Remove torchaudio._internal.fft module (#1631)

`torchaudio._internal.fft` was originally added to account for the introduction of module `torch.fft`, when `torch.fft` could refer to either a module or function. Now that `torch.fft` refers unambiguously to a module, we remove `torchaudio._internal.fft` and replace references to it with `torch.fft`.

torchaudio/_internal/fft.py

-"""Compatibility module for fft-related functions
-
-In PyTorch 1.7, the new `torch.fft` module was introduced.
-
-To use this new module, one has to explicitly import `torch.fft`. however this will change
-the reference `torch.fft` is pointing from function to module.
-And this change takes effect not only in the client code but also in already-imported libraries too.
-Similarly, if a library does the explicit import, the rest of the application code must use the
-`torch.fft.fft` function.
-
-For this reason, to migrate the deprecated functions of fft-family, we need to use the new
-implementation under `torch.fft` without explicitly importing `torch.fft` module.
-
-This module provides a simple interface for the migration, abstracting away
-the access to the underlying C functions.
-
-Once the deprecated functions are removed from PyTorch and `torch.fft` starts to always represent
-the new module, we can get rid of this module and call functions under `torch.fft` directly.
-"""
-from typing import Optional
-
-import torch
-
-
-def rfft(input: torch.Tensor, n: Optional[int] = None, dim: int = -1, norm: Optional[str] = None) -> torch.Tensor:
-    # see: https://pytorch.org/docs/master/fft.html#torch.fft.rfft
-    return torch._C._fft.fft_rfft(input, n, dim, norm)

torchaudio/compliance/kaldi.py

@@ -5,7 +5,6 @@ import torch
 from torch import Tensor
 
 import torchaudio
-import torchaudio._internal.fft
 
 __all__ = [
     'get_mel_banks',
@@ -290,7 +289,7 @@ def spectrogram(waveform: Tensor,
         snip_edges, raw_energy, energy_floor, dither, remove_dc_offset, preemphasis_coefficient)
 
     # size (m, padded_window_size // 2 + 1, 2)
-    fft = torchaudio._internal.fft.rfft(strided_input)
+    fft = torch.fft.rfft(strided_input)
 
     # Convert the FFT into a power spectrum
     power_spectrum = torch.max(fft.abs().pow(2.), epsilon).log()  # size (m, padded_window_size // 2 + 1)
@@ -572,7 +571,7 @@ def fbank(waveform: Tensor,
         snip_edges, raw_energy, energy_floor, dither, remove_dc_offset, preemphasis_coefficient)
 
     # size (m, padded_window_size // 2 + 1)
-    spectrum = torchaudio._internal.fft.rfft(strided_input).abs()
+    spectrum = torch.fft.rfft(strided_input).abs()
     if use_power:
         spectrum = spectrum.pow(2.)
 

torchaudio/functional/filtering.py

@@ -5,8 +5,6 @@ from typing import Optional
 import torch
 from torch import Tensor
 
-import torchaudio._internal.fft
-
 
 def _dB2Linear(x: float) -> float:
     return math.exp(x * math.log(10) / 20.0)
@@ -1301,7 +1299,7 @@ def _measure(
     dftBuf[measure_len_ws:dft_len_ws].zero_()
 
     # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf);
-    _dftBuf = torchaudio._internal.fft.rfft(dftBuf)
+    _dftBuf = torch.fft.rfft(dftBuf)
 
     # memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf));
     _dftBuf[:spectrum_start].zero_()
@@ -1338,7 +1336,7 @@ def _measure(
     _cepstrum_Buf[spectrum_end:dft_len_ws >> 1].zero_()
 
     # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf);
-    _cepstrum_Buf = torchaudio._internal.fft.rfft(_cepstrum_Buf)
+    _cepstrum_Buf = torch.fft.rfft(_cepstrum_Buf)
 
     result: float = float(
         torch.sum(_cepstrum_Buf[cepstrum_start:cepstrum_end].abs().pow(2))