Re-land: upsample_bilinear: fix output data-type.
#7168
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I thought |
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haha, you need to first set the |
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Hmmm. Let me try rebasing the PR. |
ysiraichi
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@JackCaoG Could you take a look at this PR whenever you have some time? |
JackCaoG
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Jun 4, 2024
| @@ -2118,6 +2118,59 @@ def fn(inp, s): | |||
| self.assertEqual(out, Xout.cpu()) | |||
| self.assertEqual("f16", torch_xla._XLAC._get_xla_tensor_shape_type(Xout)) | |||
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| # We skip TPU for 2 reasons: | |||
| # 1. upsample_bilinear on f64 tensors doesn't work on TPUs | |||
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will f64 works prior to your change on TPU? I don't want this to become a regression.
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My change doesn't affect TPU. The lowering for upsample_bilinear is different for CUDA and TPU. This change only affects non-TPU and non-Neuron devices.
JackCaoG
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Jun 4, 2024
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Re-land: #7111
Previously, it was reverted because it broke TPU CI. That happened due to the newly added test in that PR. Specifically, because it called
upsample_bilinearwith f64 data-type. Since the bug was only on non-TPU and non-Neuron device types, we simply skip the test for those. It's safe to do that because there's a short-circuit that avoids the bug for those devices.cc @miladm @JackCaoG