I have saved a model during training. However, I am facing issues when trying to load the model from a saved checkpoint. The model class name is CSLRModel . On the python code CSLRModel.load_from_checkpoint(ckpt_path, **kwargs) , I am getting the following error:

return CSLRModel.load_from_checkpoint(ckpt_path, encoder_seq, config, config.num_classes_gloss)
  File "/data/envs/ohdev/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 137, in load_from_checkpoint
    return _load_from_checkpoint(
  File "/data/envs/ohdev/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 158, in _load_from_checkpoint
    checkpoint = pl_load(checkpoint_path, map_location=map_location)
  File "/data/envs/ohdev/lib/python3.8/site-packages/lightning_lite/utilities/cloud_io.py", line 48, in _load
    return torch.load(f, map_location=map_location)
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/serialization.py", line 789, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/serialization.py", line 1131, in _load
    result = unpickler.load()
  File "/usr/lib/python3.8/pickle.py", line 1212, in load
    dispatch[key[0]](self)
  File "/usr/lib/python3.8/pickle.py", line 1253, in load_binpersid
    self.append(self.persistent_load(pid))
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/serialization.py", line 1101, in persistent_load
    load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/serialization.py", line 1083, in load_tensor
    wrap_storage=restore_location(storage, location),
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/serialization.py", line 1058, in restore_location
    result = map_location(storage, location)
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/envs/ohdev/lib/python3.8/site-packages/torch/nn/modules/module.py", line 246, in _forward_unimplemented
    raise NotImplementedError(f"Module [{type(self).__name__}] is missing the required \"forward\" function")
NotImplementedError: Module [ModuleList] is missing the required "forward" function
Not sure what I am doing wrong here. The CSLRModel class does have forward function, and I was able to use the model class to successfully train and save the checkpoints. It is only in loading from a checkpoint that I am facing an error.
              
class CSLRModel(pl.LightningModule):
  def __init__(self, encoder_seq, config, num_classes_gloss):
    super().__init__()
    self.save_hyperparameters()
    self.epoch_no = 0
    ## path hardcoded. Resolve
    gloss_dict = np.load('/data/cslr_datasets/PHOENIX-2014/phoenix2014-release/phoenix-2014-multisigner/preprocess/gloss_dict.npy', allow_pickle=True).item()
    self.decoder = Decode(gloss_dict, num_classes_gloss, 'beam')
    self.config = config
    self.encoder_seq = encoder_seq
    self.num_encoders = len(encoder_seq)
    self.num_classes_gloss = num_classes_gloss
    self.classifiers = nn.ModuleDict() ## to use features of an encoder to classify
    for enc in self.encoder_seq:
      if enc.use_ctc:
        self.classifiers[f'{enc.encoder_id}'] = nn.Linear(enc.out_size, self.num_classes_gloss)
    self.externals = {}
    self.initialize_losses(config.losses)
  def forward(self, x, len_x, label, len_label, is_training=True):
    for i, enc in enumerate(self.encoder_seq):
      x, len_x, internal_losses = enc(x,len_x)
      self.loss_value.update(internal_losses)
      if enc.use_ctc:
        logits = self.classifiers[f'{enc.encoder_id}'](x)
        self.externals[f'encoder{i+1}.logits'] = logits
        self.loss_value[f'{enc.encoder_id}.CTCLoss'] = self.loss_fn['CTC'](
                                                                logits.transpose(0,1).log_softmax(-1), 
                                                                label.cpu().int(), 
                                                                len_x.cpu().int(), 
                                                                len_label.cpu().int()).mean()
    return self.compute_external_losses(), logits, len_x
Pasting the __init__() and forward() functions of the model definition. Not pasting the rest of the definition since it would make the post long.

Note: I was able to load the model from checkpoint when I used self.save_hyperparameters() in __init__(). Is this required?
              
Hey, it makes a ton of sense now.
Here is how load_from_checkpoint works internally:

1.) We instantiate the class (CSLRModel) with the necessary init arguments

2.) We load the state dict to the class instance
For 1.) we need to get the init arguments somewhere. There are 2 options to do this.
save_hyperparameters() just serializes the init arguments so that you don’t need to do anything (at least for easily serializable stuff, different if you pass a nn.Module)
You pass them in as keyword arguments to load_from_checkpoint
Since you didn’t do any of these it makes sense that you couldn’t load the model. Hope that makes it a bit clearer!
Cheers,

Justus
              
Yeah, I had read about these two options in the docs, and I tried both. My query was as follows:

1] Approach 1: Use self.save_hyperparameters() in model __init__() and only pass ckpt_path when loading during test time i.e., CSLRModel(ckpt_path). This one works for me, but I do not prefer it since the self.save_hyperparameters() statement takes a little long to execute.
2] Approach 2: Do not use self.save_hyperparameters() and load during test time as follows: CSLRModel(ckpt_path, encoder_seq, config, num_classes_gloss) i.e., pass both saved checkpoint path and hyperparameters. However, when I use this approach, I get the above mentioned error.
NotImplementedError: Module [ModuleList] is missing the required "forward" function