[release/2.2] [ROCm] Correct numerical issues in layer norm backwards kernel (#140259)#1767
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pruthvistony merged 1 commit intorelease/2.2from Dec 6, 2024
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…ch#140259) It was raised that the backwards layer norm on AMD was slightly off the accuracy of the equivalent NVIDIA implementation. On AMD we call into a helper kernel `cuLoadWriteStridedInputs` which processes strided input and accumulates the partial gradients into shared memory. In this kernel (pytorch#87635) we truncated `mean` and `rstd` from T_ACC type to T which causes numerical issues in the warp buffers created in this kernel. This PR will use the correct accumulator type for mean and rstd. Note: Only AMD call into this call stack for backwards layer norm, so this was not an issue for NV. Pull Request resolved: pytorch#140259 Approved by: https://github.com/jianyuh (cherry picked from commit 001f736)
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Jenkins build for 5ef76a334d756b4e912e48c71d11ba4afb2e2887 commit finished as FAILURE |
pruthvistony
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Dec 6, 2024
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cherry-pick --onto release/2.1 |
rocm-mici
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Dec 6, 2024
… kernel (pytorch#140259) (#1767) It was raised that the backwards layer norm on AMD was slightly off the accuracy of the equivalent NVIDIA implementation. On AMD we call into a helper kernel `cuLoadWriteStridedInputs` which processes strided input and accumulates the partial gradients into shared memory. In this kernel (pytorch#87635) we truncated `mean` and `rstd` from T_ACC type to T which causes numerical issues in the warp buffers created in this kernel. This PR will use the correct accumulator type for mean and rstd. Note: Only AMD call into this call stack for backwards layer norm, so this was not an issue for NV. Pull Request resolved: pytorch#140259 Approved by: https://github.com/jianyuh (cherry picked from commit 001f736) Fixes #ISSUE_NUMBER
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Created branch release/2.1_cherry-pick_pr-1767 and #1777 |
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!cherry-pick --onto release/2.3 release/2.4 release/2.5 |
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Can't perform the cherry-pick keyword: unexpected error |
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!cherry-pick --onto release/2.3 release/2.4 release/2.5 |
rocm-mici
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Dec 13, 2024
… kernel (pytorch#140259) (#1767) It was raised that the backwards layer norm on AMD was slightly off the accuracy of the equivalent NVIDIA implementation. On AMD we call into a helper kernel `cuLoadWriteStridedInputs` which processes strided input and accumulates the partial gradients into shared memory. In this kernel (pytorch#87635) we truncated `mean` and `rstd` from T_ACC type to T which causes numerical issues in the warp buffers created in this kernel. This PR will use the correct accumulator type for mean and rstd. Note: Only AMD call into this call stack for backwards layer norm, so this was not an issue for NV. Pull Request resolved: pytorch#140259 Approved by: https://github.com/jianyuh (cherry picked from commit 001f736) Fixes #ISSUE_NUMBER
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Nothing to cherry-pick onto the release/2.3 branch Nothing to cherry-pick onto the release/2.4 branch Created branch release/2.5_cherry-pick_pr-1767 and #1794 |
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It was raised that the backwards layer norm on AMD was slightly off the accuracy of the equivalent NVIDIA implementation.
On AMD we call into a helper kernel
cuLoadWriteStridedInputswhich processes strided input and accumulates the partial gradients into shared memory.In this kernel (pytorch#87635) we truncated
meanandrstdfrom T_ACC type to T which causes numerical issues in the warp buffers created in this kernel. This PR will use the correct accumulator type for mean and rstd.Note: Only AMD call into this call stack for backwards layer norm, so this was not an issue for NV.
Pull Request resolved: pytorch#140259
Approved by: https://github.com/jianyuh
(cherry picked from commit 001f736)
Fixes #ISSUE_NUMBER