Unifying TBE API using List (Frontend)

Summary:
X-link: pytorch/FBGEMM#3711

X-link: facebookresearch/FBGEMM#793

**Backend**: D68054868

---


As the number of arguments in TBE keeps growing, some of the optimizers run into number of arguments limitation (i.e., 64) during pytorch operation registration. 

**For long-term growth and maintenance, we hence redesign TBE API by packing some of the arguments into list. Note that not all arguments are packed.**

We pack the arguments as a list for each type.
For **common** arguments, we pack 
- weights and arguments of type `Momentum` into TensorList
- other tensors and optional tensors to list of optional tensors `aux_tensor`
- `int` arguments into `aux_int`
- `float` arguments into `aux_float`
- `bool` arguments into `aux_bool`.

Similarly for **optimizer-specific** arguments, we pack
- arguments of type `Momentum` that are *__not__ optional* into TensorList
- *optional* tensors to list of optional tensors `optim_tensor`
- `int` arguments into `optim_int`
- `float` arguments into `optim_float`
- `bool` arguments into `optim_bool`.

We see issues with pytorch registration across packing SymInt in python-C++, so we unroll and pass SymInt arguments individually. 

**This significantly reduces number of arguments.** For example, `split_embedding_codegen_lookup_rowwise_adagrad_with_counter_function`, which currently has 61 arguments only have 26 arguments with this API design. 

Please refer to the design doc on which arguments are packed and signature.
Design doc:
https://docs.google.com/document/d/1dCBg7dcf7Yq9FHVrvXsAmFtBxkDi9o6u0r-Ptd4UDPE/edit?tab=t.0#heading=h.6bip5pwqq8xb

Full signature for each optimizer lookup function will be provided shortly.

Reviewed By: sryap

Differential Revision: D68055168

torchrec/distributed/batched_embedding_kernel.py

@@ -190,7 +190,7 @@ def __init__(
         state: Dict[Any, Any] = {}
         param_group: Dict[str, Any] = {
             "params": [],
-            "lr": emb_module.optimizer_args.learning_rate,
+            "lr": emb_module.optimizer_args.learning_rate_tensor,
         }
 
         params: Dict[str, Union[torch.Tensor, ShardedTensor]] = {}
@@ -383,7 +383,7 @@ def get_optimizer_pointwise_shard_metadata_and_global_metadata(
         state: Dict[Any, Any] = {}
         param_group: Dict[str, Any] = {
             "params": [],
-            "lr": emb_module.optimizer_args.learning_rate,
+            "lr": emb_module.optimizer_args.learning_rate_tensor,
         }
 
         params: Dict[str, Union[torch.Tensor, ShardedTensor]] = {}

torchrec/modules/fused_embedding_modules.py

@@ -68,7 +68,7 @@ def __init__(  # noqa C901
         state: Dict[Any, Any] = {}
         param_group: Dict[str, Any] = {
             "params": [],
-            "lr": emb_module.optimizer_args.learning_rate,
+            "lr": emb_module.optimizer_args.learning_rate_tensor,
         }
 
         params: Dict[str, torch.Tensor] = {}