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Add DeepSeek V4 Pro GB300 nightly and expand Kimi K25 nightly test (#28103)

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This commit is contained in:
Baizhou Zhang
2026-06-25 12:13:52 -07:00
committed by GitHub
parent f9a3720e2b
commit 3344b73c80
9 changed files with 218 additions and 19 deletions
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.github/workflows/nightly-test-nvidia.yml
+18 -3
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@@ -539,7 +539,20 @@ jobs:
# GB300 (Grace-Blackwell NVL4) performance tests - 4 GPU (ARM64)
nightly-test-perf-4-gpu-gb300:
if: github.repository == 'sgl-project/sglang' && (inputs.job_filter == '' || inputs.job_filter == 'all' || inputs.job_filter == 'nightly-test-perf-4-gpu-gb300')
runs-on: 4-gpu-gb300
name: nightly-test-perf-4-gpu-gb300 (${{ matrix.model }})
runs-on: 4-gpu-gb300-nightly
strategy:
fail-fast: false
matrix:
include:
- model: glm5-nvfp4
suite: nightly-4-gpu-gb300-glm5-nvfp4
- model: qwen35-fp8
suite: nightly-4-gpu-gb300-qwen35-fp8
- model: deepseek-v4-pro-fp4
suite: nightly-4-gpu-gb300-deepseek-v4-pro-fp4
- model: kimi-k25-nvfp4
suite: nightly-4-gpu-gb300-kimi-k25-nvfp4
steps:
- name: Checkout code
uses: actions/checkout@v4
@@ -549,8 +562,10 @@ jobs:
- uses: ./.github/actions/check-maintenance
- name: Install dependencies
env:
GRACE_BLACKWELL: "1"
run: |
bash scripts/ci/cuda/ci_install_dependency.sh
bash scripts/ci/cuda/ci_install_deepep.sh
- name: Run test
timeout-minutes: 600
@@ -560,7 +575,7 @@ jobs:
GPU_CONFIG: "4-gpu-gb300"
run: |
cd test
python3 run_suite.py --hw cuda --suite nightly-4-gpu-gb300 --nightly --continue-on-error --timeout-per-file 7200
python3 run_suite.py --hw cuda --suite ${{ matrix.suite }} --nightly --continue-on-error --timeout-per-file 7200
- name: Publish traces to storage repo
if: always()
test/registered/gb300/test_deepseek_v4_pro_fp4.py
+152
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@@ -0,0 +1,152 @@
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
register_cuda_ci(
est_time=7200, suite="nightly-4-gpu-gb300-deepseek-v4-pro-fp4", nightly=True
)
MODEL_PATH = "deepseek-ai/DeepSeek-V4-Pro"
SERVER_LAUNCH_TIMEOUT = 3600
DEEPEP_CONFIG = '{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
LOW_LATENCY_ARGS = [
"--trust-remote-code",
"--moe-runner-backend",
"flashinfer_mxfp4",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"--chunked-prefill-size",
"8192",
"--disable-flashinfer-autotune",
"--swa-full-tokens-ratio",
"0.1",
"--mem-fraction-static",
"0.85",
]
BALANCED_ARGS = [
"--trust-remote-code",
"--dp",
"4",
"--enable-dp-attention",
"--moe-a2a-backend",
"deepep",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"1",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"2",
"--mem-fraction-static",
"0.85",
"--cuda-graph-max-bs",
"128",
"--max-running-requests",
"256",
"--deepep-config",
DEEPEP_CONFIG,
]
HIGH_THROUGHPUT_ARGS = [
"--trust-remote-code",
"--dp",
"4",
"--enable-dp-attention",
"--moe-a2a-backend",
"megamoe",
"--mem-fraction-static",
"0.85",
"--cuda-graph-max-bs",
"128",
"--max-running-requests",
"256",
]
BALANCED_ENV = {
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "256",
}
HIGH_THROUGHPUT_ENV = {
"SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK": "8320",
}
PERFORMANCE_BATCH_SIZES = {
"low-latency": [1, 4, 16],
"balanced": [64],
"high-throughput": [128],
}
class TestDeepSeekV4ProFp4(unittest.TestCase):
"""DeepSeek-V4-Pro FP4 on GB300 (4x B200 NVL4, tp=4)."""
def test_deepseek_v4_pro_fp4(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=LOW_LATENCY_ARGS,
variant="low-latency",
launch_timeout=SERVER_LAUNCH_TIMEOUT,
),
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=BALANCED_ARGS,
env=BALANCED_ENV,
variant="balanced",
launch_timeout=SERVER_LAUNCH_TIMEOUT,
),
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=HIGH_THROUGHPUT_ARGS,
env=HIGH_THROUGHPUT_ENV,
variant="high-throughput",
launch_timeout=SERVER_LAUNCH_TIMEOUT,
),
]
failures = []
accuracy_params = AccuracyTestParams(
dataset="gsm8k",
baseline_accuracy=0.935,
temperature=1.0,
top_p=1.0,
)
for variant in variants:
try:
run_combined_tests(
models=[variant],
test_name=f"DeepSeek-V4-Pro-FP4 ({variant.variant})",
accuracy_params=accuracy_params,
performance_params=PerformanceTestParams(
batch_sizes=PERFORMANCE_BATCH_SIZES[variant.variant],
profile_dir="performance_profiles_gb300",
),
)
except AssertionError as e:
failures.append(f"{variant.variant}: {e}")
if failures:
raise AssertionError(
"DeepSeek-V4-Pro-FP4 failures:\n" + "\n".join(failures)
)
if __name__ == "__main__":
unittest.main()
test/registered/gb300/test_glm5_fp8.py
+4 -1
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@@ -7,7 +7,10 @@ from sglang.test.run_combined_tests import run_combined_tests
from sglang.test.test_utils import ModelLaunchSettings
register_cuda_ci(
est_time=7200, suite="nightly-4-gpu-gb300", nightly=True, disabled="not needed"
est_time=7200,
suite="nightly-4-gpu-gb300-glm5-fp8",
nightly=True,
disabled="not needed",
)
MODEL_PATH = "zai-org/GLM-5.1-FP8"
test/registered/gb300/test_glm5_nvfp4.py
+1 -1
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@@ -6,7 +6,7 @@ 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
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300", nightly=True)
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300-glm5-nvfp4", nightly=True)
MODEL_PATH = "nvidia/GLM-5-NVFP4"
test/registered/gb300/test_kimi_k25.py
+4 -1
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@@ -7,7 +7,10 @@ from sglang.test.run_combined_tests import run_combined_tests
from sglang.test.test_utils import ModelLaunchSettings
register_cuda_ci(
est_time=7200, suite="nightly-4-gpu-gb300", nightly=True, disabled="not needed"
est_time=7200,
suite="nightly-4-gpu-gb300-kimi-k25",
nightly=True,
disabled="not needed",
)
MODEL_PATH = "moonshotai/Kimi-K2.5"
test/registered/gb300/test_kimi_k25_nvfp4.py
+26 -10
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@@ -6,9 +6,12 @@ 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
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300", nightly=True)
register_cuda_ci(
est_time=7200, suite="nightly-4-gpu-gb300-kimi-k25-nvfp4", nightly=True
)
MODEL_PATH = "nvidia/Kimi-K2.5-NVFP4"
DRAFT_MODEL_PATH = "lightseekorg/kimi-k2.5-eagle3-mla"
COMMON_ARGS = [
"--trust-remote-code",
@@ -19,30 +22,43 @@ COMMON_ARGS = [
"--kv-cache-dtype=fp8_e4m3",
"--moe-runner-backend=flashinfer_trtllm",
"--mem-fraction-static=0.8",
"--enable-multimodal",
"--enable-metrics",
"--speculative-algorithm=EAGLE3",
f"--speculative-draft-model-path={DRAFT_MODEL_PATH}",
"--speculative-draft-model-quantization=unquant",
]
TP_EAGLE_ARGS = [
"--speculative-num-steps=3",
"--speculative-eagle-topk=1",
"--speculative-num-draft-tokens=4",
]
DP_EAGLE_ARGS = [
"--speculative-num-steps=1",
"--speculative-eagle-topk=1",
"--speculative-num-draft-tokens=2",
]
class TestKimiK25Nvfp4(unittest.TestCase):
"""Kimi-K2.5 NVFP4 on GB300 (4x GB300 NVL4, tp=4).
No EAGLE/MTP support for Kimi-K2.5 — only TP and TP+DP+DPA variants.
"""
"""Kimi-K2.5 NVFP4 + EAGLE3 on GB300 (4x GB300 NVL4, tp=4)."""
def test_kimi_k25_nvfp4(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=COMMON_ARGS,
variant="TP4",
extra_args=COMMON_ARGS + TP_EAGLE_ARGS,
variant="TP4+EAGLE3",
),
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=COMMON_ARGS + ["--dp-size=4", "--enable-dp-attention"],
variant="TP4+DP4+DPA",
extra_args=COMMON_ARGS
+ ["--dp-size=4", "--enable-dp-attention"]
+ DP_EAGLE_ARGS,
variant="TP4+DP4+DPA+EAGLE3",
),
]
test/registered/gb300/test_qwen35_fp8.py
+1 -1
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@@ -6,7 +6,7 @@ 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
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300", nightly=True)
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300-qwen35-fp8", nightly=True)
MODEL_PATH = "Qwen/Qwen3.5-397B-A17B-FP8"
test/registered/gb300/test_qwen35_nvfp4.py
+4 -1
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@@ -7,7 +7,10 @@ from sglang.test.run_combined_tests import run_combined_tests
from sglang.test.test_utils import ModelLaunchSettings
register_cuda_ci(
est_time=7200, suite="nightly-4-gpu-gb300", nightly=True, disabled="not needed"
est_time=7200,
suite="nightly-4-gpu-gb300-qwen35-nvfp4",
nightly=True,
disabled="not needed",
)
MODEL_PATH = "nvidia/Qwen3.5-397B-A17B-NVFP4"
test/run_suite.py
+8 -1
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@@ -128,8 +128,15 @@ NIGHTLY_SUITES = {
"nightly-eval-vlm-2-gpu",
"nightly-perf-text-2-gpu",
"nightly-perf-vlm-2-gpu",
# GB300 (4x GB300 NVL4) nightly suite
# GB300 (4x GB300 NVL4) nightly suites
"nightly-4-gpu-gb300",
"nightly-4-gpu-gb300-deepseek-v4-pro-fp4",
"nightly-4-gpu-gb300-glm5-fp8",
"nightly-4-gpu-gb300-glm5-nvfp4",
"nightly-4-gpu-gb300-kimi-k25",
"nightly-4-gpu-gb300-kimi-k25-nvfp4",
"nightly-4-gpu-gb300-qwen35-fp8",
"nightly-4-gpu-gb300-qwen35-nvfp4",
# Nightly precision regression (per-layer hidden state comparison)
"nightly-precision-8-gpu-h200",
],