Automatically move CUDA non XLA Tensors to XLA Device and back to CUDA device #6644
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vanbasten23
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Mar 1, 2024
vanbasten23
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Mar 1, 2024
vanbasten23
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Mar 5, 2024
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Is this an experimental pr or you want to merge this? |
Ideally we would merge this, or is there a reason not to? |
JackCaoG
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Mar 8, 2024
JackCaoG
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JackCaoG
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changm
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JackCaoG
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Mar 11, 2024
JackCaoG
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JackCaoG
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Mar 11, 2024
torch_xla/core/dynamo_bridge.py
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| @@ -387,6 +446,12 @@ def optimized_mod(*args): | |||
| nonlocal xla_args_need_update | |||
| nonlocal skip_checking_input_sharding_threashold | |||
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| original_device: torch.device = _get_input_arg_device(args) | |||
| is_cuda_args: bool = _args_on_cuda(args) | |||
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_args_on_cuda will call _get_input_arg_device which is redundant.
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I still think it's a little cleaner to do the redundant call, but removed the call here.
ysiraichi
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Mar 11, 2024
JackCaoG
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Mar 12, 2024
JackCaoG
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Mar 12, 2024
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Sorry for being late. It's looking good! |
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yitongh
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yitongh
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Currently only works for inference. The assumptions don't hold for training with Autograd yet.