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91 lines
2.4 KiB
Python
91 lines
2.4 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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from vllm import LLM, 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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if current_platform.is_rocm():
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pytest.skip(
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"Encoder self-attention is not implemented on ROCm.", allow_module_level=True
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)
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MODEL_NAME = "intfloat/multilingual-e5-small"
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PROMPTS = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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TOKEN_IDS = [
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# Using ID={0, 1, 2, 3} results in NaN values,
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# so we add this offset of 1000
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[1000],
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[1000, 1001],
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[1000, 1002, 1001],
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[1000, 1003, 1001, 1002],
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]
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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(
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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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)
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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_multiple_pooling_params(llm: LLM):
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pooling_params = [
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PoolingParams(),
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PoolingParams(),
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PoolingParams(),
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PoolingParams(),
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]
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# Multiple PoolingParams should be matched with each prompt
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outputs = llm.encode(PROMPTS, pooling_params=pooling_params, pooling_task="embed")
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assert len(PROMPTS) == len(outputs)
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# Exception raised, if the size of params does not match the size of prompts
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with pytest.raises(ValueError):
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outputs = llm.encode(
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PROMPTS, pooling_params=pooling_params[:3], pooling_task="embed"
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)
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# Single PoolingParams should be applied to every prompt
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single_pooling_params = PoolingParams()
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outputs = llm.encode(
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PROMPTS, pooling_params=single_pooling_params, pooling_task="embed"
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)
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assert len(PROMPTS) == len(outputs)
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# pooling_params is None, default params should be applied
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outputs = llm.encode(PROMPTS, pooling_params=None, pooling_task="embed")
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assert len(PROMPTS) == len(outputs)
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def test_right_side_truncation(llm: LLM):
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# Embeddings models should truncate the end of the prompt
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tokenizer = llm.get_tokenizer()
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assert tokenizer.truncation_side == "right"
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