lllyasviel / lllyasviel/ControlNet
Floating point exception when training with pytorch 2.0.0
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Description
Uncertain if this is limited to my specific cpu architecture or another dependency but the following code:
ldm/modules/diffusionmodules/util.py line 165:
`freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=timesteps.device)`
generates a floating point exception. This seems related to other cpu vectorized floating-point operations issues introduced in pytorch 2.0.0.
Ultimately the fix is simply to make the arange tensor in the gpu automatically.
`freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half).to(device=timesteps.device)`
I left the additional .to(device) statement, though I believe it's superfluous.
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Research direction
Start at ldm/modules/diffusionmodules/util.py line 165 and reproduce the floating point exception with the reported training code on PyTorch 2.0.0. Verify that the frequency tensor is created on the timesteps device and that the reported exception no longer occurs.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 55/100