configuration_paddleocr_vl.py

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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation

class PaddleOCRVisionConfig(PretrainedConfig):
    model_type = "paddleocr_vl"
    base_config_key = "vision_config"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=14,
        hidden_act="gelu_pytorch_tanh",
        layer_norm_eps=1e-6,
        attention_dropout=0.0,
        spatial_merge_size=2,
        temporal_patch_size=2,
        tokens_per_second=2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        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.image_size = image_size
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.spatial_merge_size = spatial_merge_size
        self.temporal_patch_size = temporal_patch_size
        self.tokens_per_second = tokens_per_second



class PaddleOCRVLConfig(PretrainedConfig):
    """
    Configuration class.

    This class stores the configuration of an Ernie model, defining the model architecture.
    It inherits from PretrainedConfig and can be used to control model outputs.
    """

    model_type = "paddleocr_vl"
    keys_to_ignore_at_inference = ["past_key_values"]
    sub_configs = {"vision_config": PaddleOCRVisionConfig}

    # Default tensor parallel plan for base model `Qwen3`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=768,
        intermediate_size=11008,
        max_position_embeddings=32768,
        num_hidden_layers=2,
        num_attention_heads=2,
        image_token_id=101304,
        video_token_id=101305,
        vision_start_token_id=101306,
        rms_norm_eps=1e-6,
        use_cache=False,
        use_flash_attention=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        head_dim=128,
        hidden_act="silu",
        use_bias=False,
        rope_theta=10000,
        weight_share_add_bias=True,
        ignored_index=-100,
        attention_probs_dropout_prob=0.0,
        hidden_dropout_prob=0.0,
        compression_ratio: float = 1.0,
        num_key_value_heads=None,
        max_sequence_length=None,
        tie_word_embeddings=False,
        vision_config=None,
        rope_scaling=None,
        **kwargs,
    ):
        """
        Initialize configuration with default or specified parameters.

        Args:
            vocab_size (int): Size of the vocabulary (number of unique tokens)
            hidden_size (int): Dimensionality of the encoder layers and the pooler layer
            intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer
            max_position_embeddings (int): Maximum sequence length the model can handle
            num_hidden_layers (int): Number of hidden layers in the Transformer encoder
            num_attention_heads (int): Number of attention heads for each attention layer
            rms_norm_eps (float): The epsilon used by the RMS normalization layers
            use_cache (bool): Whether to use caching for faster generation (decoding)
            use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation
            pad_token_id (int): Token ID used for padding sequences
            bos_token_id (int): Token ID used for beginning-of-sequence
            eos_token_id (int): Token ID used for end-of-sequence
            use_bias (bool): Whether to use bias terms in linear layers
            rope_theta (float): The base period of the RoPE embeddings
            weight_share_add_bias (bool): Whether to share bias weights in certain layers
            ignored_index (int): Target value that is ignored during loss computation
            attention_probs_dropout_prob (float): Dropout probability for attention weights
            hidden_dropout_prob (float): Dropout probability for hidden layers
            compression_ratio (float): Ratio for KV cache compression (1.0 = no compression)
            num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention)
            max_sequence_length (int): Maximum sequence length for positional embeddings
            **kwargs: Additional keyword arguments passed to parent class
        """

        # Set default for tied embeddings if not specified.
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **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"]()        
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.use_flash_attention = use_flash_attention
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.image_token_id = image_token_id
        self.video_token_id = video_token_id
        self.vision_start_token_id = vision_start_token_id
        self.head_dim = head_dim
        self.hidden_act=hidden_act
        self.sliding_window = None
        self.hidden_size = hidden_size
        self.use_bias = use_bias
        self.weight_share_add_bias = weight_share_add_bias
        self.rope_theta = rope_theta
        self.ignored_index = ignored_index
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.hidden_dropout_prob = hidden_dropout_prob
        self.compression_ratio = compression_ratio
        self.num_key_value_heads = num_key_value_heads
        self.max_sequence_length = max_sequence_length
        self.rope_scaling = rope_scaling
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            if self.rope_scaling["type"] == "mrope":
                self.rope_scaling["type"] = "default"
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self, ignore_keys={"mrope_section"})        
        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)