[Quant][CPU] Enable fp8 qlinear #155678
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[Quant][CPU] Enable fp8 qlinear #155678
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/155678
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 56fda90 with merge base 9b498d3 ( UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
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**Summary** Enable fp8 qlinear on CPU. It's part of the plan to enable fp8 static quantization on CPU. This PR only adds FP8 support of the existing int8 qlinear op. It does not add a new op nor does it affect frontend or quantization flow. The schema of the qlinear op is not changed either. So, the FP8 qlinear shares the same op as INT8 qlinear and the difference is that src/wei dtype is fp8 instead of int8. The output dtype can be fp8/float32/bfloat16. The implementation uses the oneDNN library. The differences of qlinear from `_scaled_mm` are that - Qlinear supports post op fusion while `_scaled_mm` does not - Weights are prepacked for qlinear **Test plan** ``` pytest test/quantization/core/test_quantized_op.py -k "qlinear and fp8" ``` Pull Request resolved: pytorch#155678 Approved by: https://github.com/leslie-fang-intel, https://github.com/jerryzh168
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Summary
Enable fp8 qlinear on CPU. It's part of the plan to enable fp8 static quantization on CPU. This PR only adds FP8 support of the existing int8 qlinear op. It does not add a new op nor does it affect frontend or quantization flow. The schema of the qlinear op is not changed either.
So, the FP8 qlinear shares the same op as INT8 qlinear and the difference is that src/wei dtype is fp8 instead of int8. The output dtype can be fp8/float32/bfloat16. The implementation uses the oneDNN library.
The differences of qlinear from
_scaled_mmare that_scaled_mmdoes notTest plan
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @jerryzh168