Copy configuration_minimax_m3_vl.py from nvidia/MiniMax-M3-NVFP4-Preview0615

configuration_minimax_m3_vl.py

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+"""HuggingFace configs for the MiniMax VL family (M2 VL / M3 VL).
+
+This file is bundled into every converted HF checkpoint so that loading via
+``AutoConfig.from_pretrained(..., trust_remote_code=True)`` works without any
+runtime dependency on sglang or other internal packages — only stock
+``transformers`` is required.
+
+The class definitions intentionally mirror
+``sglang.srt.configs.minimax_vl``; if either side changes, keep them in sync.
+
+The file is named ``configuration_minimax_m3_vl.py`` (matching the legacy
+``model_type="minimax_m3_vl"`` and the converter's ``auto_map`` entry) so
+that ckpts produced by this converter remain loadable by older sglang versions
+that only know the ``MiniMaxM3VL*`` names. The canonical class is
+``MiniMaxM3VLConfig``; ``MiniMaxM3VLConfig`` is a thin BC alias whose only
+purpose is to be referenced from ``auto_map``.
+"""
+
+from typing import Optional
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+
+
+def _coerce_sub_config(
+    sub_config: Optional[dict], default_model_type: str
+) -> Optional[PretrainedConfig]:
+    """Convert a config dict to a ``PretrainedConfig`` instance.
+
+    If ``model_type`` is registered in HF ``CONFIG_MAPPING`` the corresponding
+    config class is used; otherwise we fall back to a generic
+    ``PretrainedConfig`` so all dict keys still become real attributes (M3's
+    text backbone uses ``model_type="minimax_m2"`` which is not in
+    ``CONFIG_MAPPING``).
+    """
+    if not isinstance(sub_config, dict):
+        return sub_config
+    model_type = sub_config.get("model_type", default_model_type)
+    cls = CONFIG_MAPPING.get(model_type, PretrainedConfig)
+    return cls(**sub_config)
+
+
+class MiniMaxVLBaseConfig(PretrainedConfig):
+    """Base config shared by every MiniMax VL variant.
+
+    Handles vision/text sub-config coercion. Concrete subclasses only need to
+    declare a unique ``model_type`` string.
+    """
+
+    def __init__(
+        self,
+        vision_config: Optional[dict] = None,
+        text_config: Optional[dict] = None,
+        image_token_index: int = 200025,
+        video_token_index: int = 200026,
+        image_seq_length: int = 576,
+        process_image_mode: str = "dynamic_res",
+        projector_hidden_act: str = "gelu",
+        multimodal_projector_bias: bool = True,
+        vision_feature_layer: int = -1,
+        vision_feature_select_strategy: str = "full",
+        img_token_compression_config: Optional[dict] = None,
+        image_grid_pinpoints: Optional[str] = None,
+        **kwargs,
+    ):
+        self.vision_config = _coerce_sub_config(vision_config, "clip_vision_model")
+        self.text_config = _coerce_sub_config(text_config, "mixtral")
+
+        self.image_token_index = image_token_index
+        self.video_token_index = video_token_index
+        self.image_seq_length = image_seq_length
+        self.process_image_mode = process_image_mode
+        self.projector_hidden_act = projector_hidden_act
+        self.multimodal_projector_bias = multimodal_projector_bias
+        self.vision_feature_layer = vision_feature_layer
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.img_token_compression_config = img_token_compression_config or {}
+        self.image_grid_pinpoints = image_grid_pinpoints
+
+        super().__init__(**kwargs)
+
+    def __post_init__(self, **kwargs):
+        super().__post_init__(**kwargs)
+        if hasattr(self, "vision_config"):
+            self.vision_config = _coerce_sub_config(self.vision_config, "clip_vision_model")
+        if hasattr(self, "text_config"):
+            self.text_config = _coerce_sub_config(self.text_config, "mixtral")
+
+
+class MiniMaxM2VLConfig(MiniMaxVLBaseConfig):
+    """MiniMax M2 VL: vision tower + M2 (Mixtral-style MoE) text backbone."""
+
+    model_type = "minimax_m2_vl"
+
+
+class MiniMaxM3VLConfig(MiniMaxVLBaseConfig):
+    """MiniMax M3 VL: vision tower + M3 (mixed sparse/dense MoE) text backbone."""
+
+    model_type = "minimax_m3_vl"
+
+
+class MiniMaxM2MiniVLConfig(MiniMaxM2VLConfig):
+    """Legacy alias kept so old ``model_type="minimax_m2_mini_vl"`` ckpts load."""
+
+    model_type = "minimax_m2_mini_vl"
+
+
+class MiniMaxM3VLConfig(MiniMaxM3VLConfig):
+    """Legacy alias kept so old ``model_type="minimax_m3_vl"`` ckpts load."""
+
+    model_type = "minimax_m3_vl"