| |
| |
| |
|
|
| 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) |
|
|
