Quantization: Embeddings quantization, new packing format, Rtn quantizer#2238
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Quantization: Embeddings quantization, new packing format, Rtn quantizer#2238
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We are trying to improve our test coverage. Can you please add unit test for new files created in this PR? |
xiaoyu-work
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Nov 3, 2025
skywall
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Mar 10, 2026
Merge in AITEC/eiq-olive from feature/EITO-565-rebase-to-newest-version-of-olive-0.9.3 to main * commit 'fd44fa6a51e382d59a88e4fceec49042b7e2caa5': (370 commits) ruff safe fixes update rebased on badge readme fix import things I've missed during rebasing ruff Revert "ruff stuff" ruff stuff Bump up version to 0.10.1 Fix cache output model name bug (microsoft#2249) HfModelHandler: Check for tokenizer_config.json instead of try/else (microsoft#2247) Quantization: Keep embeddings tied in SelectiveMixedPrecision, Clean overrides (microsoft#2246) TieWordEmbeddings: return model when no tieing detected (microsoft#2242) Static Quantization: Always patch `MinMaxCalibrator` (microsoft#2241) Release branch 0.10.0 Add custom onnx model name support for output dir (microsoft#2235) TieWordEmbeddings: unquantized and quantized support (microsoft#2240) Quantization: Embeddings quantization, new packing format, Rtn quantizer (microsoft#2238) Add support for Quark onnx quantization (microsoft#2236) Spelling fixes (microsoft#2234) LLMAugmentedDataLoader: No decode phase for non-GQA model (microsoft#2204) ...
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Describe your changes
QuantEmbeddingmodule added to do input embedding quantizationGatherBlockQuantizedwith torch script and dynamo mode supported. model builder doesn't support it since it requires change in the builder scriptRtnpass that can be composed on top of other quantization passes. For example, gptq on the transformer layers and then rtn on the embedding and lm head to take advantage of weight tieingChecklist before requesting a review
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