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fix: replace deprecated torch.cuda.amp with torch.amp #445

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fix: replace deprecated torch.cuda.amp with torch.amp #445

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2 changes: 1 addition & 1 deletion env/SE3Transformer/se3_transformer/runtime/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def evaluate(model: nn.Module,
for callback in callbacks:
callback.on_batch_start()

with torch.cuda.amp.autocast(enabled=args.amp):
with torch.amp.autocast('cuda', enabled=args.amp):
pred = model(*input)

for callback in callbacks:
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4 changes: 2 additions & 2 deletions env/SE3Transformer/se3_transformer/runtime/training.py
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Expand Up @@ -90,7 +90,7 @@ def train_epoch(model, train_dataloader, loss_fn, epoch_idx, grad_scaler, optimi
for callback in callbacks:
callback.on_batch_start()

with torch.cuda.amp.autocast(enabled=args.amp):
with torch.amp.autocast('cuda', enabled=args.amp):
pred = model(*inputs)
loss = loss_fn(pred, target) / args.accumulate_grad_batches

Expand Down Expand Up @@ -127,7 +127,7 @@ def train(model: nn.Module,
model = DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank)

model.train()
grad_scaler = torch.cuda.amp.GradScaler(enabled=args.amp)
grad_scaler = torch.amp.GradScaler('cuda', enabled=args.amp)
if args.optimizer == 'adam':
optimizer = FusedAdam(model.parameters(), lr=args.learning_rate, betas=(args.momentum, 0.999),
weight_decay=args.weight_decay)
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2 changes: 1 addition & 1 deletion rfdiffusion/Track_module.py
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Expand Up @@ -233,7 +233,7 @@ def reset_parameter(self):
nn.init.zeros_(self.embed_e1.bias)
nn.init.zeros_(self.embed_e2.bias)

@torch.cuda.amp.autocast(enabled=False)
@torch.amp.autocast('cuda', enabled=False)
def forward(self, msa, pair, R_in, T_in, xyz, state, idx, motif_mask, cyclic_reses=None, top_k=64, eps=1e-5):
B, N, L = msa.shape[:3]

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