Update llama.py

unsloth/models/llama.py

@@ -1065,9 +1065,15 @@ class LlamaExtendedRotaryEmbedding(torch.nn.Module):
         config = None, # [TODO] Hack to pass in config - need to remove later
     ):
         super().__init__()
-        print(1068)
-        # if config is not None: return # [TODO] Hack to pass in config - need to remove later
-        
+        if config is not None:
+            # [TODO] Hack to pass in config - need to remove later
+            base = config.rope_theta
+            partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+            dim = int((config.hidden_size // config.num_attention_heads))
+            device = "cuda"
+            max_position_embeddings = config.max_position_embeddings
+        pass
+            
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
@@ -1080,7 +1086,6 @@ class LlamaExtendedRotaryEmbedding(torch.nn.Module):
         )
         inv_freq = self.apply_scaling(inv_freq)
         self.register_buffer("inv_freq", inv_freq, persistent = False)
-        print(1083)
 
         # Build here to make `torch.jit.trace` work.
         self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
@@ -1090,7 +1095,6 @@ class LlamaExtendedRotaryEmbedding(torch.nn.Module):
         # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
         self.current_rope_size = seq_len
-        print(1093)
         
         t = torch.arange(self.current_rope_size, device="cpu", dtype=torch.int64).float()