Fix split module interaction with dead code

Summary:
This change fixes split_module's interaction with dead code. Previously if a dead region was split out, split module would throw an error while attempting to access the outputs for the partition even though the partition has no outputs.

This change adds a new unit test to cover the dead code case and changes the output check to allow no output. The split module with no output will now output None like a normal python function

Unit Test Added:
test_split_module_dead_code

A module with dead code:
```
class ModWithDeadCode(torch.nn.Module):
            def forward(self, x):
                output = x * 2 # we want this
                dead_line = x + 2 # this is dead
                return output
```

Before:
```
torch/fx/passes/split_module.py, line 357, in split_module
base_mod_env[list(partition.outputs)[0]] = output_val
IndexError: list index out of range
```

After:
```
class GraphModule(torch.nn.Module):
    def forward(self, x):
        # No stacktrace found for following nodes
        submod_2 = self.submod_2(x)
        submod_1 = self.submod_1(x);  x = None
        return submod_1

    class GraphModule(torch.nn.Module):
        def forward(self, x):
            # No stacktrace found for following nodes
            add = x + 2;  x = None
            return None

    class GraphModule(torch.nn.Module):
        def forward(self, x):
            # No stacktrace found for following nodes
            mul = x * 2;  x = None
            return mul
```
Submod 2 is correctly extracted

Test Plan: Tested with new unit test

Differential Revision: D47196732

fbshipit-source-id: 82d7627ffc0b38b4c4a5d68dcfce4816d96ffddd

test/test_fx_experimental.py

@@ -785,6 +785,37 @@ def mod_partition(node: Node):
 
         self.assertEqual(orig_out, submodules_out)
 
+    def test_split_module_dead_code(self):
+        class ModWithDeadCode(torch.nn.Module):
+            def forward(self, x):
+                output = x * 2 # we want this
+                dead_line = x + 2 # this is dead
+                return output
+
+        mod = ModWithDeadCode()
+        traced = torch.fx.symbolic_trace(mod)
+
+        # split into before (0), target (1), and after(2)
+        saw_mul = False
+        def split_callback(n):
+            nonlocal saw_mul
+            if n.target == operator.mul:
+                saw_mul = True
+                return 1
+
+            if not saw_mul:
+                return 0
+            if saw_mul:
+                return 2
+
+        split = split_module(traced, mod, split_callback)
+
+        x = torch.randn((5,))
+        torch.testing.assert_close(
+            split(x), traced(x)
+        )
+
+
     def test_split_module_kwargs_expansion(self):
         class ModuleWithKwargsExpansion(torch.nn.Module):
             def forward(self, x, **kwargs):

torch/fx/passes/split_module.py

@@ -320,7 +320,11 @@ def record_cross_partition_use(
         output_vals = tuple(
             partition.environment[orig_nodes[name]] for name in partition.outputs
         )
-        output_vals = output_vals[0] if len(output_vals) == 1 else output_vals  # type: ignore[assignment]
+        num_output_vals = len(output_vals)
+        if num_output_vals == 0:
+            output_vals = None
+        elif num_output_vals == 1:
+            output_vals = output_vals[0]
         partition.graph.output(output_vals)
 
         if keep_original_order:
@@ -346,12 +350,14 @@ def record_cross_partition_use(
             partition.submod_name,
             tuple(base_mod_env[name] for name in partition.inputs),
         )
-        if len(partition.outputs) > 1:
+
+        num_outputs = len(partition.outputs)
+        if num_outputs > 1:
             # Unpack multiple return values from submodule
             output_val_proxy = torch.fx.proxy.Proxy(output_val)
             for i, output_name in enumerate(partition.outputs):
                 base_mod_env[output_name] = output_val_proxy[i].node  # type: ignore[index]
-        else:
+        elif num_outputs == 1:
             base_mod_env[list(partition.outputs)[0]] = output_val
 
     for node in m.graph.nodes: