Fix max_position_embeddings default value for llama2 to 4096 huggingf…

…ace#28241 (huggingface#28754)

* Changed max_position_embeddings default value from 2048 to 4096

* force push

* Fixed formatting issues. Fixed missing argument in write_model.

* Reverted to the default value 2048 in the Llama config. Added comments for the llama_version argument.

* Fixed issue with default value value of max_position_embeddings in docstring

* Updated help message for llama versions

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/llama/convert_llama_weights_to_hf.py

@@ -80,7 +80,9 @@ def write_json(text, path):
         json.dump(text, f)
 
 
-def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True):
+def write_model(
+    model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True, llama_version=1
+):
     # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
     if not os.path.isfile(os.path.join(input_base_path, "params.json")):
         input_base_path = os.path.join(input_base_path, model_size)
@@ -102,7 +104,16 @@ def write_model(model_path, input_base_path, model_size, tokenizer_path=None, sa
     if base > 10000.0:
         max_position_embeddings = 16384
     else:
-        max_position_embeddings = 2048
+        # Depending on the Llama version, the default max_position_embeddings has different values.
+        if llama_version == 1:
+            max_position_embeddings = 2048
+        elif llama_version == 2:
+            max_position_embeddings = 4096
+        else:
+            raise NotImplementedError(
+                f"Version {llama_version} of llama is not supported yet. "
+                "Current supported versions of llama are [1, 2]."
+            )
 
     tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
     if tokenizer_path is not None:
@@ -301,6 +312,14 @@ def main():
         help="Location to write HF model and tokenizer",
     )
     parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+    # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
+    parser.add_argument(
+        "--llama_version",
+        choices=[1, 2],
+        default=1,
+        type=int,
+        help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
+    )
     args = parser.parse_args()
     spm_path = os.path.join(args.input_dir, "tokenizer.model")
     if args.model_size != "tokenizer_only":
@@ -310,6 +329,7 @@ def main():
             model_size=args.model_size,
             safe_serialization=args.safe_serialization,
             tokenizer_path=spm_path,
+            llama_version=args.llama_version,
         )
     else:
         write_tokenizer(args.output_dir, spm_path)