[fix]修复mori报错

vllm/config.py

@@ -4755,7 +4755,7 @@ class VllmConfig:
             batch_size_capture_list = []
             if self.model_config is not None and \
                 not self.model_config.enforce_eager:
-                if self.model_config.use_mla and self.compilation_config.full_cuda_graph and self.scheduler_config.max_num_seqs<=512:
+                if self.model_config.use_mla and self.scheduler_config.max_num_seqs<=512:
                     cuda_graph_sizes = [self.scheduler_config.max_num_seqs]
                 else:
                     cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes 

vllm/distributed/device_communicators/cuda_communicator.py

@@ -87,6 +87,8 @@ class CudaCommunicator(DeviceCommunicatorBase):
                 from .all2all import DeepEPLLAll2AllManager
                 self.all2all_manager = DeepEPLLAll2AllManager(self.cpu_group)
                 logger.info("Using DeepEP Low-Latency all2all manager.")
+            elif all2all_backend == "mori":
+                pass
             else:
                 raise ValueError(f"Unknown all2all backend: {all2all_backend}")
 

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

@@ -369,7 +369,7 @@ class MoriMoE(FusedMoE):
             apply_router_weight_on_input=self.apply_router_weight_on_input,
             use_nn_moe=self.use_nn_moe,
             num_local_tokens=dispatch_recv_num_token,
-            config_select_bs=hidden_states.shape[0]*self.ep_size/self.dp_size,
+            expect_m=hidden_states.shape[0],
             scales=dispatch_scales if self.use_int8_dispatch else None
         )
         

vllm/model_executor/layers/quantization/slimquant_w4a8_marlin.py

@@ -293,7 +293,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
                 activation: str = "silu",
                 use_nn_moe: Optional[bool] = False,
                 num_local_tokens: Optional[torch.Tensor] = None,
-                config_select_bs: Optional[int] = None,
+                expect_m: Optional[int] = None,
                 routed_scaling_factor: Optional[float] = None,
                 scales: Optional[torch.Tensor] = None,
                 **_
@@ -320,7 +320,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
                 a2_scale=layer.w2_input_scale,
                 use_nn_moe=use_nn_moe,
                 num_local_tokens=num_local_tokens,
-                config_select_bs=config_select_bs,
+                expect_m=expect_m,
             )
 
     def apply(