Rewrited the OpInfer as a class with functions "infer_shape","infer_type"
and "infer_format". Used op name to find the subclass that have implemented
these functions. Two common class "_Elemwise" and "_Reduce" was provided.

Op BiasAddGrad supported "FractalNZ" format.

Changed the expander to class, and used the class name to identify the operator.
Moved the original op logic into the `_expand` function; added a `_check` function to check op inputs.

Use decorator to register the whitelist formats that operator supports,
and the decorator will change the `_check` function to check the formats.

Decoupled from the front-end interfaces.
1. Removed the call to "Renormalize".
   Completed the infer-format in model_builder.
   Only used the device shape and device format to
   infer an abstract shape without considering padding.
2. Removed the call to python's Primitive interfaces.
   The "Renormalize" relies on the PrimitivePy, so they can be
   removed together. After that, the functions "ConstAttrToInput",
   "DeleteAttrInInput" and related can be removed.
3. Reuse the AkgKernelJsonGenerator in GraphKernelExpander.
   1) set the attribute "extract_opinfo_from_anf" to true, so that
   the generator can handle the basic operator with anf info.
   2) added a function "extract_expand_info" in expander.py
   to convert the json into a more friendly format. The attrs
   was converted to a dict instead of a list.
4. Scalars only support DefaultFormat.
   Removed the argument "format" from graph_builder.value
5. Moved the expander op list from graph_kernel_helper.cc to graph_kernel_expander.cc

add tile expander

add BroadcastTo in model

fix BroadcastTo op calling error and infer shape

rewrite tile expander

not split broadcast_to

add SqrtGrad expander