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pytorch istft runs slower than torchaudio istft especially at higher n_fft

   September 17, 2020  1 Comment on pytorch istft runs slower than torchaudio istft especially at higher n_fft

🐛 Bug

There seems to be a significant performance drop after migrating the istft from torchaudio to pytorch.
migration discussed here #34827
I’ve also noticed the performance gap increases with greater n_fft.

To Reproduce

pip install torch==1.6.0

import torch
import time
n_fft = 2**15
x = torch.rand(size=[1, n_fft//2 + 1, 10, 2])
start = time.time()
y = torch.functional.istft(x,n_fft)
end = time.time()
print(end - start)

pip install torchaudio==0.5.1

import torch
import torchaudio
import time
n_fft = 2**15
x = torch.rand(size=[1, n_fft//2 + 1, 10, 2])
start = time.time()
y = torchaudio.functional.istft(x,n_fft)
end = time.time()
print(end - start)

Expected behavior

I would expect similar timings between:

torch.functional.istft in torch==1.6.0
and
torchaudio.functional.istft in torchaudio==0.5.1

Environment running torch.functional.istft

PyTorch version: 1.6.0
Is debug build: No
CUDA used to build PyTorch: 10.2

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.12.0

Python version: 3.6
Is CUDA available: No
CUDA runtime version: 10.1.243
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] torch==1.6.0
[pip3] torchaudio==0.6.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.3.1
[pip3] torchvision==0.6.1+cu101
[conda] Could not collect

Environment running torchaudio.functional.istft

PyTorch version: 1.5.1+cu101
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.12.0

Python version: 3.6
Is CUDA available: No
CUDA runtime version: 10.1.243
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] torch==1.5.1+cu101
[pip3] torchaudio==0.5.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.3.1
[pip3] torchvision==0.6.1+cu101
[conda] Could not collect

Additional context

Both environments are from google colab

cc @ezyang @gchanan @zou3519 @VitalyFedyunin @ngimel

Full dicussion on github.com

1 possible answer(s) on “pytorch istft runs slower than torchaudio istft especially at higher n_fft”

  1. I confirm that I could reproduce the issue.

    Total Par Call
    PyTorch 1.6.0 9.0404 0.904
    torchaudio 0.5.0 0.1052 0.010
    script 
    import time

import torch
import torchaudio

n_fft = 2**15
x = torch.rand(size=[1, n_fft//2 + 1, 10, 2])

start = time.monotonic()
n = 10
for _ in range(n):
    # y = torchaudio.functional.istft(x,n_fft)
    y = torch.functional.istft(x,n_fft)
elapsed = time.monotonic() - start

print(elapsed, elapsed / n)
    Env 1: PyTorch 1.6.0 
    $ python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 1.5.0
Is debug build: No
CUDA used to build PyTorch: 10.2

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: version 3.10.2

Python version: 3.6
Is CUDA available: No
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: Quadro GP100
GPU 1: Quadro GP100

Nvidia driver version: 418.116.00
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] torch==1.5.0
[pip3] torchaudio==0.5.0a0+3305d5c
[conda] blas                      1.0                         mkl
[conda] mkl                       2020.1                      217
[conda] mkl-service               2.3.0            py36he904b0f_0
[conda] mkl_fft                   1.1.0            py36h23d657b_0
[conda] mkl_random                1.1.1            py36h0573a6f_0
[conda] pytorch                   1.5.0           py3.6_cuda10.2.89_cudnn7.6.5_0    pytorch
[conda] torchaudio                0.5.0                      py36    pytorch
    Env 2: torchaudio 0.5.0 
    $ python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 1.6.0
Is debug build: No
CUDA used to build PyTorch: 10.2

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: version 3.10.2

Python version: 3.6
Is CUDA available: No
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: Quadro GP100
GPU 1: Quadro GP100

Nvidia driver version: 418.116.00
cuDNN version: Could not collect

Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] torch==1.6.0
[pip3] torchaudio==0.6.0a0+f17ae39
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               10.2.89              hfd86e86_1
[conda] mkl                       2020.1                      217
[conda] mkl-service               2.3.0            py36he904b0f_0
[conda] mkl_fft                   1.1.0            py36h23d657b_0
[conda] mkl_random                1.1.1            py36h0573a6f_0
[conda] numpy                     1.18.5           py36ha1c710e_0
[conda] numpy-base                1.18.5           py36hde5b4d6_0
[conda] pytorch                   1.6.0           py3.6_cuda10.2.89_cudnn7.6.5_0    pytorch
[conda] torchaudio                0.6.0                      py36    pytorch