Fix Trainer and Args to mention AdamW, not Adam. (#9685)

* Fix Trainer and Args to mention AdamW, not Adam.

* Update the docs for Training Arguments.

* Change arguments adamw_* to adam_*

* Fixed links to AdamW in TrainerArguments docs

* Fix line length in Training Args docs.

src/transformers/training_args.py

@@ -104,15 +104,16 @@ class TrainingArguments:
             left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
             requires more memory).
         learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
-            The initial learning rate for Adam.
+            The initial learning rate for :class:`~transformers.AdamW` optimizer.
         weight_decay (:obj:`float`, `optional`, defaults to 0):
-            The weight decay to apply (if not zero).
+            The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in 
+            :class:`~transformers.AdamW` optimizer.
         adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
-            The beta1 hyperparameter for the Adam optimizer.
+            The beta1 hyperparameter for the :class:`~transformers.AdamW` optimizer.
         adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
-            The beta2 hyperparameter for the Adam optimizer.
+            The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer.
         adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
-            The epsilon hyperparameter for the Adam optimizer.
+            The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer.
         max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
             Maximum gradient norm (for gradient clipping).
         num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
@@ -288,11 +289,11 @@ class TrainingArguments:
         metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."},
     )
 
-    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for Adam."})
-    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay if we apply some."})
-    adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for Adam optimizer"})
-    adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for Adam optimizer"})
-    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for Adam optimizer."})
+    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
+    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
+    adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
+    adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
+    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
     max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."})
 
     num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
@@ -407,7 +408,7 @@ class TrainingArguments:
     label_smoothing_factor: float = field(
         default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
     )
-    adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace Adam by Adafactor."})
+    adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
     group_by_length: bool = field(
         default=False,
         metadata={"help": "Whether or not to group samples of roughly the same length together when batching."},