Add a portable _fft_r2c kernel

[msluszniak]

Summary

Addresses the first half of #21950.

_fft_r2c.out only had an optimized (pocketfft-backed) kernel. A program that leaves it undelegated builds fine and writes a .pte, but a runtime carrying only the portable kernels cannot load it. The failure surfaces as 0x14 OperatorMissing from load_method, long after the export reported success.

_fft_r2c.out and _fft_c2r.out are two of only three ops in optimized.yaml with no counterpart in the portable functions.yaml:

portable ops: 206   optimized ops: 23
in optimized but NOT in portable (3):
   _fft_c2r.out
   _fft_r2c.out
   linear.out

The third, linear.out, ExecuTorch decomposes anyway, so the FFT pair is the real gap.

Approach

This is a direct O(n^2) evaluation of the transform sum, not a fast Fourier transform, in keeping with the portable library's role as the dependency-free reference; kernels/optimized keeps the asymptotically faster pocketfft path for anyone who links it. Audio front-ends transforming a few hundred points per frame, which is where this op tends to show up, are the intended case.

Two details worth calling out:

• Multi-dimensional transforms run the real transform along the last requested dimension and complex transforms along the rest, matching pocketfft's multi-axis r2c. A complex pass has to read a whole line before overwriting it; lines up to 128 elements (2 KB for double) use a stack buffer and longer ones ask the runtime for temporary memory. The single-dimension case, which is what torch.fft.rfft lowers to, needs no line buffer at all.
• The quarter-turn twiddle factors are returned exactly rather than through cos/sin, so a real input's Nyquist bin comes out with a zero imaginary part instead of rounding noise around 1e-16. That is what pocketfft produces and what the existing tests expect.

Test plan

Registers the shared op_fft_r2c_test for portable alongside aten and optimized. All five cases pass against both kernel libraries:

$ ./cmake-out/kernels/test/portable_kernels_test --gtest_filter='OpFftR2c*'
[  PASSED  ] 5 tests.
$ ./cmake-out/kernels/test/optimized_kernels_test --gtest_filter='OpFftR2c*'
[  PASSED  ] 5 tests.

End to end on the model from the issue (torch.fft.rfft(x).abs().pow(2) over [8, 512], exported with no partitioner so the op stays on the CPU), run through executor_runner built with portable kernels only:

	result
before	exit 134 (abort on load)
after	exit 0, output [262144., 1.2e-27, 2.4e-27, ...]

which is the correct abs(rfft(ones))**2: 512**2 in bin 0 and zero elsewhere.

Accuracy against numpy, since the tests above use exactly representable values:

case	max abs error
rfft, length 5 (odd, no Nyquist bin, no quarter-turn twiddles)	2.5e-15
rfft, 4x512	1.8e-13 (1.4e-15 relative to the largest output)
rfft2, 6x8	1.8e-14

I wanted to add the length-5 case as a regular test, but tensors_are_close() falls through to a bitwise memcmp for complex dtypes, so a complex comparison cannot have a tolerance and only exactly representable expected values can pass. That is why the existing cases all use small integers. Happy to fix that separately if it would be welcome.

Not addressed here

The issue also asks for lowering to fail when an op is left undelegated and no kernel exists, rather than emitting a .pte that dies at load. That one is a change to the AOT/runtime contract, since the export side does not know which kernel library the runtime will link (portable, optimized, or a selective build), so it needs its own discussion rather than riding along here.

[pytorch-bot]

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