qlora-support

modeling_mpt.py

@@ -291,7 +291,12 @@ class MPTForCausalLM(MPTPreTrainedModel):
  return_dict = return_dict if return_dict is not None else self.config.return_dict
  use_cache = use_cache if use_cache is not None else self.config.use_cache
  outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, inputs_embeds=inputs_embeds)
- logits = F.linear(outputs.last_hidden_state, self.transformer.wte.weight)
+ 
+ last_hidden_state = outputs.last_hidden_state
+ if self.model_parallel:
+ last_hidden_state = last_hidden_state.to(self.transformer.wte.weight.device)
+ logits = F.linear(last_hidden_state, self.transformer.wte.weight)
+
  if self.logit_scale is not None:
  if self.logit_scale == 0:
  warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')