Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Improving the MLP example. #6593

Merged
merged 1 commit into from
Nov 21, 2023
Merged

Improving the MLP example. #6593

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 20 additions & 18 deletions examples/pytorch/ogb/ogbn-products/mlp/mlp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -99,23 +99,24 @@ def train(

preds = torch.zeros(labels.shape[0], n_classes)

for _input_nodes, output_nodes, subgraphs in dataloader:
subgraphs = [b.to(device) for b in subgraphs]
new_train_idx = list(range(len(output_nodes)))
with dataloader.enable_cpu_affinity():
for _input_nodes, output_nodes, subgraphs in dataloader:
subgraphs = [b.to(device) for b in subgraphs]
new_train_idx = list(range(len(output_nodes)))

pred = model(subgraphs[0].srcdata["feat"])
preds[output_nodes] = pred.cpu().detach()
pred = model(subgraphs[0].srcdata["feat"])
preds[output_nodes] = pred.cpu().detach()

loss = criterion(
pred[new_train_idx], labels[output_nodes][new_train_idx]
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss = criterion(
pred[new_train_idx], labels[output_nodes][new_train_idx]
)
optimizer.zero_grad()
loss.backward()
optimizer.step()

count = len(new_train_idx)
loss_sum += loss.item() * count
total += count
count = len(new_train_idx)
loss_sum += loss.item() * count
total += count

preds = preds.to(train_idx.device)
return (
Expand Down Expand Up @@ -143,11 +144,12 @@ def evaluate(
eval_times = 1 # Due to the limitation of memory capacity, we calculate the average of logits 'eval_times' times.

for _ in range(eval_times):
for _input_nodes, output_nodes, subgraphs in dataloader:
subgraphs = [b.to(device) for b in subgraphs]
with dataloader.enable_cpu_affinity():
for _input_nodes, output_nodes, subgraphs in dataloader:
subgraphs = [b.to(device) for b in subgraphs]

pred = model(subgraphs[0].srcdata["feat"])
preds[output_nodes] = pred
pred = model(subgraphs[0].srcdata["feat"])
preds[output_nodes] = pred

preds /= eval_times

Expand Down