h100-sglang-diffusion

Original：🇺🇸 English

Translated

SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.

9installs

Sourcebbuf/sglang-auto-driven-skills

Added on2026-04-22

NPX Install

npx skill4agent add bbuf/sglang-auto-driven-skills h100-sglang-diffusion

SKILL.md Content

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H100 — SGLang Diffusion

Overview

Use this skill to do SGLang diffusion development on the H100 box through

h100_sglang

. The default container is

sglang_bbuf

and the repo lives at

/data/bbuf/repos/sglang

Prefer this skill when:

Validating diffusion Triton / CUDA JIT kernels
Running diffusion model smoke tests (
```
DiffGenerator
```
, flux, etc.)
Comparing eager vs
```
torch.compile
```
diffusion performance
Verifying
```
python[diffusion]
```
editable install changes

This environment is already prepared:

```
sglang_bbuf
```
is running on
```
lmsysorg/sglang:dev
```
the repo is cloned at
```
/data/bbuf/repos/sglang
```
editable installs for
```
python[all]
```
and
```
python[diffusion]
```
are already done
```
/data/.cache
```
is mounted to
```
/root/.cache
```
Infiniband paths are mounted for RDMA-aware workflows:
```
/sys/class/infiniband
```
,
```
/dev/infiniband
```
, and
```
/usr/sbin/show_gids
```

Quick Start

Check the host, container, and GPU state.

bash

ssh h100_sglang 'hostname && whoami'
ssh h100_sglang 'docker ps --format "table {{.Names}}\t{{.Status}}" | sed -n "1,20p"'
ssh h100_sglang 'nvidia-smi --query-gpu=index,name,utilization.gpu,memory.used,memory.total --format=csv,noheader,nounits'

Enter the container and confirm HF token visibility.

bash

ssh h100_sglang 'docker exec -it sglang_bbuf /bin/zsh'
cd /data/bbuf/repos/sglang
echo ${HF_TOKEN:+set}

HF_TOKEN

is missing, export it before any Hub-backed diffusion run:

bash

export HF_TOKEN=<your-hf-token>
export HUGGINGFACE_HUB_TOKEN="$HF_TOKEN"

For non-interactive

docker exec ... bash -lc "<cmd>"

runs, export both variables inline instead of relying on shell startup:

bash

ssh h100_sglang 'docker exec sglang_bbuf env HF_TOKEN=<your-hf-token> HUGGINGFACE_HUB_TOKEN=<your-hf-token> zsh -lc "..."'

Pick a free GPU.

Use a GPU with

utilization and only a few MiB allocated. Always set

CUDA_VISIBLE_DEVICES=<gpu_id>

for diffusion validation commands.

If the container is not running, start it.

bash

ssh h100_sglang 'docker start sglang_bbuf'

Safe Remote Workflow

Inspect the repo state before editing.

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /data/bbuf/repos/sglang && git branch --show-current && git status --short"'

Fast-forward to latest clean
```
main
```
before creating a validation worktree.

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /data/bbuf/repos/sglang && git fetch origin && git checkout main && git pull --ff-only origin main"'

Never write directly into
```
/data/bbuf/repos/sglang
```
when it is dirty.
Use one of these isolation strategies.

Create a detached worktree for remote-only experiments:

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /data/bbuf/repos/sglang && git worktree add --detach /tmp/sglang_validate_h100 HEAD"'

Stream the local working tree into the container (validates exactly what is local right now):

bash

COPYFILE_DISABLE=1 tar --exclude=.git -cf - . | \
ssh h100_sglang 'docker exec -i sglang_bbuf sh -lc "rm -rf /tmp/sglang_local_validate && mkdir -p /tmp/sglang_local_validate && tar -xf - -C /tmp/sglang_local_validate"'
ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "find /tmp/sglang_local_validate -name '\''._*'\'' -delete"'

For patch-oriented validation:

fast-forward remote
```
main
```
create a detached worktree from that commit
stream or
```
git apply
```
only the focused local diff into the worktree

This keeps

/data/bbuf/repos/sglang

clean while still validating the exact local delta.

Diffusion Validation Workflow

1. Syntax / Import Check

Always start here before running any GPU kernel or model test.

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /tmp/sglang_local_validate && python -m compileall python/sglang/jit_kernel/diffusion/triton python/sglang/multimodal_gen/runtime/layers"'

For broader coverage:

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /tmp/sglang_local_validate && python -m compileall python/sglang"'

2. JIT Kernel Smoke

Run a targeted smoke script covering the changed primitives before any model-level test.

Cover at least these when relevant:

```
rms_norm_fn
```
```
RMSNorm
```
under
```
torch.compile
```
```
norm_infer
```
```
apply_rotary_embedding
```

Pipe the smoke script through

docker exec -i

bash

ssh h100_sglang 'docker exec -i sglang_bbuf env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=python python' < /path/to/local_smoke.py

3. Fused Modulation Regression

Run this after any change to

jit_kernel/diffusion/triton

bash

ssh h100_sglang 'docker exec sglang_bbuf env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=python zsh -lc "cd /tmp/sglang_local_validate && pytest -q python/sglang/jit_kernel/tests/test_qwen_image_modulation.py -q"'

4. General Diffusion Tests

bash

ssh h100_sglang 'docker exec sglang_bbuf env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=python zsh -lc "cd /tmp/sglang_local_validate && pytest -q path/to/diffusion_test.py -q"'

5. Model-Level Smoke (

DiffGenerator

)

Only after steps 1–4 pass.

Use a real

.py

file with

if __name__ == "__main__":

guard —

multiprocessing.spawn

will fail if the entry point is stdin or unguarded top-level code.

bash

# stream the script file to the container
scp /path/to/local_smoke_model.py h100_sglang:/tmp/smoke_model.py
ssh h100_sglang 'docker exec sglang_bbuf env CUDA_VISIBLE_DEVICES=0 HF_TOKEN=<your-hf-token> HUGGINGFACE_HUB_TOKEN=<your-hf-token> PYTHONPATH=/tmp/sglang_local_validate/python zsh -lc "python /tmp/smoke_model.py"'

Treat checkpoint, dependency, and environment failures separately from code regressions.

6. Server-Level Smoke

Only attempt after model-level smoke passes.

bash

ssh h100_sglang 'docker exec sglang_bbuf env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=python zsh -lc "cd /tmp/sglang_local_validate && python -m sglang.launch_server --model-path <model> --port 30000 &"'

Torch Compile Attribution

When a benchmark compares eager vs

torch.compile

, do not stop at the speedup number. Capture matching eager and compile traces or perf dumps, then run:

bash

ssh h100_sglang 'docker exec sglang_bbuf zsh -lc "cd /tmp/sglang_local_validate && python scripts/analyze_diffusion_torch_compile.py"'

Cleanup

bash

ssh h100_sglang 'docker exec sglang_bbuf rm -rf /tmp/sglang_local_validate /tmp/sglang_validate_h100 /tmp/smoke_model.py'

h100-sglang-diffusion

NPX Install

Tags

SKILL.md Content

H100 — SGLang Diffusion

Overview

Quick Start

Safe Remote Workflow

Diffusion Validation Workflow

1. Syntax / Import Check

2. JIT Kernel Smoke

3. Fused Modulation Regression

4. General Diffusion Tests

5. Model-Level Smoke (
`DiffGenerator`
)

6. Server-Level Smoke

Torch Compile Attribution

Cleanup

h100-sglang-diffusion

NPX Install

Tags

SKILL.md Content

H100 — SGLang Diffusion

Overview

Quick Start

Safe Remote Workflow

Diffusion Validation Workflow

1. Syntax / Import Check

2. JIT Kernel Smoke

3. Fused Modulation Regression

4. General Diffusion Tests

5. Model-Level Smoke (DiffGenerator)

6. Server-Level Smoke

Torch Compile Attribution

Cleanup

5. Model-Level Smoke (
`DiffGenerator`
)