[Feature][Quantization] auto_round format add support for regex

[n1ck-guo]

Purpose

auto_round format add support for regex

Test Plan

Load auto_round quantized model with extra_config including regular expressions and full name of layers.

Test Result

With the change, each layer of linear that satisfies the regex in extra_config (for example, ".*mlp.down_proj": {"bits": 16}) can obtain the correct bits
Successful load mixed bits quantization model with auto-round quant_method

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request introduces support for regular expressions in AutoRound's extra_config, which is a valuable feature for defining quantization settings for groups of layers. However, the current implementation has a critical correctness issue where literal layer names can be misinterpreted as regex patterns, potentially leading to incorrect quantization. My review provides a comment with a suggested code change to address this by using a heuristic to differentiate between literal names and regex patterns, which also improves performance.

[n1ck-guo]

@mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 could you please help to review this pr.

[yewentao256]

Could you introduce more context about this PR?
Eg, which model is using it. Without this PR what it would be like, and with this PR, etc
Also, showing lm_eval result for accuracy and vllm bench for perf is helpful.

[n1ck-guo]

@yewentao256 Currently, auto_round is implemented for mixed-precision quantization models by saving the full name of all models. In the future, with the support of this pr, we hope to add support for regularization. This pr primarily reads the regularization configuration. All the model quantized by auto_round will use this pr in future.

For example, if I use this script to generate a quantized qwen model:

from auto_round import AutoRound
model_path = "Qwen/Qwen3-15B-A2B-Base/"
layer_config = {
    "self_attn.[koqv]_proj$": {"bits": 8},
}
ar = AutoRound(model=model_path, scheme="W4A16", layer_config=layer_config, iters=1)
ar.quantize_and_save("Qwen3-15B-A2B-Base-vllm-regex-test")

This config.json will include a parameter to show that all non-expert linear will fallback to 16 bits. For old version, it should look like this:

"quantization_config": {
    "autoround_version": "0.8.0.dev",
    "bits": 4,
    "data_type": "int",
    "extra_config": {
      "model.layers.0.self_attn.k_proj": {
        "bits": 8
      },
      "model.layers.0.self_attn.o_proj": {
        "bits": 8
      },
      "model.layers.0.self_attn.q_proj": {
        "bits": 8
      },
      "model.layers.0.self_attn.v_proj": {
        "bits": 8
      },
      "model.layers.1.self_attn.k_proj": {
        "bits": 8
      },
      "model.layers.1.self_attn.o_proj": {
        "bits": 8
      },
      "model.layers.1.self_attn.q_proj": {
        "bits": 8
      },

And with the support of this pr, it can be simplified to

"quantization_config": {
    "autoround_version": "0.8.0.dev",
    "bits": 4,
    "data_type": "int",
    "extra_config": {
        "self_attn.[koqv]_proj$": {"bits": 8},
}

[n1ck-guo]

This pr will not affect the accuracy of the model, this is our test result：
model is build by the script above.

task	bf16	this pr	main branch
average	0.6324	0.6305	0.6299
arc_challenge	0.4838	0.5026	0.5077
arc_easy	0.7866	0.7942	0.7963
boolq	0.8361	0.8287	0.8269
hellaswag	0.5888	0.5765	0.5772
lambada_openai	0.7402	0.7339	0.7320
mmlu	0.7347	0.7213	0.7200
openbookqa	0.3180	0.3140	0.3060
piqa	0.7894	0.7862	0.7840
truthfulqa_mc1	0.3745	0.3758	0.3733
wikitext	0.5871	0.6022	0.6023
winogrande	0.7174	0.7001	0.7032

[mgoin]

LGTM, just one issue