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[CI] Refactor CI (#952)

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1. remove some useless test func and file
2. fix format.sh problem
3. enable full test for singlecard and multicard
4. move long term test to long_term folder. For this kind of test, it
only runs by labeled and daily test. Include: spec decode、accuracy test

## After refactor:
There are 4 test modules
- `singlecard`: contains the test running on one NPU. It'll be run for
each PR and daily test.
- `multicard`: contains the test running on multi NPUs. It'll be run for
each PR and daily test.
- `long_term`: contains the test that cost much time(Now include `spec
decode` and `accuracy` test). It'll be run for the PR with
`long-term-test` labeled and daily test.
- `e2e`: contains the test for doc and pd feature. It'll be run for the
PR with `pd-test` labeled and daily test.

## Todo:
1. some test are skipped, they should be fixed and reenabled in the
future.
2. pyhccl test for multicard doesn't work at all. It should be enabled
as well.
3. ensure long-term-test pass by daily test.

### Know issue
Now, `ready` labels is required to start pd test or long term test. And
when `long-term-test` or `pd-test` is labeled after another one, the old
labeled test will be re-run again. So the labeled test should be ran in
the following step:

1. decide which test need run, then label it. `long-term-test` or
`pd-test` or both.
2. add `ready-for-test` label, then the test will be ran.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-05-28 06:31:35 +08:00
committed by GitHub
parent 9f5ab59e30
commit e2a0c19cea
34 changed files with 171 additions and 1288 deletions
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674
tests/utils.py
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@@ -17,572 +17,12 @@
# limitations under the License.
#
import asyncio
import copy
import functools
import os
import signal
import subprocess
import sys
import time
import warnings
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Type, Union
from typing import Callable
import openai
import pytest
import requests
import torch
import torch.nn.functional as F
import vllm.envs as envs
from openai.types.completion import Completion
from typing_extensions import ParamSpec
from vllm.distributed import (ensure_model_parallel_initialized,
init_distributed_environment)
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.openai.cli_args import make_arg_parser
from vllm.platforms import current_platform
from vllm.transformers_utils.tokenizer import get_tokenizer
from vllm.utils import FlexibleArgumentParser, GB_bytes, get_open_port
from vllm_ascend.utils import vllm_version_is
from .model_utils import TextTextLogprobs
if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
from vllm.model_executor.model_loader.loader import get_model_loader # type: ignore[import] # isort: skip
else:
from vllm.model_executor.model_loader import get_model_loader
VLLM_PATH = Path(__file__).parent.parent
"""Path to root of the vLLM repository."""
class RemoteOpenAIServer:
DUMMY_API_KEY = "token-abc123" # vLLM's OpenAI server does not need API key
def __init__(self,
model: str,
vllm_serve_args: List[str],
*,
env_dict: Optional[Dict[str, str]] = None,
auto_port: bool = True,
max_wait_seconds: Optional[float] = None) -> None:
if auto_port:
if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
raise ValueError("You have manually specified the port "
"when `auto_port=True`.")
# Don't mutate the input args
vllm_serve_args = vllm_serve_args + [
"--port", str(get_open_port())
]
parser = FlexibleArgumentParser(
description="vLLM's remote OpenAI server.")
parser = make_arg_parser(parser)
args = parser.parse_args(["--model", model, *vllm_serve_args])
self.host = str(args.host or 'localhost')
self.port = int(args.port)
# download the model before starting the server to avoid timeout
is_local = os.path.isdir(model)
if not is_local:
engine_args = AsyncEngineArgs.from_cli_args(args)
model_config = engine_args.create_model_config()
load_config = engine_args.create_load_config()
model_loader = get_model_loader(load_config)
model_loader.download_model(model_config)
env = os.environ.copy()
# the current process might initialize cuda,
# to be safe, we should use spawn method
env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
if env_dict is not None:
env.update(env_dict)
self.proc = subprocess.Popen(
["vllm", "serve", model, *vllm_serve_args],
env=env,
stdout=sys.stdout,
stderr=sys.stderr,
)
max_wait_seconds = max_wait_seconds or 240
self._wait_for_server(url=self.url_for("health"),
timeout=max_wait_seconds)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.proc.terminate()
try:
self.proc.wait(8)
except subprocess.TimeoutExpired:
# force kill if needed
self.proc.kill()
def _wait_for_server(self, *, url: str, timeout: float):
# run health check
start = time.time()
while True:
try:
if requests.get(url).status_code == 200:
break
except Exception:
# this exception can only be raised by requests.get,
# which means the server is not ready yet.
# the stack trace is not useful, so we suppress it
# by using `raise from None`.
result = self.proc.poll()
if result is not None and result != 0:
raise RuntimeError("Server exited unexpectedly.") from None
time.sleep(0.5)
if time.time() - start > timeout:
raise RuntimeError(
"Server failed to start in time.") from None
@property
def url_root(self) -> str:
return f"http://{self.host}:{self.port}"
def url_for(self, *parts: str) -> str:
return self.url_root + "/" + "/".join(parts)
def get_client(self, **kwargs):
if "timeout" not in kwargs:
kwargs["timeout"] = 600
return openai.OpenAI(
base_url=self.url_for("v1"),
api_key=self.DUMMY_API_KEY,
max_retries=0,
**kwargs,
)
def get_async_client(self, **kwargs):
if "timeout" not in kwargs:
kwargs["timeout"] = 600
return openai.AsyncOpenAI(base_url=self.url_for("v1"),
api_key=self.DUMMY_API_KEY,
max_retries=0,
**kwargs)
def _test_completion(
client: openai.OpenAI,
model: str,
prompt: str,
token_ids: List[int],
):
results = []
# test with text prompt
completion = client.completions.create(model=model,
prompt=prompt,
max_tokens=5,
temperature=0.0)
results.append({
"test": "single_completion",
"text": completion.choices[0].text,
"finish_reason": completion.choices[0].finish_reason,
"usage": completion.usage,
})
# test using token IDs
completion = client.completions.create(
model=model,
prompt=token_ids,
max_tokens=5,
temperature=0.0,
)
results.append({
"test": "token_ids",
"text": completion.choices[0].text,
"finish_reason": completion.choices[0].finish_reason,
"usage": completion.usage,
})
# test seeded random sampling
completion = client.completions.create(model=model,
prompt=prompt,
max_tokens=5,
seed=33,
temperature=1.0)
results.append({
"test": "seeded_sampling",
"text": completion.choices[0].text,
"finish_reason": completion.choices[0].finish_reason,
"usage": completion.usage,
})
# test seeded random sampling with multiple prompts
completion = client.completions.create(model=model,
prompt=[prompt, prompt],
max_tokens=5,
seed=33,
temperature=1.0)
results.append({
"test":
"seeded_sampling",
"text": [choice.text for choice in completion.choices],
"finish_reason":
[choice.finish_reason for choice in completion.choices],
"usage":
completion.usage,
})
# test simple list
batch = client.completions.create(
model=model,
prompt=[prompt, prompt],
max_tokens=5,
temperature=0.0,
)
results.append({
"test": "simple_list",
"text0": batch.choices[0].text,
"text1": batch.choices[1].text,
})
# test streaming
batch = client.completions.create(
model=model,
prompt=[prompt, prompt],
max_tokens=5,
temperature=0.0,
stream=True,
)
texts = [""] * 2
for chunk in batch:
assert len(chunk.choices) == 1
choice = chunk.choices[0]
texts[choice.index] += choice.text
results.append({
"test": "streaming",
"texts": texts,
})
return results
def _test_completion_close(
client: openai.OpenAI,
model: str,
prompt: str,
):
results = []
# test with text prompt
completion = client.completions.create(model=model,
prompt=prompt,
max_tokens=1,
logprobs=5,
temperature=0.0)
logporbs = completion.choices[0].logprobs.top_logprobs[0]
logporbs = {k: round(v, 2) for k, v in logporbs.items()}
results.append({
"test": "completion_close",
"logprobs": logporbs,
})
return results
def _test_embeddings(
client: openai.OpenAI,
model: str,
text: str,
):
results = []
# test with text input
embeddings = client.embeddings.create(
model=model,
input=text,
encoding_format="float",
)
results.append({
"test": "single_embedding",
"embedding": embeddings.data[0].embedding,
"usage": embeddings.usage,
})
return results
def _test_image_text(
client: openai.OpenAI,
model_name: str,
image_url: str,
):
results = []
# test pure text input
messages = [{
"role":
"user",
"content": [
{
"type": "text",
"text": "How do you feel today?"
},
],
}]
chat_completion = client.chat.completions.create(model=model_name,
messages=messages,
temperature=0.0,
max_tokens=1,
logprobs=True,
top_logprobs=5)
top_logprobs = chat_completion.choices[0].logprobs.content[0].top_logprobs
for x in top_logprobs:
x.logprob = round(x.logprob, 2)
results.append({
"test": "pure_text",
"logprobs": top_logprobs,
})
messages = [{
"role":
"user",
"content": [
{
"type": "image_url",
"image_url": {
"url": image_url
}
},
{
"type": "text",
"text": "What's in this image?"
},
],
}]
chat_completion = client.chat.completions.create(model=model_name,
messages=messages,
temperature=0.0,
max_tokens=1,
logprobs=True,
top_logprobs=5)
top_logprobs = chat_completion.choices[0].logprobs.content[0].top_logprobs
results.append({
"test": "text_image",
"logprobs": top_logprobs,
})
return results
def compare_two_settings(model: str,
arg1: List[str],
arg2: List[str],
env1: Optional[Dict[str, str]] = None,
env2: Optional[Dict[str, str]] = None,
*,
method: str = "generate",
max_wait_seconds: Optional[float] = None) -> None:
"""
Launch API server with two different sets of arguments/environments
and compare the results of the API calls.
Args:
model: The model to test.
arg1: The first set of arguments to pass to the API server.
arg2: The second set of arguments to pass to the API server.
env1: The first set of environment variables to pass to the API server.
env2: The second set of environment variables to pass to the API server.
"""
compare_all_settings(
model,
[arg1, arg2],
[env1, env2],
method=method,
max_wait_seconds=max_wait_seconds,
)
def compare_all_settings(model: str,
all_args: List[List[str]],
all_envs: List[Optional[Dict[str, str]]],
*,
method: str = "generate",
max_wait_seconds: Optional[float] = None) -> None:
"""
Launch API server with several different sets of arguments/environments
and compare the results of the API calls with the first set of arguments.
Args:
model: The model to test.
all_args: A list of argument lists to pass to the API server.
all_envs: A list of environment dictionaries to pass to the API server.
"""
trust_remote_code = False
for args in all_args:
if "--trust-remote-code" in args:
trust_remote_code = True
break
tokenizer_mode = "auto"
for args in all_args:
if "--tokenizer-mode" in args:
tokenizer_mode = args[args.index("--tokenizer-mode") + 1]
break
tokenizer = get_tokenizer(
model,
trust_remote_code=trust_remote_code,
tokenizer_mode=tokenizer_mode,
)
can_force_load_format = True
for args in all_args:
if "--load-format" in args:
can_force_load_format = False
break
prompt = "Hello, my name is"
token_ids = tokenizer(prompt).input_ids
ref_results: List = []
for i, (args, env) in enumerate(zip(all_args, all_envs)):
if can_force_load_format:
# we are comparing the results and
# usually we don't need real weights.
# we force to use dummy weights by default,
# and it should work for most of the cases.
# if not, we can use VLLM_TEST_FORCE_LOAD_FORMAT
# environment variable to force the load format,
# e.g. in quantization tests.
args = args + ["--load-format", envs.VLLM_TEST_FORCE_LOAD_FORMAT]
compare_results: List = []
results = ref_results if i == 0 else compare_results
with RemoteOpenAIServer(model,
args,
env_dict=env,
max_wait_seconds=max_wait_seconds) as server:
client = server.get_client()
# test models list
models = client.models.list()
models = models.data
served_model = models[0]
results.append({
"test": "models_list",
"id": served_model.id,
"root": served_model.root,
})
if method == "generate":
results += _test_completion(client, model, prompt, token_ids)
elif method == "generate_close":
results += _test_completion_close(client, model, prompt)
elif method == "generate_with_image":
results += _test_image_text(
client, model,
"https://upload.wikimedia.org/wikipedia/commons/0/0b/RGBA_comp.png"
)
elif method == "encode":
results += _test_embeddings(client, model, prompt)
else:
raise ValueError(f"Unknown method: {method}")
if i > 0:
# if any setting fails, raise an error early
ref_args = all_args[0]
ref_envs = all_envs[0]
compare_args = all_args[i]
compare_envs = all_envs[i]
for ref_result, compare_result in zip(ref_results,
compare_results):
ref_result = copy.deepcopy(ref_result)
compare_result = copy.deepcopy(compare_result)
if "embedding" in ref_result and method == "encode":
sim = F.cosine_similarity(
torch.tensor(ref_result["embedding"]),
torch.tensor(compare_result["embedding"]),
dim=0,
)
assert sim >= 0.999, (
f"Embedding for {model=} are not the same.\n"
f"cosine_similarity={sim}\n")
del ref_result["embedding"]
del compare_result["embedding"]
assert ref_result == compare_result, (
f"Results for {model=} are not the same.\n"
f"{ref_args=} {ref_envs=}\n"
f"{compare_args=} {compare_envs=}\n"
f"{ref_result=}\n"
f"{compare_result=}\n")
def init_test_distributed_environment(
tp_size: int,
pp_size: int,
rank: int,
distributed_init_port: str,
local_rank: int = -1,
) -> None:
distributed_init_method = f"tcp://localhost:{distributed_init_port}"
init_distributed_environment(
world_size=pp_size * tp_size,
rank=rank,
distributed_init_method=distributed_init_method,
local_rank=local_rank)
ensure_model_parallel_initialized(tp_size, pp_size)
def multi_process_parallel(
tp_size: int,
pp_size: int,
test_target: Any,
) -> None:
import ray
# Using ray helps debugging the error when it failed
# as compared to multiprocessing.
# NOTE: We need to set working_dir for distributed tests,
# otherwise we may get import errors on ray workers
ray.init(runtime_env={"working_dir": VLLM_PATH})
distributed_init_port = get_open_port()
refs = []
for rank in range(tp_size * pp_size):
refs.append(
test_target.remote(tp_size, pp_size, rank, distributed_init_port))
ray.get(refs)
ray.shutdown()
@contextmanager
def error_on_warning(category: Type[Warning] = Warning):
"""
Within the scope of this context manager, tests will fail if any warning
of the given category is emitted.
"""
with warnings.catch_warnings():
warnings.filterwarnings("error", category=category)
yield
_P = ParamSpec("_P")
@@ -627,115 +67,3 @@ def fork_new_process_for_each_test(
f" args {args} and kwargs {kwargs}")
return wrapper
def large_gpu_mark(min_gb: int) -> pytest.MarkDecorator:
"""
Get a pytest mark, which skips the test if the GPU doesn't meet
a minimum memory requirement in GB.
This can be leveraged via `@large_gpu_test` to skip tests in environments
without enough resources, or called when filtering tests to run directly.
"""
try:
if current_platform.is_cpu():
memory_gb = 0
else:
memory_gb = current_platform.get_device_total_memory() / GB_bytes
except Exception as e:
warnings.warn(
f"An error occurred when finding the available memory: {e}",
stacklevel=2,
)
memory_gb = 0
return pytest.mark.skipif(
memory_gb < min_gb,
reason=f"Need at least {min_gb}GB GPU memory to run the test.",
)
def large_gpu_test(*, min_gb: int):
"""
Decorate a test to be skipped if no GPU is available or it does not have
sufficient memory.
Currently, the CI machine uses L4 GPU which has 24 GB VRAM.
"""
mark = large_gpu_mark(min_gb)
def wrapper(f: Callable[_P, None]) -> Callable[_P, None]:
return mark(f)
return wrapper
async def completions_with_server_args(
prompts: List[str],
model_name: str,
server_cli_args: List[str],
num_logprobs: Optional[int],
max_wait_seconds: int = 240,
max_tokens: Union[int, list] = 5,
) -> List[Completion]:
'''Construct a remote OpenAI server, obtain an async client to the
server & invoke the completions API to obtain completions.
Args:
prompts: test prompts
model_name: model to spin up on the vLLM server
server_cli_args: CLI args for starting the server
num_logprobs: Number of logprobs to report (or `None`)
max_wait_seconds: timeout interval for bringing up server.
Default: 240sec
max_tokens: max_tokens value for each of the given input prompts.
if only one max_token value is given, the same value is used
for all the prompts.
Returns:
OpenAI Completion instance
'''
if isinstance(max_tokens, int):
max_tokens = [max_tokens] * len(prompts)
assert len(max_tokens) == len(prompts)
outputs = None
with RemoteOpenAIServer(model_name,
server_cli_args,
max_wait_seconds=max_wait_seconds) as server:
client = server.get_async_client()
outputs = [ client.completions.create(model=model_name,
prompt=[p],
temperature=0,
stream=False,
max_tokens=max_tok,
logprobs=num_logprobs) \
for p, max_tok in zip(prompts, max_tokens) ]
outputs = await asyncio.gather(*outputs)
assert outputs is not None, "Completion API call failed."
return outputs
def get_client_text_generations(completions: List[Completion]) -> List[str]:
'''Extract generated tokens from the output of a
request made to an Open-AI-protocol completions endpoint.
'''
assert all([len(x.choices) == 1 for x in completions])
return [x.choices[0].text for x in completions]
def get_client_text_logprob_generations(
completions: List[Completion]) -> List[TextTextLogprobs]:
'''Operates on the output of a request made to an Open-AI-protocol
completions endpoint; obtains top-rank logprobs for each token in
each :class:`SequenceGroup`
'''
text_generations = get_client_text_generations(completions)
text = ''.join(text_generations)
return [(text_generations, text,
(None if x.logprobs is None else x.logprobs.top_logprobs))
for completion in completions for x in completion.choices]