: Backend, BackendCapabilities, get_backend_capabilities(), list_available_backends(), get_default_backend(), get_system_info()
  • Provenance: SourceLocation, SourceSpan, Provenance, ProvenanceTracker, get_provenance(), get_metadata(), provenance_tracker()
  • Training: LossFunction, Optimizer, Callback, TrainingHistory, Trainer, mse_loss(), bce_loss(), cross_entropy_loss(), sgd(), adam(), rmsprop(), early_stopping(), model_checkpoint(), logger(), fit()
  • Persistence: ModelPackage, model_package(), save_model(), load_model(), save_full_model(), load_full_model()
  • DSL: Var, PredicateBuilder, RuleBuilder, var_dsl(), pred_dsl(), rule_builder()
  • Performance: MemorySnapshot, Profiler, Timer, memory_snapshot(), profiler(), timer(), get_memory_info()
  • Streaming: StreamingExecutor, DataGenerator, ResultAccumulator, streaming_executor(), result_accumulator(), process_stream()
  • Exceptions: CompilationError, ExecutionError, ValidationError, BackendError, ConfigurationError
  • Utils: ExecutionContext, CompilationContext, quick_execute(), validate_inputs(), batch_compile(), batch_predict(), execution_context(), compilation_context()

Total API: 80+ functions, 35+ classes, 5 custom exceptions, 6 compilation strategies, 3 serialization formats, 6 rich displays, 4 operator overloads