diff --git a/pytorch_lightning/callbacks/base.py b/pytorch_lightning/callbacks/base.py
index 82a0e6b0436a6..b804241aa1b7e 100644
--- a/pytorch_lightning/callbacks/base.py
+++ b/pytorch_lightning/callbacks/base.py
@@ -14,11 +14,11 @@ class Callback(abc.ABC):
     Abstract base class used to build new callbacks.
     """
 
-    def setup(self, trainer, stage: str):
+    def setup(self, trainer, pl_module, stage: str):
         """Called when fit or test begins"""
         pass
 
-    def teardown(self, trainer, stage: str):
+    def teardown(self, trainer, pl_module, stage: str):
         """Called when fit or test ends"""
         pass
 
@@ -30,11 +30,11 @@ def on_init_end(self, trainer):
         """Called when the trainer initialization ends, model has not yet been set."""
         pass
 
-    def on_fit_start(self, trainer):
+    def on_fit_start(self, trainer, pl_module):
         """Called when fit begins"""
         pass
 
-    def on_fit_end(self, trainer):
+    def on_fit_end(self, trainer, pl_module):
         """Called when fit ends"""
         pass
 
@@ -46,11 +46,11 @@ def on_sanity_check_end(self, trainer, pl_module):
         """Called when the validation sanity check ends."""
         pass
 
-    def on_train_batch_start(self, trainer, pl_module):
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the validation batch begins."""
         pass
 
-    def on_train_batch_end(self, trainer, pl_module):
+    def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the validation batch ends."""
         pass
 
@@ -90,19 +90,19 @@ def on_batch_start(self, trainer, pl_module):
         """Called when the training batch begins."""
         pass
 
-    def on_validation_batch_start(self, trainer, pl_module):
+    def on_validation_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the validation batch begins."""
         pass
 
-    def on_validation_batch_end(self, trainer, pl_module):
+    def on_validation_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the validation batch ends."""
         pass
 
-    def on_test_batch_start(self, trainer, pl_module):
+    def on_test_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the test batch begins."""
         pass
 
-    def on_test_batch_end(self, trainer, pl_module):
+    def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """Called when the test batch ends."""
         pass
 
@@ -118,6 +118,14 @@ def on_train_end(self, trainer, pl_module):
         """Called when the train ends."""
         pass
 
+    def on_pretrain_routine_start(self, trainer, pl_module):
+        """Called when the pretrain routine begins."""
+        pass
+
+    def on_pretrain_routine_end(self, trainer, pl_module):
+        """Called when the pretrain routine ends."""
+        pass
+
     def on_validation_start(self, trainer, pl_module):
         """Called when the validation loop begins."""
         pass
diff --git a/pytorch_lightning/callbacks/lr_logger.py b/pytorch_lightning/callbacks/lr_logger.py
index 7ec73b8c88811..27fbb81800241 100755
--- a/pytorch_lightning/callbacks/lr_logger.py
+++ b/pytorch_lightning/callbacks/lr_logger.py
@@ -64,7 +64,7 @@ def on_train_start(self, trainer, pl_module):
         # Initialize for storing values
         self.lrs = {name: [] for name in names}
 
-    def on_train_batch_start(self, trainer, pl_module):
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         latest_stat = self._extract_lr(trainer, 'step')
         if trainer.logger and latest_stat:
             trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
diff --git a/pytorch_lightning/callbacks/progress.py b/pytorch_lightning/callbacks/progress.py
index 4ab990f74724e..c3cd9137c9ed7 100644
--- a/pytorch_lightning/callbacks/progress.py
+++ b/pytorch_lightning/callbacks/progress.py
@@ -138,19 +138,19 @@ def on_train_start(self, trainer, pl_module):
     def on_epoch_start(self, trainer, pl_module):
         self._train_batch_idx = 0
 
-    def on_train_batch_end(self, trainer, pl_module):
+    def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         self._train_batch_idx += 1
 
     def on_validation_start(self, trainer, pl_module):
         self._val_batch_idx = 0
 
-    def on_validation_batch_end(self, trainer, pl_module):
+    def on_validation_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         self._val_batch_idx += 1
 
     def on_test_start(self, trainer, pl_module):
         self._test_batch_idx = 0
 
-    def on_test_batch_end(self, trainer, pl_module):
+    def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         self._test_batch_idx += 1
 
 
@@ -318,8 +318,8 @@ def on_epoch_start(self, trainer, pl_module):
             self.main_progress_bar.reset(convert_inf(total_batches))
         self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch + 1}')
 
-    def on_train_batch_end(self, trainer, pl_module):
-        super().on_train_batch_end(trainer, pl_module)
+    def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+        super().on_train_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
         if self.is_enabled and self.train_batch_idx % self.refresh_rate == 0:
             self.main_progress_bar.update(self.refresh_rate)
             self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
@@ -329,8 +329,8 @@ def on_validation_start(self, trainer, pl_module):
         self.val_progress_bar = self.init_validation_tqdm()
         self.val_progress_bar.total = convert_inf(self.total_val_batches)
 
-    def on_validation_batch_end(self, trainer, pl_module):
-        super().on_validation_batch_end(trainer, pl_module)
+    def on_validation_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+        super().on_validation_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
         if self.is_enabled and self.val_batch_idx % self.refresh_rate == 0:
             self.val_progress_bar.update(self.refresh_rate)
             self.main_progress_bar.update(self.refresh_rate)
@@ -349,8 +349,8 @@ def on_test_start(self, trainer, pl_module):
         self.test_progress_bar = self.init_test_tqdm()
         self.test_progress_bar.total = convert_inf(self.total_test_batches)
 
-    def on_test_batch_end(self, trainer, pl_module):
-        super().on_test_batch_end(trainer, pl_module)
+    def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+        super().on_test_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
         if self.is_enabled and self.test_batch_idx % self.refresh_rate == 0:
             self.test_progress_bar.update(self.refresh_rate)
 
diff --git a/pytorch_lightning/core/hooks.py b/pytorch_lightning/core/hooks.py
index 1218dcbe6760f..922f500ce8c25 100644
--- a/pytorch_lightning/core/hooks.py
+++ b/pytorch_lightning/core/hooks.py
@@ -77,7 +77,31 @@ def on_train_end(self) -> None:
         """
         # do something at the end of training
 
-    def on_train_batch_start(self, batch: Any) -> None:
+    def on_pretrain_routine_start(self) -> None:
+        """
+        Called at the beginning of the pretrain routine (between fit and train start).
+
+        - fit
+        - pretrain_routine start
+        - pretrain_routine end
+        - training_start
+
+        """
+        # do something at the start of the pretrain routine
+
+    def on_pretrain_routine_end(self) -> None:
+        """
+        Called at the end of the pretrain routine (between fit and train start).
+
+        - fit
+        - pretrain_routine start
+        - pretrain_routine end
+        - training_start
+
+        """
+        # do something at the end of the pretrain routine
+
+    def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
         """
         Called in the training loop before anything happens for that batch.
 
@@ -85,12 +109,19 @@ def on_train_batch_start(self, batch: Any) -> None:
 
         Args:
             batch: The batched data as it is returned by the training DataLoader.
+            batch_idx: the index of the batch
+            dataloader_idx: the index of the dataloader
         """
         # do something when the batch starts
 
-    def on_train_batch_end(self) -> None:
+    def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
         """
         Called in the training loop after the batch.
+
+        Args:
+            batch: The batched data as it is returned by the training DataLoader.
+            batch_idx: the index of the batch
+            dataloader_idx: the index of the dataloader
         """
         # do something when the batch end
 
diff --git a/pytorch_lightning/trainer/callback_hook.py b/pytorch_lightning/trainer/callback_hook.py
index 7c62743455317..b10c86af451b0 100644
--- a/pytorch_lightning/trainer/callback_hook.py
+++ b/pytorch_lightning/trainer/callback_hook.py
@@ -14,12 +14,12 @@ class TrainerCallbackHookMixin(ABC):
     def setup(self, stage: str):
         """Called in the beginning of fit and test"""
         for callback in self.callbacks:
-            callback.setup(self, stage)
+            callback.setup(self, self.get_model(), stage)
 
     def teardown(self, stage: str):
         """Called at the end of fit and test"""
         for callback in self.callbacks:
-            callback.teardown(self, stage)
+            callback.teardown(self, self.get_model(), stage)
 
     def on_init_start(self):
         """Called when the trainer initialization begins, model has not yet been set."""
@@ -31,15 +31,15 @@ def on_init_end(self):
         for callback in self.callbacks:
             callback.on_init_end(self)
 
-    def on_fit_start(self):
+    def on_fit_start(self, model):
         """Called when the trainer initialization begins, model has not yet been set."""
         for callback in self.callbacks:
-            callback.on_fit_start(self)
+            callback.on_fit_start(self, model)
 
     def on_fit_end(self):
         """Called when the trainer initialization begins, model has not yet been set."""
         for callback in self.callbacks:
-            callback.on_fit_end(self)
+            callback.on_fit_end(self, self.get_model())
 
     def on_sanity_check_start(self):
         """Called when the validation sanity check starts."""
@@ -101,6 +101,16 @@ def on_train_end(self):
         for callback in self.callbacks:
             callback.on_train_end(self, self.get_model())
 
+    def on_pretrain_routine_start(self, model):
+        """Called when the train begins."""
+        for callback in self.callbacks:
+            callback.on_pretrain_routine_start(self, model)
+
+    def on_pretrain_routine_end(self, model):
+        """Called when the train ends."""
+        for callback in self.callbacks:
+            callback.on_pretrain_routine_end(self, model)
+
     def on_batch_start(self):
         """Called when the training batch begins."""
         for callback in self.callbacks:
@@ -111,35 +121,35 @@ def on_batch_end(self):
         for callback in self.callbacks:
             callback.on_batch_end(self, self.get_model())
 
-    def on_train_batch_start(self):
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
         """Called when the training batch begins."""
         for callback in self.callbacks:
-            callback.on_train_batch_start(self, self.get_model())
+            callback.on_train_batch_start(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
-    def on_train_batch_end(self):
+    def on_train_batch_end(self, batch, batch_idx, dataloader_idx):
         """Called when the training batch ends."""
         for callback in self.callbacks:
-            callback.on_train_batch_end(self, self.get_model())
+            callback.on_train_batch_end(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
-    def on_validation_batch_start(self):
+    def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
         """Called when the validation batch begins."""
         for callback in self.callbacks:
-            callback.on_validation_batch_start(self, self.get_model())
+            callback.on_validation_batch_start(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
-    def on_validation_batch_end(self):
+    def on_validation_batch_end(self, batch, batch_idx, dataloader_idx):
         """Called when the validation batch ends."""
         for callback in self.callbacks:
-            callback.on_validation_batch_end(self, self.get_model())
+            callback.on_validation_batch_end(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
-    def on_test_batch_start(self):
+    def on_test_batch_start(self, batch, batch_idx, dataloader_idx):
         """Called when the test batch begins."""
         for callback in self.callbacks:
-            callback.on_test_batch_start(self, self.get_model())
+            callback.on_test_batch_start(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
-    def on_test_batch_end(self):
+    def on_test_batch_end(self, batch, batch_idx, dataloader_idx):
         """Called when the test batch ends."""
         for callback in self.callbacks:
-            callback.on_test_batch_end(self, self.get_model())
+            callback.on_test_batch_end(self, self.get_model(), batch, batch_idx, dataloader_idx)
 
     def on_validation_start(self):
         """Called when the validation loop begins."""
diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
index add9bb24c672a..75d09db2cec65 100644
--- a/pytorch_lightning/trainer/evaluation_loop.py
+++ b/pytorch_lightning/trainer/evaluation_loop.py
@@ -312,9 +312,9 @@ def _evaluate(
 
                 # callbacks
                 if test_mode:
-                    self.on_test_batch_start()
+                    self.on_test_batch_start(batch, batch_idx, dataloader_idx)
                 else:
-                    self.on_validation_batch_start()
+                    self.on_validation_batch_start(batch, batch_idx, dataloader_idx)
 
                 # -----------------
                 # RUN EVALUATION STEP
@@ -336,13 +336,13 @@ def _evaluate(
                         model_ref = self.get_model()
                         with self.profiler.profile('test_step_end'):
                             output = model_ref.test_step_end(output)
-                    self.on_test_batch_end()
+                    self.on_test_batch_end(batch, batch_idx, dataloader_idx)
                 else:
                     if self.is_overridden('validation_step_end'):
                         model_ref = self.get_model()
                         with self.profiler.profile('validation_step_end'):
                             output = model_ref.validation_step_end(output)
-                    self.on_validation_batch_end()
+                    self.on_validation_batch_end(batch, batch_idx, dataloader_idx)
 
                 # track outputs for collation
                 if output is not None:
diff --git a/pytorch_lightning/trainer/lr_finder.py b/pytorch_lightning/trainer/lr_finder.py
index 23ad702956e84..e028bf60eda7e 100755
--- a/pytorch_lightning/trainer/lr_finder.py
+++ b/pytorch_lightning/trainer/lr_finder.py
@@ -382,7 +382,7 @@ def on_batch_start(self, trainer, pl_module):
 
         self.lrs.append(trainer.lr_schedulers[0]['scheduler'].lr[0])
 
-    def on_train_batch_end(self, trainer, pl_module):
+    def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         """ Called when the training batch ends, logs the calculated loss """
         if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
             return
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 4b342328df297..0d596d7dd611c 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -958,7 +958,7 @@ def fit(
         self.config_validator.verify_loop_configurations(model)
 
         # callbacks
-        self.on_fit_start()
+        self.on_fit_start(model)
         if self.is_function_implemented('on_fit_start', model):
             model.on_fit_start()
 
@@ -1055,13 +1055,12 @@ def fit(
             self.accelerator_backend.setup(model)
             results = self.accelerator_backend.train(model)
 
-        # callbacks
+        # on fit end callback
         self.on_fit_end()
-
-        # model hooks
         if self.is_function_implemented('on_fit_end'):
             model.on_fit_end()
 
+        # teardown callback
         self.teardown('fit')
         if self.is_function_implemented('teardown'):
             model.teardown('fit')
@@ -1156,6 +1155,11 @@ def run_pretrain_routine(self, model: LightningModule):
         # register auto-resubmit when on SLURM
         self.register_slurm_signal_handlers()
 
+        # on pretrain routine start
+        self.on_pretrain_routine_start(ref_model)
+        if self.is_function_implemented('on_pretrain_routine_start'):
+            ref_model.on_pretrain_routine_start()
+
         # print model summary
         if self.is_global_zero and self.weights_summary is not None and not self.testing:
             if self.weights_summary in ModelSummary.MODES:
@@ -1198,6 +1202,11 @@ def run_pretrain_routine(self, model: LightningModule):
             with torch.cuda.device(f'cuda:{self.root_gpu}'):
                 torch.cuda.empty_cache()
 
+        # on pretrain routine end
+        self.on_pretrain_routine_end(ref_model)
+        if self.is_function_implemented('on_pretrain_routine_end'):
+            ref_model.on_pretrain_routine_end()
+
         # CORE TRAINING LOOP
         self.train()
 
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 993e8ccd53fd0..e1e7737247106 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -703,11 +703,12 @@ def run_training_batch(self, batch, batch_idx):
                     return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
 
         with self.profiler.profile('on_train_batch_start'):
-            # callbacks
-            self.on_train_batch_start()
+            # forward support for multiple loaders
+            dataloader_idx = 0
+            self.on_train_batch_start(batch, batch_idx, dataloader_idx)
             # hooks
             if self.is_function_implemented('on_train_batch_start'):
-                response = self.get_model().on_train_batch_start(batch)
+                response = self.get_model().on_train_batch_start(batch, batch_idx, dataloader_idx)
                 if response == -1:
                     return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
 
@@ -798,11 +799,12 @@ def run_training_batch(self, batch, batch_idx):
                 self.get_model().on_batch_end()
 
         with self.profiler.profile('on_train_batch_end'):
-            # callbacks
-            self.on_train_batch_end()
+            # forward support for multiple loaders
+            dataloader_idx = 0
+            self.on_train_batch_end(batch, batch_idx, dataloader_idx)
             # model hooks
             if self.is_function_implemented('on_train_batch_end'):
-                self.get_model().on_train_batch_end()
+                self.get_model().on_train_batch_end(batch, batch_idx, dataloader_idx)
 
         # collapse all metrics into one dict
         batch_log_metrics = {k: v for d in batch_log_metrics for k, v in d.items()}
diff --git a/tests/callbacks/test_callbacks.py b/tests/callbacks/test_callbacks.py
index 83de82c71de67..90a3d9117c376 100644
--- a/tests/callbacks/test_callbacks.py
+++ b/tests/callbacks/test_callbacks.py
@@ -36,16 +36,18 @@ def __init__(self):
             self.on_test_batch_end_called = False
             self.on_train_start_called = False
             self.on_train_end_called = False
+            self.on_pretrain_routine_start_called = False
+            self.on_pretrain_routine_end_called = False
             self.on_validation_start_called = False
             self.on_validation_end_called = False
             self.on_test_start_called = False
             self.on_test_end_called = False
 
-        def setup(self, trainer, stage: str):
+        def setup(self, trainer, pl_module, stage: str):
             assert isinstance(trainer, Trainer)
             self.setup_called = True
 
-        def teardown(self, trainer, step: str):
+        def teardown(self, trainer, pl_module, step: str):
             assert isinstance(trainer, Trainer)
             self.teardown_called = True
 
@@ -57,11 +59,11 @@ def on_init_end(self, trainer):
             assert isinstance(trainer, Trainer)
             self.on_init_end_called = True
 
-        def on_fit_start(self, trainer):
+        def on_fit_start(self, trainer, pl_module):
             assert isinstance(trainer, Trainer)
             self.on_fit_start_called = True
 
-        def on_fit_end(self, trainer):
+        def on_fit_end(self, trainer, pl_module):
             assert isinstance(trainer, Trainer)
             self.on_fit_end_called = True
 
@@ -89,27 +91,27 @@ def on_batch_end(self, trainer, pl_module):
             _check_args(trainer, pl_module)
             self.on_batch_end_called = True
 
-        def on_train_batch_start(self, trainer, pl_module):
+        def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_train_batch_start_called = True
 
-        def on_train_batch_end(self, trainer, pl_module):
+        def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_train_batch_end_called = True
 
-        def on_validation_batch_start(self, trainer, pl_module):
+        def on_validation_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_validation_batch_start_called = True
 
-        def on_validation_batch_end(self, trainer, pl_module):
+        def on_validation_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_validation_batch_end_called = True
 
-        def on_test_batch_start(self, trainer, pl_module):
+        def on_test_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_test_batch_start_called = True
 
-        def on_test_batch_end(self, trainer, pl_module):
+        def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             _check_args(trainer, pl_module)
             self.on_test_batch_end_called = True
 
@@ -121,6 +123,14 @@ def on_train_end(self, trainer, pl_module):
             _check_args(trainer, pl_module)
             self.on_train_end_called = True
 
+        def on_pretrain_routine_start(self, trainer, pl_module):
+            _check_args(trainer, pl_module)
+            self.on_pretrain_routine_start_called = True
+
+        def on_pretrain_routine_end(self, trainer, pl_module):
+            _check_args(trainer, pl_module)
+            self.on_pretrain_routine_end_called = True
+
         def on_validation_start(self, trainer, pl_module):
             _check_args(trainer, pl_module)
             self.on_validation_start_called = True
@@ -168,6 +178,8 @@ def on_test_end(self, trainer, pl_module):
     assert not test_callback.on_test_batch_end_called
     assert not test_callback.on_train_start_called
     assert not test_callback.on_train_end_called
+    assert not test_callback.on_pretrain_routine_start_called
+    assert not test_callback.on_pretrain_routine_end_called
     assert not test_callback.on_validation_start_called
     assert not test_callback.on_validation_end_called
     assert not test_callback.on_test_start_called
@@ -197,6 +209,8 @@ def on_test_end(self, trainer, pl_module):
     assert not test_callback.on_test_batch_end_called
     assert not test_callback.on_train_start_called
     assert not test_callback.on_train_end_called
+    assert not test_callback.on_pretrain_routine_start_called
+    assert not test_callback.on_pretrain_routine_end_called
     assert not test_callback.on_validation_start_called
     assert not test_callback.on_validation_end_called
     assert not test_callback.on_test_start_called
@@ -222,6 +236,8 @@ def on_test_end(self, trainer, pl_module):
     assert test_callback.on_validation_batch_end_called
     assert test_callback.on_train_start_called
     assert test_callback.on_train_end_called
+    assert test_callback.on_pretrain_routine_start_called
+    assert test_callback.on_pretrain_routine_end_called
     assert test_callback.on_validation_start_called
     assert test_callback.on_validation_end_called
     assert not test_callback.on_test_batch_start_called
diff --git a/tests/callbacks/test_progress_bar.py b/tests/callbacks/test_progress_bar.py
index 779077c437585..713bdf3c3c2c4 100644
--- a/tests/callbacks/test_progress_bar.py
+++ b/tests/callbacks/test_progress_bar.py
@@ -153,25 +153,25 @@ class CurrentProgressBar(ProgressBar):
         val_batches_seen = 0
         test_batches_seen = 0
 
-        def on_train_batch_start(self, trainer, pl_module):
-            super().on_train_batch_start(trainer, pl_module)
+        def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+            super().on_train_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx)
             assert self.train_batch_idx == trainer.batch_idx
 
-        def on_train_batch_end(self, trainer, pl_module):
-            super().on_train_batch_end(trainer, pl_module)
+        def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+            super().on_train_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
             assert self.train_batch_idx == trainer.batch_idx + 1
             if not self.is_disabled and self.train_batch_idx % self.refresh_rate == 0:
                 assert self.main_progress_bar.n == self.train_batch_idx
             self.train_batches_seen += 1
 
-        def on_validation_batch_end(self, trainer, pl_module):
-            super().on_validation_batch_end(trainer, pl_module)
+        def on_validation_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+            super().on_validation_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
             if not self.is_disabled and self.val_batch_idx % self.refresh_rate == 0:
                 assert self.val_progress_bar.n == self.val_batch_idx
             self.val_batches_seen += 1
 
-        def on_test_batch_end(self, trainer, pl_module):
-            super().on_test_batch_end(trainer, pl_module)
+        def on_test_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+            super().on_test_batch_end(trainer, pl_module, batch, batch_idx, dataloader_idx)
             if not self.is_disabled and self.test_batch_idx % self.refresh_rate == 0:
                 assert self.test_progress_bar.n == self.test_batch_idx
             self.test_batches_seen += 1
diff --git a/tests/loggers/test_all.py b/tests/loggers/test_all.py
index 7978aa8e41ace..90d0f3a89ee9a 100644
--- a/tests/loggers/test_all.py
+++ b/tests/loggers/test_all.py
@@ -5,13 +5,11 @@
 import platform
 from unittest import mock
 
-import cloudpickle
 import pytest
 
 import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer, Callback
 from pytorch_lightning.loggers import (
-    CSVLogger,
     TensorBoardLogger,
     MLFlowLogger,
     NeptuneLogger,
@@ -36,7 +34,6 @@ def _get_logger_args(logger_class, save_dir):
 
 @pytest.mark.parametrize("logger_class", [
     TensorBoardLogger,
-    CSVLogger,
     CometLogger,
     MLFlowLogger,
     NeptuneLogger,
@@ -88,7 +85,6 @@ def log_metrics(self, metrics, step):
 
 
 @pytest.mark.parametrize("logger_class", [
-    CSVLogger,
     TensorBoardLogger,
     CometLogger,
     MLFlowLogger,
@@ -152,7 +148,6 @@ def name(self):
 
 @pytest.mark.parametrize("logger_class", [
     TensorBoardLogger,
-    CSVLogger,
     CometLogger,
     MLFlowLogger,
     NeptuneLogger,
@@ -175,7 +170,6 @@ def test_loggers_pickle(tmpdir, monkeypatch, logger_class):
 
     # test pickling loggers
     pickle.dumps(logger)
-    cloudpickle.dumps(logger)
 
     trainer = Trainer(
         max_epochs=1,
@@ -220,7 +214,7 @@ class RankZeroLoggerCheck(Callback):
     # this class has to be defined outside the test function, otherwise we get pickle error
     # due to the way ddp process is launched
 
-    def on_train_batch_start(self, trainer, pl_module):
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
         is_dummy = isinstance(trainer.logger.experiment, DummyExperiment)
         if trainer.is_global_zero:
             assert not is_dummy
@@ -232,7 +226,6 @@ def on_train_batch_start(self, trainer, pl_module):
 @pytest.mark.skipif(platform.system() == "Windows", reason="Distributed training is not supported on Windows")
 @pytest.mark.parametrize("logger_class", [
     TensorBoardLogger,
-    # CSVLogger,  # todo
     CometLogger,
     MLFlowLogger,
     NeptuneLogger,
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index d6641c2f7ab24..2444d9905f6a1 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -368,7 +368,7 @@ def _new_model():
         def increment_epoch(self):
             self.num_epochs_seen += 1
 
-        def increment_batch(self, _):
+        def increment_batch(self, batch, batch_idx, dataloader_idx):
             self.num_batches_seen += 1
 
         def increment_on_load_checkpoint(self, _):
@@ -691,7 +691,7 @@ class InterruptCallback(Callback):
         def __init__(self):
             super().__init__()
 
-        def on_train_batch_start(self, trainer, pl_module):
+        def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
             raise KeyboardInterrupt
 
     class HandleInterruptCallback(Callback):
@@ -988,17 +988,3 @@ def setup(self, stage):
     trainer.test(ckpt_path=None)
     assert trainer.stage == 'test'
     assert trainer.get_model().stage == 'test'
-
-
-def test_trainer_ddp_spawn_none_checkpoint(tmpdir):
-    model = EvalModelTemplate()
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=1,
-        checkpoint_callback=None,
-        distributed_backend="ddp_spawn"
-    )
-    assert trainer.checkpoint_callback is None
-    result = trainer.fit(model)
-    assert trainer.checkpoint_callback is None
-    assert result == 1
diff --git a/tests/utilities/test_dtype_device_mixin.py b/tests/utilities/test_dtype_device_mixin.py
index 08f808bda9ceb..ac6e4923a72ff 100644
--- a/tests/utilities/test_dtype_device_mixin.py
+++ b/tests/utilities/test_dtype_device_mixin.py
@@ -27,7 +27,7 @@ def __init__(self, *args, **kwargs):
 
 class DeviceAssertCallback(Callback):
 
-    def on_train_batch_start(self, trainer, model):
+    def on_train_batch_start(self, trainer, model, batch, batch_idx, dataloader_idx):
         rank = trainer.local_rank
         assert isinstance(model, TopModule)
         # index = None also means first device