115 lines
3.7 KiB
Python
115 lines
3.7 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, ClassificationRequestOutput, PoolingParams, PoolingRequestOutput
|
|
from vllm.distributed import cleanup_dist_env_and_memory
|
|
from vllm.tasks import PoolingTask
|
|
|
|
MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
|
|
|
|
prompt = "The chef prepared a delicious meal."
|
|
prompt_token_ids = [785, 29706, 10030, 264, 17923, 15145, 13]
|
|
num_labels = 2
|
|
|
|
|
|
@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,
|
|
seed=0,
|
|
)
|
|
|
|
yield weakref.proxy(llm)
|
|
|
|
del llm
|
|
|
|
cleanup_dist_env_and_memory()
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_str_prompts(llm: LLM):
|
|
outputs = llm.classify(prompt, use_tqdm=False)
|
|
assert len(outputs) == 1
|
|
assert isinstance(outputs[0], ClassificationRequestOutput)
|
|
assert outputs[0].prompt_token_ids == prompt_token_ids
|
|
assert len(outputs[0].outputs.probs) == num_labels
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_token_ids_prompts(llm: LLM):
|
|
outputs = llm.classify([prompt_token_ids], use_tqdm=False)
|
|
assert len(outputs) == 1
|
|
assert isinstance(outputs[0], ClassificationRequestOutput)
|
|
assert outputs[0].prompt_token_ids == prompt_token_ids
|
|
assert len(outputs[0].outputs.probs) == num_labels
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_list_prompts(llm: LLM):
|
|
outputs = llm.classify([prompt, prompt_token_ids], use_tqdm=False)
|
|
assert len(outputs) == 2
|
|
for i in range(len(outputs)):
|
|
assert isinstance(outputs[i], ClassificationRequestOutput)
|
|
assert outputs[i].prompt_token_ids == prompt_token_ids
|
|
assert len(outputs[i].outputs.probs) == num_labels
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_token_classify(llm: LLM):
|
|
outputs = llm.encode(prompt, pooling_task="token_classify", use_tqdm=False)
|
|
assert len(outputs) == 1
|
|
assert isinstance(outputs[0], PoolingRequestOutput)
|
|
assert outputs[0].prompt_token_ids == prompt_token_ids
|
|
assert outputs[0].outputs.data.shape == (len(prompt_token_ids), num_labels)
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_pooling_params(llm: LLM):
|
|
def get_outputs(use_activation):
|
|
outputs = llm.classify(
|
|
prompt,
|
|
pooling_params=PoolingParams(use_activation=use_activation),
|
|
use_tqdm=False,
|
|
)
|
|
return torch.tensor([x.outputs.probs for x in outputs])
|
|
|
|
default = get_outputs(use_activation=None)
|
|
w_activation = get_outputs(use_activation=True)
|
|
wo_activation = get_outputs(use_activation=False)
|
|
|
|
assert torch.allclose(default, w_activation, atol=1e-2), (
|
|
"Default should use activation."
|
|
)
|
|
assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
|
|
"wo_activation should not use activation."
|
|
)
|
|
assert torch.allclose(softmax(wo_activation), w_activation, atol=1e-2), (
|
|
"w_activation should be close to activation(wo_activation)."
|
|
)
|
|
|
|
|
|
@pytest.mark.skip_global_cleanup
|
|
def test_score_api(llm: LLM):
|
|
err_msg = "Score API is only enabled for num_labels == 1."
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
llm.score("ping", "pong", use_tqdm=False)
|
|
|
|
|
|
@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
|
|
def test_unsupported_tasks(llm: LLM, task: PoolingTask):
|
|
err_msg = f"Unsupported task: '{task}' Supported tasks.+"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
llm.encode(prompt, pooling_task=task, use_tqdm=False)
|