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docs/Changelog.md

@@ -20880,14 +20880,14 @@ This version of the operator has been available since version 17 of the default
 <dt><tt>input</tt> (non-differentiable) : T1</dt>
 <dd>For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.</dd>
 <dt><tt>dft_length</tt> (optional, non-differentiable) : T2</dt>
-<dd>The length of the signal.If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. </dd>
+<dd>The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. </dd>
 </dl>
 
 #### Outputs
 
 <dl>
 <dt><tt>output</tt> : T1</dt>
-<dd>The Fourier Transform of the input vector.If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.</dd>
+<dd>The Fourier Transform of the input vector. If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.</dd>
 </dl>
 
 #### Type Constraints
@@ -23989,6 +23989,66 @@ This version of the operator has been available since version 20 of the default
 <dd>Constrain output types to be numerics.</dd>
 </dl>
 
+### <a name="DFT-20"></a>**DFT-20**</a>
+
+  Computes the discrete Fourier Transform (DFT) of the input.
+
+  Assuming the input has shape `[M, N]`, where `N` is the dimension over which the
+  DFT is computed and `M` denotes the conceptual "all other dimensions",
+  the DFT `y[m, k]` of of shape `[M, N]` is defined as
+
+  $$y[k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[n] \, ,$$
+
+  and the inverse transform is defined as
+
+  $$x[n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[k] \, ,$$
+
+  where $j$ is the imaginary unit.
+
+  The actual shape of the output is specified in the "output" section.
+
+  Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html
+
+#### Version
+
+This version of the operator has been available since version 20 of the default ONNX operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>inverse</tt> : int (default is 0)</dt>
+<dd>Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.</dd>
+<dt><tt>onesided</tt> : int (default is 0)</dt>
+<dd>If `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.</dd>
+</dl>
+
+#### Inputs (1 - 3)
+
+<dl>
+<dt><tt>input</tt> (non-differentiable) : T1</dt>
+<dd>For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.</dd>
+<dt><tt>axis</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>The axis as a scalar on which to perform the DFT. Default is `-1` (last axis). Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. </dd>
+<dt><tt>dft_length</tt> (optional, non-differentiable) : T2</dt>
+<dd>The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T1</dt>
+<dd>The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T1</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+<dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain scalar length types to integers.</dd>
+</dl>
+
 ### <a name="Gelu-20"></a>**Gelu-20**</a>
 
   Gelu takes one input data (Tensor<T>) and produces one

docs/Operators.md

@@ -43,7 +43,7 @@ For an operator input/output's differentiability, it can be differentiable,
 |<a href="#Cos">Cos</a>|<a href="Changelog.md#Cos-7">7</a>|
 |<a href="#Cosh">Cosh</a>|<a href="Changelog.md#Cosh-9">9</a>|
 |<a href="#CumSum">CumSum</a>|<a href="Changelog.md#CumSum-14">14</a>, <a href="Changelog.md#CumSum-11">11</a>|
-|<a href="#DFT">DFT</a>|<a href="Changelog.md#DFT-17">17</a>|
+|<a href="#DFT">DFT</a>|<a href="Changelog.md#DFT-20">20</a>, <a href="Changelog.md#DFT-17">17</a>|
 |<a href="#DeformConv">DeformConv</a>|<a href="Changelog.md#DeformConv-19">19</a>|
 |<a href="#DepthToSpace">DepthToSpace</a>|<a href="Changelog.md#DepthToSpace-13">13</a>, <a href="Changelog.md#DepthToSpace-11">11</a>, <a href="Changelog.md#DepthToSpace-1">1</a>|
 |<a href="#DequantizeLinear">DequantizeLinear</a>|<a href="Changelog.md#DequantizeLinear-19">19</a>, <a href="Changelog.md#DequantizeLinear-13">13</a>, <a href="Changelog.md#DequantizeLinear-10">10</a>|
@@ -6718,46 +6718,64 @@ expect(node, inputs=[x, axis], outputs=[y], name="test_cumsum_2d_negative_axis")
 
 ### <a name="DFT"></a><a name="dft">**DFT**</a>
 
-  Computes the discrete Fourier transform of input.
+  Computes the discrete Fourier Transform (DFT) of the input.
+
+  Assuming the input has shape `[M, N]`, where `N` is the dimension over which the
+  DFT is computed and `M` denotes the conceptual "all other dimensions",
+  the DFT `y[m, k]` of of shape `[M, N]` is defined as
+
+  $$y[k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[n] \, ,$$
+
+  and the inverse transform is defined as
+
+  $$x[n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[k] \, ,$$
+
+  where $j$ is the imaginary unit.
+
+  The actual shape of the output is specified in the "output" section.
+
+  Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html
 
 #### Version
 
-This version of the operator has been available since version 17 of the default ONNX operator set.
+This version of the operator has been available since version 20 of the default ONNX operator set.
+
+Other versions of this operator: <a href="Changelog.md#DFT-17">17</a>
 
 #### Attributes
 
 <dl>
-<dt><tt>axis</tt> : int (default is 1)</dt>
-<dd>The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index.</dd>
 <dt><tt>inverse</tt> : int (default is 0)</dt>
-<dd>Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.</dd>
+<dd>Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.</dd>
 <dt><tt>onesided</tt> : int (default is 0)</dt>
-<dd>If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.</dd>
+<dd>If `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.</dd>
 </dl>
 
-#### Inputs (1 - 2)
+#### Inputs (1 - 3)
 
 <dl>
 <dt><tt>input</tt> (non-differentiable) : T1</dt>
-<dd>For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.</dd>
+<dd>For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.</dd>
+<dt><tt>axis</tt> (optional, non-differentiable) : tensor(int64)</dt>
+<dd>The axis as a scalar on which to perform the DFT. Default is `-1` (last axis). Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. </dd>
 <dt><tt>dft_length</tt> (optional, non-differentiable) : T2</dt>
-<dd>The length of the signal.If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. </dd>
+<dd>The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal. </dd>
 </dl>
 
 #### Outputs
 
 <dl>
 <dt><tt>output</tt> : T1</dt>
-<dd>The Fourier Transform of the input vector.If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length.</dd>
+<dd>The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.</dd>
 </dl>
 
 #### Type Constraints
 
 <dl>
-<dt><tt>T1</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
+<dt><tt>T1</tt> : tensor(bfloat16), tensor(float16), tensor(float), tensor(double)</dt>
 <dd>Constrain input and output types to float tensors.</dd>
 <dt><tt>T2</tt> : tensor(int32), tensor(int64)</dt>
-<dd>Constrain scalar length types to int64_t.</dd>
+<dd>Constrain scalar length types to integers.</dd>
 </dl>