[MoE Refactor] Move the shared/fused expert output sum into MoERunnerBase (#35949)

Signed-off-by: Bill Nell <bnell@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>

vllm/model_executor/models/nemotron_h.py

@@ -216,7 +216,6 @@ class NemotronHMoE(nn.Module):
             top_k=config.num_experts_per_tok,
             hidden_size=self.moe_hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -231,6 +230,9 @@ class NemotronHMoE(nn.Module):
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
             routed_input_transform=self.fc1_latent_proj,
+            routed_output_transform=self.fc2_latent_proj,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
             router_logits_dtype=self.gate.out_dtype,
         )
 
@@ -244,38 +246,15 @@ class NemotronHMoE(nn.Module):
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
-        # SharedFusedMoE handles:
-        #   - shared experts (with original hidden_states)
-        #   - routed_input_transform (fc1_latent_proj) for latent MoE
-        #   - multistream parallelism between shared and routed experts
-        shared_output, final_hidden_states = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        # Fix FP16 overflow
-        # See DeepseekV2DecoderLayer for more details.
-        if hidden_states.dtype != torch.float16:
-            final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        # TODO: See SharedFusedMoE.apply_routed_input_transform
-        # for bandwidth optimization
-        if self.use_latent_moe:
-            final_hidden_states, _ = self.fc2_latent_proj(final_hidden_states)
-
-        if self.shared_experts is not None:
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 

vllm/model_executor/models/olmoe.py

@@ -98,7 +98,6 @@ class OlmoeMoE(nn.Module):
             top_k=top_k,
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
-            reduce_results=True,
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,

vllm/model_executor/models/openpangu.py

@@ -206,7 +206,6 @@ class OpenPanguMoE(nn.Module):
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             use_grouped_topk=True,
@@ -214,8 +213,8 @@ class OpenPanguMoE(nn.Module):
             topk_group=1,
             prefix=f"{prefix}.experts",
             scoring_func="sigmoid",
-            # we do scaling outside, set factor to 1.0 to avoid double mul
-            routed_scaling_factor=1.0,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_routed_scale_to_output=True,
             e_score_correction_bias=self.gate.e_score_correction_bias,
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
@@ -234,33 +233,15 @@ class OpenPanguMoE(nn.Module):
 
         router_logits, _ = self.gate(hidden_states)
 
-        fused_moe_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.shared_experts is None:
-            assert shared_output is None
-
-        if hidden_states.dtype != torch.float16:
-            final_hidden_states *= self.routed_scaling_factor
-        elif self.shared_experts is not None:
-            assert shared_output is not None
-            shared_output *= 1.0 / self.routed_scaling_factor
-
-        if self.shared_experts is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 

vllm/model_executor/models/param2moe.py

@@ -359,7 +359,6 @@ class Param2MoEMoEBlock(nn.Module):
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -388,24 +387,11 @@ class Param2MoEMoEBlock(nn.Module):
             self.gate.weight.float(),
         ).to(hidden_states.dtype)
 
-        final_hidden = self.experts(
+        expert_output = self.experts(
             hidden_states=hidden_states,
             router_logits=router_logits,
         )
 
-        if self.shared_experts is not None:
-            shared_output, expert_output = final_hidden
-        else:
-            shared_output, expert_output = None, final_hidden
-
-        if shared_output is not None:
-            expert_output = expert_output + shared_output
-
-        if self.tp_size > 1:
-            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
-                expert_output
-            )
-
         return expert_output.view(num_tokens, hidden_dim)
 
 

vllm/model_executor/models/phimoe.py

@@ -281,7 +281,6 @@ class PhiMoE(nn.Module):
             hidden_size=hidden_size,
             intermediate_size=intermediate_size,
             params_dtype=params_dtype,
-            reduce_results=True,
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,

vllm/model_executor/models/qwen2_moe.py

@@ -170,7 +170,6 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -187,12 +186,6 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.shared_expert is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         return final_hidden_states.view(orig_shape)
 

vllm/model_executor/models/qwen3_moe.py

@@ -212,7 +212,6 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_topk_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -234,22 +233,15 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
 
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
-        shared_out, fused_out = self.experts(
+        final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        final_hidden_states = (
-            shared_out + fused_out if shared_out is not None else fused_out
-        )
 
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         # return to 1d if input is 1d
         return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states

vllm/model_executor/models/qwen3_next.py

@@ -153,7 +153,6 @@ class Qwen3NextSparseMoeBlock(nn.Module):
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=getattr(config, "norm_topk_prob", True),
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -183,18 +182,11 @@ class Qwen3NextSparseMoeBlock(nn.Module):
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        if self.shared_expert is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-
         if self.is_sequence_parallel:
             final_hidden_states = tensor_model_parallel_all_gather(
                 final_hidden_states, 0
             )
             final_hidden_states = final_hidden_states[:num_tokens]
-        elif self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
-                final_hidden_states
-            )
 
         return final_hidden_states.view(orig_shape)
 

vllm/model_executor/models/sarvam.py

@@ -341,7 +341,6 @@ class SarvamMLAMoE(nn.Module):
             top_k=self.top_k,
             hidden_size=self.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=self.norm_expert_prob,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -370,20 +369,7 @@ class SarvamMLAMoE(nn.Module):
             router_logits=router_logits,
         )
 
-        if self.shared_experts is not None:
-            shared_output, expert_output = final_hidden
-        else:
-            shared_output, expert_output = None, final_hidden
-
-        if shared_output is not None:
-            expert_output = expert_output + shared_output
-
-        if self.tp_size > 1:
-            expert_output = self.experts.maybe_all_reduce_tensor_model_parallel(
-                expert_output
-            )
-
-        return expert_output.view(num_tokens, hidden_dim)
+        return final_hidden.view(num_tokens, hidden_dim)
 
 
 class SarvamMLABlock(nn.Module):

vllm/model_executor/models/step3_text.py

@@ -14,7 +14,6 @@ from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (
     get_pp_group,
     get_tensor_model_parallel_world_size,
-    tensor_model_parallel_all_reduce,
 )
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -71,7 +70,6 @@ class FusedMoEBlock(nn.Module):
             top_k=config.moe_top_k,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_expert_weight,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -94,8 +92,6 @@ class FusedMoEBlock(nn.Module):
         final_hidden_states = self.experts(
             hidden_states=hidden_states, router_logits=router_logits
         )
-        if self.tp_size > 1:
-            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
         return final_hidden_states.view(orig_shape)
 

vllm/model_executor/models/step3p5.py

@@ -379,7 +379,6 @@ class FusedMoEBlock(nn.Module):
             top_k=config.moe_top_k,
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
             renormalize=config.norm_expert_weight,
             quant_config=quant_config,
             activation=activation,
@@ -397,30 +396,16 @@ class FusedMoEBlock(nn.Module):
         hidden_states = hidden_states.view(-1, hidden_dim)
 
         if self.experts.is_internal_router:
-            # In this case, the gate/router runs inside the FusedMoE class
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=hidden_states
             )
         else:
-            # router_logits: (num_tokens, n_experts)
+            # TODO(bnell): this gate could be moved into the FusedMoE?
             router_logits, _ = self.gate(hidden_states)
-            fused_moe_out = self.experts(
+            final_hidden_states = self.experts(
                 hidden_states=hidden_states, router_logits=router_logits
             )
 
-        shared_output, final_hidden_states = fused_moe_out
-        if self.share_expert is None:
-            assert shared_output is None
-
-        if self.share_expert is not None:
-            assert shared_output is not None
-            final_hidden_states += shared_output
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
-                final_hidden_states
-            )
-
         return final_hidden_states.view(num_tokens, hidden_dim)
 
 

vllm/model_executor/models/transformers/moe.py

@@ -204,8 +204,6 @@ class MoEMixin(MixtureOfExperts):
         )
         assert intermediate_size is not None
 
-        # If there are shared experts, the results are
-        # reduced after mlp.forward() not inside FusedMoE
         num_shared_experts = getattr_iter(
             text_config,
             [
@@ -214,17 +212,6 @@ class MoEMixin(MixtureOfExperts):
             ],
             0,
         )
-        reduce_results = num_shared_experts == 0
-
-        def add_all_reduce(mlp: nn.Module):
-            """Adds an all-reduce to the output of `mlp.forward()`."""
-
-            class MLPWithAllReduce(mlp.__class__):
-                def forward(self, *args, **kwargs):
-                    output = super().forward(*args, **kwargs)
-                    return self.experts.maybe_all_reduce_tensor_model_parallel(output)
-
-            mlp.__class__ = MLPWithAllReduce
 
         # Unused kwargs since we use custom_routing_function:
         # - `scoring_func` and `e_score_correction_bias` only used for grouped
@@ -289,14 +276,11 @@ class MoEMixin(MixtureOfExperts):
                         if "bias" in experts_param_name:
                             has_bias = True
                             break
-                    # Double check there are no shared experts
-                    nonlocal reduce_results
-                    if reduce_results:
+                    # If the config does not specify num_shared_experts, but
+                    # the model has shared experts, we assume there is one.
+                    if self.num_shared_experts == 0:
                         for mlp_param_name, _ in mlp.named_parameters():
                             if "shared_expert" in mlp_param_name:
-                                reduce_results = False
-                                # If the config does not specify num_shared_experts, but
-                                # the model has shared experts, we assume there is one.
                                 self.num_shared_experts = 1
                                 break
                     # Replace experts module with FusedMoE
@@ -305,7 +289,6 @@ class MoEMixin(MixtureOfExperts):
                         top_k=top_k,
                         hidden_size=hidden_size,
                         intermediate_size=intermediate_size,
-                        reduce_results=reduce_results,
                         renormalize=renormalize,
                         # Hard coded because topk happens in Transformers
                         use_grouped_topk=False,
@@ -326,13 +309,6 @@ class MoEMixin(MixtureOfExperts):
                     self.moe_layers.append(fused_experts)
                     self.expert_weights.append(fused_experts.get_expert_weights())
                     self.num_moe_layers += 1
-                    # If results are not all-reduced in FusedMoE, ensure they
-                    # are all-reduced at the end of mlp.forward() if tensor
-                    # parallel or expert parallel is enabled
-                    if not reduce_results and (
-                        fused_experts.tp_size > 1 or fused_experts.ep_size > 1
-                    ):
-                        add_all_reduce(mlp)
                 else:
                     _recursive_replace(child_module, prefix=qual_name)