Ensure PyTorch cuda compatability #21
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Previously, many arrays were being created without specifying the device. This omission caused device errors when using CoLA with PyTorch and cuda. This PR adds the device kwargs to all uses of the following xnp methods: - zeros - ones - array - canonical - eye
gpleiss
commented
Aug 21, 2023
| @@ -121,7 +121,7 @@ def lu_solve(a, b): | |||
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| def get_device(array): | |||
| if not isinstance(array, jax.core.Tracer): | |||
| if not isinstance(array, jax.core.Tracer) and hasattr(array, 'device'): | |||
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I added this second check because, at least in the tests, the Jax xnp seems to be used with numpy arrays which do not have a device attribute.
Codecov Report
@@ Coverage Diff @@
## main #21 +/- ##
==========================================
+ Coverage 81.93% 82.00% +0.07%
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Files 39 39
Lines 3277 3290 +13
==========================================
+ Hits 2685 2698 +13
Misses 592 592
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Previously, many arrays were being created without specifying the device. This omission caused device errors when using CoLA with PyTorch and cuda.
This PR adds the device kwargs to all uses of the following xnp methods: