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56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import weakref
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import pytest
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import torch
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import torch.nn.functional as F
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from vllm import LLM, PoolingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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MODEL_NAME = "intfloat/multilingual-e5-small"
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prompts = ["The chef prepared a delicious meal."]
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@pytest.fixture(scope="module")
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def llm():
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# pytest caches the fixture so we use weakref.proxy to
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# enable garbage collection
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llm = LLM(model=MODEL_NAME,
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max_num_batched_tokens=32768,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.75,
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enforce_eager=True,
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seed=0)
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yield weakref.proxy(llm)
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del llm
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cleanup_dist_env_and_memory()
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@pytest.mark.skip_global_cleanup
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def test_pooling_params(llm: LLM):
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def get_outputs(normalize):
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outputs = llm.embed(prompts,
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pooling_params=PoolingParams(normalize=normalize),
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use_tqdm=False)
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return torch.tensor([x.outputs.embedding for x in outputs])
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default = get_outputs(normalize=None)
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w_normal = get_outputs(normalize=True)
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wo_normal = get_outputs(normalize=False)
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assert torch.allclose(default, w_normal,
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atol=1e-2), "Default should use normal."
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assert not torch.allclose(w_normal, wo_normal,
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atol=1e-2), "wo_normal should not use normal."
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assert torch.allclose(
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w_normal, F.normalize(wo_normal, p=2, dim=-1),
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atol=1e-2), "w_normal should be close to normal(wo_normal)."
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