[gpt-oss] Add gpt-oss bf16 support

vllm/transformers_utils/configs/eagle.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import Optional, Union
+
+from transformers import AutoConfig, PretrainedConfig
+
+import vllm.envs as envs
+from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
+
+
+class EAGLEConfig(PretrainedConfig):
+    model_type = "eagle"
+
+    def __init__(self,
+                 model: Union[PretrainedConfig, dict, None] = None,
+                 truncated_vocab_size: Optional[int] = None,
+                 method: Optional[str] = 'eagle',
+                 **kwargs):
+
+        model_config: Union[PretrainedConfig, DeepseekV2Config, None]
+        if isinstance(model, dict):
+            archs = model.get("architectures", [])
+            target_archs = ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]
+            if any(target_arch in archs for target_arch in target_archs):
+                # AutoConfig does not support DeepSeek MoE models yet
+                model_config = DeepseekV2Config(**model)
+            else:
+                model_config = AutoConfig.for_model(**model)
+        else:
+            model_config = model
+
+        for k, v in kwargs.items():
+            if k != "architectures" and k != "model_type" and hasattr(
+                    model_config, k):
+                setattr(model_config, k, v)
+
+        self.model = model_config
+
+        if self.model is None:
+            self.truncated_vocab_size = None
+        else:
+            self.truncated_vocab_size = self.model.vocab_size if \
+                truncated_vocab_size is None else truncated_vocab_size
+
+        if not envs.VLLM_USE_V1:
+            kwargs["architectures"] = ["EAGLEModel"]
+        else:
+            # Eagle model name should follow naming convention of
+            # LlamaForCausalLM -> EagleLlamaForCausalLM
+            if method == "eagle":
+                assert self.model is not None, \
+                    "model should not be None when method is eagle"
+                kwargs["architectures"] = [
+                    f"Eagle{arch}" if not arch.startswith("Eagle") \
+                        else arch for arch in self.model.architectures
+                ]
+            elif method == "eagle3":
+                assert self.model is not None, \
+                    "model should not be None when method is eagle3"
+                kwargs["architectures"] = [
+                    f"Eagle3{arch}" if not arch.startswith("Eagle3") \
+                        else arch for arch in self.model.architectures
+                ]
+            else:
+                raise ValueError(f"Invalid method {method}. \
+                    Supported methods are eagle and eagle3.")
+
+        super().__init__(**kwargs)
+
+        if self.model is not None:
+            for k, v in self.model.to_dict().items():
+                if k not in kwargs:
+                    setattr(self, k, v)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        **kwargs,
+    ) -> "EAGLEConfig":
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs)
+        return cls.from_dict(config_dict, **kwargs)