feat: use pytorch built-in interpolation method for LinearInterpolation

[yoyololicon]

I was using the mlsa filter module and found some places that can be optimised.
I replaced the convolution-based linear interpolation with pytorch built-in method and did some benchmarks.

Before

[--------------- linear_interpolation ---------------]
              |  scale_factor=128  |  scale_factor=256
1 threads: -------------------------------------------
      N=512   |        419.0       |        853.9     
      N=1024  |        849.4       |       1716.3     
      N=2048  |       1884.4       |       4325.4     
2 threads: -------------------------------------------
      N=512   |        281.6       |        562.4     
      N=1024  |        556.1       |       1153.5     
      N=2048  |       1188.4       |       2449.0     
4 threads: -------------------------------------------
      N=512   |        221.7       |        422.7     
      N=1024  |        419.9       |        864.5     
      N=2048  |        882.8       |       1788.6     

Times are in milliseconds (ms).

After

[--------------- linear_interpolation ---------------]
              |  scale_factor=128  |  scale_factor=256
1 threads: -------------------------------------------
      N=512   |       190.1        |        378.8     
      N=1024  |       380.2        |        758.5     
      N=2048  |       764.1        |       1536.3     
2 threads: -------------------------------------------
      N=512   |       113.6        |        221.4     
      N=1024  |       226.9        |        460.4     
      N=2048  |       451.6        |        902.7     
4 threads: -------------------------------------------
      N=512   |        75.6        |        147.9     
      N=1024  |       147.1        |        294.3     
      N=2048  |       295.2        |        603.4     

Times are in milliseconds (ms).

The benchmark script

import torch
from torch.profiler import profile, record_function, ProfilerActivity
import torch.utils.benchmark as benchmark
from diffsptk import LinearInterpolation
from itertools import product

def benchmark_linear_interpolation():
    # Create an instance of the LinearInterpolation class
    lin_interp = LinearInterpolation(100)

    # Generate some random input data
    x = torch.randn(16, 600).t()

    # Run the forward pass of the filter and measure the execution time
    with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
        with record_function("LinearInterpolation"):
            y = lin_interp(x)

    print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))

    lengths = [512, 1024, 2048]
    scale_factors = [128, 256]
    results = []
    for N, S in product(lengths, scale_factors):
        label = "linear_interpolation"
        sub_label = f"N={N}"
        x = torch.randn(32, N, 49)
        interp = LinearInterpolation(S)

        for num_threads in [1, 2, 4]:
            results.append(
                benchmark.Timer(
                    stmt="y = interp(x)",
                    globals={"x": x, "interp": interp},
                    label=label,
                    num_threads=num_threads,
                    sub_label=sub_label,
                    description=f"scale_factor={S}",
                ).blocked_autorange(min_run_time=1)
            )

    compare = benchmark.Compare(results)
    compare.print()


if __name__ == "__main__":
    benchmark_linear_interpolation()

I only test the script on a Macbook with an M1 chip, but it should have similar results on other machines and OS.

TODO

I haven't run the test scripts yet due to some problems with building sptk.
If anyone can help run the tests, I would appreciate it.

[takenori-y]

Thank you for improving the code!

I added some minor comments. Could you apply the linter and formatter?

[yoyololicon]

Sure, will do this today.

[takenori-y]

Thank you for your response. I will merge.

[yoyololicon]

Oh, did it pass the test? I can't see any workflow running... 😅

[takenori-y]

No worries. I intentionally skipped the CI because I have already performed the test on my machine.

[yoyololicon]

Nice! all good then.