nvidia--tensorrt/plugin/fftPlugin

FFTPlugin

Table Of Contents

• Description
• Structure
  • Inputs
  • Outputs
  • Parameters
  • Complex layout and normalization
• Additional resources
• License

Description

FFTPlugin computes a Fast Fourier Transform with cuFFT. It transforms the trailing ndims dimensions of the input and batches over the leading dimensions. It supports complex-to-complex (C2C), onesided real-to-complex (R2C), and onesided complex-to-real (C2R) transforms in FP32, FP16, and BF16, for 1D, 2D, and 3D signals.

The plugin backs the ONNX DFT operator. The ONNX parser routes DFT nodes to this plugin, so most users never construct the plugin directly.

Structure

Inputs

FFTPlugin takes one or two inputs.

• input: the signal.
  • C2C: complex, [..., N, 2] (last dim is [real, imag]).
  • R2C: real, [..., N].
  • C2R: complex, [..., N/2 + 1, 2].
• fft_length (optional, C2R only): an int64 shape input giving the original signal length N. Required to reconstruct odd-length signals, since both even N and odd N map to the same N/2 + 1 frequency bins. Marked as a shape input via the ONNX tensorrt_plugin_shape_input_indices attribute.

Outputs

FFTPlugin produces a single output, with the same element type as input.

• C2C: complex, [..., N, 2].
• R2C: complex, [..., N/2 + 1, 2].
• C2R: real, [..., N].

Parameters

Parameter	Type	Description
inverse	int32	0 for the forward transform, 1 for the inverse.
onesided	int32	0 for C2C, 1 for the onesided R2C (forward) or C2R (inverse) transform.
ndims	int32	Number of trailing dimensions to transform: 1, 2, or 3.

The creator rejects values outside these ranges at plugin creation.

Complex layout and normalization

Complex values use the interleaved [..., 2] (real, imaginary) layout shared by ONNX DFT, PyTorch's torch.view_as_real, and the existing TensorRT STFT importer. TensorRT does not need a first-class complex type because the layers above the plugin already pack complex as real.

The plugin follows cuFFT's unnormalized convention: neither the forward nor the inverse transform divides by N. To match PyTorch's normalized inverse, divide the inverse output by N.

FP16 and BF16 transforms require power-of-two signal lengths along every transformed dimension (a cuFFT restriction). The plugin rejects non-power-of-two FP16/BF16 shapes rather than producing incorrect output.

Additional resources

• ONNX DFT operator
• cuFFT documentation
• TensorRT plugin API

Changelog

• 2026/07/07: Reject out-of-range inverse and onesided values at plugin creation
• 2026/06/22: Initial release of this plugin

Known issues

• The transform axis must be the trailing signal axis (axis == -2). A DFT on an interior axis is reported as kUNSUPPORTED_NODE. Constant-fold or transpose the axis first.
• FP16 and BF16 require power-of-two signal lengths along every transformed dimension (a cuFFT restriction). Non-power-of-two shapes are rejected rather than producing incorrect output.
• dft_length padding/truncation is not supported. For the onesided inverse (C2R) it is used only to disambiguate odd N.

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.