[compile] Fix caching error over pytree slice node. (#35308)

Signed-off-by: zhxchen17 <zhxchen17@fb.com>

tests/compile/test_aot_compile.py

@@ -16,6 +16,7 @@ import torch
 import vllm.model_executor.layers.activation
 from vllm.compilation.caching import (
     StandaloneCompiledArtifacts,
+    VllmSerializableFunction,
 )
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (
@@ -156,6 +157,26 @@ def test_save_and_load(monkeypatch: pytest.MonkeyPatch):
             assert torch.allclose(ret, expected)
 
 
+@pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
+def test_save_and_load_slice(monkeypatch: pytest.MonkeyPatch):
+    def foo(x: torch.Tensor):
+        return x[slice(0, x.shape[0])]
+
+    vllm_config = make_vllm_config()
+
+    example_input = torch.randn(10, 10)
+    torch._dynamo.mark_dynamic(example_input, 0)
+    gm = torch.fx.symbolic_trace(foo)
+    assert "getitem_1 = x[slice(0, getitem, None)]" in gm.code
+    with use_vllm_config(vllm_config):
+        payload = VllmSerializableFunction.serialize_compile_artifacts(
+            VllmSerializableFunction(gm, (example_input,), "", foo)
+        )
+        fn = VllmSerializableFunction.deserialize_compile_artifacts(payload)
+
+    assert gm.code == fn.graph_module.code
+
+
 @pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")
 def test_cache_load_returns_tuple_consistency(monkeypatch: pytest.MonkeyPatch):
     """

vllm/compilation/caching.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import contextlib
 import hashlib
 import inspect
 import os
@@ -144,6 +145,18 @@ class StandaloneCompiledArtifacts:
         self.loaded_submodule_store = {}
 
 
+@contextlib.contextmanager
+def patch_pytree_map_over_slice():
+    pytree._private_register_pytree_node(
+        slice, lambda x: ([x.start, x.stop, x.step], None), lambda x, c: slice(*x)
+    )
+
+    try:
+        yield
+    finally:
+        pytree._deregister_pytree_node(slice)
+
+
 class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
     """
     A wrapper around a compiled function by vllm. It will forward the tensor
@@ -235,7 +248,10 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
                 lambda inp: torch.empty_like(inp, device="meta"),
                 state["example_inputs"],
             )
-        with patch.object(GraphPickler, "reducer_override", _graph_reducer_override):
+        with (
+            patch.object(GraphPickler, "reducer_override", _graph_reducer_override),
+            patch_pytree_map_over_slice(),
+        ):
             state["graph_module"] = GraphPickler.dumps(
                 state["graph_module"], Options(ops_filter=None)
             )
@@ -261,7 +277,8 @@ class VllmSerializableFunction(SerializableCallable):  # type: ignore[misc]
 
         state = pickle.loads(data)
         fake_mode = FakeTensorMode(shape_env=ShapeEnv())
-        state["graph_module"] = GraphPickler.loads(state["graph_module"], fake_mode)
+        with patch_pytree_map_over_slice():
+            state["graph_module"] = GraphPickler.loads(state["graph_module"], fake_mode)
         state["graph_module"].recompile()
         state["example_inputs"] = GraphPickler.loads(state["example_inputs"], fake_mode)