LightWeightConv layer warnings during training

If just install specified requirements + Pillow and fairseq following warnings appear during training start:

No module named 'lightconv_cuda'

If install lightconv-layer from fairseq, the folllowing warning displayed:

WARNING: Unsupported filter length passed - skipping forward pass

Pytorch 1.7 Cuda 10.2 Fairseq 1.0.0a0+19793a7

[ghost]

@KinamSalad lightconv_cuda is not a package. It is a pytorch extension write in C++. The shitty thing is fairseq don’t automatically install it when you build fairseq(and I don’t see and guiding or warning about installing it either) so you have to build it yourself. You can install it by following steps:

1. Installing fairseq from source https://github.com/pytorch/fairseq
2. Go to the folder https://github.com/pytorch/fairseq/tree/master/fairseq/modules/lightconv_layer
3. Create lightconv_cuda_foward.cu and lightconv_cuda_backward.cu by: python cuda_function_gen.py
4. Install lightconv_layer by: python setup.py install

Note: This installation depend a lot on cuda env so.

• You have to install cuda from Nvidia runtime file or deb file. https://developer.nvidia.com/cuda-downloads
• If you install cudatookit from pip it will not work(cudatoolkit package is not full cuda installation)
• If you are in cloud(GCP in my case) you should not use ready build image with cuda. Because they don’t install cuda in normal fashion so the code can’t find environment variable they need. In my case they prompt $CUDA_HOME variable not found and I can’t find where cuda is installed either.
• I verified it with cuda 11 and torch 1.7, 1.8 on GCP.

[v-nhandt21]

@GuangChen2016 I did not install fairseq, LightweightConv of LEECHOONGHO script worked for me except the soft-DTW of Parallel Tacotron2

ps: The softDTW in this repo can not work with large batch-size. You can check the appendix of this paper, may it help: https://arxiv.org/pdf/2205.04421.pdf