This enables inductor micro benchmark on CPU (x86):
* Running on AWS metal runner for more accurate benchmark
* I add a new `arch` column, which will be either x86_64 or arm64 for CPU or GPU name for GPU. We can use this later to differentiate between different setup, i.e. cuda (a100) vs cuda (a10g) or cpu (x86_64) vs cpu (arm64)
The next step would be to run this one cpu arm64, and cuda (a10g).
### Testing
Here is the CSV results from my test run https://github.com/pytorch/pytorch/actions/runs/10709344180
```
name,metric,target,actual,dtype,device,arch,is_model
mlp_layer_norm_gelu,flops_utilization,0.8,17.36,bfloat16,cpu,x86_64,False
gather_gemv,memory_bandwidth(GB/s),990,170.80,int8,cpu,x86_64,False
gather_gemv,memory_bandwidth(GB/s),1060,204.78,bfloat16,cpu,x86_64,False
Mixtral-8x7B-v0.1,token_per_sec,175,26.68,int8,cpu,x86_64,True
Mixtral-8x7B-v0.1,memory_bandwidth(GB/s),1130,171.91,int8,cpu,x86_64,True
Mixtral-8x7B-v0.1,compilation_time(s),162,47.36,int8,cpu,x86_64,True
gemv,memory_bandwidth(GB/s),870,236.36,int8,cpu,x86_64,False
gemv,memory_bandwidth(GB/s),990,305.71,bfloat16,cpu,x86_64,False
Llama-2-7b-chat-hf,token_per_sec,94,14.01,bfloat16,cpu,x86_64,True
Llama-2-7b-chat-hf,memory_bandwidth(GB/s),1253,185.18,bfloat16,cpu,x86_64,True
Llama-2-7b-chat-hf,compilation_time(s),162,74.99,bfloat16,cpu,x86_64,True
Llama-2-7b-chat-hf,token_per_sec,144,25.09,int8,cpu,x86_64,True
Llama-2-7b-chat-hf,memory_bandwidth(GB/s),957,165.83,int8,cpu,x86_64,True
Llama-2-7b-chat-hf,compilation_time(s),172,70.69,int8,cpu,x86_64,True
layer_norm,memory_bandwidth(GB/s),950,172.03,bfloat16,cpu,x86_64,False
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135042
Approved by: https://github.com/yanboliang
