update config (Adding the Attributes) (#1)

- update config (Adding the Attributes) (c0cae42dcb497ec01e4624565cabda1defd65486)

configuration_dots.py

@@ -1,77 +1,78 @@
-from typing import Any, Optional
-from transformers.configuration_utils import PretrainedConfig
-from transformers.models.qwen2 import Qwen2Config
-from transformers import Qwen2_5_VLProcessor, AutoProcessor
-from transformers.models.auto.configuration_auto import CONFIG_MAPPING
-
-
-class DotsVisionConfig(PretrainedConfig):
-    model_type: str = "dots_vit"
-
-    def __init__(
-        self,
-        embed_dim: int = 1536,  # vision encoder embed size
-        hidden_size: int = 1536,  # after merger hidden size
-        intermediate_size: int = 4224,
-        num_hidden_layers: int = 42,
-        num_attention_heads: int = 12,
-        num_channels: int = 3,
-        patch_size: int = 14,
-        spatial_merge_size: int = 2,
-        temporal_patch_size: int = 1,
-        rms_norm_eps: float = 1e-5,
-        use_bias: bool = False,
-        attn_implementation="flash_attention_2",  # "eager","sdpa","flash_attention_2"
-        initializer_range=0.02,
-        init_merger_std=0.02,
-        is_causal=False,  # ve causal forward
-        post_norm=True,
-        gradient_checkpointing=False,
-        **kwargs: Any,
-    ):
-        super().__init__(**kwargs)
-        self.embed_dim = embed_dim
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_channels = num_channels
-        self.patch_size = patch_size
-        self.spatial_merge_size = spatial_merge_size
-        self.temporal_patch_size = temporal_patch_size
-        self.rms_norm_eps = rms_norm_eps
-        self.use_bias = use_bias
-        self.attn_implementation = attn_implementation
-        self.initializer_range = initializer_range
-        self.init_merger_std = init_merger_std
-        self.is_causal = is_causal
-        self.post_norm = post_norm
-        self.gradient_checkpointing = gradient_checkpointing
-
-
-
-class DotsOCRConfig(Qwen2Config):
-    model_type = "dots_ocr"
-    def __init__(self, 
-        image_token_id = 151665, 
-        video_token_id = 151656,
-        vision_config: Optional[dict] = None, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.image_token_id = image_token_id
-        self.video_token_id = video_token_id
-        self.vision_config = DotsVisionConfig(**(vision_config or {}))
-
-    def save_pretrained(self, save_directory, **kwargs):
-        self._auto_class = None
-        super().save_pretrained(save_directory, **kwargs)
-
-
-class DotsVLProcessor(Qwen2_5_VLProcessor):
-    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
-        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-        self.image_token = "<|imgpad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
-        self.image_token_id = 151665 if not hasattr(tokenizer, "image_token_id") else tokenizer.image_token_id
-
-
-AutoProcessor.register("dots_ocr", DotsVLProcessor)
-CONFIG_MAPPING.register("dots_ocr", DotsOCRConfig)
+from typing import Any, Optional
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.qwen2 import Qwen2Config
+from transformers import Qwen2_5_VLProcessor, AutoProcessor
+from transformers.models.auto.configuration_auto import CONFIG_MAPPING
+
+
+class DotsVisionConfig(PretrainedConfig):
+    model_type: str = "dots_vit"
+
+    def __init__(
+        self,
+        embed_dim: int = 1536,  # vision encoder embed size
+        hidden_size: int = 1536,  # after merger hidden size
+        intermediate_size: int = 4224,
+        num_hidden_layers: int = 42,
+        num_attention_heads: int = 12,
+        num_channels: int = 3,
+        patch_size: int = 14,
+        spatial_merge_size: int = 2,
+        temporal_patch_size: int = 1,
+        rms_norm_eps: float = 1e-5,
+        use_bias: bool = False,
+        attn_implementation="flash_attention_2",  # "eager","sdpa","flash_attention_2"
+        initializer_range=0.02,
+        init_merger_std=0.02,
+        is_causal=False,  # ve causal forward
+        post_norm=True,
+        gradient_checkpointing=False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.rms_norm_eps = rms_norm_eps
+        self.use_bias = use_bias
+        self.attn_implementation = attn_implementation
+        self.initializer_range = initializer_range
+        self.init_merger_std = init_merger_std
+        self.is_causal = is_causal
+        self.post_norm = post_norm
+        self.gradient_checkpointing = gradient_checkpointing
+
+
+
+class DotsOCRConfig(Qwen2Config):
+    model_type = "dots_ocr"
+    def __init__(self,
+        image_token_id = 151665,
+        video_token_id = 151656,
+        vision_config: Optional[dict] = None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_config = DotsVisionConfig(**(vision_config or {}))
+
+    def save_pretrained(self, save_directory, **kwargs):
+        self._auto_class = None
+        super().save_pretrained(save_directory, **kwargs)
+
+
+class DotsVLProcessor(Qwen2_5_VLProcessor):
+    attributes = ["image_processor", "tokenizer"]
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.image_token = "<|imgpad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.image_token_id = 151665 if not hasattr(tokenizer, "image_token_id") else tokenizer.image_token_id
+
+
+AutoProcessor.register("dots_ocr", DotsVLProcessor)
+CONFIG_MAPPING.register("dots_ocr", DotsOCRConfig)