Revert "chore: update torch v2.5.1" (#2063)

python/sglang/srt/utils.py

@@ -410,23 +410,37 @@ def monkey_patch_vllm_dummy_weight_loader():
     Monkey patch the dummy weight loader in vllm to call process_weights_after_loading.
     """
 
-    from vllm.config import VllmConfig
     from vllm.model_executor.model_loader.loader import (
+        CacheConfig,
+        DeviceConfig,
         DummyModelLoader,
+        LoRAConfig,
+        ModelConfig,
+        ParallelConfig,
+        SchedulerConfig,
         _initialize_model,
         initialize_dummy_weights,
         nn,
         set_default_torch_dtype,
     )
 
-    def load_model(self, *, vllm_config: VllmConfig) -> nn.Module:
-        with set_default_torch_dtype(vllm_config.model_config.dtype):
-            with torch.device(vllm_config.device_config.device):
+    def load_model(
+        self,
+        *,
+        model_config: ModelConfig,
+        device_config: DeviceConfig,
+        lora_config: Optional[LoRAConfig],
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        cache_config: CacheConfig,
+    ) -> nn.Module:
+        with set_default_torch_dtype(model_config.dtype):
+            with torch.device(device_config.device):
                 model = _initialize_model(
-                    vllm_config.model_config,
+                    model_config,
                     self.load_config,
-                    vllm_config.lora_config,
-                    vllm_config.cache_config,
+                    lora_config,
+                    cache_config,
                 )
 
             for _, module in model.named_modules():
@@ -498,60 +512,6 @@ def maybe_set_triton_cache_manager() -> None:
         os.environ["TRITON_CACHE_MANAGER"] = manager
 
 
-def monkey_patch_vllm_model_config():
-    from typing import Dict, Set, Tuple, Union
-
-    from transformers import PretrainedConfig
-    from vllm.config import ModelConfig, TaskOption, _Task
-
-    def _resolve_task(
-        self,
-        task_option: Union[TaskOption, _Task],
-        hf_config: PretrainedConfig,
-    ) -> Tuple[Set[_Task], _Task]:
-
-        architectures = getattr(hf_config, "architectures", [])
-        if isinstance(architectures, str):
-            architectures = [architectures]
-
-        non_generation_models = {
-            "LlamaEmbeddingModel",
-            "MistralModel",
-            "LlamaForSequenceClassification",
-            "LlamaForSequenceClassificationWithNormal_Weights",
-            "InternLM2ForRewardModel",
-        }
-
-        is_generation = not any(arch in non_generation_models for arch in architectures)
-
-        auto_map = getattr(hf_config, "auto_map", {})
-        has_sequence_classification = any(
-            "ForSequenceClassification" in v for v in auto_map.values()
-        )
-
-        task_support: Dict[_Task, bool] = {
-            "generate": is_generation,
-            "embedding": (not is_generation) or has_sequence_classification,
-        }
-
-        supported_tasks_lst = [
-            task for task, is_supported in task_support.items() if is_supported
-        ]
-        supported_tasks = set(supported_tasks_lst)
-
-        if task_option not in supported_tasks:
-            msg = (
-                f"This model does not support the '{task_option}' task. "
-                f"Supported tasks: {supported_tasks}"
-            )
-            raise ValueError(msg)
-        selected_task = task_option
-
-        return supported_tasks, selected_task
-
-    setattr(ModelConfig, "_resolve_task", _resolve_task)
-
-
 class CustomCacheManager(FileCacheManager):
     # Adapted from: https://github.com/tdoublep/vllm/blob/3307522289fdfefe323b6c00d0db696651989a2f/vllm/triton_utils/custom_cache_manager.py
     def __init__(self, key, override=False, dump=False):