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[diffusion] CI: add minimax-h3 2-gpu consistency coverage (#33281)

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This commit is contained in:
Mick
2026-08-03 22:40:08 +08:00
committed by GitHub
parent 3953788596
commit 0ba46c88e5
7 changed files with 164 additions and 53 deletions
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.github/workflows/diffusion-ci-gt-gen.yml
+4 -1
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@@ -364,7 +364,8 @@ jobs:
--target-time 1800 \
--max-time 2400 \
--max-partitions 10 \
--parametrized-only
--parametrized-only \
${{ inputs.case_ids != '' && format('--case-ids {0}', inputs.case_ids) || '' }}
multimodal-diffusion-gen-1gpu:
needs: compute-diffusion-partitions
@@ -480,6 +481,8 @@ jobs:
- name: Generate outputs
env:
HF_TOKEN: ${{ secrets.MINIMAX_H3_HF_TOKEN || secrets.SGLANG_DIFFUSION_CI_HF_TOKEN || secrets.HF_TOKEN }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.MINIMAX_H3_HF_TOKEN || secrets.SGLANG_DIFFUSION_CI_HF_TOKEN || secrets.HF_TOKEN }}
RUNAI_STREAMER_MEMORY_LIMIT: 0
PARTITION_PLAN_JSON: ${{ needs.compute-diffusion-partitions.outputs.plan-2gpu }}
run: |
.github/workflows/pr-test-multimodal-gen.yml
+2 -49
View File
@@ -301,6 +301,8 @@ jobs:
- name: Run diffusion server tests
timeout-minutes: 240
env:
HF_TOKEN: ${{ secrets.MINIMAX_H3_HF_TOKEN || secrets.SGLANG_DIFFUSION_CI_HF_TOKEN || secrets.HF_TOKEN }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.MINIMAX_H3_HF_TOKEN || secrets.SGLANG_DIFFUSION_CI_HF_TOKEN || secrets.HF_TOKEN }}
RUNAI_STREAMER_MEMORY_LIMIT: 0
CONTINUE_ON_ERROR_FLAG: ${{ inputs.continue_on_error == 'true' && '--continue-on-error' || '' }}
PARTITION_PLAN_JSON: ${{ needs.compute-diffusion-partitions.outputs.plan-2gpu }}
@@ -435,55 +437,6 @@ jobs:
- uses: ./.github/actions/upload-cuda-coredumps
if: failure()
multimodal-gen-test-4-h100:
# Temporarily disabled while the 4-gpu-h100 runner is unstable
if: ${{ false }}
runs-on: 4-gpu-h100
timeout-minutes: 90
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ inputs.git_ref || github.sha }}
- uses: ./.github/actions/check-pr-test-health
- uses: ./.github/actions/check-maintenance
- name: Download artifacts
if: inputs.sgl_kernel == 'true'
uses: actions/download-artifact@v4
with:
path: python/sglang/kernels/aot/dist/
merge-multiple: true
pattern: wheel-python3.10-cuda*
- name: Install dependencies
timeout-minutes: 20
run: |
CUSTOM_BUILD_SGL_KERNEL=${{inputs.sgl_kernel}} bash scripts/ci/cuda/ci_install_dependency.sh diffusion
- name: Run MiniMax-H3 PR smoke test
timeout-minutes: 45
env:
RUNAI_STREAMER_MEMORY_LIMIT: 0
SGLANG_TEST_WAIT_SECS: 1800
SGLANG_DIFFUSION_ARTIFACT_DIR: ${{ github.workspace }}/diffusion-failures
run: |
cd python
python3 sglang/multimodal_gen/test/run_suite.py --suite 4-gpu-h100
- name: Upload diffusion failure artifacts
if: always()
uses: actions/upload-artifact@v4
with:
name: diffusion-failures-${{ github.job }}-${{ github.run_attempt }}
path: diffusion-failures/
if-no-files-found: ignore
- uses: ./.github/actions/upload-cuda-coredumps
if: failure()
multimodal-gen-unit-test:
if: |
((github.event_name == 'schedule' || inputs.test_parallel_dispatch == 'true') || (inputs.caller_needs_failure != 'true' && !cancelled())) &&
python/sglang/multimodal_gen/test/server/gpu_cases.py
+55
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@@ -658,6 +658,61 @@ MINIMAX_H3_FOUR_GPU_H100_CASES = [
]
TWO_GPU_CASES = [
DiffusionTestCase(
"minimax_h3_t2va_2gpu_h100",
DiffusionServerArgs(
model_path="MiniMaxAI/MiniMax-H3",
modality="video",
tp_size=2,
ulysses_degree=1,
extras=[
"--model-variant",
"fl2va",
"--performance-mode",
"memory",
"--layerwise-offload-components",
"dit,text_encoder,vae",
"--dit-offload-prefetch-size",
"1",
"--dit-layerwise-resident-layers",
"20",
"--enable-torch-compile",
"false",
],
),
DiffusionSamplingParams(
prompt=(
"A static night view of a narrow London alley in soft rain, wet "
"pavement reflecting a yellow streetlamp, the blue K. West sign "
"glowing above a doorway, cardboard boxes near the wall, a pale "
"parked car in the distance, and a slender glam-rock figure "
"holding a guitar under the lamp, brick storefronts, muted teal "
"and amber colors, subtle rain shimmer only."
),
output_size="1344x768",
seconds=4,
output_format="mp4",
num_outputs_per_prompt=1,
extras={
"task": "t2va",
"conditions": [],
"target": {
"short_edge": 768,
"aspect_ratio": "16:9",
"duration_seconds": 4.0,
},
"num_inference_steps": 8,
"flow_shift": 12.0,
"audio_flow_shift": 3.0,
"seed": 42,
},
),
run_perf_check=True,
run_consistency_check=True,
run_component_accuracy_check=False,
run_models_api_check=False,
run_t2v_input_reference_check=False,
),
DiffusionTestCase(
"flux2_modelopt_fp8_tp2_t2i",
DiffusionServerArgs(
python/sglang/multimodal_gen/test/server/perf_baselines/h100.json
+27
View File
@@ -2790,6 +2790,33 @@
"expected_median_denoise_ms": 0.0,
"estimated_full_test_time_s": 55.4
},
"minimax_h3_t2va_2gpu_h100": {
"stages_ms": {
"InputValidationStage": 0.05,
"MiniMaxH3PartitionAdmissionStage": 0.03,
"MiniMaxH3TextEncodingStage": 620.13,
"MiniMaxH3VisualEncodingStage": 0.11,
"MiniMaxH3AudioEncodingStage": 0.03,
"MiniMaxH3LatentPreparationStage": 18.72,
"MiniMaxH3TimestepPreparationStage": 0.34,
"MiniMaxH3DenoisingStage": 16839.0,
"MiniMaxH3DecodingStage": 29741.66,
"per_frame_generation": null
},
"denoise_step_ms": {
"0": 211.7,
"1": 2427.55,
"2": 2344.0,
"3": 2332.99,
"4": 2332.69,
"5": 2343.56,
"6": 2326.79
},
"expected_e2e_ms": 47538.57,
"expected_avg_denoise_ms": 2045.61,
"expected_median_denoise_ms": 2332.99,
"estimated_full_test_time_s": 235.9
},
"mova_360p_tp2": {
"stages_ms": {},
"denoise_step_ms": {},
python/sglang/multimodal_gen/test/server/test_server_common.py
+19
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@@ -64,6 +64,7 @@ from sglang.multimodal_gen.test.test_utils import (
load_action_consistency_gt,
load_consistency_gt,
save_consistency_failure_artifact,
save_missing_consistency_gt_artifact,
wait_for_req_perf_record,
)
@@ -610,6 +611,24 @@ class DiffusionServerBase:
if not gt_exists(
case.id, num_gpus, is_video=is_video, output_format=output_format
):
if is_video:
output_frames = pop_realtime_key_frames(case.id)
if output_frames is None:
output_frames = extract_key_frames_from_video(content)
else:
output_frames = [image_bytes_to_numpy(content)]
artifact_path = save_missing_consistency_gt_artifact(
artifact_dir=os.environ.get("SGLANG_DIFFUSION_ARTIFACT_DIR"),
case_id=case.id,
num_gpus=num_gpus,
output_frames=output_frames,
is_video=is_video,
output_format=output_format,
)
if artifact_path is not None:
logger.info(
"[Artifact] Saved missing consistency GT: %s", artifact_path
)
if _get_consistency_gt_dir() is not None:
names = ", ".join(
get_consistency_gt_candidates(
python/sglang/multimodal_gen/test/test_utils.py
+26 -2
View File
@@ -39,12 +39,12 @@ logger = init_logger(__name__)
# NPU/ascend) is read from sgl-project/ci-data-diffusion, where the GT-gen workflows
# publish.
SGL_TEST_FILES_CI_DATA_REPO = "sgl-project/ci-data-diffusion"
SGL_TEST_FILES_CI_DATA_REVISION = "6d689f4833e8c106ff0d853865f50496d1f2b453"
SGL_TEST_FILES_CI_DATA_REVISION = "739c6c9b7cb972149cc3472cf19fe4bb29cf15c3"
# The NPU pin is kept as a separate branch so ascend GT can be bumped independently
# when it's regenerated on its own cadence.
if current_platform.is_npu():
SGL_TEST_FILES_CI_DATA_REVISION = "6d689f4833e8c106ff0d853865f50496d1f2b453"
SGL_TEST_FILES_CI_DATA_REVISION = "d180ad38872dff3d1ad03e4610cffcda874d3eb8"
SGL_TEST_FILES_CONSISTENCY_GT_ROOT = (
"https://raw.githubusercontent.com/"
@@ -2003,6 +2003,30 @@ def _save_generated_artifact_images(
return generated_files
def save_missing_consistency_gt_artifact(
artifact_dir: str | Path | None,
case_id: str,
num_gpus: int,
output_frames: list[np.ndarray],
is_video: bool,
output_format: str | None = None,
) -> Path | None:
if not artifact_dir:
return None
out_dir = Path(artifact_dir) / "missing_consistency_gt"
out_dir.mkdir(parents=True, exist_ok=True)
filenames = _consistency_gt_filenames(
case_id,
num_gpus,
is_video=is_video,
output_format=output_format,
)
for frame, filename in zip(output_frames, filenames):
Image.fromarray(_ensure_rgb_uint8_image(frame)).save(out_dir / filename)
return out_dir
def _write_consistency_failure_index(
out_dir: Path,
records: list[dict[str, Any]],
scripts/ci/utils/diffusion/compute_diffusion_partitions.py
+31 -1
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@@ -255,6 +255,11 @@ def main():
action="store_true",
help="Only partition DiffusionTestCase parametrized cases.",
)
parser.add_argument(
"--case-ids",
nargs="*",
help="Only schedule partitions containing these parametrized case IDs.",
)
args = parser.parse_args()
script_dir = Path(__file__).resolve().parent
@@ -285,6 +290,19 @@ def main():
)
validate_suite_case_coverage(suites)
requested_case_ids = set(args.case_ids or [])
if requested_case_ids:
known_case_ids = {
case.case_id
for suite_name, suite_info in suites.items()
if suite_name in SUITE_OUTPUT_NAMES
for case in suite_info.cases
}
unknown_case_ids = sorted(requested_case_ids - known_case_ids)
if unknown_case_ids:
print(f"Error: Unknown case IDs: {' '.join(unknown_case_ids)}")
sys.exit(1)
print("=== Diffusion Partition Computation ===")
print(f"Min partition time: {args.min_time}s ({args.min_time/60:.1f} min)")
print(f"Target partition time: {args.target_time}s ({args.target_time/60:.1f} min)")
@@ -316,7 +334,19 @@ def main():
)
output_name = SUITE_OUTPUT_NAMES[suite_name]
output_github_value(f"matrix-{output_name}", build_matrix(partition_count))
matrix = build_matrix(partition_count)
if requested_case_ids:
matrix = {
"include": [
{"part": idx}
for idx, partition in enumerate(partitions)
if any(
item.kind == "case" and item.item_id in requested_case_ids
for item in partition
)
]
}
output_github_value(f"matrix-{output_name}", matrix)
output_github_scalar(f"partition-count-{output_name}", str(partition_count))
output_github_value(
f"plan-{output_name}", build_partition_plan(suite_name, partitions)