[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>

.github/workflows/_e2e_test.yaml

@@ -103,6 +103,7 @@ jobs:
           pytest -sv tests/e2e/singlecard/test_vlm.py
           pytest -sv tests/e2e/singlecard/test_xlite.py
           pytest -sv tests/e2e/singlecard/pooling/
+          pytest -sv tests/e2e/singlecard/compile/test_norm_quant_fusion.py
 
           # ------------------------------------ v1 spec decode test ------------------------------------ #
           pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

tests/e2e/singlecard/compile/backend.py

@@ -17,65 +17,12 @@
 from copy import deepcopy
 from typing import Any, Callable, List, Optional, Sequence
 
-import pytest
-import torch
 import torch.fx as fx
-import torch.nn as nn
-import torch_npu
-import vllm.config
 from torch._inductor.decomposition import select_decomp_table
 from vllm.compilation.fx_utils import OpOverload
-from vllm.config import ModelConfig, VllmConfig, get_current_vllm_config
+from vllm.config import get_current_vllm_config
 
 from vllm_ascend.compilation.compiler_interface import compile_fx
-from vllm_ascend.compilation.passes.quant_fusion_pass import \
-    AddRMSNormQuantFusionPass
-
-
-class TestModel(nn.Module):
-    """
-    A minimal test model that simulates the pattern:
-        AddRMSNorm → Quantization
-    """
-
-    def __init__(self, hidden_size: int, eps: float = 1e-6, device="npu"):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.eps = eps
-        self.rms_norm_weight = nn.Parameter(
-            torch.randn(hidden_size, device=device))
-        self.quant_scale = torch.tensor([1.0], device=device)
-        self.quant_offset = torch.tensor([0.0], device=device)
-
-    def forward(self, x):
-        """
-        Forward pass:
-          1. Perform npu_add_rms_norm
-          2. Quantize the normalized output to int8
-        Returns both quantized output and updated residual.
-        """
-        residual = torch.zeros_like(x)
-
-        norm_output, _, new_residual = torch_npu.npu_add_rms_norm(
-            x, residual, self.rms_norm_weight, self.eps)
-
-        quantized_output = torch_npu.npu_quantize(norm_output,
-                                                  self.quant_scale,
-                                                  self.quant_offset,
-                                                  torch.qint8, -1, False)
-
-        return quantized_output, new_residual
-
-    def ops_in_model_before(self) -> List[OpOverload]:
-        """Return the list of expected operators BEFORE fusion."""
-        return [
-            torch.ops.npu.npu_add_rms_norm.default,
-            torch.ops.npu.npu_quantize.default
-        ]
-
-    def ops_in_model_after(self) -> List[OpOverload]:
-        """Return the list of expected operators AFTER successful fusion."""
-        return [torch.ops.npu.npu_add_rms_norm_quant.default]
 
 
 class TestBackend:
@@ -85,14 +32,12 @@ class TestBackend:
     records the FX graph before and after the transformation.
     """
 
-    def __init__(self):
+    def __init__(self, custom_passes: Optional[List[Any]] = None):
         vllm_config = get_current_vllm_config()
         compile_config = vllm_config.compilation_config
-        self.custom_passes = [
-            AddRMSNormQuantFusionPass(vllm_config=vllm_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
@@ -105,6 +50,7 @@ class TestBackend:
         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)
@@ -136,11 +82,13 @@ class TestBackend:
         )
         return compiled_fn, None
 
-    def __call__(self, gm: fx.GraphModule, example_inputs: List[Any]):
+    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,
@@ -180,40 +128,3 @@ class TestBackend:
             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"
-
-
-@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("hidden_size", [64])
-@pytest.mark.parametrize("num_tokens", [257])
-@pytest.mark.parametrize("eps", [1e-5, 1e-6])
-def test_rmsnorm_quant_fusion(dtype: torch.dtype, hidden_size: int,
-                              num_tokens: int, eps: float):
-    """
-    End-to-end test for AddRMSNorm+Quantize fusion.
-    Compares: Operator presence/absence before and after graph transformation
-    """
-    torch.set_default_dtype(dtype)
-    torch.manual_seed(1)
-
-    vllm_config = VllmConfig(model_config=ModelConfig(dtype=dtype))
-
-    with vllm.config.set_current_vllm_config(vllm_config):
-        backend = TestBackend()
-        model = TestModel(hidden_size, eps, device="npu")
-        model = model.to("npu")
-
-        x = torch.rand(num_tokens,
-                       hidden_size,
-                       device="npu",
-                       dtype=dtype,
-                       requires_grad=False)
-
-        result_unfused = model(x)
-        print("Unfused result:", [t.shape for t in result_unfused])
-        model_fused = torch.compile(model, backend=backend)
-        result_fused = model_fused(x)
-        print("Fused result:", [t.shape for t in result_fused])
-
-        print("=== Checking operator fusion ===")
-        backend.check_before_ops(model.ops_in_model_before())
-        backend.check_after_ops(model.ops_in_model_after())

tests/e2e/singlecard/compile/test_norm_quant_fusion.py

@@ -0,0 +1,113 @@
+#
+# 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 typing import List
+
+import pytest
+import torch
+import torch.nn as nn
+import torch_npu
+import vllm.config
+from vllm.compilation.fx_utils import OpOverload
+from vllm.config import ModelConfig, VllmConfig
+
+from tests.e2e.singlecard.compile.backend import TestBackend
+from vllm_ascend.compilation.passes.norm_quant_fusion_pass import \
+    AddRMSNormQuantFusionPass
+
+
+class TestModel(nn.Module):
+    """
+    A minimal test model that simulates the pattern:
+        AddRMSNorm → Quantization
+    """
+
+    def __init__(self, hidden_size: int, eps: float = 1e-6, device="npu"):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.eps = eps
+        self.rms_norm_weight = nn.Parameter(
+            torch.randn(hidden_size, device=device))
+        self.quant_scale = torch.tensor([1.0], device=device)
+        self.quant_offset = torch.tensor([0.0], device=device)
+
+    def forward(self, x):
+        """
+        Forward pass:
+          1. Perform npu_add_rms_norm
+          2. Quantize the normalized output to int8
+        Returns both quantized output and updated residual.
+        """
+        residual = torch.zeros_like(x)
+
+        norm_output, _, new_residual = torch_npu.npu_add_rms_norm(
+            x, residual, self.rms_norm_weight, self.eps)
+
+        quantized_output = torch_npu.npu_quantize(norm_output,
+                                                  self.quant_scale,
+                                                  self.quant_offset,
+                                                  torch.qint8, -1, False)
+
+        return quantized_output, new_residual
+
+    def ops_in_model_before(self) -> List[OpOverload]:
+        """Return the list of expected operators BEFORE fusion."""
+        return [
+            torch.ops.npu.npu_add_rms_norm.default,
+            torch.ops.npu.npu_quantize.default
+        ]
+
+    def ops_in_model_after(self) -> List[OpOverload]:
+        """Return the list of expected operators AFTER successful fusion."""
+        return [torch.ops.npu.npu_add_rms_norm_quant.default]
+
+
+@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("hidden_size", [64])
+@pytest.mark.parametrize("num_tokens", [257])
+@pytest.mark.parametrize("eps", [1e-5, 1e-6])
+def test_rmsnorm_quant_fusion(dtype: torch.dtype, hidden_size: int,
+                              num_tokens: int, eps: float):
+    """
+    End-to-end test for AddRMSNorm+Quantize fusion.
+    Compares: Operator presence/absence before and after graph transformation
+    """
+    torch.set_default_dtype(dtype)
+    torch.manual_seed(1)
+
+    vllm_config = VllmConfig(model_config=ModelConfig(dtype=dtype))
+
+    with vllm.config.set_current_vllm_config(vllm_config):
+        backend = TestBackend(
+            custom_passes=[AddRMSNormQuantFusionPass(vllm_config=vllm_config)])
+        model = TestModel(hidden_size, eps, device="npu")
+        model = model.to("npu")
+
+        x = torch.rand(num_tokens,
+                       hidden_size,
+                       device="npu",
+                       dtype=dtype,
+                       requires_grad=False)
+
+        result_unfused = model(x)
+        print("Unfused result:", [t.shape for t in result_unfused])
+        model_fused = torch.compile(model, backend=backend)
+        result_fused = model_fused(x)
+        print("Fused result:", [t.shape for t in result_fused])
+
+        print("=== Checking operator fusion ===")
+        backend.check_before_ops(model.ops_in_model_before())
+        backend.check_after_ops(model.ops_in_model_after())

tests/ut/test_ascend_config.py

@@ -41,14 +41,14 @@ class TestAscendConfig(TestBase):
         self.assertFalse(ascend_config.multistream_overlap_shared_expert)
 
         ascend_compilation_config = ascend_config.ascend_compilation_config
-        self.assertTrue(ascend_compilation_config.enable_quantization_fusion)
+        self.assertTrue(ascend_compilation_config.fuse_norm_quant)
 
     @_clean_up_ascend_config
     def test_init_ascend_config_with_additional_config(self):
         test_vllm_config = VllmConfig()
         test_vllm_config.additional_config = {
             "ascend_compilation_config": {
-                "enable_quantization_fusion": False,
+                "fuse_norm_quant": False,
             },
             "multistream_overlap_shared_expert": True,
             "expert_map_path": "test_expert_map_path",
@@ -60,7 +60,7 @@ class TestAscendConfig(TestBase):
         self.assertFalse(ascend_config.enable_npugraph_ex)
 
         ascend_compilation_config = ascend_config.ascend_compilation_config
-        self.assertFalse(ascend_compilation_config.enable_quantization_fusion)
+        self.assertFalse(ascend_compilation_config.fuse_norm_quant)
 
     @_clean_up_ascend_config
     def test_init_ascend_config_enable_npugraph_ex(self):

vllm_ascend/ascend_config.py

@@ -190,19 +190,18 @@ class AscendCompilationConfig:
     deployed on Ascend platforms.
     """
 
-    def __init__(self, enable_quantization_fusion: bool = True, **kwargs):
+    def __init__(self, fuse_norm_quant: bool = True, **kwargs):
         """
         Initialize the configuration.
         
         Args:
-            enable_quantization_fusion (bool): Whether to enable quantization fusion optimization.
+            fuse_norm_quant (bool): Whether to enable norm and quant fusion optimization.
-                When set to True, the system will optimize quantization-related operations,
+                When set to True, the system will optimize norm and quant operations.
-                reducing the number of quantization/dequantization nodes.
                 Default: True
                 
             **kwargs: Additional optional parameters for forward compatibility and configuration extension.
         """
-        self.enable_quantization_fusion = enable_quantization_fusion
+        self.fuse_norm_quant = fuse_norm_quant
         # Add more compilation related configs here as needed
 
 

vllm_ascend/compilation/graph_fusion_pass_manager.py

@@ -46,8 +46,8 @@ class GraphFusionPassManager:
         # By default, we enable the graph fusion and quantization fusion pass.
         self.ascend_compilation_config: dict = config.additional_config.get(
             "ascend_compilation_config", {})
-        if self.ascend_compilation_config.get("enable_quantization_fusion",
+        if self.ascend_compilation_config.get("fuse_norm_quant", True):
-                                              True):
+            from .passes.norm_quant_fusion_pass import \
-            from .passes.quant_fusion_pass import AddRMSNormQuantFusionPass
+                AddRMSNormQuantFusionPass
             self.passes.append(AddRMSNormQuantFusionPass(config))
         # Add more passes here as needed

vllm_ascend/platform.py

@@ -88,8 +88,7 @@ class NPUPlatform(Platform):
         Get the custom compile backend. Previously, we used EagerAdaptor by default. 
         To use graph fusion operations, we defined our own backend compiler.
         """
-        from vllm_ascend.compilation.compiler_interface import AscendCompiler
+        return "vllm_ascend.compilation.compiler_interface.AscendCompiler"
-        return AscendCompiler.__module__ + "." + AscendCompiler.__name__
 
     @classmethod
     def pre_register_and_update(cls,
@@ -225,8 +224,8 @@ class NPUPlatform(Platform):
         if compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
             compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
 
-        from vllm_ascend.compilation.compiler_interface import AscendCompiler
+        # get custom compile backend for graph fusion
-        compilation_config.oot_compiler = AscendCompiler.__module__ + "." + AscendCompiler.__name__
+        compilation_config.oot_compiler = cls.get_compile_backend()
 
         if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
             compilation_config.mode = CompilationMode.NONE