fix autoscheme accuracy drop bug w/o low_gpu, add CI test#1658
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WeiweiZhang1 wants to merge 3 commits intomainfrom
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fix autoscheme accuracy drop bug w/o low_gpu, add CI test#1658WeiweiZhang1 wants to merge 3 commits intomainfrom
WeiweiZhang1 wants to merge 3 commits intomainfrom
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Signed-off-by: WeiweiZhang1 <weiwei1.zhang@intel.com>
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Pull request overview
Fixes an AutoScheme scoring regression that causes accuracy degradation when running with low_gpu_mem_usage=False, and adds a CUDA test intended to prevent the regression from reoccurring.
Changes:
- Enable gradient-based scoring (
grad_mode=True) for AutoScheme wrapper layers in the non-low_gpu_mem_usagescoring path. - Add a CUDA regression test comparing accuracy between
low_gpu_mem_usage=TrueandFalsefor mixed-bit AutoScheme. - Add a CLI warning about potential issues using
--enable_torch_compilewith AutoScheme.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
auto_round/auto_scheme/delta_loss.py |
Restores gradient-driven scoring behavior when low_gpu_mem_usage=False by enabling wrapper grad_mode. |
test/test_cuda/algorithms/test_auto_scheme.py |
Adds a regression test to compare accuracy parity across low_gpu_mem_usage modes. |
auto_round/__main__.py |
Warns users about potential torch.compile issues when using AutoScheme. |
wenhuach21
changed the title
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|
/azp run Unit-Test-CUDA-AutoRound |
|
Azure Pipelines successfully started running 1 pipeline(s). |
wenhuach21
reviewed
Apr 3, 2026
| **common_kwargs, | ||
| ) | ||
| model_baseline, _ = ar_baseline.quantize() | ||
| acc_baseline = evaluate_accuracy(model_baseline, ar_baseline.tokenizer, task="piqa", limit=200) |
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This unit test is not robust if we introduce bugs that affect both cases. It’s better to use a reference float value and verify both results against it, ensuring that the accuracy meets or exceeds this threshold.
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Description
bugfix for:
#1654
Type of Change
Related Issues
Fixes or relates to #
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