update utils.py

vllm/model_executor/model_loader/utils.py

@@ -33,52 +33,45 @@ def set_default_torch_dtype(dtype: torch.dtype):
 
 def is_transformers_impl_compatible(
         arch: str,
-        module: Optional["transformers.PreTrainedModel"] = None) -> bool:
+        module: Optional[transformers.PreTrainedModel] = None) -> bool:
     mod = module or getattr(transformers, arch, None)
     if mod is None:
         return False
-    return mod.is_backend_compatible()
+    if hasattr(mod, "supports_backend"):
+        return mod.is_backend_compatible()
+    else:
+        return mod._supports_flex_attn
 
 
-def resolve_transformers_arch(model_config: ModelConfig,
-                              architectures: list[str]):
+def resolve_transformers_fallback(model_config: ModelConfig,
+                                  architectures: list[str]):
     for i, arch in enumerate(architectures):
-        if arch == "TransformersForCausalLM":
+        if arch == "TransformersModel":
             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)
-            for name, module in sorted(auto_map.items(), key=lambda x: x[0])
-        }
-        custom_model_module = auto_modules.get("AutoModel")
+        custom_module = None
+        auto_map = getattr(model_config.hf_config, "auto_map", None)
+        if auto_map is not None and "AutoModel" in auto_map:
+            custom_module = get_class_from_dynamic_module(
+                model_config.hf_config.auto_map["AutoModel"],
+                model_config.model)
         # TODO(Isotr0py): Further clean up these raises.
         # perhaps handled them in _ModelRegistry._raise_for_unsupported?
         if model_config.model_impl == ModelImpl.TRANSFORMERS:
-            if not is_transformers_impl_compatible(arch, custom_model_module):
+            if not is_transformers_impl_compatible(arch, custom_module):
                 raise ValueError(
                     f"The Transformers implementation of {arch} is not "
                     "compatible with vLLM.")
-            architectures[i] = "TransformersForCausalLM"
+            architectures[i] = "TransformersModel"
         if model_config.model_impl == ModelImpl.AUTO:
-            if not is_transformers_impl_compatible(arch, custom_model_module):
+            if not is_transformers_impl_compatible(arch, custom_module):
                 raise ValueError(
                     f"{arch} has no vLLM implementation and the Transformers "
-                    "implementation is not compatible with vLLM. Try setting "
-                    "VLLM_USE_V1=0.")
+                    "implementation is not compatible with vLLM.")
             logger.warning(
                 "%s has no vLLM implementation, falling back to Transformers "
                 "implementation. Some features may not be supported and "
                 "performance may not be optimal.", arch)
-            architectures[i] = "TransformersForCausalLM"
+            architectures[i] = "TransformersModel"
     return architectures
 
 
@@ -92,7 +85,7 @@ def get_model_architecture(
                                 'Qwen2MoeForCausalLM', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 
                                 'BaichuanForCausalLM', 'BloomForCausalLM', 'MedusaModel', 'MixtralForCausalLM', 
                                 'MLPSpeculatorPreTrainedModel', 'FalconForCausalLM', 'DeepseekV2ForCausalLM', 
-                                'DeepseekV3ForCausalLM', 'DeepSeekMTPModel'] 
+                                'DeepseekV3ForCausalLM', 'DeepSeekMTPModel']  
     if any(arch in architectures for arch in support_nn_architectures): 
         if os.getenv('LLAMA_NN') != '0': 
              if (architectures == ['QWenLMHeadModel'] or architectures == ['ChatGLMModel'] ) and visions != []:
@@ -119,7 +112,10 @@ def get_model_architecture(
             else:
                 os.environ['AWQ_PAD'] = '0'
     else:
-        os.environ['LLAMA_NN'] = '0'
+        if os.getenv('LLAMA_NN') == '1': 
+            os.environ['LLAMA_NN'] = '1'
+        else:
+            os.environ['LLAMA_NN'] = '0'
         os.environ['LM_NN'] = '0'
         os.environ['GEMM_PAD'] = '0'
         os.environ['FA_PAD'] = '0'
@@ -141,7 +137,8 @@ def get_model_architecture(
                             for arch in architectures)
     if (not is_vllm_supported
             or model_config.model_impl == ModelImpl.TRANSFORMERS):
-        architectures = resolve_transformers_arch(model_config, architectures)
+        architectures = resolve_transformers_fallback(model_config,
+                                                      architectures)
 
     model_cls, arch = ModelRegistry.resolve_model_cls(architectures)
     if model_config.task == "embed":
@@ -205,4 +202,4 @@ def configure_quant_config(quant_config: QuantizationConfig,
         logger.warning(
             "The model class %s has not defined `packed_modules_mapping`, "
             "this may lead to incorrect mapping of quantized or ignored "
-            "modules", model_class.__name__)
+            "modules", model_class.__name__)
\ No newline at end of file