1b6cb920e6
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io> Signed-off-by: wang.yuqi <noooop@126.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
115 lines
3.7 KiB
Python
115 lines
3.7 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 logging
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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.distributed import cleanup_dist_env_and_memory
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from vllm.platforms import current_platform
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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():
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# ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
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# that supports encoder-only models on ROCm.
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attention_config = None
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if current_platform.is_rocm():
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attention_config = {"backend": "FLEX_ATTENTION"}
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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(
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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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attention_config=attention_config,
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)
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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_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_token_embed(llm: LLM, caplog_vllm):
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with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
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outputs = llm.encode(prompt, pooling_task="token_embed", use_tqdm=False)
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assert "deprecated" in caplog_vllm.text
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multi_vector = outputs[0].outputs.data
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assert multi_vector.shape == (11, 384)
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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("task", ["token_classify", "classify"])
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def test_unsupported_tasks(llm: LLM, task: PoolingTask):
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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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