set USE_FUSED_RMS_QUANT=0 and USE_FUSED_SILU_MUL_QUANT=0

add VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD

vllm/envs.py

@@ -167,6 +167,7 @@ if TYPE_CHECKING:
     VLLM_USE_LIGHTOP: bool = False
     VLLM_USE_OPT_CAT: bool = False
     VLLM_USE_OPT_MOE_SUM: bool = False
+    VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD: bool = False
     VLLM_USE_LIGHTOP_MOE_SUM: bool = False
     VLLM_USE_LIGHTOP_MOE_ALIGN: bool = False
     VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
@@ -1111,6 +1112,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "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_USE_LIGHTOP_MOE_SUM_MUL_ADD":
+        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD", "True").lower() in
+                 ("true", "1")),  
     # vLLM will use lightop moe_sum 
     "VLLM_USE_LIGHTOP_MOE_SUM":
         lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM", "True").lower() in

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1895,7 +1895,7 @@ def fused_experts_impl(
                                 block_shape=block_shape,
                                 use_nn_moe=use_nn_moe)
 
-        if envs.VLLM_USE_LIGHTOP: 
+        if envs.VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD: 
             from lightop import op as op
             op.moe_sum(input=intermediate_cache3.view(*intermediate_cache3.size()),
                     output=out_hidden_states[begin_chunk_idx:end_chunk_idx], bias=shared_output[begin_chunk_idx:end_chunk_idx], 

vllm/model_executor/model_loader/utils.py

@@ -247,6 +247,8 @@ def get_model_architecture(
             if architectures in [['DeepseekV3ForCausalLM'], ['DeepSeekMTPModel']]:
                 if not envs.is_set("VLLM_USE_LIGHTOP"):
                     os.environ['VLLM_USE_LIGHTOP'] = '1'
+                if not envs.is_set("VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD"):
+                    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'
                 
@@ -258,12 +260,11 @@ def get_model_architecture(
             if architectures in [['DeepseekV3ForCausalLM'], ['DeepSeekMTPModel']]:
                 if not envs.is_set("VLLM_USE_LIGHTOP"):
                     os.environ['VLLM_USE_LIGHTOP'] = '1'
+                if not envs.is_set("VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD"):
+                    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("USE_FUSED_RMS_QUANT"):
-                    os.environ['USE_FUSED_RMS_QUANT'] = '1'
-                if not envs.is_set("USE_FUSED_SILU_MUL_QUANT"):
-                    os.environ['USE_FUSED_SILU_MUL_QUANT'] = '1'
+                    
         # awq相关配置
         try:
             if os.getenv('AWQ_MOE_SZ') == None:

vllm/model_executor/models/deepseek_v2.py

@@ -214,7 +214,7 @@ class DeepseekV2MoE(nn.Module):
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
-        if envs.VLLM_USE_LIGHTOP:
+        if envs.VLLM_USE_LIGHTOP_MOE_SUM_MUL_ADD:
             final_hidden_states = self.experts(
                 hidden_states=hidden_states,
                 router_logits=router_logits,
@@ -230,14 +230,14 @@ class DeepseekV2MoE(nn.Module):
                 final_hidden_states = self.experts(hidden_states=hidden_states,
                                                 router_logits=router_logits)
         
-        if shared_output is not None:
-            if hidden_states.dtype != torch.float16:
-                final_hidden_states = final_hidden_states + shared_output
-            else:
-                # Fix FP16 overflow
-                # See DeepseekV2DecoderLayer for more details.
-                final_hidden_states = final_hidden_states + shared_output \
-                    * (1. / self.routed_scaling_factor)
+            if shared_output is not None:
+                if hidden_states.dtype != torch.float16:
+                    final_hidden_states = final_hidden_states + shared_output
+                else:
+                    # Fix FP16 overflow
+                    # See DeepseekV2DecoderLayer for more details.
+                    final_hidden_states = final_hidden_states + shared_output \
+                        * (1. / self.routed_scaling_factor)
 
         if self.tp_size > 1:
             if envs.VLLM_ENABLE_TBO: