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import os
import unittest
from types import SimpleNamespace
import openai
import requests
from transformers import AutoTokenizer
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.server_fixtures.disaggregation_fixture import (
PDDisaggregationServerBase,
)
from sglang.test.test_utils import (
DEFAULT_DRAFT_MODEL_EAGLE,
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_TARGET_MODEL_EAGLE,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
popen_launch_pd_server,
)
register_cuda_ci(est_time=400, suite="stage-b-test-large-2-gpu")
class TestDisaggregationAccuracy(PDDisaggregationServerBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
# Non blocking start servers
cls.start_prefill()
cls.start_decode()
# Block until both
cls.wait_server_ready(cls.prefill_url + "/health", process=cls.process_prefill)
cls.wait_server_ready(cls.decode_url + "/health", process=cls.process_decode)
cls.launch_lb()
@classmethod
def start_prefill(cls):
prefill_args = [
"--trust-remote-code",
"--disaggregation-mode",
"prefill",
"--tp",
"1",
]
prefill_args += cls.transfer_backend + cls.rdma_devices
cls.process_prefill = popen_launch_pd_server(
cls.model,
cls.prefill_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=prefill_args,
)
@classmethod
def start_decode(cls):
decode_args = [
"--trust-remote-code",
"--disaggregation-mode",
"decode",
"--tp",
"1",
"--base-gpu-id",
"1",
]
decode_args += cls.transfer_backend + cls.rdma_devices
cls.process_decode = popen_launch_pd_server(
cls.model,
cls.decode_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=decode_args,
)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host=f"http://{self.base_host}",
port=int(self.lb_port),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"Evaluation metrics: {metrics}")
self.assertGreater(metrics["accuracy"], 0.62)
def test_logprob(self):
prompt = "The capital of france is "
response = requests.post(
self.lb_url + "/generate",
json={
"text": prompt,
"sampling_params": {"temperature": 0},
"return_logprob": True,
"return_input_logprob": True,
"logprob_start_len": 0,
},
)
j = response.json()
completion_tokens = j["meta_info"]["completion_tokens"]
input_logprobs = j["meta_info"]["input_token_logprobs"]
output_logprobs = j["meta_info"]["output_token_logprobs"]
assert (
len(output_logprobs) == completion_tokens
), f"output_logprobs and completion_tokens should have the same length, but got {len(output_logprobs)} and {completion_tokens}"
assert (
len(input_logprobs) > 0
), f"input_logprobs should have at least one token, but got {len(input_logprobs)}"
def test_structured_output(self):
json_schema = json.dumps(
{
"type": "object",
"properties": {
"name": {"type": "string", "pattern": "^[\\w]+$"},
"population": {"type": "integer"},
},
"required": ["name", "population"],
}
)
# JSON
response = requests.post(
f"{self.lb_url}/generate",
json={
"text": "Here is the information of the capital of France in the JSON format.\n",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 64,
"json_schema": json_schema,
},
},
)
output = response.json()["text"]
# ensure the output is a valid JSON
json.loads(output)
def test_first_token_finish(self):
client = openai.Client(api_key="empty", base_url=f"{self.lb_url}/v1")
tokenizer = AutoTokenizer.from_pretrained(self.model)
eos_token = tokenizer.eos_token_id
prompt = "The best programming language for AI is"
# First token EOS
res = client.completions.create(
model="dummy", prompt=prompt, logit_bias={eos_token: 42}
).model_dump()
print(f"{res=}")
assert res["usage"]["completion_tokens"] == 1, (
"Expected completion_tokens to be 1 when first token is EOS, "
f"but got {res['usage']['completion_tokens']}"
)
# First token EOS with ignore_eos
res = client.completions.create(
model="dummy",
prompt=prompt,
logit_bias={eos_token: 42},
extra_body={"ignore_eos": True},
).model_dump()
print(f"{res=}")
assert res["usage"]["completion_tokens"] > 1, (
"Expected completion_tokens to be greater than 1 when ignore_eos is True, "
f"but got {res['usage']['completion_tokens']}"
)
# First token with specified stop token
stop_token_id = tokenizer.encode(" hello", add_special_tokens=False)[0]
res = client.completions.create(
model="dummy",
prompt=prompt,
logit_bias={stop_token_id: 42},
stop=[" hello"],
).model_dump()
print(f"{res=}")
assert res["usage"]["completion_tokens"] == 1, (
"Expected completion_tokens to be 1 when first token is stop token, "
f"but got {res['usage']['completion_tokens']}"
)
class TestDisaggregationMooncakeFailure(PDDisaggregationServerBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
# set DISAGGREGATION_TEST_FAILURE_PROB to simulate failure
os.environ["DISAGGREGATION_TEST_FAILURE_PROB"] = "0.05"
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
# Non blocking start servers
cls.start_prefill()
cls.start_decode()
# Block until both
cls.wait_server_ready(cls.prefill_url + "/health", process=cls.process_prefill)
cls.wait_server_ready(cls.decode_url + "/health", process=cls.process_decode)
cls.launch_lb()
@classmethod
def tearDownClass(cls):
os.environ.pop("DISAGGREGATION_TEST_FAILURE_PROB")
super().tearDownClass()
@classmethod
def start_prefill(cls):
prefill_args = [
"--trust-remote-code",
"--disaggregation-mode",
"prefill",
"--tp",
"1",
]
prefill_args += cls.transfer_backend + cls.rdma_devices
cls.process_prefill = popen_launch_pd_server(
cls.model,
cls.prefill_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=prefill_args,
)
@classmethod
def start_decode(cls):
decode_args = [
"--trust-remote-code",
"--disaggregation-mode",
"decode",
"--tp",
"1",
"--base-gpu-id",
"1",
]
decode_args += cls.transfer_backend + cls.rdma_devices
cls.process_decode = popen_launch_pd_server(
cls.model,
cls.decode_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=decode_args,
)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host=f"http://{self.base_host}",
port=int(self.lb_port),
)
# Expect lots of failure but the server cannot crash
try:
metrics = run_eval_few_shot_gsm8k(args)
print(f"Evaluation metrics: {metrics}")
except Exception as e:
print(f"Test encountered expected errors: {e}")
# Check if servers are still healthy
try:
response = requests.get(self.prefill_url + "/health_generate")
assert response.status_code == 200
response = requests.get(self.decode_url + "/health_generate")
assert response.status_code == 200
except Exception as health_check_error:
# If health check fails, re-raise the original exception
raise e from health_check_error
class TestDisaggregationMooncakeSpec(PDDisaggregationServerBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.model = DEFAULT_TARGET_MODEL_EAGLE
cls.draft_model = DEFAULT_DRAFT_MODEL_EAGLE
cls.spec_args = [
"--speculative-algorithm",
"EAGLE",
"--speculative-draft-model-path",
cls.draft_model,
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"4",
"--speculative-num-draft-tokens",
"16",
"--cuda-graph-max-bs",
"8",
]
print(f"{cls.base_host=} {cls.lb_port=} {cls.prefill_port=} {cls.decode_port=}")
# Non blocking start servers
cls.start_prefill()
cls.start_decode()
# Block until both
cls.wait_server_ready(cls.prefill_url + "/health", process=cls.process_prefill)
cls.wait_server_ready(cls.decode_url + "/health", process=cls.process_decode)
cls.launch_lb()
@classmethod
def start_prefill(cls):
prefill_args = [
"--trust-remote-code",
"--disaggregation-mode",
"prefill",
"--tp",
"1",
] + cls.spec_args
prefill_args += cls.transfer_backend + cls.rdma_devices
cls.process_prefill = popen_launch_pd_server(
cls.model,
cls.prefill_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=prefill_args,
)
@classmethod
def start_decode(cls):
decode_args = [
"--trust-remote-code",
"--disaggregation-mode",
"decode",
"--tp",
"1",
"--base-gpu-id",
"1",
] + cls.spec_args
decode_args += cls.transfer_backend + cls.rdma_devices
cls.process_decode = popen_launch_pd_server(
cls.model,
cls.decode_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=decode_args,
)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=2,
host=f"http://{self.base_host}",
port=int(self.lb_port),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"Evaluation metrics: {metrics}")
self.assertGreater(metrics["accuracy"], 0.20)
class TestDisaggregationSimulatedRetract(PDDisaggregationServerBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
os.environ["SGLANG_TEST_RETRACT"] = "true"
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
# Non blocking start servers
cls.start_prefill()
cls.start_decode()
# Block until both
cls.wait_server_ready(cls.prefill_url + "/health", process=cls.process_prefill)
cls.wait_server_ready(cls.decode_url + "/health", process=cls.process_decode)
cls.launch_lb()
@classmethod
def tearDownClass(cls):
os.environ.pop("SGLANG_TEST_RETRACT")
super().tearDownClass()
@classmethod
def start_prefill(cls):
prefill_args = [
"--trust-remote-code",
"--disaggregation-mode",
"prefill",
"--tp",
"1",
]
prefill_args += cls.transfer_backend + cls.rdma_devices
cls.process_prefill = popen_launch_pd_server(
cls.model,
cls.prefill_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=prefill_args,
)
@classmethod
def start_decode(cls):
decode_args = [
"--trust-remote-code",
"--disaggregation-mode",
"decode",
"--tp",
"1",
"--base-gpu-id",
"1",
]
decode_args += cls.transfer_backend + cls.rdma_devices
cls.process_decode = popen_launch_pd_server(
cls.model,
cls.decode_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=decode_args,
)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host=f"http://{self.base_host}",
port=int(self.lb_port),
)
metrics = run_eval_few_shot_gsm8k(args)
print(f"Evaluation metrics: {metrics}")
self.assertGreater(metrics["accuracy"], 0.62)
if __name__ == "__main__":
unittest.main()
|