{
"project_name": "God-Graph Tensor Optimization Initiative",
"version": "0.4.0-tensor-alpha",
"objective": "Transform God-Graph into a next-generation LLM infrastructure framework by optimizing tensor elements on top of existing generic node/edge support",
"vision": "Create a high-performance graph framework with native tensor support for graph neural networks, sparse computations, and large-scale machine learning workloads",
"phase_1_research_and_design": {
"timeline": "Weeks 1-4",
"objectives": [
"Research state-of-the-art tensor libraries and GNN frameworks",
"Analyze memory layout strategies for graph-tensor integration",
"Design type system for tensor-aware graph operations"
],
"tasks": [
{
"id": "R1",
"name": "Tensor Library Ecosystem Analysis",
"description": "Evaluate Rust tensor libraries for integration",
"subtasks": [
{
"id": "R1.1",
"name": "ndarray evaluation",
"details": "Assess ndarray crate for N-dimensional array support, BLAS integration, and memory layout options (C/F-order)"
},
{
"id": "R1.2",
"name": "dfdx evaluation",
"details": "Analyze dfdx for shape-checked tensors, automatic differentiation, and GPU acceleration via CUDA"
},
{
"id": "R1.3",
"name": "candle evaluation",
"details": "Evaluate Hugging Face's candle for lightweight tensor operations and GGML integration"
},
{
"id": "R1.4",
"name": "tch-rs evaluation",
"details": "Assess PyTorch bindings for production-ready tensor operations and autograd support"
}
],
"deliverables": ["Tensor library comparison matrix", "Integration feasibility report"],
"priority": "P0"
},
{
"id": "R2",
"name": "Sparse Tensor Format Research",
"description": "Research sparse tensor formats optimized for graph data",
"subtasks": [
{
"id": "R2.1",
"name": "COO format analysis",
"details": "Coordinate format for edge lists - optimal for construction and iteration"
},
{
"id": "R2.2",
"name": "CSR/CSC format analysis",
"details": "Compressed Sparse Row/Column for efficient neighbor access and matrix operations"
},
{
"id": "R2.3",
"name": "Block sparse formats",
"details": "BSR/BSC formats for batched operations and SIMD optimization"
},
{
"id": "R2.4",
"name": "Hybrid dense-sparse layouts",
"details": "Mixed precision and structured sparsity patterns (2:4 sparsity for Ampere GPUs)"
}
],
"deliverables": ["Sparse format selection guide", "Memory efficiency benchmarks"],
"priority": "P0"
},
{
"id": "R3",
"name": "GNN Operation Pattern Analysis",
"description": "Study graph neural network computation patterns",
"subtasks": [
{
"id": "R3.1",
"name": "Message passing patterns",
"details": "Analyze scatter-gather operations, aggregation functions (sum, mean, max, attention)"
},
{
"id": "R3.2",
"name": "Graph convolution patterns",
"details": "Study GCN, GAT, GraphSAGE operations and their tensor requirements"
},
{
"id": "R3.3",
"name": "Batching strategies",
"details": "Research graph batching for mini-batch GNN training (neighbor sampling, subgraph extraction)"
}
],
"deliverables": ["GNN operation specification", "Tensor API design draft"],
"priority": "P1"
}
]
},
"phase_2_core_tensor_infrastructure": {
"timeline": "Weeks 5-10",
"objectives": [
"Implement tensor trait system for backend abstraction",
"Add dense and sparse tensor storage backends",
"Integrate with existing generic node/edge system"
],
"tasks": [
{
"id": "T1",
"name": "Tensor Trait System Design",
"description": "Create abstract tensor traits for backend-agnostic operations",
"subtasks": [
{
"id": "T1.1",
"name": "TensorBase trait",
"details": "Define core tensor operations: shape, dtype, device, clone, to_dense, to_sparse",
"code_example": "pub trait TensorBase: Clone + Send + Sync { fn shape(&self) -> &[usize]; fn dtype(&self) -> DType; fn device(&self) -> Device; }"
},
{
"id": "T1.2",
"name": "TensorOps trait",
"details": "Define mathematical operations: add, mul, matmul, transpose, sum, mean",
"code_example": "pub trait TensorOps: TensorBase { fn matmul(&self, other: &Self) -> Self; fn transpose(&self, axes: &[usize]) -> Self; }"
},
{
"id": "T1.3",
"name": "SparseTensor trait",
"details": "Extend TensorOps with sparse-specific operations: nnz, row_indices, col_indices, values",
"code_example": "pub trait SparseTensor: TensorOps { fn nnz(&self) -> usize; fn coo(&self) -> COOView; }"
},
{
"id": "T1.4",
"name": "GradientSupport trait",
"details": "Add automatic differentiation support for GNN training",
"code_example": "pub trait GradientSupport: TensorOps { fn backward(&self) -> GradientTape; fn requires_grad(&self) -> bool; }"
}
],
"deliverables": ["Tensor trait hierarchy", "Backend abstraction layer"],
"priority": "P0",
"estimated_lines_of_code": 800
},
{
"id": "T2",
"name": "Dense Tensor Backend",
"description": "Implement dense tensor storage with multiple backend options",
"subtasks": [
{
"id": "T2.1",
"name": "NdArrayBackend",
"details": "Wrap ndarray::ArrayBase for N-dimensional dense tensors with BLAS acceleration",
"dependencies": ["ndarray", "ndarray-blas"],
"features": ["C/F-order layouts", "BLAS matmul", "slicing", "broadcasting"]
},
{
"id": "T2.2",
"name": "DfdxBackend",
"details": "Optional dfdx integration for shape-checked tensors with autograd",
"dependencies": ["dfdx (optional)"],
"features": ["Compile-time shape checks", "GPU acceleration", "automatic differentiation"]
},
{
"id": "T2.3",
"name": "Memory layout optimization",
"details": "Implement 64-byte alignment, cache-friendly layouts, and SIMD-friendly strides",
"optimizations": ["#[repr(align(64))]", "Contiguous innermost dimension", "Software prefetching"]
},
{
"id": "T2.4",
"name": "SIMD vectorization",
"details": "Leverage wide crate for SIMD operations on stable Rust",
"features": ["f64x4 operations", "Auto-vectorization hints", "Platform-specific optimizations"]
}
],
"deliverables": ["DenseTensor struct", "Backend implementations", "Performance benchmarks"],
"priority": "P0",
"estimated_lines_of_code": 1500
},
{
"id": "T3",
"name": "Sparse Tensor Backend",
"description": "Implement sparse tensor formats optimized for graph operations",
"subtasks": [
{
"id": "T3.1",
"name": "COO (Coordinate) format",
"details": "Store (row, col, value) tuples - optimal for construction and iteration",
"struct": "pub struct COOTensor { row_indices: Vec<usize>, col_indices: Vec<usize>, values: DenseTensor, shape: [usize; 2] }",
"operations": ["from_edges", "to_csr", "sparse_matmul", "elementwise"]
},
{
"id": "T3.2",
"name": "CSR (Compressed Sparse Row) format",
"details": "Compressed row format for efficient neighbor access - matches God-Graph's bucket structure",
"struct": "pub struct CSRTensor { row_offsets: Vec<usize>, col_indices: Vec<usize>, values: DenseTensor, shape: [usize; 2] }",
"operations": ["from_coo", "row_slice", "sparse_matmul", "transpose"]
},
{
"id": "T3.3",
"name": "Block Sparse Row (BSR) format",
"details": "Block-sparse format for batched GNN operations and SIMD optimization",
"struct": "pub struct BSRTensor { row_offsets: Vec<usize>, col_indices: Vec<usize>, blocks: DenseTensor, block_shape: [usize; 2] }",
"operations": ["from_csr", "block_matmul", "batched_operations"]
},
{
"id": "T3.4",
"name": "Sparse-dense conversions",
"details": "Efficient conversion between sparse and dense representations",
"operations": ["to_dense", "from_dense", "sparsity_pattern", "fill_ratio"]
}
],
"deliverables": ["SparseTensor enum", "COO/CSR/BSR implementations", "Conversion utilities"],
"priority": "P0",
"estimated_lines_of_code": 2000
},
{
"id": "T4",
"name": "Tensor-Aware Node/Edge Types",
"description": "Extend existing generic system to support tensor data natively",
"subtasks": [
{
"id": "T4.1",
"name": "TensorNode wrapper",
"details": "Create TensorNode<T> that wraps existing NodeIndex with tensor data",
"code_example": "pub struct TensorNode<T: TensorBase> { index: NodeIndex, data: T, _marker: PhantomData<T> }",
"features": ["Zero-cost abstraction", "Compatible with existing NodeIndex", "Tensor trait bounds"]
},
{
"id": "T4.2",
"name": "TensorEdge wrapper",
"details": "Create TensorEdge<E> for edge features (attention weights, relation types)",
"code_example": "pub struct TensorEdge<E: TensorBase> { index: EdgeIndex, data: E, endpoints: (NodeIndex, NodeIndex) }",
"features": ["Sparse edge features", "Attention weight support", "Multi-relation encoding"]
},
{
"id": "T4.3",
"name": "Graph<Tensor> specialization",
"details": "Optimize Graph<TensorNode, TensorEdge> for common GNN patterns",
"optimizations": ["Batched tensor operations", "Fused message passing", "Memory pooling"]
},
{
"id": "T4.4",
"name": "Backward compatibility layer",
"details": "Ensure existing code works without tensor features",
"strategy": "Feature flags: tensor, tensor-sparse, tensor-gpu"]
}
],
"deliverables": ["TensorNode<T>", "TensorEdge<E>", "Specialized implementations"],
"priority": "P0",
"estimated_lines_of_code": 1200
}
]
},
"phase_3_gnn_primitives": {
"timeline": "Weeks 11-16",
"objectives": [
"Implement core GNN operations as tensor primitives",
"Add message passing framework",
"Provide pre-built GNN layer implementations"
],
"tasks": [
{
"id": "G1",
"name": "Message Passing Framework",
"description": "Build flexible message passing abstraction for GNNs",
"subtasks": [
{
"id": "G1.1",
"name": "Message function trait",
"details": "Define message computation on edges",
"code_example": "pub trait MessageFn<H: TensorBase> { fn message(&self, src: &H, edge: &H, dst: &H) -> H; }",
"built_ins": ["IdentityMessage", "LinearMessage", "AttentionMessage"]
},
{
"id": "G1.2",
"name": "Aggregation trait",
"details": "Define neighbor aggregation functions",
"code_example": "pub trait Aggregator<H: TensorBase> { fn aggregate(&self, messages: &[H]) -> H; }",
"built_ins": ["SumAggregator", "MeanAggregator", "MaxAggregator", "AttentionAggregator"]
},
{
"id": "G1.3",
"name": "Update function trait",
"details": "Define node state update after aggregation",
"code_example": "pub trait UpdateFn<H: TensorBase> { fn update(&self, old_state: &H, new_message: &H) -> H; }",
"built_ins": ["GRUUpdate", "LSTMUpdate", "ResidualUpdate"]
},
{
"id": "G1.4",
"name": "MessagePassingLayer struct",
"details": "Combine message, aggregate, update into unified layer",
"code_example": "pub struct MessagePassingLayer<M, A, U> { message_fn: M, aggregator: A, update_fn: U }",
"operations": ["forward", "backward", "reset_parameters"]
}
],
"deliverables": ["Message passing traits", "Built-in implementations", "Example configurations"],
"priority": "P0",
"estimated_lines_of_code": 1800
},
{
"id": "G2",
"name": "Graph Convolution Layers",
"description": "Implement popular GNN layer architectures",
"subtasks": [
{
"id": "G2.1",
"name": "GCNConv layer",
"details": "Graph Convolutional Network layer with normalized adjacency",
"formula": "H' = D^(-1/2) A D^(-1/2) H W",
"operations": ["forward", "propagate", "reset_parameters"]
},
{
"id": "G2.2",
"name": "GATConv layer",
"details": "Graph Attention Network with multi-head attention",
"formula": "α_ij = softmax(LeakyReLU(a^T [Wh_i || Wh_j]))",
"operations": ["compute_attention", "aggregate", "multi_head_forward"]
},
{
"id": "G2.3",
"name": "GraphSAGE layer",
"details": "Inductive representation learning with neighbor sampling",
"aggregators": ["MeanAggregator", "LSTMAggregator", "PoolingAggregator"],
"operations": ["sample_neighbors", "aggregate", "forward"]
},
{
"id": "G2.4",
"name": "GINE layer",
"details": "Graph Isomorphism Network with edge features",
"formula": "h'_i = (1 + ε) · h_i + Σ_j φ(h_i + h_j + e_ij)",
"operations": ["edge_encoding", "aggregate", "update"]
}
],
"deliverables": ["GCNConv", "GATConv", "GraphSAGE", "GINE implementations"],
"priority": "P1",
"estimated_lines_of_code": 2500
},
{
"id": "G3",
"name": "Graph Pooling Operations",
"description": "Implement hierarchical graph pooling for coarsening",
"subtasks": [
{
"id": "G3.1",
"name": "TopK pooling",
"details": "Select top-k nodes by projection score",
"operations": ["compute_scores", "select_nodes", "pool_features"]
},
{
"id": "G3.2",
"name": "DiffPool",
"details": "Differentiable pooling via soft cluster assignment",
"operations": ["cluster_assignment", "pooled_adjacency", "pooled_features"]
},
{
"id": "G3.3",
"name": "Graclus",
"details": "Greedy graph clustering for pooling",
"operations": ["max_weight_matching", "cluster_graph", "pool_features"]
}
],
"deliverables": ["TopKPool", "DiffPool", "Graclus implementations"],
"priority": "P2",
"estimated_lines_of_code": 1500
},
{
"id": "G4",
"name": "Graph Normalization Layers",
"description": "Implement normalization techniques for stable GNN training",
"subtasks": [
{
"id": "G4.1",
"name": "GraphNorm",
"details": "Graph-specific batch normalization",
"operations": ["compute_stats", "normalize", "scale_shift"]
},
{
"id": "G4.2",
"name": "PairNorm",
"details": "Prevent over-smoothing in deep GNNs",
"operations": ["pairwise_distance", "normalize", "forward"]
},
{
"id": "G4.3",
"name": "DropEdge",
"details": "Random edge dropout for regularization",
"operations": ["sample_edges", "drop", "forward"]
}
],
"deliverables": ["GraphNorm", "PairNorm", "DropEdge implementations"],
"priority": "P2",
"estimated_lines_of_code": 800
}
]
},
"phase_4_performance_optimization": {
"timeline": "Weeks 17-22",
"objectives": [
"Implement parallel and GPU acceleration",
"Add memory pooling and caching",
"Optimize for large-scale graphs"
],
"tasks": [
{
"id": "P1",
"name": "Parallel Tensor Operations",
"description": "Extend existing parallel module for tensor computations",
"subtasks": [
{
"id": "P1.1",
"name": "Rayon integration",
"details": "Use rayon for parallel tensor operations",
"operations": ["par_matmul", "par_elementwise", "par_reduce"]
},
{
"id": "P1.2",
"name": "Batched GNN inference",
"details": "Parallel processing of graph batches",
"operations": ["batch_forward", "parallel_propagate", "fused_aggregation"]
},
{
"id": "P1.3",
"name": "Multi-GPU support",
"details": "Distribute tensor operations across multiple GPUs",
"operations": ["device_placement", "peer_to_peer_copy", "synchronized_forward"]
}
],
"deliverables": ["Parallel tensor traits", "Batched operations", "Multi-GPU support"],
"priority": "P1",
"estimated_lines_of_code": 1500
},
{
"id": "P2",
"name": "GPU Acceleration Backend",
"description": "Add GPU support for tensor operations",
"subtasks": [
{
"id": "P2.1",
"name": "CUDA backend via dfdx",
"details": "Optional dfdx integration for CUDA acceleration",
"feature_flag": "tensor-gpu",
"operations": ["cuda_matmul", "cuda_conv", "cuda_attention"]
},
{
"id": "P2.2",
"name": "WGPU backend",
"details": "Cross-platform GPU compute via WebGPU",
"feature_flag": "tensor-wgpu",
"operations": ["wgpu_dispatch", "buffer_transfer", "compute_shaders"]
},
{
"id": "P2.3",
"name": "Unified tensor device",
"details": "Abstract device placement (CPU/GPU)",
"code_example": "pub enum Device { Cpu, Cuda(usize), Wgpu }",
"operations": ["to_device", "clone_to", "pin_memory"]
}
],
"deliverables": ["GPU backends", "Device abstraction", "Transfer utilities"],
"priority": "P1",
"estimated_lines_of_code": 2000
},
{
"id": "P3",
"name": "Memory Pooling and Caching",
"description": "Implement memory optimization for large-scale graphs",
"subtasks": [
{
"id": "P3.1",
"name": "Tensor memory pool",
"details": "Reuse tensor allocations to reduce allocations",
"operations": ["acquire", "release", "pool_stats"]
},
{
"id": "P3.2",
"name": "Gradient checkpointing",
"details": "Trade computation for memory in backpropagation",
"operations": ["checkpoint", "recompute", "memory_budget"]
},
{
"id": "P3.3",
"name": "Sparse tensor cache",
"details": "Cache frequently accessed sparse patterns",
"operations": ["cache_lookup", "evict_lru", "prefetch"]
}
],
"deliverables": ["MemoryPool<Tensor>", "GradientCheckpoint", "SparseCache"],
"priority": "P2",
"estimated_lines_of_code": 1200
},
{
"id": "P4",
"name": "Large-Scale Graph Optimizations",
"description": "Optimize for graphs with millions/billions of nodes",
"subtasks": [
{
"id": "P4.1",
"name": "Neighbor sampling",
"details": "Sample fixed-size neighborhoods for mini-batch training",
"algorithms": ["UniformSampling", "WeightedSampling", "RandomWalk"]
},
{
"id": "P4.2",
"name": "Subgraph extraction",
"details": "Efficiently extract subgraphs for batch processing",
"operations": ["extract_induced", "extract_khop", "partition_graph"]
},
{
"id": "P4.3",
"name": "Out-of-core execution",
"details": "Process graphs larger than RAM with disk streaming",
"operations": ["stream_nodes", "checkpoint_state", "resume_training"]
}
],
"deliverables": ["NeighborSampler", "SubgraphExtractor", "OutOfCoreExecutor"],
"priority": "P1",
"estimated_lines_of_code": 1800
}
]
},
"phase_5_api_and_ecosystem": {
"timeline": "Weeks 23-26",
"objectives": [
"Design ergonomic high-level APIs",
"Build example applications and tutorials",
"Create comprehensive documentation"
],
"tasks": [
{
"id": "A1",
"name": "High-Level API Design",
"description": "Create user-friendly APIs for common GNN workflows",
"subtasks": [
{
"id": "A1.1",
"name": "GNN builder pattern",
"details": "Fluent API for constructing GNN models",
"code_example": "GnnBuilder::new().with_gcn(64).with_gat(4, 32).with_pooling(TopK(0.5)).build()",
"features": ["Layer chaining", "Automatic shape inference", "Weight initialization"]
},
{
"id": "A1.2",
"name": "Training loop abstraction",
"details": "Simplified training loop with callbacks",
"code_example": "trainer.fit(model, data).with_optimizer(Adam(1e-3)).with_loss(CrossEntropy).with_callbacks([EarlyStopping, Checkpoint]).train()",
"features": ["Optimizer integration", "Loss functions", "Callback system"]
},
{
"id": "A1.3",
"name": "Dataset loaders",
"details": "Pre-built loaders for common graph datasets",
"datasets": ["Cora", "CiteSeer", "PubMed", "OGB", "TUDataset"]
}
],
"deliverables": ["GnnBuilder", "Trainer API", "Dataset loaders"],
"priority": "P0",
"estimated_lines_of_code": 1500
},
{
"id": "A2",
"name": "Serialization and Export",
"description": "Add model persistence and interoperability",
"subtasks": [
{
"id": "A2.1",
"name": "Serde integration",
"details": "Serialize tensor graphs to/from JSON, BSON, MessagePack",
"feature_flag": "tensor-serde",
"formats": ["JSON", "BSON", "MessagePack", "Bincode"]
},
{
"id": "A2.2",
"name": "ONNX export",
"details": "Export GNN models to ONNX format for deployment",
"operations": ["to_onnx", "optimize_onnx", "verify_export"]
},
{
"id": "A2.3",
"name": "Safetensors support",
"details": "Safe tensor serialization (used by Hugging Face)",
"operations": ["save_safetensors", "load_safetensors", "verify_checksum"]
}
],
"deliverables": ["Serde impls", "ONNX exporter", "Safetensors integration"],
"priority": "P1",
"estimated_lines_of_code": 1000
},
{
"id": "A3",
"name": "Documentation and Examples",
"description": "Create comprehensive documentation and example applications",
"subtasks": [
{
"id": "A3.1",
"name": "API documentation",
"details": "Rustdoc with examples for all public APIs",
"coverage_target": "95% public API documented"
},
{
"id": "A3.2",
"name": "Tutorial notebooks",
"details": "Step-by-step tutorials for common use cases",
"topics": ["Getting started", "Building your first GNN", "Custom message passing", "Large-scale training", "Production deployment"]
},
{
"id": "A3.3",
"name": "Example applications",
"details": "Complete example applications demonstrating real-world use",
"examples": ["Node classification on Cora", "Graph classification on TUDataset", "Link prediction on OGB", "Molecular property prediction", "Recommendation system"]
},
{
"id": "A3.4",
"name": "Performance guide",
"details": "Documentation on optimizing GNN performance",
"topics": ["Memory optimization", "GPU acceleration", "Distributed training", "Profiling tools"]
}
],
"deliverables": ["Rustdoc", "Tutorial series", "Example gallery", "Performance guide"],
"priority": "P0",
"estimated_lines_of_code": 500
},
{
"id": "A4",
"name": "Testing and Benchmarking",
"description": "Comprehensive test suite and performance benchmarks",
"subtasks": [
{
"id": "A4.1",
"name": "Unit tests",
"details": "Test all tensor operations and GNN layers",
"coverage_target": "85%+ code coverage"
},
{
"id": "A4.2",
"name": "Integration tests",
"details": "End-to-end GNN training tests",
"test_cases": ["GCN training", "GAT training", "Multi-GPU training", "Serialization roundtrip"]
},
{
"id": "A4.3",
"name": "Performance benchmarks",
"details": "Benchmark tensor operations and GNN layers",
"metrics": ["Throughput (samples/sec)", "Memory usage", "GPU utilization", "Scaling (nodes/edges)"]
},
{
"id": "A4.4",
"name": "Correctness validation",
"details": "Compare results with PyTorch Geometric reference implementations",
"validation": ["Forward pass accuracy", "Gradient correctness", "Numerical stability"]
}
],
"deliverables": ["Test suite", "Benchmark suite", "Validation reports"],
"priority": "P0",
"estimated_lines_of_code": 3000
}
]
},
"feature_flags": {
"description": "Cargo feature flags for optional tensor functionality",
"flags": {
"tensor": {
"description": "Core tensor support (dense tensors with ndarray backend)",
"dependencies": ["ndarray", "ndarray-blas"],
"estimated_binary_size_increase": "2-5 MB"
},
"tensor-sparse": {
"description": "Sparse tensor formats (COO, CSR, BSR)",
"dependencies": [],
"requires": ["tensor"]
},
"tensor-gpu": {
"description": "GPU acceleration via dfdx CUDA backend",
"dependencies": ["dfdx (with cuda feature)"],
"requires": ["tensor"],
"platform": "linux, windows (with CUDA toolkit)"
},
"tensor-wgpu": {
"description": "Cross-platform GPU compute via wgpu",
"dependencies": ["wgpu", "wgpu-math"],
"requires": ["tensor"]
},
"tensor-autograd": {
"description": "Automatic differentiation for training",
"dependencies": ["dfdx"],
"requires": ["tensor"]
},
"tensor-serde": {
"description": "Serialization support for tensors",
"dependencies": ["serde", "bincode"],
"requires": ["tensor"]
},
"tensor-gnn": {
"description": "Pre-built GNN layers (GCN, GAT, GraphSAGE)",
"dependencies": [],
"requires": ["tensor", "tensor-sparse"]
}
}
},
"dependencies_summary": {
"core_dependencies": [
{
"name": "ndarray",
"version": "0.15",
"purpose": "N-dimensional array base for dense tensors",
"features": ["blas", "serde"]
},
{
"name": "wide",
"version": "0.7",
"purpose": "SIMD operations on stable Rust",
"optional": true
},
{
"name": "rayon",
"version": "1.10",
"purpose": "Data parallelism for CPU parallelization",
"optional": true
}
],
"optional_dependencies": [
{
"name": "dfdx",
"version": "0.13",
"purpose": "Shape-checked tensors with autograd and GPU support",
"feature": "tensor-gpu, tensor-autograd"
},
{
"name": "wgpu",
"version": "0.18",
"purpose": "Cross-platform GPU compute",
"feature": "tensor-wgpu"
},
{
"name": "safetensors",
"version": "0.4",
"purpose": "Safe tensor serialization",
"feature": "tensor-serde"
}
]
},
"milestone_timeline": {
"M1": {
"name": "Research Complete",
"week": 4,
"deliverables": ["Tensor library comparison", "Sparse format guide", "GNN operation spec"],
"gate_criteria": "Design review approved by team"
},
"M2": {
"name": "Core Tensor Infrastructure",
"week": 10,
"deliverables": ["Tensor traits", "Dense/sparse backends", "TensorNode/TensorEdge"],
"gate_criteria": "All tensor operations pass unit tests"
},
"M3": {
"name": "GNN Primitives Complete",
"week": 16,
"deliverables": ["Message passing framework", "GCN/GAT/GraphSAGE layers", "Pooling ops"],
"gate_criteria": "GNN layers match PyTorch Geometric reference"
},
"M4": {
"name": "Performance Optimization",
"week": 22,
"deliverables": ["Parallel ops", "GPU backend", "Memory pooling", "Large-scale optimizations"],
"gate_criteria": "Benchmark shows 10x speedup over baseline"
},
"M5": {
"name": "Release Candidate",
"week": 26,
"deliverables": ["High-level API", "Documentation", "Examples", "Test suite"],
"gate_criteria": "85%+ test coverage, all benchmarks passing"
}
},
"risk_assessment": {
"technical_risks": [
{
"risk": "Tensor library integration complexity",
"probability": "Medium",
"impact": "High",
"mitigation": "Start with ndarray (simplest), add dfdx as optional backend later"
},
{
"risk": "GPU backend maintenance burden",
"probability": "High",
"impact": "Medium",
"mitigation": "Make GPU backends optional, rely on community contributions"
},
{
"risk": "Performance not matching PyTorch",
"probability": "Medium",
"impact": "Medium",
"mitigation": "Focus on specific use cases where Rust excels (low-latency inference, embedded)"
}
],
"ecosystem_risks": [
{
"risk": "Limited Rust ML ecosystem adoption",
"probability": "Medium",
"impact": "High",
"mitigation": "Ensure ONNX export for interoperability, target production deployment scenarios"
},
{
"risk": "Competition from established frameworks",
"probability": "High",
"impact": "Medium",
"mitigation": "Differentiate via performance, safety, and integration with existing Rust ecosystem"
}
]
},
"success_metrics": {
"technical_metrics": [
{
"metric": "Tensor operation throughput",
"target": "Within 2x of PyTorch for equivalent operations",
"measurement": "Benchmark suite comparing matmul, conv, attention"
},
{
"metric": "Memory efficiency",
"target": "30% lower memory usage than PyTorch for inference",
"measurement": "Peak memory during GNN inference on Cora dataset"
},
{
"metric": "Compilation time",
"target": "< 2 minutes for typical GNN model",
"measurement": "cargo build --release on example applications"
},
{
"metric": "Test coverage",
"target": "85%+ code coverage",
"measurement": "cargo tarpaulin --all-features"
}
],
"adoption_metrics": [
{
"metric": "Crates.io downloads",
"target": "1000+ downloads in first month after release",
"measurement": "Crates.io API"
},
{
"metric": "GitHub stars",
"target": "200+ stars in first 3 months",
"measurement": "GitHub API"
},
{
"metric": "Community contributions",
"target": "5+ external PRs in first 6 months",
"measurement": "GitHub PR count"
}
]
},
"total_estimated_effort": {
"weeks": 26,
"total_lines_of_code": "~25,000 LOC",
"team_size_recommendation": "2-3 Rust engineers with ML/tensor background",
"key_skills_required": [
"Advanced Rust (traits, generics, unsafe for optimizations)",
"Linear algebra and numerical computing",
"Graph neural network architectures",
"GPU programming (CUDA/WebGPU) - optional",
"Performance profiling and optimization"
]
}
}