[scan] Autograd#135631
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/135631
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Skylion007
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ydwu4
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Overall we're on the right track! Add a few comments. The additonal_inputs would need some special handling and some more testing.
| g_c_T = flat_grads[:num_leaves_init] | ||
| g_ys = flat_grads[num_leaves_init:] | ||
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| if reverse: |
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Mind adding a comment for this? To explain how we handle reverse?
ydwu4
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ydwu4
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ydwu4
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Sep 13, 2024
Added checks for the case if not all variables in Autograd require gradients. Not yet handled
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as |
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@pytorchbot label "no-stale" |
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❌ 🤖 pytorchbot command failed: Try |
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This PR will be combined with bohnstingl#4 and pursued further in #146285 |
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This PR introduces the Autograd feature for scan with partial gradient support. It is a combination of the already opened PRs: #135631 and bohnstingl#4 Pull Request resolved: #146285 Approved by: https://github.com/ydwu4 Co-authored-by: Yidi Wu <yidi@meta.com>
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This PR introduces the Autograd feature for scan with partial gradient support. It is a combination of the already opened PRs: pytorch#135631 and bohnstingl#4 Pull Request resolved: pytorch#146285 Approved by: https://github.com/ydwu4 Co-authored-by: Yidi Wu <yidi@meta.com>
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This PR introduces the Autograd feature for scan with partial gradient support. It is a combination of the already opened PRs: pytorch#135631 and bohnstingl#4 Pull Request resolved: pytorch#146285 Approved by: https://github.com/ydwu4 Co-authored-by: Yidi Wu <yidi@meta.com>
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This is the new PR to realize autograd for scan. This is a derivative of bohnstingl#2
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