Summary:
Pull Request resolved: pytorch#36093

Unwrap any tuples (including NamedTuples) in the module forward
function input list to be arglist.
1. Supports multiple tuple inputs, and traces their use through CallMethods and
TupleIndex
2. Does not unwrap inner use of other tuples that did not show up in the
original toplevel graph inputs

We work from the ScriptModule level instead of the Graph level because:
1. If the ScriptModule was previously called with the original set of inputs, the GraphExecutor caches the ExecutionPlan (specifically, ArgumentSpecCreator is derived from the Graph and type check the inputs passed in)
2. Since we are changing this graph's inputs, we clone the module and clear the GraphExecutor.

Since we work from ScriptModule level, we cannot take advantage of jit level syntactic sugar like run_pass(), so I jit exposed this as a cpp extension. Let me know if there are other ideas about this.

Test Plan:
buck test caffe2/torch/fb/model_transform:signature_translation_test
Todo: Verify use in bento

Untranslated graph:
```
> graph(%self : __torch__.test_jit.SparseNNWrapper,
>       %inputs.1 : NamedTuple(dense : Tensor, sparse : Dict(int, Tensor))):
>   %2 : __torch__.test_jit.SparseNN = prim::GetAttr[name="main_module"](%self)
>   %4 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23
>   return (%4)
```

Translated graph:
```
> graph(%self : __torch__.test_jit.___torch_mangle_1.SparseNNWrapper,
>       %inputs.1_0 : Tensor,
>       %inputs.1_1 : Dict(int, Tensor)):
>   %2 : __torch__.test_jit.___torch_mangle_2.SparseNN = prim::GetAttr[name="main_module"](%self)
>   %3 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1_0, %inputs.1_1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23
>   return (%3)
```

Reviewed By: houseroad

Differential Revision: D20313673

fbshipit-source-id: fddd07c9537dc8b6f480a14d697bea10ecc74470

Summary:
Pull Request resolved: pytorch#36093

Unwrap any tuples (including NamedTuples) in the module forward
function input list to be arglist.
1. Supports multiple tuple inputs, and traces their use through CallMethods and
TupleIndex
2. Does not unwrap inner use of other tuples that did not show up in the
original toplevel graph inputs

We work from the ScriptModule level instead of the Graph level because:
1. If the ScriptModule was previously called with the original set of inputs, the GraphExecutor caches the ExecutionPlan (specifically, ArgumentSpecCreator is derived from the Graph and type check the inputs passed in)
2. Since we are changing this graph's inputs, we clone the module and clear the GraphExecutor.

Since we work from ScriptModule level, we cannot take advantage of jit level syntactic sugar like run_pass(), so I jit exposed this as a cpp extension. Let me know if there are other ideas about this.

Test Plan:
buck test caffe2/torch/fb/model_transform:signature_translation_test
Todo: Verify use in bento

Untranslated graph:
```
> graph(%self : __torch__.test_jit.SparseNNWrapper,
>       %inputs.1 : NamedTuple(dense : Tensor, sparse : Dict(int, Tensor))):
>   %2 : __torch__.test_jit.SparseNN = prim::GetAttr[name="main_module"](%self)
>   %4 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23
>   return (%4)
```

Translated graph:
```
> graph(%self : __torch__.test_jit.___torch_mangle_1.SparseNNWrapper,
>       %inputs.1_0 : Tensor,
>       %inputs.1_1 : Dict(int, Tensor)):
>   %2 : __torch__.test_jit.___torch_mangle_2.SparseNN = prim::GetAttr[name="main_module"](%self)
>   %3 : Tensor = prim::CallMethod[name="forward"](%2, %inputs.1_0, %inputs.1_1) # /data/users/ansha/fbsource/fbcode/buck-out/dev/gen/caffe2/test/jit#binary,link-tree/test_jit.py:12141:23
>   return (%3)
```

Reviewed By: houseroad

Differential Revision: D20313673

fbshipit-source-id: fddd07c9537dc8b6f480a14d697bea10ecc74470