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NPU Nightly Pipeline Skip Test Case Adaptation and Recovery Testing (#26353)

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This commit is contained in:
liuxianglong17
2026-05-29 09:29:25 +08:00
committed by GitHub
parent 1c2857b064
commit 6258947039
17 changed files with 152 additions and 119 deletions
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test/registered/ascend/basic_function/HiCache/test_npu_hierarchical_cache_mla.py
+1 -1
View File
@@ -12,7 +12,6 @@ register_npu_ci(
est_time=400,
suite="nightly-16-npu-a3",
nightly=True,
disabled="run failed",
)
@@ -89,6 +88,7 @@ class TestNpuHierarchicalCacheMla(CustomTestCase):
gsp_question_len=1792,
gsp_output_len=1,
other_server_args=other_args,
timeout_for_server_launch=1000,
)
TTFT = res["mean_ttft_ms"]
TTFTS.append(TTFT)
test/registered/ascend/basic_function/HiCache/test_npu_hierarchical_cache_ttft_mha.py
-1
View File
@@ -11,7 +11,6 @@ register_npu_ci(
est_time=400,
suite="nightly-2-npu-a3",
nightly=True,
disabled="run failed",
)
test/registered/ascend/basic_function/memory_and_scheduling/test_npu_no_chunked_prefill.py
+74
View File
@@ -0,0 +1,74 @@
import unittest
from types import SimpleNamespace
from sglang.srt.utils import kill_process_tree
from sglang.test.ascend.test_ascend_utils import LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
popen_launch_server,
run_bench_serving,
)
register_npu_ci(est_time=400, suite="nightly-1-npu-a3", nightly=True)
class TestNoChunkedPrefill(CustomTestCase):
"""Testcase: Verify Llama-3.1-8B-Instruct accuracy ≥ 0.65 and serving normal with chunked prefill disabled.
[Test Category] Parameter
[Test Target] --chunked-prefill-size
"""
@classmethod
def setUpClass(cls):
cls.model = LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--attention-backend",
"ascend",
"--disable-cuda-graph",
"--chunked-prefill-size",
"-1",
"--disable-radix-cache",
]
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=other_args,
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mmlu(self):
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mmlu",
num_examples=64,
num_threads=32,
)
metrics = run_eval(args)
self.assertGreaterEqual(metrics["score"], 0.65)
def test_no_chunked_prefill_without_radix_cache(self):
res = run_bench_serving(
model=LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH,
num_prompts=10,
request_rate=float("inf"),
other_server_args=["--disable-radix-cache", "--chunked-prefill-size", "-1"],
)
assert res["completed"] == 10
if __name__ == "__main__":
unittest.main()
test/registered/ascend/basic_function/parallel_strategy/expert_parallelism/test_npu_deepep_auto_qwen3_next.py
+8 -2
View File
@@ -20,7 +20,6 @@ register_npu_ci(
est_time=200,
suite="nightly-8-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/58",
)
@@ -56,7 +55,11 @@ class TestQwen3Next(CustomTestCase):
"--watchdog-timeout",
9000,
"--disable-radix-cache",
"--disable-cuda-graph",
"--cuda-graph-bs",
2,
4,
6,
8,
"--max-prefill-tokens",
28672,
"--max-total-tokens",
@@ -69,6 +72,9 @@ class TestQwen3Next(CustomTestCase):
-1,
],
env={
# In NPU scenarios, operators only support BF16 precision.
# This environment variable needs to be set for quantizing weights.
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_OP_EXPANSION_MODE": "AIV",
test/registered/ascend/basic_function/parallel_strategy/expert_parallelism/test_npu_deepep_low_latency_qwen3_next.py
+13 -3
View File
@@ -20,7 +20,6 @@ register_npu_ci(
est_time=200,
suite="nightly-8-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/58",
)
@@ -56,7 +55,11 @@ class TestQwen3Next(CustomTestCase):
"--watchdog-timeout",
9000,
"--disable-radix-cache",
"--disable-cuda-graph",
"--cuda-graph-bs",
2,
4,
6,
8,
"--chunked-prefill-size",
1024,
"--max-prefill-tokens",
@@ -69,11 +72,18 @@ class TestQwen3Next(CustomTestCase):
"low_latency",
],
env={
# The product of the following two environment variables must be greater than --max-prefill-tokens
# divide by dp size
"DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS": "3000",
"DEEPEP_NORMAL_LONG_SEQ_ROUND": "10",
# In NPU scenarios, operators only support BF16 precision.
# This environment variable needs to be set for quantizing weights.
"SGLANG_DEEPEP_BF16_DISPATCH": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_OP_EXPANSION_MODE": "AIV",
"HCCL_ALGO": "level0:NA;level1:ring",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "20",
"SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "160",
"HCCL_BUFFSIZE": "2048",
**os.environ,
},
test/registered/ascend/basic_function/parameter/test_npu_fim_completion.py
-1
View File
@@ -20,7 +20,6 @@ register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="run failed",
)
test/registered/ascend/basic_function/parameter/test_npu_no_chunked_prefill.py
-39
View File
@@ -1,39 +0,0 @@
import unittest
from sglang.test.ascend.test_ascend_utils import LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.test_utils import CustomTestCase, run_bench_serving, run_mmlu_test
register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="run failed",
)
class TestNoChunkedPrefill(CustomTestCase):
"""Testcase: Verify Llama-3.1-8B-Instruct accuracy ≥ 0.65 and serving normal with chunked prefill disabled.
[Test Category] Parameter
[Test Target] --chunked-prefill-size
"""
def test_no_chunked_prefill(self):
run_mmlu_test(
disable_radix_cache=False, enable_mixed_chunk=False, chunked_prefill_size=-1
)
def test_no_chunked_prefill_without_radix_cache(self):
res = run_bench_serving(
model=LLAMA_3_1_8B_INSTRUCT_WEIGHTS_PATH,
num_prompts=10,
request_rate=float("inf"),
other_server_args=["--disable-radix-cache", "--chunked-prefill-size", "-1"],
)
assert res["completed"] == 10
if __name__ == "__main__":
unittest.main()
test/registered/ascend/basic_function/parameter/test_npu_no_overlap_scheduler.py
+2 -2
View File
@@ -1,13 +1,13 @@
import unittest
from sglang.test.ascend.test_ascend_utils import run_mmlu_test
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.test_utils import CustomTestCase, run_mmlu_test
from sglang.test.test_utils import CustomTestCase
register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="run failed",
)
test/registered/ascend/basic_function/parameter/test_npu_warmups.py
-1
View File
@@ -16,7 +16,6 @@ register_npu_ci(
est_time=400,
suite="nightly-4-npu-a3",
nightly=True,
disabled="run failed",
)
test/registered/ascend/interface/test_npu_enable_thinking.py
+3 -2
View File
@@ -17,7 +17,6 @@ register_npu_ci(
est_time=400,
suite="nightly-2-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/32",
)
@@ -40,7 +39,9 @@ class TestEnableThinking(CustomTestCase):
"--mem-fraction-static",
0.95,
"--tp",
16,
2,
"--reasoning-parser",
"qwen3",
]
cls.process = popen_launch_server(
cls.model,
test/registered/ascend/interface/test_npu_openai_function_calling.py
+23 -26
View File
@@ -18,7 +18,6 @@ register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/39",
)
@@ -429,8 +428,10 @@ class TestOpenAIServerFunctionCalling(CustomTestCase):
def test_function_call_required(self):
"""
Test: Whether tool_choice: "required" works as expected
- When tool_choice == "required", the model should return one or more tool_calls.
Test: Whether tool_choice: "required" works as expected.
- When tool_choice == "required", the model MUST return one or more tool_calls.
- The model may choose ANY of the provided tools; we only verify that
a tool call exists and the selected name is among the candidates.
"""
client = openai.Client(api_key=self.api_key, base_url=self.base_url)
@@ -472,47 +473,42 @@ class TestOpenAIServerFunctionCalling(CustomTestCase):
},
"required": ["city"],
},
"strict": True,
},
},
]
messages = [{"role": "user", "content": "What is the capital of France?"}]
valid_tool_names = {t["function"]["name"] for t in tools}
messages = [{"role": "user", "content": "Tell me about Paris"}]
response = client.chat.completions.create(
model=self.model,
max_tokens=2048,
messages=messages,
temperature=0.8,
top_p=0.8,
temperature=0,
stream=False,
tools=tools,
tool_choice="required",
)
tool_calls = response.choices[0].message.tool_calls
self.assertIsNotNone(tool_calls, "No tool_calls in the response")
self.assertIsNotNone(
tool_calls, "tool_choice='required' must produce tool_calls"
)
self.assertGreater(len(tool_calls), 0, "tool_calls list should be non-empty")
function_name = tool_calls[0].function.name
self.assertIn(
function_name,
valid_tool_names,
f"Function name '{function_name}' is not among the provided tools: {valid_tool_names}",
)
# Verify the arguments are parseable JSON
arguments = tool_calls[0].function.arguments
args_obj = json.loads(arguments)
self.assertEqual(
function_name,
"get_weather",
f"Function name should be 'get_weather', got: {function_name}",
)
self.assertIn(
"city", args_obj, f"Function arguments should have 'city', got: {args_obj}"
)
# Make the test more robust by checking type and accepting valid responses
city_value = args_obj["city"]
self.assertIsInstance(
city_value,
str,
f"Parameter city should be a string, got: {type(city_value)}",
)
self.assertTrue(
"Paris" in city_value or "France" in city_value,
f"Parameter city should contain either 'Paris' or 'France', got: {city_value}",
args_obj, dict, "Function arguments should be a JSON object"
)
def test_function_call_specific(self):
@@ -560,6 +556,7 @@ class TestOpenAIServerFunctionCalling(CustomTestCase):
},
"required": ["city"],
},
"strict": True,
},
},
]
test/registered/ascend/llm_models/test_npu_llama4_scount_17b_16e.py
+2 -6
View File
@@ -7,12 +7,7 @@ from sglang.test.ascend.test_ascend_utils import (
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.test_utils import CustomTestCase
register_npu_ci(
est_time=400,
suite="nightly-4-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/25",
)
register_npu_ci(est_time=400, suite="nightly-4-npu-a3", nightly=True)
class TestLlama4(GSM8KAscendMixin, CustomTestCase):
@@ -24,6 +19,7 @@ class TestLlama4(GSM8KAscendMixin, CustomTestCase):
model = LLAMA_4_SCOUT_17B_16E_INSTRUCT_WEIGHTS_PATH
accuracy = 0.9
timeout_for_server_launch = 1000
other_args = [
"--chat-template",
"llama-4",
test/registered/ascend/llm_models/test_npu_minicpm3_4b.py
+1 -6
View File
@@ -5,12 +5,7 @@ from sglang.test.ascend.test_ascend_utils import MINICPM3_4B_WEIGHTS_PATH
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.test_utils import CustomTestCase
register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="https://github.com/Ascend/sglang/issues/23",
)
register_npu_ci(est_time=400, suite="nightly-1-npu-a3", nightly=True)
class TestMiniCPM3(GSM8KAscendMixin, CustomTestCase):
test/registered/ascend/vlm_models/test_npu_deepseek_vl2.py
-1
View File
@@ -8,7 +8,6 @@ register_npu_ci(
est_time=400,
suite="nightly-4-npu-a3",
nightly=True,
disabled="run failed",
)
test/registered/ascend/vlm_models/test_npu_kimi_vl_a3b_instruct.py
+14 -18
View File
@@ -1,32 +1,28 @@
import unittest
from sglang.test.ascend.gsm8k_ascend_mixin import GSM8KAscendMixin
from sglang.test.ascend.test_ascend_utils import KIMI_VL_A3B_INSTRUCT_WEIGHTS_PATH
from sglang.test.ascend.vlm_utils import TestVLMModels
from sglang.test.ci.ci_register import register_npu_ci
from sglang.test.test_utils import CustomTestCase
register_npu_ci(
est_time=400,
suite="nightly-4-npu-a3",
nightly=True,
disabled="run failed",
)
class TestKimiVLA3BInstruct(GSM8KAscendMixin, CustomTestCase):
model = "/root/.cache/modelscope/hub/models/Kimi/Kimi-VL-A3B-Instruct"
accuracy = 0.66
other_args = [
"--trust-remote-code",
"--max-running-requests",
2048,
"--mem-fraction-static",
0.7,
"--attention-backend",
"ascend",
"--tp-size",
"4",
"--disable-cuda-graph",
]
class TestKimiVLA3BInstruct(TestVLMModels):
"""Testcase: Verify that the inference accuracy of the moonshotai/Kimi-VL-A3B-Instruct model on the MMMU dataset is no less than 0.2.
[Test Category] Model
[Test Target] moonshotai/Kimi-VL-A3B-Instruct
"""
model = KIMI_VL_A3B_INSTRUCT_WEIGHTS_PATH
mmmu_accuracy = 0.2
def test_vlm_mmmu_benchmark(self):
self._run_vlm_mmmu_test()
if __name__ == "__main__":
test/registered/ascend/vlm_models/test_npu_llama_3_2_11b_vision_instruct.py
+11 -9
View File
@@ -1,20 +1,22 @@
import unittest
from sglang.test.ascend.test_ascend_utils import (
LLAMA_3_2_11B_VISION_INSTRUCT_WEIGHTS_PATH,
)
from sglang.test.ascend.vlm_utils import TestVLMModels
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=400,
suite="nightly-1-npu-a3",
nightly=True,
disabled="run failed",
)
register_npu_ci(est_time=400, suite="nightly-1-npu-a3", nightly=True)
class TestLlama3211BVisionInstruct(TestVLMModels):
model = (
"/root/.cache/modelscope/hub/models/LLM-Research/Llama-3.2-11B-Vision-Instruct"
)
"""Testcase: Verify that the inference accuracy of the LLM-Research/Llama-3.2-11B-Vision-Instruct model on the MMMU dataset is no less than 0.2.
[Test Category] Model
[Test Target] LLM-Research/Llama-3.2-11B-Vision-Instruct
"""
model = LLAMA_3_2_11B_VISION_INSTRUCT_WEIGHTS_PATH
mmmu_accuracy = 0.2
other_args = [
"--trust-remote-code",
test/registered/ascend/vlm_models/test_npu_minicpm_o_2_6.py
-1
View File
@@ -8,7 +8,6 @@ register_npu_ci(
est_time=400,
suite="nightly-4-npu-a3",
nightly=True,
disabled="run failed",
)