Update on "[ExecuTorch][to_backend] Enable passing Delegation Spec to to_backend"

Support Entire Graph Delegation Flow through EdgeProgramManager's to_backend.

### Motivation
A current usecase for backend lowering is through the `to_backend(backend_id, exported_program, compile_spec)` API which lowers the entire exported program to the specified backend_id. However, lowering via the EdgeProgramManager only allows for partitioner based lowering. the EdgeProgramManager is the main component which enables support for multiple methods, as a result backends which leverage the old `to_backend(backend_id, ...)` api can not export executorch models with multiple methods.

### Design
We override EdgeProgramManager to also allow Partitioner to be replaceable by DelegationSpec. DelegationSpec is essentially a wrapper around the backend_id and the compile_spec, so any where a partitioenr is specified to lower a graph, the delegation spec can also be used to do entier graph lowering.

### Intended Flow
```
del_spec = DelegationSpec("BackendWithCompilerDemo", [CompileSpec(...)])
encode_graph = torch.export.export(Encoder(), sample_inputs)
decode_graph = torch.export.export(Decoder(), sample_inputs)
edge_manager = to_edge({
    "encode": encode_graph,
    "decode": decode_graph,
})
lowered_edge_manager = edge_manager.to_backend(del_spec)
# or if you want to specify which methods to lower to with del_spec
lowered_edge_manager= edge_manager.to_backend({
    "encode": del_spec,
})
```

Differential Revision: [D69086565](https://our.internmc.facebook.com/intern/diff/D69086565/)

cc cccclai

[ghstack-poisoned]

Author: mcr229

exir/backend/canonical_partitioners/all_node_partitioner.py

@@ -16,6 +16,7 @@
 )
 from torch._export.utils import is_buffer, is_lifted_tensor_constant, is_param
 
+
 def is_non_tensor_placeholder(node: torch.fx.Node, ep: ExportedProgram) -> bool:
     """
     Returns true if the node is a placeholder node and it is not a tensor

exir/backend/test/test_backends.py

@@ -1278,7 +1278,7 @@ def __init__(self):
 
             def forward(self, x):
                 return [torch.sin(x)]
-            
+ 
 
         sin_module = SinModule()
         model_inputs = (torch.ones(1),)
@@ -1372,14 +1372,14 @@ def inputs(self):
         sin_module = SinModule()
         max_value_sin = sin_module.inputs()[0].shape[0]
         sin_partitioner = AllNodePartitioner(
-            "BackendWithCompilerDemo", 
+            "BackendWithCompilerDemo",
             [CompileSpec("max_value", bytes([max_value_sin]))],
         )
 
         add_mul_module = AddMulModule()
         max_value_add_mul = add_mul_module.inputs()[0].shape[0]
         add_mul_partitioner = AllNodePartitioner(
-            "BackendWithCompilerDemo", 
+            "BackendWithCompilerDemo",
             [CompileSpec("max_value", bytes([max_value_add_mul]))],
         )
 
@@ -1430,8 +1430,8 @@ def inputs(self):
         executorch_module = _load_for_executorch_from_buffer(buff)
 
         for method_name, module in {
-            "sin": sin_module, 
-            "add_mul": add_mul_module
+            "sin": sin_module,
+            "add_mul": add_mul_module,
         }.items():
             inputs_flattened, _ = tree_flatten(module.inputs())
             model_outputs = executorch_module.run_method(