Update convert_llama.py

neural_speed/convert/convert_llama.py

@@ -155,6 +155,7 @@ class Params:
     rope_scale: float
     bos_token_id: int
     eos_token_id: int
+    pad_token_id: int
 
     @staticmethod
     def guessed(model: 'LazyModel') -> 'Params':
@@ -188,6 +189,7 @@ def loadHFTransformerJson(model: 'LazyModel', config_path: Path) -> 'Params':
             rope_scale = config["rope_scaling"]["factor"] if "factor" in config["rope_scaling"] else 1
         bos_token_id = config["bos_token_id"]
         eos_token_id = config["eos_token_id"]
+        pad_token_id = config["pad_token_id"] if "pad_token_id" in config else -1
 
         return Params(
             n_vocab=n_vocab,
@@ -202,6 +204,7 @@ def loadHFTransformerJson(model: 'LazyModel', config_path: Path) -> 'Params':
             rope_scale=rope_scale,
             bos_token_id = bos_token_id,
             eos_token_id = eos_token_id,
+            pad_token_id = pad_token_id,
         )
 
     # LLaMA v2 70B params.json
@@ -219,6 +222,7 @@ def loadOriginalParamsJson(model: 'LazyModel', config_path: Path) -> 'Params':
         ffn_hidden_size = config["intermediate_size"]
         bos_token_id = config["bos_token_id"]
         eos_token_id = config["eos_token_id"]
+        pad_token_id = config["pad_token_id"] if "pad_token_id" in config else -1
         # hack to determine LLaMA v1 vs v2 vs CodeLlama
 
         if n_vocab == -1:
@@ -234,6 +238,7 @@ def loadOriginalParamsJson(model: 'LazyModel', config_path: Path) -> 'Params':
             ffn_hidden_size=ffn_hidden_size,
             bos_token_id = bos_token_id,
             eos_token_id = eos_token_id,
+            pad_token_id = pad_token_id,
         )
 
     @staticmethod
@@ -1092,7 +1097,7 @@ def write_file_header(self, params: Params, file_type: NEFileType) -> None:
         # but bos_token_id = 1 in llama.cpp
         self.fout.write(struct.pack("i", params.bos_token_id))
         self.fout.write(struct.pack("i", params.eos_token_id))
-        self.fout.write(struct.pack("i", -1))
+        self.fout.write(struct.pack("i", params.pad_token_id))
         self.fout.write(struct.pack("i", -1))
 
     def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None: