fix generating args

Former-commit-id: 52805a8441bd7b324bd89489de60f18f103c8e4c

src/utils/config.py

@@ -87,6 +87,8 @@ class ModelArguments:
         if self.checkpoint_dir is not None: # support merging multiple lora weights
             self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
 
+        if self.quantization_bit is not None:
+            assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
 
 @dataclass
 class DataTrainingArguments:
@@ -125,7 +127,7 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."}
     )
-    num_beams: Optional[int] = field(
+    eval_num_beams: Optional[int] = field(
         default=None,
         metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"}
     )
@@ -164,7 +166,7 @@ class DataTrainingArguments:
                 dataset_attr = DatasetAttr(
                     "file",
                     file_name=dataset_info[name]["file_name"],
-                    file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
+                    file_sha1=dataset_info[name].get("file_sha1", None)
                 )
 
             if "columns" in dataset_info[name]:
@@ -262,7 +264,7 @@ class GeneratingArguments:
         default=50,
         metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."}
     )
-    infer_num_beams: Optional[int] = field(
+    num_beams: Optional[int] = field(
         default=1,
         metadata={"help": "Number of beams for beam search. 1 means no beam search."}
     )
@@ -276,7 +278,4 @@ class GeneratingArguments:
     )
 
     def to_dict(self) -> Dict[str, Any]:
-        data_dict = asdict(self)
-        num_beams = data_dict.pop("infer_num_beams")
-        data_dict["num_beams"] = num_beams
-        return data_dict
+        return asdict(self)