6.4 KiB
Phase 12: Superset Expansion & Next-Gen AI/ML Platform Plan
Executive Summary
With RustyTorch++ 1.0 delivering full PyTorch parity and exceeding all performance goals in Phase 11, Phase 12 expands into superset capabilities that no existing AI/ML framework provides as a unified, GPU-native, memory-safe platform.
Phase 12 introduces Graph Neural Networks, Diffusion Models, Multimodal AI, Reinforcement Learning at scale, advanced privacy & safety tooling, Polygraph IR super-fusion, neural compression, and autonomous AI-assisted optimization agents — all deeply integrated into the RustyTorch++ architecture and Stratoswarm orchestration.
This plan specifies what crates, modules, APIs, kernels, and tooling must be built to deliver these capabilities, along with acceptance tests, KPIs, and rollout sequence.
Phase 12 Goals
Superset Model Coverage — Add domains under-served by existing stacks: GNNs, geometric deep learning, 3D vision, diffusion models, multimodal fusion, RL at scale.
Next-Level Compilation & Runtime — Introduce Polygraph IR for cross-domain fusion, speculative/assisted training, and live bidirectional AOT.
Privacy & Robustness by Design — First-class DP, secure aggregation, red-team harness, synthetic data generation.
Zero-Friction Extensibility — Typed safe custom op macros, polyglot zero-copy SDKs, developer CLI suite.
Autonomous Platform Agents — Auto-data engineering, auto-parallel planning, auto-quant accuracy guardians, closed-loop kernel synthesis.
Enterprise & Research Differentiation — Make regulated deployment, reproducibility, and collaborative experimentation native.
Deliverables by Area
- Graph & Geometric Learning
Crate: rtx-geomFeatures:
Message passing (GCN, GAT, GraphSAGE, GIN)
Dynamic ragged batching & heterogeneous graphs
GPU graph samplers (neighbor, random walk, k-hop)
Geometric ops: k-NN search, radius search, point cloud transformsDependencies: rtx-tensor, rtx-runtime, new rtx-sparse module.Exit Tests:
2× throughput vs DGL/PyG for billion-edge inference
Memory overhead < 1.3× dense baseline
Correctness: gradients match analytical on small graphs
- Diffusion & Generative Suite
Crate: rtx-diffuseFeatures:
UNet/DiT blocks, fused conv-attn kernels
Scheduler zoo (DDIM, DPM++, PNDM, LMS)
LoRA/LoCon/ControlNet adapter loading
High-res tiling with KV reuse & weight streamingDependencies: rtx-tensor, rtx-synthesis for AOT conv/attn fusion.Exit Tests:
1.5× img/sec vs PyTorch pipelines on same GPU
Deterministic outputs for fixed seed/scheduler
Fully quantized inference (INT8/FP8) parity with FP32 PSNR ≥ 38 dB
- Reinforcement Learning at Scale
Crate: rtx-rlFeatures:
GPU-based simulators API (Isaac-like interface)
Multi-agent replay buffers & prioritized sampling
PPO/A2C/SAC/DPO/ORPO implementations
Elastic topology: actor–learner scaling via StratoswarmDependencies: rtx-dist, rtx-graph.Exit Tests:
PPO at ≥1.3× sample throughput vs Ray RLlib
Actor-learner pipeline scales to ≥256 actors with >0.8× efficiency
Deterministic simulation reproducibility tests pass
- Multimodal AI
Crate: rtx-multimodalFeatures:
Vision transformers (ViT, CLIP)
Audio transformers (Conformer, Whisper-class)
Video transformers (TimeSformer, MViT)
Speech-TTS with chunked prefill and prefix cachingDependencies: rtx-fft, rtx-tensor, rtx-infer.Exit Tests:
Streaming ASR latency p95 < 250 ms
TTS MOS ≥ 4.2 on eval set
Video transformer inference at ≥0.8× PyTorch baseline speed
- Polygraph IR & Super-Fusion
Crate: rtx-polygraphFeatures:
Unified IR for dense, sparse, graph, FFT, control-flow ops
Cross-domain fusion legality checks
Persistent kernel cache keyed by workload signatures
AOT pipelines with live patchingDependencies: rtx-compiler, rtx-synthesis.Exit Tests:
≥25% step-time reduction on multi-domain models
Cache hit ratio ≥ 85% after warm-up
Fusion correctness validated via differential testing
- Privacy, Safety & Robustness
Crates: rtx-privacy, rtx-robustFeatures:
DP-SGD, label-DP, secure aggregation
On-GPU redaction transforms
Adversarial attack/defense toolkit for vision/audio/LLMs
LLM jailbreak/fuzz harnessDependencies: rtx-optim, rtx-graph, rtx-infer.Exit Tests:
DP budget tracking accurate to < 0.1% error
Successful defense against ≥90% of red-team jailbreak prompts
Adversarial accuracy drop < 5% at ε=8 perturbations
- Neural Compression & Memory Efficiency
Crate: rtx-compressFeatures:
Product-quantized KV cache (PQ/OPQ)
Vector-quantized checkpoints (4/8-bit)
Layerwise mixed-precision search
Zero-copy Arrow-based RAG integrationDependencies: rtx-infer, rtx-tensor.Exit Tests:
KV cache memory usage ↓ ≥60% with < 1% quality loss
Checkpoint load time ↓ ≥40%
RAG query latency ↓ ≥20%
- Developer Experience & Tooling
Crates/Tools:
rtx-opmacros: #[rtx_op] macro generates forward/backward, tests, docs
CLI: rtx-doctor (env & perf), rtx-flame (kernel flamegraphs), rtx-trace (shareable traces)
Polyglot SDKs with DLPack zero-copy for Python, JS, Java, GoExit Tests:
Custom op round-trip (macro→build→train) in < 3 min
Flamegraph generation ≤ 10 s for 1k kernel calls
SDKs achieve ≥95% parity with Python API
- Autonomous Platform Agents
Crates: rtx-auto (submodules for data, parallel, quant, kernels)Features:
Auto-data engineering (ETL rewrites, sharding, caching)
Auto-parallel planner (chooses DP/TP/PP/SP config)
Auto-quant accuracy guardian
Auto-kernel synthesizer with profiler feedback loopExit Tests:
≥70% of merged proposals yield ≥10% KPI improvement
Zero production regressions in canary→prod rollouts
Rollback rehearsals succeed in < 30 s
Rollout Sequence
GNN + Polygraph IR (rtx-geom, rtx-polygraph)
Diffusion & fused conv-attn (rtx-diffuse)
Auto-Quant & Accuracy Guardian (rtx-auto)
Typed custom ops + CLI tooling (rtx-opmacros, tools)
Privacy & robustness suite (rtx-privacy, rtx-robust)
Multimodal + RL scale-up (rtx-multimodal, rtx-rl)
Compression + RAG zero-copy (rtx-compress)
Full autonomous agents loop (rtx-auto all submodules)
KPIs for Phase 12
Performance Superset: ≥25% average step-time reduction over Phase 11 baselines
Coverage Superset: Support ≥95% of all major AI/ML domains in one stack
Scalability: Proven to ≥1024 GPUs in distributed mode
Privacy/Safety: Passes 90%+ red-team adversarial tests
Developer Velocity: Custom op to production in < 5 minutes
Compression: Model memory footprint ↓ ≥50% without measurable quality drop