Pytorch Lightning Automatic Logging - AttributeError: 'NoneType' object has no attribute '_results' - pytorch-lightning

Question

Unable to use Automatic Logging (self.log) when calling training_step() on Pytorch Lightning, what am I missing? Here is a minimal example:
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
class LitModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.l1 = nn.Linear(100, 4)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1)))
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y.long())
self.log("train_loss", loss) # <-- error
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
pl_model = LitModel()
x = torch.rand((10,100))
y = torch.randint(0,4, size=(10,))
batch = (x,y)
loss = pl_model.training_step(batch, 0)
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-34-b9419bfca30f> in <module>
25 y = torch.randint(0,4, size=(10,))
26 batch = (x,y)
---> 27 loss = pl_model.training_step(batch, 0)
<ipython-input-34-b9419bfca30f> in training_step(self, batch, batch_idx)
14 y_hat = self(x)
15 loss = F.cross_entropy(y_hat, y.long())
---> 16 self.log("train_loss", loss)
17 return loss
18
D:\programs\anaconda3\lib\site-packages\pytorch_lightning\core\lightning.py in log(self, name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, tbptt_reduce_fx, tbptt_pad_token, enable_graph, sync_dist, sync_dist_op, sync_dist_group, add_dataloader_idx, batch_size, metric_attribute, rank_zero_only)
405 on_epoch = self.__auto_choose_log_on_epoch(on_epoch)
406
--> 407 results = self.trainer._results
408 assert results is not None
409 assert self._current_fx_name is not None
AttributeError: 'NoneType' object has no attribute '_results'

Answer 1

This is NOT the correct usage of LightningModule class. You can't call a hook (namely .training_step()) manually and expect everything to work fine.
You need to setup a Trainer as suggested by PyTorch Lightning at the very start of its tutorial - it is a requirement. The functions (or hooks) that you define in a LightningModule merely tells Lightning "what to do" in a specific situation (in this case, at each training step). It is the Trainer that actually "orchestrates" the training by instantiating the necessary environment (including Logging functionality) and feeding it into the Lightning Module whenever needed.
So, do it the way Lightning suggests and it will work.