[Bugfix] Avoid merging empty-only partitions into splitting-op subgraphs (#36595)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

tests/compile/test_graph_partition.py

@@ -7,7 +7,7 @@ import pytest
 import torch
 from torch.fx.experimental.proxy_tensor import make_fx
 
-from vllm.compilation.backends import split_graph
+from vllm.compilation.backends import _is_empty_allocation_node, split_graph
 from vllm.compilation.passes.fx_utils import find_op_nodes
 
 # This import automatically registers `torch.ops.silly.attention`
@@ -186,10 +186,25 @@ def test_consecutive_ops_in_split():
     ] + ["output"]
 
 
-def test_empty_only_partition_is_merged():
+def _get_empty_nodes(split_item):
+    return [
+        node for node in split_item.graph.graph.nodes if _is_empty_allocation_node(node)
+    ]
+
+
+def _subgraphs_with_empty_nodes(split_items, *, is_splitting_graph):
+    return [
+        split_item
+        for split_item in split_items
+        if split_item.is_splitting_graph == is_splitting_graph
+        and _get_empty_nodes(split_item)
+    ]
+
+
+def test_empty_only_partition_stays_separate_after_splitting_predecessor():
     """
-    Test that an empty-allocation-only partition is merged into its previous
-    partition during Dynamo FX splitting.
+    Empty-only subgraphs should not be merged when the only predecessor is
+    a splitting-op subgraph.
     """
 
     def model_fn(x: torch.Tensor) -> torch.Tensor:
@@ -204,9 +219,65 @@ def test_empty_only_partition_is_merged():
     split_ops = ["aten::sin", "aten::cos.out"]
     split_gm, split_items = split_graph(gm, split_ops)
 
-    # Without the merge, this graph is split into 3 partitions where the
-    # middle partition contains only aten::empty_like.
-    assert len(split_items) == 2, "Empty-only partition should be merged"
+    # Graph partitioning for this pattern is:
+    # [sin], [empty_like], [cos.out].
+    assert len(split_items) == 3, (
+        "Empty-only partition should not merge into splitting-op subgraph"
+    )
+
+    splitting_with_empty = _subgraphs_with_empty_nodes(
+        split_items, is_splitting_graph=True
+    )
+    assert len(splitting_with_empty) == 0, (
+        "Splitting-op subgraphs should not contain empty allocation nodes: "
+        f"{[item.submod_name for item in splitting_with_empty]}"
+    )
+
+    output_original = gm(x)
+    output_split = split_gm(x)
+    assert torch.allclose(output_original, output_split), "Output mismatch after split"
+
+
+def test_empty_only_partition_is_merged():
+    """
+    Empty-only subgraphs should still be merged when a non-splitting predecessor
+    exists. The merged empty node must remain outside splitting-op subgraphs.
+    """
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        base = x + 1
+        y = torch.sin(base)
+        out = torch.empty_like(base)
+        torch.ops.aten.cos.out(base, out=out)
+        return out + y
+
+    x = torch.randn(4, 3)
+    gm = make_fx(model_fn)(x)
+    split_gm, split_items = split_graph(gm, ["aten::sin", "aten::cos.out"])
+
+    # Partitioning should be:
+    # [add, empty_like], [sin], [cos.out], [add].
+    assert len(split_items) == 4, (
+        "Empty-only partition should be merged into non-splitting predecessor"
+    )
+
+    splitting_with_empty = _subgraphs_with_empty_nodes(
+        split_items, is_splitting_graph=True
+    )
+    assert len(splitting_with_empty) == 0, (
+        "Splitting-op subgraphs should not contain empty allocation nodes: "
+        f"{[item.submod_name for item in splitting_with_empty]}"
+    )
+
+    non_splitting_with_empty = _subgraphs_with_empty_nodes(
+        split_items, is_splitting_graph=False
+    )
+    assert len(non_splitting_with_empty) == 1, (
+        "Exactly one non-splitting subgraph should contain the merged empty node"
+    )
+    assert len(_get_empty_nodes(non_splitting_with_empty[0])) == 1, (
+        "Expected exactly one empty allocation node in merged subgraph"
+    )
 
     output_original = gm(x)
     output_split = split_gm(x)
@@ -220,18 +291,37 @@ def test_builtin_empty_only_partition_is_merged():
     """
 
     def model_fn(x: torch.Tensor) -> torch.Tensor:
-        out1 = torch.empty_like(x)
-        torch.ops.silly.attention(x, x, x, out1)
-        out2 = torch.empty_like(x)
-        torch.ops.silly.attention(out1, out1, out1, out2)
-        return out2
+        hidden = x + 1
+        out1 = torch.empty_like(hidden)
+        torch.ops.silly.attention(hidden, hidden, hidden, out1)
+        out2 = torch.empty_like(hidden)
+        torch.ops.silly.attention(out1, out1, hidden, out2)
+        return out2 + hidden
 
     gm = torch.fx.symbolic_trace(model_fn)
     split_gm, split_items = split_graph(gm, ["silly::attention"])
 
-    # Without the empty-only merge, this graph creates 4 partitions:
-    # [empty_like], [attention], [empty_like], [attention].
-    assert len(split_items) == 3, "Builtin empty-only partition should be merged"
+    # Without empty-only merge, this graph would split into:
+    # [add, empty_like], [attention], [empty_like], [attention], [add].
+    assert len(split_items) == 4, "Builtin empty-only partition should be merged"
+
+    splitting_with_empty = _subgraphs_with_empty_nodes(
+        split_items, is_splitting_graph=True
+    )
+    assert len(splitting_with_empty) == 0, (
+        "Splitting-op subgraphs should not contain empty allocation nodes: "
+        f"{[item.submod_name for item in splitting_with_empty]}"
+    )
+
+    non_splitting_with_empty = _subgraphs_with_empty_nodes(
+        split_items, is_splitting_graph=False
+    )
+    assert len(non_splitting_with_empty) == 1, (
+        "Exactly one non-splitting subgraph should contain merged empty nodes"
+    )
+    assert len(_get_empty_nodes(non_splitting_with_empty[0])) == 2, (
+        "Expected two builtin empty_like nodes in merged non-splitting subgraph"
+    )
 
     x = torch.randn(2, 3, device="cuda")
     output_original = gm(x)