feat: Add Alpamayo end-to-end Expert diffusion support

[Turoad]

What does this PR do?

Type of change: New feature

Overview: Add end-to-end TensorRT inference for Alpamayo 1.5 (10B VLM + Diffusion Expert) autonomous driving model. After VLM decode produces a KV cache, the Expert runner performs 10-step flow-matching Euler integration to generate 6 candidate trajectories — all on GPU without host round-trips.

New files

• AlpamayoExpertRunner (cpp/runtime/alpamayoExpertRunner.h/.cpp): Loads Expert TRT engine, manages GPU buffers (KV reshape, noisy action, timestep, attention mask), runs 10-step Euler diffusion
• CUDA kernels (cpp/kernels/alpamayoExpertKernels/): kvCacheReshapeRepeat, buildPositionIds, fillTimestep, eulerUpdate

Modified files

• llmInferenceRuntime: Expert integration after VLM decode, StopAfterEOS for <traj_future_start> token, single-seq and multi-seq candidate modes, KV cache dump for offline validation
• llm_inference.cpp: CLI flags --expertEngine, --numCandidates, --numDiffusionSteps, --multiSeq, --dumpKVCache
• QwenViTRunner: preprocessPreparedVisual() for pre-processed multi-camera images (bypasses runtime image decoding)
• CMakeLists: curand linkage, SM 110 (Jetson Thor) architecture

Validation

Validated on Jetson AGX Thor with 300 samples:

Config	mean minADE	median minADE	Steady-state latency
FP8 VLM + BF16 Expert 6x (recommended)	0.799m	0.637m	~2.83s/sample
PyTorch reference	0.827m	0.700m	3.62s/sample

Usage

llm_inference \
  --engineDir <vlm_engine> \
  --multimodalEngineDir <visual_engine> \
  --expertEngine <expert_trt_engine> \
  --numCandidates 6 \
  --numDiffusionSteps 10 \
  --inputFile input.json \
  --outputFile output.json

🚀 Pull Request Checklist

✅ Pre-commit Checks

• Code formatted with clang-format (style=file)
• codespell passed (0 errors)
• License headers added (SPDX Apache-2.0)

🧪 Tests

• Compiled and tested on Jetson AGX Thor (ARM64, CUDA 12.8, TRT 10.x)
• 300-sample precision validation (minADE aligned with PyTorch reference)
• Speed validation (no regression vs baseline)

📄 Documentation

• CLI flags documented in commit message

⚙️ Compatibility

• Backward compatible — Expert features are opt-in via --expertEngine flag
• No changes to existing inference paths when Expert is not configured

Additional Information

Related issue: #32

[nvluxiaoz]

Thanks a lot for this MR! The core team is also working on supporting Alpamayo in a new release. We will take this MR as a reference and properly cite this great contribution.

[Turoad]

Thanks for the response! Glad to hear the core team is also working on Alpamayo support.

I'd love to collaborate rather than have parallel efforts — a few thoughts:

1. This PR is validated: 300-sample precision benchmark on Jetson AGX Thor, steady-state 2.83s/sample, aligned with PyTorch reference. Happy to share the full test harness and dataset scripts if helpful.

2. Happy to adapt: If the core team's implementation has a different architecture or API design, I'm glad to refactor this PR to align with your internal conventions — just let me know the target structure.

3. Incremental merge? If the full PR is too large to review at once, I can split it into smaller pieces (e.g., kernels first, then ExpertRunner, then integration).

What would make this most useful for the team — merge as-is, adapt to your internal branch, or contribute specific pieces? I'm flexible on the path, just want to make sure the validated work doesn't go to waste.

@nvluxiaoz

[genie-ahughes]

@Turoad I'd love the test harness and dataset scripts you offered above. Trying to reproduce your end-to-end pipeline on a Jetson AGX Thor against nvidia/Alpamayo-R1-10B. Anywhere you can drop them (gist, branch on the fork, attachment) would be hugely appreciated — particularly the action-expert ONNX export script so the engine I/O matches your alpamayoExpertRunner.cpp contract (single fused kv_cache binding etc.). Thanks!