d4d93db2c5
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Nicolò Lucchesi <nlucches@redhat.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Compare the classification outputs of HF and vLLM models.
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Run `pytest tests/models/test_cls_models.py`.
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"""
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import pytest
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import torch
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from transformers import AutoModelForSequenceClassification
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from vllm.platforms import current_platform
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@pytest.mark.parametrize(
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"model",
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[
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pytest.param("jason9693/Qwen2.5-1.5B-apeach",
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marks=[pytest.mark.core_model, pytest.mark.cpu_model]),
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],
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)
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@pytest.mark.parametrize("dtype",
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["half"] if current_platform.is_rocm() else ["float"])
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def test_classification_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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monkeypatch,
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) -> None:
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if current_platform.is_rocm():
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# ROCm Triton FA does not currently support sliding window attention
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# switch to use ROCm CK FA backend
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monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", "False")
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.classify(example_prompts)
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with hf_runner(model,
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dtype=dtype,
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auto_cls=AutoModelForSequenceClassification) as hf_model:
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hf_outputs = hf_model.classify(example_prompts)
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# check logits difference
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for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
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hf_output = torch.tensor(hf_output)
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vllm_output = torch.tensor(vllm_output)
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# the tolerance value of 1e-2 is selected based on the
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# half datatype tests in
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# tests/models/embedding/language/test_embedding.py
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assert torch.allclose(hf_output, vllm_output,
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1e-3 if dtype == "float" else 1e-2)
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