[Bugfix] Eliminate tuple inputs to submodules in graph partitioning (#28533)

Signed-off-by: Yanan Cao <gmagogsfm@gmail.com>

.buildkite/test-pipeline.yaml

@@ -445,6 +445,7 @@ steps:
     - vllm/
     - tests/compile
   commands:
+    - pytest -v -s compile/test_graph_partition.py
     - pytest -v -s compile/test_config.py
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py

tests/compile/test_graph_partition.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import operator
+
+import pytest
+import torch
+from torch.fx.experimental.proxy_tensor import make_fx
+
+from vllm.compilation.backends import split_graph
+
+
+def test_getitem_moved_to_producer_subgraph():
+    """
+    Test that getitem operations are moved to the same subgraph as their input,
+    preventing tuple inputs to submodules.
+    """
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        # torch.split returns a tuple, creating real getitem operations
+        # Should become first submodule that produces tuple
+        chunks = torch.split(x, x.shape[0] // 2, dim=0)
+
+        # Following ops should become second submodule that consumes tuple
+        result_0 = torch.relu(chunks[0])
+        result_1 = torch.relu(chunks[1])
+        return torch.cat([result_0, result_1], dim=0)
+
+    x = torch.randn(4, 3)
+    gm = make_fx(model_fn)(x)
+
+    has_getitem = any(
+        node.op == "call_function" and node.target == operator.getitem
+        for node in gm.graph.nodes
+    )
+    assert has_getitem, "Test setup failed: graph should contain getitem operations"
+
+    # Split on tuple producer aten::split
+    split_ops = ["aten::split.Tensor"]
+    split_gm, split_items = split_graph(gm, split_ops)
+    assert len(split_items) == 2, "Graph should be split into 2 submodules"
+
+    for split_item in split_items:
+        submodule = split_item.graph
+
+        getitem_on_placeholder = []
+        for node in submodule.graph.nodes:
+            if (
+                node.op == "call_function"
+                and node.target == operator.getitem
+                and node.args[0].op == "placeholder"
+            ):
+                getitem_on_placeholder.append(node)
+
+        assert len(getitem_on_placeholder) == 0, (
+            f"Submodule {split_item.submod_name} has getitem operations on "
+            f"placeholder nodes: {[n.name for n in getitem_on_placeholder]}. "
+            "This means tuple inputs were not properly eliminated."
+        )
+
+    new_x = torch.randn(4, 3)
+    output_original = gm(new_x)
+    output_split = split_gm(new_x)
+
+    assert torch.allclose(output_original, output_split), "Output mismatch"
+
+
+def test_no_tuple_inputs_with_multiple_consumers():
+    """
+    Test that when a tuple is consumed by multiple split operations,
+    getitem operations are properly moved to avoid tuple inputs.
+    """
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        # torch.split returns a tuple, creating real getitem operations
+        # Should become first submodule that produces tuple
+        chunks = torch.split(x, x.shape[0] // 2, dim=0)
+
+        # These should become second submodule consuming tuple
+        result_1 = torch.relu(chunks[0])
+        result_2 = torch.relu(chunks[1])
+
+        # Artificial graph splitting point to create another
+        # independent submodule that consumes tuple later
+        # This would become the third submodule
+        result_1 = torch.sigmoid(result_1)
+
+        # Fourth submodule that consumes tuple
+        result = torch.cat([chunks[0], chunks[1], result_1, result_2])
+        return result
+
+    x = torch.randn(4, 3)
+    gm = make_fx(model_fn)(x)
+
+    has_getitem = any(
+        node.op == "call_function" and node.target == operator.getitem
+        for node in gm.graph.nodes
+    )
+    assert has_getitem, "Test setup failed: graph should contain getitem operations"
+
+    split_ops = ["aten::split.Tensor", "aten::sigmoid"]
+    split_gm, split_items = split_graph(gm, split_ops)
+    assert len(split_items) == 4, "Graph should be split into 4 submodules"
+
+    for split_item in split_items:
+        submodule = split_item.graph
+
+        for node in submodule.graph.nodes:
+            if (
+                node.op == "call_function"
+                and node.target == operator.getitem
+                and node.args[0].op == "placeholder"
+            ):
+                pytest.fail(
+                    f"Submodule {split_item.submod_name} has getitem on "
+                    f"placeholder {node.args[0].name}, indicating it receives "
+                    "a tuple input"
+                )
+
+    new_x = torch.randn(4, 3)
+    output_original = gm(new_x)
+    output_split = split_gm(new_x)
+
+    assert torch.allclose(output_original, output_split), "Output mismatch after split"

vllm/compilation/backends.py

@@ -4,6 +4,7 @@
 import ast
 import dataclasses
 import hashlib
+import operator
 import os
 import pprint
 import time
@@ -307,12 +308,24 @@ def split_graph(
 ) -> tuple[fx.GraphModule, list[SplitItem]]:
     # split graph by ops
     subgraph_id = 0
-    node_to_subgraph_id = {}
-    split_op_graphs = []
+    node_to_subgraph_id: dict[fx.Node, int] = {}
+    split_op_graphs: list[int] = []
     for node in graph.graph.nodes:
         if node.op in ("output", "placeholder"):
             continue
 
+        # Check if this is a getitem operation on a node from an earlier subgraph.
+        # If so, assign it to the same subgraph as its input to avoid passing entire
+        # tuple as input to submodules, which is against standalone_compile and
+        # AoTAutograd input requirement.
+        if node.op == "call_function" and node.target == operator.getitem:
+            # Assign this getitem to the same subgraph as its input
+            input_node = node.args[0]
+            if input_node.op != "placeholder":
+                assert input_node in node_to_subgraph_id
+                node_to_subgraph_id[node] = node_to_subgraph_id[input_node]
+                continue
+
         if should_split(node, splitting_ops):
             subgraph_id += 1
             node_to_subgraph_id[node] = subgraph_id