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Half precision
Manage episode 301973966 series 2921809
Contenu fourni par PyTorch, Edward Yang, and Team PyTorch. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par PyTorch, Edward Yang, and Team PyTorch ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.
In this episode I talk about reduced precision floating point formats float16 (aka half precision) and bfloat16. I'll discuss what floating point numbers are, how these two formats vary, and some of the practical considerations that arise when you are working with numeric code in PyTorch that also needs to work in reduced precision. Did you know that we do all CUDA computations in float32, even if the source tensors are stored as float16? Now you know!
Further reading.
- The Wikipedia article on IEEE floating point is pretty great https://en.wikipedia.org/wiki/IEEE_754
- How bfloat16 works out when doing training https://arxiv.org/abs/1905.12322
- Definition of acc_type in PyTorch https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/AccumulateType.h
83 episodes
Partager
Manage episode 301973966 series 2921809
Contenu fourni par PyTorch, Edward Yang, and Team PyTorch. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par PyTorch, Edward Yang, and Team PyTorch ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.
In this episode I talk about reduced precision floating point formats float16 (aka half precision) and bfloat16. I'll discuss what floating point numbers are, how these two formats vary, and some of the practical considerations that arise when you are working with numeric code in PyTorch that also needs to work in reduced precision. Did you know that we do all CUDA computations in float32, even if the source tensors are stored as float16? Now you know!
Further reading.
- The Wikipedia article on IEEE floating point is pretty great https://en.wikipedia.org/wiki/IEEE_754
- How bfloat16 works out when doing training https://arxiv.org/abs/1905.12322
- Definition of acc_type in PyTorch https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/AccumulateType.h
83 episodes
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Similaire à PyTorch Developer Podcast
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continue reading
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continue reading
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continue reading
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continue reading
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