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Fix DMRL save() failing when log_metrics=True #724

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Merged
qtuantruong merged 2 commits into from
Sep 13, 2026
Merged

Fix DMRL save() failing when log_metrics=True #724

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6d9c29c
Fix DMRL save() failing when log_metrics=True
Shxiao101 Sep 12, 2026
1b77daf
Remove DMRL save/load test per review
Shxiao101 Sep 13, 2026
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36 changes: 19 additions & 17 deletions cornac/models/dmrl/recom_dmrl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,10 +114,6 @@ def __init__(
self.num_neg = num_neg
self.num_factors = num_factors
self.log_metrics = log_metrics
if log_metrics:
from torch.utils.tensorboard import SummaryWriter

self.tb_writer = SummaryWriter("temp/tb_data/run_1")

if self.num_factors == 1:
# deactivate disentangled portion of loss if theres only 1 factor
Expand Down Expand Up @@ -261,9 +257,13 @@ def _fit_dmrl(self, train_set: Dataset, val_set: Dataset = None):
decay_c=1e-3, num_factors=self.num_factors, num_neg=self.num_neg
)

# add hyperparams to tensorboard
if self.log_metrics:
self.tb_writer.add_hparams(
from torch.utils.tensorboard import SummaryWriter

# kept local: a writer attribute would break Recommender.save/deepcopy
tb_writer = SummaryWriter("temp/tb_data/run_1")
# add hyperparams to tensorboard
tb_writer.add_hparams(
{
"learning_rate": self.learning_rate,
"decay_c": self.decay_c,
Expand Down Expand Up @@ -379,38 +379,38 @@ def _fit_dmrl(self, train_set: Dataset, val_set: Dataset = None):

if self.log_metrics:
# tb_x = epoch * len(dataloader) + i + 1
self.tb_writer.add_scalar("Loss/train", last_loss, j)
self.tb_writer.add_scalar(
tb_writer.add_scalar("Loss/train", last_loss, j)
tb_writer.add_scalar(
"Loss/val", running_loss_val / devider, j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"Gradient Norm/train", np.mean(self.model.grad_norms), j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"Param Norm/train", np.mean(self.model.param_norms), j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"User-Item based rating", np.mean(self.model.ui_ratings), j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"User-Text based rating", np.mean(self.model.ut_ratings), j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"User-Itm Attention", np.mean(self.model.ui_attention), j
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"User-Text Attention", np.mean(self.model.ut_attention), j
)
for name, param in self.model.named_parameters():
self.tb_writer.add_scalar(
tb_writer.add_scalar(
name + "/grad_norm",
np.mean(self.model.grad_dict[name]),
j,
)
self.tb_writer.add_histogram(
tb_writer.add_histogram(
name + "/grad", param.grad, global_step=epoch
)
self.tb_writer.add_scalar(
tb_writer.add_scalar(
"Learning rate", optimizer.param_groups[0]["lr"], j
)
self.model.reset_grad_metrics()
Expand All @@ -426,6 +426,8 @@ def _fit_dmrl(self, train_set: Dataset, val_set: Dataset = None):
print(f"Epoch: {epoch} is done")
# scheduler.step()
print("Finished training!")
if self.log_metrics:
tb_writer.close()
# self.eval_train_set_performance() # evaluate the model on the training set after training if necessary

def eval_train_set_performance(self) -> Tuple[float, float]:
Expand Down
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