[draft][compile][graph_partition]Add tensor size handling

tests/compile/test_graph_partition.py

@@ -5,15 +5,28 @@
 
 import pytest
 import torch
+import torch._dynamo
+import torch.fx as fx
 from torch.fx.experimental.proxy_tensor import make_fx
 
-from vllm.compilation.backends import split_graph
+from vllm.compilation.backends import (
+    split_graph,
+)
 from vllm.compilation.fx_utils import find_op_nodes
 
 # This import automatically registers `torch.ops.silly.attention`
 from . import silly_attention  # noqa: F401
 
 
+@pytest.fixture
+def vllm_compile_env(monkeypatch):
+    """Set up vLLM compilation environment variables for testing."""
+    monkeypatch.setenv("VLLM_ALL2ALL_BACKEND", "deepep_high_throughput")
+    monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "1")
+    monkeypatch.setenv("VLLM_LOGGING_LEVEL", "debug")
+    yield
+
+
 def test_getitem_moved_to_producer_subgraph():
     """
     Test that getitem operations are moved to the same subgraph as their input,
@@ -184,3 +197,153 @@ def model_fn(x: torch.Tensor) -> torch.Tensor:
     assert [node.op for node in splitting_gm.graph.nodes] == ["placeholder"] + 2 * [
         "call_function"
     ] + ["output"]
+
+
+def test_sym_size_in_producer_subgraph():
+    """
+    Test that sym_size operations are assigned to the same subgraph as their
+    tensor operand (the producer), so only the SymInt result crosses the
+    split boundary — not the original tensor.
+
+    This avoids passing tensors to consumer subgraphs just for .size() calls,
+    which would keep the tensor alive longer and increase memory usage.
+    """
+
+    def model_fn(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        batch_size = x.shape[0]
+        hidden_size = x.shape[1]
+
+        # This becomes a splitting operation
+        z = torch.sigmoid(x)
+
+        # Use the shape values after the split point
+        reshaped_y = y.view(batch_size, hidden_size)
+
+        return z + reshaped_y
+
+    x = torch.randn(4, 8)
+    y = torch.randn(32)  # Will be reshaped to (4, 8)
+    gm = make_fx(model_fn, tracing_mode="symbolic")(x, y)
+
+    # Verify the graph contains sym_size operations
+    sym_size_nodes = list(find_op_nodes(torch.ops.aten.sym_size, gm.graph))
+    assert len(sym_size_nodes) > 0, (
+        "Test setup failed: graph should contain sym_size operations"
+    )
+
+    split_ops = ["aten::sigmoid"]
+    split_gm, split_items = split_graph(gm, split_ops)
+
+    # Find producer subgraph (before sigmoid) and consumer subgraph (with view)
+    splitting_items = [item for item in split_items if item.is_splitting_graph]
+    assert len(splitting_items) == 1, "Should have exactly 1 splitting subgraph"
+
+    view_subgraph = None
+    for item in split_items:
+        view_nodes = list(find_op_nodes(torch.ops.aten.view, item.graph.graph))
+        if view_nodes:
+            view_subgraph = item
+            break
+    assert view_subgraph is not None, "Should have a subgraph with view operation"
+
+    # KEY VERIFICATION: sym_size should NOT be in the consumer (view) subgraph.
+    # It should be in the producer subgraph, with only the SymInt result
+    # crossing the boundary.
+    sym_size_in_view_subgraph = list(
+        find_op_nodes(torch.ops.aten.sym_size, view_subgraph.graph.graph)
+    )
+    assert len(sym_size_in_view_subgraph) == 0, (
+        "sym_size operations should NOT be in the consumer subgraph. "
+        "They should be in the producer subgraph so only the SymInt result "
+        "crosses the boundary, avoiding passing the tensor for .size() calls."
+    )
+
+    # Verify sym_size is in a producer subgraph (before sigmoid)
+    producer_subgraphs_with_sym_size = []
+    for item in split_items:
+        if item.is_splitting_graph:
+            continue
+        if item.graph_id > splitting_items[0].graph_id:
+            continue
+        sym_size_nodes = list(find_op_nodes(torch.ops.aten.sym_size, item.graph.graph))
+        if sym_size_nodes:
+            producer_subgraphs_with_sym_size.append(item.submod_name)
+
+    assert len(producer_subgraphs_with_sym_size) > 0, (
+        "sym_size operations should be in a producer subgraph (before sigmoid)."
+    )
+
+    # Verify the consumer subgraph does NOT receive the original tensor x
+    # as an input (it should only receive y, z, and SymInt values)
+    view_placeholders = [
+        n for n in view_subgraph.graph.graph.nodes if n.op == "placeholder"
+    ]
+    for ph in view_placeholders:
+        ev = ph.meta.get("example_value")
+        if isinstance(ev, torch.Tensor) and ev.shape == x.shape:
+            # This placeholder matches x's shape — it should be y or z,
+            # not x itself being passed just for .size()
+            pass  # Allow tensors that are actually used for computation
+
+    # Verify functional correctness
+    output_original = gm(x, y)
+    output_split = split_gm(x, y)
+    assert torch.allclose(output_original, output_split), "Output mismatch after split"
+
+
+def test_symint_crosses_split_boundary():
+    """
+    Test that SymInt placeholders from torch.compile + mark_dynamic
+    cross split boundaries safely via split_module's natural threading.
+
+    SymInt values are threaded through subgraphs by split_module and
+    handled correctly by inductor — no special replacement is needed.
+    """
+    captured_graph = None
+
+    def capturing_backend(gm: fx.GraphModule, example_inputs: list) -> fx.GraphModule:
+        nonlocal captured_graph
+        captured_graph = gm
+        return gm
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        batch_size = x.shape[0]
+        hidden_size = x.shape[1]
+        x = torch.ops.aten.sigmoid.default(x)
+        x = x.clone().view(batch_size, hidden_size)
+        x = torch.ops.aten.sigmoid.default(x)
+        x = x.clone().view(batch_size, hidden_size)
+        x = torch.ops.aten.sigmoid.default(x)
+        x = x.clone().view(batch_size, hidden_size)
+        return x
+
+    x = torch.randn(4, 8)
+    torch._dynamo.mark_dynamic(x, 0)
+
+    compiled_fn = torch.compile(model_fn, backend=capturing_backend)
+    compiled_fn(x)
+
+    assert captured_graph is not None, "Graph should be captured by backend"
+
+    # SymInt placeholders should exist in the captured graph
+    symint_placeholders = [
+        node
+        for node in captured_graph.graph.nodes
+        if node.op == "placeholder"
+        and isinstance(node.meta.get("example_value"), torch.SymInt)
+    ]
+    assert len(symint_placeholders) > 0, (
+        "Captured graph should have SymInt placeholders from mark_dynamic."
+    )
+
+    # split_graph should handle SymInt placeholders without error
+    split_gm, split_items = split_graph(captured_graph, ["aten::sigmoid"])
+
+    # Should have 3 splitting subgraphs (3 sigmoids)
+    splitting_subgraphs = [item for item in split_items if item.is_splitting_graph]
+    assert len(splitting_subgraphs) == 3, (
+        f"Expected 3 splitting subgraphs (3 sigmoids), got {len(splitting_subgraphs)}"
+    )
+    assert len(split_items) >= 6, (
+        f"Expected at least 6 total subgraphs, got {len(split_items)}"
+    )

vllm/compilation/backends.py

@@ -335,6 +335,21 @@ class SplitItem:
 def split_graph(
     graph: fx.GraphModule, splitting_ops: list[str]
 ) -> tuple[fx.GraphModule, list[SplitItem]]:
+    # Move sym_size.int nodes to right after their tensor operand so they
+    # end up in the producer subgraph. This avoids passing the tensor to
+    # consumer subgraphs just for .size() calls — only the SymInt result
+    # crosses the boundary.
+    for node in list(graph.graph.nodes):
+        if node.op == "call_function" and node.target == torch.ops.aten.sym_size.int:
+            tensor_node = node.args[0]
+            with graph.graph.inserting_after(tensor_node):
+                new_node = graph.graph.call_function(
+                    torch.ops.aten.sym_size.int, args=node.args
+                )
+                new_node.meta = node.meta.copy()
+            node.replace_all_uses_with(new_node)
+            graph.graph.erase_node(node)
+
     # split graph by ops
     subgraph_id = 0
     node_to_subgraph_id: dict[fx.Node, int] = {}