1.优化大EP，合入grouped gemm

2.解决mtp >1 大EP推理all gather卡住问题

vllm/envs.py

@@ -164,6 +164,7 @@ if TYPE_CHECKING:
     VLLM_USE_FLASH_ATTN_PA: bool = False
     VLLM_USE_APEX_RN: bool = False
     VLLM_USE_GLOBAL_CACHE13: bool = False
+    VLLM_USE_ALLTOALL_EP: bool = False
 
 def get_default_cache_root():
     return os.getenv(
@@ -1089,7 +1090,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_GLOBAL_CACHE13":
         lambda: (os.environ.get("VLLM_USE_GLOBAL_CACHE13", "False").lower() in
                  ("true", "1")),
-
+    # vLLM will use all_to_all ep mode
+    "VLLM_USE_ALLTOALL_EP":
+        lambda: (os.environ.get("VLLM_USE_ALLTOALL_EP", "True").lower() in
+                 ("true", "1")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/forward_context.py

@@ -135,8 +135,10 @@ def set_forward_context(
     if need_to_track_batchsize:
         forward_start_time = time.perf_counter()
     dp_metadata: Optional[DPMetadata] = None
-    if vllm_config.parallel_config.data_parallel_size > 1 and (
-            attn_metadata is not None or num_tokens is not None):
+    dp_size = vllm_config.parallel_config.data_parallel_size
+    use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and vllm_config.parallel_config.enable_expert_parallel
+    if not use_all2all_ep and dp_size > 1 and (
+            attn_metadata is not None or num_tokens is not None) :
         dp_metadata = DPMetadata.make(vllm_config.parallel_config,
                                       attn_metadata, num_tokens or 0,
                                       num_tokens_across_dp)

vllm/model_executor/layers/fused_moe/ep_moe/ep_moe_utlis.py

@@ -327,7 +327,8 @@ def all_to_all(group, input, output_split_sizes, input_split_sizes):
         output = input.new_empty(
             size=[sum(output_split_sizes)] + list(input.size()[1:]),
             dtype=input.dtype,
-            device=torch.cuda.current_device(),
+            #device=torch.cuda.current_device(),
+            device=input.device,
         )
 
     torch.distributed.all_to_all_single(
@@ -336,6 +337,7 @@ def all_to_all(group, input, output_split_sizes, input_split_sizes):
         output_split_sizes=output_split_sizes,
         input_split_sizes=input_split_sizes,
         group=group,
+        async_op=True
     )
     return output
 

vllm/model_executor/layers/fused_moe/ep_moe/layer.py

@@ -18,6 +18,7 @@ from vllm.model_executor.layers.fused_moe.layer import FusedMoEMethodBase, Unqua
 from vllm.model_executor.layers.fused_moe.ep_moe.token_dispatcher import MoEAlltoAllTokenDispatcher
 from vllm.model_executor.layers.fused_moe.ep_moe.ep_moe_utlis import EpMoeConfig
 from vllm.utils import direct_register_custom_op
+from lightop import groupgemm
 
 
 logger = init_logger(__name__)
@@ -33,6 +34,7 @@ class UnquantizedEPGroupedGemmMethod(UnquantizedFusedMoEMethod):
         self.moe = moe
 
         self.rocm_aiter_moe_enabled = False # is_rocm_aiter_moe_enabled()
+        self.zero_token_count = None
 
     def apply(
         self,
@@ -55,40 +57,59 @@ class UnquantizedEPGroupedGemmMethod(UnquantizedFusedMoEMethod):
 
         # process MoE
         def custom_forward(layer, hidden_states, tokens_per_expert):
-            tokens_per_expert = tokens_per_expert.cpu().numpy()
-
-            outputs = []
-            start_idx = 0
-            for i, num_tokens in enumerate(tokens_per_expert):
-                end_idx = start_idx + num_tokens
-                if num_tokens == 0:
-                    continue
-                w1 = layer.w13_weight[i]
-                w2 = layer.w2_weight[i]
-                tokens_for_this_expert = hidden_states[start_idx:end_idx]
-
-                gateup_output = torch.matmul(tokens_for_this_expert, w1.T)
-
-                # Act
-                down_input = torch.zeros(
-                    gateup_output.shape[0],
-                    gateup_output.shape[1] // 2,
-                    device=gateup_output.device,
-                    dtype=hidden_states.dtype
-                )
-                torch.ops._C.silu_and_mul(down_input,
-                                gateup_output.view(-1, w1.shape[0]))
-
-                expert_out = torch.matmul(down_input, w2.T)
-
-                outputs.append(expert_out)
-                start_idx = end_idx
-
-            if len(outputs) > 0:
-                expert_output = torch.cat(outputs, dim=0)
+            if False:
+                tokens_per_expert = tokens_per_expert.cpu().numpy()
+                outputs = []
+                start_idx = 0
+                for i, num_tokens in enumerate(tokens_per_expert):
+                    end_idx = start_idx + num_tokens
+                    if num_tokens == 0:
+                        continue
+                    w1 = layer.w13_weight[i]
+                    w2 = layer.w2_weight[i]
+                    tokens_for_this_expert = hidden_states[start_idx:end_idx]
+
+                    gateup_output = torch.matmul(tokens_for_this_expert, w1)
+
+                    # Act
+                    down_input = torch.zeros(
+                        gateup_output.shape[0],
+                        gateup_output.shape[1] // 2,
+                        device=gateup_output.device,
+                        dtype=hidden_states.dtype
+                    )
+                    torch.ops._C.silu_and_mul(down_input,
+                                    gateup_output.view(-1, w1.shape[1]))
+
+                    expert_out = torch.matmul(down_input, w2)
+
+                    outputs.append(expert_out)
+                    start_idx = end_idx
+
+                if len(outputs) > 0:
+                    expert_output = torch.cat(outputs, dim=0)
+                else:
+                    assert hidden_states.numel() == 0, f"sorted_tokens: should be empty, but got {hidden_states.shape}"
+                    expert_output = hidden_states
             else:
-                assert hidden_states.numel() == 0, f"sorted_tokens: should be empty, but got {hidden_states.shape}"
-                expert_output = hidden_states
+                if self.zero_token_count is None:
+                    self.zero_token_count = torch.zeros(1, dtype=torch.int64, device=hidden_states.device)
+                total_tokens = tokens_per_expert.sum()
+                if total_tokens > self.zero_token_count:
+                    gateup_output = groupgemm(hidden_states, layer.w13_weight, tokens_per_expert, False)
+                    # Act
+                    down_input = torch.zeros(
+                        gateup_output.shape[0],
+                        gateup_output.shape[1] // 2,
+                        device=gateup_output.device,
+                        dtype=hidden_states.dtype
+                    )
+                    torch.ops._C.silu_and_mul(down_input,
+                                        gateup_output.view(-1, layer.w13_weight.shape[2]))
+                    expert_output = groupgemm(down_input, layer.w2_weight, tokens_per_expert, False)
+                else :
+                    expert_output = hidden_states
+                
 
             return expert_output
 
@@ -157,6 +178,8 @@ class EPMoE(FusedMoE):
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
         routed_scaling_factor: Optional[float] = None,
+        enable_eplb: bool = False,
+        num_redundant_experts: int = 0,
         moe_permute_fusion: bool = True,
         moe_shared_expert_overlap: bool = False
     ):
@@ -170,7 +193,9 @@ class EPMoE(FusedMoE):
                          e_score_correction_bias,
                          apply_router_weight_on_input,
                          activation, 
-                         routed_scaling_factor=routed_scaling_factor
+                         routed_scaling_factor=routed_scaling_factor,
+                         enable_eplb=enable_eplb,
+                         num_redundant_experts=num_redundant_experts,
                          )
         
         self.ep_moe_config: EpMoeConfig = EpMoeConfig.make(

vllm/model_executor/layers/fused_moe/ep_moe/token_dispatcher.py

@@ -24,6 +24,7 @@ from vllm.platforms import current_platform
 
 
 cuda_dtoh_stream = torch.cuda.Stream()
+cuda_dtoh_sync_event = torch.cuda.Event(enable_timing=False)
 
 class MoETokenDispatcher:
     """
@@ -137,7 +138,7 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
         self.output_splits = None
         # [tp_size]. Represents the number of tokens received by the current rank from
         # other TP ranks.
-        self.output_splits_tp = None
+        #self.output_splits_tp = None
         self.permute_idx_device = torch.device("cuda") if self.config.moe_permute_fusion else None
         input_chunk_idxs = torch.arange(
             self.num_experts * self.tp_size, device=self.permute_idx_device
@@ -211,6 +212,7 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
             if self.use_all_gather:
                 # Gather is not supported for some devices such as TPUs.
                 # Use all-gather instead.
+
                 num_global_tokens_per_expert = expert_parallel_all_gather(num_local_tokens_per_expert) \
                     .reshape(self.ep_size, self.tp_size, self.num_experts) \
                     .transpose(0, 1)
@@ -233,7 +235,7 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
             # [tp_size, ep_size] -> [tp_size]
             # self.output_splits_tp represents the number of tokens received by the current
             # rank from other TP rank.
-            self.output_splits_tp = num_global_tokens_per_rank.sum(axis=1)
+            #self.output_splits_tp = num_global_tokens_per_rank.sum(axis=1)
             # [tp_size, ep_size, num_local_experts] -> [num_local_experts]
             num_tokens_per_local_expert = num_global_tokens_per_local_expert.sum(dim=(0, 1))
 
@@ -317,11 +319,16 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
             num_out_tokens=self.num_out_tokens,
             fused=self.config.moe_permute_fusion
         )
-
+        
         # Perform expert parallel AlltoAll communication
-        tokens_per_expert = self._maybe_dtoh_and_synchronize(
-            "before_ep_alltoall", tokens_per_expert
-        )
+        # tokens_per_expert = self._maybe_dtoh_and_synchronize(
+        #     "before_ep_alltoall", tokens_per_expert
+        # )
+
+        ###test##############
+        #cuda_dtoh_stream.synchronize()
+        #cuda_dtoh_sync_event.synchronize()
+        ###test##############
 
         global_input_tokens = all_to_all(
             self.ep_group.device_group, permutated_local_input_tokens, self.output_splits, self.input_splits
@@ -331,9 +338,9 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
             self.shared_experts.linear_fc1_forward_and_act(global_input_tokens)
 
         # Permutation 2: Sort tokens by local expert.
-        tokens_per_expert = self._maybe_dtoh_and_synchronize(
-            "before_permutation_2", tokens_per_expert
-        )
+        # tokens_per_expert = self._maybe_dtoh_and_synchronize(
+        #     "before_permutation_2", tokens_per_expert
+        # )
         if self.num_local_experts > 1:
             global_input_tokens = sort_chunks_by_idxs(
                     global_input_tokens,
@@ -342,7 +349,7 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
                     fused=self.config.moe_permute_fusion,
                 )
 
-        tokens_per_expert = self._maybe_dtoh_and_synchronize("before_finish", tokens_per_expert)
+        #tokens_per_expert = self._maybe_dtoh_and_synchronize("before_finish", tokens_per_expert)
 
         return global_input_tokens, tokens_per_expert
 
@@ -444,9 +451,9 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
                 self.output_splits = maybe_move_tensor_to_cpu(
                     self.output_splits, as_numpy=True, record_stream=on_side_stream
                 )
-                self.output_splits_tp = maybe_move_tensor_to_cpu(
-                    self.output_splits_tp, as_numpy=True, record_stream=on_side_stream
-                )
+                # self.output_splits_tp = maybe_move_tensor_to_cpu(
+                #     self.output_splits_tp, as_numpy=True, record_stream=on_side_stream
+                # )
                 self.num_out_tokens = maybe_move_tensor_to_cpu(
                     self.num_out_tokens, record_stream=on_side_stream
                 )
@@ -455,6 +462,8 @@ class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
                         self.num_global_tokens_per_local_expert, record_stream=on_side_stream
                     )
 
+                #cuda_dtoh_sync_event.record()
+
         if point == self.cuda_sync_point:
             # Synchronize with the dtoh stream at self.cuda_sync_point.
             cuda_dtoh_stream.synchronize()

vllm/model_executor/layers/fused_moe/layer.py

@@ -772,12 +772,16 @@ class FusedMoE(torch.nn.Module):
         self.moe_config = moe
         self.quant_config = quant_config
 
-        self.quant_method = self.create_quant_method(moe, quant_config, prefix)
+        quant_method = self.create_quant_method(moe, quant_config, prefix)
+
+        assert quant_method is not None
+        assert isinstance(quant_method, FusedMoEMethodBase)
+        self.quant_method = quant_method
 
         if self.enable_eplb:
             from vllm.model_executor.layers.quantization.fp8 import (
                 Fp8MoEMethod)
-            if not isinstance(self.quant_method, Fp8MoEMethod):
+            if not isinstance(quant_method, Fp8MoEMethod):
                 # TODO: Add support for additional quantization methods.
                 # The implementation for other quantization methods does not
                 # contain essential differences, but the current quant API

vllm/model_executor/models/deepseek_mtp.py

@@ -11,6 +11,7 @@ import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
 
+import vllm.envs as envs
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -24,6 +25,7 @@ from vllm.sequence import IntermediateTensors
 from vllm.compilation.decorators import support_torch_compile
 from .deepseek_v2 import (DeepseekV2DecoderLayer,
                           get_spec_layer_idx_from_weight_name)
+from vllm.distributed import get_dp_group
 from .interfaces import SupportsPP
 from .utils import maybe_prefix
 from vllm import _custom_ops as ops
@@ -174,6 +176,10 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                                                      prefix, "model"))
         self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
 
+        parallel_config = vllm_config.parallel_config
+        dp_size = get_dp_group().world_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+
 
     def forward(
         self,
@@ -205,6 +211,10 @@ class DeepSeekMTP(nn.Module, SupportsPP):
             ("gate_up_proj", "up_proj", 1),
         ]
 
+        if self.use_all2all_ep:
+            ep_moe_shared_experts_keys = "mlp.shared_experts"
+            ep_moe_shared_experts_mapping = {ep_moe_shared_experts_keys:"mlp.experts.shared_experts"}
+
         expert_params_mapping = FusedMoE.make_expert_params_mapping(
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -233,6 +243,9 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                 if (("mlp.experts." in name) and name not in params_dict):
                     continue
                 name = name.replace(weight_name, param_name)
+
+                if self.use_all2all_ep:
+                    name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
@@ -248,6 +261,9 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                         continue
                     name = name.replace(weight_name, param_name)
 
+                    if self.use_all2all_ep:
+                        name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
+
                     param = params_dict[name]
                     weight_loader = param.weight_loader
                     weight_loader(param,
@@ -257,6 +273,8 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                                   expert_id=expert_id)
                     break
                 else:
+                    if self.use_all2all_ep:
+                        name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue

vllm/model_executor/models/deepseek_v2.py

@@ -155,9 +155,9 @@ class DeepseekV2MoE(nn.Module):
                                     self.n_local_physical_experts)
         
         dp_size = get_dp_group().world_size
-        self.use_ep_opt = dp_size > 1 and parallel_config.enable_expert_parallel
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
         
-        moe_cls = FusedMoE if not self.use_ep_opt else EPMoE
+        moe_cls = FusedMoE if not self.use_all2all_ep else EPMoE
         self.experts = moe_cls(
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
@@ -172,12 +172,14 @@ class DeepseekV2MoE(nn.Module):
             prefix=f"{prefix}.experts",
             scoring_func=config.scoring_func,
             e_score_correction_bias=self.gate.e_score_correction_bias,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts,
             routed_scaling_factor=self.routed_scaling_factor)
 
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *
                                  config.n_shared_experts)
-            shared_expert_cls = DeepseekV2MLP if not self.use_ep_opt else EPSharedExperts
+            shared_expert_cls = DeepseekV2MLP if not self.use_all2all_ep else EPSharedExperts
             self.shared_experts = shared_expert_cls(
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
@@ -187,8 +189,8 @@ class DeepseekV2MoE(nn.Module):
                 ),
                 prefix=f"{prefix}.shared_experts",
             )
-
-            self.experts.set_shared_experts(self.shared_experts)
+            if self.use_all2all_ep:
+                self.experts.set_shared_experts(self.shared_experts)
 
         from vllm.two_batch_overlap.two_batch_overlap import tbo_all_reduce
         self.tbo_all_reduce = tbo_all_reduce
@@ -196,13 +198,13 @@ class DeepseekV2MoE(nn.Module):
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         num_tokens, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
-        if not self.use_ep_opt:
+        if not self.use_all2all_ep:
             if self.n_shared_experts is not None:
                 shared_output = self.shared_experts(hidden_states)
 
         router_logits, _ = self.gate(hidden_states)
 
-        if not self.use_ep_opt:
+        if not self.use_all2all_ep:
             if hidden_states.dtype != torch.float16:
                 final_hidden_states = self.experts(
                     hidden_states=hidden_states,
@@ -216,7 +218,7 @@ class DeepseekV2MoE(nn.Module):
             final_hidden_states = self.experts(hidden_states=hidden_states,
                                                 router_logits=router_logits)
         
-        if not self.use_ep_opt:
+        if not self.use_all2all_ep:
             if shared_output is not None:
                 if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
                     final_hidden_states = final_hidden_states + shared_output
@@ -637,8 +639,6 @@ class DeepseekV2DecoderLayer(nn.Module):
             hidden_states=hidden_states,
         )
 
-        #ops.print_tensor(hidden_states)
-
         if hidden_states.dtype == torch.float16 and not self.dpsk_fp16_quick:
             # Fix FP16 overflow
             # We scale both hidden_states and residual before
@@ -808,7 +808,7 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
 
         parallel_config = vllm_config.parallel_config
         dp_size = get_dp_group().world_size
-        self.use_ep_opt = dp_size > 1 and parallel_config.enable_expert_parallel
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
 
     def set_eplb_state(
         self,
@@ -891,7 +891,7 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
             ("gate_up_proj", "up_proj", 1),
         ]
 
-        if self.use_ep_opt:
+        if self.use_all2all_ep:
             ep_moe_shared_experts_keys = "mlp.shared_experts"
             ep_moe_shared_experts_mapping = {ep_moe_shared_experts_keys:"mlp.experts.shared_experts"}
 
@@ -928,7 +928,7 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                     continue
                 name = name.replace(weight_name, param_name)
 
-                if self.use_ep_opt:
+                if self.use_all2all_ep:
                     name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
 
                 # Skip loading extra bias for GPTQ models.
@@ -957,7 +957,7 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                     # Instead, create a new variable
                     name_mapped = name.replace(weight_name, param_name)
 
-                    if self.use_ep_opt:
+                    if self.use_all2all_ep:
                         name_mapped = name_mapped.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
 
                     if is_pp_missing_parameter(name_mapped, self):
@@ -985,7 +985,7 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                         # So we simply skip it
                         continue
                     
-                    if self.use_ep_opt:
+                    if self.use_all2all_ep:
                         name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:

vllm/v1/spec_decode/eagle.py

@@ -86,6 +86,9 @@ class EagleProposer:
                                    1,
                                    device=device,
                                    dtype=torch.int32)
+        
+        self.dp_size = self.vllm_config.parallel_config.data_parallel_size
+        self.enable_expert_parallel = vllm_config.parallel_config.enable_expert_parallel
 
     def propose(
         self,
@@ -510,6 +513,14 @@ class EagleProposer:
                 self.hidden_states[:num_tokens],
             )
 
+            if self.dp_size > 1 and self.enable_expert_parallel and self.num_speculative_tokens > 1:
+                for _ in range(self.num_speculative_tokens - 1):
+                    self.model(
+                        self.input_ids[:num_tokens],
+                        self.positions[:num_tokens],
+                        self.hidden_states[:num_tokens],
+                    )
+
     def validate_same_kv_cache_group(self,
                                      kv_cache_config: KVCacheConfig) -> None:
         """

vllm/v1/worker/gpu_model_runner.py

@@ -319,6 +319,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # from the KV cache of `shared_kv_cache_layers[layer_name]`.
         self.shared_kv_cache_layers: dict[str, str] = {}
 
+        dp_size = self.vllm_config.parallel_config.data_parallel_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+
     def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
         """
         Update the order of requests in the batch based on the attention
@@ -1229,7 +1232,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # TODO(tms) : There are many cases where padding is enabled for
         # prefills, causing unnecessary and excessive padding of activations.
 
-        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
+        if dp_size == 1 or self.vllm_config.model_config.enforce_eager or self.use_all2all_ep:
             # Early exit.
             return 0, None