[ROCm] Correct numerical issues in layer norm backwards kernel #140259
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/140259
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 496b18a with merge base 565a794 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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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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…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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…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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…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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…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
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… kernel (pytorch#140259) (#1766) 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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… 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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… 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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… kernel (pytorch#140259) (#1766) 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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… kernel (pytorch#140259) (#1766) 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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… kernel (pytorch#140259) (#1766) 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)
rocm-mici
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… 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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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 (#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.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @hongxiayang @naromero77amd