[Bug]: optimizer state not saved

[chelseagzr]

Describe the bug

Thank you for developing and maintaining this invaluable module!

We would like to save the state of the optimizer at the end of each epoch.
The save_optimizer_state parameter of the fine_tune function seems to be designed for this purpose.
However, the state of the optimizer is not saved even if we set save_optimizer_state=True.

Thank you!

To Reproduce

%pip install scipy==1.10.1 datasets transformers torch==2.0 flair==0.13.1 

import torch
import flair
from flair.data import Corpus
from flair.datasets import TREC_6
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import TextClassifier
from flair.trainers import ModelTrainer

# 1. get the corpus
corpus: Corpus = TREC_6()

# 2. what label do we want to predict?
label_type = 'question_class'

# 3. create the label dictionary
label_dict = corpus.make_label_dictionary(label_type=label_type)

# 4. initialize transformer document embeddings (many models are available)
document_embeddings = TransformerDocumentEmbeddings('distilbert-base-uncased', fine_tune=True)

# 5. create the text classifier
classifier = TextClassifier(document_embeddings, label_dictionary=label_dict, label_type=label_type)

# 6. initialize trainer
trainer = ModelTrainer(classifier, corpus)

# 7. run training with fine-tuning
trainer.fine_tune('resources/taggers/question-classification-with-transformer',
                  learning_rate=5.0e-5,
                  mini_batch_size=4,
                  max_epochs=10,
                  save_optimizer_state=True,
                  save_model_each_k_epochs=1
)

checkpoint = torch.load('resources/taggers/question-classification-with-transformer/model_epoch_1.pt', map_location=flair.device)

Expected behavior

When save_optimizer_state is true, the checkpoint contains the state_dict of the optimizer.

Logs and Stack traces

No response

Screenshots

No response

Additional Context

No response

Environment

Versions:

Flair

0.13.1

Pytorch

2.0.0+cu117

Transformers

4.40.0

GPU

True

[helpmefindaname]

Hi @chelseagzr

thank you for reporting, currently saving/loading the optimizer state is not possible, and that flag should have been removed when the trainer was reworked.

I discussed with @alanakbik to introduce this again, but change it to not only store the optimizer state but also lr-scheduler and plugin states.
I am thinking of a state that can be loaded via the trainer (e.g. trainer = ModelTrainer.load_checkpoint("checkpoint.pt") to load the states, while still allowing Classifier.load("checkpoint.pt") to load the model without training states.

[chelseagzr]

Thank you for the timely response! It would be great if saving the state of a trainer can be enabled!