Update w8a8_int8.py

vllm/model_executor/layers/quantization/w8a8_int8.py

-from typing import Any, Callable, Dict, List, Optional
-
-import torch
-from vllm.model_executor.utils import set_weight_attrs
-from vllm.distributed import get_tensor_model_parallel_world_size
-from torch.nn.parameter import Parameter
-from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
-                                                  FusedMoeWeightScaleSupported)
-from vllm.model_executor.parameter import (BasevLLMParameter,
-                                           ChannelQuantScaleParameter,
-                                           ModelWeightParameter,
-                                           PerTensorScaleParameter)
-from vllm.model_executor.layers.quantization.utils.int8_utils import (
-    per_token_group_quant_int8,
-    per_token_quant_int8)
-from vllm import _custom_ops as ops
-from vllm.utils import W8a8GetCacheJSON
-
-import os
-from vllm import _custom_ops as ops
-
-W8A8_TRITONJSON=W8a8GetCacheJSON()
-
-def baseline_scaled_mm(a: torch.Tensor,
-                      b: torch.Tensor,
-                      scale_a: torch.Tensor,
-                      scale_b: torch.Tensor,
-                      out_dtype: torch.dtype,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-
-    scales= scale_a* scale_b.T
-    gemmout= torch.mm(
-        a.to(dtype=torch.float32), b.to(dtype=torch.float32))
-    output = (scales *gemmout).to(out_dtype)
-    if bias is not None:
-        output = output + bias
-    return output.to(out_dtype)
-
-
-class W8A8Int8Config(QuantizationConfig):
-    """Config class for W8A8 Int8 Quantization.
-
-    - Weight: static, per-channel, symmetric
-    - Activation: dynamic, per-token, symmetric
-    """
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
-        return [torch.float16, torch.bfloat16]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 75
-
-    @classmethod
-    def get_name(self) -> str:
-        return "w8a8_int8"
-
-    @classmethod
-    def get_config_filenames(cls) -> List[str]:
-        return []
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "W8A8Int8Config":
-        return cls()
-
-    def get_quant_method(
-        self,
-        layer: torch.nn.Module,
-        prefix: str,
-    ) -> Optional["QuantizeMethodBase"]:
-
-        if isinstance(layer, LinearBase):
-            return W8A8Int8LinearMethod(self)
-        elif isinstance(layer, FusedMoE):
-            return W8A8Int8MoEMethod(self)
-        return None
-
-    def get_scaled_act_names(self) -> List[str]:
-        return []
-
-
-class W8A8Int8LinearMethod(LinearMethodBase):
-
-    def __init__(self, quantization_config: W8A8Int8Config):
-        self.quantization_config = quantization_config
-        self.tritonsingleton= W8a8GetCacheJSON()
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
-        
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        n=layer.weight.shape[0]
-        k=layer.weight.shape[1]
-        
-        if self.w8a8_strategy==1:
-            if {n,k} not in self.tritonsingleton.weight_shapes:
-                self.tritonsingleton.weight_shapes.append({n,k})
-                json_file=self.tritonsingleton.get_w8a8json_name(n,k)
-                configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
-                
-                if configs_dict:
-                    self.tritonsingleton.triton_json_dict.update(configs_dict)
-                    
-                    for key, value in configs_dict.items():
-                        m=int(key.split('_')[0])
-                        ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,device=layer.weight.device,best_config=value)
-        else: 
-            weight_data=layer.weight.data
-            _weight=weight_data.T.contiguous().reshape(n,-1)
-            layer.weight.data=_weight
-            
-        layer.weight = Parameter(layer.weight.t(), requires_grad=False)
-        layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: List[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-
-        weight_loader = extra_weight_attrs.get("weight_loader")
-        self.logical_widths = output_partition_sizes
-
-        weight = ModelWeightParameter(
-            data=torch.empty(
-                sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8
-            ),
-            input_dim=1,
-            output_dim=0,
-            weight_loader=weight_loader,
-        )
-        layer.register_parameter("weight", weight)
-
-        weight_scale = ChannelQuantScaleParameter(
-            data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
-            output_dim=0,
-            weight_loader=weight_loader,
-        )
-        layer.register_parameter("weight_scale", weight_scale)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-    ):
-        x_q, x_scale = per_token_quant_int8(x)
-
-        if self.w8a8_strategy==1:
-            m=x_q.shape[0]
-            k=x_q.shape[1]
-            n=layer.weight.shape[1]
-            
-            if len(W8A8_TRITONJSON.triton_json_dict)==0:
-                best_config=None
-                
-            elif f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict:
-                if m<=16:
-                    m_=m
-                elif m<=64:
-                    m_= (m + 3) & -4 #取值到最近的4的倍数
-                elif m<=160:
-                    m_=(m + 7) & -8
-                    
-                elif m<200: #256
-                    m_=160
-                elif m<480: #512
-                    m_=256
-                elif m<960: #1024
-                    m_=512
-                elif m<2048:
-                    m_=1024
-                elif m<4096:
-                    m_=2048
-                elif m<6000:
-                    m_=4096
-                else:
-                    m_=8192  
-
-                best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
-                
-            else: 
-                best_config=None
-                
-            if best_config==None:
-                print("m:{},n:{},k:{}".format(m,n,k))
-                print("config not found!")
-
-            return ops.triton_scaled_mm(x_q,
-                                        layer.weight,
-                                        scale_a=x_scale,
-                                        scale_b=layer.weight_scale,
-                                        out_dtype=x.dtype,
-                                        bias=bias,best_config=best_config)
-        elif self.w8a8_strategy==2:
-            return ops.cutlass_scaled_mm(x_q,
-                                        layer.weight,
-                                        scale_a=x_scale,
-                                        scale_b=layer.weight_scale,
-                                        out_dtype=x.dtype,
-                                        bias=bias)
-        else:
-            return ops.rocblas_scaled_mm(x_q,
-                                        layer.weight,
-                                        scale_a=x_scale,
-                                        scale_b=layer.weight_scale,
-                                        out_dtype=x.dtype,
-                                        bias=bias) 
-
-
-class W8A8Int8MoEMethod:
-    """MoE method for INT8.
-    Supports loading INT8 checkpoints with static weight scale and
-    dynamic/static activation scale.
-    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
-    activation scaling. The weight scaling factor will be initialized after
-    the model weights are loaded.
-    Args:
-        quant_config: The quantization config.
-    """
-
-    def __new__(cls, *args, **kwargs):
-
-        if not hasattr(cls, "_initialized"):
-            original_init = cls.__init__
-            new_cls = type(
-                cls.__name__,
-                (FusedMoEMethodBase,),
-                {
-                    "__init__": original_init,
-                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
-                },
-            )
-            obj = super(new_cls, new_cls).__new__(new_cls)
-            obj.__init__(*args, **kwargs)
-            return obj
-        return super().__new__(cls)
-
-    def __init__(self, quant_config):
-        self.quant_config = quant_config
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        num_experts: int,
-        hidden_size: int,
-        intermediate_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-        tp_size = get_tensor_model_parallel_world_size()
-
-        # WEIGHTS
-        w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts, 2 * intermediate_size, hidden_size, dtype=torch.int8
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight", w13_weight)
-        set_weight_attrs(w13_weight, extra_weight_attrs)
-
-        w2_weight = torch.nn.Parameter(
-            torch.empty(num_experts, hidden_size, intermediate_size, dtype=torch.int8),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_weight", w2_weight)
-        set_weight_attrs(w2_weight, extra_weight_attrs)
-
-        w13_weight_scale = torch.nn.Parameter(
-            torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
-            requires_grad=False,
-        )
-        w2_weight_scale = torch.nn.Parameter(
-            torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
-        layer.register_parameter("w2_weight_scale", w2_weight_scale)
-
-        extra_weight_attrs.update(
-            {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
-        )
-
-        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
-        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
-
-        w13_input_scale = None
-        layer.register_parameter("w13_input_scale", w13_input_scale)
-
-        w2_input_scale = None
-        layer.register_parameter("w2_input_scale", w2_input_scale)
-
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
-        layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
-        layer.w13_weight_scale = Parameter(
-            layer.w13_weight_scale.data, requires_grad=False
-        )
-        layer.w2_weight_scale = Parameter(
-            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",
-        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
-        
-        # 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_int8_w8a8=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,
-        )
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.distributed import get_tensor_model_parallel_world_size
+from torch.nn.parameter import Parameter
+from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.model_executor.layers.quantization.utils.int8_utils import (
+    per_token_group_quant_int8,
+    per_token_quant_int8)
+from vllm import _custom_ops as ops
+from vllm.utils import W8a8GetCacheJSON
+
+import os
+from vllm import _custom_ops as ops
+
+W8A8_TRITONJSON=W8a8GetCacheJSON()
+
+def baseline_scaled_mm(a: torch.Tensor,
+                      b: torch.Tensor,
+                      scale_a: torch.Tensor,
+                      scale_b: torch.Tensor,
+                      out_dtype: torch.dtype,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+    scales= scale_a* scale_b.T
+    gemmout= torch.mm(
+        a.to(dtype=torch.float32), b.to(dtype=torch.float32))
+    output = (scales *gemmout).to(out_dtype)
+    if bias is not None:
+        output = output + bias
+    return output.to(out_dtype)
+
+
+class W8A8Int8Config(QuantizationConfig):
+    """Config class for W8A8 Int8 Quantization.
+
+    - Weight: static, per-channel, symmetric
+    - Activation: dynamic, per-token, symmetric
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 75
+
+    @classmethod
+    def get_name(self) -> str:
+        return "w8a8_int8"
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "W8A8Int8Config":
+        return cls()
+
+    def get_quant_method(
+        self,
+        layer: torch.nn.Module,
+        prefix: str,
+    ) -> Optional["QuantizeMethodBase"]:
+
+        if isinstance(layer, LinearBase):
+            return W8A8Int8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return W8A8Int8MoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class W8A8Int8LinearMethod(LinearMethodBase):
+
+    def __init__(self, quantization_config: W8A8Int8Config):
+        self.quantization_config = quantization_config
+        self.tritonsingleton= W8a8GetCacheJSON()
+        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        n=layer.weight.shape[0]
+        k=layer.weight.shape[1]
+        
+        if self.w8a8_strategy==1:
+            if {n,k} not in self.tritonsingleton.weight_shapes:
+                self.tritonsingleton.weight_shapes.append({n,k})
+                json_file=self.tritonsingleton.get_w8a8json_name(n,k)
+                configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
+                
+                if configs_dict:
+                    self.tritonsingleton.triton_json_dict.update(configs_dict)
+                    
+                    for key, value in configs_dict.items():
+                        m=int(key.split('_')[0])
+                        ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,device=layer.weight.device,best_config=value)
+        else: 
+            weight_data=layer.weight.data
+            _weight=weight_data.T.contiguous().reshape(n,-1)
+            layer.weight.data=_weight
+            
+        layer.weight = Parameter(layer.weight.t(), requires_grad=False)
+        layer.weight_scale = Parameter(layer.weight_scale.data, requires_grad=False)
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        self.logical_widths = output_partition_sizes
+
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                sum(output_partition_sizes), input_size_per_partition, dtype=torch.int8
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        weight_scale = ChannelQuantScaleParameter(
+            data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ):
+        x_q, x_scale = per_token_quant_int8(x)
+
+        if self.w8a8_strategy==1:
+            m=x_q.shape[0]
+            k=x_q.shape[1]
+            n=layer.weight.shape[1]
+            
+            if len(W8A8_TRITONJSON.triton_json_dict)==0:
+                best_config=None
+                
+            elif f"1_{n}_{k}" in  W8A8_TRITONJSON.triton_json_dict:
+                if m<=16:
+                    m_=m
+                elif m<=64:
+                    m_= (m + 3) & -4 #取值到最近的4的倍数
+                elif m<=160:
+                    m_=(m + 7) & -8
+                    
+                elif m<200: #256
+                    m_=160
+                elif m<480: #512
+                    m_=256
+                elif m<960: #1024
+                    m_=512
+                elif m<2048:
+                    m_=1024
+                elif m<4096:
+                    m_=2048
+                elif m<6000:
+                    m_=4096
+                else:
+                    m_=8192  
+
+                best_config=W8A8_TRITONJSON.triton_json_dict[f"{m_}_{n}_{k}"]
+                
+            else: 
+                best_config=None
+                
+            #if best_config==None:
+            #    print("m:{},n:{},k:{}".format(m,n,k))
+            #    print("config not found!")
+
+            return ops.triton_scaled_mm(x_q,
+                                        layer.weight,
+                                        scale_a=x_scale,
+                                        scale_b=layer.weight_scale,
+                                        out_dtype=x.dtype,
+                                        bias=bias,best_config=best_config)
+        elif self.w8a8_strategy==2:
+            return ops.cutlass_scaled_mm(x_q,
+                                        layer.weight,
+                                        scale_a=x_scale,
+                                        scale_b=layer.weight_scale,
+                                        out_dtype=x.dtype,
+                                        bias=bias)
+        else:
+            return ops.rocblas_scaled_mm(x_q,
+                                        layer.weight,
+                                        scale_a=x_scale,
+                                        scale_b=layer.weight_scale,
+                                        out_dtype=x.dtype,
+                                        bias=bias) 
+
+
+class W8A8Int8MoEMethod:
+    """MoE method for INT8.
+    Supports loading INT8 checkpoints with static weight scale and
+    dynamic/static activation scale.
+    Also supports loading quantized FP16/BF16 model checkpoints with dynamic
+    activation scaling. The weight scaling factor will be initialized after
+    the model weights are loaded.
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __new__(cls, *args, **kwargs):
+
+        if not hasattr(cls, "_initialized"):
+            original_init = cls.__init__
+            new_cls = type(
+                cls.__name__,
+                (FusedMoEMethodBase,),
+                {
+                    "__init__": original_init,
+                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
+                },
+            )
+            obj = super(new_cls, new_cls).__new__(new_cls)
+            obj.__init__(*args, **kwargs)
+            return obj
+        return super().__new__(cls)
+
+    def __init__(self, quant_config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        tp_size = get_tensor_model_parallel_world_size()
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, 2 * intermediate_size, hidden_size, dtype=torch.int8
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(num_experts, hidden_size, intermediate_size, dtype=torch.int8),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
+            requires_grad=False,
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
+        )
+
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        w13_input_scale = None
+        layer.register_parameter("w13_input_scale", w13_input_scale)
+
+        w2_input_scale = None
+        layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
+        layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
+        layer.w13_weight_scale = Parameter(
+            layer.w13_weight_scale.data, requires_grad=False
+        )
+        layer.w2_weight_scale = Parameter(
+            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",
+        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
+        
+        # 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_int8_w8a8=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,
+        )