diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py index cd92d0e7dd..c0bdae5d3b 100644 --- a/unsloth/models/mistral.py +++ b/unsloth/models/mistral.py @@ -201,31 +201,31 @@ def MistralForCausalLM_fast_forward( # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) self.model._has_no_labels = labels is None - if past_key_values is not None and \ - hasattr(self.model.layers[0].self_attn, "paged_attention"): - outputs = LlamaModel_fast_forward_inference( - self.model, - input_ids, - past_key_values, - ) - else: - outputs = self.model( - input_ids=input_ids, - causal_mask=causal_mask, - attention_mask=attention_mask, - position_ids=position_ids, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) + # if past_key_values is not None and \ + # hasattr(self.model.layers[0].self_attn, "paged_attention"): + # outputs = LlamaModel_fast_forward_inference( + # self.model, + # input_ids, + # past_key_values, + # ) + # else: + outputs = self.model( + input_ids=input_ids, + causal_mask=causal_mask, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) pass hidden_states = outputs[0] bsz, q_len, hd = hidden_states.shape - if bsz == 1 and q_len == 1: + if False:#bsz == 1 and q_len == 1: logits = torch.mv(self.lm_head.weight, hidden_states.ravel()) logits = logits.unsqueeze(0).unsqueeze(0) else: