Add support for NVIDIA DGX Spark (GB10 / sm_121a, arm64)

[boots-coder]

Add NVIDIA DGX Spark (GB10 / sm_121a) support

Summary

This PR adds support for NVIDIA DGX Spark (Project Digits, GB10 chip — Grace
CPU + consumer Blackwell GPU at sm_121a, aarch64, 128 GB unified memory) to
slime's docker build pipeline. GB10 is an explicit gap in the current support
matrix:

• slime's published images (slimerl/slime:*) are x86_64-only.
• The arm64 base slimerl/sglang:v0.5.9 ships CUDA 12.9 whose ptxas has no
  sm_121a target, so triton JIT crashes on first kernel.
• The ENABLE_CUDA_13=1 branch in the upstream Dockerfile is aimed at GB200/GB300
  (sm_100a) with an x86-only sgl-router wheel.

This PR rebases a second Dockerfile (docker/Dockerfile.gb10) on
nvcr.io/nvidia/vllm:26.03-py3 (arm64), which ships CUDA 13.2, PyTorch 2.11.0a0
with compute_120 PTX, Triton 3.6.0, and flash-attn 2.7.4.post1 — the minimal
viable baseline for GB10. Fifteen small blockers are resolved in the Dockerfile
and supporting patches.

End-to-end validation: Qwen2.5-0.5B + GRPO + dapo-math-17k, 1 GB10 GPU,
colocated actor + rollout, one full rollout → reward → policy-update cycle
completed in 2m10s (step 0 metrics logged, weight sync to SGLang succeeded,
checkpoint saved).

What's in the patch

New files

• docker/Dockerfile.gb10 — digest-pinned, arm64-native build on NGC vllm base
• docker/patch/gb10/patch_sgl_kernel.py — adds SGL_KERNEL_GB10_ONLY CMake option
• docker/patch/gb10/sgl-kernel-arch.patch — unified diff of the same
• docker/patch/gb10/cuda_profiler_api.h — 20-line shim for a CUDA 13 dropped header
• scripts/run-qwen2.5-0.5B-gb10-smoke.sh — minimal 1-GPU smoke script
• NOTES_GB10.md — walkthrough of the 15 blockers with root causes

Existing file edits

• slime/utils/arguments.py — remove trailing comma that turned an argparse
help= string into a single-element tuple (causes 'tuple' has no attribute 'strip'
  during --help; affects all platforms, not just GB10)

The 15 blockers (quick reference)

#	Blocker	Resolution
1	ptxas fatal: sm_121a	Rebase on NGC CUDA 13.2 image
2	libnvrtc.so.12 missing from sgl_kernel wheel	Build sgl_kernel from source
3	sgl_kernel wheel ABI-mismatch vs NGC libtorch	Build against NGC torch
4	sgl_kernel arm64 wheels only have sm_100 variant	Build sm_121 variant
5	sgl_kernel source build OOM (7 archs × cutlass)	SGL_KERNEL_GB10_ONLY CMake option
6	TE: CUDNN::cudnn_engines_precompiled not found	Install nvidia-cudnn-cu13==9.20.0.48 pypi wheel, symlink
7	TE: nvtx3/nvToolsExt.h missing	Copy headers from NVIDIA/NVTX repo
8	TE ptx.cuh: "use smXXXf, not smXXX"	NVTE_CUDA_ARCHS="120f;121f"
9	CMake 3.31 rejects f suffix	Upgrade to cmake==4.3.1, override PIP_CONSTRAINT
10	cuda_profiler_api.h missing in CUDA 13	20-line shim header
11	'tuple' object has no attribute 'strip'	Remove trailing comma in arguments.py
12	sglang_router x86_64 only	Use upstream sglang-router==0.3.2 (arm64 wheel)
13	antlr4==4.13.2 breaks omegaconf	Pin antlr4-python3-runtime==4.9.3
14	megatron.training not found post pip install -e	PYTHONPATH=/root/src/Megatron-LM
15	tilelang needs libz3.so	apt install libz3-dev

Testing

Run inside the image:

cd /root/slime
python train.py --help   # prints 3714 lines, exits 0
bash scripts/run-qwen2.5-0.5B-gb10-smoke.sh   # end-to-end GRPO run, 2m10s

Scope this PR does NOT cover

• FA3 (Hopper-only) kernels: deliberately skipped; GB10 cannot run sm_90a binaries.
• Any multi-GPU or multi-node GB10 topology (DGX Spark is a single-GPU SKU).
• Convergence / training quality validation (separate ongoing benchmark).

Reproducibility

cd slime
docker build -f docker/Dockerfile.gb10 -t slime-gb10:latest .

Base image is pinned by digest:

nvcr.io/nvidia/vllm@sha256:13e327dad79e6e417f6687fec2ba76b0386d597082ec0ee003c1e964ec6ad0e7

[boots-coder]

End-to-end smoke on a real GB10 (DGX Spark). Qwen2.5-0.5B + GRPO + dapo-math-17k, 1 GPU colocated, 1 rollout × 1 policy step. Full cycle: 1m26s, Ray job succeeded, 8/8 weight-sync HTTP calls returned 200 OK.

[boots-coder]

100-rollout convergence run + held-out test set evaluation on GB10

Following the smoke test posted earlier, I ran a full 100-rollout GRPO convergence experiment on the GB10 (single GPU, colocated mode), then evaluated on the held-out GSM8K test set (1,319 questions, greedy decoding).

Test Set Results (Pass@1, greedy, 1319 questions)

Model	Correct	Accuracy	No \boxed{} output	Avg response length
Baseline (Qwen2.5-0.5B-Instruct)	575 / 1319	43.59%	33 (2.5%)	1071 chars
+ 100-step GRPO (iter_99)	662 / 1319	50.19%	3 (0.2%)	907 chars
Improvement	+87	+6.60 pp	−30 (format compliance ↑)	−15.3% (more concise)

Three dimensions improved simultaneously:

1. Accuracy +6.60 pp on never-seen test questions
2. Format compliance 97.5% → 99.8% — model learned to reliably output \boxed{}
3. Responses 15% shorter — RL optimized for quality, not length

Training Configuration

• Model: Qwen2.5-0.5B-Instruct (494M params)
• Data: GSM8K train split (7,473 questions), --rm-type math (grade_answer_verl)
• Scale: 20 prompts × 8 samples × 100 rollouts = 16,000 total samples (< 1 epoch)
• Hyperparams: lr=1e-6 constant, GRPO with eps-clip=0.2/0.28, colocated single GPU
• Training time: 1h 11min (41.9s/rollout avg)
• Eval time: 28min (2 × 1319 greedy, HF transformers backend)

Training Stability

Metric	Value
grad_norm	0.93 mean (range 0.73–1.18, no explosion)
entropy_loss	0.295 → 0.172 (monotonic decrease)
kl_loss vs ref	0.0 → 0.044 (smooth, controlled)
Rollout throughput	5,495 tok/gpu/s
Actor train TFLOPS	16.83 (mean)

Reward Curve (training rollout, temp=1.0)

Rollout raw_reward trajectory: 0.375 → 0.656 (peak 0.763 at rollout 58).
Note: rollout metrics use temp=1.0 sampling on training data (8 samples/prompt), so they're higher than the greedy test-set Pass@1 reported above.

Reproduction

All scripts, configs, and the updated Dockerfile.gb10 (with 6 missing runtime deps fixed in commit 8f55ecb1) are in this PR.