minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[diffusion] nightly: track SGLang-Diffusion only (#29434)

Browse Source
This commit is contained in:
Mick
2026-06-28 14:23:17 +08:00
committed by GitHub
parent 643e1cc779
commit 97ddc66209
5 changed files with 85 additions and 58 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.github/workflows/nightly-test-nvidia.yml
+8 -8
View File
@@ -26,7 +26,7 @@ on:
- 'nightly-test-specialized-8-gpu-b200'
- 'nightly-test-perf-4-gpu-gb300'
- 'nightly-test-kernel-1-gpu-h100'
- 'nightly-test-diffusion-comparison'
- 'nightly-test-diffusion'
- 'nightly-test-kernel-8-gpu-h200'
- 'nightly-test-precision-8-gpu-h200'
workflow_call:
@@ -628,9 +628,9 @@ jobs:
- uses: ./.github/actions/upload-cuda-coredumps
if: failure()
# Diffusion cross-framework comparison
nightly-test-diffusion-comparison:
if: github.repository == 'sgl-project/sglang' && (inputs.job_filter == '' || inputs.job_filter == 'all' || inputs.job_filter == 'nightly-test-diffusion-comparison')
# SGLang-Diffusion nightly benchmark
nightly-test-diffusion:
if: github.repository == 'sgl-project/sglang' && (inputs.job_filter == '' || inputs.job_filter == 'all' || inputs.job_filter == 'nightly-test-diffusion')
runs-on: 4-gpu-h100
timeout-minutes: 300
steps:
@@ -643,7 +643,7 @@ jobs:
run: |
bash scripts/ci/cuda/ci_install_dependency.sh diffusion
- name: Run cross-framework comparison
- name: Run diffusion benchmark
env:
GITHUB_SHA: ${{ github.sha }}
GITHUB_RUN_ID: ${{ github.run_id }}
@@ -676,11 +676,11 @@ jobs:
--dashboard dashboard.md \
--charts-dir comparison-charts
- name: Upload comparison artifacts
- name: Upload benchmark artifacts
if: always()
uses: actions/upload-artifact@v4
with:
name: diffusion-comparison-${{ github.run_id }}
name: diffusion-benchmark-${{ github.run_id }}
path: |
comparison-results.json
dashboard.md
@@ -783,7 +783,7 @@ jobs:
- nightly-test-perf-4-gpu-b200
- nightly-test-specialized-8-gpu-b200
- nightly-test-perf-4-gpu-gb300
- nightly-test-diffusion-comparison
- nightly-test-diffusion
- nightly-test-precision-8-gpu-h200
- consolidate-metrics
runs-on: ubuntu-latest
scripts/ci/utils/diffusion/comparison_configs.json
+33 -17
View File
@@ -118,23 +118,6 @@
}
}
},
{
"id": "ltx2_twostage_t2v",
"model": "Lightricks/LTX-2",
"task": "text-to-video",
"prompt": "A cat and a dog baking a cake together in a kitchen.",
"width": 768,
"height": 512,
"num_frames": 121,
"seed": 42,
"num_gpus": 2,
"frameworks": {
"sglang": {
"serve_args": "--warmup --enable-cfg-parallel --pipeline-class-name LTX2TwoStagePipeline",
"extra_env": {}
}
}
},
{
"id": "ltx2.3_twostage_ti2v_2gpus",
"model": "Lightricks/LTX-2.3",
@@ -153,6 +136,39 @@
}
}
},
{
"id": "ideogram4_fp8_t2i_2gpu",
"model": "ideogram-ai/ideogram-4-fp8",
"task": "text-to-image",
"prompt": "A futuristic cyberpunk city at night, neon lights reflecting on wet streets",
"width": 1024,
"height": 1024,
"seed": 42,
"num_gpus": 2,
"frameworks": {
"sglang": {
"serve_args": "--warmup --tp-size 2 --attention-backend fa",
"extra_env": {}
}
}
},
{
"id": "cosmos3_super_t2v_2gpu",
"model": "nvidia/Cosmos3-Super",
"task": "text-to-video",
"prompt": "A cat and a dog baking a cake together in a kitchen.",
"width": 1280,
"height": 720,
"num_frames": 81,
"seed": 42,
"num_gpus": 2,
"frameworks": {
"sglang": {
"serve_args": "--warmup --tp-size 2",
"extra_env": {"SGLANG_DISABLE_COSMOS3_GUARDRAILS": "1"}
}
}
},
{
"id": "wan22_i2v_a14b_720p",
"model": "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
scripts/ci/utils/diffusion/generate_diffusion_dashboard.py
+6 -6
View File
@@ -1,4 +1,4 @@
"""Generate a Markdown dashboard for diffusion cross-framework comparisons.
"""Generate a Markdown dashboard for SGLang-Diffusion nightly benchmarks.
Reads current comparison results + historical data from sgl-project/ci-data repo
and produces a Markdown report with tables and trend charts saved as PNG files.
@@ -252,7 +252,7 @@ def generate_dashboard(
Returns the markdown string.
"""
lines: list[str] = []
lines.append("# Diffusion Cross-Framework Performance Dashboard\n")
lines.append("# SGLang-Diffusion Nightly Performance Dashboard\n")
ts = current.get("timestamp", datetime.now(timezone.utc).isoformat())
sha = current.get("commit_sha", "unknown")
lines.append(f"*Generated: {_short_date(ts)} | Commit: `{_short_sha(sha)}`*\n")
@@ -297,8 +297,8 @@ def generate_dashboard(
all_frameworks.insert(0, "sglang")
other_frameworks = [fw for fw in all_frameworks if fw != "sglang"]
# ---- Section 1: Cross-Framework Comparison (current run) ----
lines.append("## Cross-Framework Performance Comparison\n")
# ---- Section 1: SGLang-Diffusion performance (current run) ----
lines.append("## SGLang-Diffusion Performance\n")
# Compute risk assessments for all cases
risk_map: dict[str, tuple[str, str]] = {}
@@ -345,7 +345,7 @@ def generate_dashboard(
row += f" {_fmt_speedup(sg_lat, case_fws.get(ofw))} |"
lines.append(row)
# ---- Section 2: Cross-Framework Speedup Trend (only if multiple frameworks) ----
# ---- Section 2: Speedup-over-time vs. other frameworks (rendered only when present) ----
if history and other_frameworks:
lines.append("\n## SGLang vs vLLM-Omni Speedup Over Time\n")
@@ -750,7 +750,7 @@ def _create_alert_issue(alert_reasons: list[str]) -> None:
def main():
parser = argparse.ArgumentParser(
description="Generate diffusion cross-framework comparison dashboard"
description="Generate SGLang-Diffusion nightly benchmark dashboard"
)
parser.add_argument(
"--results",
scripts/ci/utils/diffusion/publish_comparison_results.py
+2 -2
View File
@@ -1,4 +1,4 @@
"""Publish diffusion comparison results to sgl-project/ci-data repo.
"""Publish SGLang-Diffusion nightly benchmark results to sgl-project/ci-data repo.
Pushes comparison-results.json, dashboard.md, and chart PNG files to the
ci-data repository for historical tracking. Chart PNGs are stored under
@@ -196,7 +196,7 @@ def publish_comparison(
def main():
parser = argparse.ArgumentParser(
description="Publish diffusion comparison results to ci-data"
description="Publish SGLang-Diffusion nightly benchmark results to ci-data"
)
parser.add_argument(
"--results",
scripts/ci/utils/diffusion/run_comparison.py
+36 -25
View File
@@ -1,7 +1,9 @@
"""Cross-framework comparison benchmark for diffusion serving.
"""Diffusion serving benchmark for SGLang-Diffusion nightly CI.
Launches servers (SGLang, vLLM-Omni, LightX2V) for each test case, sends a
single request, measures end-to-end latency, and writes comparison-results.json.
Launches an SGLang-Diffusion server for each test case, sends a single
request, measures end-to-end latency, and writes comparison-results.json.
The runner still supports extra frameworks via --frameworks, but the nightly
config tracks SGLang-Diffusion only.
Usage:
# Full run (requires GPU)
@@ -84,6 +86,10 @@ def _build_sglang_cmd(case: dict, fw_cfg: dict, port: int) -> list[str]:
cmd += ["--num-gpus", str(case["num_gpus"])]
if fw_cfg.get("serve_args", "").strip():
cmd += fw_cfg["serve_args"].strip().split()
# No explicit --warmup-resolutions: server-based warmup now defaults to the
# model's sampling-default resolution (see warmup_request_builder), which
# already matches these single-resolution cases — the default warmup is
# sufficient, so we don't pin a resolution here.
return cmd
@@ -399,14 +405,16 @@ def send_image_request_sglang(
if "data" not in data or len(data["data"]) == 0:
raise RuntimeError(f"Image request returned no data: {data}")
# Report client-side e2e latency to match vllm-omni / lightx2v (fair
# cross-framework comparison); server-side perf_dump is diagnostic only.
if perf_dump_path:
server_latency = _read_perf_dump(perf_dump_path)
if server_latency is not None:
print(
f" Image generated in {server_latency:.2f}s (server-side), "
f"client={client_latency:.2f}s"
f" Image generated in {client_latency:.2f}s (client e2e; "
f"server-side {server_latency:.2f}s, diagnostic)"
)
return server_latency
return client_latency
print(f" Image generated in {client_latency:.2f}s")
return client_latency
@@ -450,14 +458,16 @@ def send_video_request_sglang(
client_latency = time.time() - start
# Report client-side e2e latency to match vllm-omni / lightx2v (fair
# cross-framework comparison); server-side perf_dump is diagnostic only.
if perf_dump_path:
server_latency = _read_perf_dump(perf_dump_path)
if server_latency is not None:
print(
f" Video generated in {server_latency:.2f}s (server-side), "
f"client={client_latency:.2f}s"
f" Video generated in {client_latency:.2f}s (client e2e; "
f"server-side {server_latency:.2f}s, diagnostic)"
)
return server_latency
return client_latency
print(f" Video generated in {client_latency:.2f}s")
return client_latency
@@ -536,14 +546,16 @@ def send_image_conditioned_request_sglang(
client_latency = time.time() - start
# Report client-side e2e latency to match vllm-omni / lightx2v (fair
# cross-framework comparison); server-side perf_dump is diagnostic only.
if perf_dump_path:
server_latency = _read_perf_dump(perf_dump_path)
if server_latency is not None:
print(
f" Generated in {server_latency:.2f}s (server-side), "
f"client={client_latency:.2f}s"
f" Generated in {client_latency:.2f}s (client e2e; "
f"server-side {server_latency:.2f}s, diagnostic)"
)
return server_latency
return client_latency
print(f" Generated in {client_latency:.2f}s (sglang, image-conditioned)")
return client_latency
@@ -789,18 +801,17 @@ def run_single(
base_url = f"http://{DEFAULT_HOST}:{port}"
wait_for_health(base_url, framework)
# Warmup requests (not measured, no perf dump)
# Use few steps to be fast — server's own warmup (warmup_steps=3) handles
# torch.compile compilation; these external warmups just stabilize triton
# kernel specializations across requests.
WARMUP_STEPS = 3
warmup_case = {**case, "num_inference_steps": WARMUP_STEPS}
for wi in range(1, 3):
print(f" Sending warmup request ({wi}/2, {WARMUP_STEPS} steps)...")
try:
send_request(base_url, warmup_case, framework, config)
except Exception as e:
raise RuntimeError(f"Warmup request {wi} failed: {e}") from e
# No client-side warmup: each framework relies on its own server-side
# warmup before traffic. sglang's serve_args pass --warmup, which `serve`
# resolves to server-based (synthetic) warmup that primes kernels at
# startup, before the health check passes. This goes through the internal
# warmup path that bypasses sampling-param preset validation (e.g.
# Ideogram-4's preset-locked num_inference_steps), so no per-case warmup
# special-casing is needed here.
# NOTE: vllm-omni / lightx2v configure no server-side warmup; if
# cross-framework comparison is restored, they must add their own warmup
# to stay on equal footing — otherwise their measured request pays the
# full cold-start.
# Measured request — pass perf_dump_path for SGLang server-side timing
if perf_dump_path and os.path.exists(perf_dump_path):
@@ -964,7 +975,7 @@ def run_comparison(
def main():
parser = argparse.ArgumentParser(
description="Cross-framework diffusion serving comparison benchmark"
description="SGLang-Diffusion serving benchmark (nightly CI)"
)
parser.add_argument(
"--config",