Use source_fn_stack in xnnpack tutorial

docs/source/native-delegates-executorch-xnnpack-delegate.md

@@ -25,18 +25,18 @@ The partitioner is implemented by backend delegates to mark nodes suitable for l
 
 ##### Module-based partitioning
 
-`source_fn` is embedded in the node’s metadata and gives information on where these nodes come from. For example, modules like `torch.nn.Linear` when captured and exported `to_edge` generate groups of nodes for their computation. The group of nodes associated with computing the linear module then has a `source_fn` of `torch.nn.Linear. Partitioning based on `source_fn` allows us to identify groups of nodes which are lowerable via XNNPACK.
+`source_fn_stack` is embedded in the node’s metadata and gives information on where these nodes come from. For example, modules like `torch.nn.Linear` when captured and exported `to_edge` generate groups of nodes for their computation. The group of nodes associated with computing the linear module then has a `source_fn_stack` of `torch.nn.Linear. Partitioning based on `source_fn_stack` allows us to identify groups of nodes which are lowerable via XNNPACK.
 
 For example after capturing `torch.nn.Linear` you would find the following key in the metadata for the addmm node associated with linear:
 ```python
->>> print(linear_node.meta["source_fn"])
-'source_fn': ('fn', <class 'torch.nn.modules.linear.Linear'>)
+>>> print(linear_node.meta["source_fn_stack"])
+'source_fn_stack': ('fn', <class 'torch.nn.modules.linear.Linear'>)
 ```
 
 
 ##### Op-based partitioning
 
-The `XnnpackPartitioner` also partitions using op targets. It traverses the graph and identifies individual nodes which are lowerable to XNNPACK. A drawback to module-based partitioning is that operators which come from [decompositions](https://github.com/pytorch/pytorch/blob/main/torch/_decomp/decompositions.py) may be skipped. For example, an operator like `torch.nn.Hardsigmoid` is decomposed into add, muls, divs, and clamps. While hardsigmoid is not lowerable, we can lower the decomposed ops. Relying on `source_fn` metadata would skip these lowerables because they belong to a non-lowerable module, so in order to improve model performance, we greedily lower operators based on the op targets as well as the `source_fn`.
+The `XnnpackPartitioner` also partitions using op targets. It traverses the graph and identifies individual nodes which are lowerable to XNNPACK. A drawback to module-based partitioning is that operators which come from [decompositions](https://github.com/pytorch/pytorch/blob/main/torch/_decomp/decompositions.py) may be skipped. For example, an operator like `torch.nn.Hardsigmoid` is decomposed into add, muls, divs, and clamps. While hardsigmoid is not lowerable, we can lower the decomposed ops. Relying on `source_fn_stack` metadata would skip these lowerables because they belong to a non-lowerable module, so in order to improve model performance, we greedily lower operators based on the op targets as well as the `source_fn_stack`.
 
 ##### Passes