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Omar SobhandClaude Sonnet 4.6 57e5252caa docs: add full repository review spec (2026-06-26)

Comprehensive layered architectural review covering all 109 crates,
306K LOC, 13,351 tests. Identifies 9 gaps (G0-G8) with the highest-
priority being 45 unimplemented! panics across rtx-backend-cuda/rocm/sycl
and the rtx-distributed workspace exclusion. Includes 17-item 4-phase
roadmap through 90 days.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

2026-06-26 12:48:03 +00:00

33 KiB

Raw Blame History

RustyTorch Full Repository Review

Date: 2026-06-26
Crates: 109
LOC: 306,423
Tests: 13,351+
Incomplete stubs: 51
Toolchain: nightly-2025-10-25 (Rust 2024 edition)

1. Project Overview

RustyTorch is a production-grade, GPU-accelerated ML framework in pure Rust. It aims to match PyTorch's feature breadth while eliminating the Python GIL, delivering memory-safe tensor operations, automatic differentiation, and domain-specific stacks for medical imaging, neuroimaging, and scientific computing.

1.1 Platform Role

Layer 8 — ML Infrastructure for the QuantumRedClaw platform:

QPUDIDP consumes RustyTorch for surrogate model training (MLP, normalizing flows, MC Dropout)
qstar neural decoders depend on GPU compute provided by this framework
Python interop via PyO3/maturin bridges to Jupyter notebooks

1.2 Maturity Snapshot

Metric	Value
Total crates	109
Total Rust LOC	306,423
`#[test]` attributes	13,351+
`todo!/unimplemented!`	51
CI pipelines	5 (format, clippy, build, test, GPU)
Demo applications	30+
Rust toolchain	nightly-2025-10-25
Edition	2024
Build target dir	`/hot/targets/rustyverse/rustytorch`
TIER progress	TIER 0–3 complete (Dec 28, 2025)

1.3 Key Differentiators vs. Ecosystem

Feature	RustyTorch	tch-rs	burn	candle
Pure Rust (no C/C++)	✅	❌ (libtorch)	✅	✅
Multi-backend dispatch	✅	❌	✅	partial
Flash Attention	✅	via ext	❌	❌
Mixture of Experts	✅	❌	❌	❌
Mamba/SSM	✅	❌	❌	❌
Speculative decoding	✅	❌	❌	❌
Medical imaging stack	✅	❌	❌	❌
Neuroimaging (EEG/MEG)	✅	❌	❌	❌
Neural Operators (FNO)	✅	❌	❌	❌
Federated learning	✅	❌	❌	❌

1.4 GPU Backend Support

Backend	Hardware	Status
CUDA (sm_86 default, sm_120 Blackwell)	NVIDIA RTX/A100/H100/5060 Ti	Production
Metal (MPS)	Apple M-series	Production
ROCm/HIP	AMD RDNA 2/3	Partial (15 ops unimplemented)
SYCL/DPC++	Intel Arc	Partial (15 ops unimplemented)
CubeCL (WGPU)	Cross-platform	Production
CPU (OpenBLAS/MKL/manual)	x86/ARM	Production

2. Layer-by-Layer Review

Layer 1: Core Infrastructure

2.1.1 rtx-backend

Status: Production-Ready
Purpose: Compile-time backend dispatch trait — zero runtime overhead
Path: crates/core/rtx-backend/

Key API surface (522 LOC, src/lib.rs):

Backend trait: 430+ methods covering tensor creation, element-wise ops, reductions, LLM-specific ops (flash_attention, rope, rms_norm)
AutodiffBackend: marker trait for gradient-capable backends
QuantizedBackend: quantization support

Test coverage: Feature-gated; integration via backend crates
Gaps: Trait is intentionally abstract. The gap is in the concrete backends (see rtx-backend-cuda, rtx-backend-rocm, rtx-backend-sycl below).

2.1.2 rtx-tensor

Status: Production-Ready
Purpose: PyTorch-compatible GPU-native tensor primitives
Path: crates/core/rtx-tensor/

Key API surface:

Tensor, Device, DType, Shape
ComplexTensor, SparseCOO, SparseCSR
CublasManager, CudnnContext, CuSolverContext, CuSparseContext
35+ tensor operation modules, 7 Metal-specific modules

Test coverage: 1,047 #[test] in src, 184 in tests/; loom concurrency tests; 10+ benchmark files
Notable: Real CUDA kernel compilation via cudarc 0.18.x, real MSL shaders for Metal
Gaps: 2 GPU memory access tests marked RED: unimplemented in storage/core/ (flagged as intentional TDD red phase)

2.1.3 rtx-autograd

Status: Production-Ready
Purpose: Tape-based automatic differentiation, forward and reverse modes
Path: crates/core/rtx-autograd/

Key API surface:

Autodiff<B>: decorator wrapping any Backend<B> to add gradients
grad(), jvp(), vjp(), jacfwd(), jacrev(), hessian(), vmap()
Checkpointing: checkpoint(), EveryNthStrategy, SqrtCheckpointStrategy, AdaptiveCheckpointStrategy
AutogradProfiler with Chrome trace export

Test coverage: 223 #[test] in tests/, 128 in src/; gradient_benchmarks.rs
Gaps: None detected.

2.1.4 rtx-memory

Status: Production-Ready
Purpose: GPU memory pooling, KV cache, ZeRO-style partitioning
Path: crates/core/rtx-memory/

Key API surface:

MemoryPoolManager, TransformerMemoryPools
KvCacheManager, AsyncKvCacheManager, EntropyTracker
ZeroMemoryManager, PagedAttentionManager
Allocators: Buddy, Slab, Stack

Test coverage: 190 #[test] in src/, 190 in tests/; 3 benchmark files (pool, allocation strategies, simple GPU)
Gaps: None detected. Fragmentation target <5% is documented.

2.1.5 rtx-kernel

Status: Production-Ready
Purpose: CUDA PTX + Metal MSL kernel compilation, caching, execution
Path: crates/core/rtx-kernel/

Key API surface:

KernelSystem, KernelCompiler (NVRTC-based), KernelCache, KernelExecutor
MetalKernelCompiler, MetalKernelCache, MetalKernelExecutor
LaunchConfig, KernelParam (type-safe binding)

Gaps: None detected.

2.1.6 rtx-flash-attention

Status: Production-Ready (with one stub in backward pass)
Purpose: Memory-efficient attention, O(n) memory, 5-8× speedup
Path: crates/training/rtx-flash-attention/

Key API surface:

FlashAttention, FlashAttentionFactory, FlashAttentionConfig
SdpaBackendSelector (hardware-aware: FlashAttn2, SDPA, naive fallback)
flash_attention_forward(), flash_attention_backward()

Test coverage: 97 #[test] in src/; flash_attention_bench.rs
Notable: sm_120 (Blackwell/RTX 5060 Ti) support added in commit c2f4796
Gaps: Backward pass gradient computation has a placeholder fallback path for non-CUDA; acceptable since CUDA is the target.

2.1.7 rtx-lora

Status: Production-Ready
Purpose: LoRA and QLoRA parameter-efficient fine-tuning
Path: crates/core/rtx-lora/

Key API surface:

LoraConfig, LoraAdapter, LoraWeight, QLoraWeight
NF4Block (4-bit quantization), AdapterManager, MergeStrategy

Test coverage: Tests in adapter.rs, config.rs, weight.rs, quantized.rs, merge.rs
Gaps: None detected.

2.1.8 rtx-moe

Status: Production-Ready
Purpose: Mixture of Experts (SwitchTransformer, ExpertChoice, TokenChoice routing)
Path: crates/core/rtx-moe/

Key API surface: SwitchTransformer, ExpertChoice, TokenChoice routing strategies; load balancing; expert capacity controls
Gaps: None detected from exploration. (Dedicated Metal MSL kernels in rtx-tensor/metal_moe_ops.)

2.1.9 rtx-mamba

Status: Production-Ready (conv1d history buffer improved in D309)
Purpose: Mamba/State Space Models, O(n) attention alternative
Path: crates/core/rtx-mamba/

Key API surface: MambaRecurrence, selective scan, MambaStep (new ergonomic step API from commit f751414)
Gaps: None detected.

2.1.10 rtx-speculative-decoding

Status: Production-Ready
Purpose: 2-3× inference speedup via draft model + target verification
Path: crates/core/rtx-speculative-decoding/ and crates/production/rtx-inference/

Key API surface: SpeculativeEngine, AdvancedSpeculativeDecoder, EagleDraftModel, MedusaDraftModel, tree-based decoding
Metal ops: metal_speculative_ops in rtx-tensor
Gaps: None detected.

2.1.11 rtx-fusion

Status: Production-Ready
Purpose: Kernel fusion DSL eliminating intermediate tensor writes (10-25% speedup)
Path: crates/core/rtx-fusion/

Key API surface:

Fusion<B> backend wrapper
FusionAnalyzer (pattern detection), FusedKernelCache (LRU), FusionConfig, FusionStats
Sync points: matmul, reductions, flash_attention, data access

Gaps: None detected.

2.1.12 rtx-cubecl

Status: Production-Ready
Purpose: Single-source Rust → GPU kernel synthesis via CubeCL
Path: crates/core/rtx-cubecl/

Key API surface: CubeclBackend, CubeclClient, CubeclDevice, CubeclTensor
Backends: cpu, wgpu, cuda, hip, vulkan, metal
Tier strategy: ~60% ops via portable CubeCL kernels; ~40% via hand-crafted tier-2 (Flash Attention, GEMM, conv)
Gaps: None detected.

2.1.13 rtx-bindings

Status: Partial
Purpose: Python (PyO3), C FFI, ONNX, DLPack interop
Path: crates/core/rtx-bindings/

Key API surface: c_api (enabled), python (disabled), onnx (basic), dlpack (basic)
Test coverage: test files exist but are conditionally disabled
Gaps:

[G1] PyO3 Python bindings disabled due to version conflicts
[G7] ONNX/DLPack are basic stubs — no full model export or zero-copy interchange

2.1.14 rtx-macros

Status: Production-Ready
Purpose: Derive macros: #[derive(Module)], #[derive(Config)]
Path: crates/core/rtx-macros/

Key API surface:

Module derive: generates parameters(), to_device(), train(), field attrs: #[param], #[module], #[constant]
Config derive: builder pattern with #[config(default = "value")]

Gaps: None detected.

2.1.15 rtx-backend-cuda / rtx-backend-rocm / rtx-backend-sycl

Status: Partial — Critical gap
Path: crates/core/rtx-backend-{cuda,rocm,sycl}/src/lib.rs

Each of these three backend adapters is missing the same 15 operations:

sin, cos, pow, clamp, relu, sigmoid, tanh,
var, var_dim, leaky_relu, elu, gt_scalar,
conv2d, max_pool2d, avg_pool2d

These are unimplemented!() panics — any code path that routes through the backend dispatch layer for these ops will panic at runtime on CUDA, ROCm, and SYCL.

Note: rtx-tensor has its own GPU implementations (via cudarc + cuDNN directly) that do not go through the backend dispatch path. But any consumer using the generic Backend trait abstraction hits these panics.

This is gap G0 — the highest-priority fix in this review.

Layer 2: Training & Optimization

2.2.1 rtx-transformers

Status: Production-Ready (core); Phase 2-3 modules deferred
Path: crates/training/rtx-transformers/

Key API surface:

Layers: Mamba, MoE, RoPE, Linear Attention, Spectral Norm, Block Sparse Attention, GeLU, SwiGLU, GatedMLP
Architectures: BERT, GPT, TransformerBlock, MultiHeadAttention
Optimizers: Adam, AdamW, Shampoo
New (commit 24bd5cf): mamba_step, ClonedMemoryUpdater, GatedMemoryUpdater, SetEncoderTeacher

Test coverage: 1,845 #[test] — highest in the workspace
Notable: Also has integration tests at tests/d350_gpu_backbone_training.rs, tests/mamba_temporal_learning.rs, tests/real_selective_scan.rs
Gaps: [G4] Phase 2 modules commented out: validation_framework, meta-learning, advanced tokenization, multi-modal text, continual learning

2.2.2 rtx-distributed

Status: Production-Ready (code) — excluded from workspace
Path: crates/training/rtx-distributed/

Key API surface:

Collective ops: AllReduce, Broadcast, Gather, Scatter via NCCL/RCCL/MPI
Parallelism: DistributedDataParallel, FSDP2, TensorParallel, PipelineParallel
Elastic training: ElasticCluster, FaultDetector, ActivationCheckpointManager
DCP: Distributed Checkpoint with DeviceMesh, HardwareTopology

Test coverage: 439 #[test]
Gaps: [G2] Excluded from workspace Cargo.toml due to RNCCL path dependency — cannot be tested in CI, cannot be used as a dependency without manual workspace inclusion.

2.2.3 rtx-rl

Status: Production-Ready
Path: crates/training/rtx-rl/

Key API surface: PPO, DQN, SAC, ActorLearner, Environment, ReplayBuffer, RLHF
Test coverage: 6 test files (PPO, SAC, RLHF, actor-learner, environment, replay buffer); rl_bench benchmark
Gaps: None detected.

2.2.4 rtx-compress

Status: Production-Ready
Path: crates/training/rtx-compress/

Key API surface: CompressionPipeline, CompressionConfig, CompressedStorage, quantization, pruning, distillation, KV cache, LoRA
Test coverage: 164 #[test] in tests/; compression_bench
Gaps: Some advanced quantization modes (AWQ, GPTQ, SmoothQuant) are in progress — basic pipeline is complete.

2.2.5 rtx-nas

Status: Production-Ready
Path: crates/training/rtx-nas/

Key API surface: DARTS, PC-DARTS, FairNAS, RandomSearch, Architecture, DARTSCell, SearchSpace
Test coverage: 165 #[test]
Gaps: None detected.

2.2.6 rtx-federated

Status: Production-Ready
Path: crates/training/rtx-federated/

Key API surface:

Aggregation: FedAvg, FedProx, SCAFFOLD, FedNova, AsyncAggregation
Byzantine-robust: Krum, MultiKrum, TrimmedMean, AnomalyDetector, ReputationSystem
Privacy: DifferentialPrivacy, HomomorphicEncryption (tfhe), SMPC, PrivacyBudget
Personalization: MetaLearning, ClientClustering, MultiTaskLearning, TransferLearning

Test coverage: federated_bench; federated_demo example
Gaps: None detected. 80+ dependencies including cryptographic primitives.

2.2.7 rtx-auto

Status: Production-Ready
Path: crates/training/rtx-auto/

Key API surface: AutonomousAgent, DataEngineeringAgent, ParallelPlannerAgent, QuantGuardianAgent, KernelSynthesizerAgent, ProposalValidator, RollbackManager
Test coverage: Integration test for AutonomousOptimizer initialization
Gaps: None detected.

2.2.8 rtx-evolution

Status: Production-Ready
Path: crates/training/rtx-evolution/

Key API surface: EvolutionOrchestrator, TelemetryAnalyzer, SafeSandbox, KnowledgeGraph, MultiObjectiveOptimizer, ParetoFrontier, HyperparameterTuner
Test coverage: orchestrator_tests.rs
Gaps: None detected.

Layer 3: Model Architectures

2.3.1 rtx-vision

Status: Production-Ready
Path: crates/models/rtx-vision/

Key API surface: ViT, ConvNeXt V2, EfficientNetV2, MobileViT, DenseNet, NFNet, EdgeViT, MaxViT, CoAtNet (150+ exported types)
Preprocessing: ImageTensor, Augmentation, ImageProcessor
Gaps: None detected.

2.3.2 rtx-multimodal

Status: Production-Ready (quantum/neuromorphic stubs remain)
Path: crates/models/rtx-multimodal/

Key API surface: MultimodalSystem, VisionTransformer, AudioTransformer, CrossModalAttention, ModalityFusion (7 strategies)
Test coverage: 122 #[test]; multimodal_bench
Gaps: [G8] Quantum enhancement and neuromorphic preprocessing modules have TODO stubs.

2.3.3 rtx-diffuse

Status: Production-Ready
Path: crates/models/rtx-diffuse/

Key API surface: UNet, DiT (Diffusion Transformer), DDIMSampler, DPMSolverPP, UniPCSampler (5-10 step fast inference), ControlNet, IP-Adapter, T2I-Adapter, ClassifierFreeGuidance, LoRA diffusion
Test coverage: 141 #[test] in src/; validation tests; diffusion_bench
Gaps: None detected.

2.3.4 rtx-timeseries

Status: Production-Ready (quantum stubs remain)
Path: crates/models/rtx-timeseries/

Key API surface: ARIMAModel, SARIMAModel, ProphetModel, NeuralProphetModel, ExponentialSmoothingModel, StateSpaceModel, TransformerForecastModel, Forecaster, TimeSeriesAnalyzer (seasonality, stationarity, anomaly detection)
Test coverage: Criterion benchmarks
Gaps: [G8] Quantum forecasting modules marked // TODO: Implement.

2.3.5 rtx-nlg

Status: Production-Ready
Path: crates/models/rtx-nlg/

Key API surface: TextGenerator, ModelServer, StreamingGenerator, BatchGenerator, ConversationManager, BeamSearch, NucleusSampling, RepetitionPenalty, ToxicityFilter
Test coverage: 3 benchmark suites (generation, translation, summarization)
Gaps: None detected.

Layer 4: Production & Deployment

2.4.1 rtx-serving-api

Status: Production-Ready
Path: crates/production/rtx-serving-api/

Key API surface: ServingServer, AdvancedServingServer, ContinuousBatchingController, QueueManager, RateLimitManager, CircuitBreaker, GrammarSampler (BNF parser), StructuredGenerator (JSON schema), MultiModelManager, WebSocket streaming, BudgetConfig
Stack: Axum (HTTP), Tonic (gRPC), tokio-tungstenite (WebSocket), Prometheus
Test coverage: 140 #[test] in src/; cache_performance benchmark
Gaps: None detected.

2.4.2 rtx-inference

Status: Production-Ready
Path: crates/production/rtx-inference/

Key API surface: InferenceEngine, PagedKvCache (GPU→CPU→NVMe tiering), BatchScheduler (SLA lanes), AdvancedSpeculativeDecoder, EagleDraftModel, MedusaDraftModel, QuantizationConfig, MetricsCollector
Test coverage: Integration tests with proptest
Features: onnx-runtime, burn, burn-wgpu, candle, candle-cuda, candle-metal
Gaps: None detected.

2.4.3 rtx-streaming

Status: Production-Ready
Path: crates/production/rtx-streaming/

Key API surface: StreamingServer, ConnectionManager, TokenGenerator, BackpressureHandler, AdaptiveProcessor, Kafka integration, edge computing support
Design target: sub-millisecond latency, >1000 concurrent connections
Test coverage: streaming_bench
Gaps: None detected.

2.4.4 rtx-wasm-inference

Status: Production-Ready
Path: crates/production/rtx-wasm-inference/

Key API surface: WasmInferenceEngine, ComputeBackend (Cpu/WebGpu/Auto), TensorCore, DType
Features: WASM SIMD, SharedArrayBuffer threads, WebGPU acceleration, TypeScript/JavaScript bindings
Build: opt-level = "s", LTO enabled, wasm-opt with SIMD
Gaps: None detected.

2.4.5 rtx-onnx

Status: Partial
Path: crates/production/rtx-onnx/

Key API surface: OnnxSession, OnnxSessionConfig, ExecutionProviderType (CPU/CUDA/CoreML/TensorRT/DirectML), tensor_bridge (rtx ↔ ort bidirectional)
Test coverage: Integration tests with tokio
Gaps: [G7] Session-only integration (import/run). No full model export path from rtx-graph IR.

Layer 5: Specialized Domains

2.5.1 Medical Imaging (8 crates)

Status: Production-Ready
Crates: rtx-medical-core, rtx-medical-io, rtx-mri2fe, rtx-registration, rtx-segmentation, rtx-materials, rtx-fem-export, rtx-mesh-gen

Test coverage: 165 #[test] in rtx-medical-core
Key capabilities: DICOM/NIfTI I/O, MRI→FEM pipeline for biomechanical simulation, image registration, segmentation, tissue material properties, FEM mesh export
Gaps: None detected.

2.5.2 Neuroimaging — rtx-neuro Suite (17 crates)

Status: Production-Ready
Crates: rtx-neuro (super-crate), rtx-neuro-core (47K LOC consolidated), rtx-neuro-io, rtx-neuro-signal, rtx-neuro-forward, rtx-neuro-inverse, rtx-neuro-connectivity, rtx-neuro-stats, rtx-neuro-anatomy, rtx-neuro-db, rtx-neuro-lsl, rtx-neuro-realtime, rtx-neuro-artifacts, rtx-neuro-gnn, rtx-neuro-pinn, rtx-neuro-fem, rtx-neuro-python

Test coverage: 266 #[test] in rtx-neuro-core
Key capabilities: EEG/MEG I/O, Lab Streaming Layer (LSL) real-time, signal processing, forward/inverse modeling (source localization), GNN connectivity, artifact detection, PINN coupling, FEM coupling
Gaps:

rtx-neuro-python requires separate maturin build — not in CI workflow
Python bindings build is manual, not automated

2.5.3 Physics-Informed & Scientific Computing (8 crates)

Status: Production-Ready
Crates: rtx-neural-operator, rtx-piddm, rtx-digital-twin, rtx-fea, rtx-cfd, rtx-science, rtx-geom, rtx-synthesis

Test coverage: rtx-fea has 371 #[test] in src/ and 176 in tests/; rtx-cfd has 179 in src/ and 142 in tests/
Key capabilities: FNO/DeepONet (mesh-agnostic PDE solving), Physics-Informed Diffusion, Digital Twins, Finite Element Analysis, CFD with GPU acceleration
Gaps: 4 unimplemented! in rtx-fea tests (test-only, not hot path).

2.5.4 Other Specialized (6 crates)

Status: Production-Ready
Crates: rtx-compiler, rtx-platform, rtx-nmf, rtx-ml-classic, rtx-sklearn-py, rtx-polygraph
Gaps: None detected. rtx-sklearn-py bridges to scikit-learn via Python interop (subject to same PyO3 constraint as G1).

3. Cross-Cutting Concerns

3.1 Backend Op Completeness (G0 — Critical)

15 operations are unimplemented!() panics in rtx-backend-cuda, rtx-backend-rocm, and rtx-backend-sycl:

sin    cos    pow    clamp   relu
sigmoid  tanh   var    var_dim  leaky_relu
elu    gt_scalar  conv2d   max_pool2d  avg_pool2d

Impact: Any code using the generic Backend trait dispatch path will panic at runtime on these ops across all three backends. The rtx-tensor crate has its own cudarc/cuDNN implementations that bypass the trait, so it is unaffected. But consumers using the backend abstraction (e.g., Fusion<CudaBackend>, Autodiff<CudaBackend>) are exposed.
Fix: Implement these 15 ops in each of the three backends (45 total functions). Start with rtx-backend-cuda as the highest-value target, then mirror to ROCm/SYCL.
Files: crates/core/rtx-backend-cuda/src/lib.rs, crates/core/rtx-backend-rocm/src/lib.rs, crates/core/rtx-backend-sycl/src/lib.rs

3.2 Python Bindings Disabled (G1 — High)

Affected crate: crates/core/rtx-bindings/
Current state: python feature is disabled with comment "version conflicts". C API works.
Impact: Blocks Jupyter notebook use case. Blocks qstar Python integration. rtx-neuro-python and rtx-sklearn-py also affected.
Fix: Update to PyO3 ≥0.22.x (which stabilized the GIL API). Add maturin build step to CI. Unlock the python feature flag.

3.3 rtx-distributed Workspace Exclusion (G2 — High)

Affected crate: crates/training/rtx-distributed/
Current state: Manually excluded from Cargo.toml members due to RNCCL path dependency.
Impact: 439 tests not run in CI. Cannot use rtx-distributed as a dependency without manual workspace modification. Multi-GPU training is not CI-verified.
Fix: Make RNCCL an optional dependency with a CPU/mock fallback for CI. Conditional compilation: #[cfg(feature = "nccl")].

3.4 Async Debt (G3 — Medium)

Scope: 277 async fn declarations across 97 files that never use .await internally.
Affected crates: rtx-graph, rtx-memory, rtx-tokenization, rtx-validation, rtx-nlg, rtx-config, rtx-monitoring, rtx-serving-api, rtx-science, rtx-evolution, rtx-federated, rtx-flash-attention, rtx-rl, rtx-transformers
Current state: Functions are unnecessarily async, adding allocator overhead for the poll machinery on every call.
Fix:

For functions that could be async (I/O, GPU sync): add the actual .await call points
For functions that have no async work: remove the async keyword
Tooling: cargo clippy -- -W clippy::async_yields_async

3.5 Deferred rtx-transformers Modules (G4 — Medium)

Affected crate: crates/training/rtx-transformers/
Current state: Phase 2 and Phase 3 modules are commented out with // TODO: Re-enable markers.
Phase 2 deferred: validation_framework, meta-learning, advanced tokenization
Phase 3 deferred: multi-modal text integration, continual learning
Fix: Re-enable one module at a time. Start with validation_framework (lowest risk, standalone). Each re-enablement needs its own integration test before merge.

3.6 rtx-tts Disabled (G5 — Medium)

Affected crate: crates/models/rtx-tts/ (185 #[test] in src — significant investment)
Current state: Excluded from workspace. Depends on rtx_nn module API that has changed.
Fix: Audit current rtx-nn public API against rtx-tts imports; update use paths and type signatures to match. No architectural change needed.

3.7 Partial ONNX / DLPack Interop (G7 — Low)

Affected crates: rtx-bindings, rtx-onnx, rtx-onnx-codegen
Current state:

ONNX: Session creation and inference work; rtx-onnx-codegen has a todo!("Constant tensor") — model export from rtx-graph is incomplete
DLPack: Basic structure only; no zero-copy __dlpack__ array interchange Fix: Implement ONNX export using rtx-graph IR (graph walking → protobuf emission). Implement DLPack DLManagedTensor structs with from_tensor/to_tensor methods.

3.8 GPU Memory Access Tests (G6 — Low)

Affected crate: crates/core/rtx-tensor/src/storage/
Current state: 2 tests explicitly marked // RED: GPU memory access is unimplemented — intentional TDD red phase.
Fix: Implement read_gpu_memory() / write_gpu_memory() helpers in storage core; flip tests to green.

3.9 Quantum/Neuromorphic TODOs (G8 — Low)

Affected crates: crates/models/rtx-timeseries/, crates/models/rtx-multimodal/
Current state: // TODO: Implement stubs for quantum enhancement and neuromorphic preprocessing modules.
Options: Either implement (adds value for research use cases) or remove stubs and document as future work. Don't leave dead stubs in production code.

3.10 CI Coverage Gaps

No maturin Python wheel build job in .gitea/workflows/
No WASM build/size-check job (rtx-wasm-inference)
rtx-distributed tests not run (see G2)
GPU CI job runs but depends on self-hosted runner availability

4. Gaps Registry

ID	Severity	Crate(s)	Description	Recommended Fix	Effort
G0	Critical	rtx-backend-cuda, rtx-backend-rocm, rtx-backend-sycl	15 ops are `unimplemented!()` panics per backend (45 total): sin, cos, pow, clamp, relu, sigmoid, tanh, var, var_dim, leaky_relu, elu, gt_scalar, conv2d, max_pool2d, avg_pool2d	Implement all 15 in each backend; start with CUDA	M
G1	High	rtx-bindings, rtx-neuro-python, rtx-sklearn-py	PyO3 Python bindings disabled (version conflict); blocks Jupyter/qstar integration	Update to PyO3 ≥0.22, add maturin CI step	M
G2	High	rtx-distributed	Excluded from workspace due to RNCCL path dep; 439 tests unrun; multi-GPU CI broken	Make RNCCL optional `#[cfg(feature = "nccl")]`, add CPU/mock fallback	L
G3	Medium	97 files	277 async fns without `.await` — unnecessary allocation overhead per call	Batch sweep: remove async or add real await points; use clippy lint	M
G4	Medium	rtx-transformers	Phase 2-3 modules commented out (validation_framework, meta-learning, multi-modal text, continual learning)	Re-enable incrementally, one module per PR with integration test	M
G5	Medium	rtx-tts	Disabled: rtx_nn API surface changed after rtx-tts was written	Audit rtx-nn current API; update import paths and type sigs	S
G6	Low	rtx-tensor/storage	2 GPU memory access tests in TDD red phase	Implement read/write GPU memory helpers	S
G7	Low	rtx-bindings, rtx-onnx, rtx-onnx-codegen	ONNX model export incomplete (`todo!("Constant tensor")`); DLPack zero-copy not implemented	Full ONNX export via rtx-graph IR; DLPack `DLManagedTensor`	L
G8	Low	rtx-timeseries, rtx-multimodal	Quantum/neuromorphic TODO stubs in non-hot-path modules	Implement or remove — no dead stubs in production	S-M

Effort key: S = days, M = 1-2 weeks, L = 3-4 weeks

5. Prioritized Roadmap

Phase 1 — Critical & Blocking Fixes (Weeks 1-2)

Goal: Eliminate runtime panics in the backend dispatch path; restore CI integrity; re-enable Python ecosystem.

#	Action	Gap	Owner crates	Verification
1	Implement 15 missing backend ops in rtx-backend-cuda	G0	rtx-backend-cuda	All 15 ops covered by tests; `cargo test -F cuda` passes
2	Mirror same 15 ops to rtx-backend-rocm and rtx-backend-sycl	G0	rtx-backend-rocm, rtx-backend-sycl	Feature-gated tests pass
3	Update PyO3 to ≥0.22, re-enable `python` feature in rtx-bindings	G1	rtx-bindings	`cargo test -F python`; `maturin build` succeeds
4	Add maturin build job to `.gitea/workflows/ci.yml`	G1	CI	Wheel builds on push to main
5	Make RNCCL optional in rtx-distributed; re-add to workspace	G2	rtx-distributed	`cargo test` (CPU path) runs in CI; all 439 tests pass
6	Fix rtx-tts API mismatch with rtx-nn	G5	rtx-tts	`cargo test` for rtx-tts passes

Phase 2 — Technical Debt Reduction (Weeks 3-4)

Goal: Eliminate async overhead, restore deferred modules, complete TDD red phases.

#	Action	Gap	Verification
7	Async sweep — 97 files	G3	`cargo clippy -- -W clippy::async_yields_async` returns zero warnings
8	Re-enable rtx-transformers `validation_framework` module	G4	Integration test passes
9	Re-enable rtx-transformers `meta-learning` module	G4	Integration test passes
10	Implement GPU memory access helpers in rtx-tensor/storage	G6	2 RED tests flip to green
11	Resolve quantum/neuromorphic TODOs in rtx-timeseries and rtx-multimodal (implement or remove)	G8	No `// TODO` stubs in non-test code

Phase 3 — 20-Demo Push (Weeks 5-10)

Goal: Complete the 20 next-generation demo applications per resume_nextplan.md.

Phase 3A — Medical (Weeks 5-6)

DrugBinder: molecular docking with GNN + RL
CardioSim: cardiac simulation with FEM coupling
TumorBoard AI: multi-modal oncology decision support

Phase 3B — Finance (Weeks 6-7)

QuantumPort: portfolio optimization with quantum-inspired algorithms
MarketSim: market simulation with agent-based modeling
RiskFlow: risk factor analysis with normalizing flows
AlgoArena: algorithmic trading benchmarking

Phase 3C — AI/ML Core (Weeks 7-8)

NeuralOp Studio: interactive FNO/DeepONet playground
WorldGen: video diffusion for world model training
EmbodiedSim: robotics simulation with physics coupling
FoundationForge: foundation model training harness

Phase 3D — Engineering (Weeks 8-9)

AeroFlow: aerodynamics CFD with GPU acceleration
WeatherCast: NWP with neural operators
SeismicAI: seismic inversion with PINNs
StructuralPINN: structural mechanics with physics-informed networks

Phase 3E — Cluster (Weeks 9-10)

ClusterViz: distributed training monitoring dashboard
DistributedLLM: multi-node LLM training benchmark
FederatedMed: federated medical imaging across hospital networks

Phase 4 — Ecosystem Maturity (Weeks 11-12)

Goal: Production-grade interoperability, documentation, and CI completeness.

#	Action	Gap	Verification
12	Full ONNX model export: rtx-graph IR → protobuf emission	G7	Round-trip test: train rtx model, export ONNX, run via ort session
13	DLPack zero-copy: implement `DLManagedTensor` structs	G7	Array interchange test with numpy
14	Re-enable rtx-transformers Phase 3 modules (multi-modal text, continual learning)	G4	Integration tests pass
15	Add WASM build + size-check CI job for rtx-wasm-inference	—	WASM bundle <5MB (gzipped); `wasm-pack test` passes
16	mdBook: complete all 8 layer chapters with API examples	—	`mdbook build` succeeds; no broken links
17	rtx-neuro-python: add maturin build to CI	G1	Python wheel installable; EEG demo runs

Appendix A: Test Density by Crate

Crates with the highest test investment (top 15 by #[test] count):

Rank	Crate	`#[test]` count
1	rtx-transformers	1,845
2	rtx-tensor	1,047+ (src + tests)
3	rtx-distributed	439
4	rtx-fea	371 (src) + 176 (tests)
5	rtx-nn	334
6	rtx-neuro-core	266
7	rtx-autograd	223 (tests) + 128 (src)
8	rtx-metal	201
9	rtx-runtime	192 (src) + 190 (tests)
10	rtx-memory	190
11	rtx-tts	185 (currently excluded)
12	rtx-preprocessing	184 (tests)
13	rtx-tensor (tests/)	184
14	rtx-interpret	183 (src) + 145 (tests)
15	rtx-cfd	179 (src) + 142 (tests)

Appendix B: Unimplemented Stub Inventory

All 51 todo!/unimplemented! occurrences:

Count	Location	Operations
15	`rtx-backend-cuda/src/lib.rs`	sin, cos, pow, clamp, relu, sigmoid, tanh, var, var_dim, leaky_relu, elu, gt_scalar, conv2d, max_pool2d, avg_pool2d
15	`rtx-backend-rocm/src/lib.rs`	(same 15 ops)
15	`rtx-backend-sycl/src/lib.rs`	(same 15 ops)
4	`rtx-fea/tests/`	Test-only stubs (not hot path)
1	`rtx-transformers/src/layers/geglu.rs`	Numerical gradient checking (TDD red phase)
1	`rtx-onnx-codegen/src/ops/mod.rs`	`todo!("Constant tensor")` in ONNX codegen

Total: 51 — all non-test stubs are in backend op implementations or ONNX codegen.

Appendix C: CI Workflow Coverage

Workflow	Triggers	What it checks
ci.yml	Push/PR	rustfmt, clippy (pedantic), build Linux+macOS, cargo test
gpu-tests.yml	Push to main	CUDA kernel tests (self-hosted GPU runner)
benchmarks.yml	Manual / release	Criterion benchmarks, performance regression
docs.yml	Push to main	mdbook build, doc links
release.yml	Tag push	Build + publish crates

Gap: No WASM build job. No maturin/PyO3 wheel build job. rtx-distributed excluded from all jobs.

Spec self-review: no TBD/TODO placeholders; all 8 gaps in registry; all 5 layers covered; roadmap has 17 concrete action items across 4 phases.

33 KiB Raw Blame History Unescape Escape

RustyTorch Full Repository Review

1. Project Overview

1.1 Platform Role

1.2 Maturity Snapshot

1.3 Key Differentiators vs. Ecosystem

1.4 GPU Backend Support

2. Layer-by-Layer Review

Layer 1: Core Infrastructure

2.1.1 rtx-backend

2.1.2 rtx-tensor

2.1.3 rtx-autograd

2.1.4 rtx-memory

2.1.5 rtx-kernel

2.1.6 rtx-flash-attention

2.1.7 rtx-lora

2.1.8 rtx-moe

2.1.9 rtx-mamba

2.1.10 rtx-speculative-decoding

2.1.11 rtx-fusion

2.1.12 rtx-cubecl

2.1.13 rtx-bindings

2.1.14 rtx-macros

2.1.15 rtx-backend-cuda / rtx-backend-rocm / rtx-backend-sycl

Layer 2: Training & Optimization

2.2.1 rtx-transformers

2.2.2 rtx-distributed

2.2.3 rtx-rl

2.2.4 rtx-compress

2.2.5 rtx-nas

2.2.6 rtx-federated

2.2.7 rtx-auto

2.2.8 rtx-evolution

Layer 3: Model Architectures

2.3.1 rtx-vision

2.3.2 rtx-multimodal

2.3.3 rtx-diffuse

2.3.4 rtx-timeseries

2.3.5 rtx-nlg

Layer 4: Production & Deployment

2.4.1 rtx-serving-api

2.4.2 rtx-inference

2.4.3 rtx-streaming

2.4.4 rtx-wasm-inference

2.4.5 rtx-onnx

Layer 5: Specialized Domains

2.5.1 Medical Imaging (8 crates)

2.5.2 Neuroimaging — rtx-neuro Suite (17 crates)

2.5.3 Physics-Informed & Scientific Computing (8 crates)

2.5.4 Other Specialized (6 crates)

3. Cross-Cutting Concerns

3.1 Backend Op Completeness (G0 — Critical)

3.2 Python Bindings Disabled (G1 — High)

3.3 rtx-distributed Workspace Exclusion (G2 — High)

3.4 Async Debt (G3 — Medium)

3.5 Deferred rtx-transformers Modules (G4 — Medium)

3.6 rtx-tts Disabled (G5 — Medium)

3.7 Partial ONNX / DLPack Interop (G7 — Low)

3.8 GPU Memory Access Tests (G6 — Low)

3.9 Quantum/Neuromorphic TODOs (G8 — Low)

3.10 CI Coverage Gaps

4. Gaps Registry

5. Prioritized Roadmap

Phase 1 — Critical & Blocking Fixes (Weeks 1-2)

Phase 2 — Technical Debt Reduction (Weeks 3-4)

Phase 3 — 20-Demo Push (Weeks 5-10)

Phase 4 — Ecosystem Maturity (Weeks 11-12)

Appendix A: Test Density by Crate

Appendix B: Unimplemented Stub Inventory

Appendix C: CI Workflow Coverage

33 KiB

Raw Blame History