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Merged
merged 1 commit into from
Feb 18, 2022
Merged

bce with logits #307

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3 changes: 1 addition & 2 deletions RecommenderSystems/dlrm/models/dlrm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -211,7 +211,6 @@ def __init__(self, args):
feature_size = self.interaction.output_feature_size(args.embedding_vec_size, args.bottom_mlp[-1])
self.top_mlp = MLP(feature_size, args.top_mlp)
self.scores = nn.Linear(args.top_mlp[-1], 1)
self.sigmoid = nn.Sigmoid()


def forward(self, dense_fields, sparse_fields) -> flow.Tensor:
Expand All @@ -223,7 +222,7 @@ def forward(self, dense_fields, sparse_fields) -> flow.Tensor:
features = self.interaction(dense_fields, embedding)
features = self.top_mlp(features)
scores = self.scores(features)
return self.sigmoid(scores)
return scores


def make_dlrm_module(args):
Expand Down
9 changes: 5 additions & 4 deletions RecommenderSystems/dlrm/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ def __init__(self):
growth_interval=2000,
)

self.loss = flow.nn.BCELoss(reduction="none").to("cuda")
self.loss = flow.nn.BCEWithLogitsLoss(reduction="none").to("cuda")
if self.execution_mode == "graph":
if self.dataset_format == 'petastorm':
self.eval_graph = DLRMValGraph(self.dlrm_module, args.use_fp16)
Expand Down Expand Up @@ -190,7 +190,8 @@ def eval(self, save_model=False):
labels = []
preds = []
for _ in range(self.eval_batchs):
pred, label = self.inference()
logits, label = self.inference()
pred = logits.sigmoid()
label_ = label.numpy().astype(np.float32)
labels.append(label_)
preds.append(pred.numpy())
Expand Down Expand Up @@ -242,8 +243,8 @@ def train_one_step(self):
return self.train_graph()
else:
labels, dense_fields, sparse_fields = self.load_data(self.train_dataloader)
predicts = self.dlrm_module(dense_fields, sparse_fields)
loss = self.loss(predicts, labels)
logits = self.dlrm_module(dense_fields, sparse_fields)
loss = self.loss(logits, labels)
loss = flow.mean(loss)
loss.backward()
self.opt.step()
Expand Down