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https://github.com/vllm-project/vllm.git
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Andreas KaratzasandGitHub
b96bcd0b47
Signed-off-by: Andreas Karatzas <[email protected]> Signed-off-by: Andreas Karatzas <[email protected]>
100 lines
3.4 KiB
Python
100 lines
3.4 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, EmbeddingRequestOutput, PoolingParams
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from vllm.tasks import PoolingTask
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MODEL_NAME = "intfloat/multilingual-e5-small"
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prompt = "The chef prepared a delicious meal."
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prompt_token_ids = [0, 581, 21861, 133888, 10, 8, 150, 60744, 109911, 5, 2]
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embedding_size = 384
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@pytest.fixture(scope="module")
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def llm(vllm_runner):
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with vllm_runner(
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MODEL_NAME,
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max_model_len=None,
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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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enable_chunked_prefill=None,
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) as runner:
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assert embedding_size == runner.llm.model_config.embedding_size
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# pytest caches yielded fixtures until after teardown, so use a proxy to
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# avoid retaining the LLM while VllmRunner.__exit__ releases ROCm memory.
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yield weakref.proxy(runner.llm)
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@pytest.mark.skip_global_cleanup
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def test_str_prompts(llm: LLM):
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outputs = llm.embed(prompt, use_tqdm=False)
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assert len(outputs) == 1
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assert isinstance(outputs[0], EmbeddingRequestOutput)
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assert outputs[0].prompt_token_ids == prompt_token_ids
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assert len(outputs[0].outputs.embedding) == embedding_size
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@pytest.mark.skip_global_cleanup
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def test_token_ids_prompts(llm: LLM):
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outputs = llm.embed([prompt_token_ids], use_tqdm=False)
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assert len(outputs) == 1
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assert isinstance(outputs[0], EmbeddingRequestOutput)
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assert outputs[0].prompt_token_ids == prompt_token_ids
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assert len(outputs[0].outputs.embedding) == embedding_size
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@pytest.mark.skip_global_cleanup
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def test_list_prompts(llm: LLM):
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outputs = llm.embed([prompt, prompt_token_ids], use_tqdm=False)
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assert len(outputs) == 2
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for i in range(len(outputs)):
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assert isinstance(outputs[i], EmbeddingRequestOutput)
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assert outputs[i].prompt_token_ids == prompt_token_ids
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assert len(outputs[i].outputs.embedding) == embedding_size
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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(
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[prompt],
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pooling_params=PoolingParams(use_activation=normalize),
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use_tqdm=False,
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)
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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, atol=1e-2), "Default should use normal."
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assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
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"wo_normal should not use normal."
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)
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assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
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"w_normal should be close to normal(wo_normal)."
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)
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@pytest.mark.parametrize(
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"task", ["token_classify", "classify", "token_embed", "plugin"]
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)
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def test_unsupported_tasks(llm: LLM, task: PoolingTask):
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if task == "plugin":
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err_msg = "No IOProcessor plugin installed."
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elif task == "token_embed":
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err_msg = "Try switching the model's pooling_task via.+"
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else:
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err_msg = "Classification API is not supported by this model.+"
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with pytest.raises(ValueError, match=err_msg):
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llm.encode(prompt, pooling_task=task, use_tqdm=False)
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