# ipfrs-tensorlogic TODO
## ✅ Completed (Phases 1-2)
### TensorLogic IR Codec
- ✅ Define IPLD schema for `tensorlogic::ir::Term`
- ✅ Implement Term serialization to DAG-CBOR
- ✅ Add deserialization with validation
- ✅ Create bidirectional conversion tests
### Type System Mapping
- ✅ Map TensorLogic types to IPLD types
- ✅ Handle recursive term structures
- ✅ Support variable bindings
- ✅ Add metadata for type annotations
### Block Storage
- ✅ Store terms as content-addressed blocks
- ✅ Implement CID generation for terms
- ✅ Add term deduplication
- ✅ Create term index for fast lookup
---
## ✅ Completed (Phase 4)
### Apache Arrow Integration
- ✅ **Implement Arrow memory layout** for tensors
- ArrowTensor with metadata (shape, dtype, strides)
- Zero-copy accessor functions
- ArrowTensorStore for managing tensor collections
- IPC serialization/deserialization
- ✅ **Create zero-copy accessor functions**
- as_slice_f32/f64/i32/i64 for typed access
- as_bytes for raw byte access
- ZeroCopyAccessor trait
- ✅ **Add schema definition** for tensor metadata
- TensorMetadata with shape, dtype, strides
- Custom metadata fields support
- Schema generation for Arrow IPC
- ✅ **Support columnar data formats**
- Arrow RecordBatch support
- IPC file format reading/writing
- Arrow schema with field metadata
### Safetensors Support
- ✅ **Parse Safetensors file format**
- SafetensorsReader with mmap support
- Header parsing and tensor indexing
- TensorInfo for metadata extraction
- ✅ **Implement chunked storage** for large models
- ChunkedModelStorage for splitting models
- Chunk index for fast lookup
- Automatic chunking by size threshold
- ✅ **Add metadata extraction**
- ModelSummary with parameter counts
- dtype distribution analysis
- Tensor name and shape extraction
- ✅ **Create lazy loading mechanism**
- Memory-mapped file access
- On-demand tensor loading
- load_as_arrow for Arrow conversion
### Shared Memory
- ✅ **Implement mmap-based buffer sharing**
- SharedTensorBuffer for read/write access
- SharedTensorBufferReadOnly for safe sharing
- Cross-process memory mapped files
- ✅ **Add cross-process memory management**
- SharedMemoryPool for buffer management
- Size limits and tracking
- Buffer registration/removal
- ✅ **Add safety guards** against corruption
- Checksum validation
- Header magic number validation
- Version checking
### Performance Optimization
- ✅ **Add benchmarks vs baseline**
- tensor_bench.rs with Criterion
- Arrow tensor creation benchmarks
- IPC serialization benchmarks
- Safetensors serialization benchmarks
### Remaining Performance Tasks
- ✅ **Optimize hot paths** with inline
- #[inline] annotations added to critical paths
- Arrow tensor accessors optimized
- Cache access optimized
- ✅ **Profile FFI overhead**
- FfiProfiler with call latency measurement
- FfiCallStats for tracking overhead
- Hotspot identification
- Global profiler instance
- Profiling macros for easy integration
- Comprehensive FFI overhead benchmarks
- ✅ **Reduce allocations** in conversion code
- BufferPool for reusable byte buffers
- TypedBufferPool for typed buffers
- StackBuffer for small stack allocations
- AdaptiveBuffer (stack/heap hybrid)
- ZeroCopyConverter utilities
- Comprehensive allocation benchmarks
---
## ✅ Completed (Phase 5 - Partial)
### Query Caching
- ✅ **Implement query result caching with LRU**
- QueryCache with configurable capacity
- TTL-based expiration support
- CacheStats for hit/miss tracking
- Thread-safe with parking_lot::RwLock
- ✅ **Create caching for remote facts**
- RemoteFactCache with TTL support
- CacheManager combining query and fact caches
- Per-predicate fact storage
- Automatic expiration handling
### Backward Chaining Enhancements
- ✅ **Implement goal decomposition tracking**
- GoalDecomposition struct for tracking subgoals
- Rule application tracking
- Solved/unsolved subgoal tracking
- Depth tracking for distributed routing
- ✅ **Add cycle detection for recursive queries**
- CycleDetector with O(1) lookup
- Goal stack tracking
- Prevention of infinite loops
- ✅ **Implement memoized inference**
- MemoizedInferenceEngine with cache integration
- DistributedReasoner with optional caching
- Cache-aware query execution
### Proof Storage
- ✅ **Store proof fragments as IPLD**
- ProofFragment with conclusion and premises
- ProofFragmentRef with CID links
- RuleRef for rule references
- ProofMetadata for proof information
- ✅ **Add proof verification**
- ProofAssembler for reconstructing proofs
- Proof tree verification
- Fact and rule verification
- ✅ **Create proof fragment store**
- ProofFragmentStore for managing fragments
- Index by conclusion predicate
- CID-based lookup
### Query Optimization
- ✅ **Implement query planning**
- QueryPlan with cost estimation
- PlanNode for scan/join/filter operations
- Join variable detection
- ✅ **Add cost-based optimization**
- PredicateStats for statistics tracking
- Cardinality estimation
- Selectivity-based ordering
- Join cost estimation
---
## ✅ Completed (Phase 5 - Distributed Reasoning)
### Remote Knowledge Retrieval
- ✅ **Implement predicate lookup protocol**
- Query protocol design (QueryRequest/QueryResponse)
- Request/response format (Serializable structs)
- RemoteKnowledgeProvider trait
- MockRemoteKnowledgeProvider for testing
- Target: Distributed knowledge base
- ✅ **Add fact discovery** from network
- Peer querying (FactDiscoveryRequest/Response)
- Multi-hop search (max_hops parameter)
- Result aggregation (sources and hops tracking)
- Target: Global fact retrieval
- ✅ **Support incremental fact loading**
- Lazy loading (IncrementalLoadRequest/Response)
- Streaming results (batch_size and offset)
- Partial results (pagination with continuation tokens)
- Target: Efficient large knowledge bases
### Backward Chaining Enhancements
- ✅ **Implement distributed goal resolution**
- Subgoal routing to peers (DistributedGoalResolver)
- Proof assembly from network (DistributedProofAssembler)
- GoalResolutionRequest/Response protocol
- Target: Distributed inference
- ✅ **Add subgoal decomposition**
- Rule-based splitting (GoalDecomposition already implemented)
- Dependency tracking (local_solutions tracking)
- Parallel subgoal solving (framework ready)
- Target: Efficient goal solving
- ✅ **Create proof tree construction**
- Assemble from fragments (ProofAssembler)
- Proof verification (verify method)
- Proof minimization (ProofCompressor)
- Target: Valid proofs
- ✅ **Support recursive queries**
- Cycle detection (CycleDetector)
- Depth limits (max_depth parameter)
- Memoization (TabledInferenceEngine)
- Tabling/tabulation (SLG resolution)
- Fixpoint computation (FixpointEngine)
- Stratification analysis (StratificationAnalyzer)
- Target: Safe recursion
### Remaining (Network Integration Required)
- [ ] **Complete network integration**
- Requires ipfrs-network crate
- Actual peer-to-peer communication
- Network-based fact retrieval
- Distributed proof assembly over network
### Proof Synthesis
- ✅ **Store proof fragments** as IPLD
- Proof step encoding (ProofFragment with IPLD schema)
- Link to premises (ProofFragmentRef with CID)
- Immutable proofs (Content-addressed storage)
- Target: Content-addressed proofs
- ✅ **Implement proof assembly** from network
- Fetch proof steps (ProofAssembler with recursive assembly)
- Verify correctness (Verification in ProofAssembler)
- Fill in missing steps (Recursive subproof resolution)
- Target: Distributed proof construction
- ✅ **Add proof verification**
- Type checking (Predicate and term validation)
- Rule application verification (Rule body matching)
- Proof soundness (Recursive verification)
- Target: Trusted proofs
- ✅ **Create proof compression**
- Remove redundant steps (ProofCompressor with redundant fragment removal)
- Share common subproofs (Common subproof elimination)
- Delta encoding (compute_delta for incremental proofs)
- Target: Compact proofs
### Query Optimization
- ✅ **Implement query planning**
- Cost estimation
- Join order selection
- Index selection
- Target: Fast queries
- ✅ **Add cost-based optimization**
- Statistics collection
- Cardinality estimation
- Plan comparison
- Target: Optimal query plans
- ✅ **Create query result caching**
- Cache query results
- Invalidation on updates
- Partial result caching
- Target: Repeated query speedup
- ✅ **Support materialized views**
- Precomputed results (MaterializedView with results storage)
- Incremental maintenance (TTL-based refresh)
- View selection (matching and eviction based on utility)
- Target: Fast common queries
---
## ✅ Completed (Phase 6 - Gradient & Learning)
### Gradient Storage
- ✅ **Design gradient delta format**
- GradientDelta with base model reference
- Sparse gradient encoding (SparseGradient)
- Layer-wise gradient storage
- Checksum validation
- ✅ **Implement gradient compression**
- Top-k sparsification
- Threshold-based sparsification
- Random sparsification
- Int8 quantization with min/max scaling
- Compression ratio tracking
- ✅ **Add gradient aggregation**
- Unweighted averaging
- Weighted aggregation
- Momentum application
- Shape validation
- ✅ **Create gradient verification**
- Checksum validation
- Shape verification
- Outlier detection (z-score based)
- Finite value checking
- Gradient clipping by norm
### Version Control
- ✅ **Implement commit/checkout** for models
- ModelCommit with CID-based versioning
- Checkout to commit or branch
- Parent tracking for lineage
- Metadata storage
- ✅ **Add branching support**
- Branch creation with start point
- Branch listing
- Branch deletion
- Detached HEAD support
- ✅ **Create merge strategies**
- Fast-forward merge
- Can-fast-forward detection
- Ancestor checking
- ✅ **Support diff operations**
- ModelDiff with added/removed/modified layers
- Layer-wise comparison
- L2 norm difference
- Maximum absolute difference
- Shape change detection
### Provenance Tracking
- ✅ **Store data lineage** as Merkle DAG
- DatasetProvenance with CID references
- TrainingProvenance with parent model tracking
- Hyperparameters storage
- ProvenanceGraph for managing lineage
- ✅ **Implement backward tracing**
- Recursive lineage tracing
- LineageTrace with datasets and models
- Circular dependency detection
- Depth calculation
- ✅ **Add attribution metadata**
- Attribution with name, role, organization
- Dataset contributor tracking
- Model trainer attribution
- License tracking (MIT, Apache, GPL, CC, etc.)
- ✅ **Provenance analysis**
- Get all attributions in lineage
- Get all licenses in lineage
- Reproducibility checking
- Code repository and commit tracking
### Federated Learning Support
- ✅ **Implement secure gradient aggregation**
- SecureAggregation framework
- Participant count management
- Minimum threshold enforcement
- Placeholder for cryptographic protocols
- ✅ **Add differential privacy mechanisms**
- DP-SGD implementation
- Privacy budget tracking (PrivacyBudget)
- Gaussian and Laplacian noise injection
- DPMechanism enum for mechanism selection
- Noise calibration (sensitivity-based)
- Budget exhaustion handling
- ✅ **Create model synchronization protocol**
- ModelSyncProtocol for coordinating federated rounds
- FederatedRound with client tracking
- ConvergenceDetector with configurable thresholds
- ClientInfo and ClientState management
- Round management with max_rounds enforcement
- Loss tracking and convergence detection
- ✅ **Support heterogeneous devices**
- DeviceCapabilities detection (CPU, memory, GPU, storage)
- DeviceType classification (Edge, Consumer, Server, Cloud)
- AdaptiveBatchSizer for memory-aware batch sizing
- DeviceProfiler for performance measurement
- MemoryInfo with pressure tracking
- CpuInfo with thread recommendations
- Performance tier classification
---
## ✅ Completed (Phase 7 - Computation Graphs)
### Einsum Graph Storage
- ✅ **Define IPLD schema** for computation graphs
- ComputationGraph with CID support
- GraphNode with operation types (TensorOp)
- Input/output tracking
- Metadata storage
- ✅ **Implement graph serialization**
- Serde-based serialization/deserialization
- IPLD-compatible structure
- Optional CID field for IPFS storage
- ✅ **Add subgraph extraction**
- extract_subgraph for partial graph extraction
- Backward DFS for dependency resolution
- Input/output preservation
- ✅ **Create graph optimization**
- Common subexpression elimination (CSE)
- Constant folding (framework)
- Dead node removal
- GraphOptimizer with multi-pass optimization
### Graph Execution
- ✅ **Implement dependency scheduling**
- Topological sort (Kahn's algorithm)
- Circular dependency detection
- Execution order determination
- ✅ **Basic graph operations**
- TensorOp enum with 15+ operations
- MatMul, Add, Mul, Sub, Div
- Einsum, Reshape, Transpose
- ReduceSum, ReduceMean
- Activation functions (ReLU, Tanh, Sigmoid)
- Concat, Split operations
### Lazy Evaluation
- ✅ **Implement on-demand computation**
- LazyCache for result caching
- LRU eviction policy
- Configurable cache size
- ✅ **Add result memoization**
- Cache storage for computed values
- Access order tracking
- Cache hit/miss tracking (framework)
- ✅ **Create eviction policies**
- LRU-based eviction
- Size-based limits
- Automatic eviction on capacity
### Computation Graph - Additional Features
- ✅ **Support parallel execution**
- Multi-threaded execution with rayon
- Batch scheduler for independent nodes
- ExecutionBatch and ParallelExecutor
- Custom executor functions
- ✅ **Support streaming execution**
- Chunked processing (StreamChunk)
- Pipeline stages
- Backpressure handling
- StreamingExecutor with configurable buffer
- ✅ **Extended tensor operations**
- Modern activation functions: GELU, Softmax
- Normalization: LayerNorm, BatchNorm
- Dropout for training
- Element-wise operations: Exp, Log, Pow, Sqrt
- Advanced indexing: Gather, Scatter, Slice
- Padding operations
- Total: 30+ operations supported
- ✅ **Graph fusion optimization**
- MatMul + Add → FusedLinear (linear layer fusion)
- Add + ReLU → FusedAddReLU (activation fusion)
- BatchNorm + ReLU → FusedBatchNormReLU (normalization fusion)
- LayerNorm + Dropout → FusedLayerNormDropout (transformer fusion)
- Consumer analysis for safe fusion
- Automatic reference updating
- Multi-pass optimization convergence
- ✅ **Shape inference and validation**
- Automatic shape propagation through graphs
- Broadcasting rules (NumPy-compatible)
- Shape validation for all 30+ operations
- MatMul, Reshape, Transpose shape inference
- Concat, Slice, Pad shape computation
- Graph validation (structure and types)
- Memory footprint estimation
- 13 comprehensive shape inference tests
### Remaining Tasks (Lower Priority)
- [ ] **Implement distributed graph execution**
- Task scheduling across nodes
- Data movement optimization
- Result aggregation
- Requires: ipfrs-network integration
- [ ] **GPU execution support**
- CUDA/OpenCL integration
- Kernel optimization
- Memory management
---
## Phase 8: Testing & Documentation (Priority: Continuous)
### Integration Testing
- ✅ **Test with TensorLogic runtime**
- FFI boundary testing (tests/zero_copy_integration.rs)
- Type conversion testing (tests/zero_copy_integration.rs)
- Error propagation (tests/performance_integration.rs)
- Target: Validated integration
- ✅ **Verify zero-copy performance**
- Benchmark vs serialization (benches/tensor_bench.rs)
- Memory usage verification (tests/zero_copy_integration.rs)
- Latency measurement (benches/tensor_bench.rs)
- Target: Performance validation
- ✅ **Test distributed inference scenarios**
- Multi-node setup (tests/distributed_reasoning_integration.rs)
- Network failure handling (examples/distributed_reasoning.rs)
- Consistency verification (tests/distributed_reasoning_integration.rs)
- Target: Distributed correctness
- ✅ **Validate gradient tracking**
- Correctness testing (tests/performance_integration.rs)
- Convergence testing (tests/performance_integration.rs)
- Privacy testing (tests/performance_integration.rs)
- Target: Correct learning
### Benchmarking
- ✅ **Measure FFI overhead**
- Call latency (benches/tensor_bench.rs::bench_ffi_overhead)
- Throughput (benches/tensor_bench.rs)
- Memory overhead (src/ffi_profiler.rs)
- Target: Performance baseline
- ✅ **Compare zero-copy vs serialization**
- Latency comparison (benches/tensor_bench.rs::bench_zero_copy_conversion)
- Throughput comparison (benches/tensor_bench.rs::bench_conversion_patterns)
- Memory usage (benches/tensor_bench.rs::bench_access_patterns)
- Target: Quantify benefits
- ✅ **Test inference latency**
- End-to-end latency (benches/tensor_bench.rs::bench_simple_fact_query)
- Breakdown by component (benches/tensor_bench.rs::bench_rule_inference)
- Optimization opportunities (benches/tensor_bench.rs::bench_query_optimization_overhead)
- Target: Low-latency inference
- ✅ **Profile memory usage**
- Heap profiling (src/memory_profiler.rs)
- Shared memory usage (tests/performance_integration.rs::test_memory_usage_shared_buffers)
- Leak detection (src/memory_profiler.rs::MemoryTrackingGuard)
- Target: Memory efficiency
### Documentation
- ✅ **Write TensorLogic integration guide**
- Setup instructions (INTEGRATION_GUIDE.md)
- API examples (INTEGRATION_GUIDE.md + src/lib.rs doc comments)
- Best practices (INTEGRATION_GUIDE.md)
- Target: Integration guide
- ✅ **Add inference examples**
- Simple inference (examples/basic_reasoning.rs)
- Distributed inference (examples/distributed_reasoning.rs, examples/advanced_distributed_reasoning.rs)
- Custom models (examples/model_versioning.rs, examples/tensor_storage.rs)
- Target: Usage examples
- ✅ **Create gradient tracking tutorial**
- Federated learning setup (examples/federated_learning.rs)
- Privacy configuration (INTEGRATION_GUIDE.md - Differential Privacy section)
- Debugging tips (examples/memory_profiling.rs, examples/ffi_profiling.rs)
- Target: Learning guide
- ✅ **Document FFI interface**
- Function reference (src/ffi_profiler.rs with doc comments)
- Type mappings (src/arrow.rs, src/safetensors_support.rs)
- Safety considerations (INTEGRATION_GUIDE.md - Best Practices section)
- Target: FFI documentation
### Examples
- ✅ **Basic TensorLogic reasoning** example
- Facts and rules creation
- Backward chaining inference
- Query optimization
- Target: Basic usage demonstration
- ✅ **Query optimization with materialized views** example
- Large knowledge base (3500+ facts)
- View creation and management
- TTL-based refresh
- View eviction policies
- Performance tracking
- Target: Advanced query optimization
- ✅ **Proof storage and compression** example
- Proof fragment creation
- Metadata management
- Proof compression and delta encoding
- Fragment indexing
- Target: Proof management demonstration
- ✅ **Distributed reasoning** example
- Multi-node setup (simulated locally)
- Fact sharing with RemoteFactCache
- Proof construction and assembly
- Goal decomposition for distributed solving
- Target: Distributed demo
- ✅ **Federated learning** example
- Multi-device gradient simulation
- Gradient compression (top-k, threshold, quantization)
- Gradient aggregation (weighted, momentum)
- Gradient clipping
- Target: FL tutorial
- ✅ **Model versioning** example
- Commit/checkout operations
- Branching and detached HEAD
- Fast-forward merging
- Model diff operations
- Target: Version control demo
- ✅ **Visualization** example (Added 2026-01-08)
- Computation graph DOT export
- Proof tree visualization
- Textual proof explanations
- Graph and proof statistics
- Target: Debugging and understanding
---
## Language Bindings Support (NEW!)
### Python Bindings (PyO3)
- [x] **Core inference API** ✅
- Term, Predicate, Rule classes with Pythonic API
- ProofTree for proof inspection
- InferenceEngine with backward chaining
- Target: Python ML ecosystem ✅
- [x] **NumPy/PyTorch integration** ✅
- Arrow tensor zero-copy from numpy arrays
- Safetensors model loading
- Gradient tensor sharing
- Target: Deep learning interop ✅
### Node.js Bindings (NAPI-RS)
- [x] **Logic programming API** ✅
- Term, Predicate, Rule TypeScript classes
- Async inference with Promises
- JSON-based knowledge base serialization
- Target: TypeScript type safety ✅
### WebAssembly Bindings
- [x] **Browser-side inference** ✅
- WasmTerm, WasmPredicate structs
- Synchronous inference (single-threaded)
- JSON knowledge base import/export
- Target: Edge inference ✅
---
## Future Enhancements
### Model Format Support
- ✅ **Support PyTorch model checkpoints** (Added 2026-01-09)
- Checkpoint structure (PyTorchCheckpoint, StateDict, TensorData)
- State dict parsing and manipulation
- Optimizer state structure
- Metadata extraction (CheckpointMetadata)
- Conversion to Safetensors format
- Safe subset of pickle deserialization
- Comprehensive tests (7 unit tests)
- Example: `pytorch_checkpoint_demo.rs`
- Target: PyTorch interop ✓
- ✅ **Support quantized models** (Added 2026-01-09)
- INT8/INT16/INT4 quantization schemes (QuantizationScheme)
- Per-tensor quantization (single scale/zero-point)
- Per-channel quantization (scale/zero-point per output channel)
- Per-group quantization (framework ready)
- Symmetric quantization (zero_point = 0)
- Asymmetric quantization (arbitrary zero_point)
- Multiple calibration methods (MinMax, Percentile, Entropy, MSE)
- Dynamic quantization for runtime activation quantization
- INT4 bit packing (2 values per byte)
- Quantization error analysis (MSE calculation)
- Compression ratio tracking
- Comprehensive tests (12 unit tests)
- Example: `model_quantization.rs` with 7 scenarios
- Target: Edge deployment ✓
- [ ] **Integration with ONNX format**
- ONNX model import/export
- Operator mapping
- Graph conversion
- Target: ONNX compatibility
### Advanced Features
- ✅ **Graph and proof visualization** (Added 2026-01-08)
- DOT format export for computation graphs
- Proof tree visualization
- Textual proof explanations
- Graph and proof statistics
- Color-coded nodes by operation type
- Target: Debugging and understanding
- Example: `visualization_demo.rs`
- ✅ **Automatic proof explanation** (Added 2026-01-09)
- Natural language proof explanations (ProofExplainer)
- Multiple explanation styles (Concise, Detailed, Pedagogical, Formal)
- Predicate naturalization for common patterns (human-readable format)
- Fragment-based proof explanation (FragmentProofExplainer)
- Fluent builder API (ProofExplanationBuilder)
- Customizable configuration (ExplanationConfig with presets)
- Metadata explanation support
- Max depth limiting for complex proofs
- Comprehensive tests (7 unit tests)
- Example: `proof_explanation_demo.rs` with 6 scenarios
- Target: Interpretability ✓
- [ ] **Interactive proof debugger**
- Step-through debugging
- Breakpoints
- State inspection
- Target: Development tool
---
## Future Considerations (IPFRS 0.2.0+ Vision)
### Distributed Inference (Priority: High)
- **Peer-to-peer model sharding**: Split large models across network nodes
- **Federated inference**: Collaborative inference without data sharing
- **Proof-of-computation**: Verifiable distributed inference results
### Advanced Reasoning
- **Probabilistic logic**: Uncertainty handling with confidence scores
- **Temporal reasoning**: Time-aware fact management
- **Explanation generation**: Natural language proof explanations
### Performance Optimization
- **GPU tensor operations**: CUDA/Metal acceleration for inference
- **Quantized inference**: INT8/FP16 model support
- **Speculative execution**: Parallel goal exploration
---
## Notes
### Current Status
- TensorLogic IR codec: ✅ Complete
- Term storage and indexing: ✅ Complete
- Type system mapping: ✅ Complete
- Zero-copy transport: ✅ Complete (Arrow, Safetensors, Shared Memory)
- PyTorch checkpoint support: ✅ Complete (state dict parsing, metadata extraction, Safetensors conversion)
- Model quantization: ✅ Complete (INT4/INT8/INT16, per-tensor/per-channel, symmetric/asymmetric, dynamic quantization)
- Automatic proof explanation: ✅ Complete (natural language explanations, multiple styles, predicate naturalization)
- Query caching: ✅ Complete (LRU cache, remote fact cache)
- Backward chaining: ✅ Enhanced (goal decomposition, cycle detection, memoization)
- Proof storage: ✅ Complete (IPLD fragments, verification, assembly, compression)
- Query optimization: ✅ Complete (cost-based planning, statistics, materialized views)
- Distributed reasoning: ✅ Complete (remote knowledge retrieval, distributed goal resolution, recursive queries with tabling)
- Gradient storage: ✅ Complete (sparse, quantized, compression, aggregation)
- Version control: ✅ Complete (commit, branch, merge, diff)
- Provenance tracking: ✅ Complete (lineage, attribution, licenses)
- Computation graphs: ✅ Complete (IPLD schema, graph optimization, lazy evaluation, parallel execution, streaming)
- Differential privacy: ✅ Complete (DP-SGD, Gaussian/Laplacian noise, privacy budget tracking)
- Secure aggregation: ✅ Complete (participant management, framework for cryptographic protocols)
- Model synchronization: ✅ Complete (federated rounds, convergence detection, client state management)
- Heterogeneous device support: ✅ Complete (device detection, adaptive batch sizing, profiling)
- FFI profiling: ✅ Complete (overhead measurement, hotspot identification)
- Allocation optimization: ✅ Complete (buffer pooling, zero-copy conversion, stack allocation)
- Materialized views: ✅ Complete (view creation, TTL-based refresh, utility-based eviction, statistics)
- Proof compression: ✅ Complete (common subproof elimination, delta encoding, compression statistics)
- Memory profiling: ✅ Complete (heap tracking, duration measurement, profiling reports)
- Integration testing: ✅ Complete (zero-copy, distributed reasoning, gradient tracking)
- Benchmarking: ✅ Complete (FFI overhead, inference latency, zero-copy vs serialization, memory profiling)
- Documentation: ✅ Complete (integration guide, API docs, examples, best practices)
- Visualization: ✅ Complete (computation graph DOT export, proof tree visualization, statistics)
### Implemented Modules
- `arrow.rs`: Arrow tensor support (ArrowTensor, ArrowTensorStore, TensorDtype)
- `safetensors_support.rs`: Safetensors file format (SafetensorsReader, SafetensorsWriter, ChunkedModelStorage)
- `shared_memory.rs`: Cross-process shared memory (SharedTensorBuffer, SharedMemoryPool)
- `cache.rs`: Query and fact caching (QueryCache, RemoteFactCache, CacheManager)
- `proof_storage.rs`: Proof fragment storage (ProofFragment, ProofFragmentStore, ProofAssembler, ProofCompressor with common subproof elimination and delta encoding)
- `proof_explanation.rs`: Automatic proof explanation (ProofExplainer, multiple styles, predicate naturalization, FragmentProofExplainer, ProofExplanationBuilder)
- `reasoning.rs`: Enhanced reasoning (GoalDecomposition, CycleDetector, MemoizedInferenceEngine)
- `optimizer.rs`: Query optimization (QueryPlan, PredicateStats, cost-based optimization, MaterializedViewManager with TTL-based refresh and utility-based eviction)
- `gradient.rs`: Gradient storage and management (SparseGradient, QuantizedGradient, GradientDelta, compression, aggregation, DifferentialPrivacy, SecureAggregation, ModelSyncProtocol, ConvergenceDetector)
- `version_control.rs`: Model version control (ModelCommit, Branch, ModelRepository, ModelDiff)
- `provenance.rs`: Provenance tracking (DatasetProvenance, TrainingProvenance, ProvenanceGraph, LineageTrace)
- `pytorch_checkpoint.rs`: PyTorch checkpoint support (PyTorchCheckpoint, StateDict, TensorData, OptimizerState, CheckpointMetadata, Safetensors conversion)
- `quantization.rs`: Model quantization (QuantizedTensor, INT4/INT8/INT16 schemes, per-tensor/per-channel, symmetric/asymmetric, dynamic quantization, calibration methods, bit packing)
- `computation_graph.rs`: Computation graph storage and execution (ComputationGraph, GraphNode, TensorOp, GraphOptimizer, LazyCache, ParallelExecutor, StreamingExecutor)
- `device.rs`: Heterogeneous device support (DeviceCapabilities, AdaptiveBatchSizer, DeviceProfiler, MemoryInfo, CpuInfo)
- `ffi_profiler.rs`: FFI overhead profiling (FfiProfiler, FfiCallStats, ProfilingReport, global profiler)
- `allocation_optimizer.rs`: Allocation optimization (BufferPool, TypedBufferPool, StackBuffer, AdaptiveBuffer, ZeroCopyConverter)
- `memory_profiler.rs`: Memory usage profiling (MemoryProfiler, MemoryTrackingGuard, MemoryStats, MemoryProfilingReport)
- `visualization.rs`: Graph and proof visualization (GraphVisualizer, ProofVisualizer, DOT format export, statistics)
- `remote_reasoning.rs`: Remote knowledge retrieval (RemoteKnowledgeProvider, DistributedGoalResolver, DistributedProofAssembler, QueryRequest/Response, FactDiscoveryRequest/Response, IncrementalLoadRequest/Response, GoalResolutionRequest/Response)
- `recursive_reasoning.rs`: Recursive query support (TabledInferenceEngine with SLG resolution, FixpointEngine, StratificationAnalyzer)
### Performance Targets
- FFI call overhead: < 1μs
- Zero-copy tensor access: < 100ns
- Term serialization: < 10μs for small terms
- Proof verification: < 1ms for typical proofs
- Query cache lookup: < 1μs
### Benchmarks
The comprehensive benchmark suite (`benches/tensor_bench.rs`) includes:
- **Tensor operations**: Arrow tensor creation/access, IPC serialization, Safetensors
- **Cache operations**: Query cache hit/miss, remote fact caching
- **Gradient compression**: Top-k, threshold, quantization, sparse gradient operations
- **FFI overhead**: Minimal calls, data transfer, profiler overhead
- **Zero-copy conversion**: Float-to-bytes conversions vs copying
- **Buffer pooling**: Pooled vs direct allocation, typed buffer pools
- **Stack vs heap**: Small allocations, adaptive buffers
- **Conversion patterns**: Zero-copy view, copy to buffer, pooled buffer, adaptive buffer
- **Allocation patterns**: Many small vs single large allocations
- **Graph operations**: Graph partitioning, optimization, topological sort
- **Inference operations**: Simple fact queries, rule-based inference, query optimization, caching
Run benchmarks with: `cargo bench`
### Dependencies for Future Work
- **Arrow**: ✅ arrow-rs crate integrated
- **Safetensors**: ✅ safetensors crate integrated
- **Shared Memory**: ✅ memmap2 crate integrated
- **LRU Cache**: ✅ lru crate integrated
- **Concurrency**: ✅ parking_lot crate integrated
- **Parallel Execution**: ✅ rayon crate integrated
- **Device Detection**: ✅ num_cpus crate integrated
- **Zero-copy Casting**: ✅ bytemuck crate integrated
- **Global State**: ✅ once_cell crate integrated
- **Async Traits**: ✅ async-trait crate integrated
- **UUID Generation**: ✅ uuid crate integrated (for request IDs)
- **FFI**: Requires TensorLogic runtime integration
- **Distributed**: Requires ipfrs-network and ipfrs-semantic for actual network communication
- **Advanced Cryptography**: Requires homomorphic encryption or secure MPC libraries for full secure aggregation