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from peft import get_peft_model, PeftModel
import os
from copy import deepcopy
import torch
from diffusers.training_utils import EMAModel
class EMAModel_LoRA(EMAModel):
def __init__(self, lora_config, **kwargs):
super().__init__(**kwargs)
self.lora_config = lora_config
@classmethod
def from_pretrained(cls, path, model_cls, lora_config, origin_model_path) -> "EMAModel":
# 1. load origin model
origin_model = model_cls.from_pretrained(origin_model_path) # origin_model
# 2. convert to lora model automatically and load lora weight
lora_model = PeftModel.from_pretrained(origin_model, path) # lora_origin_model
# 3. ema the whole lora_model
ema_model = cls(lora_config, parameters=lora_model.parameters(), model_cls=model_cls, model_config=origin_model.config)
# 4. load ema_kwargs, e.g decay...
ema_kwargs = torch.load(os.path.join(path, 'ema_kwargs.pt'))
ema_model.load_state_dict(ema_kwargs)
return ema_model
def save_pretrained(self, path):
if self.model_cls is None:
raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__.")
if self.model_config is None:
raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__.")
# 1. init a base model randomly
model = self.model_cls.from_config(self.model_config)
# 2. convert lora_model
lora_model = get_peft_model(model, self.lora_config)
# 3. ema_lora_model weight to lora_model
self.copy_to(lora_model.parameters())
# 4. save lora weight
lora_model.save_pretrained(path) # only lora weight
# 5. save ema_kwargs, e.g decay...
state_dict = self.state_dict() # lora_model weight
state_dict.pop("shadow_params", None)
torch.save(state_dict, os.path.join(path, 'ema_kwargs.pt'))
def maybe_zero_3(param, ignore_status=False, name=None):
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
if hasattr(param, "ds_id"):
if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
if not ignore_status:
logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
with zero.GatheredParameters([param]):
param = param.data.detach().cpu().clone()
else:
param = param.detach().cpu().clone()
return param
# Borrowed from peft.utils.get_peft_model_state_dict
def get_peft_state_maybe_zero_3(named_params, bias):
if bias == "none":
to_return = {k: t for k, t in named_params if "lora_" in k}
elif bias == "all":
to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
elif bias == "lora_only":
to_return = {}
maybe_lora_bias = {}
lora_bias_names = set()
for k, t in named_params:
if "lora_" in k:
to_return[k] = t
bias_name = k.split("lora_")[0] + "bias"
lora_bias_names.add(bias_name)
elif "bias" in k:
maybe_lora_bias[k] = t
for k, t in maybe_lora_bias:
if bias_name in lora_bias_names:
to_return[bias_name] = t
else:
raise NotImplementedError
to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()}
return to_return
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