Overdrive cpp extension

[bhargavkathivarapu]

New PR for overdrive C++ extension similar to lfilter . Relates to #580 ( old PR )

[mthrok]

Hi @bhargavkathivarapu

Thanks for the contribution, and sorry for taking so long to get back to you.
Overall it looks good to me.
@cpuhrsch Can you take a second look?

[mthrok]

I think you can get rid of these comments now.

[bhargavkathivarapu]

@mthrok sure will remove them.
Also I will try to implement the parallel_for as mentioned by @cpuhrsch in the old PR comments ( #580 ).

[mthrok]

@bhargavkathivarapu sounds good. Could you try measure the performance improvement?
Something like the following and change the shape of tensor to variety of shapes.
If numactl is missing, you can install it with sudo apt-get install -y numactl on Ubuntu.

OMP_NUM_THREADS=1 numactl --membind 0 --cpubind 0 python -m timeit -n 100 -r 5 -s """
import torch;
import torchaudio
x = torch.zeros(32, 100, dtype=torch.float)
""" """
torchaudio.functional.overdrive(x)
"""

[cpuhrsch]

LGTM depending some basic benchmarks that show clear improvement.

[bhargavkathivarapu]

@mthrok

Changes made

• Removed commented code
• Added parallel_for in overdrive cpp.

Performance details
( 60 sec audio clip, VM with 2 vCPUs)

Numctl details ( VM with 2 vCPUs)
Command used :

OMP_NUM_THREADS=1 numactl --membind 0 --cpubind 0 python -m timeit -n 100 -r 5 -s """
import torch
import torchaudio
x = torch.zeros(2, 100, dtype=torch.float)
torchaudio.functional.overdrive(x)
"""

(2 , 100 ) = 100 loops, best of 5: 14.1 nsec per loop
(32 , 100 ) = 100 loops, best of 5: 16.8 nsec per loop
(1000, 100) = 100 loops, best of 5: 28.2 nsec per loop
(2, 100000) = 100 loops, best of 5: 40.4 nsec per loop
(100, 100000) = 100 loops, best of 5: 44.3 nsec per loop
(1000, 100000) = 100 loops, best of 5: 55.7 nsec per loop
(10000, 10000) =100 loops, best of 5: 49.7 nsec per loop

[mthrok]

@mthrok

Changes made

• Removed commented code
• Added parallel_for in overdrive cpp.

Performance details
( 60 sec audio clip, VM with 2 vCPUs)

Numctl details ( VM with 2 vCPUs)
Command used :

OMP_NUM_THREADS=1 numactl --membind 0 --cpubind 0 python -m timeit -n 100 -r 5 -s """
import torch
import torchaudio
x = torch.zeros(2, 100, dtype=torch.float)
torchaudio.functional.overdrive(x)
"""

(2 , 100 ) = 100 loops, best of 5: 14.1 nsec per loop
(32 , 100 ) = 100 loops, best of 5: 16.8 nsec per loop
(1000, 100) = 100 loops, best of 5: 28.2 nsec per loop
(2, 100000) = 100 loops, best of 5: 40.4 nsec per loop
(100, 100000) = 100 loops, best of 5: 44.3 nsec per loop
(1000, 100000) = 100 loops, best of 5: 55.7 nsec per loop
(10000, 10000) =100 loops, best of 5: 49.7 nsec per loop

@bhargavkathivarapu

Thanks for the update. So my understanding is that the speed is comparable to sox implementation.

Can you compare the same torchaudio.functional.overdrive function and Tensor shape before adding the C++ code? That will tell more directly how the C++ implementation improves the performance.

[mthrok]

Can you merge the latest master commit to include #1297? I believe I killed the flaky bug. 🐛

[bhargavkathivarapu]

@mthrok . Now all checks passed after merging master.

Can you compare the same torchaudio.functional.overdrive function and Tensor shape before adding the C++ code? That will tell more directly how the C++ implementation improves the performance.

( Reference 60 second clip ) Tensor shape of w = [1, 1323000]
Existing torchaudio overdrive timing

Comparison for that tensor w

Implementation	Relative run time	Run time
sox overdrive	1X	38.7ms
overdrive new(cpp)	2X	78.2ms
overdrive old (python)	~2000X	77000ms

[mthrok]

@mthrok . Now all checks passed after merging master.

Can you compare the same torchaudio.functional.overdrive function and Tensor shape before adding the C++ code? That will tell more directly how the C++ implementation improves the performance.

( Reference 60 second clip ) Tensor shape of w = [1, 1323000]
Existing torchaudio overdrive timing

Comparison for that tensor w

Implementation Relative run time Run time
sox overdrive 1X 38.7ms
overdrive new(cpp) 2X 78.2ms
overdrive old (python) ~2000X 77000ms

Wonderful! Thanks!