forked from Karylab-cklius/vllm
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
"""Utilities for selecting and loading models."""
|
|
import contextlib
|
|
from typing import Type
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from transformers import PretrainedConfig
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.model_executor.models import * # pylint: disable=wildcard-import
|
|
from vllm.model_executor.weight_utils import initialize_dummy_weights
|
|
|
|
# TODO(woosuk): Lazy-load the model classes.
|
|
_MODEL_REGISTRY = {
|
|
"AquilaModel": AquilaForCausalLM,
|
|
"BaiChuanForCausalLM": BaiChuanForCausalLM, # baichuan-7b
|
|
"BaichuanForCausalLM": BaichuanForCausalLM, # baichuan-13b
|
|
"BloomForCausalLM": BloomForCausalLM,
|
|
"FalconForCausalLM": FalconForCausalLM,
|
|
"GPT2LMHeadModel": GPT2LMHeadModel,
|
|
"GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
|
|
"GPTJForCausalLM": GPTJForCausalLM,
|
|
"GPTNeoXForCausalLM": GPTNeoXForCausalLM,
|
|
"InternLMForCausalLM": InternLMForCausalLM,
|
|
"LlamaForCausalLM": LlamaForCausalLM,
|
|
"LLaMAForCausalLM": LlamaForCausalLM, # For decapoda-research/llama-*
|
|
"MPTForCausalLM": MPTForCausalLM,
|
|
"OPTForCausalLM": OPTForCausalLM,
|
|
"QWenLMHeadModel": QWenLMHeadModel,
|
|
"RWForCausalLM": FalconForCausalLM,
|
|
}
|
|
|
|
|
|
@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(config: PretrainedConfig) -> Type[nn.Module]:
|
|
architectures = getattr(config, "architectures", [])
|
|
for arch in architectures:
|
|
if arch in _MODEL_REGISTRY:
|
|
return _MODEL_REGISTRY[arch]
|
|
raise ValueError(
|
|
f"Model architectures {architectures} are not supported for now. "
|
|
f"Supported architectures: {list(_MODEL_REGISTRY.keys())}")
|
|
|
|
|
|
def get_model(model_config: ModelConfig) -> nn.Module:
|
|
model_class = _get_model_architecture(model_config.hf_config)
|
|
with _set_default_torch_dtype(model_config.dtype):
|
|
# Create a model instance.
|
|
# The weights will be initialized as empty tensors.
|
|
model = model_class(model_config.hf_config)
|
|
if model_config.use_dummy_weights:
|
|
model = model.cuda()
|
|
# NOTE(woosuk): For accurate performance evaluation, we assign
|
|
# random values to the weights.
|
|
initialize_dummy_weights(model)
|
|
else:
|
|
# Load the weights from the cached or downloaded files.
|
|
model.load_weights(model_config.model, model_config.download_dir,
|
|
model_config.use_np_weights)
|
|
model = model.cuda()
|
|
return model.eval()
|