[MoE Refactor] Separate Router into OO Classes (#30623)

Signed-off-by: Bill Nell <bnell@redhat.com>

tests/kernels/moe/modular_kernel_tools/common.py

@@ -21,12 +21,12 @@ from vllm.distributed import (
     get_tensor_model_parallel_world_size,
 )
 from vllm.forward_context import set_forward_context
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.utils.import_utils import has_deep_ep, has_deep_gemm, has_pplx
 
 from .mk_objects import (

tests/kernels/moe/test_batched_moe.py

@@ -15,10 +15,10 @@ from tests.kernels.moe.utils import (
 from tests.kernels.quant_utils import native_batched_masked_quant_matmul
 from tests.kernels.utils import torch_experts
 from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
     invoke_moe_batched_triton_kernel,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl
 from vllm.utils.torch_utils import set_random_seed

tests/kernels/moe/test_block_fp8.py

@@ -11,13 +11,15 @@ from tests.kernels.quant_utils import (
 )
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import fused_experts
+from vllm.model_executor.layers.fused_moe import (
+    fused_experts,
+    fused_topk,
+)
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
     _valid_deep_gemm_shape,
     deep_gemm_moe_fp8,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_topk,
     modular_triton_fused_moe,
 )
 from vllm.platforms import current_platform

tests/kernels/moe/test_cutlass_moe.py

@@ -10,6 +10,7 @@ import torch
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
     FusedMoEQuantConfig,
@@ -19,7 +20,6 @@ from vllm.model_executor.layers.fused_moe.cutlass_moe import (
     CutlassExpertsFp8,
     run_cutlass_moe_fp8,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP,
 )

tests/kernels/moe/test_flashinfer_moe.py

@@ -12,6 +12,7 @@ from tests.kernels.quantization.nvfp4_utils import (
 from tests.kernels.utils import torch_moe
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
     FlashInferExperts,
     is_valid_flashinfer_cutlass_fused_moe,
@@ -19,7 +20,6 @@ from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (
     create_flashinfer_prepare_finalize,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe

tests/kernels/moe/test_grouped_topk.py

@@ -14,7 +14,7 @@ from vllm.config import (
     get_cached_compilation_config,
     set_current_vllm_config,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import (
+from vllm.model_executor.layers.fused_moe.router.grouped_topk_router import (
     GroupedTopk,
     fused_grouped_topk,
 )

tests/kernels/moe/test_moe.py

@@ -24,6 +24,9 @@ from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig, set_current_vllm_config
 from vllm.distributed.parallel_state import init_distributed_environment
 from vllm.forward_context import set_forward_context
+from vllm.model_executor.layers.fused_moe import (
+    fused_topk,
+)
 from vllm.model_executor.layers.fused_moe.config import (
     FUSED_MOE_UNQUANTIZED_CONFIG,
     int4_w4a16_moe_quant_config,
@@ -34,7 +37,6 @@ from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
     fused_marlin_moe,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_topk,
     modular_triton_fused_moe,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (

tests/kernels/moe/test_moe_permute_unpermute.py

@@ -9,7 +9,7 @@ import numpy as np
 import pytest
 import torch
 
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
 from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
     moe_permute,

tests/kernels/moe/test_nvfp4_moe.py

@@ -13,11 +13,11 @@ from tests.kernels.quantization.nvfp4_utils import (
 from tests.kernels.utils import torch_moe
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.config import nvfp4_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import (
     CutlassExpertsFp4,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP,
 )

tests/kernels/moe/test_pplx_cutlass_moe.py

@@ -8,9 +8,9 @@ import torch
 from tests.kernels.utils import torch_experts
 from vllm import _custom_ops as ops
 from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
 from vllm.model_executor.layers.fused_moe.cutlass_moe import CutlassBatchedExpertsFp8
-from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv

tests/kernels/moe/test_routing.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+
+import pytest
+import torch
+
+from vllm.distributed.eplb.eplb_state import EplbLayerState
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
+from vllm.model_executor.layers.fused_moe.router.router_factory import (
+    create_fused_moe_router,
+)
+from vllm.model_executor.models.llama4 import Llama4MoE
+
+# Test parameters
+MK_S = [(32, 256), (64, 512)]
+TOP_KS = [2, 4, 6]
+NUM_EXPERTS = [8, 16, 64]
+
+
+def setup_eplb_state(enable_eplb: bool, global_num_experts: int) -> EplbLayerState:
+    if not enable_eplb:
+        return EplbLayerState()
+
+    # Initialize EPLB state with proper tensors for testing
+    # For testing purposes, we use a simple 1:1 mapping (no redundant experts)
+    # expert_load_view: tracks load on each expert (shape: num_experts)
+    expert_load_view = torch.zeros(global_num_experts, dtype=torch.int32, device="cuda")
+
+    # logical_to_physical_map: maps logical experts to physical experts
+    # Shape: (num_logical_experts, max_slots)
+    # For testing, use simple 1:1 mapping with single slot per expert
+    logical_to_physical_map = torch.arange(
+        global_num_experts, dtype=torch.int64, device="cuda"
+    ).unsqueeze(-1)
+
+    # logical_replica_count: number of replicas per logical expert
+    # Shape: (num_logical_experts,)
+    # For testing, each logical expert has exactly 1 replica
+    logical_replica_count = torch.ones(
+        global_num_experts, dtype=torch.int64, device="cuda"
+    )
+
+    return EplbLayerState(
+        expert_load_view=expert_load_view,
+        logical_to_physical_map=logical_to_physical_map,
+        logical_replica_count=logical_replica_count,
+    )
+
+
+def make_test_data(
+    m: int, k: int, num_experts: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+    hidden_states = torch.randn((m, k), device="cuda") / 10
+    logits = torch.randn((m, num_experts), device="cuda")
+    return hidden_states, logits
+
+
+def make_e_score_correction_bias(
+    e_score_correction_bias_val: float,
+    num_experts: int,
+) -> torch.Tensor:
+    # return torch.randn(num_experts, device="cuda") * e_score_correction_bias_val
+    return torch.full(
+        (num_experts,), e_score_correction_bias_val, device="cuda", dtype=torch.float32
+    )
+
+
+def assert_routing_results_close(
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    baseline_weights: torch.Tensor,
+    baseline_ids: torch.Tensor,
+    rtol: float = 1e-3,
+    atol: float = 1e-3,
+):
+    """
+    Compare routing results, sorting by expert ID first to handle non-deterministic
+    ordering from sorted=False in topk.
+    """
+    # Sort both results by expert IDs for consistent comparison
+    sorted_indices_actual = torch.argsort(topk_ids, dim=-1)
+    sorted_indices_baseline = torch.argsort(baseline_ids.to(topk_ids.dtype), dim=-1)
+
+    # Gather the sorted values
+    topk_ids_sorted = torch.gather(topk_ids, 1, sorted_indices_actual)
+    topk_weights_sorted = torch.gather(topk_weights, 1, sorted_indices_actual)
+    baseline_ids_sorted = torch.gather(
+        baseline_ids.to(topk_ids.dtype), 1, sorted_indices_baseline
+    )
+    baseline_weights_sorted = torch.gather(baseline_weights, 1, sorted_indices_baseline)
+
+    # Compare
+    torch.testing.assert_close(topk_ids_sorted, baseline_ids_sorted)
+    torch.testing.assert_close(
+        topk_weights_sorted, baseline_weights_sorted, rtol=rtol, atol=atol
+    )
+
+
+def baseline_fused_topk(
+    router_logits: torch.Tensor, top_k: int, renormalize: bool
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Baseline for standard fused top-k routing.
+
+    Algorithm:
+    1. Apply softmax to router logits
+    2. Select top-k experts
+    3. Optionally renormalize the weights
+    """
+    scores = torch.softmax(router_logits, dim=-1, dtype=torch.float32)
+    # Use sorted=False to match vllm implementation (vllm_is_batch_invariant
+    # defaults to False)
+    topk_weights, topk_ids = torch.topk(scores, top_k, dim=-1, sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def baseline_fused_topk_bias(
+    router_logits: torch.Tensor,
+    top_k: int,
+    renormalize: bool,
+    e_score_correction_bias: torch.Tensor,
+    routed_scaling_factor: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Baseline for fused top-k with bias correction.
+
+    Algorithm:
+    1. Apply softmax to router logits
+    2. Add bias to scores for expert selection
+    3. Select top-k experts using biased scores
+    4. Get weights from original (unbiased) scores
+    5. Apply routed scaling factor
+    6. Optionally renormalize the weights
+    """
+    # Apply softmax to get scores
+    scores = torch.softmax(router_logits, dim=-1, dtype=torch.float32)
+
+    # Add bias for expert selection
+    scores_for_choice = scores + e_score_correction_bias.unsqueeze(0)
+
+    # Select top-k using biased scores (sorted=False to match implementation)
+    topk_ids = torch.topk(scores_for_choice, k=top_k, dim=-1, sorted=False)[1]
+
+    # Get weights from original scores (not biased)
+    topk_weights = scores.gather(1, topk_ids)
+
+    # Renormalize if needed (BEFORE applying scaling factor)
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    # Apply scaling factor (AFTER renormalization, if applicable)
+    if routed_scaling_factor != 1.0:
+        topk_weights *= routed_scaling_factor
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def baseline_grouped_topk(
+    router_logits: torch.Tensor,
+    top_k: int,
+    num_expert_group: int,
+    topk_group: int,
+    scoring_func: str,
+    renormalize: bool,
+    e_score_correction_bias: torch.Tensor | None,
+    routed_scaling_factor: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Baseline for grouped top-k routing (e.g., DeepSeek).
+
+    Algorithm:
+    1. Apply scoring function (softmax or sigmoid)
+    2. Optionally add bias
+    3. Select top-k groups based on max scores within each group
+    4. Mask scores to only include selected groups
+    5. Select top-k experts from masked scores
+    6. Apply scaling factor
+    7. Optionally renormalize
+    """
+    num_token = router_logits.shape[0]
+
+    # Apply scoring function
+    if scoring_func == "softmax":
+        scores = torch.softmax(router_logits, dim=-1, dtype=torch.float32)
+    elif scoring_func == "sigmoid":
+        scores = torch.sigmoid(router_logits.float())
+    else:
+        raise ValueError(f"Unsupported scoring function: {scoring_func}")
+
+    # Handle bias correction
+    if e_score_correction_bias is not None:
+        original_scores = scores
+        scores = scores + e_score_correction_bias.unsqueeze(0)
+        # For bias case, use sum of top-2 scores in each group
+        group_scores = (
+            scores.view(num_token, num_expert_group, -1).topk(2, dim=-1)[0].sum(dim=-1)
+        )
+    else:
+        # Use max score in each group
+        group_scores = scores.view(num_token, num_expert_group, -1).max(dim=-1).values
+
+    # Select top-k groups
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[1]
+
+    # Create mask for selected groups
+    group_mask = torch.zeros_like(group_scores)
+    group_mask.scatter_(1, group_idx, 1)
+
+    # Expand mask to all experts
+    score_mask = (
+        group_mask.unsqueeze(-1)
+        .expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
+        .reshape(num_token, -1)
+    )
+
+    # Mask scores (set non-selected to -inf)
+    tmp_scores = scores.masked_fill(~score_mask.bool(), float("-inf"))
+
+    # Select top-k experts
+    if e_score_correction_bias is not None:
+        topk_ids = torch.topk(tmp_scores, k=top_k, dim=-1, sorted=False)[1]
+        topk_weights = original_scores.gather(1, topk_ids)
+    else:
+        topk_weights, topk_ids = torch.topk(tmp_scores, k=top_k, dim=-1, sorted=False)
+
+    # Renormalize if needed
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    # Apply scaling factor
+    if routed_scaling_factor != 1.0:
+        topk_weights *= routed_scaling_factor
+
+    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+
+
+def baseline_custom_llama4(
+    router_logits: torch.Tensor, top_k: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Baseline for Llama4 custom routing.
+
+    Algorithm:
+    1. Select top-k expert indices (without softmax)
+    2. Apply sigmoid to the selected scores
+    """
+    router_scores, router_indices = torch.topk(router_logits, top_k, dim=-1)
+    router_scores = torch.sigmoid(router_scores.float())
+    return router_scores.to(torch.float32), router_indices.to(torch.int32)
+
+
+@pytest.mark.parametrize("m,k", MK_S)
+@pytest.mark.parametrize("top_k", TOP_KS)
+@pytest.mark.parametrize("global_num_experts", NUM_EXPERTS)
+@pytest.mark.parametrize("renormalize", [False, True])
+@pytest.mark.parametrize("enable_eplb", [False, True])
+def test_fused_topk(
+    m: int,
+    k: int,
+    top_k: int,
+    global_num_experts: int,
+    renormalize: bool,
+    enable_eplb: bool,
+):
+    if top_k > global_num_experts:
+        pytest.skip(f"top_k ({top_k}) > global_num_experts ({global_num_experts})")
+
+    eplb_state = setup_eplb_state(enable_eplb, global_num_experts)
+    router = create_fused_moe_router(
+        top_k=top_k,
+        global_num_experts=global_num_experts,
+        renormalize=renormalize,
+        enable_eplb=enable_eplb,
+        eplb_state=eplb_state,
+    )
+
+    hidden_states, router_logits = make_test_data(m, k, global_num_experts)
+
+    # Get router output
+    topk_weights, topk_ids = router.select_experts(hidden_states, router_logits)
+
+    # Compute baseline
+    baseline_weights, baseline_ids = baseline_fused_topk(
+        router_logits, top_k, renormalize
+    )
+
+    # Compare results
+    assert_routing_results_close(topk_weights, topk_ids, baseline_weights, baseline_ids)
+
+
+@pytest.mark.parametrize("m,k", MK_S)
+@pytest.mark.parametrize("top_k", TOP_KS)
+@pytest.mark.parametrize("global_num_experts", NUM_EXPERTS)
+@pytest.mark.parametrize("renormalize", [False, True])
+@pytest.mark.parametrize("enable_eplb", [False, True])
+@pytest.mark.parametrize("e_score_correction_bias_val", [0.9])
+@pytest.mark.parametrize("routed_scaling_factor", [1.0, 1.1])
+def test_fused_topk_bias(
+    m: int,
+    k: int,
+    top_k: int,
+    global_num_experts: int,
+    renormalize: bool,
+    enable_eplb: bool,
+    e_score_correction_bias_val: float,
+    routed_scaling_factor: float,
+):
+    if top_k > global_num_experts:
+        pytest.skip(f"top_k ({top_k}) > global_num_experts ({global_num_experts})")
+
+    eplb_state = setup_eplb_state(enable_eplb, global_num_experts)
+
+    e_score_correction_bias = make_e_score_correction_bias(
+        e_score_correction_bias_val,
+        global_num_experts,
+    )
+
+    router = create_fused_moe_router(
+        e_score_correction_bias=e_score_correction_bias,
+        routed_scaling_factor=routed_scaling_factor,
+        top_k=top_k,
+        global_num_experts=global_num_experts,
+        renormalize=renormalize,
+        enable_eplb=enable_eplb,
+        eplb_state=eplb_state,
+    )
+
+    hidden_states, router_logits = make_test_data(m, k, global_num_experts)
+
+    # Get router output
+    topk_weights, topk_ids = router.select_experts(hidden_states, router_logits)
+
+    # Compute baseline
+    baseline_weights, baseline_ids = baseline_fused_topk_bias(
+        router_logits,
+        top_k,
+        renormalize,
+        e_score_correction_bias,
+        routed_scaling_factor,
+    )
+
+    # Compare results
+    assert_routing_results_close(topk_weights, topk_ids, baseline_weights, baseline_ids)
+
+
+@pytest.mark.parametrize("m,k", MK_S)
+@pytest.mark.parametrize("top_k", TOP_KS)
+@pytest.mark.parametrize(
+    "global_num_experts,num_expert_group,topk_group",
+    [
+        (64, 8, 4),  # 8 groups of 8 experts, select 4 groups
+        (32, 4, 2),  # 4 groups of 8 experts, select 2 groups
+    ],
+)
+@pytest.mark.parametrize("renormalize", [False, True])
+@pytest.mark.parametrize("enable_eplb", [False, True])
+@pytest.mark.parametrize("e_score_correction_bias_val", [0.9])
+@pytest.mark.parametrize("routed_scaling_factor", [1.0, 1.1])
+@pytest.mark.parametrize("scoring_func", ["sigmoid", "softmax"])
+def test_grouped_topk(
+    m: int,
+    k: int,
+    top_k: int,
+    global_num_experts: int,
+    renormalize: bool,
+    enable_eplb: bool,
+    num_expert_group: int,
+    topk_group: int,
+    scoring_func: str,
+    e_score_correction_bias_val: float,
+    routed_scaling_factor: float,
+):
+    if top_k > global_num_experts:
+        pytest.skip(f"top_k ({top_k}) > global_num_experts ({global_num_experts})")
+
+    eplb_state = setup_eplb_state(enable_eplb, global_num_experts)
+
+    e_score_correction_bias = make_e_score_correction_bias(
+        e_score_correction_bias_val,
+        global_num_experts,
+    )
+
+    routing_method_type = None
+    if scoring_func == "llama4":
+        routing_method_type = RoutingMethodType.Llama4
+        scoring_func = "sigmoid"
+
+    router = create_fused_moe_router(
+        use_grouped_topk=True,
+        num_expert_group=num_expert_group,
+        topk_group=topk_group,
+        scoring_func=scoring_func,
+        routing_method_type=routing_method_type,
+        e_score_correction_bias=e_score_correction_bias,
+        routed_scaling_factor=routed_scaling_factor,
+        top_k=top_k,
+        global_num_experts=global_num_experts,
+        renormalize=renormalize,
+        enable_eplb=enable_eplb,
+        eplb_state=eplb_state,
+    )
+
+    hidden_states, router_logits = make_test_data(m, k, global_num_experts)
+
+    # Get router output
+    topk_weights, topk_ids = router.select_experts(hidden_states, router_logits)
+
+    # Compute baseline
+    baseline_weights, baseline_ids = baseline_grouped_topk(
+        router_logits,
+        top_k,
+        num_expert_group,
+        topk_group,
+        scoring_func,
+        renormalize,
+        e_score_correction_bias,
+        routed_scaling_factor,
+    )
+
+    # Compare results
+    assert_routing_results_close(topk_weights, topk_ids, baseline_weights, baseline_ids)
+
+
+@pytest.mark.parametrize("m,k", MK_S)
+@pytest.mark.parametrize("top_k", TOP_KS)
+@pytest.mark.parametrize("global_num_experts", NUM_EXPERTS)
+@pytest.mark.parametrize("renormalize", [False, True])
+@pytest.mark.parametrize("enable_eplb", [False, True])
+@pytest.mark.parametrize("custom_routing_function", [Llama4MoE.custom_routing_function])
+def test_custom(
+    m: int,
+    k: int,
+    top_k: int,
+    global_num_experts: int,
+    renormalize: bool,
+    enable_eplb: bool,
+    custom_routing_function: Callable,
+):
+    if top_k > global_num_experts:
+        pytest.skip(f"top_k ({top_k}) > global_num_experts ({global_num_experts})")
+
+    eplb_state = setup_eplb_state(enable_eplb, global_num_experts)
+
+    router = create_fused_moe_router(
+        top_k=top_k,
+        global_num_experts=global_num_experts,
+        custom_routing_function=custom_routing_function,
+        renormalize=renormalize,
+        enable_eplb=enable_eplb,
+        eplb_state=eplb_state,
+    )
+
+    hidden_states, router_logits = make_test_data(m, k, global_num_experts)
+
+    # Get router output
+    topk_weights, topk_ids = router.select_experts(hidden_states, router_logits)
+
+    # Compute baseline (Llama4 uses sigmoid)
+    baseline_weights, baseline_ids = baseline_custom_llama4(router_logits, top_k)
+
+    # Compare results
+    assert_routing_results_close(topk_weights, topk_ids, baseline_weights, baseline_ids)
+
+
+# TODO: is other test sufficient?
+# # See tests/test_routing_simulatator.py
+# @pytest.mark.parametrize("m,k", MK_S)
+# @pytest.mark.parametrize("top_k", TOP_KS)
+# @pytest.mark.parametrize("global_num_experts", NUM_EXPERTS)
+# @pytest.mark.parametrize("renormalize", [False, True])
+# @pytest.mark.parametrize("enable_eplb", [False, True])
+# @pytest.mark.parameterize("strategy", ["uniform_random", "normal_routing"])
+# def test_simulated(
+#     m: int,
+#     k: int,
+#     top_k: int,
+#     global_num_experts: int,
+#     renormalize: bool,
+#     enable_eplb: bool,
+#     strategy: str,
+#     monkeypatch,
+# ):
+#     eplb_state = setup_eplb_state(enable_eplb)
+
+#     monkeypatch.setenv("VLLM_MOE_ROUTING_SIMULATION_STRATEGY", strategy)
+#     router = create_fused_moe_router(
+#         top_k=top_k,
+#         global_num_experts=global_num_experts,
+#         enable_eplb=enable_eplb,
+#         eplb_state=eplb_state,
+#     )
+
+#     hidden_states, router_logits = make_test_data(m, k, global_num_experts)
+#     topk_weights, topk_ids = router.select_experts(hidden_states, router_logits)

tests/test_routing_simulator.py → tests/kernels/moe/test_routing_simulator.py

@@ -19,7 +19,7 @@ from vllm.distributed import (
     init_distributed_environment,
     initialize_model_parallel,
 )
-from vllm.model_executor.layers.fused_moe.routing_simulator import (
+from vllm.model_executor.layers.fused_moe.router.routing_simulator_router import (
     DistributionBasedRouting,
     RoutingSimulator,
 )
@@ -109,6 +109,8 @@ def test_routing_strategy_integration(monkeypatch, device):
             tensor_model_parallel_size=1,
             pipeline_model_parallel_size=1,
         )
+
+        for strategy in strategies:
             fused_moe = FusedMoE(
                 num_experts=num_experts,
                 top_k=top_k,
@@ -116,9 +118,9 @@ def test_routing_strategy_integration(monkeypatch, device):
                 intermediate_size=0,
                 use_grouped_topk=False,
                 renormalize=True,
+                prefix=strategy,
             )
 
-    for strategy in strategies:
             # Set environment variable
             env_name = "VLLM_MOE_ROUTING_SIMULATION_STRATEGY"
             monkeypatch.setenv(env_name, strategy)
@@ -136,7 +138,9 @@ def test_routing_strategy_integration(monkeypatch, device):
             assert topk_weights.shape == (num_tokens, top_k), (
                 f"Wrong weights shape for {strategy}"
             )
-        assert topk_ids.shape == (num_tokens, top_k), f"Wrong ids shape for {strategy}"
+            assert topk_ids.shape == (num_tokens, top_k), (
+                f"Wrong ids shape for {strategy}"
+            )
 
             # Verify expert IDs are valid
             assert topk_ids.min() >= 0, f"Invalid expert ID (negative) for {strategy}"

tests/model_executor/test_enabled_custom_ops.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.activation import (
     ReLUSquaredActivation,
     SiluAndMul,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe import (
+from vllm.model_executor.layers.fused_moe.router.fused_topk_router import (
     dispatch_topk_func,
     vllm_topk_softmax,
 )

vllm/distributed/eplb/eplb_state.py

@@ -1158,6 +1158,15 @@ class EplbState:
         return self._allreduce_list(load_pass_list)
 
 
+@dataclass
+class EplbLayerState:
+    """Runtime EPLB data stored in the MoE layer."""
+
+    expert_load_view: torch.Tensor | None = None
+    logical_to_physical_map: torch.Tensor | None = None
+    logical_replica_count: torch.Tensor | None = None
+
+
 def _node_count_with_rank_mapping(
     pg: ProcessGroup | StatelessProcessGroup,
     rank_mapping: dict[int, int],

vllm/model_executor/layers/fused_moe/__init__.py

@@ -11,9 +11,6 @@ from vllm.model_executor.layers.fused_moe.config import (
 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
     FusedMoEMethodBase,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe_router import (
-    FusedMoERouter,
-)
 from vllm.model_executor.layers.fused_moe.layer import (
     FusedMoE,
     FusedMoeWeightScaleSupported,
@@ -23,6 +20,9 @@ from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEPermuteExpertsUnpermute,
     FusedMoEPrepareAndFinalize,
 )
+from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
+    FusedMoERouter,
+)
 from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
 from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
     UnquantizedFusedMoEMethod,
@@ -83,13 +83,17 @@ if HAS_TRITON:
         BatchedTritonExperts,
     )
     from vllm.model_executor.layers.fused_moe.fused_moe import (
-        GroupedTopk,
         TritonExperts,
         TritonWNA16Experts,
         fused_experts,
-        fused_topk,
         get_config_file_name,
     )
+    from vllm.model_executor.layers.fused_moe.router.fused_topk_router import (
+        fused_topk,
+    )
+    from vllm.model_executor.layers.fused_moe.router.grouped_topk_router import (
+        GroupedTopk,
+    )
     from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
         TritonOrDeepGemmExperts,
     )

vllm/model_executor/layers/fused_moe/config.py

@@ -117,8 +117,12 @@ class RoutingMethodType(IntEnum):
     RenormalizeNaive = (4,)
     # TopK: TopK (no softmax)
     TopK = (5,)
+    # Custom
+    Custom = (6,)
+    # Simulated
+    Simulated = (7,)
     # Unspecified
-    Unspecified = 6.0
+    Unspecified = 8.0
 
 
 @dataclass

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -13,9 +13,7 @@ import torch
 import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
-from vllm._aiter_ops import rocm_aiter_ops
 from vllm.logger import init_logger
-from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.batch_invariant import (
     vllm_is_batch_invariant,
 )
@@ -34,9 +32,6 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP,
 )
-from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
-    rocm_aiter_grouped_topk,
-)
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
@@ -49,7 +44,6 @@ from vllm.model_executor.layers.fused_moe.utils import (
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import dequant_mxfp4
 from vllm.model_executor.layers.quantization.utils.mxfp6_utils import dequant_mxfp6
 from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_Scheme
-from vllm.model_executor.utils import maybe_disable_graph_partition
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
@@ -1318,375 +1312,6 @@ def try_get_optimal_moe_config(
     return config
 
 
-def vllm_topk_softmax(
-    topk_weights: torch.Tensor,
-    topk_indices: torch.Tensor,
-    token_expert_indices: torch.Tensor,
-    gating_output: torch.Tensor,
-    renormalize: bool,
-) -> tuple[torch.Tensor, ...]:
-    ops.topk_softmax(
-        topk_weights,
-        topk_indices,
-        token_expert_indices,
-        gating_output,
-        renormalize,
-    )
-
-    return topk_weights, topk_indices
-
-
-def dispatch_topk_func(
-    use_rocm_aiter: bool = False,
-) -> Callable[..., tuple[torch.Tensor, ...]]:
-    if use_rocm_aiter:
-        return rocm_aiter_ops.topk_softmax
-    return vllm_topk_softmax
-
-
-def fused_topk(
-    hidden_states: torch.Tensor,
-    gating_output: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-    indices_type: torch.dtype | None = None,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    assert hidden_states.size(0) == gating_output.size(0), "Number of tokens mismatch"
-
-    M, _ = hidden_states.size()
-
-    topk_weights = torch.empty(
-        M, topk, dtype=torch.float32, device=hidden_states.device
-    )
-    topk_ids = torch.empty(
-        M,
-        topk,
-        dtype=torch.int32 if indices_type is None else indices_type,
-        device=hidden_states.device,
-    )
-    token_expert_indices = torch.empty(
-        M, topk, dtype=torch.int32, device=hidden_states.device
-    )
-
-    topk_func = dispatch_topk_func(use_rocm_aiter=rocm_aiter_ops.is_fused_moe_enabled())
-    topk_weights, topk_ids = topk_func(
-        topk_weights, topk_ids, token_expert_indices, gating_output, renormalize
-    )
-
-    return topk_weights, topk_ids, token_expert_indices
-
-
-def fused_topk_bias(
-    hidden_states: torch.Tensor,
-    gating_output: torch.Tensor,
-    e_score_correction_bias: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-):
-    n_routed_experts = gating_output.shape[-1]
-    scores = gating_output.softmax(dim=-1)
-    scores_for_choice = scores.view(
-        -1, n_routed_experts
-    ) + e_score_correction_bias.unsqueeze(0)
-
-    # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = vllm_is_batch_invariant()
-    topk_indices = torch.topk(scores_for_choice, k=topk, dim=-1, sorted=use_sorted)[1]
-    topk_weights = scores.gather(1, topk_indices)
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-    return topk_weights.to(torch.float32), topk_indices.to(torch.int32)
-
-
-# This is used by the Deepseek-V2 and Deepseek-V3 model
-@torch.compile(
-    dynamic=True,
-    backend=current_platform.simple_compile_backend,
-    options=maybe_disable_graph_partition(current_platform.simple_compile_backend),
-)
-def grouped_topk(
-    hidden_states: torch.Tensor,
-    gating_output: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-    num_expert_group: int = 0,
-    topk_group: int = 0,
-    scoring_func: str = "softmax",
-    routed_scaling_factor: float = 1.0,
-    e_score_correction_bias: torch.Tensor | None = None,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    if (
-        envs.VLLM_USE_FUSED_MOE_GROUPED_TOPK
-        and current_platform.is_cuda()
-        and num_expert_group <= 32
-        and topk <= 32
-        and e_score_correction_bias is not None
-    ):
-        return fused_grouped_topk(
-            hidden_states=hidden_states,
-            gating_output=gating_output,
-            topk=topk,
-            renormalize=renormalize,
-            e_score_correction_bias=e_score_correction_bias,
-            num_expert_group=num_expert_group,
-            topk_group=topk_group,
-            scoring_func=scoring_func,
-            routed_scaling_factor=routed_scaling_factor,
-        )
-
-    assert hidden_states.size(0) == gating_output.size(0), "Number of tokens mismatch"
-
-    if scoring_func == "softmax":
-        scores = torch.softmax(gating_output, dim=-1)
-    elif scoring_func == "sigmoid":
-        scores = gating_output.sigmoid()
-    else:
-        raise ValueError(f"Unsupported scoring function: {scoring_func}")
-
-    num_token = scores.size(0)
-    if e_score_correction_bias is not None:
-        # Store original scores before applying correction bias. We use biased
-        # scores for expert selection but original scores for routing weights
-        original_scores = scores
-        scores = scores + e_score_correction_bias.unsqueeze(0)
-        group_scores = (
-            scores.view(num_token, num_expert_group, -1).topk(2, dim=-1)[0].sum(dim=-1)
-        )
-    else:
-        group_scores = (
-            scores.view(num_token, num_expert_group, -1).max(dim=-1).values
-        )  # [n, n_group]
-
-    # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = vllm_is_batch_invariant()
-    group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=use_sorted)[
-        1
-    ]  # [n, top_k_group]
-    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
-    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
-    score_mask = (
-        group_mask.unsqueeze(-1)
-        .expand(num_token, num_expert_group, scores.size(-1) // num_expert_group)
-        .reshape(num_token, -1)
-    )  # [n, e]
-    tmp_scores = scores.masked_fill(~score_mask.bool(), float("-inf"))  # [n, e]
-
-    if e_score_correction_bias is not None:
-        topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=use_sorted)[1]
-        # Use original unbiased scores for the routing weights
-        topk_weights = original_scores.gather(1, topk_ids)
-    else:
-        topk_weights, topk_ids = torch.topk(
-            tmp_scores, k=topk, dim=-1, sorted=use_sorted
-        )
-
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-
-    if routed_scaling_factor != 1.0:
-        topk_weights = topk_weights * routed_scaling_factor
-    return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
-
-
-# --8<-- [start:grouped_topk]
-@CustomOp.register("grouped_topk")
-class GroupedTopk(CustomOp):
-    """GroupedTopk used by the Deepseek-V2 and Deepseek-V3 model."""
-
-    # --8<-- [end:grouped_topk]
-
-    def __init__(
-        self,
-        topk: int,
-        renormalize: bool,
-        num_expert_group: int = 0,
-        topk_group: int = 0,
-        scoring_func: str = "softmax",
-        routed_scaling_factor: float = 1.0,
-        num_fused_shared_experts: int = 0,
-    ) -> None:
-        super().__init__()
-        self.native_impl = grouped_topk
-        self.topk = topk
-        self.renormalize = renormalize
-        self.num_expert_group = num_expert_group
-        self.topk_group = topk_group
-        self.scoring_func = scoring_func
-        self.routed_scaling_factor = routed_scaling_factor
-        self.num_fused_shared_experts = num_fused_shared_experts
-
-    def forward_native(
-        self,
-        hidden_states: torch.Tensor,
-        gating_output: torch.Tensor,
-        e_score_correction_bias: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return self.native_impl(
-            hidden_states,
-            gating_output,
-            self.topk,
-            self.renormalize,
-            self.num_expert_group,
-            self.topk_group,
-            self.scoring_func,
-            self.routed_scaling_factor,
-            e_score_correction_bias,
-        )
-
-    def forward_cuda(
-        self,
-        hidden_states: torch.Tensor,
-        gating_output: torch.Tensor,
-        e_score_correction_bias: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return self.forward_native(
-            hidden_states, gating_output, e_score_correction_bias
-        )
-
-    def forward_hip(
-        self,
-        hidden_states: torch.Tensor,
-        gating_output: torch.Tensor,
-        e_score_correction_bias: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if rocm_aiter_ops.is_fused_moe_enabled():
-            if not rocm_aiter_ops.is_fusion_moe_shared_experts_enabled():
-                assert self.num_fused_shared_experts == 0
-            return rocm_aiter_grouped_topk(
-                hidden_states,
-                gating_output,
-                self.topk,
-                self.renormalize,
-                self.num_expert_group,
-                self.topk_group,
-                self.scoring_func,
-                self.routed_scaling_factor,
-                e_score_correction_bias,
-                self.num_fused_shared_experts,
-            )
-        else:
-            return self.forward_native(
-                hidden_states, gating_output, e_score_correction_bias
-            )
-
-
-@torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
-def eplb_map_to_physical_and_record(
-    topk_ids: torch.Tensor,
-    expert_load_view: torch.Tensor,
-    logical_to_physical_map: torch.Tensor,
-    logical_replica_count: torch.Tensor,
-) -> torch.Tensor:
-    """
-    Map the logical expert ids to physical expert ids
-    and record the expert load metrics.
-
-    This will select a pseudo-random replica for each logical expert.
-    Only used for EPLB.
-
-    Args:
-        topk_ids: The logical expert ids.
-        expert_load_view: The expert load view.
-        logical_to_physical_map: The logical to physical map.
-        logical_replica_count: The logical replica count.
-
-    Returns:
-        The physical expert ids.
-    """
-
-    # 1. Convert the logical expert ids to physical expert ids
-    # Directly select a random replica for each logical expert
-
-    # In case `indices_type` is not `torch.long` or `torch.int`,
-    # e.g. `torch.uint32` as required by dispatch/combine kernels
-    topk_ids_long = topk_ids.long()
-    # Use (token position) modulo (replica count)
-    # to deterministically choose a replica
-    replica_count = logical_replica_count[topk_ids_long]
-    # Flatten-position based index, reshaped back to `topk_ids` shape
-    pos_indices = torch.arange(
-        topk_ids.numel(), device=topk_ids.device, dtype=torch.long
-    ).reshape_as(topk_ids)
-    # Compute pseudo-random indices by modulo
-    replica_indices = (pos_indices % replica_count).unsqueeze(-1)
-    physical_ids = (
-        logical_to_physical_map[topk_ids_long].gather(-1, replica_indices).squeeze(-1)
-    )
-
-    topk_ids = physical_ids
-
-    # 2. Record expert load metrics.
-
-    # TODO(bowen): When using `FusedMoEModularKernel`, this
-    # can be done in a more unified way, since
-    # `FusedMoEPrepareAndFinalize` will return the expert
-    # token count, in some cases directly from the kernel.
-    # However, now there are many code paths not using
-    # the modular kernel, e.g. calling `fused_experts`,
-    # so we decide to keep the logic here.
-    #
-    # If later refactor moved all the MoE kernel calls
-    # to the modular kernel, we can move this logic there
-    # to achieve better efficiency.
-
-    # `expert_load_view`: (num_physical_experts,)
-
-    # `torch.bincount` is not compilable, so use `scatter_add_` instead.
-    topk_ids_flatten = topk_ids.flatten()
-    expert_load_view.scatter_add_(
-        dim=0,
-        index=topk_ids_flatten.long(),
-        src=torch.ones_like(topk_ids_flatten).to(expert_load_view),
-    )
-    return topk_ids
-
-
-def fused_grouped_topk(
-    hidden_states: torch.Tensor,
-    gating_output: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-    e_score_correction_bias: torch.Tensor,
-    num_expert_group: int = 0,
-    topk_group: int = 0,
-    scoring_func: str = "softmax",
-    routed_scaling_factor: float = 1.0,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    assert hidden_states.size(0) == gating_output.size(0), "Number of tokens mismatch"
-
-    if scoring_func == "sigmoid":
-        # Fully fused kernel path for sigmoid
-        topk_values, topk_indices = ops.grouped_topk(
-            gating_output,  # raw logits
-            num_expert_group,
-            topk_group,
-            topk,
-            renormalize,
-            routed_scaling_factor,
-            e_score_correction_bias,
-            1,  # scoring_func=1 for sigmoid
-        )
-    elif scoring_func == "softmax":
-        # Apply softmax in Python, then use fused kernel
-        # TODO: Add support for softmax in kernel
-        scores = torch.softmax(gating_output, dim=-1)
-        topk_values, topk_indices = ops.grouped_topk(
-            scores,  # pre-computed scores
-            num_expert_group,
-            topk_group,
-            topk,
-            renormalize,
-            routed_scaling_factor,
-            e_score_correction_bias,
-            0,  # scoring_func=0 (no activation, scores already computed)
-        )
-    else:
-        raise ValueError(f"Unsupported scoring function: {scoring_func}")
-
-    # Fused kernel outputs float32 values and int32 indices directly
-    return topk_values, topk_indices
-
-
 def inplace_fused_experts(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,

vllm/model_executor/layers/fused_moe/fused_moe_method_base.py

@@ -10,13 +10,13 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe_router import (
-    FusedMoERouter,
-)
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEPermuteExpertsUnpermute,
     FusedMoEPrepareAndFinalize,
 )
+from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
+    FusedMoERouter,
+)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizeMethodBase,
 )

vllm/model_executor/layers/fused_moe/fused_moe_modular_method.py

@@ -12,11 +12,13 @@ from vllm.model_executor.layers.fused_moe.config import (
 from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
     FusedMoEMethodBase,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe_router import FusedMoERouter
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEModularKernel,
     FusedMoEPrepareAndFinalize,
 )
+from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
+    FusedMoERouter,
+)
 
 logger = init_logger(__name__)
 

vllm/model_executor/layers/fused_moe/layer.py

@@ -21,7 +21,7 @@ from vllm.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
 )
-from vllm.distributed.eplb.eplb_state import EplbState
+from vllm.distributed.eplb.eplb_state import EplbLayerState, EplbState
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
@@ -31,14 +31,24 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
     RoutingMethodType,
 )
-from vllm.model_executor.layers.fused_moe.fused_moe_router import FusedMoERouter
+from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
+    FusedMoEMethodBase,
+)
+from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
+    FusedMoEModularMethod,
+)
 from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
     init_aiter_topK_meta_data,
 )
 from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
     RoutedExpertsCapturer,
 )
-from vllm.model_executor.layers.fused_moe.routing_simulator import RoutingSimulator
+from vllm.model_executor.layers.fused_moe.router.router_factory import (
+    create_fused_moe_router,
+)
+from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
+    UnquantizedFusedMoEMethod,
+)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
 )
@@ -52,31 +62,6 @@ from vllm.utils.torch_utils import (
 )
 from vllm.v1.worker.ubatching import dbo_current_ubatch_id
 
-if current_platform.is_cuda_alike():
-    from .fused_moe import eplb_map_to_physical_and_record
-else:
-
-    def _eplb_map_to_physical_and_record(
-        topk_ids: torch.Tensor,
-        expert_load_view: torch.Tensor,
-        logical_to_physical_map: torch.Tensor,
-        logical_replica_count: torch.Tensor,
-    ) -> torch.Tensor:
-        # CPU fallback: no EPLB so just return as is
-        return topk_ids
-
-    eplb_map_to_physical_and_record = _eplb_map_to_physical_and_record
-from vllm.model_executor.layers.fused_moe.fused_moe import GroupedTopk
-from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (
-    FusedMoEModularMethod,
-)
-from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
-    UnquantizedFusedMoEMethod,
-)
-
 logger = init_logger(__name__)
 
 
@@ -288,23 +273,6 @@ def maybe_roundup_hidden_size(
     return hidden_size
 
 
-class FusedMoERouterImpl(FusedMoERouter):
-    def __init__(self, layer: "FusedMoE"):
-        super().__init__()
-        self.layer = layer
-
-    @property
-    def routing_method_type(self) -> RoutingMethodType:
-        return self.layer.routing_method_type
-
-    def select_experts(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        return self.layer._select_experts(hidden_states, router_logits)
-
-
 # --8<-- [start:fused_moe]
 @CustomOp.register("fused_moe")
 class FusedMoE(CustomOp):
@@ -440,9 +408,7 @@ class FusedMoE(CustomOp):
         self.layer_name = prefix
 
         self.enable_eplb = enable_eplb
-        self.expert_load_view: torch.Tensor | None = None
-        self.logical_to_physical_map: torch.Tensor | None = None
-        self.logical_replica_count: torch.Tensor | None = None
+        self.eplb_state = EplbLayerState()
         self.expert_placement_strategy: ExpertPlacementStrategy = (
             vllm_config.parallel_config.expert_placement_strategy
         )
@@ -538,6 +504,8 @@ class FusedMoE(CustomOp):
         self.intermediate_size_per_partition = intermediate_size // self.tp_size
         self.reduce_results = reduce_results
         self.renormalize = renormalize
+
+        # TODO(bnell): these attributes are only used by cpu/xpu/mxfp4
         self.use_grouped_topk = use_grouped_topk
         if self.use_grouped_topk:
             assert num_expert_group is not None and topk_group is not None
@@ -547,46 +515,11 @@ class FusedMoE(CustomOp):
         self.scoring_func = scoring_func
         self.routed_scaling_factor = routed_scaling_factor
         self.e_score_correction_bias = e_score_correction_bias
+        # TODO(bnell): end attributes
+
         self.apply_router_weight_on_input = apply_router_weight_on_input
         self.activation = activation
 
-        self._grouped_topk_impl: GroupedTopk | None = None
-        if self.use_grouped_topk:
-            assert self.num_expert_group is not None
-            assert self.topk_group is not None
-            self._grouped_topk_impl = GroupedTopk(
-                topk=self.top_k,
-                renormalize=self.renormalize,
-                num_expert_group=self.num_expert_group,
-                topk_group=self.topk_group,
-                scoring_func=self.scoring_func,
-                routed_scaling_factor=self.routed_scaling_factor,
-                num_fused_shared_experts=self.num_fused_shared_experts,
-            )
-
-        if self.scoring_func != "softmax" and not self.use_grouped_topk:
-            raise ValueError(
-                "Only softmax scoring function is supported for non-grouped topk."
-            )
-
-        # ToDo: Better logic to determine the routing method type
-        if routing_method_type is not None:
-            self.routing_method_type: RoutingMethodType = routing_method_type
-        else:
-            if scoring_func == "sigmoid":
-                if self.use_grouped_topk:
-                    self.routing_method_type = RoutingMethodType.DeepSeekV3
-                elif self.top_k == 1:
-                    self.routing_method_type = RoutingMethodType.Llama4
-            elif self.scoring_func == "softmax":
-                self.routing_method_type = (
-                    RoutingMethodType.Renormalize
-                    if not self.renormalize
-                    else RoutingMethodType.RenormalizeNaive
-                )
-            else:
-                self.routing_method_type = RoutingMethodType.TopK
-
         self.moe_config: FusedMoEConfig = FusedMoEConfig(
             num_experts=self.global_num_experts,
             experts_per_token=top_k,
@@ -637,8 +570,7 @@ class FusedMoE(CustomOp):
             # If you plan to add support for more quantization methods,
             # please refer to the implementation in `Fp8MoEMethod`.
             raise NotImplementedError(
-                f"EPLB is not supported {self.quant_method.__class__.__name__}. "
-                "EPLB is only supported for FP8 quantization for now."
+                f"EPLB is not supported {self.quant_method.__class__.__name__}."
             )
 
         moe_quant_params = {
@@ -663,7 +595,38 @@ class FusedMoE(CustomOp):
         self.batched_hidden_states: torch.Tensor | None = None
         self.batched_router_logits: torch.Tensor | None = None
 
-        self.router = FusedMoERouterImpl(self)
+        # TODO(bnell): in next PR move capture back to layer
+        capture: Callable[[torch.Tensor], None] | None = None
+        if (
+            self.vllm_config.model_config is not None
+            and self.vllm_config.model_config.enable_return_routed_experts
+        ):
+            # In dummy runs, the capturer is not initialized.
+            capturer = RoutedExpertsCapturer.get_instance()
+            if capturer is not None:
+                capture = lambda topk_ids: capturer.capture(self.layer_id, topk_ids)
+
+        self.router = create_fused_moe_router(
+            top_k=top_k,
+            global_num_experts=self.global_num_experts,
+            eplb_state=self.eplb_state,
+            renormalize=renormalize,
+            use_grouped_topk=use_grouped_topk,
+            num_expert_group=num_expert_group,
+            topk_group=topk_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,
+            num_fused_shared_experts=self.num_fused_shared_experts,
+            enable_eplb=enable_eplb,
+            # TODO(bnell): once we can construct the MK at init time, we
+            # can make this a value.
+            indices_type_getter=lambda: self.quant_method.topk_indices_dtype,
+            routing_method_type=routing_method_type,
+            capture=capture,
+        )
+        self.routing_method_type: RoutingMethodType = self.router.routing_method_type
 
     # Note: maybe_init_modular_kernel should only be called by
     # prepare_communication_buffer_for_model.
@@ -1492,9 +1455,9 @@ class FusedMoE(CustomOp):
         This is used later in forward pass, where we get the expert mapping
         and record the load metrics in `expert_load_view`.
         """
-        self.expert_load_view = expert_load_view[moe_layer_idx]
-        self.logical_to_physical_map = logical_to_physical_map[moe_layer_idx]
-        self.logical_replica_count = logical_replica_count[moe_layer_idx]
+        self.eplb_state.expert_load_view = expert_load_view[moe_layer_idx]
+        self.eplb_state.logical_to_physical_map = logical_to_physical_map[moe_layer_idx]
+        self.eplb_state.logical_replica_count = logical_replica_count[moe_layer_idx]
 
     def ensure_moe_quant_config_init(self):
         if self.quant_method.moe_quant_config is None:
@@ -1535,130 +1498,6 @@ class FusedMoE(CustomOp):
             device=torch.cuda.current_device(),
         )
 
-    def _select_experts(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Route the input hidden states to the top-k experts based on the
-        router logits.
-
-        Returns:
-                (topk_weights, topk_ids)
-                (tuple[torch.Tensor, torch.Tensor]):
-                The weights and expert ids.
-
-            **Compatibility**: When EPLB is not enabled, the returned ids are
-            equivalent to global logical ids, so should be compatible with
-            plain MoE implementations without redundant experts.
-        """
-        from vllm.model_executor.layers.fused_moe.fused_moe import (
-            fused_topk,
-            fused_topk_bias,
-        )
-
-        if self.enable_eplb:
-            if self.quant_method.supports_eplb:
-                if self.expert_load_view is None:
-                    raise ValueError(
-                        "enable_eplb=True requiere expert_load_view != None"
-                    )
-                if self.logical_to_physical_map is None:
-                    raise ValueError(
-                        "enable_eplb=True requiere logical_to_physical_map != None"
-                    )
-                if self.logical_replica_count is None:
-                    raise ValueError(
-                        "enable_eplb=True requiere logical_replica_count != None"
-                    )
-            else:
-                raise NotImplementedError(
-                    f"EPLB is not supported for {self.quant_method.method_name}."
-                )
-
-        def valid_grouping() -> bool:
-            # Check if num_experts is greater than num_expert_group
-            # and is divisible by num_expert_group
-            num_experts = router_logits.shape[-1]
-            if num_experts <= self.num_expert_group:
-                return False
-            return num_experts % self.num_expert_group == 0
-
-        indices_type = self.quant_method.topk_indices_dtype
-
-        # Check if we should use a routing simulation strategy
-        routing_strategy = envs.VLLM_MOE_ROUTING_SIMULATION_STRATEGY
-        if routing_strategy != "":
-            topk_weights, topk_ids = RoutingSimulator.simulate_routing(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-                strategy_name=routing_strategy,
-                top_k=self.top_k,
-                indices_type=indices_type,
-            )
-
-        # DeepSeekv2 uses grouped_top_k
-        elif self.use_grouped_topk and valid_grouping():
-            assert self._grouped_topk_impl is not None
-            topk_weights, topk_ids = self._grouped_topk_impl(
-                hidden_states=hidden_states,
-                gating_output=router_logits,
-                e_score_correction_bias=self.e_score_correction_bias,
-            )
-        elif self.e_score_correction_bias is not None:
-            topk_weights, topk_ids = fused_topk_bias(
-                hidden_states=hidden_states,
-                gating_output=router_logits,
-                e_score_correction_bias=self.e_score_correction_bias.data,
-                topk=self.top_k,
-                renormalize=self.renormalize,
-            )
-            if self.routed_scaling_factor != 1.0:
-                topk_weights *= self.routed_scaling_factor
-        elif self.custom_routing_function is None:
-            topk_weights, topk_ids, token_expert_indices = fused_topk(
-                hidden_states=hidden_states,
-                gating_output=router_logits,
-                topk=self.top_k,
-                renormalize=self.renormalize,
-                indices_type=indices_type,
-            )
-        else:
-            topk_weights, topk_ids = self.custom_routing_function(
-                hidden_states=hidden_states,
-                gating_output=router_logits,
-                topk=self.top_k,
-                renormalize=self.renormalize,
-            )
-
-        if self.enable_eplb:
-            topk_ids = eplb_map_to_physical_and_record(
-                topk_ids=topk_ids,
-                expert_load_view=self.expert_load_view,
-                logical_to_physical_map=self.logical_to_physical_map,
-                logical_replica_count=self.logical_replica_count,
-            )
-
-        if (indices_type is not None) and topk_ids.dtype != indices_type:
-            topk_ids = topk_ids.to(dtype=indices_type)
-
-        assert topk_ids.dtype == indices_type or indices_type is None
-
-        if (
-            self.vllm_config.model_config is not None
-            and self.vllm_config.model_config.enable_return_routed_experts
-        ):
-            # In dummy runs, the capturer is not initialized.
-            capturer = RoutedExpertsCapturer.get_instance()
-            if capturer is not None:  # in dummmy_run may be None
-                capturer.capture(  # noqa
-                    layer_id=self.layer_id,
-                    topk_ids=topk_ids,
-                )
-
-        return topk_weights, topk_ids
-
     def must_reduce_shared_expert_outputs(self) -> bool:
         """
         The shared_experts are typically computed using the RowParallelLinear
@@ -1761,8 +1600,12 @@ class FusedMoE(CustomOp):
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         assert self.batched_hidden_states is not None
         assert self.batched_router_logits is not None
-        assert self.batched_hidden_states.dtype == full_hidden_states.dtype
-        assert self.batched_router_logits.dtype == full_router_logits.dtype
+        assert self.batched_hidden_states.dtype == full_hidden_states.dtype, (
+            f"{self.batched_hidden_states.dtype} == {full_hidden_states.dtype}"
+        )
+        assert self.batched_router_logits.dtype == full_router_logits.dtype, (
+            f"{self.batched_router_logits.dtype} == {full_router_logits.dtype}"
+        )
         # Check size compatibility.
         assert self.batched_hidden_states.size(-1) == full_hidden_states.size(-1)
         assert self.batched_router_logits.size(-1) == full_router_logits.size(-1)
@@ -2080,15 +1923,8 @@ class FusedMoE(CustomOp):
             f"tp_size={self.tp_size},\n"
             f"ep_size={self.ep_size}, "
             f"reduce_results={self.reduce_results}, "
-            f"renormalize={self.renormalize}, "
-            f"use_grouped_topk={self.use_grouped_topk}"
         )
 
-        if self.use_grouped_topk:
-            s += f", num_expert_group={self.num_expert_group}, topk_group={self.topk_group}"  # noqa: E501
-
-        s += f", scoring_func='{self.scoring_func}', activation='{self.activation}'"  # noqa: E501
-
         return s