rfft warning

[pythonmobile]

X = dct.dct(x)
UserWarning: The function torch.rfft is deprecated and will be removed in a future PyTorch release. Use the new torch.fft module functions, instead, by importing torch.fft and calling torch.fft.fft or torch.fft.rfft. (Triggered internally at /pytorch/aten/src/ATen/native/SpectralOps.cpp:590.)
Vc = torch.rfft(v, 1, onesided=False)

[Shuang Xu]

I met the same problem

[Xuechen Liu]

@zh217 Yes I am meeting same problem as well. But in short this is not happening for torch <=1.6 from my observation, but for torch 1.7.0 or later it does. I tried:

1. torch 1.6.0 + torchaudio 0.6.0
2. torch 1.7.0 + torchaudio 0.7.0
3. torch.1.7.1 + torchaudio 0.7.2

For the 1st case I did not observe any warning but for latter two I did.

Also I think it does not affect the performance of the system overall from my experiments, we may need some update to cope though.

[3omni]

With pytorch 1.8.0 the lib does not work anymore.
AttributeError: module 'torch' has no attribute 'rfft'

[Xuechen Liu]

@margaritageleta Thanks a lot for addressing.

@zh217 Shall I fix it if you don't have time?

[Liu Yue]

you can use these code to use pytorch 1.8.0 to simulate pytorch1.5.0, when signal_ndim = 1 and onesided=False.
for this file, these two functions are totally enough.

def _rfft(x, signal_ndim=1, onesided=True):
# b = torch.Tensor([[1,2,3,4,5],[2,3,4,5,6]])
# b = torch.Tensor([[1,2,3,4,5,6],[2,3,4,5,6,7]])
# torch 1.8.0 torch.fft.rfft to torch 1.5.0 torch.rfft as signal_ndim=1
# written by mzero
odd_shape1 = (x.shape[1] % 2 != 0)
x = torch.fft.rfft(x)
x = torch.cat([x.real.unsqueeze(dim=2), x.imag.unsqueeze(dim=2)], dim=2)
if onesided == False:
_x = x[:, 1:, :].flip(dims=[1]).clone() if odd_shape1 else x[:, 1:-1, :].flip(dims=[1]).clone()
_x[:,:,1] = -1 * _x[:,:,1]
x = torch.cat([x, _x], dim=1)
return x

def _irfft(x, signal_ndim=1, onesided=True):
# b = torch.Tensor([[1,2,3,4,5],[2,3,4,5,6]])
# b = torch.Tensor([[1,2,3,4,5,6],[2,3,4,5,6,7]])
# torch 1.8.0 torch.fft.irfft to torch 1.5.0 torch.irfft as signal_ndim=1
# written by mzero
if onesided == False:
res_shape1 = x.shape[1]
x = x[:,:(x.shape[1] // 2 + 1),:]
x = torch.complex(x[:,:,0].float(), x[:,:,1].float())
x = torch.fft.irfft(x, n=res_shape1)
else:
x = torch.complex(x[:,:,0].float(), x[:,:,1].float())
x = torch.fft.irfft(x)
return x

[sunner]

Just share my experience.
Maybe we can inherit most of code while modifying only two line:

1. Substitude rfft with fft in function dct:

def dct(x, norm=None):
    """
        <Skip comment>
    """
    x_shape = x.shape
    N = x_shape[-1]
    x = x.contiguous().view(-1, N)

    v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)

    # Vc = torch.rfft(v, 1, onesided=False)           # comment this line
    Vc = torch.view_as_real(torch.fft.fft(v, dim=1))  # add this line

    k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
    W_r = torch.cos(k)
    W_i = torch.sin(k)

    V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i

    if norm == 'ortho':
        V[:, 0] /= np.sqrt(N) * 2
        V[:, 1:] /= np.sqrt(N / 2) * 2

    V = 2 * V.view(*x_shape)

    return V

2. Substitude irfft with latest version in function idct:

def idct(X, norm=None):
    """
        <Skip comment>
    """

    x_shape = X.shape
    N = x_shape[-1]

    X_v = X.contiguous().view(-1, x_shape[-1]) / 2

    if norm == 'ortho':
        X_v[:, 0] *= np.sqrt(N) * 2
        X_v[:, 1:] *= np.sqrt(N / 2) * 2

    k = torch.arange(x_shape[-1], dtype=X.dtype, device=X.device)[None, :] * np.pi / (2 * N)
    W_r = torch.cos(k)
    W_i = torch.sin(k)

    V_t_r = X_v
    V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)

    V_r = V_t_r * W_r - V_t_i * W_i
    V_i = V_t_r * W_i + V_t_i * W_r

    V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)

    
    # v = torch.irfft(V, 1, onesided=False)                             # comment this line
    v= torch.fft.irfft(torch.view_as_complex(V), n=V.shape[1], dim=1)   # add this line

    x = v.new_zeros(v.shape)
    x[:, ::2] += v[:, :N - (N // 2)]
    x[:, 1::2] += v.flip([1])[:, :N // 2]

    return x.view(*x_shape)

With these two-line modification, we can perform the same functionality under the same codebase in PyTorch 1.7

[Julia Grabinski]

I'm facing still the same issue, did you already include the improvements into the code?

[Xuechen Liu]

@GeJulia We have not since I have not heard @zh217 for a very long time.
But you definitely can have a try with the code from PR #24, although from my point of view it is WIP. I gave some minor suggestions.

[Jon Nordby]

This can be closed now. Fixed in PR #24