Add tensor parallelism support for int4_weight_only quantization #1120
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Summary: Following pytorch#988 we added TP support for int4_weight_only quantization in torchao that's using TensorCoreTiledLayout Addresses one work item in pytorch#988 Also clarified docs based on pytorch#386 Also restructructured the tests in test/dtypes/test_affine_quantized_tensor_parallel.py to not depend on torchao/utils.py to reduce the jumps people have to do to understand what is tested Test Plan: python test/dtypes/test_affine_quantized_tensor_parallel.py Reviewers: Subscribers: Tasks: Tags:
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1120
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit ca241ed with merge base 3475aed ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
jainapurva
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Oct 19, 2024
This was referenced Oct 19, 2024
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…and present it in a nicely formatted manner (pytorch#1120) * add stage metrics - total params per stage, total size * PR feedback * PR feedback, typing
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Summary:
Following #939 we added TP support for int4_weight_only quantization in torchao that's using TensorCoreTiledLayout
Addresses one work item in #988
Also clarified docs based on #386
Also restructructured the tests in test/dtypes/test_affine_quantized_tensor_parallel.py to not depend on torchao/utils.py to reduce the jumps people have to do to understand what is tested
Test Plan:
python test/dtypes/test_affine_quantized_tensor_parallel.py
Reviewers:
Subscribers:
Tasks:
Tags: