[Misc] MoE ModularKernel : Introduce TopKWeightAndReduce (#20648)

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

tests/kernels/moe/test_pplx_moe.py

@@ -32,6 +32,8 @@ from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
 from vllm.model_executor.layers.fused_moe.fused_moe import get_default_config
 from vllm.model_executor.layers.fused_moe.modular_kernel import (
     FusedMoEModularKernel)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.platforms import current_platform
 from vllm.utils import round_up
 
@@ -371,6 +373,7 @@ def pplx_prepare_finalize(
         chunk_topk_weight,
         chunk_topk_ids,
         False,
+        weight_and_reduce_impl=TopKWeightAndReduceDelegate(),
     )
 
     torch.cuda.synchronize()

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -7,6 +7,8 @@ import torch
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.triton_utils import tl, triton
 
@@ -217,6 +219,10 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return False
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,

vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py

@@ -88,6 +88,25 @@ class BatchedTritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         return ((bdge is None or bdge.supports_expert_map())
                 and (bte is None or bte.supports_expert_map()))
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        bdge = self.batched_deep_gemm_experts
+        bte = self.batched_triton_experts
+        bdge_war = bdge.finalize_weight_and_reduce_impl() if bdge else None
+        bte_war = bte.finalize_weight_and_reduce_impl() if bte else None
+        is_bdge_war = bdge_war is not None
+        is_bte_war = bte_war is not None
+
+        if is_bdge_war and is_bte_war:
+            assert bdge_war == bte_war, (
+                "Both implementations should agree on WeightAndReduce impls. "
+                f"Got bdge_war: {bdge_war}, and bte_war: {bte_war}")
+
+        if bdge_war is not None:
+            return bdge_war
+
+        assert bte_war is not None
+        return bte_war
+
     def workspace_shapes(
         self,
         a: torch.Tensor,

vllm/model_executor/layers/fused_moe/cutlass_moe.py

@@ -11,6 +11,8 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (_fp8_perm,
                                                         _fp8_quantize,
                                                         _resize_cache)
@@ -255,6 +257,10 @@ class CutlassExpertsFp8(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return not self.use_batched_format
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -12,6 +12,8 @@ from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
     _moe_permute)
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache, per_token_group_quant_fp8)
 from vllm.utils import has_deep_gemm, round_up
@@ -85,6 +87,10 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return True
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     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

vllm/model_executor/layers/fused_moe/deepep_ht_prepare_finalize.py

@@ -6,8 +6,9 @@ import deep_ep
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceContiguous, TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     moe_kernel_quantize_input)
 
@@ -187,45 +188,25 @@ class DeepEPHTPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         return (expert_x, expert_x_scale, expert_tokens_meta, expert_topk_ids,
                 expert_topk_weights)
 
-    def _apply_weights_and_reduce(self, num_tokens: int,
-                                  fused_expert_output: torch.Tensor,
-                                  topk_weights: torch.Tensor,
-                                  apply_router_weight_on_input: bool,
-                                  output_dtype: torch.dtype):
-
-        hidden_dim = fused_expert_output.size(-1)
-        if fused_expert_output.ndim == 2:
-            fused_expert_output = fused_expert_output.view(
-                num_tokens, -1, hidden_dim)
-
-        if not apply_router_weight_on_input:
-            # The DeepEP combine kernels don't do the topk weight
-            # multiplication. We multiply the weights locally.
-            m_x_topk = fused_expert_output.size(0)
-            fused_expert_output.mul_(topk_weights.view(m_x_topk, -1, 1))
-
-        out = torch.empty((num_tokens, hidden_dim),
-                          device=fused_expert_output.device,
-                          dtype=output_dtype)
-        ops.moe_sum(fused_expert_output, out)
-
-        return out
-
     def finalize(self, output: torch.Tensor, fused_expert_output: torch.Tensor,
                  topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-                 apply_router_weight_on_input: bool) -> None:
+                 apply_router_weight_on_input: bool,
+                 weight_and_reduce_impl: mk.TopKWeightAndReduce) -> None:
 
         assert self.handle is not None
 
         # fused_expert_output can have 0 tokens - This happens when none of the
         # tokens from the all2all reach this EP rank.
         if fused_expert_output.numel() != 0:
-            fused_expert_output = self._apply_weights_and_reduce(
-                num_tokens=topk_ids.size(0),
+            if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
+                weight_and_reduce_impl = TopKWeightAndReduceContiguous()
+            fused_expert_output = weight_and_reduce_impl.apply(
+                output=None,
                 fused_expert_output=fused_expert_output,
                 topk_weights=topk_weights,
+                topk_ids=topk_ids,
                 apply_router_weight_on_input=apply_router_weight_on_input,
-                output_dtype=output.dtype)
+            )
 
         combined_x, _, event = self.buffer.combine(
             x=fused_expert_output,

vllm/model_executor/layers/fused_moe/deepep_ll_prepare_finalize.py

@@ -7,6 +7,8 @@ import torch
 
 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.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     moe_kernel_quantize_input, normalize_batched_scales_shape)
 
@@ -166,8 +168,11 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
 
     def finalize(self, output: torch.Tensor, fused_expert_output: torch.Tensor,
                  topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-                 apply_router_weight_on_input: bool) -> None:
-
+                 apply_router_weight_on_input: bool,
+                 weight_and_reduce_impl: mk.TopKWeightAndReduce) -> None:
+        assert isinstance(
+            weight_and_reduce_impl, TopKWeightAndReduceDelegate
+        ), ("Weight application and reduction happens in the combine kernel.")
         assert self.handle is not None
 
         combine_topk_weights = topk_weights

vllm/model_executor/layers/fused_moe/fused_batched_moe.py

@@ -11,6 +11,8 @@ 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 (
     get_config_dtype_str, try_get_optimal_moe_config)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate, TopKWeightAndReduceNaiveBatched)
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache, moe_kernel_quantize_input, normalize_batched_scales_shape,
     normalize_scales_shape)
@@ -600,25 +602,17 @@ class BatchedPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         apply_router_weight_on_input: bool,
+        weight_and_reduce_impl: mk.TopKWeightAndReduce,
     ) -> None:
-        num_tokens = topk_ids.size(0)
-        num_local_experts = fused_expert_output.size(0)
-        K = fused_expert_output.size(-1)
-        assert output.size(0) == num_tokens and output.size(1) == K
-
-        output.fill_(0)
-
-        first_expert = num_local_experts * self.rank
-        last_expert = first_expert + num_local_experts
-
-        for expert_id in range(first_expert, last_expert):
-            matching_tokens = topk_ids == expert_id
-            topks = torch.any(matching_tokens, dim=1).flatten()
-            rows = torch.count_nonzero(topks)
-            rhs = fused_expert_output[expert_id - first_expert, :rows, :]
-            if not apply_router_weight_on_input:
-                rhs.mul_(topk_weights[matching_tokens].view(rhs.size(0), 1))
-            output[topks] = output[topks] + rhs
+        if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
+            weight_and_reduce_impl = TopKWeightAndReduceNaiveBatched(self.rank)
+        weight_and_reduce_impl.apply(
+            output=output,
+            fused_expert_output=fused_expert_output,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+        )
 
 
 class NaiveBatchedExperts(mk.FusedMoEPermuteExpertsUnpermute):
@@ -670,6 +664,10 @@ class NaiveBatchedExperts(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return False
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,
@@ -877,6 +875,10 @@ class BatchedTritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return False
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -25,6 +25,8 @@ from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
     moe_align_block_size)
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     _resize_cache, moe_kernel_quantize_input)
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
@@ -1596,6 +1598,10 @@ class TritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
     def supports_expert_map(self) -> bool:
         return True
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        # Let PrepareAndFinalize::finalize() decide the impl.
+        return TopKWeightAndReduceDelegate()
+
     def workspace_shapes(
         self,
         a: torch.Tensor,

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -23,7 +23,7 @@ from vllm.utils import cdiv
 #
 # [Router] → [Quantize-Dispatch] → [Permute-Experts-Unpermute] → [Combine]
 #
-# Each component will be independent of the others except for
+# Each component will be independent of (but may inform) the others except for
 # [Quantize-Dispatch] and `[Combine] (see below). The components can then be
 # mixed and matched with so that DP+EP can be supported easily for multiple
 # MoE kernel implementations.
@@ -32,13 +32,19 @@ from vllm.utils import cdiv
 # * FusedMoEPrepareAndFinalize - an abstract base class for preparation of MoE
 #   inputs (e.g. quantization, distribution) and finalization of Moe outputs.
 #   The prepare method must take care of any needed quantization and the
-#   finalize method must apply weights and do the final reduction of the output.
+#   finalize method, informed by the FusedMoEPermuteExpertsUnpermute method,
+#   may apply weights and/or do the final reduction of the output.
 # * FusedMoEPermuteExpertsUnpermute - an abstract base class for the main fused
-#   MoE operation. One important feature to note is that this class does not
-#   apply topk weights or reduce the final output.
+#   MoE operation, i.e matmul + act_mul + optionally quant + matmul.
+#   Some FusedMoEPermuteExpertsUnpermute implementations may choose to do
+#   the weight application and/or reduction. The class communicates this
+#   to [Finalize] via a TopKWeightAndReduce object.
 # * FusedMoEModularKernel - an interface class that combines a
 #   FusedMoEPrepareAndFinalize and a FusedMoEPermuteExpertsUnpermute to
 #   provide the standard fused MoE kernel interface.
+# * TopKWeightAndReduce - A TopKWeightAndReduce implementation chosen
+#   by the FusedMoEPermuteExpertsUnpermute implementation that is passed
+#   on to [Finalize].
 #
 # [Quantize-Prepare] and [Finalize] functionality are bundled into a single
 # class `FusedMoEPrepareAndFinalize` since they could use collective
@@ -117,6 +123,24 @@ class ExpertTokensMetadata:
             expert_num_tokens_cpu=expert_num_tokens_cpu)
 
 
+class TopKWeightAndReduce(ABC):
+    """
+    An abstract base class for weight application and reduction implementations.
+    """
+
+    @abstractmethod
+    def apply(self, output: Optional[torch.Tensor],
+              fused_expert_output: torch.Tensor, topk_weights: torch.Tensor,
+              topk_ids: torch.Tensor,
+              apply_router_weight_on_input: bool) -> torch.Tensor:
+        """
+        Apply topk_weights to the fused_experts_outputs and/or reduce.
+        If an output tensor is not passed, it will be created in the
+        function.
+        """
+        raise NotImplementedError
+
+
 # TODO: pass FusedMoEParallelConfig in as ctor parameter?
 class FusedMoEPrepareAndFinalize(ABC):
     """
@@ -173,6 +197,7 @@ class FusedMoEPrepareAndFinalize(ABC):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         apply_router_weight_on_input: bool,
+        weight_and_reduce_impl: TopKWeightAndReduce,
     ) -> None:
         """
         Perform any combine plus apply weights and perform a reduction on the
@@ -184,6 +209,8 @@ class FusedMoEPrepareAndFinalize(ABC):
         - topk_ids: The topk_ids.
         - apply_router_weight_on_input: When False, apply the weights to
           fused_expert_output.
+        - weight_and_reduce_impl: An optional TopKWeightAndReduce
+          implementation.
         """
         raise NotImplementedError
 
@@ -323,6 +350,9 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
         return envs.VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING and \
           self.supports_chunking()
 
+    def finalize_weight_and_reduce_impl(self) -> TopKWeightAndReduce:
+        raise NotImplementedError
+
     @abstractmethod
     def apply(
         self,
@@ -702,7 +732,9 @@ class FusedMoEModularKernel(torch.nn.Module):
                 a2_scale=a2_scale,
                 expert_tokens_meta=expert_tokens_meta)
 
-        self.prepare_finalize.finalize(output, fused_out, topk_weights,
-                                       topk_ids, apply_router_weight_on_input)
+        self.prepare_finalize.finalize(
+            output, fused_out, topk_weights, topk_ids,
+            apply_router_weight_on_input,
+            self.fused_experts.finalize_weight_and_reduce_impl())
 
         return output

vllm/model_executor/layers/fused_moe/pplx_prepare_finalize.py

@@ -8,6 +8,8 @@ import torch
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     _validate_scale_shape, moe_kernel_quantize_input)
 from vllm.utils import cdiv, round_up
@@ -222,7 +224,12 @@ class PplxPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         apply_router_weight_on_input: bool,
+        weight_and_reduce_impl: mk.TopKWeightAndReduce,
     ) -> None:
+        assert isinstance(
+            weight_and_reduce_impl, TopKWeightAndReduceDelegate
+        ), ("Weight application and reduction happens in the combine kernel.")
+
         # This argument is optional
         # There's not much point setting this unless it is != topk_ids.size(0)
         bound_m: Optional[torch.Tensor] = None

vllm/model_executor/layers/fused_moe/prepare_finalize.py

@@ -6,8 +6,8 @@ import torch
 
 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.moe_permute_unpermute import (
-    _moe_unpermute_and_reduce)
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceContiguous, TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import (
     moe_kernel_quantize_input)
 
@@ -62,6 +62,13 @@ class MoEPrepareAndFinalizeNoEP(mk.FusedMoEPrepareAndFinalize):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         apply_router_weight_on_input: bool,
+        weight_and_reduce_impl: mk.TopKWeightAndReduce,
     ) -> None:
-        _moe_unpermute_and_reduce(output, fused_expert_output, None,
-                                  topk_weights, apply_router_weight_on_input)
+        if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
+            weight_and_reduce_impl = TopKWeightAndReduceContiguous()
+        weight_and_reduce_impl.apply(
+            output=output,
+            fused_expert_output=fused_expert_output,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            apply_router_weight_on_input=apply_router_weight_on_input)

vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py

@@ -0,0 +1,139 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+import vllm._custom_ops as ops
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+
+
+class TopKWeightAndReduceDelegate(mk.TopKWeightAndReduce):
+    """
+    Useful in the case when some FusedMoEPermuteExpertsUnpermute
+    implementation does not perform weight application and reduction
+    but cannot address the needs of all the compatible PrepareAndFinalize
+    implementations.
+    For example, BatchedTritonExperts is compatible with both
+    PplxPrepareAndFinalize and BatchedPrepareAndFinalize. PplxPrepareAndFinalize
+    does the weight-application + reduction as part of the pplx combine kernel.
+    But the BatchedPrepareAndFinalize needs an implementation. To facilitate
+    this case, the BatchedTritonExperts could use TopKWeightAndReduceDelegate 
+    so the PrepareAndFinalize implementations could choose how to
+    weight + reduce.
+    """
+
+    def __eq__(self, other):
+        return isinstance(other, TopKWeightAndReduceDelegate)
+
+    def apply(self, output: Optional[torch.Tensor],
+              fused_expert_output: torch.Tensor, topk_weights: torch.Tensor,
+              topk_ids: torch.Tensor,
+              apply_router_weight_on_input: bool) -> torch.Tensor:
+        raise RuntimeError("The caller is expected to choose an appropriate "
+                           "TopKWeightAndReduce implementation.")
+
+
+class TopKWeightAndReduceNoOP(mk.TopKWeightAndReduce):
+    """
+    The fused_experts outputs have already been weight applied and reduced.
+    This implementation is a no-op.
+    """
+
+    def __eq__(self, other):
+        return isinstance(other, TopKWeightAndReduceNoOP)
+
+    def apply(self, output: Optional[torch.Tensor],
+              fused_expert_output: torch.Tensor, topk_weights: torch.Tensor,
+              topk_ids: torch.Tensor,
+              apply_router_weight_on_input: bool) -> torch.Tensor:
+        # Relax this if an explicit copy is necessary. Note that,
+        # if a copy is employed we have to make sure that the
+        # tensors don't overlap
+        assert output is None
+        return fused_expert_output
+
+
+class TopKWeightAndReduceContiguous(mk.TopKWeightAndReduce):
+    """
+    TopKWeightAndReduce implementation for a fused_experts output
+    of shape (m, topk, K)
+    """
+
+    def __eq__(self, other):
+        return isinstance(other, TopKWeightAndReduceContiguous)
+
+    def apply(self, output: Optional[torch.Tensor],
+              fused_expert_output: torch.Tensor, topk_weights: torch.Tensor,
+              topk_ids: torch.Tensor,
+              apply_router_weight_on_input: bool) -> torch.Tensor:
+
+        m, num_topk = topk_ids.size()
+        k = fused_expert_output.size(-1)
+        if fused_expert_output.ndim == 2:
+            fused_expert_output = fused_expert_output.view(m, num_topk, k)
+
+        assert fused_expert_output.size() == (m, num_topk, k), (
+            f"Expected fused_expert_output size {(m, num_topk, k)}. But got "
+            f"{fused_expert_output.size()}")
+
+        if not apply_router_weight_on_input:
+            fused_expert_output.mul_(topk_weights.view(m, -1, 1))
+
+        if output is None:
+            output = torch.empty((m, k),
+                                 device=fused_expert_output.device,
+                                 dtype=fused_expert_output.dtype)
+        assert output.size() == (m, k), (
+            f"Expected output size {(m, k)}. But got {output.size()}")
+
+        ops.moe_sum(fused_expert_output, output)
+        return output
+
+
+class TopKWeightAndReduceNaiveBatched(mk.TopKWeightAndReduce):
+    """
+    TopKWeightAndReduce implementation for a fused_experts output
+    of shape (num_experts, batch_size, K)
+    """
+
+    def __init__(self, rank: int):
+        self.rank = rank
+
+    def __eq__(self, other):
+        return (isinstance(other, TopKWeightAndReduceNaiveBatched)
+                and (other.rank == self.rank))
+
+    def apply(self, output: Optional[torch.Tensor],
+              fused_expert_output: torch.Tensor, topk_weights: torch.Tensor,
+              topk_ids: torch.Tensor,
+              apply_router_weight_on_input: bool) -> torch.Tensor:
+        assert fused_expert_output.ndim == 3
+        num_tokens = topk_ids.size(0)
+        num_local_experts = fused_expert_output.size(0)
+        K = fused_expert_output.size(-1)
+
+        if output is None:
+            output = torch.zeros((num_tokens, K),
+                                 device=fused_expert_output.device,
+                                 dtype=fused_expert_output.dtype)
+        else:
+            output.fill_(0)
+
+        assert output.size() == (num_tokens, K), (
+            f"Expected output size {(num_tokens, K)}, but got {output.size()}")
+
+        first_expert = num_local_experts * self.rank
+        last_expert = first_expert + num_local_experts
+
+        for expert_id in range(first_expert, last_expert):
+            matching_tokens = topk_ids == expert_id
+            topks = torch.any(matching_tokens, dim=1).flatten()
+            rows = torch.count_nonzero(topks)
+            rhs = fused_expert_output[expert_id - first_expert, :rows, :]
+            if not apply_router_weight_on_input:
+                rhs.mul_(topk_weights[matching_tokens].view(rhs.size(0), 1))
+            output[topks] = output[topks] + rhs
+
+        return output

vllm/model_executor/layers/fused_moe/triton_deep_gemm_moe.py

@@ -69,6 +69,25 @@ class TritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         return ((dge is None or dge.supports_expert_map())
                 and (te is None or te.supports_expert_map()))
 
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        dge = self.deep_gemm_expert
+        te = self.triton_expert
+        dge_war = dge.finalize_weight_and_reduce_impl() if dge else None
+        te_war = te.finalize_weight_and_reduce_impl() if te else None
+        is_dge_war = dge_war is not None
+        is_te_war = te_war is not None
+
+        if is_dge_war and is_te_war:
+            assert dge_war == te_war, (
+                "Both implementations should agree on WeightAndReduce impls. "
+                f"Got dge_war: {dge_war}, and te_war: {te_war}")
+
+        if dge_war is not None:
+            return dge_war
+
+        assert te_war is not None
+        return te_war
+
     def workspace_shapes(
         self,
         a: torch.Tensor,