Add Intel AutoRound algorithm support

[yiliu30]

Resolve #1968

Highlights

• Introduced AutoRoundModifier to enable AutoRound quantization for wNa16.
• Added an end-to-end example and unit tests.
• Verified functionality with local accuracy tests (GSM8K with a limit of 1000, the results may fluctuate due to non-determinism.)

- LLMC-AutoRound
vllm (pretrained=/storage/yiliu7/Meta-Llama-3-8B-Instruct-W4A16-G128-disbale-shuffule,tensor_parallel_size=1,max_model_len=8192,max_num_batched_tokens=32768,max_num_seqs=128,add_bos_token=True,gpu_memory_utilization=0.8,dtype=bfloat16,max_gen_toks=2048,enable_prefix_caching=False), gen_kwargs: (None), limit: 1000.0, num_fewshot: None, batch_size: 128
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.737|±  |0.0139|
|     |       |strict-match    |     5|exact_match|↑  |0.736|±  |0.0139|

- AutoRound result as ref
vllm (pretrained=/storage/yiliu7/meta-llama/Meta-Llama-3-8B-Instruct-ar/Meta-Llama-3-8B-Instruct-w4g128/,tensor_parallel_size=1,max_model_len=8192,max_num_batched_tokens=32768,max_num_seqs=128,add_bos_token=True,gpu_memory_utilization=0.8,dtype=bfloat16,max_gen_toks=2048,enable_prefix_caching=False), gen_kwargs: (None), limit: 1000.0, num_fewshot: None, batch_size: 128
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.739|±  |0.0139|
|     |       |strict-match    |     5|exact_match|↑  |0.740|±  |0.0139|

Attached eval cmd FYI.

Next stage (in later PRs)

• Extend support for additional data types.
• Add group-wise quantization recipes mapping between LLMC and AutoRound.
• Add end-to-end tests.

cc @hshen14 @thuang6 @wenhuach21

[brian-dellabetta]

Thanks for addressing my comments! A few more small things:

[yiliu30]

Hi @yiliu30 , do you have an estimate for when the next version of autoround will release? Does it have the appropriate licensing to avoid issues like vllm-project/compressed-tensors#468?

Hi @brian-dellabetta , We're planning to release the next version within the next 1–2 weeks—hope that works for you!
As for AutoRound, it's licensed under Apache License 2.0, so I guess there shouldn't be any licensing concerns.

[kylesayrs]

Really awesome job, thanks for the contribution!

[brian-dellabetta]

Thanks for adding this! We can pin autoround in a follow-up then