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Sep 16, 2026
| image = latents | ||
| else: | ||
| latents = latents.to(self.vae.dtype) | ||
| torch_accelerator_module = getattr(torch, get_device(), torch.cuda) |
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Deadcode since min supported torch version is 2.6
|
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sayakpaul
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Sep 17, 2026
| raise ValueError("Using streams for data transfer requires a CUDA device, or an Intel XPU device.") | ||
| if use_stream and onload_device.type == "cpu": | ||
| raise ValueError("Using streams for data transfer requires an accelerator onload device, got `cpu`.") | ||
| stream = TorchDeviceBackend(onload_device).Stream() if use_stream else None |
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What if the underlying device doesn't support streams?
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| torch_accelerator_module = getattr(torch, get_device(), torch.cuda) | ||
| oom_error = ( | ||
| torch.OutOfMemoryError | ||
| if is_torch_version(">=", "2.5.0") | ||
| else torch_accelerator_module.OutOfMemoryError | ||
| ) |
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Safe because we pin on >=2.6.
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| def empty_cache(self) -> None: | ||
| # Backends without a caching allocator (cpu, neuron) have nothing to clear. | ||
| empty_cache = getattr(self.module, "empty_cache", None) | ||
| if empty_cache is not None: | ||
| empty_cache() |
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Should we also warn in case empty_cache is None?
| """Tests for :class:`diffusers.utils.torch_utils.TorchDeviceBackend` on the CPU backend.""" | ||
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| def test_module_resolves_from_str_and_torch_device(self): | ||
| import torch |
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I think we can keep all the imports at the top. They seem harmless.
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What does this PR do?
Support for torch device backends keeps growing, and each new backend adds a branch at every site that touches the device module. e.g. Adding NPU streams to group offloading (#14785) looks like this:
This PR introduces a
TorchDeviceBackendobject that automatically routes to the appropriate backend module for a given device type. e.g.Fixes # (issue)
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self-reviewskill on the diff?documentation guidelines, and
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