fixed NaN with in log_prob corr < -1e-8 for SineBivariateVonMises
#3165
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Do you want to clip like in numpyro? |
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@fehiepsi yes, but couldn't find the float epsilon of a tensor in the docs. |
fehiepsi
reviewed
Dec 20, 2022
| correlation = ( | ||
| weighted_correlation * sqrt_(phi_concentration * psi_concentration) | ||
| (weighted_correlation * sqrt_(phi_concentration * psi_concentration)).clamp(min=eps) | ||
| + 1e-8 |
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Do we need to clamp and and 1e-8 here? I think we address numerical issues in norm_const property.
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If you remove 1e-8 here. Could you also remove it in numpyro for consistency?
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Yeah, I guess we don't need either. I'll fix it in NumPyro too.
| @@ -135,9 +136,10 @@ def norm_const(self): | |||
| (self.phi_concentration, self.psi_concentration), dim=-1 | |||
| ).view(-1, 2) | |||
| m = torch.arange(50, device=self.phi_loc.device).view(-1, 1) | |||
| eps = torch.finfo(corr.dtype).eps | |||
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I think you can use tiny. Its log is still finite.
fehiepsi
approved these changes
Dec 20, 2022
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Fixes
NaNinlog_probwhen the correlation is below zero inSineBivariateVonMises. Removes correlation bias (+1e-8) in normalization constant.See PR NumPyro#1515 and issue NumPyro#1511.