CPP Implementation of lfilter for CPU #290
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Raw performance test results: |
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We talked about order 40. Is this too high to test? :) |
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@vincentqb, here are the results for 40'th, code in commit. The python is now about 200x slower than the elementwise, so the CPP is about 100x times slower. |
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Hi, I refactored the CPP implementations to use a Python wrapper. Basically I tried to write the bare minimum necessary in CPP.
Performance results are similar as before. For small data, may be affected by Python wrapper addition but its a fixed cost that does not scale with data size. Chuan |
…to lfilter_cpp_try_2
vincentqb
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Oct 15, 2019
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Thanks for looking into this! The gain in performance may be worth offering this C++ implementation to the user, so I'm ok with offering it. I'd like having a simple mechanism to offer/remove the interface to the implementation.
What's the status with GPU? Is it only for CPU in its current form? Should we have a forking mechanism when on CUDA and use the python implementation instead?
This PR has a lot of commits from other PRs (e.g. deltas, SpecAugment) that are tangle with it, and so needs to be rebased to isolate what is meant to be part of this PR.
| # Perform sanity checks, input check, and memory allocation in python | ||
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| # Current limitations to be removed in future | ||
| assert(waveform.dtype == torch.float32 or waveform.dtype == torch.float64) |
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Is this implementation in C++ restricting the functionality w/r to the python one?
| def biquad(waveform, b0, b1, b2, a0, a1, a2): | ||
| # type: (Tensor, float, float, float, float, float, float) -> Tensor | ||
| r"""Performs a biquad filter of input tensor. Initial conditions set to 0. | ||
| https://en.wikipedia.org/wiki/Digital_biquad_filter | ||
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| Args: | ||
| waveform (torch.Tensor): audio waveform of dimension of `(n_channel, n_frames)` | ||
| waveform (torch.Tensor): Audio waveform of dimension of `(n_channel, n_frames)`. | ||
| Currently only supports float32. Normalized [-1, 1] |
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What about other float types?
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By the way, it'd be nice to make a comparison with jit compiled python code, #326. |
53 tasks
Merged
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cc #1238 |
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To compare with the current python implementation of
lfilter(implementation 0), we developed 2 cpp implementations.1.) an element wise implementation using accessors (implementation 1)
2.) a matrix based implementation (implementation 2)
We test these implementations against each other using random inputs, and a variety of signal lengths and filter orders:
Performance
Correctness
Questions / Comments