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Revert prefactor m in autograd documentation #3221
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3221
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit dc20fcb with merge base 82f449a ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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My bad! Thanks for taking the time to send a fix!
The prefactor m was incorrectly added to the vector-Jacobian product formula in the autograd tutorial. This change was based on the mistaken assumption that m-scaling was necessary to account for multiple terms in the summation. However, the chain rule and vector-Jacobian product formula already correctly aggregate contributions from all intermediate variables without requiring explicit scaling by m.
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@albanD No worries! Thanks for approving. I think some workflows need approval before moving forward... not 100% sure how the approval process works but I'll leave the rest in your hands :) Thanks again for the prompt approval |
I'll let @svekars check that part! |
Fixes #2168
Description
The prefactor m was incorrectly added to the vector-Jacobian product formula in the autograd tutorial as part of #3181. This change was based on the mistaken assumption that m-scaling was necessary to account for multiple terms in the summation. However, the chain rule and vector-Jacobian product formula already correctly aggregate contributions from all intermediate variables without requiring explicit scaling by m.
For a deeper explanation see #2168 (comment).
Checklist
cc @albanD @jbschlosser