Update on "[Inductor][CPP] Pass weight dtype explicitly for cpp gemm …

…template"


**Summary**
This PR mainly refactor 2 things:

1. Passing in weight's data type explicitly in `create_micro_gemm` as `input2.dtype`. When registering `CppMicroGemmConfig`, we will reuse `input.dtype` if `input2.dtype` is not explicitly registered.
2. Add an util function to get the output data type and compute data type from input data type.



cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx peterbell10 ipiszy yf225 chenyang78 kadeng muchulee8 ColinPeppler amjames desertfire chauhang

[ghstack-poisoned]

torch/_inductor/codegen/cpp_micro_gemm.py

@@ -189,10 +189,10 @@ def generate_gemm_config(
     vec_isa_cls,
     register_blockings,
     input_dtype=torch.float,
+    input2_dtype=None,
     output_dtype=None,
     compute_dtype=None,
     extra_check=None,
-    input2_dtype=None,
 ):
     if output_dtype is None:
         output_dtype = input_dtype
@@ -460,10 +460,10 @@ def check_amx_extra(config, m, n, k, alpha, num_threads):
         VecAMX,
         [(32, 32, 64), (48, 16, 64)],
         input_dtype=torch.uint8,
+        input2_dtype=torch.int8,
         output_dtype=torch.int32,
         compute_dtype=torch.int32,
         extra_check=check_amx_extra,
-        input2_dtype=torch.int8,
     ),
 )
 class CppMicroGemmAMX(CppMicroGemm):