Repository: huggingface/transformers (Python) — Feature

Issue:
Trainer: Functions to inspect model and optimizer status
### Feature request

In huggingface Trainer, are there any functions to inspect model and optimizer status? such as, how many parameters require grad, learning rate of each parameter, which optimizer group each parameter belong...

I didn't find any related function in Trainer, and I know implementing it by myself is easy, but I just want to know whether such functions already exist.

### Motivation

Such inspection is useful for correcting training.

### Your contribution

I propose a question.


Discussion:
cc @muellerzr @pacman100 
Hi, can I take over the issue?
@CKeibel Sure! No need to claim on an issue, we prioritise based on PRs open, as we find this helps prevent issues from going stale without being addressed. Once you have something opened, feel free to ping me and @muellerzr for review 🤗 
Hey, thanks for the reply. Maybe I should have read the contribution guidelines first. I'm used to asking beforehand in other open source projects. But then I will start contributing diligently. :)

Resolving patch:
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -1048,6 +1048,36 @@ def create_optimizer(self):
 
         return self.optimizer
 
+    def get_num_trainable_parameters(self):
+        """
+        Get the number of trainable parameters.
+        """
+        return sum(p.numel() for p in self.model.parameters() if p.requires_grad)
+
+    def get_learning_rates(self):
+        """
+        Returns the learning rate of each parameter from self.optimizer.
+        """
+        if self.optimizer is None:
+            raise ValueError("Trainer optimizer is None, please make sure you have setup the optimizer before.")
+        return [group["lr"] for group in self.optimizer.param_groups]
+
+    def get_optimizer_group(self, param: Optional[Union[str, torch.nn.parameter.Parameter]] = None):
+        """
+        Returns optimizer group for a parameter if given, else returns all optimizer groups for params.
+
+        Args:
+            param (`str` or `torch.nn.parameter.Parameter`, *optional*):
+                The parameter for which optimizer group needs to be returned.
+        """
+        if self.optimizer is None:
+            raise ValueError("Trainer optimizer is None, please make sure you have setup the optimizer before.")
+        if param is not None:
+            for group in self.optimizer.param_groups:
+                if param in group["params"]:
+                    return group
+        return [group["params"] for group in self.optimizer.param_groups]
+
     @staticmethod
     def get_optimizer_cls_and_kwargs(
         args: TrainingArguments, model: Optional[PreTrainedModel] = None


Test patch:
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -3769,3 +3769,41 @@ def test_hyperparameter_search_backends(self):
             list(ALL_HYPERPARAMETER_SEARCH_BACKENDS.keys()),
             list(HPSearchBackend),
         )
+
+
+@require_torch
+class OptimizerAndModelInspectionTest(unittest.TestCase):
+    def test_get_num_trainable_parameters(self):
+        model = nn.Sequential(nn.Linear(128, 64), nn.Linear(64, 32))
+        # in_features * out_features + bias
+        layer_1 = 128 * 64 + 64
+        layer_2 = 64 * 32 + 32
+        trainer = Trainer(model=model)
+        self.assertEqual(trainer.get_num_trainable_parameters(), layer_1 + layer_2)
+        # Freeze the last layer
+        for param in model[-1].parameters():
+            param.requires_grad = False
+        self.assertEqual(trainer.get_num_trainable_parameters(), layer_1)
+
+    def test_get_learning_rates(self):
+        model = nn.Sequential(nn.Linear(128, 64))
+        trainer = Trainer(model=model)
+        with self.assertRaises(ValueError):
+            trainer.get_learning_rates()
+        trainer.create_optimizer()
+        self.assertEqual(trainer.get_learning_rates(), [5e-05, 5e-05])
+
+    def test_get_optimizer_group(self):
+        model = nn.Sequential(nn.Linear(128, 64))
+        trainer = Trainer(model=model)
+        # ValueError is raised if optimizer is None
+        with self.assertRaises(ValueError):
+            trainer.get_optimizer_group()
+        trainer.create_optimizer()
+        # Get groups
+        num_groups = len(trainer.get_optimizer_group())
+        self.assertEqual(num_groups, 2)
+        # Get group of parameter
+        param = next(model.parameters())
+        group = trainer.get_optimizer_group(param)
+        self.assertIn(param, group["params"])