Add timing inside Trainer (#9196)

* Add timing inside Trainer

* Fix tests

* Add n_objs for train

* Sort logs

src/transformers/training_args.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import dataclasses
 import json
 import os
-from dataclasses import dataclass, field
+from dataclasses import asdict, dataclass, field
 from enum import Enum
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -411,7 +410,16 @@ class TrainingArguments:
             self.run_name = self.output_dir
 
         if is_torch_available() and self.device.type != "cuda" and self.fp16:
-            raise ValueError("AMP (`--fp16`) can only be used on CUDA devices.")
+            raise ValueError("Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.")
+
+    def __repr__(self):
+        # We override the default repr to remove deprecated arguments from the repr. This method should be removed once
+        # those deprecated arguments are removed form TrainingArguments. (TODO: v5)
+        self_as_dict = asdict(self)
+        del self_as_dict["per_gpu_train_batch_size"]
+        del self_as_dict["per_gpu_eval_batch_size"]
+        attrs_as_str = [f"{k}={v}" for k, v in self_as_dict.items()]
+        return f"{self.__class__.__name__}({', '.join(attrs_as_str)})"
 
     @property
     def train_batch_size(self) -> int:
@@ -523,7 +531,7 @@ class TrainingArguments:
         """
         Serializes this instance while replace `Enum` by their values (for JSON serialization support).
         """
-        d = dataclasses.asdict(self)
+        d = asdict(self)
         for k, v in d.items():
             if isinstance(v, Enum):
                 d[k] = v.value