109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
import weakref
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from typing import List
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import pytest
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from vllm import LLM, EmbeddingRequestOutput, PoolingParams
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from ...conftest import cleanup
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MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
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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(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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with llm.deprecate_legacy_api():
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yield weakref.proxy(llm)
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del llm
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cleanup()
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def assert_outputs_equal(o1: List[EmbeddingRequestOutput],
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o2: List[EmbeddingRequestOutput]):
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assert [o.outputs for o in o1] == [o.outputs for o in o2]
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@pytest.mark.skip_global_cleanup
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@pytest.mark.parametrize('prompt_token_ids', TOKEN_IDS)
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def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
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prompt_token_ids):
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pooling_params = PoolingParams()
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with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
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v1_output = llm.encode(prompt_token_ids=prompt_token_ids,
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pooling_params=pooling_params)
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v2_output = llm.encode({"prompt_token_ids": prompt_token_ids},
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pooling_params=pooling_params)
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assert_outputs_equal(v1_output, v2_output)
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@pytest.mark.skip_global_cleanup
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def test_v1_v2_api_consistency_multi_prompt_tokens(llm: LLM):
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pooling_params = PoolingParams()
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with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
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v1_output = llm.encode(prompt_token_ids=TOKEN_IDS,
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pooling_params=pooling_params)
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v2_output = llm.encode(
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[{
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"prompt_token_ids": p
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} for p in TOKEN_IDS],
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pooling_params=pooling_params,
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)
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assert_outputs_equal(v1_output, v2_output)
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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)
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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(PROMPTS, pooling_params=pooling_params[:3])
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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(PROMPTS, pooling_params=single_pooling_params)
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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)
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assert len(PROMPTS) == len(outputs)
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