Merge branch 'fix_stable_train_samples' into remove_ort

src/transformers/training_args.py

@@ -315,7 +315,7 @@ class TrainingArguments:
             Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
         warmup_steps (`int`, *optional*, defaults to 0):
             Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
-        stable_train_warmup_steps (`int`, *optional*, defaults to 10):
+        stable_train_warmup_steps (`int`, *optional*, defaults to 0):
             Number of steps to skip before collecting performance numbers for stable_train_samples_per_second.
         log_level (`str`, *optional*, defaults to `passive`):
             Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
@@ -606,7 +606,8 @@ class TrainingArguments:
                     Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined before calling `TrainingArguments`.
                     If `True`, an `Accelerator` or `PartialState` must be initialized. Note that by doing so, this could lead to issues
                     with hyperparameter tuning.
-
+        ort (:obj:`bool`, `optional`):
+            Use `ORTModule <https://github.com/microsoft/onnxruntime>`__.
         label_smoothing_factor (`float`, *optional*, defaults to 0.0):
             The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
             labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor +
@@ -922,7 +923,7 @@ class TrainingArguments:
         default=0.0, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."}
     )
     warmup_steps: int = field(default=10, metadata={"help": "Linear warmup over warmup_steps."})
-    stable_train_warmup_steps: int = field(default=10, metadata={"help": "warmup steps to skip before collecting training performance."})
+    stable_train_warmup_steps: int = field(default=0, metadata={"help": "warmup steps to skip before collecting training performance."})
 
     log_level: Optional[str] = field(
         default="passive",
@@ -1269,6 +1270,10 @@ class TrainingArguments:
             )
         },
     )
+    ort: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Enable Ort"},
+    )
     label_smoothing_factor: float = field(
         default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
     )