default padding side to right for llama

src/transformers/models/deprecated/open_llama/modeling_open_llama.py

@@ -107,7 +107,7 @@ class OpenLlamaRotaryEmbedding(torch.nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         # Build here to make `torch.jit.trace` work.
         self._set_cos_sin_cache(
@@ -171,7 +171,7 @@ class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
                 (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
             ) ** (self.dim / (self.dim - 2))
             inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq)
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
 

src/transformers/models/llama/modeling_llama.py

@@ -97,7 +97,7 @@ class LlamaRotaryEmbedding(torch.nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         # Build here to make `torch.jit.trace` work.
         self._set_cos_sin_cache(
@@ -159,7 +159,7 @@ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
                 (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
             ) ** (self.dim / (self.dim - 2))
             inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-            self.register_buffer("inv_freq", inv_freq)
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
 

src/transformers/models/llama/tokenization_llama.py

@@ -107,6 +107,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
         bos_token="<s>",
         eos_token="</s>",
         pad_token=None,
+        padding_side="right",
         sp_model_kwargs: Optional[Dict[str, Any]] = None,
         add_bos_token=True,
         add_eos_token=False,
@@ -124,6 +125,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
             eos_token=eos_token,
             unk_token=unk_token,
             pad_token=pad_token,
+            padding_side=padding_side,
             add_bos_token=add_bos_token,
             add_eos_token=add_eos_token,
             sp_model_kwargs=self.sp_model_kwargs,

src/transformers/models/llama/tokenization_llama_fast.py

@@ -108,6 +108,7 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
         unk_token="<unk>",
         bos_token="<s>",
         eos_token="</s>",
+        padding_side="right",
         add_bos_token=True,
         add_eos_token=False,
         **kwargs,
@@ -116,6 +117,7 @@ class LlamaTokenizerFast(PreTrainedTokenizerFast):
             vocab_file=vocab_file,
             tokenizer_file=tokenizer_file,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            padding_side=padding_side,
             unk_token=unk_token,
             bos_token=bos_token,
             eos_token=eos_token,