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`GradScaler` and mixed-precision training

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#899 2 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
csharp, pytorch
Domain
machine-learning

Research direction

Start with the linked PyTorch AMP examples, focusing on GradScaler and the fp16 gradient-scaling flow. Identify the TorchSharp API surface and tests that cover mixed-precision training; done means fp16 gradient scaling is exposed and verified, with fp8 support considered for the future hardware noted in the issue.

Written by the indexing model from the issue text.

Description

https://pytorch.org/docs/stable/notes/amp_examples.html

Currently, bfloat16 works well without grad scaling. But to use fp16 and fp8 (fp8 - in the future, when the support for Hopper/40XX GPUs lands) one needs to scale gradients.

Dominant language
C#
Stars
1.9k
Forks
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No merged PRs in 30d

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