feat: slimquant_w4a8量化加入ep_moe适配，修改ep_moe中调用接口为：self.quant_method.apply_ep()

vllm/model_executor/layers/fused_moe/ep_moe/layer.py

@@ -36,7 +36,7 @@ class UnquantizedEPGroupedGemmMethod(UnquantizedFusedMoEMethod):
         self.rocm_aiter_moe_enabled = False # is_rocm_aiter_moe_enabled()
         self.zero_token_count = None
 
-    def apply(
+    def apply_ep(
         self,
         layer: torch.nn.Module,
         hidden_states: torch.Tensor,
@@ -275,7 +275,7 @@ class EPMoE(FusedMoE):
         )
 
         # Matrix multiply.
-        expert_output = self.quant_method.apply(
+        expert_output = self.quant_method.apply_ep(
             layer=self,
             hidden_states=dispatched_input,
             tokens_per_expert=tokens_per_expert

vllm/model_executor/layers/quantization/slimquant_w4a8.py

@@ -21,7 +21,10 @@ from vllm.utils import W8a8GetCacheJSON
 
 import os
 from vllm import _custom_ops as ops
-
+try:
+    from lmslim.layers.fused_moe.fuse_moe_w4a8 import fused_experts_impl_w4a8_ep
+except Exception:
+    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
 W8A8_TRITONJSON=W8a8GetCacheJSON()
 
 def baseline_scaled_mm(a: torch.Tensor,
@@ -328,65 +331,79 @@ class SlimQuantW4A8Int8MoEMethod:
             layer.w2_weight_scale.data, requires_grad=False
         )
 
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-        top_k: int,
-        renormalize: bool,
-        use_grouped_topk: bool = False,
-        topk_group: Optional[int] = None,
-        num_expert_group: Optional[int] = None,
-        global_num_experts: int = -1,
-        expert_map: Optional[torch.Tensor] = None,
-        custom_routing_function: Optional[Callable] = None,
-        scoring_func: str = "softmax",
-        e_score_correction_bias: Optional[torch.Tensor] = None,
-        apply_router_weight_on_input: bool = False,
-        activation: str = "silu",
-        enable_eplb: bool = False,
-        use_nn_moe: Optional[bool] = False,
-        routed_scaling_factor: Optional[float] = None,
-        use_fused_gate: Optional[bool] = False,
-        **_  
-    ) -> torch.Tensor:
-        from vllm.model_executor.layers.fused_moe import fused_experts
-        if enable_eplb:
-            raise NotImplementedError(
-                "EPLB not supported for `SlimQuantW4A8Int8MoEMethod` yet.")   
-        # Expert selection
-        topk_weights, topk_ids = FusedMoE.select_experts(
-            hidden_states=x,
-            router_logits=router_logits,
-            use_grouped_topk=use_grouped_topk,
-            top_k=top_k,
-            renormalize=renormalize,
-            topk_group=topk_group,
-            num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function,
-            scoring_func=scoring_func,
-            e_score_correction_bias=e_score_correction_bias,
-            routed_scaling_factor=routed_scaling_factor,
-            use_fused_gate=use_fused_gate
-        )
+    def apply_ep(  #dp+ep
+                self,
+                layer: torch.nn.Module,
+                hidden_states: torch.Tensor,
+                tokens_per_expert: torch.Tensor,
+        ) -> torch.Tensor:
+        return fused_experts_impl_w4a8_ep(hidden_states,
+                                          layer.w13_weight,
+                                          layer.w2_weight,
+                                          layer.w13_weight_scale,
+                                          layer.w2_weight_scale,
+                                          tokens_per_expert)
+
+
+    def apply(# tp
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            router_logits: torch.Tensor,
+            top_k: int,
+            renormalize: bool,
+            use_grouped_topk: bool = False,
+            topk_group: Optional[int] = None,
+            num_expert_group: Optional[int] = None,
+            global_num_experts: int = -1,
+            expert_map: Optional[torch.Tensor] = None,
+            custom_routing_function: Optional[Callable] = None,
+            scoring_func: str = "softmax",
+            e_score_correction_bias: Optional[torch.Tensor] = None,
+            apply_router_weight_on_input: bool = False,
+            activation: str = "silu",
+            enable_eplb: bool = False,
+            use_nn_moe: Optional[bool] = False,
+            routed_scaling_factor: Optional[float] = None,
+            use_fused_gate: Optional[bool] = False,
+            **_
+        ) -> torch.Tensor:
+            from vllm.model_executor.layers.fused_moe import fused_experts
+            if enable_eplb:
+                raise NotImplementedError(
+                    "EPLB not supported for `SlimQuantW4A8Int8MoEMethod` yet.")
+            # Expert selection
+            topk_weights, topk_ids = FusedMoE.select_experts(
+                hidden_states=x,
+                router_logits=router_logits,
+                use_grouped_topk=use_grouped_topk,
+                top_k=top_k,
+                renormalize=renormalize,
+                topk_group=topk_group,
+                num_expert_group=num_expert_group,
+                custom_routing_function=custom_routing_function,
+                scoring_func=scoring_func,
+                e_score_correction_bias=e_score_correction_bias,
+                routed_scaling_factor=routed_scaling_factor,
+                use_fused_gate=use_fused_gate
+            )
 
-        return fused_experts(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            inplace=True,
-            use_int4_w4a8=True,
-            per_channel_quant=True,
-            activation=activation,
-            expert_map=expert_map,
-            apply_router_weight_on_input=apply_router_weight_on_input,
-            global_num_experts=global_num_experts,
-            w1_scale=(layer.w13_weight_scale),
-            w2_scale=(layer.w2_weight_scale),
-            a1_scale=layer.w13_input_scale,
-            a2_scale=layer.w2_input_scale,
-            use_nn_moe=use_nn_moe,
-        )
+            return fused_experts(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                inplace=True,
+                use_int4_w4a8=True,
+                per_channel_quant=True,
+                activation=activation,
+                expert_map=expert_map,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                w1_scale=(layer.w13_weight_scale),
+                w2_scale=(layer.w2_weight_scale),
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+                use_nn_moe=use_nn_moe,
+            )