[fix]: add Arm 4bit fused moe support (#23809)

Signed-off-by: Nikhil Gupta <nikhil.gupta2@arm.com>

cmake/cpu_extension.cmake

@@ -258,7 +258,8 @@ set(VLLM_EXT_SRC
     "csrc/cpu/layernorm.cpp"
     "csrc/cpu/mla_decode.cpp"
     "csrc/cpu/pos_encoding.cpp"
-    "csrc/cpu/torch_bindings.cpp")
+    "csrc/cpu/torch_bindings.cpp"
+    "csrc/moe/dynamic_4bit_int_moe_cpu.cpp")
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     set(VLLM_EXT_SRC

csrc/cpu/torch_bindings.cpp

@@ -88,8 +88,18 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    int tp_rank, int blocksparse_local_blocks,"
       "    int blocksparse_vert_stride, int blocksparse_block_size,"
       "    int blocksparse_head_sliding_step) -> ()");
+
   ops.impl("paged_attention_v1", torch::kCPU, &paged_attention_v1);
 
+  ops.def(
+      "dynamic_4bit_int_moe("
+      "Tensor x, Tensor topk_ids, Tensor topk_weights,"
+      "Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
+      "int group_size, bool apply_router_weight_on_input, int activation_kind"
+      ") -> Tensor");
+
+  ops.impl("dynamic_4bit_int_moe", torch::kCPU, &dynamic_4bit_int_moe_cpu);
+
   // PagedAttention V2.
   ops.def(
       "paged_attention_v2("

csrc/moe/dynamic_4bit_int_moe_cpu.cpp

@@ -0,0 +1,156 @@
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/all.h>
+
+// _dyn_quant_matmul_4bit is only available on AArch64.
+#if defined(__aarch64__)
+  #include <ATen/ops/_dyn_quant_matmul_4bit.h>
+#endif
+
+inline torch::Tensor mm(const torch::Tensor& a, const torch::Tensor& packed_w,
+                        int64_t group_size_eff, int64_t in_features,
+                        int64_t out_features) {
+#if defined(__aarch64__)
+  return at::_ops::_dyn_quant_matmul_4bit::call(a, packed_w, group_size_eff,
+                                                in_features, out_features);
+#else
+  TORCH_CHECK(false,
+              "dynamic 4-bit int MoE path requires AArch64 (ARM64); "
+              "_dyn_quant_matmul_4bit is unavailable on this architecture");
+  return {};
+#endif
+}
+
+enum ActivationKind : int64_t {
+  SwiGLU_Gu = 0,  // act = SiLU(g) * u
+  SwiGLUOAI = 1,  // act = SiLU(u) * g
+  SiLU = 2        // SiLU
+};
+
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind) {
+  TORCH_CHECK(x.dim() == 2, "x must be 2D");
+  TORCH_CHECK(topk_ids.dim() == 2 && topk_weights.dim() == 2,
+              "topk tensors must be [T, K]");
+  TORCH_CHECK(
+      w13_packed.size(0) == w2_packed.size(0),
+      "w13_packed and w2_packed must have same number of experts in dim 0");
+  TORCH_CHECK(I2 == 2 * I, "I2 must equal 2*I");
+
+  const int64_t T = x.size(0);
+  const int64_t K = topk_ids.size(1);
+  const int64_t E = w13_packed.size(0);
+  const int64_t N = T * K;
+
+  auto x_c = x.contiguous();
+  auto ids_c = topk_ids.contiguous();
+  auto gates_c = topk_weights.to(at::kFloat).contiguous();
+
+  // bucketing tokens -> experts
+  c10::SmallVector<int64_t, 64> counts(
+      E, 0);  // Small vector uses stack allocation
+  {
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    for (int64_t i = 0; i < N; ++i) {
+      const int64_t e_id = ids_ptr[i];
+      TORCH_CHECK(0 <= e_id && e_id < E, "expert id out of range");
+      counts[e_id]++;
+    }
+  }
+  c10::SmallVector<int64_t, 65> offsets(E + 1, 0);  // ( E +1 )
+  for (int64_t e = 0; e < E; ++e) offsets[e + 1] = offsets[e] + counts[e];
+
+  auto expert_tokens = at::empty({offsets[E]}, ids_c.options());
+  auto expert_gates = at::empty({offsets[E]}, gates_c.options());
+  {
+    c10::SmallVector<int64_t, 64> cursor(E, 0);
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    const auto* gts_ptr = gates_c.data_ptr<float>();
+    auto* tok_ptr = expert_tokens.data_ptr<int64_t>();
+    auto* gate_ptr = expert_gates.data_ptr<float>();
+
+    for (int64_t t = 0; t < T; ++t) {
+      const int64_t base = t * K;
+      for (int64_t k = 0; k < K; ++k) {
+        const int64_t idx = base + k;
+        const int64_t e = ids_ptr[idx];
+        const int64_t p = offsets[e] + (cursor[e]++);
+        tok_ptr[p] = t;
+        gate_ptr[p] = gts_ptr[idx];
+      }
+    }
+  }
+
+  const int64_t g_eff_13 = (group_size != -1) ? group_size : H;
+  const int64_t g_eff_2 = (group_size != -1) ? group_size : I;
+
+  // Per-expert outputs filled in parallel
+  std::vector<torch::Tensor> y_list(E);
+  y_list.resize(E);
+
+  at::parallel_for(0, E, 1, [&](int64_t e_begin, int64_t e_end) {
+    for (int64_t e = e_begin; e < e_end; ++e) {
+      const int64_t te = counts[e];
+      if (te == 0) {
+        y_list[e] = at::empty({0, H}, x_c.options());
+        continue;
+      }
+
+      const int64_t start = offsets[e];
+
+      auto sel_tokens =
+          expert_tokens.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+      auto gates_e =
+          expert_gates.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+
+      auto x_e = x_c.index_select(/*dim=*/0, sel_tokens);
+
+      if (apply_router_weight_on_input) {
+        x_e = x_e.mul(gates_e.unsqueeze(1));
+      }
+
+      auto w13_e = w13_packed.select(/*dim=*/0, e);
+      auto w2_e = w2_packed.select(/*dim=*/0, e);
+
+      // W13
+      auto y13 =
+          mm(x_e, w13_e, g_eff_13, /*in_features=*/H, /*out_features=*/I2);
+
+      auto g_part = y13.narrow(/*dim=*/1, /*start=*/0, /*length=*/I);
+      auto u_part = y13.narrow(/*dim=*/1, /*start=*/I, /*length=*/I);
+
+      torch::Tensor act;
+      if (activation_kind == ActivationKind::SwiGLUOAI) {  // SwiGLUOAI
+        constexpr double kAlpha = 1.702;                   // GPT-OSS default
+        constexpr double kLimit = 7.0;                     // GPT-OSS default
+        auto gate_c = at::clamp_max(g_part, kLimit);
+        auto up_c = at::clamp(u_part, -kLimit, kLimit);
+        auto glu = gate_c.mul(at::sigmoid(gate_c.mul(kAlpha)));
+        act = up_c.add(1.0).mul(glu);
+      } else {  // SiLU , SwiGLU_GU, vLLM maps silu to SiluAndMul()
+        act = at::silu(g_part).mul(u_part);
+      }
+
+      // W2
+      auto y = mm(act, w2_e, g_eff_2, /*in_features=*/I, /*out_features=*/H);
+
+      if (!apply_router_weight_on_input) {
+        y = y.mul(gates_e.unsqueeze(1));
+      }
+
+      // Store per-expert result
+      y_list[e] = y;
+    }
+  });
+
+  // Concatenate all expert outputs to match expert_tokens order
+  auto Y_all = at::cat(y_list, /*dim=*/0);
+  auto out = at::zeros({T, H}, x.options());
+  out =
+      at::index_add(out, /*dim=*/0, /*index=*/expert_tokens, /*source=*/Y_all);
+
+  return out;
+}

csrc/ops.h

@@ -328,6 +328,12 @@ void selective_scan_fwd(const torch::Tensor& u, const torch::Tensor& delta,
                         const std::optional<torch::Tensor>& has_initial_state,
                         const torch::Tensor& ssm_states, int64_t pad_slot_id);
 
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind);
+
 using fptr_t = int64_t;
 fptr_t init_custom_ar(const std::vector<int64_t>& fake_ipc_ptrs,
                       torch::Tensor& rank_data, int64_t rank,

vllm/model_executor/layers/fused_moe/cpu_fused_moe.py

@@ -98,13 +98,16 @@ def select_experts(
                             e_score_correction_bias=e_score_correction_bias)
     elif custom_routing_function is None:
         assert scoring_func == "softmax"
-        topk_weights = torch.nn.functional.softmax(router_logits,
-                                                   dim=1,
-                                                   dtype=torch.float32)
-        topk_weights, topk_ids = torch.topk(topk_weights, top_k, dim=-1)
+        topk_logit_vals, topk_idx = torch.topk(router_logits,
+                                               k=top_k,
+                                               dim=-1,
+                                               sorted=False)
         if renormalize:
-            topk_weights /= topk_weights.sum(dim=-1, keepdim=True)
-        return topk_weights, topk_ids.to(torch.int32)
+            topk_vals = torch.softmax(topk_logit_vals, dim=-1)
+        else:
+            logZ = torch.logsumexp(router_logits, dim=-1, keepdim=True)
+            topk_vals = (topk_logit_vals - logZ).exp()
+        return topk_vals.to(torch.float32), topk_idx.to(torch.int32)
     else:
         return custom_routing_function(hidden_states=hidden_states,
                                        gating_output=router_logits,

vllm/model_executor/layers/fused_moe/layer.py

@@ -69,8 +69,6 @@ else:
 if is_rocm_aiter_moe_enabled():
     from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
         rocm_aiter_grouped_topk as grouped_topk)
-elif current_platform.is_cpu():
-    pass
 else:
     from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
 if current_platform.is_tpu():

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -22,6 +22,7 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig, fp8_w8a8_moe_quant_config,
     int4_w4a16_moe_quant_config, int8_w8a8_moe_quant_config,
     int8_w8a16_moe_quant_config, nvfp4_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.cpu_fused_moe import select_experts
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
     is_valid_flashinfer_cutlass_fused_moe)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_wNa16 import (  # noqa
@@ -47,7 +48,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.platforms import current_platform
+from vllm.platforms import CpuArchEnum, current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
 
@@ -63,7 +64,7 @@ __all__ = [
     "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
     "CompressedTensorsW8A8Int8MoEMethod",
     "CompressedTensorsWNA16MarlinMoEMethod", "CompressedTensorsWNA16MoEMethod",
-    "CompressedTensorsW4A4MoeMethod"
+    "CompressedTensorsW4A4MoeMethod", "CompressedTensorsW4A8Int8MoEMethod"
 ]
 
 
@@ -139,6 +140,10 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
         elif quant_config._is_dynamic_token_w8a8(weight_quant, input_quant):
             return CompressedTensorsW8A8Int8MoEMethod(quant_config,
                                                       layer.moe_config)
+        elif quant_config._is_dynamic_token_w4a8_int(weight_quant,
+                                                     input_quant):
+            return CompressedTensorsW4A8Int8MoEMethod(quant_config,
+                                                      layer.moe_config)
         else:
             raise RuntimeError(
                 f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}")
@@ -1769,3 +1774,301 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             expert_map=expert_map,
             quant_config=self.moe_quant_config,
         )
+
+
+class CompressedTensorsW4A8Int8MoEMethod(CompressedTensorsMoEMethod):
+    """
+    CPU-only MoE method using dynamic 4-bit matmul kernels on Arm Platform
+    - Weights: int4 (stored as int8 values in [-8,7], packed to uint8 nibbles)
+    - Scales: Fp32 for Channelwise , bf16 for groupwise quantization
+    - Bias: Same data type as original weights
+    - Activations: FP32/Bf16 dynamic per-token (A8 Int),
+      quantized inside the kernel
+    """
+
+    def __init__(
+            self,
+            quant_config: "CompressedTensorsConfig",  # type: ignore # noqa E501
+            moe: FusedMoEConfig):
+        super().__init__(moe)
+        self.has_bias = self.moe.has_bias
+        self.quant_config = quant_config
+
+        # Validate scheme: weights=W4 (channel or group),
+        # activations=dynamic TOKEN (A8)
+        wq = self.quant_config.target_scheme_map["Linear"].get("weights")
+        aq = self.quant_config.target_scheme_map["Linear"].get(
+            "input_activations")
+
+        # Must be dynamic per-token activations
+        if aq.strategy != QuantizationStrategy.TOKEN or not aq.dynamic:
+            raise ValueError(
+                "W4A8-int MoE needs dynamic per-token activation quantization."
+            )
+
+        # Weight can be channel-wise (group_size=None) or group-wise
+        self.group_size = wq.group_size if (wq.group_size is not None) else -1
+        if wq.num_bits != 4:
+            raise ValueError(
+                "This method only supports 4-bit weights (num_bits=4).")
+
+        # CPU only
+        if not current_platform.is_cpu():
+            raise ValueError("CompressedTensorsW4A8Int8MoEMethod is CPU-only.")
+
+        # Arm: check _dyn ops availability
+        if current_platform.get_cpu_architecture() == CpuArchEnum.ARM:
+            try:
+                _ = torch.ops.aten._dyn_quant_matmul_4bit
+                _ = torch.ops.aten._dyn_quant_pack_4bit_weight
+            except AttributeError as err:
+                raise RuntimeError(
+                    f"""PyTorch {torch.__version__} lacks _dyn_quant_* 4bit ops;
+                    install a newer build.""") from err
+        self.static_input_scales = False  # always dynamic per token
+
+    # ---- parameter creation ----
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        # Shapes per local rank (TP/EP):
+        #   w13: [E, 2*I_local, H]  int8  (int4 values in [-8,7])
+        #   w2 : [E, H, I_local]    int8
+        # Scales:
+        #   channel-wise: group_size=-1 -> per-output-row, single scale per row
+        #   group-wise  : group_size=g   ->
+        #   per-output-row, (in_features/g) scales
+
+        E = num_experts
+        H = hidden_size
+        IN = intermediate_size_per_partition
+        g = self.group_size
+
+        # Per-row scale columns
+        def _n_scale_cols(in_features: int) -> int:
+            return 1 if g == -1 else (in_features // g)
+
+        # Register unpacked int4-as-int8 weights the loader will fill.
+        w13 = torch.nn.Parameter(torch.empty(E, 2 * IN, H, dtype=torch.int8),
+                                 requires_grad=False)
+        set_weight_attrs(w13, extra_weight_attrs)
+        layer.register_parameter("w13_weight", w13)
+
+        w2 = torch.nn.Parameter(torch.empty(E, H, IN, dtype=torch.int8),
+                                requires_grad=False)
+        set_weight_attrs(w2, extra_weight_attrs)
+        layer.register_parameter("w2_weight", w2)
+
+        # Register scales
+        # KleidiAI groupwise kernels accepts float32 scales
+        # KleidiAI groupwise kernels accepts bfloat16 scales
+        scale_dtype = torch.float32 if g == -1 else torch.bfloat16
+
+        w13_s = torch.nn.Parameter(torch.ones(E,
+                                              2 * IN,
+                                              _n_scale_cols(H),
+                                              dtype=scale_dtype),
+                                   requires_grad=False)
+        set_weight_attrs(
+            w13_s, {
+                "quant_method": "channel" if g == -1 else "group",
+                **extra_weight_attrs
+            })
+        layer.register_parameter("w13_weight_scale", w13_s)
+
+        w2_s = torch.nn.Parameter(torch.ones(E,
+                                             H,
+                                             _n_scale_cols(IN),
+                                             dtype=scale_dtype),
+                                  requires_grad=False)
+        set_weight_attrs(
+            w2_s, {
+                "quant_method": "channel" if g == -1 else "group",
+                **extra_weight_attrs
+            })
+        layer.register_parameter("w2_weight_scale", w2_s)
+
+        if self.has_bias:
+            w13_bias = torch.nn.Parameter(torch.zeros(E,
+                                                      2 * IN,
+                                                      dtype=params_dtype),
+                                          requires_grad=False)
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(torch.zeros(num_experts,
+                                                     hidden_size,
+                                                     dtype=params_dtype),
+                                         requires_grad=False)
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+
+        # Placeholders for packed weights (will be replaced after packing)
+        layer.register_parameter(
+            "w13_weight_packed",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        set_weight_attrs(layer.w13_weight_packed, extra_weight_attrs)
+
+        layer.register_parameter(
+            "w2_weight_packed",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        set_weight_attrs(layer.w2_weight_packed, extra_weight_attrs)
+
+        # dims for 4 bit fused matmuls
+        layer.w13_in_features = H
+        layer.w13_out_features = 2 * IN
+        layer.w2_in_features = IN
+        layer.w2_out_features = H
+        layer.group_size = g
+
+    # post-load packing to dyn-4bit KleidiAI kernel's format
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        E = layer.w13_weight.shape[0]
+        H = layer.w13_in_features
+        I2 = layer.w13_out_features
+        IN = layer.w2_in_features
+        g = layer.group_size
+
+        def _pack_matrix(int4_as_int8_2d: torch.Tensor,
+                         scales_2d: torch.Tensor,
+                         bias_1d: Optional[torch.Tensor], in_features: int,
+                         out_features: int) -> torch.Tensor:
+            # int4 values are stored as int8 in [-8,7].
+            # Shift to unsigned nibble and pack pairs along input-dim.
+            tmp = int4_as_int8_2d.add(8)  # [out, in]
+            uint8_nibbles = ((tmp[:, 1::2] << 4) | tmp[:, ::2]).to(
+                torch.uint8)  # [out, in//2]
+
+            # KleidiAI groupwise kernels accepts float32 scales
+            # KleidiAI groupwise kernels accepts bfloat16 scales
+            scale_dtype = torch.float32 if g == -1 else torch.bfloat16
+            scales = scales_2d.to(scale_dtype)
+            bias = None if bias_1d is None else bias_1d.to(torch.float32)
+            return torch.ops.aten._dyn_quant_pack_4bit_weight(
+                uint8_nibbles, scales, bias, g if g != -1 else in_features,
+                in_features, out_features)
+
+        # Pack per expert
+        w13_packed_list = []
+        w2_packed_list = []
+
+        has_w13_bias = hasattr(layer,
+                               "w13_bias") and layer.w13_bias is not None
+        has_w2_bias = hasattr(layer, "w2_bias") and layer.w2_bias is not None
+
+        for e in range(E):
+            w13_packed_list.append(
+                _pack_matrix(
+                    layer.w13_weight[e],  # [2I, H]
+                    layer.w13_weight_scale[e],  # [2I, H/g or 1]
+                    layer.w13_bias[e] if has_w13_bias else None,  # [2I]
+                    H,
+                    I2))
+            w2_packed_list.append(
+                _pack_matrix(
+                    # w2 shape is [H, IN]; we need [out, in] == [H, IN].
+                    layer.w2_weight[e],  # [H, IN]
+                    layer.w2_weight_scale[e],  # [H, IN/g or 1]
+                    layer.w2_bias[e] if has_w2_bias else None,  # [H]
+                    IN,
+                    layer.w2_out_features  # in_features=IN, out_features=H
+                ))
+
+        # each packed tensor has identical shape per expert; stack on dim 0
+        w13_packed = torch.stack(w13_packed_list, dim=0)
+        w2_packed = torch.stack(w2_packed_list, dim=0)
+
+        replace_parameter(layer, "w13_weight_packed",
+                          torch.nn.Parameter(w13_packed, requires_grad=False))
+        replace_parameter(layer, "w2_weight_packed",
+                          torch.nn.Parameter(w2_packed, requires_grad=False))
+
+        # free raw tensors/scales/bias now that they're packed into the payload.
+        replace_parameter(
+            layer, "w13_weight",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w2_weight",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w13_weight_scale",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        replace_parameter(
+            layer, "w2_weight_scale",
+            torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        if has_w13_bias:
+            replace_parameter(
+                layer, "w13_bias",
+                torch.nn.Parameter(torch.empty(0), requires_grad=False))
+        if has_w2_bias:
+            replace_parameter(
+                layer, "w2_bias",
+                torch.nn.Parameter(torch.empty(0), requires_grad=False))
+
+    def get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        # CPU dynamic 4-bit MoE path does not use modular kernels or
+        # fused_experts; quant config is not needed.
+        return None
+
+    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",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        assert not enable_eplb, "EPLB not supported for W4A8-int MoE yet."
+        assert activation in (
+            "silu", "swigluoai",
+            "swiglu"), "Only SiLU/SwiGLUGU/SwiGLUUG are supported."
+        assert expert_map is None, """expert_map/EP not implemented
+        for CPU dyn-4bit MoE."""
+
+        def _act_kind(s: str) -> int:
+            # 0 = SwiGLU_Gu (SiLU(g)*u), 1 = SwiGLU_Ug (SiLU(u)*g), 2 = SiLU
+            if s == "swiglu":
+                return 0
+            if s == "swigluoai":
+                return 1
+            if s == "silu":
+                return 2
+            raise ValueError(f"Unknown activation '{s}'")
+
+        # Apply topk softmax on router output
+        topk_weights, topk_ids = 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,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+        )
+
+        return torch.ops._C.dynamic_4bit_int_moe(
+            x, topk_ids.to(torch.long), topk_weights, layer.w13_weight_packed,
+            layer.w2_weight_packed, layer.w2_out_features,
+            layer.w2_in_features, layer.w13_out_features, layer.group_size,
+            apply_router_weight_on_input, int(_act_kind(activation)))