Use create_memref_view_from_dlpack within Tripy
#72
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markkraay
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create_memref_view_from_dlpack within Tripy
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jhalakpatel
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Aug 8, 2024
| @@ -166,11 +166,11 @@ def test_weights_loading_from_torch(self, kind): | |||
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| tripy_linear = tp.Linear(2, 3) | |||
| if kind == "gpu": | |||
| tripy_linear.weight = tp.Parameter(tp.Tensor(torch_linear.weight.to("cuda"), device=tp.device(kind))) | |||
| tripy_linear.bias = tp.Parameter(tp.Tensor(torch_linear.bias.to("cuda"), device=tp.device(kind))) | |||
| tripy_linear.weight = tp.Parameter(tp.Tensor(torch_linear.weight.detach().to("cuda"), device=tp.device(kind))) | |||
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What happens when you remove detach()?
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Torch tensors will not allow invocation of the __dlpack__ method if they have a grad tensor attached.
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can we instead handle this internally? This could lead to bad UX.
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Are you suggesting we add a check within the Array._memref function to see if the data is a torch tensor and then manually calling detach()?
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Yeah, that would be better.
jhalakpatel
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Aug 9, 2024
markkraay
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Aug 16, 2024
This function greatly simplifies Tripy's Array implementation. We want to be able to handle memref creation from all types that implement the `__dlpack__()` interface rather than the limited set we currently support. This function should allow us to achieve this. The corresponding Tripy changes are #72.
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I'm closing this PR because #95 covers all the changes. Thanks. |
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Relies on #71. Simplifies our Array class by using
create_memref_view_from_dlpack.