[BugFix] Fix weight loading for Mixtral with TP (#2208)

vllm/model_executor/models/mixtral.py

@@ -49,7 +49,6 @@ from vllm.model_executor.parallel_utils.parallel_state import (
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
-from vllm.model_executor.utils import set_weight_attrs
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -94,30 +93,6 @@ class MixtralMLP(nn.Module):
         return current_hidden_states
 
 
-class DummyModule(nn.Module):
-
-    def __init__(self) -> None:
-        super().__init__()
-
-        self.w1 = nn.Linear(0, 0, bias=False)
-        self.w2 = nn.Linear(0, 0, bias=False)
-        self.w3 = nn.Linear(0, 0, bias=False)
-
-        set_weight_attrs(self.w1.weight,
-                         {"weight_loader": self.dummy_weight_loader})
-        set_weight_attrs(self.w2.weight,
-                         {"weight_loader": self.dummy_weight_loader})
-        set_weight_attrs(self.w3.weight,
-                         {"weight_loader": self.dummy_weight_loader})
-
-    def forward(self, *args, **kwargs) -> None:
-        raise NotImplementedError()
-
-    def dummy_weight_loader(self, *args, **kwargs) -> None:  # pylint: disable=unused-argument
-        # Noop
-        return
-
-
 class MixtralMoE(nn.Module):
 
     def __init__(
@@ -147,7 +122,7 @@ class MixtralMoE(nn.Module):
                        config.hidden_size,
                        config.intermediate_size,
                        linear_method=linear_method)
-            if idx in self.expert_indicies else DummyModule()
+            if idx in self.expert_indicies else None
             for idx in range(self.num_total_experts)
         ])
         self.gate = ReplicatedLinear(config.hidden_size,
@@ -427,6 +402,10 @@ class MixtralForCausalLM(nn.Module):
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                # Skip experts that are not assigned to this worker.
+                if ("block_sparse_moe.experts." in name
+                        and name not in params_dict):
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)