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pytorchmergebot
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# Summary This pull request introduces an fp8 row-scaling kernel as an optional implementation for `scaled_mm`. The kernel selection is based on the scaling tensors of the inputs. For inputs `x` and `y` of shape `[M, K]` and `[K, N]` respectively, the following conditions must be met: - `x`'s scale should be a 1-dimensional tensor of length `M`. - `y`'s scale should be a 1-dimensional tensor of length `N`. It's important to note that this kernel is not called "rowwise, columnwise" scaling because, although the scales for `y` are semantically along its columns, this implementation only supports the TN format. This means the scaling is along the faster-moving dimension, or the "row". The following two PRs were required to enable local builds: - [PR #126185](#126185) - [PR #125523](#125523) ### Todo We still do not build our Python wheels with this architecture. @ptrblck @malfet, should we replace `sm_90` with `sm_90a`? The NVRTC TMA shadowing feels wrong, but I a not sure the right way to spoof the symbol for this compilation unit: https://github.com/pytorch/pytorch/pull/125204/files#r1586986954 #### ifdef I tried to use : `#if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12000 && \ defined(__CUDA_ARCH__) && __CUDA_ARCH__ > 900` to gate the building of the kernel. I was having a hell of a time with this.. so I am not really sure the right way to do this Kernel Credit: @jwfromm Pull Request resolved: #125204 Approved by: https://github.com/lw
pytorchmergebot
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# Summary This pull request introduces an fp8 row-scaling kernel as an optional implementation for `scaled_mm`. The kernel selection is based on the scaling tensors of the inputs. For inputs `x` and `y` of shape `[M, K]` and `[K, N]` respectively, the following conditions must be met: - `x`'s scale should be a 1-dimensional tensor of length `M`. - `y`'s scale should be a 1-dimensional tensor of length `N`. It's important to note that this kernel is not called "rowwise, columnwise" scaling because, although the scales for `y` are semantically along its columns, this implementation only supports the TN format. This means the scaling is along the faster-moving dimension, or the "row". The following two PRs were required to enable local builds: - [PR #126185](#126185) - [PR #125523](#125523) ### Todo We still do not build our Python wheels with this architecture. @ptrblck @malfet, should we replace `sm_90` with `sm_90a`? The NVRTC TMA shadowing feels wrong, but I a not sure the right way to spoof the symbol for this compilation unit: https://github.com/pytorch/pytorch/pull/125204/files#r1586986954 #### ifdef I tried to use : `#if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12000 && \ defined(__CUDA_ARCH__) && __CUDA_ARCH__ > 900` to gate the building of the kernel. I was having a hell of a time with this.. so I am not really sure the right way to do this Kernel Credit: @jwfromm Pull Request resolved: #125204 Approved by: https://github.com/lw, https://github.com/malfet
petrex
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# Summary This pull request introduces an fp8 row-scaling kernel as an optional implementation for `scaled_mm`. The kernel selection is based on the scaling tensors of the inputs. For inputs `x` and `y` of shape `[M, K]` and `[K, N]` respectively, the following conditions must be met: - `x`'s scale should be a 1-dimensional tensor of length `M`. - `y`'s scale should be a 1-dimensional tensor of length `N`. It's important to note that this kernel is not called "rowwise, columnwise" scaling because, although the scales for `y` are semantically along its columns, this implementation only supports the TN format. This means the scaling is along the faster-moving dimension, or the "row". The following two PRs were required to enable local builds: - [PR pytorch#126185](pytorch#126185) - [PR pytorch#125523](pytorch#125523) ### Todo We still do not build our Python wheels with this architecture. @ptrblck @malfet, should we replace `sm_90` with `sm_90a`? The NVRTC TMA shadowing feels wrong, but I a not sure the right way to spoof the symbol for this compilation unit: https://github.com/pytorch/pytorch/pull/125204/files#r1586986954 #### ifdef I tried to use : `#if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12000 && \ defined(__CUDA_ARCH__) && __CUDA_ARCH__ > 900` to gate the building of the kernel. I was having a hell of a time with this.. so I am not really sure the right way to do this Kernel Credit: @jwfromm Pull Request resolved: pytorch#125204 Approved by: https://github.com/lw
pytorchmergebot
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Jun 19, 2024
# Summary First PR got reverted and needed a redo This pull request introduces an fp8 row-scaling kernel as an optional implementation for `scaled_mm`. The kernel selection is based on the scaling tensors of the inputs. For inputs `x` and `y` of shape `[M, K]` and `[K, N]` respectively, the following conditions must be met: - `x`'s scale should be a 1-dimensional tensor of length `M`. - `y`'s scale should be a 1-dimensional tensor of length `N`. It's important to note that this kernel is not called "rowwise, columnwise" scaling because, although the scales for `y` are semantically along its columns, this implementation only supports the TN format. This means the scaling is along the faster-moving dimension, or the "row". The following two PRs were required to enable local builds: - [PR #126185](#126185) - [PR #125523](#125523) ### Todo We still do not build our Python wheels with this architecture. @ptrblck @malfet, should we replace `sm_90` with `sm_90a`? The NVRTC TMA shadowing feels wrong, but I a not sure the right way to spoof the symbol for this compilation unit: https://github.com/pytorch/pytorch/pull/125204/files#r1586986954 #### ifdef I tried to use : `#if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12000 && \ defined(__CUDA_ARCH__) && __CUDA_ARCH__ > 900` to gate the building of the kernel. I was having a hell of a time with this.. so I am not really sure the right way to do this Kernel Credit: @jwfromm Pull Request resolved: #128989 Approved by: https://github.com/yangsiyu007, https://github.com/vkuzo
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Summary
Fixes: #125413