[Compile] Fix noop_elimination pass and add tests for noop_elimination (#24880)

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

.buildkite/test-pipeline.yaml

@@ -394,6 +394,7 @@ steps:
     - pytest -v -s compile/test_async_tp.py
     - pytest -v -s compile/test_fusion_all_reduce.py
     - pytest -v -s compile/test_decorator.py
+    - pytest -v -s compile/test_noop_elimination.py
 
 - label: PyTorch Fullgraph Smoke Test # 15min
   timeout_in_minutes: 30

tests/compile/backend.py

@@ -65,3 +65,7 @@ class TestBackend:
             num_post = len(list(find_op_nodes(op, self.graph_post_pass)))
             assert num_pre == 0, f"Unexpected op {op.name()} in pre-pass graph"
             assert num_post > 0, f"Op {op.name()} not found in post-pass graph"
+
+    def op_count(self, op: OpOverload, before=False) -> int:
+        graph = self.graph_pre_pass if before else self.graph_post_pass
+        return len(list(find_op_nodes(op, graph)))

tests/compile/test_noop_elimination.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+import vllm
+from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.config import (CompilationConfig, CompilationLevel, PassConfig,
+                         VllmConfig)
+
+from .backend import TestBackend
+
+
+@pytest.mark.parametrize("dtype",
+                         [torch.float16, torch.bfloat16, torch.float32])
+@pytest.mark.parametrize("num_tokens", [256, 1024])
+@pytest.mark.parametrize("hidden_size", [64, 4096])
+def test_noop_elimination(dtype, num_tokens, hidden_size):
+    torch.set_default_device("cuda")
+    torch.set_default_dtype(dtype)
+    torch.manual_seed(1)
+
+    class Model(torch.nn.Module):
+
+        def forward(self, x):
+            # Chain of reshapes
+            y = x.reshape(-1, 128, 32)
+            z = y.reshape(-1, 4096)
+            # No-op reshape
+            a = z.reshape(-1, 4096)
+            # Final reshape that should remain
+            b = a.reshape(-1, 128, 32)
+            # No-op slice
+            c = b[0:b.shape[0]]
+            # The pass should replace the result of this op with `c`.
+            d = torch.slice_scatter(
+                torch.ones_like(c),  # Dummy tensor to be scattered into
+                c,  # Source tensor
+                0,  # dim
+                0,  # start
+                c.shape[0],  # end
+            )
+            return d
+
+    vllm_config = VllmConfig(compilation_config=CompilationConfig(
+        level=CompilationLevel.PIECEWISE,
+        pass_config=PassConfig(enable_noop=True),
+    ))
+    with vllm.config.set_current_vllm_config(vllm_config):
+        noop_pass = NoOpEliminationPass(vllm_config)
+
+        backend = TestBackend(noop_pass)
+
+        model = Model()
+        # First dimension dynamic
+        x = torch.rand(num_tokens, hidden_size)
+        torch._dynamo.mark_dynamic(x, 0)
+
+        result = model(x)
+
+        model2 = torch.compile(model, backend=backend)
+        result2 = model2(x)
+
+        ATOL, RTOL = (2e-3, 2e-3)
+        torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL)
+
+        # The no-op reshape and slice should be eliminated.
+        # The chain of reshapes should be fused into a single reshape.
+        assert backend.op_count(torch.ops.aten.reshape.default) == 1
+        assert backend.op_count(torch.ops.aten.slice.Tensor) == 0
+        assert backend.op_count(torch.ops.aten.slice_scatter.default) == 0
+
+
+def test_non_noop_slice_preserved():
+    """Ensure that a slice with end=-1 (dropping last row) is NOT eliminated.
+
+    Regression test for a bug where end=-1 was treated like an inferred
+    dimension (reshape semantics) leading to incorrect elimination.
+    """
+    torch.set_default_device("cuda")
+    x = torch.randn(16, 16)
+
+    class SliceModel(torch.nn.Module):
+
+        def forward(self, x):
+            base = x.clone()
+            src = torch.ones(15, 16)
+            y = torch.slice_scatter(base, src, dim=0, start=0, end=-1)
+            return x[0:-1, :], y
+
+    vllm_config = VllmConfig(compilation_config=CompilationConfig(
+        level=CompilationLevel.PIECEWISE,
+        pass_config=PassConfig(enable_noop=True),
+    ))
+    with vllm.config.set_current_vllm_config(vllm_config):
+        noop_pass = NoOpEliminationPass(vllm_config)
+        backend = TestBackend(noop_pass)
+        model = SliceModel()
+        ref = model(x)
+        compiled = torch.compile(model, backend=backend)
+        out = compiled(x)
+        torch.testing.assert_close(ref, out)
+        # The slice should remain (not a no-op).
+        assert backend.op_count(torch.ops.aten.slice.Tensor) == 1
+        assert backend.op_count(torch.ops.aten.slice_scatter.default) == 1

vllm/compilation/noop_elimination.py

@@ -62,9 +62,6 @@ class NoOpEliminationPass(VllmInductorPass):
     scaled_mm: "f16[s0, 4096]" = ...
     at = auto_functionalized(fused_add_rms_norm, input = scaled_mm, ...)
     out: "f16[s0, 4096]" = at[1]
-
-    TODO(luka): This is currently tested in test_fusion,
-     but separate tests could be good.
     """
 
     def __call__(self, graph: torch.fx.Graph):
@@ -96,17 +93,19 @@ class NoOpEliminationPass(VllmInductorPass):
                     # Invalid reshape args, skip
                     continue
 
-                if self.all_dims_equivalent(shape, input_shape):
+                if self.reshape_all_dims_equivalent(shape, input_shape):
                     node.replace_all_uses_with(input)
                     graph.erase_node(node)
                     count += 1
 
             elif is_func(node, torch.ops.aten.slice.Tensor):
+                # python slicing semantics are different from reshape
+                # Don't treat -1 as inferred dimension
                 input, dim_index, start, end = node.args[:4]
                 input_shape = input.meta["val"].shape
-                i_dim = input_shape[dim_index]
+                output_shape = node.meta["val"].shape
 
-                if start == 0 and self.dims_equivalent(end, i_dim):
+                if output_shape == input_shape:
                     node.replace_all_uses_with(input)
                     graph.erase_node(node)
                     count += 1
@@ -116,14 +115,7 @@ class NoOpEliminationPass(VllmInductorPass):
                 base_shape = base.meta["val"].shape
                 view_shape = view.meta["val"].shape
 
-                view_dim = view_shape[dim_index]
-
-                # Check that view fully covers base and the full view is used
-                # (if the view fully covered the base after slicing but was not
-                # fully used, we could replace slice_scatter with a simple slice
-                # but that's a niche case).
-                if (base_shape == view_shape and start == 0
-                        and self.dims_equivalent(end, view_dim)):
+                if base_shape == view_shape:
                     node.replace_all_uses_with(view)
                     graph.erase_node(node)
                     count += 1
@@ -132,12 +124,8 @@ class NoOpEliminationPass(VllmInductorPass):
         self.dump_graph(graph, "after_noop_elimination")
         self.end_and_log()
 
-    def all_dims_equivalent(self, dims: Iterable[Union[int, torch.fx.Node]],
-                            i_dims: Iterable[Union[int, SymInt]]):
-        return all(
-            self.dims_equivalent(s, i_s) for s, i_s in zip(dims, i_dims))
-
-    def dims_equivalent(self, dim: Union[int, torch.fx.Node],
+    # ---------------------- Reshape helpers ----------------------
+    def reshape_dims_equivalent(self, dim: Union[int, torch.fx.Node],
                                 i_dim: Union[int, SymInt]) -> bool:
         """
         This function checks if two dimensions are equivalent.
@@ -156,10 +144,18 @@ class NoOpEliminationPass(VllmInductorPass):
         In case 3, the reshape dimension is a torch.fx.Node,
         and its value is a SymInt. That value is equal to the
         input dimension.
-
         """
         # Case 1 and 2
         if dim == i_dim or dim == -1:
             return True
         # Case 3
         return isinstance(dim, torch.fx.Node) and dim.meta["val"] == i_dim
+
+    def reshape_all_dims_equivalent(
+        self,
+        dims: Iterable[Union[int, torch.fx.Node]],
+        i_dims: Iterable[Union[int, SymInt]],
+    ) -> bool:
+        return all(
+            self.reshape_dims_equivalent(s, i_s)
+            for s, i_s in zip(dims, i_dims))