From ca4b20bd949d0434e6558ae5a9bdd6747e86dec4 Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Sat, 16 Jan 2021 01:34:23 +0530
Subject: [PATCH 1/7] fix and update tests

---
 .../callbacks/model_checkpoint.py             |   4 +-
 pytorch_lightning/core/hooks.py               |   4 +-
 pytorch_lightning/loggers/mlflow.py           |   6 +-
 pytorch_lightning/trainer/properties.py       |  16 +--
 tests/trainer/properties/log_dir.py           | 126 ++++++++++--------
 5 files changed, 83 insertions(+), 73 deletions(-)

diff --git a/pytorch_lightning/callbacks/model_checkpoint.py b/pytorch_lightning/callbacks/model_checkpoint.py
index e5c960b3c002b..5a4eba2dc996f 100644
--- a/pytorch_lightning/callbacks/model_checkpoint.py
+++ b/pytorch_lightning/callbacks/model_checkpoint.py
@@ -193,7 +193,7 @@ def on_pretrain_routine_start(self, trainer, pl_module):
         """
         When pretrain routine starts we build the ckpt dir on the fly
         """
-        self.__resolve_ckpt_dir(trainer, pl_module)
+        self.__resolve_ckpt_dir(trainer)
         self.save_function = trainer.save_checkpoint
 
     def on_validation_end(self, trainer, pl_module):
@@ -447,7 +447,7 @@ def format_checkpoint_name(
         ckpt_name = f"{filename}{self.FILE_EXTENSION}"
         return os.path.join(self.dirpath, ckpt_name) if self.dirpath else ckpt_name
 
-    def __resolve_ckpt_dir(self, trainer, pl_module):
+    def __resolve_ckpt_dir(self, trainer):
         """
         Determines model checkpoint save directory at runtime. References attributes from the
         trainer's logger to determine where to save checkpoints.
diff --git a/pytorch_lightning/core/hooks.py b/pytorch_lightning/core/hooks.py
index a87ebbeb47199..a1890fc39980a 100644
--- a/pytorch_lightning/core/hooks.py
+++ b/pytorch_lightning/core/hooks.py
@@ -283,8 +283,8 @@ def on_after_backward(self) -> None:
             def on_after_backward(self):
                 # example to inspect gradient information in tensorboard
                 if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
-                    params = self.state_dict()
-                    for k, v in params.items():
+                    params = self.named_parameters()
+                    for k, v in params:
                         self.logger.experiment.add_histogram(
                             tag=k, values=v.grad, global_step=self.trainer.global_step
                         )
diff --git a/pytorch_lightning/loggers/mlflow.py b/pytorch_lightning/loggers/mlflow.py
index 4987d050c925d..fa7dec6b63b0f 100644
--- a/pytorch_lightning/loggers/mlflow.py
+++ b/pytorch_lightning/loggers/mlflow.py
@@ -21,11 +21,9 @@
 from time import time
 from typing import Any, Dict, Optional, Union
 
-
 from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
-from pytorch_lightning.utilities import rank_zero_only, rank_zero_warn, _module_available
-
+from pytorch_lightning.utilities import _module_available, rank_zero_only, rank_zero_warn
 
 LOCAL_FILE_URI_PREFIX = "file:"
 
@@ -190,7 +188,7 @@ def save_dir(self) -> Optional[str]:
 
         Return:
             Local path to the root experiment directory if the tracking uri is local.
-            Otherwhise returns `None`.
+            Otherwhise returns ``None``.
         """
         if self._tracking_uri.startswith(LOCAL_FILE_URI_PREFIX):
             return self._tracking_uri.lstrip(LOCAL_FILE_URI_PREFIX)
diff --git a/pytorch_lightning/trainer/properties.py b/pytorch_lightning/trainer/properties.py
index 3fa2af79e5530..7aa0e7371c2ad 100644
--- a/pytorch_lightning/trainer/properties.py
+++ b/pytorch_lightning/trainer/properties.py
@@ -11,10 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from abc import ABC
-from argparse import ArgumentParser, Namespace
 import inspect
 import os
+from abc import ABC
+from argparse import ArgumentParser, Namespace
 from typing import cast, List, Optional, Type, TypeVar, Union
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
@@ -63,16 +63,10 @@ class TrainerProperties(ABC):
 
     @property
     def log_dir(self):
-        if self.checkpoint_callback is not None:
-            dirpath = self.checkpoint_callback.dirpath
-            dirpath = os.path.split(dirpath)[0]
-        elif self.logger is not None:
-            if isinstance(self.logger, TensorBoardLogger):
-                dirpath = self.logger.log_dir
-            else:
-                dirpath = self.logger.save_dir
+        if self.logger is None:
+            dirpath = self.default_root_dir
         else:
-            dirpath = self._default_root_dir
+            dirpath = getattr(self.logger, 'log_dir' if isinstance(self.logger, TensorBoardLogger) else 'save_dir')
 
         if self.accelerator_backend is not None:
             dirpath = self.accelerator_backend.broadcast(dirpath)
diff --git a/tests/trainer/properties/log_dir.py b/tests/trainer/properties/log_dir.py
index 021bb04a7c917..f59cb358a253a 100644
--- a/tests/trainer/properties/log_dir.py
+++ b/tests/trainer/properties/log_dir.py
@@ -12,114 +12,132 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-import torch
+
 import pytest
-from tests.base.boring_model import BoringModel, RandomDataset
+import torch
+
 from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.utilities import APEX_AVAILABLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from tests.base.boring_model import BoringModel, RandomDataset
+
+
+class TestModel(BoringModel):
+    def __init__(self, expected_log_dir):
+        super().__init__()
+        self.expected_log_dir = expected_log_dir
+
+    def training_step(self, *args, **kwargs):
+        assert self.trainer.log_dir == self.expected_log_dir
+        return super().training_step(*args, **kwargs)
 
 
 def test_logdir(tmpdir):
     """
     Tests that the path is correct when checkpoint and loggers are used
     """
-    class TestModel(BoringModel):
-        def training_step(self, batch, batch_idx):
-            output = self.layer(batch)
-            loss = self.loss(batch, output)
+    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')
 
-            expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
-            assert self.trainer.log_dir == expected
-            return {"loss": loss}
+    model = TestModel(expected)
 
-    model = TestModel()
-
-    limit_train_batches = 2
     trainer = Trainer(
         default_root_dir=tmpdir,
-        limit_train_batches=limit_train_batches,
-        limit_val_batches=2,
-        max_epochs=1,
+        max_steps=2,
     )
 
+    assert trainer.log_dir == expected
     trainer.fit(model)
+    assert trainer.log_dir == expected
 
 
 def test_logdir_no_checkpoint_cb(tmpdir):
     """
     Tests that the path is correct with no checkpoint
     """
-    class TestModel(BoringModel):
-        def training_step(self, batch, batch_idx):
-            output = self.layer(batch)
-            loss = self.loss(batch, output)
-            expected = os.path.join(self.trainer.default_root_dir, 'lightning_logs', 'version_0')
-            assert self.trainer.log_dir == expected
-            return {"loss": loss}
-
-    model = TestModel()
+    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')
+    model = TestModel(expected)
 
-    limit_train_batches = 2
     trainer = Trainer(
         default_root_dir=tmpdir,
-        limit_train_batches=limit_train_batches,
-        limit_val_batches=2,
-        max_epochs=1,
+        max_steps=2,
         checkpoint_callback=False
     )
 
+    assert trainer.log_dir == expected
     trainer.fit(model)
+    assert trainer.log_dir == expected
 
 
 def test_logdir_no_logger(tmpdir):
     """
     Tests that the path is correct even when there is no logger
     """
-    class TestModel(BoringModel):
-        def training_step(self, batch, batch_idx):
-            output = self.layer(batch)
-            loss = self.loss(batch, output)
-            expected = os.path.join(self.trainer.default_root_dir)
-            assert self.trainer.log_dir == expected
-            return {"loss": loss}
+    expected = os.path.join(tmpdir)
+    model = TestModel(expected)
 
-    model = TestModel()
-
-    limit_train_batches = 2
     trainer = Trainer(
         default_root_dir=tmpdir,
-        limit_train_batches=limit_train_batches,
-        limit_val_batches=2,
-        max_epochs=1,
+        max_steps=2,
         logger=False,
     )
 
+    assert trainer.log_dir == expected
     trainer.fit(model)
+    assert trainer.log_dir == expected
 
 
 def test_logdir_no_logger_no_checkpoint(tmpdir):
     """
     Tests that the path is correct even when there is no logger
     """
-    class TestModel(BoringModel):
-        def training_step(self, batch, batch_idx):
-            output = self.layer(batch)
-            loss = self.loss(batch, output)
-            expected = os.path.join(self.trainer.default_root_dir)
-            assert self.trainer.log_dir == expected
-            return {"loss": loss}
-
-    model = TestModel()
+    expected = os.path.join(tmpdir)
+    model = TestModel(expected)
 
-    limit_train_batches = 2
     trainer = Trainer(
         default_root_dir=tmpdir,
-        limit_train_batches=limit_train_batches,
-        limit_val_batches=2,
-        max_epochs=1,
+        max_steps=2,
         logger=False,
         checkpoint_callback=False
     )
 
+    assert trainer.log_dir == expected
+    trainer.fit(model)
+    assert trainer.log_dir == expected
+
+
+def test_logdir_custom_callback(tmpdir):
+    """
+    Tests that the path is correct even when there is a custom callback
+    """
+    expected = os.path.join(tmpdir, 'lightning_logs', 'version_0')
+    model = TestModel(expected)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=2,
+        checkpoint_callback=ModelCheckpoint(dirpath=os.path.join(tmpdir, 'ckpts'))
+    )
+
+    assert trainer.log_dir == expected
+    trainer.fit(model)
+    assert trainer.log_dir == expected
+
+
+def test_logdir_custom_logger(tmpdir):
+    """
+    Tests that the path is correct even when there is a custom logger
+    """
+    expected = os.path.join(tmpdir, 'custom_logs', 'version_0')
+    model = TestModel(expected)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=2,
+        logger=TensorBoardLogger(save_dir=tmpdir, name='custom_logs')
+    )
+
+    assert trainer.log_dir == expected
     trainer.fit(model)
+    assert trainer.log_dir == expected

From df1723689415cf0420a2968ab66b19a0ff18619a Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Sat, 16 Jan 2021 02:00:07 +0530
Subject: [PATCH 2/7] update with ModelCheckpoint

---
 tests/trainer/properties/log_dir.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/trainer/properties/log_dir.py b/tests/trainer/properties/log_dir.py
index f59cb358a253a..2bc5c06955b69 100644
--- a/tests/trainer/properties/log_dir.py
+++ b/tests/trainer/properties/log_dir.py
@@ -45,6 +45,7 @@ def test_logdir(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_steps=2,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
 
     assert trainer.log_dir == expected
@@ -81,6 +82,7 @@ def test_logdir_no_logger(tmpdir):
         default_root_dir=tmpdir,
         max_steps=2,
         logger=False,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
     )
 
     assert trainer.log_dir == expected
@@ -117,7 +119,7 @@ def test_logdir_custom_callback(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_steps=2,
-        checkpoint_callback=ModelCheckpoint(dirpath=os.path.join(tmpdir, 'ckpts'))
+        callbacks=[ModelCheckpoint(dirpath=os.path.join(tmpdir, 'ckpts'))],
     )
 
     assert trainer.log_dir == expected
@@ -135,6 +137,7 @@ def test_logdir_custom_logger(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_steps=2,
+        callbacks=[ModelCheckpoint(dirpath=tmpdir)],
         logger=TensorBoardLogger(save_dir=tmpdir, name='custom_logs')
     )
 

From 80187ff8f1a868c34568e48fb93dd60c0c0c6c9e Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Sat, 16 Jan 2021 02:29:45 +0530
Subject: [PATCH 3/7] chlog

---
 CHANGELOG.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e3c2a33469f3b..f998f40780ac1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `reinit_scheduler_properties` with correct optimizer ([#5519](https://github.com/PyTorchLightning/pytorch-lightning/pull/5519))
 
 
+- Fixed `log_dir` property ([#5537](https://github.com/PyTorchLightning/pytorch-lightning/pull/5537))
+
+
 ## [1.1.4] - 2021-01-12
 
 ### Added

From ca248cd3930156d39de6dce7dc61f3bd9fbe96c4 Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Sun, 17 Jan 2021 02:24:57 +0530
Subject: [PATCH 4/7] wip wandb fix

---
 pytorch_lightning/loggers/wandb.py | 15 ++++++++++++---
 tests/loggers/test_wandb.py        |  8 +++++---
 2 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/pytorch_lightning/loggers/wandb.py b/pytorch_lightning/loggers/wandb.py
index f92c44ab27b7f..407c2d1c10d7e 100644
--- a/pytorch_lightning/loggers/wandb.py
+++ b/pytorch_lightning/loggers/wandb.py
@@ -23,7 +23,8 @@
 import torch.nn as nn
 
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
-from pytorch_lightning.utilities import rank_zero_only, _module_available
+from pytorch_lightning.utilities import _module_available, rank_zero_only
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.warning_utils import WarningCache
 
 _WANDB_AVAILABLE = _module_available("wandb")
@@ -98,6 +99,13 @@ def __init__(
         if wandb is None:
             raise ImportError('You want to use `wandb` logger which is not installed yet,'  # pragma: no-cover
                               ' install it with `pip install wandb`.')
+
+        if offline and log_model:
+            raise MisconfigurationException(
+                f'log_model={log_model} and offline={offline} is an invalid configuration'
+                ' since model checkpoints cannot be uploaded in offline mode.'
+            )
+
         super().__init__()
         self._name = name
         self._save_dir = save_dir
@@ -141,11 +149,12 @@ def experiment(self) -> Run:
             self._experiment = wandb.init(
                 name=self._name, dir=self._save_dir, project=self._project, anonymous=self._anonymous,
                 id=self._id, resume='allow', **self._kwargs) if wandb.run is None else wandb.run
+
             # offset logging step when resuming a run
             self._step_offset = self._experiment.step
+
             # save checkpoints in wandb dir to upload on W&B servers
-            if self._log_model:
-                self._save_dir = self._experiment.dir
+            self._save_dir = self._experiment.dir
         return self._experiment
 
     def watch(self, model: nn.Module, log: str = 'gradients', log_freq: int = 100):
diff --git a/tests/loggers/test_wandb.py b/tests/loggers/test_wandb.py
index a44b19ca39270..9b011849d7a60 100644
--- a/tests/loggers/test_wandb.py
+++ b/tests/loggers/test_wandb.py
@@ -13,13 +13,13 @@
 # limitations under the License.
 import os
 import pickle
-from unittest import mock
-from argparse import ArgumentParser
 import types
+from argparse import ArgumentParser
+from unittest import mock
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import WandbLogger
-from tests.base import EvalModelTemplate, BoringModel
+from tests.base import BoringModel, EvalModelTemplate
 
 
 def get_warnings(recwarn):
@@ -152,6 +152,8 @@ def test_wandb_logger_dirs_creation(wandb, tmpdir):
 
     assert trainer.checkpoint_callback.dirpath == str(tmpdir / 'project' / version / 'checkpoints')
     assert set(os.listdir(trainer.checkpoint_callback.dirpath)) == {'epoch=0-step=9.ckpt'}
+    assert trainer.log_dir == logger.save_dir
+    assert 'output.log' in os.listdir(trainer.log_dir)
 
 
 def test_wandb_sanitize_callable_params(tmpdir):

From 4167fff8ab21f600ab9c3a9fa65c98735be2f911 Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Sun, 17 Jan 2021 03:16:20 +0530
Subject: [PATCH 5/7] all fixed

---
 pytorch_lightning/loggers/mlflow.py |  2 +-
 pytorch_lightning/loggers/wandb.py  |  3 ++-
 tests/loggers/test_comet.py         | 21 ++++-----------------
 tests/loggers/test_mlflow.py        |  5 +++--
 tests/loggers/test_neptune.py       |  4 +++-
 tests/loggers/test_tensorboard.py   |  4 +++-
 tests/loggers/test_wandb.py         | 16 ++++++++++++++--
 7 files changed, 30 insertions(+), 25 deletions(-)

diff --git a/pytorch_lightning/loggers/mlflow.py b/pytorch_lightning/loggers/mlflow.py
index fa7dec6b63b0f..929f070deb865 100644
--- a/pytorch_lightning/loggers/mlflow.py
+++ b/pytorch_lightning/loggers/mlflow.py
@@ -188,7 +188,7 @@ def save_dir(self) -> Optional[str]:
 
         Return:
             Local path to the root experiment directory if the tracking uri is local.
-            Otherwhise returns ``None``.
+            Otherwhise returns `None`.
         """
         if self._tracking_uri.startswith(LOCAL_FILE_URI_PREFIX):
             return self._tracking_uri.lstrip(LOCAL_FILE_URI_PREFIX)
diff --git a/pytorch_lightning/loggers/wandb.py b/pytorch_lightning/loggers/wandb.py
index 407c2d1c10d7e..34121c3652509 100644
--- a/pytorch_lightning/loggers/wandb.py
+++ b/pytorch_lightning/loggers/wandb.py
@@ -154,7 +154,8 @@ def experiment(self) -> Run:
             self._step_offset = self._experiment.step
 
             # save checkpoints in wandb dir to upload on W&B servers
-            self._save_dir = self._experiment.dir
+            if self._save_dir is None:
+                self._save_dir = self._experiment.dir
         return self._experiment
 
     def watch(self, model: nn.Module, log: str = 'gradients', log_freq: int = 100):
diff --git a/tests/loggers/test_comet.py b/tests/loggers/test_comet.py
index fc61829645b6e..b272f98c8925d 100644
--- a/tests/loggers/test_comet.py
+++ b/tests/loggers/test_comet.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-from unittest.mock import patch, DEFAULT
+from unittest.mock import DEFAULT, patch
 
 import pytest
 
@@ -74,7 +74,7 @@ def test_comet_logger_online(comet):
 @patch('pytorch_lightning.loggers.comet.comet_ml')
 def test_comet_logger_no_api_key_given(comet):
     """ Test that CometLogger fails to initialize if both api key and save_dir are missing. """
-    with pytest.raises(MisconfigurationException):
+    with pytest.raises(MisconfigurationException, match='requires either api_key or save_dir'):
         comet.config.get_api_key.return_value = None
         CometLogger(workspace='dummy-test', project_name='general')
 
@@ -89,13 +89,10 @@ def test_comet_logger_experiment_name(comet):
     # Test api_key given
     with patch('pytorch_lightning.loggers.comet.CometExperiment') as comet_experiment:
         logger = CometLogger(api_key=api_key, experiment_name=experiment_name,)
-
         assert logger._experiment is None
 
         _ = logger.experiment
-
         comet_experiment.assert_called_once_with(api_key=api_key, project_name=None)
-
         comet_experiment().set_name.assert_called_once_with(experiment_name)
 
 
@@ -118,13 +115,10 @@ def save_os_environ(*args, **kwargs):
     with patch.dict(os.environ, {"COMET_EXPERIMENT_KEY": experiment_key}):
         with patch('pytorch_lightning.loggers.comet.CometExperiment', side_effect=save_os_environ) as comet_experiment:
             logger = CometLogger(api_key=api_key)
-
             assert logger.version == experiment_key
-
             assert logger._experiment is None
 
             _ = logger.experiment
-
             comet_experiment.assert_called_once_with(api_key=api_key, project_name=None)
 
     assert instantation_environ["COMET_EXPERIMENT_KEY"] == experiment_key
@@ -156,10 +150,12 @@ def test_comet_logger_dirs_creation(comet, comet_experiment, tmpdir, monkeypatch
 
     model = EvalModelTemplate()
     trainer = Trainer(default_root_dir=tmpdir, logger=logger, max_epochs=1, limit_val_batches=3)
+    assert trainer.log_dir == logger.save_dir
     trainer.fit(model)
 
     assert trainer.checkpoint_callback.dirpath == (tmpdir / 'test' / "1" / 'checkpoints')
     assert set(os.listdir(trainer.checkpoint_callback.dirpath)) == {'epoch=0-step=9.ckpt'}
+    assert trainer.log_dir == logger.save_dir
 
 
 @patch('pytorch_lightning.loggers.comet.comet_ml')
@@ -170,11 +166,8 @@ def test_comet_name_default(comet):
 
     with patch('pytorch_lightning.loggers.comet.CometExperiment'):
         logger = CometLogger(api_key=api_key)
-
         assert logger._experiment is None
-
         assert logger.name == "comet-default"
-
         assert logger._experiment is None
 
 
@@ -187,11 +180,8 @@ def test_comet_name_project_name(comet):
 
     with patch('pytorch_lightning.loggers.comet.CometExperiment'):
         logger = CometLogger(api_key=api_key, project_name=project_name)
-
         assert logger._experiment is None
-
         assert logger.name == project_name
-
         assert logger._experiment is None
 
 
@@ -205,14 +195,11 @@ def test_comet_version_without_experiment(comet):
 
     with patch('pytorch_lightning.loggers.comet.CometExperiment'):
         logger = CometLogger(api_key=api_key, experiment_name=experiment_name)
-
         assert logger._experiment is None
 
         first_version = logger.version
         assert first_version is not None
-
         assert logger.version == first_version
-
         assert logger._experiment is None
 
         _ = logger.experiment
diff --git a/tests/loggers/test_mlflow.py b/tests/loggers/test_mlflow.py
index c6072afbb69e2..c0f99220ed00b 100644
--- a/tests/loggers/test_mlflow.py
+++ b/tests/loggers/test_mlflow.py
@@ -13,11 +13,10 @@
 # limitations under the License.
 import importlib.util
 import os
-
 from unittest import mock
 from unittest.mock import MagicMock
-import pytest
 
+import pytest
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import _MLFLOW_AVAILABLE, MLFlowLogger
@@ -113,9 +112,11 @@ def test_mlflow_log_dir(client, mlflow, tmpdir):
         limit_train_batches=1,
         limit_val_batches=3,
     )
+    assert trainer.log_dir == logger.save_dir
     trainer.fit(model)
     assert trainer.checkpoint_callback.dirpath == (tmpdir / "exp-id" / "run-id" / 'checkpoints')
     assert set(os.listdir(trainer.checkpoint_callback.dirpath)) == {'epoch=0-step=0.ckpt'}
+    assert trainer.log_dir == logger.save_dir
 
 
 def test_mlflow_logger_dirs_creation(tmpdir):
diff --git a/tests/loggers/test_neptune.py b/tests/loggers/test_neptune.py
index a6b91be4b0fbf..1580ac6e9784c 100644
--- a/tests/loggers/test_neptune.py
+++ b/tests/loggers/test_neptune.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from unittest.mock import patch, MagicMock
+from unittest.mock import MagicMock, patch
 
 import torch
 
@@ -112,7 +112,9 @@ def _run_training(logger):
             limit_train_batches=0.05,
             logger=logger,
         )
+        assert trainer.log_dir is None
         trainer.fit(model)
+        assert trainer.log_dir is None
         return logger
 
     logger_close_after_fit = _run_training(NeptuneLogger(offline_mode=True))
diff --git a/tests/loggers/test_tensorboard.py b/tests/loggers/test_tensorboard.py
index 148ad550e74c7..3195279cf9896 100644
--- a/tests/loggers/test_tensorboard.py
+++ b/tests/loggers/test_tensorboard.py
@@ -35,9 +35,11 @@ def test_tensorboard_hparams_reload(tmpdir):
     model = EvalModelTemplate()
 
     trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)
+    assert trainer.log_dir == trainer.logger.log_dir
     trainer.fit(model)
 
-    folder_path = trainer.logger.log_dir
+    assert trainer.log_dir == trainer.logger.log_dir
+    folder_path = trainer.log_dir
 
     # make sure yaml is there
     with open(os.path.join(folder_path, "hparams.yaml")) as file:
diff --git a/tests/loggers/test_wandb.py b/tests/loggers/test_wandb.py
index 9b011849d7a60..c297633e9a516 100644
--- a/tests/loggers/test_wandb.py
+++ b/tests/loggers/test_wandb.py
@@ -17,8 +17,11 @@
 from argparse import ArgumentParser
 from unittest import mock
 
+import pytest
+
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import WandbLogger
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import BoringModel, EvalModelTemplate
 
 
@@ -94,6 +97,7 @@ class Experiment:
         """ """
         id = 'the_id'
         step = 0
+        dir = 'wandb'
 
         def project_name(self):
             return 'the_project_name'
@@ -109,6 +113,7 @@ def project_name(self):
     )
     # Access the experiment to ensure it's created
     assert trainer.logger.experiment, 'missing experiment'
+    assert trainer.log_dir == logger.save_dir
     pkl_bytes = pickle.dumps(trainer)
     trainer2 = pickle.loads(pkl_bytes)
 
@@ -147,13 +152,13 @@ def test_wandb_logger_dirs_creation(wandb, tmpdir):
 
     version = logger.version
     model = EvalModelTemplate()
-    trainer = Trainer(default_root_dir=tmpdir, logger=logger, max_epochs=1, limit_val_batches=3)
+    trainer = Trainer(default_root_dir=tmpdir, logger=logger, max_epochs=1, limit_val_batches=3, log_every_n_steps=1)
+    assert trainer.log_dir == logger.save_dir
     trainer.fit(model)
 
     assert trainer.checkpoint_callback.dirpath == str(tmpdir / 'project' / version / 'checkpoints')
     assert set(os.listdir(trainer.checkpoint_callback.dirpath)) == {'epoch=0-step=9.ckpt'}
     assert trainer.log_dir == logger.save_dir
-    assert 'output.log' in os.listdir(trainer.log_dir)
 
 
 def test_wandb_sanitize_callable_params(tmpdir):
@@ -184,3 +189,10 @@ def wrapper_something():
     assert params["something"] == "something"
     assert params["wrapper_something"] == "wrapper_something"
     assert params["wrapper_something_wo_name"] == "<lambda>"
+
+
+@mock.patch('pytorch_lightning.loggers.wandb.wandb')
+def test_wandb_logger_offline_log_model(wandb, tmpdir):
+    """ Test that log_model=True raises an error in offline mode """
+    with pytest.raises(MisconfigurationException, match='checkpoints cannot be uploaded in offline mode'):
+        logger = WandbLogger(save_dir=str(tmpdir), offline=True, log_model=True)

From 9f817d4191111ba5040b847ea313a80596270f94 Mon Sep 17 00:00:00 2001
From: Jirka Borovec <Borda@users.noreply.github.com>
Date: Fri, 29 Jan 2021 15:33:10 +0100
Subject: [PATCH 6/7] hint

Co-authored-by: chaton <thomas@grid.ai>
---
 pytorch_lightning/loggers/wandb.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pytorch_lightning/loggers/wandb.py b/pytorch_lightning/loggers/wandb.py
index 34121c3652509..0c3222851e13b 100644
--- a/pytorch_lightning/loggers/wandb.py
+++ b/pytorch_lightning/loggers/wandb.py
@@ -102,8 +102,9 @@ def __init__(
 
         if offline and log_model:
             raise MisconfigurationException(
-                f'log_model={log_model} and offline={offline} is an invalid configuration'
-                ' since model checkpoints cannot be uploaded in offline mode.'
+                f'Providing log_model={log_model} and offline={offline} is an invalid configuration'
+                ' since model checkpoints cannot be uploaded in offline mode.\n'
+                'Hint: Set `offline=False` to log your model.'
             )
 
         super().__init__()

From 81b82c92c2563014af63cd9a00bf75a904faefc7 Mon Sep 17 00:00:00 2001
From: rohitgr7 <rohitgr1998@gmail.com>
Date: Tue, 2 Feb 2021 22:17:21 +0530
Subject: [PATCH 7/7] rev

---
 pytorch_lightning/core/hooks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pytorch_lightning/core/hooks.py b/pytorch_lightning/core/hooks.py
index a1890fc39980a..a87ebbeb47199 100644
--- a/pytorch_lightning/core/hooks.py
+++ b/pytorch_lightning/core/hooks.py
@@ -283,8 +283,8 @@ def on_after_backward(self) -> None:
             def on_after_backward(self):
                 # example to inspect gradient information in tensorboard
                 if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
-                    params = self.named_parameters()
-                    for k, v in params:
+                    params = self.state_dict()
+                    for k, v in params.items():
                         self.logger.experiment.add_histogram(
                             tag=k, values=v.grad, global_step=self.trainer.global_step
                         )