52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""Utilities for selecting and loading models."""
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import contextlib
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from typing import Tuple, Type
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import torch
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from torch import nn
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from vllm.config import ModelConfig
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from vllm.model_executor.models import ModelRegistry
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from vllm.model_executor.models.adapters import (as_classification_model,
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as_embedding_model,
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as_reward_model)
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@contextlib.contextmanager
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def set_default_torch_dtype(dtype: torch.dtype):
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"""Sets the default torch dtype to the given dtype."""
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old_dtype = torch.get_default_dtype()
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torch.set_default_dtype(dtype)
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yield
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torch.set_default_dtype(old_dtype)
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def get_model_architecture(
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model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
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architectures = getattr(model_config.hf_config, "architectures", [])
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# Special handling for quantized Mixtral.
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# FIXME(woosuk): This is a temporary hack.
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mixtral_supported = [
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"fp8", "compressed-tensors", "gptq_marlin", "awq_marlin"
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]
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if (model_config.quantization is not None
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and model_config.quantization not in mixtral_supported
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and "MixtralForCausalLM" in architectures):
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architectures = ["QuantMixtralForCausalLM"]
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model_cls, arch = ModelRegistry.resolve_model_cls(architectures)
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if model_config.task == "embed":
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model_cls = as_embedding_model(model_cls)
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elif model_config.task == "classify":
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model_cls = as_classification_model(model_cls)
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elif model_config.task == "reward":
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model_cls = as_reward_model(model_cls)
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return model_cls, arch
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def get_architecture_class_name(model_config: ModelConfig) -> str:
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return get_model_architecture(model_config)[1]
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