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68 lines
1.9 KiB
Python
68 lines
1.9 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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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 = "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
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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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def test_pooling_params(llm: LLM):
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def get_outputs(use_activation):
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text_1 = "What is the capital of France?"
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text_2 = "The capital of France is Paris."
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outputs = llm.score(
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text_1,
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text_2,
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pooling_params=PoolingParams(use_activation=use_activation),
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use_tqdm=False,
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)
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return torch.tensor([x.outputs.score for x in outputs])
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default = get_outputs(use_activation=None)
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w_activation = get_outputs(use_activation=True)
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wo_activation = get_outputs(use_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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