[FEAT] Add transformers backend support (#5929)

python/sglang/srt/model_loader/utils.py

@@ -2,12 +2,17 @@
 
 """Utilities for selecting and loading models."""
 import contextlib
+import logging
 from typing import Tuple, Type
 
 import torch
+import transformers
 from torch import nn
+from transformers.dynamic_module_utils import get_class_from_dynamic_module
 
-from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.configs.model_config import ModelConfig, ModelImpl
+
+logger = logging.getLogger(__name__)
 
 
 @contextlib.contextmanager
@@ -19,6 +24,61 @@ def set_default_torch_dtype(dtype: torch.dtype):
     torch.set_default_dtype(old_dtype)
 
 
+def resolve_transformers_arch(model_config: ModelConfig, architectures: list[str]):
+    for i, arch in enumerate(architectures):
+        if arch == "TransformersForCausalLM":
+            continue
+        auto_map: dict[str, str] = (
+            getattr(model_config.hf_config, "auto_map", None) or dict()
+        )
+        # Make sure that config class is always initialized before model class,
+        # otherwise the model class won't be able to access the config class,
+        # the expected auto_map should have correct order like:
+        # "auto_map": {
+        #     "AutoConfig": "<your-repo-name>--<config-name>",
+        #     "AutoModel": "<your-repo-name>--<config-name>",
+        #     "AutoModelFor<Task>": "<your-repo-name>--<config-name>",
+        # },
+        auto_modules = {
+            name: get_class_from_dynamic_module(
+                module, model_config.model_path, revision=model_config.revision
+            )
+            for name, module in sorted(auto_map.items(), key=lambda x: x[0])
+        }
+        model_module = getattr(transformers, arch, None)
+        if model_module is None:
+            if "AutoModel" not in auto_map:
+                raise ValueError(
+                    f"Cannot find model module. '{arch}' is not a registered "
+                    "model in the Transformers library (only relevant if the "
+                    "model is meant to be in Transformers) and 'AutoModel' is "
+                    "not present in the model config's 'auto_map' (relevant "
+                    "if the model is custom)."
+                )
+            model_module = auto_modules["AutoModel"]
+        if model_config.impl == ModelImpl.TRANSFORMERS:
+            if not model_module.is_backend_compatible():
+                raise ValueError(
+                    f"The Transformers implementation of {arch} is not "
+                    "compatible with vLLM."
+                )
+            architectures[i] = "TransformersForCausalLM"
+        if model_config.impl == ModelImpl.AUTO:
+            if not model_module.is_backend_compatible():
+                raise ValueError(
+                    f"{arch} has no SGlang implementation and the Transformers "
+                    "implementation is not compatible with SGLang."
+                )
+            logger.warning(
+                "%s has no SGLang implementation, falling back to Transformers "
+                "implementation. Some features may not be supported and "
+                "performance may not be optimal.",
+                arch,
+            )
+            architectures[i] = "TransformersForCausalLM"
+    return architectures
+
+
 def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
     from sglang.srt.models.registry import ModelRegistry
 
@@ -34,6 +94,12 @@ def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module],
     ):
         architectures = ["QuantMixtralForCausalLM"]
 
+    supported_archs = ModelRegistry.get_supported_archs()
+    is_native_supported = any(arch in supported_archs for arch in architectures)
+
+    if not is_native_supported or model_config.impl == ModelImpl.TRANSFORMERS:
+        architectures = resolve_transformers_arch(model_config, architectures)
+
     return ModelRegistry.resolve_model_cls(architectures)