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[CI] Refactor PCG related CI (#19994)

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Signed-off-by: yuweia <ayw.sirius19@gmail.com>
Signed-off-by: Oasis-Git <ayw.sirius19@gmail.com>
This commit is contained in:
Yuwei An
2026-03-10 14:49:25 +08:00
committed by GitHub
parent 9b2e5526fb
commit 834795adb8
4 changed files with 135 additions and 485 deletions
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test/registered/piecewise_cuda_graph/test_piecewise_cuda_graph_2_gpu.py
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import unittest
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_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,
SimpleNamespace,
popen_launch_server,
)
# CI Registration - 2-GPU tests (80GB GPUs required)
register_cuda_ci(est_time=160, suite="stage-b-test-large-2-gpu")
class TestPiecewiseCudaGraphTP(CustomTestCase):
"""Test piecewise CUDA graph with normal TP"""
@classmethod
def setUpClass(cls):
cls.model = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--piecewise-cuda-graph-compiler",
"eager",
"--tp",
"2",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k_accuracy(self):
"""Test GSM8K accuracy with 8-shot setting"""
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"GSM8K Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.90)
if __name__ == "__main__":
unittest.main()
test/registered/piecewise_cuda_graph/test_piecewise_cuda_graph_large_1_gpu.py
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import unittest
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_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,
SimpleNamespace,
popen_launch_server,
)
# CI Registration - Large 1-GPU tests (80GB GPU required)
register_cuda_ci(est_time=480, suite="stage-b-test-large-1-gpu")
class TestPiecewiseCudaGraphQwen3MoE(CustomTestCase):
"""Test piecewise CUDA graph with Qwen3-Coder-30B-A3B-Instruct MoE model"""
@classmethod
def setUpClass(cls):
cls.model = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--piecewise-cuda-graph-compiler",
"eager",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k_accuracy(self):
"""Test GSM8K accuracy with 8-shot setting"""
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"GSM8K Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.90)
class TestPiecewiseCudaGraphGPTQ(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = "Qwen/Qwen3-30B-A3B-GPTQ-Int4"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
num_examples = 1319
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
# Expected accuracy: 0.948, allow some variance
self.assertGreaterEqual(metrics["score"], 0.92)
class TestPiecewiseCudaGraphAWQ(CustomTestCase):
"""Test piecewise CUDA graph with AWQ quantized model"""
@classmethod
def setUpClass(cls):
cls.model = "Qwen/QwQ-32B-AWQ"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
"""Test MGSM accuracy with AWQ model"""
num_examples = 1319
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
print(f"Output throughput: {metrics.get('throughput', 'N/A')} token/s")
# Expected accuracy: 0.680, allow some variance
self.assertGreaterEqual(metrics["score"], 0.65)
if __name__ == "__main__":
unittest.main()
test/registered/piecewise_cuda_graph/test_piecewise_cuda_graph_small_1_gpu.py
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import unittest
import torch
from sglang import Engine
from sglang.lang.chat_template import get_chat_template_by_model_path
from sglang.srt.utils import get_device_sm, kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_IMAGE_URL,
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_MODEL_NAME_FOR_TEST_MLA,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
SimpleNamespace,
popen_launch_server,
run_bench_one_batch,
)
# CI Registration - Small 1-GPU tests (24GB GPU sufficient)
register_cuda_ci(est_time=539, suite="stage-b-test-large-1-gpu")
class TestPiecewiseCudaGraphCorrectness(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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)
class TestPiecewiseCudaGraphBenchmark(CustomTestCase):
def test_latency(self):
prefill_latency, _, _ = run_bench_one_batch(
DEFAULT_MODEL_NAME_FOR_TEST, other_args=[]
)
self.assertLess(prefill_latency, 0.015)
@unittest.skipIf(get_device_sm() < 100, "Test requires CUDA SM 100 or higher")
class TestPiecewiseCudaGraphLlama31FP4(CustomTestCase):
"""MGSM test: piecewise CUDA graph with NVFP4 Llama3.1 8B on Blackwell."""
@classmethod
def setUpClass(cls):
cls.model = "nvidia/Llama-3.1-8B-Instruct-FP4"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--quantization",
"modelopt_fp4",
"--mem-fraction-static",
"0.8",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
num_examples = 1319
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.78)
class TestPiecewiseCudaGraphDeepSeek(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--piecewise-cuda-graph-compiler",
"eager",
"--piecewise-cuda-graph-max-tokens",
"4096", # should less than max_context_len
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
print(metrics)
self.assertGreater(metrics["accuracy"], 0.62)
class TestPiecewiseCudaGraphFP8(CustomTestCase):
"""Test piecewise CUDA graph with FP8 quantized model"""
@classmethod
def setUpClass(cls):
cls.model = "nvidia/Llama-3.1-8B-Instruct-FP8"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--quantization",
"modelopt_fp8",
"--kv-cache-dtype",
"bfloat16",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
"""Test MGSM accuracy with FP8 model"""
num_examples = 1319
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
self.assertGreaterEqual(metrics["score"], 0.85)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
class TestPiecewiseCudaGraphQwen25VL(CustomTestCase):
"""Test piecewise CUDA graph with Qwen2.5-VL-7B-Instruct model"""
@classmethod
def setUpClass(cls):
cls.model = "Qwen/Qwen2.5-VL-7B-Instruct"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--piecewise-cuda-graph-compiler",
"eager",
"--disable-radix-cache",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k_accuracy(self):
"""Test GSM8K accuracy with 8-shot setting"""
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"GSM8K Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.70)
class TestPiecewiseCudaGraphInternVL25(CustomTestCase):
"""Test piecewise CUDA graph with InternVL2.5-8B-Instruct model"""
@classmethod
def setUpClass(cls):
cls.model = "OpenGVLab/InternVL2_5-8B"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--piecewise-cuda-graph-compiler",
"eager",
"--disable-radix-cache",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_gsm8k_accuracy(self):
"""Test GSM8K accuracy with 8-shot setting"""
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"GSM8K Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.70)
class TestPiecewiseCudaGraphQwen25VLEmbedding(CustomTestCase):
"""Test piecewise CUDA graph with Qwen2.5-VL-3B-Instruct embedding model"""
def test_embedding(self):
model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
chat_template = get_chat_template_by_model_path(model_path)
text = f"{chat_template.image_token}What is in this picture? Answer: "
engine = Engine(
model_path=model_path,
enable_multimodal=True,
is_embedding=True,
piecewise_cuda_graph_compiler="eager",
)
out = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0]["embedding"]
engine.shutdown()
self.assertGreater(len(out), 0)
engine = Engine(
model_path=model_path,
enable_multimodal=True,
is_embedding=True,
disable_piecewise_cuda_graph=True,
)
out_without_pcg = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0][
"embedding"
]
engine.shutdown()
self.assertGreater(len(out_without_pcg), 0)
self.assertTrue(
torch.allclose(torch.tensor(out), torch.tensor(out_without_pcg))
)
if __name__ == "__main__":
unittest.main()
test/registered/piecewise_cuda_graph/test_piecewise_cuda_graph_support_1_gpu.py
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import unittest
import torch
from sglang import Engine
from sglang.lang.chat_template import get_chat_template_by_model_path
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.run_eval import run_eval
from sglang.test.test_utils import (
DEFAULT_IMAGE_URL,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
SimpleNamespace,
popen_launch_server,
)
# CI Registration
register_cuda_ci(est_time=220, suite="stage-b-test-large-1-gpu")
class TestPiecewiseCudaGraphQwen25VL(CustomTestCase):
"""Test piecewise CUDA graph with Qwen2.5-VL-7B-Instruct model"""
@classmethod
def setUpClass(cls):
cls.model = "Qwen/Qwen2.5-VL-7B-Instruct"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--enforce-piecewise-cuda-graph",
"--disable-radix-cache",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.70)
class TestPiecewiseCudaGraphInternVL25(CustomTestCase):
"""Test piecewise CUDA graph with InternVL2.5-8B model"""
@classmethod
def setUpClass(cls):
cls.model = "OpenGVLab/InternVL2_5-8B"
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--enforce-piecewise-cuda-graph",
"--disable-radix-cache",
],
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mgsm_accuracy(self):
num_examples = 2000
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mgsm_en",
num_examples=num_examples,
num_threads=min(num_examples, 1024),
)
metrics = run_eval(args)
print(f"MGSM Accuracy: {metrics['score']:.3f}")
self.assertGreaterEqual(metrics["score"], 0.70)
class TestPiecewiseCudaGraphQwen25VLEmbedding(CustomTestCase):
"""Test piecewise CUDA graph with Qwen2.5-VL-3B-Instruct embedding model"""
def test_embedding(self):
model_path = "Qwen/Qwen2.5-VL-3B-Instruct"
chat_template = get_chat_template_by_model_path(model_path)
text = f"{chat_template.image_token}What is in this picture? Answer: "
engine = Engine(
model_path=model_path,
enable_multimodal=True,
is_embedding=True,
enforce_piecewise_cuda_graph=True,
)
out = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0]["embedding"]
engine.shutdown()
self.assertGreater(len(out), 0)
engine = Engine(
model_path=model_path,
enable_multimodal=True,
is_embedding=True,
disable_piecewise_cuda_graph=True,
)
out_without_pcg = engine.encode([text], image_data=[DEFAULT_IMAGE_URL])[0][
"embedding"
]
engine.shutdown()
self.assertGreater(len(out_without_pcg), 0)
self.assertTrue(
torch.allclose(torch.tensor(out), torch.tensor(out_without_pcg))
)
if __name__ == "__main__":
unittest.main()