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Add scaling arguments to bsr_dense_addmm by pearu · Pull Request #136104 · pytorch/pytorch · GitHub
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Add scaling arguments to bsr_dense_addmm #136104
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[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/136104
Note: Links to docs will display an error until the docs builds have been completed. âś… No FailuresAs of commit 825a557 with merge base 357b7fb ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
As in the title. Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413 [ghstack-poisoned]
As in the title. Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413 The PR assumes that the existing tuning parameters are good also when using scaling arguments. This needs to be verified as a follow-up task. [ghstack-poisoned]
As in the title. Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413 The PR assumes that the existing tuning parameters are good also when using scaling arguments. This needs to be verified as a follow-up task. [ghstack-poisoned]
As in the title. Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413 The PR assumes that the existing tuning parameters are good also when using scaling arguments. This needs to be verified as a follow-up task. Also, this PR redefines triton-contiguous tensors: the tensor must have strides not larger than 1. This will now allow zero strides that previously triggered `contiguous` call although the underlying memory buffer was contiguous. Re: "a considerable slow-down occurs because tensor data is copied element-wise rather than chunk-wise" - this note should refer to a code (torch or triton?) that implements the element/chunk-wise copy so that we could verify that allowing zero strides indeed would not trigger element-wise copies. Atm, the performance increase in ViT-H benchmarks (that involve using 0 strides) is an evidence that allowing zero strides does not lead to slow-downs. [ghstack-poisoned]
cpuhrsch
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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As in the title. Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413 The PR assumes that the existing tuning parameters are good also when using scaling arguments. This needs to be verified as a follow-up task. Also, this PR redefines triton-contiguous tensors: the tensor must have strides not larger than 1. This will now allow zero strides that previously triggered `contiguous` call although the underlying memory buffer was contiguous. Re: "a considerable slow-down occurs because tensor data is copied element-wise rather than chunk-wise" - this note should refer to a code (torch or triton?) that implements the element/chunk-wise copy so that we could verify that allowing zero strides indeed would not trigger element-wise copies. Atm, the performance increase in ViT-H benchmarks (that involve using 0 strides) is an evidence that allowing zero strides does not lead to slow-downs. Pull Request resolved: pytorch#136104 Approved by: https://github.com/cpuhrsch
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As in the title.
Tackles https://github.com/pytorch/ao/pull/821/files#r1759821413
The PR assumes that the existing tuning parameters are good also when using scaling arguments. This needs to be verified as a follow-up task.
Also, this PR redefines triton-contiguous tensors: the tensor must have strides not larger than 1. This will now allow zero strides that previously triggered
contiguouscall although the underlying memory buffer was contiguous.Re: "a considerable slow-down occurs because tensor data is copied element-wise rather than chunk-wise" - this note should refer to a code (torch or triton?) that implements the element/chunk-wise copy so that we could verify that allowing zero strides indeed would not trigger element-wise copies. Atm, the performance increase in ViT-H benchmarks (that involve using 0 strides) is an evidence that allowing zero strides does not lead to slow-downs.
Stack from ghstack (oldest at bottom):