Use cutlass kernels

[varun-sundar-rabindranath]

Description:
Cutlass integration.

• Use cutlass gemm with epilogue fusion for dequantization
• Remove all existing dequant kernels and interface
• Remove cublas i8gemm files

Test:
Run examples/offline_quantized_inference.py

(vllm-test) varun@floppy-fan:~/code/neuralmagic-vllm (vllm-quantization-cutlass) $ python3 ./examples/offline_quantized_inference.py 
...
Prompt: 'Hello, my name is', Generated text: ' John and I am a recovering workaholic.\nI used to work all the time'
Prompt: 'The president of the United States is', Generated text: ' the head of state and head of government of the United States of America. The president leads the executive'
Prompt: 'The capital of France is', Generated text: ' Paris.\nThe capital of France is Paris.'
Prompt: 'The future of AI is', Generated text: ' here, and it’s more accessible than ever.\nThe future of AI is here,'

Profiling results :
Prefill 512 tokens, Branch : This branch, dtype : "torch.float", model : Quantized model - results

Note that this branch is better than the previous best [w8a8 upstream PR with custom fused kernels]

[mgoin]

very nice - make sure to run format.sh though since some formatting bits seem to be off