replace Hparams by init args (#1896)

* remove the need for hparams

* remove the need for hparams

* remove the need for hparams

* remove the need for hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* replace self.hparams

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* fixed

* finished moco

* basic

* testing

* todo

* recurse

* hparams

* persist

* hparams

* chlog

* tests

* tests

* tests

* tests

* tests

* tests

* review

* saving

* tests

* tests

* tests

* docs

* finished moco

* hparams

* review

* Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* hparams

* overwrite

* transform

* transform

* transform

* transform

* cleaning

* cleaning

* tests

* examples

* examples

* examples

* Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* chp key

* tests

* Apply suggestions from code review

* class

* updated docs

* updated docs

* updated docs

* updated docs

* save

* wip

* fix

* flake8

Co-authored-by: Jirka <jirka@pytorchlightning.ai>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

docs/source/hyperparameters.rst

@@ -75,7 +75,7 @@ Now in your main trainer file, add the Trainer args, the program args, and add t
     # ie: now --gpus --num_nodes ... --fast_dev_run all work in the cli
     parser = Trainer.add_argparse_args(parser)
 
-    hparams = parser.parse_args()
+    args = parser.parse_args()
 
 Now you can call run your program like so
 
@@ -87,39 +87,50 @@ Finally, make sure to start the training like so:
 
 .. code-block:: python
 
-    # YES
-    model = LitModel(hparams)
-    trainer = Trainer.from_argparse_args(hparams, early_stopping_callback=...)
+    # init the trainer like this
+    trainer = Trainer.from_argparse_args(args, early_stopping_callback=...)
 
-    # NO
-    # model = LitModel(learning_rate=hparams.learning_rate, ...)
-    # trainer = Trainer(gpus=hparams.gpus, ...)
+    # NOT like this
+    trainer = Trainer(gpus=hparams.gpus, ...)
 
-LightningModule hparams
-^^^^^^^^^^^^^^^^^^^^^^^
+    # init the model with Namespace directly
+    model = LitModel(args)
 
-Normally, we don't hard-code the values to a model. We usually use the command line to
-modify the network and read those values in the LightningModule
+    # or init the model with all the key-value pairs
+    dict_args = vars(args)
+    model = LitModel(**dict_args)
+
+LightningModule hyperparameters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. warning:: The use of `hparams` is no longer recommended (but still supported)
+
+LightningModule is just an nn.Module, you can use it as you normally would. However, there are
+some best practices to improve readability and reproducibility.
+
+1. It's more readable to specify all the arguments that go into a module (with default values).
+This helps users of your module know everything that is required to run this.
 
 .. testcode::
 
     class LitMNIST(LightningModule):
 
-        def __init__(self, hparams):
+        def __init__(self, layer_1_dim=128, layer_2_dim=256, learning_rate=1e-4, batch_size=32, **kwargs):
             super().__init__()
+            self.layer_1_dim = layer_1_dim
+            self.layer_2_dim = layer_2_dim
+            self.learning_rate = learning_rate
+            self.batch_size = batch_size
 
-            # do this to save all arguments in any logger (tensorboard)
-            self.hparams = hparams
-
-            self.layer_1 = torch.nn.Linear(28 * 28, hparams.layer_1_dim)
-            self.layer_2 = torch.nn.Linear(hparams.layer_1_dim, hparams.layer_2_dim)
-            self.layer_3 = torch.nn.Linear(hparams.layer_2_dim, 10)
+            self.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_dim)
+            self.layer_2 = torch.nn.Linear(self.layer_1_dim, self.layer_2_dim)
+            self.layer_3 = torch.nn.Linear(self.layer_2_dim, 10)
 
         def train_dataloader(self):
-            return DataLoader(mnist_train, batch_size=self.hparams.batch_size)
+            return DataLoader(mnist_train, batch_size=self.batch_size)
 
         def configure_optimizers(self):
-            return Adam(self.parameters(), lr=self.hparams.learning_rate)
+            return Adam(self.parameters(), lr=self.learning_rate)
 
         @staticmethod
         def add_model_specific_args(parent_parser):
@@ -130,20 +141,59 @@ modify the network and read those values in the LightningModule
             parser.add_argument('--learning_rate', type=float, default=0.002)
             return parser
 
-Now pass in the params when you init your model
+2. You can also pass in a dict or Namespace, but this obscures the parameters your module is looking
+for. The user would have to search the file to find what is parametrized.
+
+.. code-block:: python
+
+    # using a argparse.Namespace
+    class LitMNIST(LightningModule):
+
+        def __init__(self, hparams, *args, **kwargs):
+            super().__init__()
+            self.hparams = hparams
+
+            self.layer_1 = torch.nn.Linear(28 * 28, self.hparams.layer_1_dim)
+            self.layer_2 = torch.nn.Linear(self.hparams.layer_1_dim, self.hparams.layer_2_dim)
+            self.layer_3 = torch.nn.Linear(self.hparams.layer_2_dim, 10)
+
+        def train_dataloader(self):
+            return DataLoader(mnist_train, batch_size=self.hparams.batch_size)
+
+One way to get around this is to convert a Namespace or dict into key-value pairs using `**`
 
 .. code-block:: python
 
     parser = ArgumentParser()
     parser = LitMNIST.add_model_specific_args(parser)
-    hparams = parser.parse_args()
-    model = LitMNIST(hparams)
+    args = parser.parse_args()
+    dict_args = vars(args)
+    model = LitMNIST(**dict_args)
 
-The line `self.hparams = hparams` is very special. This line assigns your hparams to the LightningModule.
-This does two things:
+Within any LightningModule all the arguments you pass into your `__init__` will be stored in
+the checkpoint so that you know all the values that went into creating this model.
+
+We will also add all of those values to the TensorBoard hparams tab (unless it's an object which
+we won't). We also will store those values into checkpoints for you which you can use to init your
+models.
+
+.. code-block:: python
+
+    class LitMNIST(LightningModule):
+
+        def __init__(self, layer_1_dim, some_other_param):
+            super().__init__()
+            self.layer_1_dim = layer_1_dim
+            self.some_other_param = some_other_param
+
+            self.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_dim)
+
+            self.layer_2 = torch.nn.Linear(self.layer_1_dim, self.some_other_param)
+            self.layer_3 = torch.nn.Linear(self.some_other_param, 10)
+
+
+    model = LitMNIST(10, 20)
 
-1.  It adds them automatically to TensorBoard logs under the hparams tab.
-2.  Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
 
 Trainer args
 ^^^^^^^^^^^^
@@ -171,13 +221,13 @@ polluting the main.py file, the LightningModule lets you define arguments for ea
 
     class LitMNIST(LightningModule):
 
-        def __init__(self, hparams):
+        def __init__(self, layer_1_dim, **kwargs):
             super().__init__()
-            self.layer_1 = torch.nn.Linear(28 * 28, hparams.layer_1_dim)
+            self.layer_1 = torch.nn.Linear(28 * 28, layer_1_dim)
 
         @staticmethod
         def add_model_specific_args(parent_parser):
-            parser = ArgumentParser(parents=[parent_parser])
+            parser = ArgumentParser(parents=[parent_parser], add_help=False)
             parser.add_argument('--layer_1_dim', type=int, default=128)
             return parser
 
@@ -185,13 +235,13 @@ polluting the main.py file, the LightningModule lets you define arguments for ea
 
     class GoodGAN(LightningModule):
 
-        def __init__(self, hparams):
+        def __init__(self, encoder_layers, **kwargs):
             super().__init__()
-            self.encoder = Encoder(layers=hparams.encoder_layers)
+            self.encoder = Encoder(layers=encoder_layers)
 
         @staticmethod
         def add_model_specific_args(parent_parser):
-            parser = ArgumentParser(parents=[parent_parser])
+            parser = ArgumentParser(parents=[parent_parser], add_help=False)
             parser.add_argument('--encoder_layers', type=int, default=12)
             return parser
 
@@ -201,14 +251,14 @@ Now we can allow each model to inject the arguments it needs in the ``main.py``
 .. code-block:: python
 
     def main(args):
+        dict_args = vars(args)
 
         # pick model
         if args.model_name == 'gan':
-            model = GoodGAN(hparams=args)
+            model = GoodGAN(**dict_args)
         elif args.model_name == 'mnist':
-            model = LitMNIST(hparams=args)
+            model = LitMNIST(**dict_args)
 
-        model = LitMNIST(hparams=args)
         trainer = Trainer.from_argparse_args(args)
         trainer.fit(model)