[GPTOSS][DP/EP][Marlin] Enable GPTOSS DP/EP using Marlin kernels (#25488)

Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Co-authored-by: mgoin <mgoin64@gmail.com>

docs/design/moe_kernel_features.md

@@ -93,6 +93,8 @@ To be used with a particular `FusedMoEPrepareAndFinalize` sub-class, MoE kernels
 | gpt oss triton               | standard              | N/A              | N/A           | <sup>5</sup>                                                | Y                     | Y       | [`triton_kernel_fused_experts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.triton_kernel_fused_experts],</br>[`OAITritonExperts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.OAITritonExperts]                                                                    |
 | deep gemm+triton<sup>2</sup> | standard,</br>batched | all<sup>1</sup>  | G(128),A,T    | silu, gelu                                                  | <sup>6</sup>          | Y       | [`TritonOrDeepGemmExperts`][vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe.TritonOrDeepGemmExperts],</br>[`BatchedTritonOrDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe.BatchedTritonOrDeepGemmExperts]                                                 |
 | marlin                       | standard              | <sup>3</sup>     | <sup>3</sup>  | silu,</br>swigluoai                                         | Y                     | N       | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.fused_marlin_moe]                                                                                                                                                                                                                |
+
+| marlin experts               | standard              | N/A              | N/A           | silu,</br>swigluoai                                         | Y                     | Y       | [`MarlinExperts`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.MarlinExperts]                                                                                                                                                                                                                      |
 | trtllm                       | standard              | mxfp4,</br>nvfp4 | G(16),G(32)   | <sup>5</sup>                                                | N                     | Y       | [`TrtLlmGenExperts`][vllm.model_executor.layers.fused_moe.trtllm_moe.TrtLlmGenExperts]                                                                                                                                                                                                                      |
 | pallas                       | standard              | N/A              | N/A           | silu                                                        | N                     | N       | [`fused_moe`][vllm.model_executor.layers.fused_moe.moe_pallas.fused_moe]                                                                                                                                                                                                                                    |
 | iterative                    | standard              | N/A              | N/A           | silu                                                        | N                     | N       | [`fused_moe`][vllm.model_executor.layers.fused_moe.moe_torch_iterative.fused_moe]                                                                                                                                                                                                                           |
@@ -114,6 +116,6 @@ The following table shows "families" of modular kernels that are intended to wor
 
 | backend                          | `FusedMoEPrepareAndFinalize` subclasses                    | `FusedMoEPermuteExpertsUnpermute` subclasses                                                                               |
 |----------------------------------|------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|
-| deepep_high_throughput,</br>pplx | `DeepEPHTPrepareAndFinalize`,</br>`PplxPrepareAndFinalize` | `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`BatchedTritonOrDeepGemmExperts`,</br>`CutlassBatchedExpertsFp8` |
-| deepep_low_latency               | `DeepEPLLPrepareAndFinalize`                               | `DeepGemmExperts`,</br>`TritonExperts`,</br>`TritonOrDeepGemmExperts`,</br>`CutlassExpertsFp8`                             |
+| deepep_high_throughput           | `DeepEPHTPrepareAndFinalize`                               |  `DeepGemmExperts`,</br>`TritonExperts`,</br>`TritonOrDeepGemmExperts`,</br>`CutlassExpertsFp8`, </br>`MarlinExperts`      |
+| deepep_low_latency,</br>pplx     | `DeepEPLLPrepareAndFinalize`,</br>`PplxPrepareAndFinalize` |  `BatchedDeepGemmExperts`,</br>`BatchedTritonExperts`,</br>`BatchedTritonOrDeepGemmExperts`,</br>`CutlassBatchedExpertsFp8`|
 | flashinfer                       | `FlashInferCutlassMoEPrepareAndFinalize`                   | `FlashInferExperts`                                                                                                        |

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -303,7 +303,7 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
         assert w2.size(1) == K
 
-        E, max_num_tokens, N, K, top_k_num = mk._moe_problem_size(
+        E, max_num_tokens, N, K, top_k_num = self.moe_problem_size(
             hidden_states, w1, w2, topk_ids)
 
         workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -712,7 +712,7 @@ class CutlassExpertsFp4(mk.FusedMoEPermuteExpertsUnpermute):
         expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
         apply_router_weight_on_input: bool,
     ):
-        e, m, n, k, _ = mk._moe_problem_size(hidden_states, w1, w2, topk_ids)
+        e, m, n, k, _ = self.moe_problem_size(hidden_states, w1, w2, topk_ids)
         n = w2.shape[2] * 2
 
         run_cutlass_moe_fp4(

vllm/model_executor/layers/fused_moe/fused_batched_moe.py

@@ -906,7 +906,7 @@ class BatchedTritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
         expert_num_tokens = expert_tokens_meta.expert_num_tokens
 
-        E, max_num_tokens, N, K, top_k_num = mk._moe_problem_size(
+        E, max_num_tokens, N, K, top_k_num = self.moe_problem_size(
             hidden_states, w1, w2, topk_ids)
 
         assert w1.size(0) == E

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -4,11 +4,18 @@
 from typing import Optional
 
 import torch
+from typing_extensions import override
 
 import vllm._custom_ops as ops
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.fused_moe import moe_align_block_size
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceNoOP)
+from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    marlin_make_workspace_new, maybe_warn_marlin_atomic_add)
+    marlin_make_workspace_new, marlin_moe_intermediate_size,
+    maybe_warn_marlin_atomic_add)
 from vllm.scalar_type import ScalarType, scalar_types
 from vllm.utils import direct_register_custom_op
 
@@ -20,7 +27,7 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
                      bias2: Optional[torch.Tensor],
                      w1_scale: torch.Tensor,
                      w2_scale: torch.Tensor,
-                     gating_output: torch.Tensor,
+                     gating_output: Optional[torch.Tensor],
                      topk_weights: torch.Tensor,
                      topk_ids: torch.Tensor,
                      quant_type_id: int,
@@ -37,7 +44,10 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
                      w1_zeros: Optional[torch.Tensor] = None,
                      w2_zeros: Optional[torch.Tensor] = None,
                      workspace: Optional[torch.Tensor] = None,
+                     intermediate_cache13: Optional[torch.Tensor] = None,
+                     intermediate_cache2: Optional[torch.Tensor] = None,
                      is_k_full: bool = True,
+                     output: Optional[torch.Tensor] = None,
                      inplace: bool = False) -> torch.Tensor:
     """
     This function computes a Mixture of Experts (MoE) layer using two sets of
@@ -49,8 +59,8 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
     - w2 (torch.Tensor): The second set of expert weights.
     - w1_scale (torch.Tensor): Scale to be used for w1.
     - w2_scale (torch.Tensor): Scale to be used for w2.
-    - gating_output (torch.Tensor): The output of the gating operation
-        (before softmax).
+    - gating_output (Optional[torch.Tensor]): The output of the gating
+        operation (before softmax).
     - g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.
     - g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.
     - sort_indices1 (Optional[torch.Tensor]): The first act_order input
@@ -78,8 +88,9 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
     num_bits = 4 if quant_type in bit4_scalar_types else 8
 
     # Check constraints.
-    assert hidden_states.shape[0] == gating_output.shape[
-        0], "Number of tokens mismatch"
+    if gating_output is not None:
+        assert hidden_states.shape[0] == gating_output.shape[
+            0], "Number of tokens mismatch"
     assert hidden_states.shape[
         1] == w1.shape[1] * 16, "Hidden size mismatch w1"
     assert hidden_states.shape[1] == w2.shape[2] // (
@@ -93,7 +104,7 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
 
     M, K = hidden_states.shape
     E = w1.shape[0]
-    N = w2.shape[1] * 16
+    N = marlin_moe_intermediate_size(w1, w2)
     topk = topk_ids.shape[1]
 
     # M block size selection logic
@@ -111,20 +122,24 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
     if workspace is None:
         workspace = marlin_make_workspace_new(hidden_states.device, 4)
 
-    intermediate_cache2 = torch.empty(
-        (M * topk_ids.shape[1], N),
-        device=hidden_states.device,
-        dtype=hidden_states.dtype,
-    )
-    intermediate_cache13 = torch.empty(
-        (M * topk_ids.shape[1] * max(2 * N, K), ),
-        device=hidden_states.device,
-        dtype=hidden_states.dtype,
-    )
-    intermediate_cache1 = intermediate_cache13[:M * topk_ids.shape[1] * 2 * N]
-    intermediate_cache1 = intermediate_cache1.view(-1, 2 * N)
-    intermediate_cache3 = intermediate_cache13[:M * topk_ids.shape[1] * K]
-    intermediate_cache3 = intermediate_cache3.view(-1, K)
+    if intermediate_cache2 is None:
+        intermediate_cache2 = torch.empty(
+            (M * topk, N),
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+
+    if intermediate_cache13 is None:
+        intermediate_cache13 = torch.empty(
+            (M * topk * max(2 * N, K), ),
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+
+    intermediate_cache1 = _resize_cache(intermediate_cache13,
+                                        (M * topk, 2 * N))
+    intermediate_cache3 = _resize_cache(intermediate_cache13, (M * topk, K))
+    intermediate_cache2 = _resize_cache(intermediate_cache2, (M * topk, N))
 
     maybe_warn_marlin_atomic_add(hidden_states.device, hidden_states.dtype)
     use_atomic_add = hidden_states.dtype == torch.half or \
@@ -200,10 +215,9 @@ def fused_marlin_moe(hidden_states: torch.Tensor,
         use_fp32_reduce=True,
         is_zp_float=False).view(-1, topk, K)
 
-    output = hidden_states if inplace else torch.empty_like(hidden_states)
-    return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape),
-                     dim=1,
-                     out=output)
+    if output is None:
+        output = hidden_states if inplace else torch.empty_like(hidden_states)
+    return torch.sum(intermediate_cache3.view(-1, topk, K), dim=1, out=output)
 
 
 def fused_marlin_moe_fake(hidden_states: torch.Tensor,
@@ -211,7 +225,7 @@ def fused_marlin_moe_fake(hidden_states: torch.Tensor,
                           w2: torch.Tensor,
                           w1_scale: torch.Tensor,
                           w2_scale: torch.Tensor,
-                          gating_output: torch.Tensor,
+                          gating_output: Optional[torch.Tensor],
                           topk_weights: torch.Tensor,
                           topk_ids: torch.Tensor,
                           quant_type_id: int,
@@ -227,7 +241,10 @@ def fused_marlin_moe_fake(hidden_states: torch.Tensor,
                           w1_zeros: Optional[torch.Tensor] = None,
                           w2_zeros: Optional[torch.Tensor] = None,
                           workspace: Optional[torch.Tensor] = None,
+                          intermediate_cache13: Optional[torch.Tensor] = None,
+                          intermediate_cache2: Optional[torch.Tensor] = None,
                           is_k_full: bool = True,
+                          output: Optional[torch.Tensor] = None,
                           inplace: bool = False) -> torch.Tensor:
     return torch.empty_like(hidden_states)
 
@@ -237,3 +254,124 @@ direct_register_custom_op(
     op_func=fused_marlin_moe,
     fake_impl=fused_marlin_moe_fake,
 )
+
+
+class MarlinExperts(mk.FusedMoEPermuteExpertsUnpermute):
+
+    def __init__(self, quant_config: FusedMoEQuantConfig):
+        # TODO (varun) : Enable activation quantization
+        assert quant_config.use_mxfp4_w4a16, "Supports only mxfp4_w4a16"
+        super().__init__(quant_config)
+
+    @override
+    def moe_problem_size(
+        self,
+        a1: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+    ) -> tuple[int, int, int, int, int]:
+        assert w1.dim() == 3 and w2.dim() == 3
+
+        E = w1.size(0)
+        K = a1.size(-1)
+        N = marlin_moe_intermediate_size(w1, w2)
+
+        if a1.dim() == 2:
+            # Make sure we are using the correct a1 (pre-permute).
+            assert topk_ids.size(0) == a1.size(0), \
+                f"{topk_ids.size(0)} != {a1.size(0)}"
+            M = a1.size(0)
+        else:
+            assert a1.dim() == 3
+            assert a1.size(0) == E, f"{a1.size(0)} == {E}"
+            M = a1.size(1)  # This is max_num_tokens
+
+        assert topk_ids.dim() == 2
+        topk = topk_ids.size(1)
+
+        return E, M, N, K, topk
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    @property
+    def activation_formats(
+        self
+    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
+        return (mk.FusedMoEActivationFormat.Standard,
+                mk.FusedMoEActivationFormat.Standard)
+
+    def supports_chunking(self) -> bool:
+        return True
+
+    def workspace_shapes(
+        self, a: torch.Tensor, aq: torch.Tensor, M: int, N: int, K: int,
+        topk: int, global_num_experts: int, local_num_experts: int,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata]
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
+        # Modular Kernel provisions output buffer from workspace1. However in
+        # the fused_marlin_moe() function, the final torch.sum(), is defined
+        # essentially as,
+        # `torch.sum(workspace1, dim=1, out=output)`
+        # Having overlapping input and output tensors for torch.sum seems
+        # error prone and depends on how the torch.sum is implemented.
+        # For this reason we swap let the output buffer provision from
+        # workspace2.
+
+        # Workspace/IntermediateCache allocation matching fused_marlin_moe()
+        #workspace1 = (M * topk * max(2 * N, K),)
+        #workspace2 = (M * topk, N)
+
+        # Workspace/IntermediateCache allocation accounting for output buffer
+        # provisioning
+        workspace1 = (M * topk, max(N, K))
+        workspace2 = (M * topk * max(2 * N, K), )
+        output = (M, K)
+
+        return (workspace1, workspace2, output, a.dtype)
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: str,
+        global_num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        a1q_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
+        apply_router_weight_on_input: bool,
+    ):
+        assert self.w1_scale is not None
+        assert self.w2_scale is not None
+        return fused_marlin_moe(
+            hidden_states=hidden_states,
+            w1=w1,
+            w2=w2,
+            bias1=self.w1_bias,
+            bias2=self.w2_bias,
+            w1_scale=self.w1_scale,
+            w2_scale=self.w2_scale,
+            gating_output=None,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            quant_type_id=scalar_types.float4_e2m1f.id,  # works only for w4a16
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_num_experts,
+            activation=activation,
+            expert_map=expert_map,
+            output=output,
+            # Workspaces are swapped in workspace_shapes() to account for proper
+            # output buffer allocation. Please refer to workspace_shapes().
+            intermediate_cache13=workspace2,
+            intermediate_cache2=workspace13)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1780,7 +1780,7 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
             torch.float32, torch.float16, torch.bfloat16, torch.float8_e4m3fn
         ]
 
-        E, num_tokens, N, K, top_k_num = mk._moe_problem_size(
+        E, num_tokens, N, K, top_k_num = self.moe_problem_size(
             hidden_states, w1, w2, topk_ids)
 
         if global_num_experts == -1:

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -55,46 +55,6 @@ from vllm.v1.worker.ubatching import (dbo_current_ubatch_id, dbo_enabled,
 #
 
 
-def _moe_problem_size(
-    a1: torch.Tensor,
-    w1: torch.Tensor,
-    w2: torch.Tensor,
-    topk_ids: torch.Tensor,
-) -> tuple[int, int, int, int, int]:
-    """
-    Extract the MoE problem size from the given tensor arguments:
-    - a: The hidden states, input to the MoE layer.
-    - w1: The first set of expert weights.
-    - w2: The second set of expert weights.
-    - topk_ids: The topk ids.
-
-    Note: extracting the problem shape from the weight and activation tensors is
-    not obvious.  It needs to be done this way specifically due to subtle issues
-    with particular kernels, e.g. the int4 kernels divide the trailing dimension
-    by two, so it's not "correct" to extract N or K from the trailing dimension
-    of w1 or w2.  Similarly, some kernels transpose the weights, so this needs
-    to be kept in mind.
-    """
-    assert w1.dim() == 3 and w2.dim() == 3
-    E, N, _ = w1.size()
-    K = a1.size(-1)
-
-    if a1.dim() == 2:
-        # Make sure we are using the correct a1 (pre-permute).
-        assert topk_ids.size(0) == a1.size(0), \
-            f"{topk_ids.size(0)} != {a1.size(0)}"
-        M = a1.size(0)
-    else:
-        assert a1.dim() == 3
-        assert a1.size(0) == E, f"{a1.size(0)} == {E}"
-        M = a1.size(1)  # This is max_num_tokens
-
-    assert topk_ids.dim() == 2
-    topk = topk_ids.size(1)
-
-    return E, M, N, K, topk
-
-
 class FusedMoEActivationFormat(Enum):
     """
     The standard activation format (num_tokens, hidden dim).
@@ -391,6 +351,50 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
         """
         raise NotImplementedError
 
+    def moe_problem_size(
+        self,
+        a1: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+    ) -> tuple[int, int, int, int, int]:
+        """
+        Extract the MoE problem size from the given tensor arguments:
+        - a: The hidden states, input to the MoE layer.
+        - w1: The first set of expert weights.
+        - w2: The second set of expert weights.
+        - topk_ids: The topk ids.
+
+        Note: extracting the problem shape from the weight and activation
+        tensors is not obvious.  It needs to be done this way specifically
+        due to subtle issues with particular kernels, e.g. the int4 kernels
+        divide the trailing dimension by two, so it's not "correct" to
+        extract N or K from the trailing dimension of w1 or w2.  Similarly,
+        some kernels transpose the weights, so this needs to be kept in mind.
+
+        Note: This implementation covers most cases. However, if experts
+        require a specialized implementation, like MarlinExperts, they are free
+        to override this function.
+        """
+        assert w1.dim() == 3 and w2.dim() == 3
+        E, N, _ = w1.size()
+        K = a1.size(-1)
+
+        if a1.dim() == 2:
+            # Make sure we are using the correct a1 (pre-permute).
+            assert topk_ids.size(0) == a1.size(0), \
+                f"{topk_ids.size(0)} != {a1.size(0)}"
+            M = a1.size(0)
+        else:
+            assert a1.dim() == 3
+            assert a1.size(0) == E, f"{a1.size(0)} == {E}"
+            M = a1.size(1)  # This is max_num_tokens
+
+        assert topk_ids.dim() == 2
+        topk = topk_ids.size(1)
+
+        return E, M, N, K, topk
+
     #
     # Various helpers for accessing quantization parameters from the
     # quant_config.
@@ -674,7 +678,8 @@ class FusedMoEModularKernel(torch.nn.Module):
         apply_router_weight_on_input: bool,
     ) -> torch.Tensor:
 
-        _, M, N, K, top_k = _moe_problem_size(a1q, w1, w2, topk_ids)
+        _, M, N, K, top_k = self.fused_experts.moe_problem_size(
+            a1q, w1, w2, topk_ids)
 
         (workspace13_shape, workspace2_shape, fused_out_shape,
          workspace_dtype) = self.fused_experts.workspace_shapes(
@@ -737,7 +742,8 @@ class FusedMoEModularKernel(torch.nn.Module):
         apply_router_weight_on_input: bool,
     ) -> torch.Tensor:
 
-        _, M, N, K, top_k = _moe_problem_size(a1q, w1, w2, topk_ids)
+        _, M, N, K, top_k = self.fused_experts.moe_problem_size(
+            a1q, w1, w2, topk_ids)
 
         CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
         num_chunks = cdiv(M, CHUNK_SIZE)

vllm/model_executor/layers/quantization/mxfp4.py

@@ -15,6 +15,7 @@ from vllm.model_executor.layers.fused_moe import modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig, mxfp4_w4a4_moe_quant_config,
     mxfp4_w4a16_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.fused_marlin_moe import MarlinExperts
 from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
     OAITritonExperts)
 from vllm.model_executor.layers.fused_moe.trtllm_moe import TrtLlmGenExperts
@@ -92,7 +93,7 @@ def get_mxfp4_backend():
                 "Please `pip install vllm[flashinfer]` for best results.")
 
         # If FlashInfer is not available, try either Marlin or Triton
-        if current_platform.get_device_capability(
+        if envs.VLLM_MXFP4_USE_MARLIN or current_platform.get_device_capability(
         )[0] < 9 or not has_triton_kernels() or not is_torch_equal_or_newer(
                 "2.8.0"):
             logger.info_once("Using Marlin backend")
@@ -646,9 +647,13 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
 
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
-            return None
-
-        if self.mxfp4_backend == Mxfp4Backend.TRITON:
+            return mxfp4_w4a16_moe_quant_config(
+                w1_bias=layer.w13_bias,
+                w2_bias=layer.w2_bias,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+            )
+        elif self.mxfp4_backend == Mxfp4Backend.TRITON:
             w1_scale = self.w13_precision_config
             w2_scale = self.w2_precision_config
             return mxfp4_w4a16_moe_quant_config(
@@ -690,6 +695,8 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 }
                 return TrtLlmGenExperts(self.moe, self.moe_quant_config,
                                         **kwargs)
+            elif (self.mxfp4_backend == Mxfp4Backend.MARLIN):
+                return MarlinExperts(self.moe_quant_config)
             else:
                 return OAITritonExperts(self.moe_quant_config)
 
@@ -782,6 +789,29 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         if enable_eplb:
             raise NotImplementedError("EPLB is not supported for mxfp4")
 
+        if self.fused_experts is not None:
+            return self._route_and_experts(
+                layer,
+                x,
+                router_logits,
+                top_k,
+                renormalize,
+                use_grouped_topk,
+                topk_group,
+                num_expert_group,
+                global_num_experts,
+                expert_map,
+                custom_routing_function,
+                scoring_func,
+                e_score_correction_bias,
+                apply_router_weight_on_input,
+                activation,
+                enable_eplb,
+                expert_load_view,
+                logical_to_physical_map,
+                logical_replica_count,
+            )
+
         if self.mxfp4_backend == Mxfp4Backend.MARLIN:
             topk_weights, topk_ids, _ = FusedMoE.select_experts(
                 hidden_states=x,
@@ -815,29 +845,6 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 activation=activation,
                 expert_map=expert_map)
 
-        if self.fused_experts is not None:
-            return self._route_and_experts(
-                layer,
-                x,
-                router_logits,
-                top_k,
-                renormalize,
-                use_grouped_topk,
-                topk_group,
-                num_expert_group,
-                global_num_experts,
-                expert_map,
-                custom_routing_function,
-                scoring_func,
-                e_score_correction_bias,
-                apply_router_weight_on_input,
-                activation,
-                enable_eplb,
-                expert_load_view,
-                logical_to_physical_map,
-                logical_replica_count,
-            )
-
         assert _can_support_mxfp4(
             use_grouped_topk, topk_group, num_expert_group, expert_map,
             custom_routing_function, e_score_correction_bias,

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -187,6 +187,16 @@ def check_moe_marlin_supports_layer(layer: LinearBase, group_size: int) \
         supports_router_weight and supports_activation
 
 
+def marlin_moe_intermediate_size(w1_packed: torch.Tensor,
+                                 w2_packed: torch.Tensor):
+    """
+    Given Marlin packed weight matrices w1_packed, and w2_packed,
+    return the MoE intermediate size N 
+    """
+    marlin_tile_size = 16
+    return w2_packed.size(1) * marlin_tile_size
+
+
 def marlin_make_workspace(output_size_per_partition: int,
                           device: torch.device) -> torch.Tensor:
     max_workspace_size = (output_size_per_partition //