Update modeling_super_linear.py

modeling_super_linear.py

@@ -392,7 +392,6 @@ class Model(nn.Module):
             - Prediction tensor
             - (Optional) Expert selection probabilities if get_prob is True
         """
-        print(pred_len)
         if pred_len is None:
             pred_len = self.train_pred_len
 
@@ -421,8 +420,6 @@ class Model(nn.Module):
         else:
             out = self.moe(x)
 
-        print(pred_len)
-        print(self.train_pred_len)
         if self.train_pred_len < pred_len:
             outputs = [out]
             ar_x = torch.cat([x, out], dim=1)[:, -self.seq_len:]
@@ -474,7 +471,7 @@ class SuperLinearForCausalLM(PreTrainedModel, GenerationMixin):
                 labels: Optional[torch.Tensor] = None,        
                 **kwargs,) -> CausalLMOutputWithCrossAttentions:
 
-        
+    
         if inputs_embeds is None:
             raise ValueError("Pass the time‑series as `inputs_embeds`")
         
@@ -482,7 +479,7 @@ class SuperLinearForCausalLM(PreTrainedModel, GenerationMixin):
         x_enc = inputs_embeds
     
         # backbone returns (B, pred_len, C)
-        preds = self.backbone(x_enc, pred_len=kwargs.get("pred_len", default_value))
+        preds = self.backbone(x_enc, pred_len=kwargs.get("pred_len", None))
         return CausalLMOutputWithCrossAttentions(loss=None,logits=preds,past_key_values=None,hidden_states=None,attentions=None,)