"""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 import os @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", []) support_nn_architectures = ['LlamaForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'ChatGLMModel', 'BaichuanForCausalLM', 'BloomForCausalLM'] use_triton_fa_architectures = ['DeepseekV2ForCausalLM'] if any(arch in architectures for arch in support_nn_architectures): if os.getenv('LLAMA_NN') != '0': os.environ['LLAMA_NN'] = '1' if os.getenv('GEMM_PAD') != '1': os.environ['GEMM_PAD'] = '0' if os.getenv('FA_PAD') != '1': os.environ['FA_PAD'] = '0' else: os.environ['LLAMA_NN'] = '0' os.environ['GEMM_PAD'] = '0' os.environ['FA_PAD'] = '0' if any(arch in architectures for arch in use_triton_fa_architectures): os.environ['VLLM_USE_TRITON_FLASH_ATTN'] = '1' os.environ['VLLM_USE_FLASH_ATTN_AUTO'] = '0' # Special handling for quantized Mixtral. # FIXME(woosuk): This is a temporary hack. mixtral_supported = ["fp8", "compressed-tensors"] 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]