configure adaptive optimization operator for qwen3-30b

vllm/envs.py

@@ -187,6 +187,7 @@ if TYPE_CHECKING:
     VLLM_USE_CAT_MLA: bool = False
     VLLM_REJECT_SAMPLE_OPT: bool = False
     VLLM_USE_FUSE_SILU_AND_MUL: bool = False
+    VLLM_USE_OPT_RESHAPE_AND_CACHE: bool = False
 
 def get_default_cache_root():
     return os.getenv(
@@ -1103,46 +1104,57 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_FLASH_ATTN_PA":
     lambda: (os.environ.get("VLLM_USE_FLASH_ATTN_PA", "True").lower() in
              ("true", "1")),
+    
     # vLLM will use apex for rmsnorm
     "VLLM_USE_APEX_RN":
     lambda: (os.environ.get("VLLM_USE_APEX_RN", "False").lower() in
              ("true", "1")),
+    
     # vLLM will use global cache for moe
     "VLLM_USE_GLOBAL_CACHE13":
         lambda: (os.environ.get("VLLM_USE_GLOBAL_CACHE13", "False").lower() in
                  ("true", "1")),
+        
     # vLLM will use lightop for deepseek-v3
     "VLLM_USE_LIGHTOP":
         lambda: (os.environ.get("VLLM_USE_LIGHTOP", "False").lower() in
                  ("true", "1")),
+        
     # vLLM will use elenmentwise not triton_
     "VLLM_USE_OPT_ZEROS":
         lambda: (os.environ.get("VLLM_USE_OPT_ZEROS", "True").lower() in
                  ("true", "1")),
+        
     # vLLM will use opt cat for deepseek-v3
     "VLLM_USE_OPT_CAT":
         lambda: (os.environ.get("VLLM_USE_OPT_CAT", "False").lower() in
                  ("true", "1")),  
+        
     # vLLM will use triton moe_sum 
     "VLLM_USE_OPT_MOE_SUM":
         lambda: (os.environ.get("VLLM_USE_OPT_MOE_SUM", "False").lower() in
                  ("true", "1")),  
-    # vLLM will use lightop moe_sum_mul_add
+        
+    # vLLM will use lightop moe_sum_mul_add for deepseek-v3
     "VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD":
         lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD", "False").lower() in
                  ("true", "1")),  
-    # vLLM will use lightop moe_sum 
+        
+    # vLLM will use lightop moe_sum (qwen3-30b)
     "VLLM_USE_LIGHTOP_MOE_SUM":
-        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM", "True").lower() in
+        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM", "False").lower() in
                  ("true", "1")),  
-    # vLLM will use lightop moe_align_block_size 
+        
+    # vLLM will use lightop moe_align_block_size (qwen3-30b)
     "VLLM_USE_LIGHTOP_MOE_ALIGN":
-        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_ALIGN", "True").lower() in
+        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_ALIGN", "False").lower() in
                  ("true", "1")),    
+        
     # vLLM will use opt merge_aatn_states, not triton
     "VLLM_USE_MERGE_ATTN_STATES_OPT":
         lambda: (os.environ.get("VLLM_USE_MERGE_ATTN_STATES_OPT", "True").lower() in
                  ("true", "1")),  
+        
     # vllm will use rmsquant fused op 
     "USE_FUSED_RMS_QUANT": 
     lambda: bool(int(os.getenv("USE_FUSED_RMS_QUANT", "1"))),
@@ -1175,9 +1187,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
         lambda: (os.environ.get("VLLM_USE_PP_SYNC", "True").lower() in
                  ("true", "1")),
 
-    # vLLM will use lightop to fuse fill and moe align
+    # vLLM will use lightop to fuse fill and moe align (dpsk-v3 + qwen3-30b)
     "VLLM_USE_LIGHTOP_FILL_MOE_ALIGN":
-        lambda: (os.environ.get("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN", "True").lower() in
+        lambda: (os.environ.get("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN", "False").lower() in
                  ("true", "1")), 
 
     # vllm will use custom-allreduce rmsquant fused op
@@ -1208,10 +1220,16 @@ environment_variables: dict[str, Callable[[], Any]] = {
         lambda: (os.getenv('VLLM_REJECT_SAMPLE_OPT', 'False').lower() in
                  ("true", "1")),      
 
-    # vLLM will use fused silu+mul kernel
+    # vLLM will use fused silu+mul kernel (fp16 + qwen3-30b)
     "VLLM_USE_FUSE_SILU_AND_MUL":
         lambda: (os.environ.get("VLLM_USE_FUSE_SILU_AND_MUL", "False").lower() in
                  ("true", "1")),
+        
+     # vLLM will use optimized reshape_and_cache kernel when enabled (fp16 + qwen3-30b)
+    "VLLM_USE_OPT_RESHAPE_AND_CACHE":
+        lambda:
+        (os.environ.get("VLLM_USE_OPT_RESHAPE_AND_CACHE", "False").lower() in
+                ("true", "1")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1875,7 +1875,7 @@ def fused_experts_impl(
                                 use_nn_moe=use_nn_moe)
 
         if activation == "silu":
-            if envs.VLLM_USE_FUSE_SILU_AND_MUL:
+            if envs.VLLM_USE_FUSE_SILU_AND_MUL and intermediate_cache1.dtype == intermediate_cache2.dtype == "fp16":
                 from lightop import fuse_silu_and_mul
                 fuse_silu_and_mul(intermediate_cache1.view(-1, N),intermediate_cache2)    
             else:

vllm/model_executor/model_loader/utils.py

@@ -251,15 +251,24 @@ def get_model_architecture(
                     os.environ['VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD'] = '1'
                 if not envs.is_set("VLLM_USE_OPT_CAT"):
                     os.environ['VLLM_USE_OPT_CAT'] = '1'
-                # if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
-                #     os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'
+                if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
+                    os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'
                 if not envs.is_set("VLLM_USE_CAT_MLA"):
                     os.environ['VLLM_USE_CAT_MLA'] = '1'
             else:
                 if not envs.is_set("VLLM_USE_PD_SPLIT"):
                     os.environ['VLLM_USE_PD_SPLIT'] = '1'
+                if architectures in [['Qwen3MoeForCausalLM']]:
+                    if not envs.is_set("VLLM_USE_LIGHTOP_MOE_ALIGN"):
+                        os.environ['VLLM_USE_LIGHTOP_MOE_ALIGN'] = '1'
+                    if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
+                        os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'  
+                    if not envs.is_set("VLLM_USE_LIGHTOP_MOE_SUM"):
+                        os.environ['VLLM_USE_LIGHTOP_MOE_SUM'] = '1'    
                     if not envs.is_set("VLLM_USE_FUSE_SILU_AND_MUL"):
                         os.environ['VLLM_USE_FUSE_SILU_AND_MUL'] = '1'
+                    if not envs.is_set("VLLM_USE_OPT_RESHAPE_AND_CACHE"):
+                        os.environ['VLLM_USE_OPT_RESHAPE_AND_CACHE'] = '1'
                 
             if os.getenv('GEMM_PAD') != '1': 
                 os.environ['GEMM_PAD'] = '0'
@@ -273,15 +282,29 @@ def get_model_architecture(
                     os.environ['VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD'] = '1'
                 if not envs.is_set("VLLM_USE_OPT_CAT"):
                     os.environ['VLLM_USE_OPT_CAT'] = '1'
-                # if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
-                #     os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'
+                if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
+                    os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'
                 if not envs.is_set("VLLM_USE_CAT_MLA"):
                     os.environ['VLLM_USE_CAT_MLA'] = '1'
             else:
                 if not envs.is_set("VLLM_USE_PD_SPLIT"):
                     os.environ['VLLM_USE_PD_SPLIT'] = '1'
+                if architectures in [['Qwen3MoeForCausalLM']]:
+                    if not envs.is_set("VLLM_USE_LIGHTOP_MOE_ALIGN"):
+                        os.environ['VLLM_USE_LIGHTOP_MOE_ALIGN'] = '1'
+                    if not envs.is_set("VLLM_USE_LIGHTOP_FILL_MOE_ALIGN"):
+                        os.environ['VLLM_USE_LIGHTOP_FILL_MOE_ALIGN'] = '1'  
+                    if not envs.is_set("VLLM_USE_LIGHTOP_MOE_SUM"):
+                        os.environ['VLLM_USE_LIGHTOP_MOE_SUM'] = '1' 
                     if not envs.is_set("VLLM_USE_FUSE_SILU_AND_MUL"):
                         os.environ['VLLM_USE_FUSE_SILU_AND_MUL'] = '1'
+                    if not envs.is_set("VLLM_USE_OPT_RESHAPE_AND_CACHE"):
+                        os.environ['VLLM_USE_OPT_RESHAPE_AND_CACHE'] = '1'
+                        
+            if os.getenv('GEMM_PAD') != '1': 
+                os.environ['GEMM_PAD'] = '0'
+            if os.getenv('FA_PAD') != '1': 
+                os.environ['FA_PAD'] = '0'
                     
         # awq相关配置
         try:

vllm/v1/attention/backends/flash_attn.py

@@ -565,6 +565,16 @@ class FlashAttentionImpl(AttentionImpl):
                     layer._k_scale,
                     layer._v_scale,
                 )
+            else:
+                from lightop import reshape_and_cache_cuda
+                if envs.VLLM_USE_OPT_RESHAPE_AND_CACHE and key.dtype == value.dtype == "fp16":
+                    reshape_and_cache_cuda(
+                        key, value,
+                        key_cache, value_cache,
+                        attn_metadata.slot_mapping,
+                        self.kv_cache_dtype,
+                        layer._k_scale, layer._v_scale
+                    )
                 else:
                     reshape_and_cache_cuda(
                         key,