Variance of symbolic shape #4446
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marcellofuschi
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marcellofuschi
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May 7, 2024
| square_sum = ((self - self.mean(axis=axis, keepdim=True)).square()).sum(axis=axis, keepdim=keepdim) | ||
| return square_sum.div(max(0, prod(self.shape)/prod(square_sum.shape)-correction)) | ||
| squares = (self - self.mean(axis=axis, keepdim=True)).square() | ||
| n = prod([si for si, so in zip(self.shape, squares.sum(axis=axis, keepdim=True).shape) if si != so]) |
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For context, this is the same logic used in mean function
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It's likely correct, but this and symbolic shape mean need tests in symbolic_ops and symbolic_jit to make sure it's generating a kernel that uses variable input, and jit works. Either you can add relevant tests in those files, or i can take a look later this week (busy with mlperf this week). |
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test_symbolic_varpass