use graph.output_node

backends/test/harness/tester.py

@@ -416,12 +416,7 @@ def _calculate_reference_output(
         """
 
         # Locate the output node.
-        output_node = None
-        for node in program.graph.nodes:
-            if node.op == "output":
-                output_node = node
-                break
-        assert output_node is not None
+        output_node = program.graph.output_node()
 
         # Look for a dequantization node in the output node args. Returned values are found in the first
         # argument of the output node.

backends/transforms/test/test_create_delete_constant_placeholder.py

@@ -61,7 +61,7 @@ def _test_create_delete(kind: InputKind, persistent_buffer: bool = None):
             kwargs={},
         )
 
-    output_node = list(graph.nodes)[-1]
+    output_node = graph.output_node()
     output_node.replace_input_with(input_node, add_node)
 
     # We should now have four nodes: test_node, input, add, output

exir/backend/backend_api.py

@@ -288,12 +288,8 @@ def _partition_and_lower_one_graph_module(
                 tagged_graph_module, node_list, tag
             )
 
-        tagged_graph_module_output_node = [
-            node for node in tagged_graph_module.graph.nodes if node.op == "output"
-        ][0]
-        submodule_output_node = [
-            node for node in submodule.graph.nodes if node.op == "output"
-        ][0]
+        tagged_graph_module_output_node = tagged_graph_module.graph.output_node()
+        submodule_output_node = submodule.graph.output_node()
         # Copy the output node meta from the original output node, because
         # create_submodule_from_nodes doesn't cover the meta field
         submodule_output_node.meta = tagged_graph_module_output_node.meta
@@ -476,12 +472,8 @@ def _create_partitions_in_graph_module(
                 tagged_graph_module, node_list, tag
             )
 
-        tagged_graph_module_output_node = [
-            node for node in tagged_graph_module.graph.nodes if node.op == "output"
-        ][0]
-        submodule_output_node = [
-            node for node in submodule.graph.nodes if node.op == "output"
-        ][0]
+        tagged_graph_module_output_node = tagged_graph_module.graph.output_node()
+        submodule_output_node = submodule.graph.output_node()
         # Copy the output node meta from the original output node, because
         # create_submodule_from_nodes doesn't cover the meta field
         submodule_output_node.meta = tagged_graph_module_output_node.meta

exir/emit/_emit_program.py

@@ -57,11 +57,7 @@ class EmitterOutput:
 
 def _remove_non_user_outputs(exported_program: ExportedProgram) -> torch.fx.GraphModule:
     gm = exported_program.graph_module
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-    assert output_node is not None
+    output_node = gm.graph.output_node()
 
     mutated_outputs: List[Optional[str]] = [
         out_spec.target if out_spec.kind in (OutputKind.BUFFER_MUTATION,) else None

exir/lowered_backend_module.py

@@ -233,14 +233,11 @@ def program(
             )
         ]
 
-        output_node = [
-            node for node in lowered_exported_program.graph.nodes if node.op == "output"
-        ]
-        assert len(output_node) == 1, "There should be only one output node"
+        output_node = lowered_exported_program.graph.output_node()
 
         # Step 1. Cleaning up the graph before inserting the call_delegate node
         # Remove the original output node
-        lowered_exported_program.graph.erase_node(output_node[0])
+        lowered_exported_program.graph.erase_node(output_node)
 
         # Remove all the everything else except the input
         for node in reversed(lowered_exported_program.graph.nodes):
@@ -269,11 +266,9 @@ def program(
         )
         # Get the output list. Since the output node is a tuple of list, like ([aten_mul_tensor, aten_add_tensor],)
         # We add some handling logic to get the list `[aten_mul_tensor, aten_add_tensor]` properly
-        original_output_nodes = [
-            node
-            for node in self._original_exported_program.graph.nodes
-            if node.op == "output"
-        ][0].args[0]
+        original_output_nodes = (
+            self._original_exported_program.graph.output_node().args[0]
+        )
 
         delegate_node.meta["spec"] = tuple(
             [make_spec(node.meta["val"]) for node in original_output_nodes]
@@ -927,11 +922,7 @@ def _unsafe_adjust_original_program(  # noqa: C901
             raise RuntimeError(f"Invalid input spec {input_spec} received")
 
     # Delete buffer mutations from the output which were consumed by the delegate
-    toplevel_output_node = None
-    for node in reversed(original_program.graph.nodes):
-        if node.op == "output":
-            toplevel_output_node = node
-            break
+    toplevel_output_node = original_program.graph.output_node()
 
     assert toplevel_output_node is not None
     assert (

exir/passes/insert_write_back_for_buffers_pass.py

@@ -30,11 +30,7 @@ def _insert_copy(
     Find the all the buffers and inputs that were mutated and insert copy_
     operators to reflect mutations.
     """
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-            break
+    output_node = gm.graph.output_node()
     assert output_node is not None
     outputs = pytree.tree_flatten(output_node.args)[0]
     assert len(outputs) == len(mutated_outputs)
@@ -139,11 +135,7 @@ def insert_write_back_for_buffers_pass(
         if lifted_node is not None:
             input_name_to_node[lifted_node] = input_node
 
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-            break
+    output_node = gm.graph.output_node()
 
     # Grab the mutable buffer nodes in the outputs,
     mutated_outputs: List[Optional[str]] = []

exir/passes/quantize_io_pass.py

@@ -145,11 +145,8 @@ def quantize_output(exported_program, output_index):
     output quantization.
     """
     graph = exported_program.graph_module.graph
-    outputs = [n for n in graph.nodes if n.op == "output"]
-    if len(outputs) != 1:
-        raise NotImplementedError("Only 1 output node is supported")
 
-    output_node = outputs[0]
+    output_node = graph.output_node()
     output_list = list(output_node.args[0])
     if output_index >= len(output_list):
         raise ValueError(

exir/passes/weights_to_outputs_pass.py

@@ -46,12 +46,7 @@ def weights_to_outputs_pass(
     inputs_to_params = gs.inputs_to_parameters
 
     # Get output node
-    output_node = None
-    for node in gm.graph.nodes:
-        if node.op == "output":
-            output_node = node
-            break
-    assert output_node is not None
+    output_node = gm.graph.output_node()
 
     # Get input nodes that are weights with an associated gradient
     placeholder_nodes = [

exir/tests/test_joint_graph.py

@@ -42,11 +42,7 @@ def forward(self, x, y):
         joint_ep = _export_forward_backward(ep)
         edge = to_edge(joint_ep)
 
-        output_node = None
-        for node in edge.exported_program().graph.nodes:
-            if node.op == "output":
-                output_node = node
-                break
+        output_node = edge.exported_program().graph.output_node()
 
         orig_outputs = len(output_node.args[0])
 
@@ -58,11 +54,7 @@ def forward(self, x, y):
             if spec.kind == OutputKind.TOKEN
         ]
 
-        output_node = None
-        for node in et.exported_program().graph.nodes:
-            if node.op == "output":
-                output_node = node
-                break
+        output_node = et.exported_program().graph.output_node()
 
         weight_outputs = len(output_node.args[0])