mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-21 22:25:47 +08:00
58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import weakref
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from tests.models.utils import softmax
|
|
from vllm import LLM, PoolingParams
|
|
from vllm.distributed import cleanup_dist_env_and_memory
|
|
|
|
MODEL_NAME = "internlm/internlm2-1_8b-reward"
|
|
|
|
prompts = ["The chef prepared a delicious meal."]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def llm():
|
|
# pytest caches the fixture so we use weakref.proxy to
|
|
# enable garbage collection
|
|
llm = LLM(
|
|
model=MODEL_NAME,
|
|
max_num_batched_tokens=32768,
|
|
tensor_parallel_size=1,
|
|
gpu_memory_utilization=0.75,
|
|
enforce_eager=True,
|
|
trust_remote_code=True,
|
|
seed=0,
|
|
)
|
|
|
|
yield weakref.proxy(llm)
|
|
|
|
del llm
|
|
|
|
cleanup_dist_env_and_memory()
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_pooling_params(llm: LLM):
|
|
def get_outputs(softmax):
|
|
outputs = llm.reward(
|
|
prompts, pooling_params=PoolingParams(softmax=softmax), use_tqdm=False
|
|
)
|
|
return torch.cat([x.outputs.data for x in outputs])
|
|
|
|
default = get_outputs(softmax=None)
|
|
w_softmax = get_outputs(softmax=True)
|
|
wo_softmax = get_outputs(softmax=False)
|
|
|
|
assert torch.allclose(default, w_softmax, atol=1e-2), "Default should use softmax."
|
|
assert not torch.allclose(w_softmax, wo_softmax, atol=1e-2), (
|
|
"wo_softmax should not use softmax."
|
|
)
|
|
assert torch.allclose(softmax(wo_softmax), w_softmax, atol=1e-2), (
|
|
"w_softmax should be close to softmax(wo_softmax)."
|
|
)
|