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[ci] Use env var to control whether to use S3 bucket in CI (#13634)

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This commit is contained in:
Kevin H. Luu
2025-02-22 19:19:45 -08:00
committed by GitHub
parent 322d2a27d6
commit 2c5e637b57
30 changed files with 222 additions and 231 deletions
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7
tests/engine/test_computed_prefix_blocks.py
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@ -2,16 +2,12 @@
import pytest
from vllm.config import LoadFormat
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.llm_engine import LLMEngine
from vllm.sampling_params import SamplingParams
from ..conftest import MODEL_WEIGHTS_S3_BUCKET
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
@pytest.mark.parametrize("block_size", [16])
def test_computed_prefix_blocks(model: str, block_size: int):
# This test checks if we are able to run the engine to completion
@ -28,7 +24,6 @@ def test_computed_prefix_blocks(model: str, block_size: int):
"decoration.")
engine_args = EngineArgs(model=model,
load_format=LoadFormat.RUNAI_STREAMER,
block_size=block_size,
enable_prefix_caching=True)

8
tests/engine/test_detokenization.py
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@ -2,15 +2,11 @@
import pytest
from vllm.config import LoadFormat
from vllm.entrypoints.llm import LLM
from vllm.sampling_params import SamplingParams
from ..conftest import MODEL_WEIGHTS_S3_BUCKET
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_computed_prefix_blocks(model: str):
# This test checks if the engine generates completions both with and
# without optional detokenization, that detokenization includes text
@ -21,7 +17,7 @@ def test_computed_prefix_blocks(model: str):
"paper clips? Is there an easy to follow video tutorial available "
"online for free?")
llm = LLM(model=model, load_format=LoadFormat.RUNAI_STREAMER)
llm = LLM(model=model)
sampling_params = SamplingParams(max_tokens=10,
temperature=0.0,
detokenize=False)

21
tests/engine/test_executor.py
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@ -6,17 +6,12 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import pytest
from vllm.config import LoadFormat
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.engine.llm_engine import LLMEngine
from vllm.executor.uniproc_executor import UniProcExecutor
from vllm.sampling_params import SamplingParams
from ..conftest import MODEL_WEIGHTS_S3_BUCKET
RUNAI_STREAMER_LOAD_FORMAT = LoadFormat.RUNAI_STREAMER
class Mock:
...
@ -38,12 +33,10 @@ class CustomUniExecutor(UniProcExecutor):
CustomUniExecutorAsync = CustomUniExecutor
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_custom_executor_type_checking(model):
with pytest.raises(ValueError):
engine_args = EngineArgs(model=model,
load_format=RUNAI_STREAMER_LOAD_FORMAT,
distributed_executor_backend=Mock)
LLMEngine.from_engine_args(engine_args)
with pytest.raises(ValueError):
@ -52,8 +45,7 @@ def test_custom_executor_type_checking(model):
AsyncLLMEngine.from_engine_args(engine_args)
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_custom_executor(model, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
@ -62,7 +54,6 @@ def test_custom_executor(model, tmp_path):
engine_args = EngineArgs(
model=model,
load_format=RUNAI_STREAMER_LOAD_FORMAT,
distributed_executor_backend=CustomUniExecutor,
enforce_eager=True, # reduce test time
)
@ -77,8 +68,7 @@ def test_custom_executor(model, tmp_path):
os.chdir(cwd)
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_custom_executor_async(model, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
@ -87,7 +77,6 @@ def test_custom_executor_async(model, tmp_path):
engine_args = AsyncEngineArgs(
model=model,
load_format=RUNAI_STREAMER_LOAD_FORMAT,
distributed_executor_backend=CustomUniExecutorAsync,
enforce_eager=True, # reduce test time
)
@ -106,8 +95,7 @@ def test_custom_executor_async(model, tmp_path):
os.chdir(cwd)
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_respect_ray(model):
# even for TP=1 and PP=1,
# if users specify ray, we should use ray.
@ -116,7 +104,6 @@ def test_respect_ray(model):
engine_args = EngineArgs(
model=model,
distributed_executor_backend="ray",
load_format=RUNAI_STREAMER_LOAD_FORMAT,
enforce_eager=True, # reduce test time
)
engine = LLMEngine.from_engine_args(engine_args)

13
tests/engine/test_skip_tokenizer_init.py
View File

@ -2,22 +2,19 @@
import pytest
from vllm.config import LoadFormat
from vllm.entrypoints.llm import LLM
from vllm.sampling_params import SamplingParams
from ..conftest import MODEL_WEIGHTS_S3_BUCKET
@pytest.mark.parametrize("model",
[f"{MODEL_WEIGHTS_S3_BUCKET}/distilbert/distilgpt2"])
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_skip_tokenizer_initialization(model: str):
# This test checks if the flag skip_tokenizer_init skips the initialization
# of tokenizer and detokenizer. The generated output is expected to contain
# token ids.
llm = LLM(model=model,
skip_tokenizer_init=True,
load_format=LoadFormat.RUNAI_STREAMER)
llm = LLM(
model=model,
skip_tokenizer_init=True,
)
sampling_params = SamplingParams(prompt_logprobs=True, detokenize=True)
with pytest.raises(ValueError, match="cannot pass text prompts when"):