Batching

PyTorch Developer Podcast

コンテンツは PyTorch, Edward Yang, and Team PyTorch によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャストコンテンツは、PyTorch, Edward Yang, and Team PyTorch またはそのポッドキャストプラットフォームパートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal。

4+ y ago 13:37

MP3•エピソードのホーム

PyTorch operates on its input data in a batched manner, typically processing multiple batches of an input at once (rather than once at a time, as would be the case in typical programming). In this podcast, we talk a little about the implications of batching operations in this way, and then also about how PyTorch's API is structured for batching (hint: poorly) and how Numpy introduced a concept of ufunc/gufuncs to standardize over broadcasting and batching behavior. There is some overlap between this podcast and previous podcasts about TensorIterator and vmap; you may also be interested in those episodes.

Further reading.

ufuncs and gufuncs https://numpy.org/doc/stable/reference/ufuncs.html and https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html
A brief taxonomy of PyTorch operators by shape behavior http://blog.ezyang.com/2020/05/a-brief-taxonomy-of-pytorch-operators-by-shape-behavior/
Related episodes on TensorIterator and vmap https://pytorch-dev-podcast.simplecast.com/episodes/tensoriterator and https://pytorch-dev-podcast.simplecast.com/episodes/vmap

83 つのエピソード