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project_6/vllm/model_executor/model_loader/utils.py

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