Migrate runtime.xla_device in favor of core.xla_model.xla_device #9200
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yaoshiang
requested changes
May 22, 2025
| @@ -15,14 +15,14 @@ import torch | |||
| import torch_xla | |||
| import torch_xla.core.xla_model as xm | |||
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| t = torch.randn(2, 2, device=xm.xla_device()) | |||
| t = torch.randn(2, 2, device=torch_xla.device()) | |||
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can this be changed to device="xla"?
| print(t.device) | ||
| print(t) | ||
| ``` | ||
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| This code should look familiar. PyTorch/XLA uses the same interface as regular | ||
| PyTorch with a few additions. Importing `torch_xla` initializes PyTorch/XLA, and | ||
| `xm.xla_device()` returns the current XLA device. This may be a CPU or TPU | ||
| `torch_xla.device()` returns the current XLA device. This may be a CPU or TPU |
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can this be further simplified to simply describing that users get a devicea via torch.device("xla")?
| t0 = torch.randn(2, 2, device=xm.xla_device()) | ||
| t1 = torch.randn(2, 2, device=xm.xla_device()) | ||
| t0 = torch.randn(2, 2, device=torch_xla.device()) | ||
| t1 = torch.randn(2, 2, device=torch_xla.device()) |
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can this be device="xla"?
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fixes #7831