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[CI] Trim redundant nightly test registrations (#34070)

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Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>
This commit is contained in:
Liangsheng Yin
2026-08-08 01:42:46 -07:00
committed by GitHub
co-authored by Baizhou Zhang
parent dd5d82bead
commit f6a6f5bf1e
24 changed files with 2 additions and 3571 deletions
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test/registered/quant/test_kimi_k25_nvfp4_eagle.py
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import unittest
from sglang.test.accuracy_test_runner import AccuracyTestParams
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.performance_test_runner import PerformanceTestParams
from sglang.test.run_combined_tests import run_combined_tests
from sglang.test.test_utils import ModelLaunchSettings
# Kimi-K2.5 NVFP4 + EAGLE3 (MLA draft) speculative decoding on 4x B200, tp=4.
register_cuda_ci(est_time=3000, suite="nightly-4-gpu-b200", nightly=True)
MODEL_PATH = "nvidia/Kimi-K2.5-NVFP4"
DRAFT_MODEL_PATH = "lightseekorg/kimi-k2.5-eagle3-mla"
EXTRA_ARGS = [
"--trust-remote-code",
"--attention-backend=tokenspeed_mla",
"--moe-runner-backend=flashinfer_trtllm",
"--quantization=modelopt_fp4",
"--kv-cache-dtype=fp8_e4m3",
"--mem-fraction-static=0.85",
"--max-running-requests=16",
"--speculative-algorithm=EAGLE3",
f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
"--speculative-num-steps=3",
"--speculative-eagle-topk=1",
"--speculative-num-draft-tokens=4",
"--speculative-draft-model-quantization=unquant",
]
class TestKimiK25Nvfp4Eagle(unittest.TestCase):
"""Kimi-K2.5 NVFP4 with EAGLE3 speculative decoding on 4x B200 (tp=4).
Runs both an accuracy test (gsm8k) and a performance test (bs=1/8/16),
and gates the speculative-decoding accept length.
"""
def test_kimi_k25_nvfp4_eagle(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=EXTRA_ARGS,
variant="TP4+EAGLE3",
),
]
run_combined_tests(
models=variants,
test_name="Kimi-K2.5-NVFP4 EAGLE3",
# Thresholds from a measured tp=4 run: gsm8k 0.945, perf accept ~3.0-3.3.
accuracy_params=AccuracyTestParams(
dataset="gsm8k",
baseline_accuracy=0.92,
num_examples=200,
api="completion",
),
performance_params=PerformanceTestParams(
batch_sizes=[1, 8, 16],
spec_accept_length_threshold=2.8,
result_dir="performance_results_kimi_k25_nvfp4_eagle",
),
)
if __name__ == "__main__":
unittest.main()
test/registered/quant/test_kimi_k26_nvfp4_dflash.py
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import unittest
from sglang.test.accuracy_test_runner import AccuracyTestParams
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.performance_test_runner import PerformanceTestParams
from sglang.test.run_combined_tests import run_combined_tests
from sglang.test.test_utils import ModelLaunchSettings
# Kimi-K2.6 NVFP4 (pure-MLA target, fp8 KV) + DFlash speculative decoding on 8x B200, tp=8.
register_cuda_ci(est_time=3600, suite="nightly-8-gpu-b200", nightly=True)
MODEL_PATH = "nvidia/Kimi-K2.6-NVFP4"
DRAFT_MODEL_PATH = "nvidia/Kimi-K2.6-DFlash"
# trtllm_mla verify only; cuteDSL fold verify depends on the flashinfer version.
EXTRA_ARGS = [
"--trust-remote-code",
"--quantization=modelopt_fp4",
"--moe-runner-backend=flashinfer_trtllm",
"--fp4-gemm-backend=flashinfer_cutlass",
"--attention-backend=trtllm_mla",
"--kv-cache-dtype=fp8_e4m3",
"--mem-fraction-static=0.85",
"--max-running-requests=16",
"--speculative-algorithm=DFLASH",
f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
"--speculative-num-draft-tokens=8",
"--speculative-draft-attention-backend=fa4",
"--speculative-draft-model-quantization=unquant",
"--speculative-draft-window-size=4096",
]
class TestKimiK26Nvfp4Dflash(unittest.TestCase):
"""Kimi-K2.6 NVFP4 (pure-MLA, fp8 KV) with DFlash speculative decoding on 8x B200 (tp=8).
Runs both an accuracy test (gsm8k) and a performance test (bs=1/8/16), and gates the
speculative-decoding accept length. Guards the pure-MLA fp8-KV DFlash path.
"""
def test_kimi_k26_nvfp4_dflash(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
tp_size=8,
extra_args=EXTRA_ARGS,
variant="TP8+DFLASH",
),
]
run_combined_tests(
models=variants,
test_name="Kimi-K2.6-NVFP4 DFlash",
# Thresholds from a measured tp=8 run: gsm8k 0.936 (full set), accept length ~2.66.
accuracy_params=AccuracyTestParams(
dataset="gsm8k",
baseline_accuracy=0.92,
num_examples=200,
api="completion",
),
performance_params=PerformanceTestParams(
batch_sizes=[1, 8, 16],
spec_accept_length_threshold=2.0,
result_dir="performance_results_kimi_k26_nvfp4_dflash",
),
)
if __name__ == "__main__":
unittest.main()