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Co-authored-by: Lei Wen <wenlei03@qiyi.com>

Co-authored-by: Danny Guinther <dguinther@neuralmagic.com>

Signed-off-by: Tao He <sighingnow@gmail.com>

Co-authored-by: SangBin Cho <sangcho@sangcho-LT93GQWG9C.local>

Co-authored-by: Lily Liu <lilyliupku@gmail.com>

Signed-off-by: Tao He <sighingnow@gmail.com>
Co-authored-by: simon-mo <simon.mo@hey.com>

Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

Co-authored-by: Avnish Narayan <avnish@anyscale.com>

Co-authored-by: ElizaWszola <eliza@neuralmagic.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>

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* fix: engine-useray complain

* fix: typo

Co-authored-by: Chen Shen <scv119@gmail.com>
Co-authored-by: Shreyas Krishnaswamy <shrekris@anyscale.com>
Co-authored-by: Avnish Narayan <avnish@anyscale.com>

Co-authored-by: DouHappy <2278958187@qq.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

TP/quantization/weight loading refactor part 2 - Refactor quantized linear logic and extend quantization support to all models (#1622)

Refactor the tensor parallelism, quantization, and weight-loading codes.

Summary of the new features enabled by this PR:
- **All models** are able to be quantized with AWQ and SqueezeLLM, and [soon GPTQ](https://github.com/vllm-project/vllm/pull/1580).
- Model loading code became much simpler.
- Support model parallelism for all MQA/GQA models when the number of key/value heads is smaller than the tensor parallel size.