def test_update(nlp: TorchLanguage):
texts = ["This is a test sentence to check\u3000model.update!"]
labels = [{}]
pipe: TrfModel = nlp.get_pipe(TRANSFORMERS_MODEL)
optimizer = nlp.resume_training()
eps = 1e-5
def sum_param(params):
return sum(p.sum().item() for p in params)
def train():
docs, golds = nlp._format_docs_and_golds(texts, labels)
before = sum_param(pipe.optim_parameters())
nlp._update_pipes(docs, golds)
h = get_last_hidden_state_from_docs(docs)
loss = h.sum() + torch.tensor(0.0, requires_grad=True)
add_loss_to_docs(docs, loss)
nlp._update_params(docs, optimizer)
return abs(before - sum_param(pipe.optim_parameters()))
assert train() > eps
# freeze model
pipe.cfg["freeze"] = True
assert train() < eps
# restore freeze state
pipe.cfg["freeze"] = False
def train(
cfg: Config,
nlp: TorchLanguage,
train_data: InputData,
val_data: InputData,
savedir: Path,
) -> None:
eval_fn = EVAL_FN_MAP[cfg.task]
optim = nlp.resume_training()
scheduler = load_scheduler(cfg, optim)
for i in range(cfg.niter):
random.shuffle(train_data)
train_epoch(cfg, nlp, optim, train_data, val_data, i, eval_fn)
scheduler.step(
) # type: ignore # (https://github.com/pytorch/pytorch/pull/26531)
scores = eval_fn(cfg, nlp, val_data)
nlp.meta.update({
"score": scores,
"config": OmegaConf.to_container(cfg)
})
save_model(nlp, savedir / str(i))