54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from unittest.mock import patch
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import pytest
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from transformers import PretrainedConfig
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from vllm import LLM
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from .registry import HF_EXAMPLE_MODELS
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@pytest.mark.parametrize("model_arch", HF_EXAMPLE_MODELS.get_supported_archs())
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def test_can_initialize(model_arch):
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model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
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model_info.check_available_online(on_fail="skip")
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model_info.check_transformers_version(on_fail="skip")
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# Avoid OOM
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def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
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if hf_config.model_type == "deepseek_vl_v2":
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hf_config.update({"architectures": ["DeepseekVLV2ForCausalLM"]})
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if hasattr(hf_config, "text_config"):
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text_config: PretrainedConfig = hf_config.text_config
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else:
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text_config = hf_config
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text_config.update({
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"num_layers": 1,
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"num_hidden_layers": 1,
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"num_experts": 2,
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"num_experts_per_tok": 2,
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"num_local_experts": 2,
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})
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return hf_config
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# Avoid calling model.forward()
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def _initialize_kv_caches(self) -> None:
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self.cache_config.num_gpu_blocks = 0
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self.cache_config.num_cpu_blocks = 0
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with patch.object(LLM.get_engine_class(), "_initialize_kv_caches",
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_initialize_kv_caches):
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LLM(
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model_info.default,
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tokenizer=model_info.tokenizer,
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tokenizer_mode=model_info.tokenizer_mode,
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speculative_model=model_info.speculative_model,
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num_speculative_tokens=1 if model_info.speculative_model else None,
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trust_remote_code=model_info.trust_remote_code,
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load_format="dummy",
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hf_overrides=hf_overrides,
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)
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