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change torch.cuda.amp.GradScaler to torch.GradScaler("cuda") #3257

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Merged
merged 4 commits into from
Jan 30, 2025
Merged

change torch.cuda.amp.GradScaler to torch.GradScaler("cuda") #3257

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85c31ba
change torch.cuda.amp.GradScaler to torch.GradScaler("cuda")
0ssamaak0 Jan 24, 2025
0c71332
changing torch.GradScaler to torch.amp.GradScaler
0ssamaak0 Jan 26, 2025
627983b
Merge branch 'main' into patch-1
0ssamaak0 Jan 28, 2025
50173b2
Merge branch 'main' into patch-1
svekars Jan 28, 2025
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4 changes: 2 additions & 2 deletions recipes_source/recipes/amp_recipe.py
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Expand Up @@ -150,7 +150,7 @@ def make_model(in_size, out_size, num_layers):
# The same ``GradScaler`` instance should be used for the entire convergence run.
# If you perform multiple convergence runs in the same script, each run should use
# a dedicated fresh ``GradScaler`` instance. ``GradScaler`` instances are lightweight.
scaler = torch.cuda.amp.GradScaler()
scaler = torch.GradScaler("cuda")

for epoch in range(0): # 0 epochs, this section is for illustration only
for input, target in zip(data, targets):
Expand Down Expand Up @@ -182,7 +182,7 @@ def make_model(in_size, out_size, num_layers):

net = make_model(in_size, out_size, num_layers)
opt = torch.optim.SGD(net.parameters(), lr=0.001)
scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
scaler = torch.GradScaler("cuda" ,enabled=use_amp)
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Same as above.

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I followed this
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Thanks for the quick reply. GradScaler can be used on its own but because you are modifying amp recipe, I would suggest to use torch.amp.GradScaler as it's also mentioned in the code - https://github.com/pytorch/pytorch/blob/main/torch/cuda/amp/grad_scaler.py#L12
Also, if you run the recipe, you will notice the warning message suggesting to use torch.amp.GradScaler

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I have changed the code accordingly.

Thank you

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Thanks!


start_timer()
for epoch in range(epochs):
Expand Down