Sync from v0.13

vllm/transformers_utils/configs/bagel.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from transformers import PretrainedConfig, SiglipVisionConfig
+from transformers.models.qwen2 import Qwen2Config
+
+
+class BagelConfig(PretrainedConfig):
+    """Configuration class for BAGEL model."""
+
+    model_type = "bagel"
+
+    def __init__(
+        self,
+        visual_gen: bool = True,
+        visual_und: bool = True,
+        llm_config: dict | Qwen2Config | None = None,
+        vit_config: dict | SiglipVisionConfig | None = None,
+        vae_config: dict | None = None,
+        latent_patch_size: int = 2,
+        max_latent_size: int = 32,
+        vit_max_num_patch_per_side: int = 70,
+        connector_act: str = "gelu_pytorch_tanh",
+        interpolate_pos: bool = False,
+        timestep_shift: float = 1.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.visual_gen = visual_gen
+        self.visual_und = visual_und
+
+        # Convert dict configs to proper config objects
+        if isinstance(llm_config, dict):
+            self.llm_config = Qwen2Config(**llm_config)
+        else:
+            self.llm_config = llm_config or Qwen2Config()
+
+        if isinstance(vit_config, dict):
+            self.vit_config = SiglipVisionConfig(**vit_config)
+        else:
+            self.vit_config = vit_config or SiglipVisionConfig()
+
+        self.vae_config = vae_config or {"z_channels": 16, "downsample": 8}
+        self.latent_patch_size = latent_patch_size
+        self.max_latent_size = max_latent_size
+        self.vit_max_num_patch_per_side = vit_max_num_patch_per_side
+        self.connector_act = connector_act
+        self.interpolate_pos = interpolate_pos
+        self.timestep_shift = timestep_shift
+
+    @property
+    def hidden_size(self) -> int:
+        """Return the hidden size of the language model."""
+        return self.llm_config.hidden_size