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