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

Update GLM tests to 5.2 and delete redundant tests (#29686)

Browse Source
This commit is contained in:
Baizhou Zhang
2026-06-29 23:04:26 -07:00
committed by GitHub
parent b6907d9664
commit cc238270b1
16 changed files with 39 additions and 508 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/registered/8-gpu-models/test_deepseek_v32.py
-200
View File
@@ -1,200 +0,0 @@
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, is_blackwell_system
from sglang.test.tool_call_test_runner import ToolCallTestParams
register_cuda_ci(est_time=5400, suite="nightly-8-gpu-common", nightly=True)
DEEPSEEK_V32_MODEL_PATH = "deepseek-ai/DeepSeek-V3.2"
BASE_ARGS = [
"--trust-remote-code",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
]
TOOL_CALL_ARGS = [
"--tool-call-parser=deepseekv32",
"--reasoning-parser=deepseek-v3",
]
DP_ARGS = [
"--tp=8",
"--dp=8",
"--enable-dp-attention",
]
# Accuracy thresholds
GSM8K_BASELINE = 0.935
GPQA_BASELINE = 0.83
class TestDeepseekV32(unittest.TestCase):
"""Unified test class for DeepSeek V3.2 performance and accuracy.
Tests multiple variants with both performance and accuracy tests:
- dp: Standard TP=8 + DP=8 with dp-attention
- dp+mtp: DP + EAGLE speculative decoding
- tp: Pure TP=8 only
- tp+mtp: Pure TP=8 + EAGLE speculative decoding
"""
def test_deepseek_v32_all_variants(self):
"""Run performance and accuracy for all DeepSeek V3.2 variants."""
TP_ARGS = [
"--tp=8",
]
MTP_ARGS = [
"--speculative-algorithm=EAGLE",
"--speculative-num-steps=3",
"--speculative-eagle-topk=1",
"--speculative-num-draft-tokens=4",
"--mem-frac=0.85",
]
variants = [
# Variant: "dp" - Standard TP=8 + DP=8 with dp-attention
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + TOOL_CALL_ARGS,
variant="DP8",
),
# Variant: "dp+mtp" - DP + EAGLE speculative decoding
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + TOOL_CALL_ARGS + MTP_ARGS,
variant="DP8+MTP",
),
# Variant: "tp" - Pure TP=8 only
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + TP_ARGS + TOOL_CALL_ARGS,
variant="TP8",
),
# Variant: "tp+mtp" - Pure TP=8 + EAGLE speculative decoding
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + TP_ARGS + TOOL_CALL_ARGS + MTP_ARGS,
variant="TP8+MTP",
),
]
run_combined_tests(
models=variants,
test_name="DeepSeek-V3.2",
accuracy_params=AccuracyTestParams(
dataset="gsm8k", baseline_accuracy=GSM8K_BASELINE
),
performance_params=PerformanceTestParams(
batch_sizes=[1, 8, 16, 64],
profile_dir="performance_profiles_deepseek_v32",
),
tool_call_params=ToolCallTestParams(
test_thinking=True, test_reasoning_usage=True
),
)
@unittest.skipIf(is_blackwell_system(), "Requires H200 system")
def test_deepseek_v32_dsa_backends(self):
"""Test DSA attention backend variants (H200 only).
Tests three DSA backend configurations:
- flashmla: flashmla_sparse prefill + flashmla_kv decode
- fa3: FA3 prefill + FA3 decode
- fp8kvcache: default backends with FP8 KV cache
"""
DSA_FLASHMLA_ARGS = [
"--attention-backend=dsa",
"--dsa-prefill-backend=flashmla_sparse",
"--dsa-decode-backend=flashmla_kv",
]
DSA_FA3_ARGS = [
"--attention-backend=dsa",
"--dsa-prefill-backend=fa3",
"--dsa-decode-backend=fa3",
]
DSA_FP8KV_ARGS = [
"--attention-backend=dsa",
"--kv-cache-dtype=fp8_e4m3",
]
dsa_variants = [
# flashmla backend
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + DSA_FLASHMLA_ARGS,
),
# fa3 backend
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + DSA_FA3_ARGS,
),
# fp8 kv cache
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + DSA_FP8KV_ARGS,
),
]
run_combined_tests(
models=dsa_variants,
test_name="DeepSeek-V3.2 DSA Backends",
accuracy_params=AccuracyTestParams(
dataset="gsm8k", baseline_accuracy=GSM8K_BASELINE
),
performance_params=None,
)
@unittest.skipIf(
not is_blackwell_system(),
"Hardware agnostic - just using B200 for efficiency reasons",
)
def test_deepseek_v32_b200(self):
"""Test DeepSeek V3.2 with GPQA evaluation using thinking mode (B200 only).
This test runs GPQA evaluation with the reasoning parser enabled.
"""
B200_REASONING_ARGS = [
"--tool-call-parser=deepseekv32",
"--reasoning-parser=deepseek-v3",
]
variants = [
ModelLaunchSettings(
DEEPSEEK_V32_MODEL_PATH,
tp_size=8,
extra_args=BASE_ARGS + DP_ARGS + B200_REASONING_ARGS,
),
]
run_combined_tests(
models=variants,
test_name="DeepSeek-V3.2 GPQA (B200)",
accuracy_params=AccuracyTestParams(
dataset="gpqa",
baseline_accuracy=GPQA_BASELINE,
num_examples=198,
num_threads=198,
max_tokens=120000,
thinking_mode="deepseek-v3",
temperature=0.1,
repeat=4,
),
performance_params=None, # Skip performance test for GPQA
)
if __name__ == "__main__":
unittest.main()
test/registered/8-gpu-models/test_glm_51_fp8.py → test/registered/8-gpu-models/test_glm52_fp8.py
+9 -9
View File
@@ -9,7 +9,7 @@ from sglang.test.test_utils import ModelLaunchSettings
# Runs on both H200 and B200 via nightly-8-gpu-common suite
register_cuda_ci(est_time=1800, suite="nightly-8-gpu-common", nightly=True)
GLM_51_FP8_MODEL_PATH = "zai-org/GLM-5.1-FP8"
GLM_52_FP8_MODEL_PATH = "zai-org/GLM-5.2-FP8"
COMMON_ARGS = [
"--trust-remote-code",
@@ -27,27 +27,27 @@ MTP_ARGS = [
]
class TestGlm51Fp8(unittest.TestCase):
"""GLM-5.1 FP8 on H200/B200 (8-GPU, tp=8)."""
class TestGlm52Fp8(unittest.TestCase):
"""GLM-5.2 FP8 on H200/B200 (8-GPU, tp=8)."""
def test_glm51_fp8(self):
def test_glm52_fp8(self):
dp_args = ["--dp=8", "--enable-dp-attention"]
variants = [
ModelLaunchSettings(
GLM_51_FP8_MODEL_PATH,
GLM_52_FP8_MODEL_PATH,
tp_size=8,
extra_args=COMMON_ARGS,
variant="TP8",
),
ModelLaunchSettings(
GLM_51_FP8_MODEL_PATH,
GLM_52_FP8_MODEL_PATH,
tp_size=8,
extra_args=COMMON_ARGS + dp_args,
variant="TP8+DP8",
),
ModelLaunchSettings(
GLM_51_FP8_MODEL_PATH,
GLM_52_FP8_MODEL_PATH,
tp_size=8,
extra_args=COMMON_ARGS + dp_args + MTP_ARGS,
variant="TP8+DP8+MTP",
@@ -56,10 +56,10 @@ class TestGlm51Fp8(unittest.TestCase):
run_combined_tests(
models=variants,
test_name="GLM-5.1-FP8",
test_name="GLM-5.2-FP8",
accuracy_params=AccuracyTestParams(dataset="gsm8k", baseline_accuracy=0.92),
performance_params=PerformanceTestParams(
profile_dir="performance_profiles_glm_51_fp8",
profile_dir="performance_profiles_glm_52_fp8",
),
)
test/registered/cuda_graph/piecewise/test_pcg_glm5_fp4.py → test/registered/cuda_graph/piecewise/test_pcg_glm52_fp4.py
+5 -5
View File
@@ -13,20 +13,20 @@ from sglang.test.test_utils import (
register_cuda_ci(est_time=900, stage="base-c", runner_config="4-gpu-b200")
GLM5_FP4_MODEL = "nvidia/GLM-5-NVFP4"
GLM52_FP4_MODEL = "nvidia/GLM-5.2-NVFP4"
class TestPCGGlm5Fp4(CustomTestCase):
"""PCG prefill on GLM-5-NVFP4 (DSA model, TP=4, B200).
class TestPCGGlm52Fp4(CustomTestCase):
"""PCG prefill on GLM-5.2-NVFP4 (DSA model, TP=4, B200).
GLM-5 uses GlmMoeDsaForCausalLM (DSA attention). This test verifies that
GLM-5.2 uses GlmMoeDsaForCausalLM (DSA attention). This test verifies that
piecewise CUDA graph works correctly after the DSA indexer was updated to
cache k_fp8/k_scale for PCG-compatible prefill.
"""
@classmethod
def setUpClass(cls):
cls.model = GLM5_FP4_MODEL
cls.model = GLM52_FP4_MODEL
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
test/registered/cuda_graph/piecewise/test_pcg_glm5_fp8_tp8.py → test/registered/cuda_graph/piecewise/test_pcg_glm52_fp8_tp8.py
+4 -4
View File
@@ -13,15 +13,15 @@ from sglang.test.test_utils import (
register_cuda_ci(est_time=900, stage="base-c", runner_config="8-gpu-h200")
GLM5_FP8_MODEL = "zai-org/GLM-5-FP8"
GLM52_FP8_MODEL = "zai-org/GLM-5.2-FP8"
class TestBCGGlm5Fp8TP8(CustomTestCase):
"""Breakable CUDA graph prefill on GLM-5-FP8 (DSA model, TP=8, H200)."""
class TestBCGGlm52Fp8TP8(CustomTestCase):
"""Breakable CUDA graph prefill on GLM-5.2-FP8 (DSA model, TP=8, H200)."""
@classmethod
def setUpClass(cls):
cls.model = GLM5_FP8_MODEL
cls.model = GLM52_FP8_MODEL
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
test/registered/debug_utils/test_nightly_precision_regression.py
+2 -2
View File
@@ -2,7 +2,7 @@
compare day-over-day against a rolling baseline.
Env knobs:
SGLANG_PRECISION_MODELS comma-separated model ids (default GLM-5.1-FP8)
SGLANG_PRECISION_MODELS comma-separated model ids (default GLM-5.2-FP8)
SGLANG_PRECISION_BASELINE_DIR local baseline dir
SGLANG_PRECISION_DIFF_THRESHOLD per-tensor rel_diff cutoff (default 1e-3)
SGLANG_PRECISION_FORCE_UPDATE=1 skip comparison, refresh baseline
@@ -49,7 +49,7 @@ except Exception: # pragma: no cover
register_cuda_ci(est_time=3600, suite="nightly-precision-8-gpu-h200", nightly=True)
DEFAULT_MODELS_FOR_NIGHTLY_PRECISION = "zai-org/GLM-5.1-FP8"
DEFAULT_MODELS_FOR_NIGHTLY_PRECISION = "zai-org/GLM-5.2-FP8"
DEFAULT_DIFF_THRESHOLD = 1e-3
# Fallback when the layer count can't be resolved: never silently shrink coverage.
DUMPER_FILTER_ALL_LAYERS = (
test/registered/gb300/test_glm5_nvfp4.py → test/registered/gb300/test_glm52_nvfp4.py
+5 -5
View File
@@ -8,7 +8,7 @@ from sglang.test.test_utils import ModelLaunchSettings
register_cuda_ci(est_time=7200, suite="nightly-4-gpu-gb300-glm5-nvfp4", nightly=True)
MODEL_PATH = "nvidia/GLM-5-NVFP4"
MODEL_PATH = "nvidia/GLM-5.2-NVFP4"
COMMON_ARGS = [
"--trust-remote-code",
@@ -35,10 +35,10 @@ DP_MTP_ARGS = [
]
class TestGlm5Nvfp4(unittest.TestCase):
"""GLM-5 NVFP4 on GB300 (4x GB300 NVL4, tp=4)."""
class TestGlm52Nvfp4(unittest.TestCase):
"""GLM-5.2 NVFP4 on GB300 (4x GB300 NVL4, tp=4)."""
def test_glm5_nvfp4(self):
def test_glm52_nvfp4(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
@@ -58,7 +58,7 @@ class TestGlm5Nvfp4(unittest.TestCase):
run_combined_tests(
models=variants,
test_name="GLM-5-NVFP4",
test_name="GLM-5.2-NVFP4",
accuracy_params=AccuracyTestParams(dataset="gsm8k", baseline_accuracy=0.92),
performance_params=PerformanceTestParams(
profile_dir="performance_profiles_gb300",
test/registered/gb300/test_glm5_fp8.py
-72
View File
@@ -1,72 +0,0 @@
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-glm5-fp8",
nightly=True,
disabled="not needed",
)
MODEL_PATH = "zai-org/GLM-5.1-FP8"
COMMON_ARGS = [
"--trust-remote-code",
"--reasoning-parser=glm45",
"--tool-call-parser=glm47",
"--mem-fraction-static=0.9",
"--enable-metrics",
]
MTP_ARGS = [
"--speculative-algorithm=EAGLE",
"--speculative-num-steps=3",
"--speculative-eagle-topk=1",
"--speculative-num-draft-tokens=4",
]
class TestGlm5Fp8(unittest.TestCase):
"""GLM-5.1 FP8 on GB300 (4x GB300 NVL4, tp=4)."""
def test_glm5_fp8(self):
variants = [
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=COMMON_ARGS,
variant="TP4",
),
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=COMMON_ARGS + ["--dp-size=4", "--enable-dp-attention"],
variant="TP4+DP4+DPA",
),
ModelLaunchSettings(
MODEL_PATH,
tp_size=4,
extra_args=COMMON_ARGS
+ ["--dp-size=4", "--enable-dp-attention"]
+ MTP_ARGS,
variant="TP4+DP4+DPA+MTP",
),
]
run_combined_tests(
models=variants,
test_name="GLM-5.1-FP8",
accuracy_params=AccuracyTestParams(dataset="gsm8k", baseline_accuracy=0.92),
performance_params=PerformanceTestParams(
profile_dir="performance_profiles_gb300",
),
)
if __name__ == "__main__":
unittest.main()
test/registered/models_e2e/test_deepseek_v32_fp4_mtp_dp.py
-68
View File
@@ -1,68 +0,0 @@
import unittest
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.send_one import BenchArgs, send_one_prompt
from sglang.test.server_fixtures.default_fixture import DefaultServerBase
from sglang.test.test_utils import is_in_ci, write_github_step_summary
register_cuda_ci(est_time=400, stage="base-c", runner_config="4-gpu-b200")
DSV32_FP4_MODEL = "nvidia/DeepSeek-V3.2-NVFP4"
class TestDeepseekV32FP4DPSpec(GSM8KMixin, DefaultServerBase):
model = DSV32_FP4_MODEL
timeout = 1200
other_args = [
"--tp",
"4",
"--dp",
"4",
"--enable-dp-attention",
"--attention-backend",
"dsa",
"--moe-runner-backend",
"flashinfer_trtllm",
"--quantization",
"modelopt_fp4",
"--tool-call-parser",
"deepseekv32",
"--reasoning-parser",
"deepseek-v3",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"--model-loader-extra-config",
'{"enable_multithread_load": true,"num_threads": 64}',
]
gsm8k_accuracy_thres = 0.93
gsm8k_num_questions = 500
gsm8k_num_threads = 500
gsm8k_num_shots = 20
gsm8k_accept_length_thres = 2.7
def test_z_bs_1_speed(self):
args = BenchArgs(port=int(self.base_url.split(":")[-1]), max_new_tokens=2048)
acc_length, speed = send_one_prompt(args)
print(f"{acc_length=:.2f} {speed=:.2f}")
if is_in_ci():
write_github_step_summary(
f"### test_bs_1_speed (deepseek-v32 mtp dp)\n"
f"{acc_length=:.2f}\n"
f"{speed=:.2f} token/s\n"
)
self.assertGreater(acc_length, 2.7)
self.assertGreater(speed, 90)
if __name__ == "__main__":
unittest.main()
test/registered/models_e2e/test_deepseek_v32_fp4_mtp_tp.py
-73
View File
@@ -1,73 +0,0 @@
import unittest
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.send_one import BenchArgs, send_one_prompt
from sglang.test.server_fixtures.default_fixture import DefaultServerBase
from sglang.test.test_utils import is_in_ci, write_github_step_summary
register_cuda_ci(est_time=400, stage="base-c", runner_config="4-gpu-b200")
DSV32_FP4_MODEL = "nvidia/DeepSeek-V3.2-NVFP4"
class TestDeepseekV32FP4TPSpec(GSM8KMixin, DefaultServerBase):
model = DSV32_FP4_MODEL
timeout = 1200
other_args = [
"--tp",
"4",
"--attention-backend",
"dsa",
"--moe-runner-backend",
"flashinfer_trtllm",
"--quantization",
"modelopt_fp4",
"--tool-call-parser",
"deepseekv32",
"--reasoning-parser",
"deepseek-v3",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"--model-loader-extra-config",
'{"enable_multithread_load": true,"num_threads": 64}',
]
gsm8k_accuracy_thres = 0.93
gsm8k_num_questions = 500
gsm8k_num_threads = 500
gsm8k_num_shots = 20
gsm8k_accept_length_thres = 2.7
def test_z_bs_1_speed(self):
args = BenchArgs(
port=int(self.base_url.split(":")[-1]),
max_new_tokens=2048,
prompt=(
"Human: Think carefully before answering. Build a fully functional FastAPI todo server. "
"Start with a short design plan, then output the complete Python code, then show how to run it "
"and test three endpoints.\n\nAssistant:"
),
)
acc_length, speed = send_one_prompt(args)
print(f"{acc_length=:.2f} {speed=:.2f}")
if is_in_ci():
write_github_step_summary(
f"### test_bs_1_speed (deepseek-v32 mtp tp)\n"
f"{acc_length=:.2f}\n"
f"{speed=:.2f} token/s\n"
)
self.assertGreater(acc_length, 2.7)
self.assertGreater(speed, 150)
if __name__ == "__main__":
unittest.main()
test/registered/models_e2e/test_dsa_dsv32_dp_mtp.py
-28
View File
@@ -1,28 +0,0 @@
import unittest
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.kits.spec_decoding_kit import SpecDecodingMixin
from sglang.test.server_fixtures.dsa_mtp_fixture import (
DsaMtpEvalConfigDefaults,
DsaMtpServerBase,
)
register_cuda_ci(
est_time=600,
stage="extra-b",
runner_config="8-gpu-h200",
)
class TestDeepseekV32DPMTP(
DsaMtpServerBase, DsaMtpEvalConfigDefaults, GSM8KMixin, SpecDecodingMixin
):
model = "deepseek-ai/DeepSeek-V3.2"
mem_fraction_static = 0.85
enable_dp_attention = True
bs_1_speed_thres = 90
if __name__ == "__main__":
unittest.main()
test/registered/models_e2e/test_dsa_dsv32_tp_mtp.py
-27
View File
@@ -1,27 +0,0 @@
import unittest
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.kits.spec_decoding_kit import SpecDecodingMixin
from sglang.test.server_fixtures.dsa_mtp_fixture import (
DsaMtpEvalConfigDefaults,
DsaMtpServerBase,
)
register_cuda_ci(
est_time=400,
stage="extra-b",
runner_config="8-gpu-h200",
)
class TestDeepseekV32TPMTP(
DsaMtpServerBase, DsaMtpEvalConfigDefaults, GSM8KMixin, SpecDecodingMixin
):
model = "deepseek-ai/DeepSeek-V3.2"
mem_fraction_static = 0.85
bs_1_speed_thres = 180
if __name__ == "__main__":
unittest.main()
test/registered/models_e2e/test_dsa_glm5_dp_mtp.py → test/registered/models_e2e/test_dsa_glm52_dp_mtp.py
+2 -2
View File
@@ -15,10 +15,10 @@ register_cuda_ci(
)
class TestGLM5DPMTP(
class TestGLM52DPMTP(
DsaMtpServerBase, DsaMtpEvalConfigDefaults, GSM8KMixin, SpecDecodingMixin
):
model = "zai-org/GLM-5-FP8"
model = "zai-org/GLM-5.2-FP8"
mem_fraction_static = 0.88
enable_dp_attention = True
bs_1_speed_thres = 70
test/registered/models_e2e/test_dsa_glm5_hisparse.py → test/registered/models_e2e/test_dsa_glm52_hisparse.py
+5 -5
View File
@@ -9,18 +9,18 @@ from sglang.test.server_fixtures.default_fixture import DefaultServerBase
register_cuda_ci(est_time=720, stage="extra-b", runner_config="8-gpu-h200")
GLM5_FP8_MODEL_PATH = "zai-org/GLM-5-FP8"
GLM52_FP8_MODEL_PATH = "zai-org/GLM-5.2-FP8"
class TestGLM5HiSparse(DefaultServerBase, GSM8KMixin):
"""GLM-5 FP8 with HiSparse (host-to-device sparse KV offload) on DSA decode.
class TestGLM52HiSparse(DefaultServerBase, GSM8KMixin):
"""GLM-5.2 FP8 with HiSparse (host-to-device sparse KV offload) on DSA decode.
HiSparse targets the high-concurrency regime and is not used together with
EAGLE MTP, so this variant runs without speculative decoding (unlike the
DSA-MTP variants in test_dsa_glm5_{dp,tp}_mtp.py).
DSA-MTP variants in test_dsa_glm52_{dp,tp}_mtp.py).
"""
model = GLM5_FP8_MODEL_PATH
model = GLM52_FP8_MODEL_PATH
other_args = [
"--trust-remote-code",
"--tp",
test/registered/models_e2e/test_dsa_glm5_tp_mtp.py → test/registered/models_e2e/test_dsa_glm52_tp_mtp.py
+2 -2
View File
@@ -15,10 +15,10 @@ register_cuda_ci(
)
class TestGLM5TPMTP(
class TestGLM52TPMTP(
DsaMtpServerBase, DsaMtpEvalConfigDefaults, GSM8KMixin, SpecDecodingMixin
):
model = "zai-org/GLM-5-FP8"
model = "zai-org/GLM-5.2-FP8"
mem_fraction_static = 0.8
bs_1_speed_thres = 150
test/registered/radix_cache/unified_radix_tree/test_unified_radix_cache_kl_nightly.py
+5 -5
View File
@@ -23,7 +23,7 @@ from sglang.test.test_utils import (
popen_launch_server,
)
GLM5_MODEL = "zai-org/GLM-5.1-FP8"
GLM5_MODEL = "zai-org/GLM-5.2-FP8"
GLM5_LAUNCH_TIMEOUT = 3600
register_cuda_ci(est_time=900, suite="nightly-8-gpu-h200", nightly=True)
@@ -141,7 +141,7 @@ class AccuracyTwoPassMixin:
class TestGLM5HiRadixCacheL3Accuracy(AccuracyTwoPassMixin, CustomTestCase):
"""GLM-5.1-FP8 + HiCache L3 (file backend), with HiRadixTree."""
"""GLM-5.2-FP8 + HiCache L3 (file backend), with HiRadixTree."""
@classmethod
def setUpClass(cls):
@@ -159,7 +159,7 @@ class TestGLM5HiRadixCacheL3Accuracy(AccuracyTwoPassMixin, CustomTestCase):
"--page-size",
"64",
"--mem-fraction-static",
"0.85",
"0.8",
"--model-loader-extra-config",
'{"enable_multithread_load": true, "num_threads": 64}',
"--enable-hierarchical-cache",
@@ -189,7 +189,7 @@ class TestGLM5HiRadixCacheL3Accuracy(AccuracyTwoPassMixin, CustomTestCase):
class TestGLM5UnifiedRadixCacheL3Accuracy(AccuracyTwoPassMixin, CustomTestCase):
"""GLM-5.1-FP8 + HiCache L3 (file backend), with UnifiedRadixTree."""
"""GLM-5.2-FP8 + HiCache L3 (file backend), with UnifiedRadixTree."""
@classmethod
def setUpClass(cls):
@@ -207,7 +207,7 @@ class TestGLM5UnifiedRadixCacheL3Accuracy(AccuracyTwoPassMixin, CustomTestCase):
"--page-size",
"64",
"--mem-fraction-static",
"0.85",
"0.8",
"--model-loader-extra-config",
'{"enable_multithread_load": true, "num_threads": 64}',
"--enable-hierarchical-cache",
test/run_suite.py
-1
View File
@@ -131,7 +131,6 @@ NIGHTLY_SUITES = {
# 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",