[Fusion] normalize fusion naming and enable e2e test (#4693)

### What this PR does / why we need it?
This PR standardizes the fusion naming, changing
`enable_quantization_fusion` to `fuse_norm_quant`, and enables e2e
testing.

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: wxsIcey <1790571317@qq.com>

tests/e2e/singlecard/compile/backend.py

@@ -0,0 +1,130 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from copy import deepcopy
+from typing import Any, Callable, List, Optional, Sequence
+
+import torch.fx as fx
+from torch._inductor.decomposition import select_decomp_table
+from vllm.compilation.fx_utils import OpOverload
+from vllm.config import get_current_vllm_config
+
+from vllm_ascend.compilation.compiler_interface import compile_fx
+
+
+class TestBackend:
+    """
+    A custom compilation backend for testing operator fusion passes.
+    It applies the AddRMSNormQuantFusionPass during graph compilation and
+    records the FX graph before and after the transformation.
+    """
+
+    def __init__(self, custom_passes: Optional[List[Any]] = None):
+        vllm_config = get_current_vllm_config()
+        compile_config = vllm_config.compilation_config
+        self.inductor_config = compile_config.inductor_compile_config
+        self.inductor_config["graph_fusion_manager"] = self.post_pass
+        self.custom_passes = custom_passes
+
+        # Placeholders to store FX graphs for verification
+        self.graph_pre_pass = None
+        self.graph_post_pass = None
+
+    def post_pass(self,
+                  graph: fx.Graph,
+                  runtime_shape: int | None = None) -> fx.Graph:
+        """
+        Apply custom graph transformation passes.
+        """
+        self.graph_pre_pass = deepcopy(graph)
+        if self.custom_passes is not None:
+            for pass_ in self.custom_passes:
+                pass_(graph)
+        self.graph_post_pass = deepcopy(graph)
+        return graph
+
+    def compile(
+            self,
+            graph: fx.GraphModule,
+            example_inputs: list[Any],
+            compiler_config: dict[str, Any],
+            runtime_shape: Optional[int] = None,
+            key: Optional[str] = None
+    ) -> tuple[Optional[Callable], Optional[Any]]:
+        """
+        Compile the FX graph using vLLM's Ascend compiler interface.
+        Wraps the post-pass logic into the inner_compile callback.
+        """
+
+        def compile_inner(graph, example_inputs):
+            current_pass_manager = compiler_config["graph_fusion_manager"]
+            return current_pass_manager(graph, runtime_shape)
+
+        decompositions = select_decomp_table()
+        compiled_fn = compile_fx(
+            graph=graph,
+            example_inputs=example_inputs,
+            inner_compile=compile_inner,
+            decompositions=decompositions,
+        )
+        return compiled_fn, None
+
+    def __call__(self, gm: fx.GraphModule,
+                 example_inputs: Optional[List[Any]]):
+        """
+        Make the backend callable by torch.compile().
+        Returns a compiled executable function.
+        """
+        assert example_inputs is not None
+        compiled_fn, _ = self.compile(
+            gm,
+            example_inputs,
+            compiler_config={"graph_fusion_manager": self.post_pass},
+            runtime_shape=None,
+            key=None,
+        )
+        return compiled_fn
+
+    def find_nodes_by_target(self, graph: fx.GraphModule,
+                             target: OpOverload) -> List[fx.Node]:
+        """Helper to find all FX nodes that call a specific operator."""
+        return [
+            node for node in graph.graph.nodes
+            if hasattr(node, 'target') and node.target == target
+        ]
+
+    def check_before_ops(self,
+                         ops: Sequence[OpOverload],
+                         fully_replaced: bool = True):
+        """
+        Verify that the original (unfused) operators exist before the pass
+        and are fully removed afterward (if fully_replaced=True).
+        """
+        for op in ops:
+            num_pre = len(self.find_nodes_by_target(self.graph_pre_pass, op))
+            num_post = len(self.find_nodes_by_target(self.graph_post_pass, op))
+            print(f"Op {op}: pre={num_pre}, post={num_post}")
+
+            assert num_pre > 0, f"Op {op} not found in pre-pass graph"
+            if fully_replaced:
+                assert num_post == 0, f"Unexpected op {op} in post-pass graph: {num_post} nodes remain"
+
+    def check_after_ops(self, ops: Sequence[OpOverload]):
+        """Verify that the fused operator appears in the transformed graph."""
+        for op in ops:
+            num_post = len(self.find_nodes_by_target(self.graph_post_pass, op))
+            print(f"Op {op}: post={num_post}")
+            assert num_post > 0, f"Op {op} not found in post-pass graph"