Add flag to fx.passes.split_module to normalize input names (#157733)

This is useful for vLLM, which runs AOTAutograd directly on graphs after
they have been split.

I created a new flag for this instead of reusing
`keep_original_node_name` (please let me know if you think I should reuse this).
The reasoning is:
- The names of the placeholder nodes is different from the targets of
  the placehoder nodes. The targets are the actual input names.
- Backwards compatibility: this API has been out for ~4 years, it
  looks public, and it has extensive public use. For example, this change
  would actually be BC-breaking to vLLM (they rely on the subgraph input
  names being different at the moment).

Test Plan:
- new tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157733
Approved by: https://github.com/ezyang

test/expect/TestFXAPIBackwardCompatibility.test_function_back_compat-fx_backcompat_function_signatures.expect

@@ -64,7 +64,7 @@ torch.fx.node.map_aggregate(a: torch.fx.node.Argument, fn: Callable[[torch.fx.no
 torch.fx.node.map_arg(a: torch.fx.node.Argument, fn: Callable[[torch.fx.node.Node], torch.fx.node.Argument]) -> torch.fx.node.Argument
 torch.fx.passes.reinplace.reinplace(gm, *sample_args)
 torch.fx.passes.runtime_assert.insert_deferred_runtime_asserts(gm: torch.fx.graph_module.GraphModule, shape_env: Any, name: str, export: bool = False) -> None
-torch.fx.passes.split_module.split_module(m: torch.fx.graph_module.GraphModule, root_m: torch.nn.modules.module.Module, split_callback: Callable[[torch.fx.node.Node], int], qualname_map: Optional[Dict[str, str]] = None, keep_original_order: Optional[bool] = False, keep_original_node_name: Optional[bool] = False)
+torch.fx.passes.split_module.split_module(m: torch.fx.graph_module.GraphModule, root_m: torch.nn.modules.module.Module, split_callback: Callable[[torch.fx.node.Node], int], qualname_map: Optional[Dict[str, str]] = None, keep_original_order: Optional[bool] = False, keep_original_node_name: Optional[bool] = False, keep_original_input_name: bool = True)
 torch.fx.proxy.Attribute.__init__(self, root: torch.fx.proxy.Proxy, attr: str)
 torch.fx.proxy.Proxy.__init__(self, node: torch.fx.node.Node, tracer: 'Optional[TracerBase]' = None)
 torch.fx.proxy.Proxy.keys(self)

test/test_fx_experimental.py

@@ -791,6 +791,46 @@ terrible spacing
 
         self.assertEqual(orig_out, submodules_out)
 
+    def test_split_module_input_names(self):
+        class Mod(torch.nn.Module):
+            def forward(self, x, a0, a1, b0, b1, c0, c1):
+                x = x + (a0 ** 2) + (a1 / 2)
+                x = x + (b0 ** 2) + (b1 / 2)
+                x = x + (c0 ** 2) + (c1 / 2)
+                return x
+
+        mod = Mod()
+        traced = torch.fx.symbolic_trace(mod)
+
+        seen = 0
+
+        def split(n):
+            nonlocal seen
+            result = seen // 4
+            seen += 1
+            return result
+
+        split = split_module(traced, mod, split, keep_original_input_name=False)
+
+        # All the submodules should take in the inputs in the same order.
+        args = [torch.tensor(2.), torch.tensor(3.), torch.tensor(4.)]
+        output0 = split.submod_0(*args)
+        output1 = split.submod_1(*args)
+        output2 = split.submod_2(*args)
+        self.assertEqual(output0, output1)
+        self.assertEqual(output1, output2)
+
+        # Each submodule should have normalized input names
+        def check_ph(gm):
+            nodes = list(gm.graph.nodes)
+            self.assertEqual(nodes[0].target, "arg_0")
+            self.assertEqual(nodes[1].target, "arg_1")
+            self.assertEqual(nodes[2].target, "arg_2")
+
+        check_ph(split.submod_0)
+        check_ph(split.submod_1)
+        check_ph(split.submod_2)
+
     def test_split_module_dead_code(self):
         class ModWithDeadCode(torch.nn.Module):
             def forward(self, x):

torch/fx/passes/split_module.py

@@ -58,6 +58,7 @@ def split_module(
     qualname_map: Optional[dict[str, str]] = None,
     keep_original_order: Optional[bool] = False,
     keep_original_node_name: Optional[bool] = False,
+    keep_original_input_name: bool = True,
 ):
     """
     Creates subgraphs out of main graph
@@ -76,7 +77,10 @@ def split_module(
             names in the original module.
         keep_original_order: Optional[bool]: keep the original order of the GraphModule
             or use the Topological order of the new constructed GraphModule
-
+        keep_original_node_name: Optional[bool]: If the partitioned graphs should
+            have the same node names as the original graph.
+        keep_original_input_name: bool: If the partitioned graphs should
+            have the same input names as the original graph.
 
     Returns:
         GraphModule: the module after split.
@@ -419,11 +423,28 @@ def split_module(
     for partition_name in sorted_partitions:
         partition = partitions[partition_name]
         new_inputs: dict[str, None] = {}
+
+        counter = 0
+
         for inp in partition.inputs:
             orig_node = orig_nodes[inp]
             # We don't pass in get_attr nodes as inputs to the partition, but
             # instead set them as targets and use getattr within the module
 
+            def add_placeholder():
+                if keep_original_input_name:
+                    name = inp
+                else:
+                    nonlocal counter
+                    name = f"arg_{counter}"
+                    counter += 1
+                placeholder = partition.graph.placeholder(
+                    name,
+                    type_expr=orig_nodes[inp].type,
+                )
+                new_inputs[inp] = None
+                return placeholder
+
             if orig_node.op == "get_attr":
                 assert isinstance(orig_node.target, str)
 
@@ -432,17 +453,9 @@ def split_module(
                     placeholder = partition.graph.get_attr(orig_node.target)
                     partition.targets[orig_node.target] = orig_attr
                 else:
-                    placeholder = partition.graph.placeholder(
+                    placeholder = add_placeholder()
-                        inp,
-                        type_expr=orig_nodes[inp].type,
-                    )
-                    new_inputs[inp] = None
             else:
-                placeholder = partition.graph.placeholder(
+                placeholder = add_placeholder()
-                    inp,
-                    type_expr=orig_nodes[inp].type,
-                )
-                new_inputs[inp] = None
             placeholder.meta = orig_nodes[inp].meta.copy()
             partition.environment[orig_nodes[inp]] = placeholder
         partition.inputs = new_inputs