[BugFix][Fusion] Fix graph fusion failure problem (#5253)

Currently, the vllm pull request
(https://github.com/vllm-project/vllm/pull/24252) is causing operator
fusion to fail. This issue was previously fixed by patching the backend.
The root cause has been identified, and the problem can be resolved with
this pull request.

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

---------

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

vllm_ascend/compilation/compiler_interface.py

@@ -26,6 +26,7 @@ from torch._inductor.compile_fx import (graph_returns_tuple,
 from torch._inductor.decomposition import select_decomp_table
 from torch.fx import GraphModule
 from vllm.compilation.compiler_interface import CompilerInterface
+from vllm.config.utils import Range
 
 from vllm_ascend.ascend_config import get_ascend_config
 
@@ -46,13 +47,13 @@ def fusion_pass_compile(
     graph: fx.GraphModule,
     example_inputs: list[Any],
     compiler_config: dict[str, Any],
-    runtime_shape: Optional[int] = None,
+    compile_range: Range,
     key: Optional[str] = None,
 ) -> tuple[Optional[Callable], Optional[Any]]:
 
     def compile_inner(graph, example_inputs):
         current_pass_manager = compiler_config["graph_fusion_manager"]
-        graph = current_pass_manager(graph, runtime_shape)
+        graph = current_pass_manager(graph, compile_range)
         return graph
 
     decompositions = select_decomp_table()
@@ -71,7 +72,7 @@ def npugraph_ex_compile(
     graph: fx.GraphModule,
     example_inputs: list[Any],
     compiler_config: dict[str, Any],
-    runtime_shape: Optional[int] = None,
+    compile_range: Range,
     key: Optional[str] = None,
 ) -> tuple[Optional[Callable], Optional[Any]]:
     # When currently using the FULL_DECODE_ONLY mode,
@@ -124,14 +125,14 @@ class AscendCompiler(CompilerInterface):
         graph: fx.GraphModule,
         example_inputs: list[Any],
         compiler_config: dict[str, Any],
-        runtime_shape: Optional[int] = None,
+        compile_range: Range,
         key: Optional[str] = None,
     ) -> tuple[Optional[Callable], Optional[Any]]:
 
         ascend_config = get_ascend_config()
         if ascend_config.enable_npugraph_ex:
             return npugraph_ex_compile(graph, example_inputs, compiler_config,
-                                       runtime_shape, key)
+                                       compile_range, key)
         else:
             return fusion_pass_compile(graph, example_inputs, compiler_config,
-                                       runtime_shape, key)
+                                       compile_range, key)

vllm_ascend/compilation/graph_fusion_pass_manager.py

@@ -17,6 +17,7 @@
 #
 
 from torch import fx as fx
+from vllm.compilation.inductor_pass import get_pass_context
 from vllm.compilation.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig
 
@@ -32,10 +33,13 @@ class GraphFusionPassManager:
     def __init__(self):
         self.passes: list[VllmInductorPass] = []
 
-    def __call__(self, graph: fx.Graph, runtime_shape) -> fx.Graph:
+    def __call__(self, graph: fx.Graph, compile_range) -> fx.Graph:
+        compile_range = get_pass_context().compile_range
+
         for pass_ in self.passes:
-            if pass_.is_applicable(runtime_shape):
+            if pass_.is_applicable_for_range(compile_range):
                 pass_(graph)
+        graph.recompile()
         return graph
 
     def add(self, pass_: VllmInductorPass):

vllm_ascend/compilation/passes/norm_quant_fusion_pass.py

@@ -20,6 +20,7 @@ import torch._inductor.pattern_matcher as pm
 from torch._inductor.pattern_matcher import PatternMatcherPass
 from vllm.compilation.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig
+from vllm.config.compilation import Range
 from vllm.logger import logger
 
 
@@ -308,7 +309,7 @@ class AddRMSNormQuantFusionPass(VllmInductorPass):
         logger.debug("Replaced %s patterns", self.matched_count)
         self.end_and_log()
 
-    def is_applicable(self, runtime_shape: int | None = None) -> bool:
+    def is_applicable_for_range(self, compile_range: Range) -> bool:
         """
         Check if the pass is applicable for the current configuration.
         """

vllm_ascend/compilation/passes/qknorm_rope_fusion_pass.py

@@ -22,6 +22,7 @@ from torch._inductor.pattern_matcher import (PatternMatcherPass,
 from vllm.attention.layer import Attention
 from vllm.compilation.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.config.compilation import Range
 from vllm.logger import logger
 
 
@@ -283,7 +284,7 @@ class QKNormRopeFusionPass(VllmInductorPass):
                 pattern_idx += 1
         self.end_and_log()
 
-    def is_applicable(self, runtime_shape):
+    def is_applicable_for_range(self, compile_range: Range) -> bool:
         """
         Check if the pass is applicable for the current configuration.
         """