Fix Transformers backend tensor parallel for multimodal models

vllm/model_executor/models/transformers.py

@@ -505,30 +505,47 @@ def tensor_parallel(self):
         Apply the model's tensor parallelization plan.
         Currently only supports linear layers.
         """
-        tp_plan = getattr(self.model.config, "base_model_tp_plan", None) or {}
+        # Look for tp plans in all of the PreTrainedModels found in self.model
+        is_pretrained_model = lambda m: isinstance(m, PreTrainedModel)
+        supports_tp_plan = lambda m: m.config.base_model_tp_plan is not None
+        pretrained_models = filter(is_pretrained_model, self.model.modules())
+        models_with_tp_plan = filter(supports_tp_plan, pretrained_models)
 
-        if not tp_plan and self.tp_size > 1:
+        if not any(models_with_tp_plan) and self.tp_size > 1:
             raise ValueError(
                 f"{type(self.model)} does not support tensor parallel yet!")
 
-        # Some weight loaders expect linear layers to inherit from vLLM's
-        # LinearBase class, so we set a default style which causes any
-        # unspecified linear layers to be replaced with ReplicatedLinear
-        tp_plan[".*"] = "replicate"
-
-        def _tensor_parallel(module: nn.Module, prefix: str = ""):
+        def _tensor_parallel(module: nn.Module,
+                             prefix: str = "",
+                             tp_plan=None):
+            tp_plan = tp_plan or {}
+
+            # If the current module is a PreTrainedModel, set the tp_plan for
+            # all of its children
+            if isinstance(module, PreTrainedModel):
+                tp_plan = module.config.base_model_tp_plan or {}
+                tp_plan = {
+                    maybe_prefix(prefix, k): v
+                    for k, v in tp_plan.items()
+                }
+
+            # Some weight loaders expect linear layers to inherit from vLLM's
+            # LinearBase class, so we set a default style which causes any
+            # unspecified linear layers to be replaced with ReplicatedLinear
             for child_name, child_module in module.named_children():
                 qual_name = maybe_prefix(prefix, child_name)
-                for pattern, style in tp_plan.items():
-                    if re.match(pattern, qual_name) and isinstance(
-                            child_module, nn.Linear):
-                        new_module = replace_linear_class(
-                            child_module, style, self.quant_config)
-                        setattr(module, child_name, new_module)
-                        log_replacement(qual_name, child_module, new_module)
-                        break
+                if isinstance(child_module, nn.Linear):
+                    generator = (p for p in tp_plan if re.match(p, qual_name))
+                    pattern = next(generator, None)
+                    style = tp_plan.get(pattern, "replicate")
+                    new_module = replace_linear_class(child_module, style,
+                                                      self.quant_config)
+                    setattr(module, child_name, new_module)
+                    log_replacement(qual_name, child_module, new_module)
                 else:
-                    _tensor_parallel(child_module, prefix=qual_name)
+                    _tensor_parallel(child_module,
+                                     prefix=qual_name,
+                                     tp_plan=tp_plan)
 
         _tensor_parallel(self.model)