[Fix] Fix default torch dtype loading (#1969)

* Support OlympiadBench Benchmark

* Support OlympiadBench Benchmark

* Support OlympiadBench Benchmark

* update dataset path

* Update olmpiadBench

* Update olmpiadBench

* Update olmpiadBench

* Add HLE dataset

* Add HLE dataset

* Add HLE dataset

* Add AIME2025 oss info

* Fix torch dtype error

---------

Co-authored-by: sudanl <sudanl@foxmail.com>

Author: liushz

opencompass/models/huggingface_above_v4_33.py

@@ -124,20 +124,48 @@ def _get_meta_template(meta_template):
     return APITemplateParser(meta_template or default_meta_template)
 
 
-def _set_model_kwargs_torch_dtype(model_kwargs):
+def _set_model_kwargs_torch_dtype(model_kwargs, path=None):
     import torch
-    if 'torch_dtype' not in model_kwargs:
-        torch_dtype = torch.float16
+    from transformers import AutoConfig
+
+    # If torch_dtype already exists and is not a string, return directly
+    if 'torch_dtype' in model_kwargs and not isinstance(model_kwargs['torch_dtype'], str):
+        return model_kwargs
+
+    # Mapping from string to torch data types
+    dtype_map = {
+        'torch.float16': torch.float16, 'float16': torch.float16,
+        'torch.bfloat16': torch.bfloat16, 'bfloat16': torch.bfloat16,
+        'torch.float': torch.float, 'float': torch.float,
+        'torch.float32': torch.float32, 'float32': torch.float32,
+        'auto': 'auto', 'None': None
+    }
+
+    # 1. Priority: Use torch_dtype from model_kwargs if available
+    if 'torch_dtype' in model_kwargs:
+        torch_dtype = dtype_map.get(model_kwargs['torch_dtype'], torch.float16)
+
+    # 2. Secondary: Try to read from model config
+    elif path is not None:
+        try:
+            config = AutoConfig.from_pretrained(path)
+            if hasattr(config, 'torch_dtype'):
+                config_dtype = config.torch_dtype
+                if isinstance(config_dtype, str):
+                    torch_dtype = dtype_map.get(config_dtype, torch.float16)
+                else:
+                    torch_dtype = config_dtype
+            else:
+                torch_dtype = torch.float16
+        except Exception:
+            torch_dtype = torch.float16
+
+    # 3. Default: Use float16 as fallback
     else:
-        torch_dtype = {
-            'torch.float16': torch.float16,
-            'torch.bfloat16': torch.bfloat16,
-            'torch.float': torch.float,
-            'auto': 'auto',
-            'None': None,
-        }.get(model_kwargs['torch_dtype'])
-    if torch_dtype is not None:
-        model_kwargs['torch_dtype'] = torch_dtype
+        torch_dtype = torch.float16
+
+    # Update model_kwargs with the resolved torch_dtype
+    model_kwargs['torch_dtype'] = torch_dtype
     return model_kwargs
 
 
@@ -218,12 +246,12 @@ def _load_tokenizer(self, path: Optional[str], kwargs: dict, pad_token_id: Optio
         raise ValueError('pad_token_id is not set for this tokenizer. Please set `pad_token_id={PAD_TOKEN_ID}` in model_cfg.')
 
     def _load_model(self, path: str, kwargs: dict, peft_path: Optional[str] = None, peft_kwargs: dict = dict()):
-        from transformers import AutoModel, AutoModelForCausalLM
+        from transformers import AutoConfig, AutoModel, AutoModelForCausalLM
 
         DEFAULT_MODEL_KWARGS = dict(device_map='auto', trust_remote_code=True)
         model_kwargs = DEFAULT_MODEL_KWARGS
         model_kwargs.update(kwargs)
-        model_kwargs = _set_model_kwargs_torch_dtype(model_kwargs)
+        model_kwargs = _set_model_kwargs_torch_dtype(model_kwargs, path)
         self.logger.debug(f'using model_kwargs: {model_kwargs}')
         if is_npu_available():
             model_kwargs['device_map'] = 'npu'