Question: How to finetune on custom loss function?

[gopal86]

Hi, I wanted to fine tune the model on my own dataset however with my own custom loss. Could you give an example on how to do that? It would be very helpful for my research purpose! I am unclear on how to do that on your model

Thanks Gopal

[gorold]

Firstly, you can check out the fine-tuning section in the README to get familiar with how to run a fine-tuning job. Then, to change the loss function, you can specify the loss function for the config file using model.loss_func._target_=... as shown below:

python -m cli.finetune \
  run_name=example_run \ 
  model=moirai_1.0_R_small \ 
  data=etth1 \ 
  val_data=etth1  \
  model.loss_func._target_=uni2ts.loss.packed.PackedMSELoss

We have already implemented some common loss functions used in time series forecasting, but if you want your own custom loss, you'll have to implement it by subclassing PackedLoss.

[jmoffatt32]

I'm trying to run a fine-tune with PackedMSELoss function. However, when running the fine-tuning script, I am getting an error:

  File "/home/ubuntu/verb-workspace/linkt-uni2ts/.venv/lib/python3.11/site-packages/torch/nn/functional.py", line 3328, in mse_loss
    if not (target.size() == input.size()):
                             ^^^^^^^^^^
AttributeError: 'AffineTransformed' object has no attribute 'size'

From the debugging I have done, the issue seems to be that the model is passing a distribution (defined by the distr_output kwarg) to it's forward method, when the PackedMSELoss is expecting a tensor instead of a distribution. Is this understanding correct? How can I fix this issue, to use point based loss functions instead of distribution based loss funcs?

I have attached my model config below for additional reference:

_target_: uni2ts.model.moirai.MoiraiFinetune
_args_:
  module_kwargs:
    _target_: builtins.dict
    distr_output:
      _target_: uni2ts.distribution.MixtureOutput
      components:
        - _target_: uni2ts.distribution.StudentTOutput
        - _target_: uni2ts.distribution.NormalFixedScaleOutput
        - _target_: uni2ts.distribution.NegativeBinomialOutput
        - _target_: uni2ts.distribution.LogNormalOutput
    d_model: 384
    num_layers: 6
    patch_sizes: ${as_tuple:[8, 16, 32, 64, 128]}
    max_seq_len: 512
    attn_dropout_p: 0.0
    dropout_p: 0.0
    scaling: true
  min_patches: 2
  min_mask_ratio: 0.15
  max_mask_ratio: 0.5
  max_dim: 128
  loss_func:
    _target_: uni2ts.loss.packed.PackedMSELoss
  lr: 1e-3
  weight_decay: 1e-1
  beta1: 0.9
  beta2: 0.98
  num_training_steps: ${mul:${trainer.max_epochs},${train_dataloader.num_batches_per_epoch}}
  num_warmup_steps: 0
  checkpoint_path:
    _target_: huggingface_hub.hf_hub_download
    repo_id: Salesforce/moirai-1.0-R-small
    filename: model.ckpt

[gorold]

You could call distr.mean and feed it to the loss function. Not too sure how that will work out though. Some alternatives:

1. Fine-tune with the NLL, you can still get a point prediction from a distribution
2. Replace the output head with a new one for point forecast.