push best checkpoint to hub

angle_emb/angle.py

@@ -1048,39 +1048,6 @@ def __call__(self,
         return loss
 
 
-class EvaluateCallback(TrainerCallback):
-    """
-    Custom TrainerCallback for Angle.
-    This callback will compute corrcoef for each epoch.
-
-    :param model: PreTrainedModel.
-    :param valid_ds: Dataset.
-    :param evaluate_fn: Callable. It will receive valid_ds as input like `evaluate_fn(valid_ds)`.
-    :param save_dir: Optional[str]. specify dir to save model with best results.
-    """
-    def __init__(self,
-                 model: PreTrainedModel,
-                 valid_ds: Dataset,
-                 evaluate_fn: Callable,
-                 save_dir: Optional[str] = None):
-        super().__init__()
-        self.model = model
-        self.valid_ds = valid_ds
-        self.evaluate_fn = evaluate_fn
-        self.save_dir = save_dir
-        self.best_corrcoef = 0
-
-    def on_epoch_end(self, args, state, control, **kwargs):
-        corrcoef, accuracy = self.evaluate_fn(self.valid_ds)
-        if corrcoef > self.best_corrcoef:
-            self.best_corrcoef = corrcoef
-            print('new best corrcoef!')
-            if self.save_dir is not None:
-                self.model.save_pretrained(self.save_dir)
-                print(f'save to {self.save_dir}')
-        print(f'corrcoef: {corrcoef}, accuracy: {accuracy}, best corrcoef: {self.best_corrcoef}')
-
-
 class AnglE:
     """
     AnglE. Everything is here👋
@@ -1493,10 +1460,14 @@ def fit(self,
             best_ckpt_dir = None
             if output_dir is not None:
                 best_ckpt_dir = os.path.join(output_dir, 'best-checkpoint')
-                self.tokenizer.save_pretrained(best_ckpt_dir)
-            evaluate_callback = EvaluateCallback(self.backbone, valid_ds,
+            evaluate_callback = EvaluateCallback(self, valid_ds,
                                                  partial(self.evaluate, batch_size=batch_size, device=self.device),
-                                                 save_dir=best_ckpt_dir)
+                                                 save_dir=best_ckpt_dir,
+                                                 push_to_hub=push_to_hub,
+                                                 hub_model_id=hub_model_id,
+                                                 hub_private_repo=hub_private_repo)
+            # set False to ensure only best checkpoint is pushed
+            argument_kwargs['push_to_hub'] = False
             callbacks = [evaluate_callback]
 
         CustomTrainer = AngleESETrainer if apply_ese else AngleTrainer
@@ -1662,3 +1633,48 @@ def save_pretrained(self, output_dir: str, exist_ok: bool = True):
         os.makedirs(output_dir)
         self.tokenizer.save_pretrained(output_dir)
         self.backbone.save_pretrained(output_dir)
+
+
+class EvaluateCallback(TrainerCallback):
+    """
+    Custom TrainerCallback for Angle.
+    This callback will compute corrcoef for each epoch.
+
+    :param model: PreTrainedModel.
+    :param valid_ds: Dataset.
+    :param evaluate_fn: Callable. It will receive valid_ds as input like `evaluate_fn(valid_ds)`.
+    :param save_dir: Optional[str]. specify dir to save model with best results.
+    """
+    def __init__(self,
+                 model: AnglE,
+                 valid_ds: Dataset,
+                 evaluate_fn: Callable,
+                 save_dir: Optional[str] = None,
+                 push_to_hub: bool = False,
+                 hub_model_id: Optional[str] = None,
+                 hub_private_repo: bool = True):
+        super().__init__()
+        self.model = model
+        self.valid_ds = valid_ds
+        self.evaluate_fn = evaluate_fn
+        self.save_dir = save_dir
+        self.best_corrcoef = 0
+        self.push_to_hub = push_to_hub
+        self.hub_model_id = hub_model_id
+        self.hub_private_repo = hub_private_repo
+
+    def on_epoch_end(self, args, state, control, **kwargs):
+        corrcoef, accuracy = self.evaluate_fn(self.valid_ds)
+        if corrcoef > self.best_corrcoef:
+            self.best_corrcoef = corrcoef
+            print('new best corrcoef!')
+            if self.save_dir is not None:
+                self.model.save_pretrained(self.save_dir)
+                print(f'save to {self.save_dir}')
+                if self.push_to_hub:
+                    self.model.push_to_hub(
+                        self.hub_model_id,
+                        private=self.hub_private_repo,
+                        exist_ok=True,
+                        commit_message='new best checkpoint')
+        print(f'corrcoef: {corrcoef}, accuracy: {accuracy}, best corrcoef: {self.best_corrcoef}')