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Auto-generated TensorFlow FFT ops

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PiperOrigin-RevId: 316208389
Change-Id: Ie8bb752225253f0d6b375cb5759bac400451a1e9
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Smit Hinsu 2020-06-12 17:36:15 -07:00 committed by TensorFlower Gardener
parent 1fbc648c9c
commit fe34364219
1 changed files with 272 additions and 0 deletions
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272
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
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@ -2984,6 +2984,63 @@ i.e. `exp(x) - 1` or `e^(x) - 1`, where `x` is the input tensor.
TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}
def TF_FFTOp : TF_Op<"FFT", [NoSideEffect]> {
let summary = "Fast Fourier transform.";
let description = [{
Computes the 1-dimensional discrete Fourier transform over the inner-most
dimension of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_FFT2DOp : TF_Op<"FFT2D", [NoSideEffect]> {
let summary = "2D fast Fourier transform.";
let description = [{
Computes the 2-dimensional discrete Fourier transform over the inner-most
2 dimensions of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_FFT3DOp : TF_Op<"FFT3D", [NoSideEffect]> {
let summary = "3D fast Fourier transform.";
let description = [{
Computes the 3-dimensional discrete Fourier transform over the inner-most 3
dimensions of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_FakeQuantWithMinMaxArgsOp : TF_Op<"FakeQuantWithMinMaxArgs", [NoSideEffect, SameOperandsAndResultType]> {
let summary = [{
Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
@ -3743,6 +3800,161 @@ table will be immutable.
);
}
def TF_IFFTOp : TF_Op<"IFFT", [NoSideEffect]> {
let summary = "Inverse fast Fourier transform.";
let description = [{
Computes the inverse 1-dimensional discrete Fourier transform over the
inner-most dimension of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IFFT2DOp : TF_Op<"IFFT2D", [NoSideEffect]> {
let summary = "Inverse 2D fast Fourier transform.";
let description = [{
Computes the inverse 2-dimensional discrete Fourier transform over the
inner-most 2 dimensions of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IFFT3DOp : TF_Op<"IFFT3D", [NoSideEffect]> {
let summary = "Inverse 3D fast Fourier transform.";
let description = [{
Computes the inverse 3-dimensional discrete Fourier transform over the
inner-most 3 dimensions of `input`.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IRFFTOp : TF_Op<"IRFFT", [NoSideEffect]> {
let summary = "Inverse real-valued fast Fourier transform.";
let description = [{
Computes the inverse 1-dimensional discrete Fourier transform of a real-valued
signal over the inner-most dimension of `input`.
The inner-most dimension of `input` is assumed to be the result of `RFFT`: the
`fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If
`fft_length` is not provided, it is computed from the size of the inner-most
dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to
compute `input` is odd, it should be provided since it cannot be inferred
properly.
Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller
than the corresponding dimension of `input`, the dimension is cropped. If it is
larger, the dimension is padded with zeros.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input,
I32Tensor:$fft_length
);
let results = (outs
TF_F32OrF64Tensor:$output
);
TF_DerivedResultTypeAttr Treal = TF_DerivedResultTypeAttr<0>;
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IRFFT2DOp : TF_Op<"IRFFT2D", [NoSideEffect]> {
let summary = "Inverse 2D real-valued fast Fourier transform.";
let description = [{
Computes the inverse 2-dimensional discrete Fourier transform of a real-valued
signal over the inner-most 2 dimensions of `input`.
The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`:
The inner-most dimension contains the `fft_length / 2 + 1` unique components of
the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
from the size of the inner-most 2 dimensions of `input`. If the FFT length used
to compute `input` is odd, it should be provided since it cannot be inferred
properly.
Along each axis `IRFFT2D` is computed on, if `fft_length` (or
`fft_length / 2 + 1` for the inner-most dimension) is smaller than the
corresponding dimension of `input`, the dimension is cropped. If it is larger,
the dimension is padded with zeros.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input,
I32Tensor:$fft_length
);
let results = (outs
TF_F32OrF64Tensor:$output
);
TF_DerivedResultTypeAttr Treal = TF_DerivedResultTypeAttr<0>;
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IRFFT3DOp : TF_Op<"IRFFT3D", [NoSideEffect]> {
let summary = "Inverse 3D real-valued fast Fourier transform.";
let description = [{
Computes the inverse 3-dimensional discrete Fourier transform of a real-valued
signal over the inner-most 3 dimensions of `input`.
The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`:
The inner-most dimension contains the `fft_length / 2 + 1` unique components of
the DFT of a real-valued signal. If `fft_length` is not provided, it is computed
from the size of the inner-most 3 dimensions of `input`. If the FFT length used
to compute `input` is odd, it should be provided since it cannot be inferred
properly.
Along each axis `IRFFT3D` is computed on, if `fft_length` (or
`fft_length / 2 + 1` for the inner-most dimension) is smaller than the
corresponding dimension of `input`, the dimension is cropped. If it is larger,
the dimension is padded with zeros.
}];
let arguments = (ins
TensorOf<[TF_Complex128, TF_Complex64]>:$input,
I32Tensor:$fft_length
);
let results = (outs
TF_F32OrF64Tensor:$output
);
TF_DerivedResultTypeAttr Treal = TF_DerivedResultTypeAttr<0>;
TF_DerivedOperandTypeAttr Tcomplex = TF_DerivedOperandTypeAttr<0>;
}
def TF_IdentityNOp : TF_Op<"IdentityN", [NoSideEffect]> {
let summary = [{
Returns a list of tensors with the same shapes and contents as the input
@ -6349,6 +6561,66 @@ the dimension is padded with zeros.
TF_DerivedResultTypeAttr Tcomplex = TF_DerivedResultTypeAttr<0>;
}
def TF_RFFT2DOp : TF_Op<"RFFT2D", [NoSideEffect]> {
let summary = "2D real-valued fast Fourier transform.";
let description = [{
Computes the 2-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 2 dimensions of `input`.
Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
`fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
of `output`: the zero-frequency term, followed by the `fft_length / 2`
positive-frequency terms.
Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the
corresponding dimension of `input`, the dimension is cropped. If it is larger,
the dimension is padded with zeros.
}];
let arguments = (ins
TF_F32OrF64Tensor:$input,
I32Tensor:$fft_length
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Treal = TF_DerivedOperandTypeAttr<0>;
TF_DerivedResultTypeAttr Tcomplex = TF_DerivedResultTypeAttr<0>;
}
def TF_RFFT3DOp : TF_Op<"RFFT3D", [NoSideEffect]> {
let summary = "3D real-valued fast Fourier transform.";
let description = [{
Computes the 3-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 3 dimensions of `input`.
Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the
`fft_length / 2 + 1` unique components of the FFT for the inner-most dimension
of `output`: the zero-frequency term, followed by the `fft_length / 2`
positive-frequency terms.
Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the
corresponding dimension of `input`, the dimension is cropped. If it is larger,
the dimension is padded with zeros.
}];
let arguments = (ins
TF_F32OrF64Tensor:$input,
I32Tensor:$fft_length
);
let results = (outs
TensorOf<[TF_Complex128, TF_Complex64]>:$output
);
TF_DerivedOperandTypeAttr Treal = TF_DerivedOperandTypeAttr<0>;
TF_DerivedResultTypeAttr Tcomplex = TF_DerivedResultTypeAttr<0>;
}
def TF_RandomGammaGradOp : TF_Op<"RandomGammaGrad", [NoSideEffect, ResultsBroadcastableShape]>,
WithBroadcastableBinOpBuilder {
let summary = [{