[Misc][Kernel]: Add GPTQAllSpark Quantization

[wyajieha]

This PR mainly added specific optimizations for the Ampere architecture A16W8 quantization, supporting the GPTQ quantization model in the scenario where GroupSize=-1 and act_desc is False, and its performance in this scenario is better than Marlin.

Operator performance test (Marlin VS AllSpark) can be performed through the following command：
python3 benchmarks/kernels/benchmark_marlin.py --limit-num-bits 8 --limit-act-order 0 --limit-k-full 1 --limit-group-size -1

The following figure shows the performance comparison of Marlin vs. AllSpark under different M settings for the common Gemm scale of the model on the A100 GPU. The blue line shows the acceleration ratio of Marlin A16W8 Gemm compared to Torch FP16 Gemm, and the orange line shows the acceleration ratio of AllSpark A16W8 Gemm compared to Torch FP16 Gemm. In scenarios where N and K are small and M is large, AllSpark performs significantly better than Marlin. In other scenarios, the performance is basically the same.

Use the following command to perform performance test on the Qwen2-7B-Instruct-quantized.w8a16 model on a single A100 card
CUDA_VISIBLE_DEVICES=1 python3 benchmarks/benchmark_throughput.py --backend=vllm --model Qwen2-7B-Instruct-quantized.w8a16/ --quantization gptq_allspark（or gptq_marlin） --input-len 2048 --output-len 256 --num-prompts=1000 --trust-remote-code --dtype=float16 --kv-cache-dtype=auto --device=cuda

The performance results of the whole network are as follows:

Metrics	Marlin A16W8	AllSpark A16W8
QPS	4.25	5.01(+17.8%)
TPS	9797.44	11551.60 (+17.9%)
Output TPS	1088.60	1283.51(+17.9%)

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can either: Add ready label to the PR or enable auto-merge.

🚀