[quant][pt2][be] Refactor QAT q-dq patterns #112279
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Summary: This commit refactors q-dq patterns used in QAT fusion, reducing code duplication. This is important for future efforts to support quantizing bias. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/112279
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 2ad5548 with merge base c120e56 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
andrewor14
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Oct 27, 2023
Summary: This commit refactors q-dq patterns used in QAT fusion, reducing code duplication. This is important for future efforts to support quantizing bias. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar ghstack-source-id: b5b3749 Pull Request resolved: #112279
jerryzh168
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Oct 31, 2023
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DacinTitus
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Nov 4, 2023
Summary: This commit refactors q-dq patterns used in QAT fusion, reducing code duplication. This is important for future efforts to support quantizing bias. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar ghstack-source-id: a9ecab9 Pull Request resolved: pytorch/pytorch#112279
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Nov 7, 2023
Summary: This commit refactors q-dq patterns used in QAT fusion, reducing code duplication. This is important for future efforts to support quantizing bias. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar Pull Request resolved: pytorch#112279 Approved by: https://github.com/jerryzh168 ghstack dependencies: pytorch#112159
Skylion007
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Nov 14, 2023
Summary: This commit refactors q-dq patterns used in QAT fusion, reducing code duplication. This is important for future efforts to support quantizing bias. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar Pull Request resolved: pytorch#112279 Approved by: https://github.com/jerryzh168 ghstack dependencies: pytorch#112159
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Stack from ghstack (oldest at bottom):
Summary: This commit refactors q-dq patterns used in QAT fusion,
reducing code duplication. This is important for future efforts
to support quantizing bias.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar
Differential Revision: D50856379