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import time
import torch
import torch.nn.functional as F
def train_runtime(model, data, epochs, device):
if hasattr(data, 'features'):
x = torch.tensor(data.features, dtype=torch.float, device=device)
else:
x = None
mask = data.train_mask if hasattr(data, 'train_mask') else data.train_idx
y = torch.tensor(data.labels, dtype=torch.long, device=device)[mask]
model = model.to(device)
model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
if torch.cuda.is_available():
torch.cuda.synchronize()
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
torch.mps.synchronize()
t_start = time.perf_counter()
for epoch in range(epochs):
optimizer.zero_grad()
out = model(x)
loss = F.nll_loss(out[mask], y.view(-1))
loss.backward()
optimizer.step()
if torch.cuda.is_available():
torch.cuda.synchronize()
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
torch.mps.synchronize()
t_end = time.perf_counter()
return t_end - t_start
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