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Optimize FFT logic for complex strided input to avoid oversized memory allocation #2939

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Optimize FFT logic for complex strided input to avoid oversized memory allocation #2939

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Optimize FFT for strided input to avoid oversized allocation
vlad-perevezentsev Jun 1, 2026
881cf69
Merge master into fix_fft_logic
vlad-perevezentsev Jun 1, 2026
fbb13bd
Update changelog
vlad-perevezentsev Jun 1, 2026
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ This release is compatible with NumPy 2.4.5.
* Updated `searchsorted` implementations to align with the 2025.12 array API spec [gh-2902](https://github.com/IntelPython/dpnp/pull/2902)
* Updated tests to align with NumPy 2.4.5 compatibility [gh-2920](https://github.com/IntelPython/dpnp/pull/2920)
* Replaced `.pxi` includes in `dpnp.tensor` with modular `.pxd`/`.pyx` Cython imports [#2913](https://github.com/IntelPython/dpnp/pull/2913)
* Improved performance of `dpnp.fft` functions for complex strided input by avoiding oversized allocations and extra copies [#2939](https://github.com/IntelPython/dpnp/pull/2939)

### Deprecated

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19 changes: 16 additions & 3 deletions dpnp/fft/dpnp_utils_fft.py
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Expand Up @@ -408,12 +408,25 @@ def _fft(a, norm, out, forward, in_place, c2c, axes, batch_fft=True):
a = dpnp.reshape(a, local_shape)
index = 1

if not a.flags.c_contiguous:
# cuFFT requires input arrays to be C-contiguous (row-major)
# for correct execution
if (
dpnp.is_cuda_backend(a) and not a.flags.c_contiguous
): # pragma: no cover
if dpnp.is_cuda_backend(a): # pragma: no cover
a = dpnp.ascontiguousarray(a)
else:
# Check if the memory footprint of the strides exceeds
# the number of elements.
# If so, copy to contiguous to avoid oversized allocation
# for the output array and unnecessary copy to contiguous
# after oneMKL FFT
_strides = dpnp.get_usm_ndarray(a).strides
_shape = a.shape
# max element offset reachable by the strides
max_disp = sum(
st * (sh - 1) for st, sh in zip(_strides, _shape) if st > 0
)
if (max_disp + 1) > a.size:
a = dpnp.ascontiguousarray(a)

# w/a for cuFFT to avoid "Invalid strides" error when
# the last dimension is 1 and there are multiple axes
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