Implement pad as a CUDA kernel
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`Ops.pad` was a fairly slow operation on GPU. It iterates over all sequences and copies each sequence into the padded array. This results in a lot of kernel launches. In the biaffine parser, padding the inputs was more costly than applying the biaffine layers. This change optimizes the `pad` op using a custom CUDA kernel. The kernel get an array of pointers to the CuPy arrays that are provided as a list. The output array is then filled, parallelizing over the 'time steps'. This should provides the largest amount of parallelism, since we usually have n_steps * hidden_size to parallelize over.
shadeMe
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Apr 17, 2023
👷 Deploy request for thinc-ai pending review.Visit the deploys page to approve it
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shadeMe
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Apr 19, 2023
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@explosion-bot please test_slow_gpu |
URL: https://buildkite.com/explosion-ai/thinc-slow-gpu-tests/builds/41 |
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Description
Ops.padwas a fairly slow operation on GPU. It iterates over all sequences and copies each sequence into the padded array. This results in a lot of kernel launches. In the biaffine parser, padding the inputs was more costly than applying the biaffine layers.This change optimizes the
padop using a custom CUDA kernel. The kernel get an array of pointers to the CuPy arrays that are provided as a list. The output array is then filled, parallelizing over the 'time steps'. This should provides the largest amount of parallelism, since we usually have n_steps * hidden_size to parallelize over.Before:
After:
Warning: please do not review yet! There are still some todo items and I still want to be able to rebase the branch.master.Ops.padcode.Types of change
Checklist