mlflow logger complains about missing run_id
🐛 Bug
When using MLflow logger, log_param() function require run_id
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-23-d048545e1854> in <module>
      9 trainer.fit(model=experiment, 
     10            train_dataloader=train_dl,
---> 11            val_dataloaders=test_dl)

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
    452         self.call_hook('on_fit_start')
    453 
--> 454         results = self.accelerator_backend.train()
    455         self.accelerator_backend.teardown()
    456 

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_backend.py in train(self)
     51 
     52         # train or test
---> 53         results = self.train_or_test()
     54         return results
     55 

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/accelerators/base_accelerator.py in train_or_test(self)
     48             results = self.trainer.run_test()
     49         else:
---> 50             results = self.trainer.train()
     51         return results
     52 

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in train(self)
    499 
    500                 # run train epoch
--> 501                 self.train_loop.run_training_epoch()
    502 
    503                 if self.max_steps and self.max_steps <= self.global_step:

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_epoch(self)
    525             # TRAINING_STEP + TRAINING_STEP_END
    526             # ------------------------------------
--> 527             batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
    528 
    529             # when returning -1 from train_step, we end epoch early

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in run_training_batch(self, batch, batch_idx, dataloader_idx)
    660                     opt_idx,
    661                     optimizer,
--> 662                     self.trainer.hiddens
    663                 )
    664 

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens)
    739         """
    740         # lightning module hook
--> 741         result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
    742 
    743         if result is None:

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py in training_step(self, split_batch, batch_idx, opt_idx, hiddens)
    300         with self.trainer.profiler.profile('model_forward'):
    301             args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
--> 302             training_step_output = self.trainer.accelerator_backend.training_step(args)
    303             training_step_output = self.trainer.call_hook('training_step_end', training_step_output)
    304 

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_backend.py in training_step(self, args)
     59                 output = self.__training_step(args)
     60         else:
---> 61             output = self.__training_step(args)
     62 
     63         return output

~/anaconda3/envs/ns_dl_2020_torch/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_backend.py in __training_step(self, args)
     67         batch = self.to_device(batch)
     68         args[0] = batch
---> 69         output = self.trainer.model.training_step(*args)
     70         return output
     71 

<ipython-input-21-31b6dc3ffd67> in training_step(self, batch, batch_idx, optimizer_idx)
     28         for key, val in train_loss.items():
     29             self.log(key, val.item())
---> 30             self.logger.experiment.log_param(key=key, value=val.item())
     31 
     32         return train_loss

TypeError: log_param() missing 1 required positional argument: 'run_id'

Expected behavior
The MlflowLogger should behave the same as the mlflow api where only key and value argment is needed for log_param() function
Code sample
mlf_logger = MLFlowLogger(
    experiment_name='test',
    tracking_uri="file:./ml-runs"
)

Cllass VAEexperiment(LightningModule):
...
    def training_step(self, batch, batch_idx, optimizer_idx = 0):
        ....
        for key, val in train_loss.items():
            self.logger.experiment.log_param(key=key, value=val.item())
       ....
       return train_loss

trainer = Trainer(logger=mlf_logger,
                  default_root_dir='../logs',
                  early_stop_callback=False,
                  gpus=1, 
                  auto_select_gpus=True,
                  max_epochs=40)

trainer.fit(model=experiment, 
           train_dataloader=train_dl, 
           val_dataloaders=test_dl)
Environment
pytorch-lightning==0.10.0
torch==1.6.0
torchsummary==1.5.1
torchvision==0.7.0