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https://github.com/vllm-project/vllm.git
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1cf3753b90
Signed-off-by: EduardDurech <39579228+EduardDurech@users.noreply.github.com> Co-authored-by: AllenHaoHuang <allenhuangdd@gmail.com>
111 lines
4.3 KiB
Python
111 lines
4.3 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 warnings
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import pytest
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import torch.cuda
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from vllm.model_executor.models import (is_pooling_model,
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is_text_generation_model,
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supports_multimodal)
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from vllm.model_executor.models.adapters import (as_embedding_model,
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as_reward_model,
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as_seq_cls_model)
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from vllm.model_executor.models.registry import (_MULTIMODAL_MODELS,
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_SPECULATIVE_DECODING_MODELS,
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_TEXT_GENERATION_MODELS,
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ModelRegistry)
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from vllm.platforms import current_platform
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from ..utils import create_new_process_for_each_test
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from .registry import HF_EXAMPLE_MODELS
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@pytest.mark.parametrize("model_arch", ModelRegistry.get_supported_archs())
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def test_registry_imports(model_arch):
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# Skip if transformers version is incompatible
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model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
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model_info.check_transformers_version(on_fail="skip")
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# Ensure all model classes can be imported successfully
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model_cls = ModelRegistry._try_load_model_cls(model_arch)
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assert model_cls is not None
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if model_arch in _SPECULATIVE_DECODING_MODELS:
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return # Ignore these models which do not have a unified format
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if (model_arch in _TEXT_GENERATION_MODELS
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or model_arch in _MULTIMODAL_MODELS):
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assert is_text_generation_model(model_cls)
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# All vLLM models should be convertible to a pooling model
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assert is_pooling_model(as_seq_cls_model(model_cls))
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assert is_pooling_model(as_embedding_model(model_cls))
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assert is_pooling_model(as_reward_model(model_cls))
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if model_arch in _MULTIMODAL_MODELS:
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assert supports_multimodal(model_cls)
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@create_new_process_for_each_test()
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@pytest.mark.parametrize("model_arch,is_mm,init_cuda,is_ce", [
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("LlamaForCausalLM", False, False, False),
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("MllamaForConditionalGeneration", True, False, False),
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("LlavaForConditionalGeneration", True, True, False),
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("BertForSequenceClassification", False, False, True),
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("RobertaForSequenceClassification", False, False, True),
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("XLMRobertaForSequenceClassification", False, False, True),
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])
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def test_registry_model_property(model_arch, is_mm, init_cuda, is_ce):
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model_info = ModelRegistry._try_inspect_model_cls(model_arch)
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assert model_info is not None
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assert model_info.supports_multimodal is is_mm
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assert model_info.supports_cross_encoding is is_ce
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if init_cuda and current_platform.is_cuda_alike():
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assert not torch.cuda.is_initialized()
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ModelRegistry._try_load_model_cls(model_arch)
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if not torch.cuda.is_initialized():
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warnings.warn(
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"This model no longer initializes CUDA on import. "
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"Please test using a different one.",
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stacklevel=2)
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@create_new_process_for_each_test()
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@pytest.mark.parametrize(
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"model_arch,is_pp,init_cuda",
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[
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# TODO(woosuk): Re-enable this once the MLP Speculator is supported
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# in V1.
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# ("MLPSpeculatorPreTrainedModel", False, False),
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("DeepseekV2ForCausalLM", True, False),
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("Qwen2VLForConditionalGeneration", True, True),
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])
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def test_registry_is_pp(model_arch, is_pp, init_cuda):
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model_info = ModelRegistry._try_inspect_model_cls(model_arch)
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assert model_info is not None
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assert model_info.supports_pp is is_pp
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if init_cuda and current_platform.is_cuda_alike():
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assert not torch.cuda.is_initialized()
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ModelRegistry._try_load_model_cls(model_arch)
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if not torch.cuda.is_initialized():
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warnings.warn(
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"This model no longer initializes CUDA on import. "
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"Please test using a different one.",
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stacklevel=2)
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def test_hf_registry_coverage():
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untested_archs = (ModelRegistry.get_supported_archs() -
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HF_EXAMPLE_MODELS.get_supported_archs())
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assert not untested_archs, (
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"Please add the following architectures to "
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f"`tests/models/registry.py`: {untested_archs}")
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