Adding optional trial argument to model_init (#7759)

* Adding optional trial argument to model_init

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/trainer.py

@@ -173,6 +173,9 @@ class Trainer:
         model_init (:obj:`Callable[[], PreTrainedModel]`, `optional`):
             A function that instantiates the model to be used. If provided, each call to
             :meth:`~transformers.Trainer.train` will start from a new instance of the model as given by this function.
+
+            The function may have zero argument, or a single one containing the optuna/Ray Tune trial object, to be able to choose
+            different architectures according to hyper parameters (such as layer count, sizes of inner layers, dropout probabilities etc).
         compute_metrics (:obj:`Callable[[EvalPrediction], Dict]`, `optional`):
             The function that will be used to compute metrics at evaluation. Must take a
             :class:`~transformers.EvalPrediction` and return a dictionary string to metric values.
@@ -212,15 +215,16 @@ class Trainer:
         assert (
             model is not None or model_init is not None
         ), "You must provide a model to use `Trainer`, either by using the `model` argument or the `model_init` argument."
+        self.model_init = model_init
         if model is None and model_init is not None:
-            model = model_init()
+            model = self.call_model_init()
         self.model = model.to(args.device) if model is not None else None
         default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
         self.data_collator = data_collator if data_collator is not None else default_collator
         self.train_dataset = train_dataset
         self.eval_dataset = eval_dataset
         self.tokenizer = tokenizer
-        self.model_init = model_init
+
         self.compute_metrics = compute_metrics
         self.optimizer, self.lr_scheduler = optimizers
         if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
@@ -532,6 +536,17 @@ class Trainer:
                 torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                 torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
 
+    def call_model_init(self, trial=None):
+        model_init_argcount = len(inspect.signature(self.model_init).parameters)
+        if model_init_argcount == 0:
+            model = self.model_init()
+        elif model_init_argcount == 1:
+            model = self.model_init(trial)
+        else:
+            raise Exception("model_init should have 0 or 1 argument.")
+
+        return model
+
     def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", Dict[str, Any]] = None):
         """
         Main training entry point.
@@ -550,7 +565,9 @@ class Trainer:
         if self.model_init is not None:
             # Seed must be set before instantiating the model when using model_init.
             set_seed(self.args.seed)
-            model = self.model_init()
+
+            model = self.call_model_init(trial)
+
             self.model = model.to(self.args.device)
 
             # Reinitializes optimizer and scheduler