Real valued FFTs #1657
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Real valued FFTs #1657
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Note: The transpose rule is not correct yet (hence the failing tests).
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just want to pin this to the issue tracker #1877 |
mattjj
approved these changes
Jan 12, 2020
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Now with |
skye
reviewed
Jan 13, 2020
| self._CheckAgainstNumpy(onp_fn, np_fn, args_maker, check_dtypes=False, | ||
| tol=1e-4) | ||
| self._CompileAndCheck(np_fn, args_maker, check_dtypes=True) | ||
| # Test gradient for differentiable types. | ||
| if dtype in inexact_dtypes: | ||
| tol = 0.15 # TODO(skye): can we be more precise? | ||
| jtu.check_grads(np_fn, args_maker(), order=1, atol=tol, rtol=tol) |
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order=2 already tests all derivatives up to order=2. So the separate order=1 was just redundant.
| def _irfft_with_zeroed_inputs(irfft_fun): | ||
| # irfft isn't defined on the full domain of inputs, so in order to have a | ||
| # well defined derivative on the whole domain of the function, we zero-out | ||
| # the imaginary part of the first and possibly the last elements. |
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This logic is pretty complicated for a test... would be it be possible/simpler to limit the test inputs to irfft?
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The challenge here is that in order for the finite difference calculation to be correct, I need to apply the restriction to the perturbation as well. I don't think our check_grads machinery is set-up to do that unless we modify the function passed into it.
skye
approved these changes
Jan 13, 2020
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Note: The transpose rule is not correct yet (hence the failing tests).Everything should be working -- this is ready for review!