skip fp8 and ActivationQuantFusionPass

vllm/compilation/fusion.py

@@ -586,23 +586,23 @@ class FusionPass(VllmInductorPass):
 
         for epsilon in [1e-5, 1e-6]:
             # Fuse rms_norm + static fp8 quant
-            RMSNormStaticQuantPattern(epsilon,
-                                      FP8_DTYPE).register(self.patterns)
+            # RMSNormStaticQuantPattern(epsilon,
+            #                           FP8_DTYPE).register(self.patterns)
 
             # Matches for patterns below have 2 or more outputs,
             # so we need to process them manually (see process_matches)
 
             # Fuse rms_norm + static fp8 quant
-            FusedAddRMSNormStaticQuantPattern(epsilon, FP8_DTYPE).register(
-                self.patterns, self.record_match)
+            # FusedAddRMSNormStaticQuantPattern(epsilon, FP8_DTYPE).register(
+            #     self.patterns, self.record_match)
 
             # Fuse rms_norm + dynamic per-token fp8 quant
-            RMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
-                self.patterns, self.record_match)
+            # RMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
+            #     self.patterns, self.record_match)
 
             # Fuse fused_add_rms_norm + dynamic per-token fp8 quant
-            FusedAddRMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
-                self.patterns, self.record_match)
+            # FusedAddRMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
+            #     self.patterns, self.record_match)
 
             # WARNING: This is a hack to clear the pattern matcher cache
             # and allow multiple values of epsilon.

vllm/compilation/pass_manager.py

@@ -6,7 +6,7 @@ from torch import fx as fx
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 
-from .activation_quant_fusion import ActivationQuantFusionPass
+# from .activation_quant_fusion import ActivationQuantFusionPass
 from .collective_fusion import AsyncTPPass
 from .fix_functionalization import FixFunctionalizationPass
 from .fusion import FusionPass
@@ -56,9 +56,9 @@ class PostGradPassManager(CustomGraphPass):
             if self.pass_config.enable_async_tp:
                 self.passes += [AsyncTPPass(config)]
 
-        if self.pass_config.enable_fusion:
-            self.passes += [FusionPass.instance(config)]
-            self.passes += [ActivationQuantFusionPass(config)]
+        # if self.pass_config.enable_fusion:
+        #     self.passes += [FusionPass.instance(config)]
+        #     self.passes += [ActivationQuantFusionPass(config)]
 
         if self.pass_config.enable_attn_fusion:
             self.passes += [AttnFusionPass(config)]

vllm/compilation/sequence_parallelism.py

@@ -444,16 +444,16 @@ class SequenceParallelismPass(VllmInductorPass):
 
         for epsilon in [1e-5, 1e-6]:
             # RMSNorm + Static FP8 quantization patterns
-            fp8_quant_op = torch.ops._C.static_scaled_fp8_quant.default
-            FirstAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device,
-                fp8_quant_op).register(self.patterns)
-            MiddleAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device,
-                fp8_quant_op).register(self.patterns)
-            LastAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device,
-                fp8_quant_op).register(self.patterns)
+            # fp8_quant_op = torch.ops._C.static_scaled_fp8_quant.default
+            # FirstAllReduceRMSNormStaticFP8Pattern(
+            #     epsilon, self.model_dtype, self.device,
+            #     fp8_quant_op).register(self.patterns)
+            # MiddleAllReduceRMSNormStaticFP8Pattern(
+            #     epsilon, self.model_dtype, self.device,
+            #     fp8_quant_op).register(self.patterns)
+            # LastAllReduceRMSNormStaticFP8Pattern(
+            #     epsilon, self.model_dtype, self.device,
+            #     fp8_quant_op).register(self.patterns)
 
             # Normal RMSNorm patterns
             FirstAllReduceRMSNormPattern(epsilon, self.model_dtype,