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Support real-valued input in torch.fft.irfft converter #2721

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Support real-valued input in torch.fft.irfft converter #2721

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6 changes: 6 additions & 0 deletions coremltools/converters/mil/frontend/torch/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8749,6 +8749,12 @@ def fft_ifftn(context, node):
def fft_irfft(context, node):
"""Lowers torch.fft.irfft by the dialect op `complex_irfft` from complex_dialect_ops.py."""
input_data, n, dim, norm = _get_inputs(context, node, expected=[4])
# torch.fft.irfft accepts a real input and treats it as a complex tensor with a zero
# imaginary part. The complex_irfft dialect op only takes complex64, so promote a real
# input to complex here before lowering.
if not types.is_complex(input_data.dtype):
imag_data = mb.fill(shape=mb.shape(x=input_data), value=0.0)
input_data = mb.complex(real_data=input_data, imag_data=imag_data)
irfft_res = mb.complex_irfft(data=input_data, n=n, dim=dim, norm=norm)
context.add(irfft_res, node.name)

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21 changes: 21 additions & 0 deletions coremltools/converters/mil/frontend/torch/test/test_torch_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12973,6 +12973,27 @@ def forward(self, x):
(2, 3, 4), FftModel(), backend=backend, compute_unit=compute_unit
)

@pytest.mark.parametrize(
"compute_unit, backend, n, dim, norm",
itertools.product(
compute_units,
backends,
[None, 1, 5],
[0, 1, -1],
[None, "forward", "backward", "ortho"],
),
)
def test_fft_irfft_real_input(self, compute_unit: ct.ComputeUnit, backend, n, dim, norm):
# torch.fft.irfft accepts a real input (treated as complex with a zero imaginary part).
# The input is not wrapped with torch.complex here, unlike test_fft_basic. See GH #2130.
class FftModel(torch.nn.Module):
def forward(self, x):
return torch.fft.irfft(x, n=n, dim=dim, norm=norm)

TorchBaseTest.run_compare_torch(
(2, 3, 4), FftModel(), backend=backend, compute_unit=compute_unit
)

@pytest.mark.parametrize(
"compute_unit, backend",
itertools.product(
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