fix lint (#854)

xtuner/model/dpo.py

@@ -52,10 +52,13 @@ def __init__(self,
  self.beta = beta
 
  if ref_llm is not None:
- ref_llm = self._build_llm_from_cfg(ref_llm, kwargs.get("use_varlen_attn"), kwargs.get("max_position_embeddings"))
+ ref_llm = self.build_llm_from_cfg(
+ ref_llm, kwargs.get('use_varlen_attn', False),
+ kwargs.get('max_position_embeddings', None))
  self.ref_llm = disable_grad(ref_llm)
  else:
- self.ref_llm = None if self.use_lora else create_reference_model(self.llm)
+ self.ref_llm = None if self.use_lora else create_reference_model(
+ self.llm)
 
  def _gather_masked_logits(self, logits, labels, mask):
  logits = torch.gather(

xtuner/model/sft.py

@@ -80,7 +80,8 @@ def __init__(self,
  max_position_embeddings=None):
  super().__init__()
 
- self.llm = self._build_llm_from_cfg(llm, use_varlen_attn, max_position_embeddings)
+ self.llm = self.build_llm_from_cfg(llm, use_varlen_attn,
+ max_position_embeddings)
 
  if tokenizer is not None:
  if isinstance(tokenizer, dict):
@@ -115,19 +116,19 @@ def __init__(self,
  # the sequence.
  self.use_varlen_attn = use_varlen_attn
 
-
- def _build_llm_from_cfg(self, llm_cfg, use_varlen_attn, max_position_embeddings):
+ def build_llm_from_cfg(self, llm_cfg, use_varlen_attn,
+   max_position_embeddings):
  # For forward
  with LoadWoInit():
  if isinstance(llm_cfg, dict):
- llm = self._dispatch_lm_model_cfg(llm_cfg, max_position_embeddings)
+ llm = self._dispatch_lm_model_cfg(llm_cfg,
+ max_position_embeddings)
  llm = self._build_from_cfg_or_module(llm)
 
  llm.config.use_cache = False
  dispatch_modules(llm, use_varlen_attn=use_varlen_attn)
  return llm
 
-
  def gradient_checkpointing_enable(self):
  self.activation_checkpointing_enable()