Add jit.ignore to prototype optimizers (pytorch#2958)

Summary:
Pull Request resolved: pytorch#2958

`torch.compile` doesn't seem to cause errors if we deprecate an optimizer that is no longer used, but `torch.jit.script` will. `torch.jit.script` seems to check and ensure all decision branches are alive. See [simplified Bento example](https://fburl.com/anp/rbktkl08)

To make prototype optimizers easily deprecated once included in production, we wrap the invoker function with `torch.jit.ignore`. This means that we need to always keep auto-generating the `lookup_{}.py` even the optimizers are deprecated and their backends are removed.

**Usage**
Add  `"is_prototype_optimizer": True` for the optimizer in `/codegen/genscript/optimizers.py`
Example:
```
def ensemble_rowwise_adagrad_optimizer:
   return {
      "optimizer": "ensemble_rowwise_adagrad",
      "is_prototype_optimizer": True,
   }
```

Reviewed By: q10

Differential Revision: D60943180

fbshipit-source-id: d43e11dfddf248b2b3113cb42d2c70c02e002ef8

fbgemm_gpu/codegen/training/python/split_embedding_codegen_lookup_invoker.template

@@ -38,7 +38,12 @@ torch.ops.load_library("//deeplearning/fbgemm/fbgemm_gpu:embedding_inplace_updat
 
 {%- endif %}
 
-
+{%- if is_prototype_optimizer %}
+# Decorate the prototype optimizers which may be deprecated in the future with jit.ignore to avoid
+# possible errors from torch.jit.script. 
+# Note that backends can be removed but the lookup invoker is still needed for backward compatibility
+@torch.jit.ignore
+{%- endif %}
 def invoke(
     common_args: CommonArgs,
     optimizer_args: OptimizerArgs,