[Question]: Multi-Task Learning with use_all_task

[zrjohnnyl]

Question

How can I correctly train two tasks simultaneously on a single corpus using the parameter use_all_task=True? When I attempted to train two models together on one corpus, I encountered a RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn.

multitask_dataset = CONLL_03_DUTCH()
tasks = ['ner', 'pos']
model_1 = initialize_tagger(multitask_dataset, shared_embedding, tasks[0])
model_2 = initialize_tagger(multitask_dataset, shared_embedding, tasks[1])
multitask_model = MultitaskModel([model_1, model_2], use_all_tasks=True, task_ids=tasks)
trainer = ModelTrainer(multitask_model, multitask_dataset)
trainer.fine_tune('resources/taggers/sota-ner-flert',
learning_rate=5.0e-6,
max_epochs=20)
╭─────────────────────────────────────── Traceback (most recent call last) ───────────────────────────────────────╮
│ in :60 │
│ │
│ ❱ 60 trainer.fine_tune('resources/taggers/sota-ner-flert', │
│ │
│ /pyzr/active_venv/lib/python3.10/site-packages/flair/trainers/trainer.py:253 in fine_tune │
│ │
│ ❱ 253 │ │ return self.train_custom( │
│ │
│ /pyzr/active_venv/lib/python3.10/site-packages/flair/trainers/trainer.py:606 in train_custom │
│ │
│ ❱ 606 │ │ │ │ │ │ │ self._backward(scaler.scale(loss)) │
│ │
│ /pyzr/active_venv/lib/python3.10/site-packages/flair/trainers/trainer.py:124 in _backward │
│ │
│ ❱ 124 │ │ loss.backward() │
│ │
│ /pyzr/active_venv/lib/python3.10/site-packages/torch/_tensor.py:487 in backward │
│ │
│ ❱ 487 │ │ torch.autograd.backward( │
│ │
│ /pyzr/active_venv/lib/python3.10/site-packages/torch/autograd/init.py:200 in backward │
│ │
│ ❱ 200 │ Variable._execution_engine.run_backward( # Calls into the C++ engine to run the bac │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn