update quantization doc: add x86 backend as default backend of server inference #86794
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/86794
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ghstack-source-id: e3f8c232ce566213b31fd5448a07a86ca41a0d3f Pull Request resolved: #86794
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XiaobingSuper
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ghstack-source-id: 4bb0ca12ffe3465d9970b4c6698fac29c9bc6289 Pull Request resolved: #86794
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Since x86 is now the default qengine replacing fbgemm, can we only recommend x86 for server CPUs? We can leave a note that fbgemm is still available but not recommended. |
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XiaobingSuper
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ghstack-source-id: 08dd01ebb39df926006c3126cde54d300844381b Pull Request resolved: #86794
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ghstack-source-id: 5a2fbb19f228039521387b824bd9c35bba7aca62 Pull Request resolved: #86794
jgong5
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Oct 13, 2022
docs/source/quantization.rst
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| @@ -742,30 +748,31 @@ Backend/Hardware Support | |||
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| Today, PyTorch supports the following backends for running quantized operators efficiently: | |||
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| * x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via `fbgemm <https://github.com/pytorch/FBGEMM>`_ | |||
| * x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via `x86` to apply the optimization of `fbgemm <https://github.com/pytorch/FBGEMM>`_ and `onednn <https://github.com/oneapi-src/oneDNN>`_ (see the details at `RFC <https://github.com/pytorch/pytorch/issues/83888>`_) | |||
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| * x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via `x86` to apply the optimization of `fbgemm <https://github.com/pytorch/FBGEMM>`_ and `onednn <https://github.com/oneapi-src/oneDNN>`_ (see the details at `RFC <https://github.com/pytorch/pytorch/issues/83888>`_) | |
| * x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via `x86` optimized by `fbgemm <https://github.com/pytorch/FBGEMM>`_ and `onednn <https://github.com/oneapi-src/oneDNN>`_ (see the details at `RFC <https://github.com/pytorch/pytorch/issues/83888>`_) |
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XiaobingSuper
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ghstack-source-id: 4f27ab24f6341b4f69d3a98a396ba4e070f2fa2f Pull Request resolved: #86794
jgong5
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Oct 13, 2022
XiaobingSuper
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@pytorchbot rebase |
kit1980
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Nov 18, 2022
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@XiaobingSuper feel free to merge when ready. |
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@pytorchbot successfully started a rebase job. Check the current status here |
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Rebase failed due to Command Raised by https://github.com/pytorch/pytorch/actions/runs/3493543061 |
…d of server inference" [ghstack-poisoned]
XiaobingSuper
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ghstack-source-id: 6e9d7bef60d4f6312c66971b9643bcbcedfc05df Pull Request resolved: #86794
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@kit1980 @jerryzh168, code is rebased. |
kit1980
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Dec 2, 2022
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@pytorchbot merge |
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 |
kulinseth
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Dec 10, 2022
… inference (pytorch#86794) Pull Request resolved: pytorch#86794 Approved by: https://github.com/jgong5, https://github.com/kit1980
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