Add configuration_brain_ocr.py

configuration_brain_ocr.py

@@ -0,0 +1,263 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Modified from HunyuanVL configuration for BrainOCR.
+
+from transformers import PretrainedConfig
+
+
+class BrainOCRVisionConfig(PretrainedConfig):
+    model_type = "brain_ocr"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_act="gelu",
+        hidden_size=1152,
+        intermediate_size=4304,
+        interpolate_mode="bilinear",
+        rms_norm_eps=1e-05,
+        learnable_mlp_pooling_size=0,
+        num_attention_heads=16,
+        num_key_value_heads=None,
+        num_channels=3,
+        num_hidden_layers=27,
+        out_hidden_size=4096,
+        patch_size=16,
+        remove_prenorm=True,
+        spatial_merge_size=2,
+        temporal_patch_size=1,
+        resize_resolution=2048,
+        img_max_token_num=4096,
+        max_image_size=2048,
+        video_max_image_size=768,
+        video_min_image_size=256,
+        min_image_size=512,
+        anyres_vit_max_image_size=2048,
+        max_vit_seq_len=16384,
+        text_hidden_size=3072,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.interpolate_mode = interpolate_mode
+        self.learnable_mlp_pooling_size = learnable_mlp_pooling_size
+        self.num_attention_heads = num_attention_heads
+        if not num_key_value_heads:
+            self.num_key_value_heads = num_attention_heads
+        else:
+            self.num_key_value_heads = num_key_value_heads
+        self.num_channels = num_channels
+        self.num_hidden_layers = num_hidden_layers
+        self.out_hidden_size = out_hidden_size
+        self.patch_size = patch_size
+        self.remove_prenorm = remove_prenorm
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.rms_norm_eps = rms_norm_eps
+
+        self.resize_resolution = resize_resolution
+        self.img_max_token_num = img_max_token_num
+        self.max_image_size = max_image_size
+        self.min_image_size = min_image_size
+        self.video_max_image_size = video_max_image_size
+        self.video_min_image_size = video_min_image_size
+        self.anyres_vit_max_image_size = anyres_vit_max_image_size
+        self.max_vit_seq_len = max_vit_seq_len
+        self.text_hidden_size = text_hidden_size
+
+
+class BrainOCRTextConfig(PretrainedConfig):
+    r"""
+    Configuration class for BrainOCR text model.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 290943):
+            Vocabulary size of the model.
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer.
+        num_key_value_heads (`int`, *optional*):
+            Number of key_value heads for Grouped Query Attention.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        head_dim (`int`, *optional*, defaults to 128):
+            The attention head dimension.
+    """
+
+    model_type = "brain_ocr_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=290943,
+        hidden_size=4096,
+        intermediate_size: int = 11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        eod_token_id=3,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        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.head_dim = head_dim
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """Validate the `rope_scaling` configuration."""
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and "
+                f"`factor` or `type` and `alpha`, got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        rope_scaling_alpha = self.rope_scaling.get("alpha", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                "`rope_scaling`'s type field must be one of ['linear', 'dynamic'], "
+                f"got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None and rope_scaling_alpha is None:
+            raise ValueError(
+                "`rope_scaling`'s factor or alpha field must be have one, "
+                "got both of none"
+            )
+        if rope_scaling_factor is not None and (
+            not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                "`rope_scaling`'s factor field must be a float > 1.0, "
+                f"got {rope_scaling_factor}"
+            )
+        if rope_scaling_alpha is not None and (
+            not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0
+        ):
+            raise ValueError(
+                "`rope_scaling`'s alpha field must be a float > 1.0, "
+                f"got {rope_scaling_alpha}"
+            )
+
+
+class BrainOCRConfig(PretrainedConfig):
+    model_type = "brain_ocr"
+    sub_configs = {
+        "vision_config": BrainOCRVisionConfig,
+        "text_config": BrainOCRTextConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        im_start_id=120118,
+        im_end_id=120119,
+        image_token_id=120120,
+        im_newline_id=120121,
+        video_start_id=120122,
+        video_end_id=120123,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"](**kwargs)
+
+        self.image_token_id = image_token_id
+        self.im_start_id = im_start_id
+        self.im_end_id = im_end_id
+        self.im_newline_id = im_newline_id
+        self.video_start_id = video_start_id
+        self.video_end_id = video_end_id
+
+        self.vision_config.text_hidden_size = self.text_config.hidden_size
+
+        self._attn_implementation = kwargs.pop("attn_implementation", None)
+
+    def __setattr__(self, key, value):
+        if (
+            (text_config := super().__getattribute__("__dict__").get("text_config"))
+            is not None
+            and key not in ["dtype", "_attn_implementation_internal"]
+            and key in text_config.__dict__
+        ):
+            setattr(text_config, key, value)
+        else:
+            super().__setattr__(key, value)
+
+    def __getattribute__(self, key):
+        if "text_config" in super().__getattribute__("__dict__") and key not in [
+            "_name_or_path",
+            "model_type",
+            "dtype",
+            "_attn_implementation_internal",
+        ]:
+            text_config = super().__getattribute__("text_config")
+            if key in text_config.__dict__:
+                return getattr(text_config, key)
+
+        return super().__getattribute__(key)