Support auto_round integration 3.x

[Kaihui-intel]

Type of Change

feature

Description

• update config params
• update get_autoround_default_run_fn
• update prepare/convert
• return paking model
• enhance ut
• support lm_head
• pre-ci version

Expected Behavior & Potential Risk

the expected behavior that triggered by this PR

How has this PR been tested?

how to reproduce the test (including hardware information)

Dependency Change?

any library dependency introduced or removed

[github-actions]

⛈️ Required checks status: Has failure 🔴

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🟢 Code Scan Tests workflow

Check ID	Status	Error details
Code-Scan	success		✅
Code-Scan (Bandit Code Scan Bandit)	success		✅
Code-Scan (DocStyle Code Scan DocStyle)	success		✅
Code-Scan (Pylint Code Scan Pylint)	success		✅

These checks are required after the changes to neural_compressor/torch/algorithms/weight_only/autoround.py, neural_compressor/torch/algorithms/weight_only/utility.py, neural_compressor/torch/quantization/algorithm_entry.py, neural_compressor/torch/quantization/config.py, neural_compressor/torch/utils/environ.py.

🟢 Model Tests 3x workflow

Check ID	Status	Error details
Model-Test-3x	success		✅
Model-Test-3x (Generate Report GenerateReport)	success		✅
Model-Test-3x (Run PyTorch Model opt_125m_woq_gptq_int4)	success		✅
Model-Test-3x (Run PyTorch Model opt_125m_woq_gptq_int4_dq_bnb)	success		✅
Model-Test-3x (Run PyTorch Model opt_125m_woq_gptq_int4_dq_ggml)	success		✅

These checks are required after the changes to neural_compressor/torch/algorithms/weight_only/autoround.py, neural_compressor/torch/algorithms/weight_only/utility.py, neural_compressor/torch/quantization/algorithm_entry.py, neural_compressor/torch/quantization/config.py, neural_compressor/torch/utils/environ.py.

🔴 Unit Tests basic workflow

Check ID	Status	Error details
UT-Basic	failure		❌
UT-Basic (Coverage Compare CollectDatafiles)	no_status		❓
UT-Basic (Unit Test FWKs adaptor Test FWKs adaptor)	success		✅
UT-Basic (Unit Test FWKs adaptor baseline Test FWKs adaptor baseline)	success		✅
UT-Basic (Unit Test ITEX Test ITEX)	success		✅
UT-Basic (Unit Test ITEX baseline Test ITEX baseline)	success		✅
UT-Basic (Unit Test Pruning Test PyTorch Pruning)	success		✅
UT-Basic (Unit Test Pruning Test TensorFlow Pruning)	success		✅
UT-Basic (Unit Test Pruning baseline Test PyTorch Pruning baseline)	success		✅
UT-Basic (Unit Test Pruning baseline Test TensorFlow Pruning baseline)	failure	download	❌
UT-Basic (Unit Test TF newAPI Test TF newAPI)	success		✅
UT-Basic (Unit Test TF newAPI baseline Test TF newAPI baseline)	success		✅
UT-Basic (Unit Test User facing API Test User facing API)	success		✅
UT-Basic (Unit Test User facing API baseline Test User facing API baseline)	success		✅
UT-Basic (Unit Test other basic case Test other basic case)	success		✅
UT-Basic (Unit Test other cases baseline Test other cases baseline)	success		✅

These checks are required after the changes to .azure-pipelines/scripts/ut/env_setup.sh.

🟢 Unit Tests basic no coverage workflow

Check ID	Status	Error details
UT-Basic-No-Coverage	success		✅
UT-Basic-No-Coverage (Unit Test FWKs adaptor Test FWKs adaptor)	success		✅
UT-Basic-No-Coverage (Unit Test Pruning Test PyTorch Pruning)	success		✅
UT-Basic-No-Coverage (Unit Test Pruning Test TensorFlow Pruning)	success		✅
UT-Basic-No-Coverage (Unit Test User facing API Test User facing API)	success		✅
UT-Basic-No-Coverage (Unit Test other basic case Test other basic case)	success		✅

These checks are required after the changes to .azure-pipelines/scripts/ut/env_setup.sh.

🔴 Unit Tests 3x-PyTorch workflow

Check ID	Status	Error details
UT-3x-Torch	failure		❌
UT-3x-Torch (Coverage Compare CollectDatafiles)	no_status		❓
UT-3x-Torch (Unit Test 3x Torch Unit Test 3x Torch)	success		✅
UT-3x-Torch (Unit Test 3x Torch baseline Unit Test 3x Torch baseline)	failure	download	❌

These checks are required after the changes to neural_compressor/torch/algorithms/weight_only/autoround.py, neural_compressor/torch/algorithms/weight_only/utility.py, neural_compressor/torch/quantization/algorithm_entry.py, neural_compressor/torch/quantization/config.py, neural_compressor/torch/utils/environ.py, test/3x/torch/quantization/weight_only/test_autoround.py.

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[yiliu30]

Can we 1) create an InputCaptureModule during the prepare stage and 2) initialize an original AutoRound at the convert stage, receiving a) the original model and b) the data captured by the InputCaptureModule as a dataset?

[Kaihui-intel]

Can we 1) create an InputCaptureModule during the prepare stage and 2) initialize an original AutoRound at the convert stage, receiving a) the original model and b) the data captured by the InputCaptureModule as a dataset?

Thanks, synced offline with yi&xin, now we have adopted this suggestion. 8b54922