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73 lines
2.0 KiB
Python
73 lines
2.0 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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from tests.models.utils import softmax
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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 = "jason9693/Qwen2.5-1.5B-apeach"
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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(
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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_pooling_params(llm: LLM):
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def get_outputs(activation):
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outputs = llm.classify(
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prompts, pooling_params=PoolingParams(activation=activation), use_tqdm=False
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)
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return torch.tensor([x.outputs.probs for x in outputs])
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default = get_outputs(activation=None)
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w_activation = get_outputs(activation=True)
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wo_activation = get_outputs(activation=False)
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assert torch.allclose(default, w_activation, atol=1e-2), (
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"Default should use activation."
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)
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assert not torch.allclose(w_activation, wo_activation, atol=1e-2), (
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"wo_activation should not use activation."
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)
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assert torch.allclose(softmax(wo_activation), w_activation, atol=1e-2), (
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"w_activation should be close to activation(wo_activation)."
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)
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@pytest.mark.skip_global_cleanup
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def test_encode_api(llm: LLM):
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# chunked prefill does not support all pooling
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err_msg = "pooling_task must be one of.+"
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with pytest.raises(ValueError, match=err_msg):
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llm.encode(prompts, use_tqdm=False)
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def test_score_api(llm: LLM):
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err_msg = "Score API is only enabled for num_labels == 1."
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with pytest.raises(ValueError, match=err_msg):
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llm.score("ping", "pong", use_tqdm=False)
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