AttributeError: 'Trainer' object has no attribute 'val_dataloader'

[kiddycharles]

I am getting the following error when running trainer.validate(model, valid_dl)
Full traceback:

AttributeError                            Traceback (most recent call last)
Cell In[19], line 2
      1 # see performance for validation dataset
----> 2 trainer.validate(model, valid_dl)
      4 # see performance for test dataset
      5 trainer.test(model, test_dl)

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:697, in Trainer.validate(self, model, dataloaders, ckpt_path, verbose, datamodule)
    695     model = self._maybe_unwrap_optimized(model)
    696     self.strategy._lightning_module = model
--> 697 return call._call_and_handle_interrupt(
    698     self, self._validate_impl, model, dataloaders, ckpt_path, verbose, datamodule
    699 )

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py:38, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
     36         return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
     37     else:
---> 38         return trainer_fn(*args, **kwargs)
     40 except _TunerExitException:
     41     trainer._call_teardown_hook()

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:745, in Trainer._validate_impl(self, model, dataloaders, ckpt_path, verbose, datamodule)
    742 self._validated_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
    744 # run validate
--> 745 results = self._run(model, ckpt_path=self.ckpt_path)
    747 assert self.state.stopped
    748 self.validating = False

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1112, in Trainer._run(self, model, ckpt_path)
   1108 self._checkpoint_connector.restore_training_state()
   1110 self._checkpoint_connector.resume_end()
-> 1112 results = self._run_stage()
   1114 log.detail(f"{self.__class__.__name__}: trainer tearing down")
   1115 self._teardown()

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1188, in Trainer._run_stage(self)
   1185 self.strategy.dispatch(self)
   1187 if self.evaluating:
-> 1188     return self._run_evaluate()
   1189 if self.predicting:
   1190     return self._run_predict()

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1228, in Trainer._run_evaluate(self)
   1223 self._evaluation_loop.trainer = self
   1225 with self.profiler.profile(f"run_{self.state.stage}_evaluation"), _evaluation_context(
   1226     self.accelerator, self._inference_mode
   1227 ):
-> 1228     eval_loop_results = self._evaluation_loop.run()
   1230 # remove the tensors from the eval results
   1231 for result in eval_loop_results:

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:199, in Loop.run(self, *args, **kwargs)
    197 try:
    198     self.on_advance_start(*args, **kwargs)
--> 199     self.advance(*args, **kwargs)
    200     self.on_advance_end()
    201     self._restarting = False

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py:152, in EvaluationLoop.advance(self, *args, **kwargs)
    150 if self.num_dataloaders > 1:
    151     kwargs["dataloader_idx"] = dataloader_idx
--> 152 dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
    154 # store batch level output per dataloader
    155 self._outputs.append(dl_outputs)

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py:199, in Loop.run(self, *args, **kwargs)
    197 try:
    198     self.on_advance_start(*args, **kwargs)
--> 199     self.advance(*args, **kwargs)
    200     self.on_advance_end()
    201     self._restarting = False

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py:137, in EvaluationEpochLoop.advance(self, data_fetcher, dl_max_batches, kwargs)
    134 self.batch_progress.increment_started()
    136 # lightning module methods
--> 137 output = self._evaluation_step(**kwargs)
    138 output = self._evaluation_step_end(output)
    140 self.batch_progress.increment_processed()

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py:234, in EvaluationEpochLoop._evaluation_step(self, **kwargs)
    223 """The evaluation step (validation_step or test_step depending on the trainer's state).
    224 
    225 Args:
   (...)
    231     the outputs of the step
    232 """
    233 hook_name = "test_step" if self.trainer.testing else "validation_step"
--> 234 output = self.trainer._call_strategy_hook(hook_name, *kwargs.values())
    236 return output

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1494, in Trainer._call_strategy_hook(self, hook_name, *args, **kwargs)
   1491     return
   1493 with self.profiler.profile(f"[Strategy]{self.strategy.__class__.__name__}.{hook_name}"):
-> 1494     output = fn(*args, **kwargs)
   1496 # restore current_fx when nested context
   1497 pl_module._current_fx_name = prev_fx_name

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pytorch_lightning/strategies/strategy.py:390, in Strategy.validation_step(self, *args, **kwargs)
    388 with self.precision_plugin.val_step_context():
    389     assert isinstance(self.model, ValidationStep)
--> 390     return self.model.validation_step(*args, **kwargs)

File ~/stock/torchTS/torchts/nn/model.py:119, in TimeSeriesModel.validation_step(self, batch, batch_idx)
    112 def validation_step(self, batch, batch_idx):
    113     """Validates model for one step.
    114 
    115     Args:
    116         batch (torch.Tensor): Output of the torch.utils.data.DataLoader
    117         batch_idx (int): Integer displaying index of this batch
    118     """
--> 119     val_loss = self._step(batch, batch_idx, len(self.trainer.val_dataloader))
    120     self.log("val_loss", val_loss, on_step=True,
    121         on_epoch=True,
    122         prog_bar=True,
    123         logger=True,)
    124     return val_loss

AttributeError: 'Trainer' object has no attribute 'val_dataloader'

My Trainer object:
trainer = Trainer(max_epochs=15)

train

start = time.time()
trainer.fit(model, train_dataloaders=train_dl)
print("Training time taken %f"%(time.time() - start), flush=True)

Any idea on how to fix this? My PyTorch-lightning version is 2.2.1. I am new to the PyTorching-lightning library and following the documentation. I was so confused, Thank you.

[A7mad99]

did you find a solution?

[JiayouZhang]

I guess it's called val_dataloader**s**?

[Lakshit-Gupta]

import pytorch_lightning as pla
import torch.nn.functional as F
import torch

class TFTLightningModule(pla.LightningModule):
def init(self, model):
super().init()
self.model = model # Store the model
self.loss_fn = SMAPE() # Example loss function

def forward(self, x):
    #print(f"Input shape: {x.shape}")
    x = x.to(self.device)
    return self.model(x)

def training_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self(x)
    loss = self.loss_fn(y_hat, y)
    self.log("train_loss", loss)
    return loss

def validation_step(self, batch, batch_idx):
    x, y,*additional_data = batch
    #x = batch['x']  # Access input data
    #y = batch['y']  # Access target data
    #print(f"Batch x shape: {x.shape}, Batch y shape: {y.shape}")
    y_hat = self(x)
    loss = self.loss_fn(y_hat, y)
    self.log("val_loss", loss, prog_bar=True)
    return loss

def configure_optimizers(self):
    return torch.optim.Adam(self.parameters(), lr=1e-3)

Wrap TFT in LightningModule

model = TFTLightningModule(model)

define you own funcctions above it should work