configuration_v1.py

from typing import Optional

from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
    Qwen2_5_VLVisionConfig,
)


class V1Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen2_5_VLModel`]. It is used to instantiate a
    Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 152064):
            Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen2_5_VLModel`]
        hidden_size (`int`, *optional*, defaults to 8192):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 29568):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 80):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 64):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 32768):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 1000000.0):
            The base period of the RoPE embeddings.
        use_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        max_window_layers (`int`, *optional*, defaults to 80):
            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        vision_config (`Dict`, *optional*):
            The config for the visual encoder initialization.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`List[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`List[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE

    ```python
    >>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig

    >>> # Initializing a Qwen2_5_VL style configuration
    >>> configuration = Qwen2_5_VLConfig()

    >>> # Initializing a model from the Qwen2-VL-7B style configuration
    >>> model = Qwen2_5_VLForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "v1"
    sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `Qwen2_5_VL`
    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=152064,
        hidden_size=8192,
        intermediate_size=29568,
        num_hidden_layers=80,
        num_attention_heads=64,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=32768,
        initializer_range=0.02,
        rms_norm_eps=1e-05,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=1000000.0,
        use_sliding_window=False,
        sliding_window=4096,
        max_window_layers=80,
        attention_dropout=0.0,
        vision_config=None,
        rope_scaling=None,
        region_token_id: int = 151662,  # <|fim_pad|>
        copy_token_start: int = 151665,
        copy_token_num: int = 30000,
        copy_scaler: float = 0.1,
        use_embeddings_as_keys: bool = False,
        normalize_copy_states: bool = False,
        copy_extraction_layer: int = -1,
        tie_copy_heads: bool = False,
        use_cfg: bool = False,
        copy_hidden_size: Optional[int] = None,
        z_loss_weight: float = 1e-5,
        z_loss_top_k: int = 40,
        use_gate: bool = False,
        label_smoothing: bool = False,
        separate_copy_loss: bool = False,
        do_copy: bool = True,
        **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.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.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window
        self.max_window_layers = max_window_layers

        # for backward compatibility
        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.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_dropout = attention_dropout
        self.rope_scaling = rope_scaling

        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        # and change type from 'mrope' to 'default' because `mrope` does default RoPE calculations
        # one can set it to "linear"/"dynamic" etc. to have scaled RoPE
        # TODO: @raushan update config in the hub
        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"})

        self.region_token_id = region_token_id
        self.copy_token_start = copy_token_start
        self.copy_token_num = copy_token_num
        self.copy_scaler = copy_scaler
        self.use_embeddings_as_keys = use_embeddings_as_keys
        self.normalize_copy_states = normalize_copy_states
        self.copy_extraction_layer = copy_extraction_layer
        self.tie_copy_heads = tie_copy_heads
        self.use_cfg = use_cfg

        if copy_hidden_size is None:
            copy_hidden_size = self.hidden_size
        self.copy_hidden_size = copy_hidden_size
        self.z_loss_weight = z_loss_weight
        self.z_loss_top_k = z_loss_top_k
        self.use_gate = use_gate
        self.label_smoothing = label_smoothing
        self.separate_copy_loss = separate_copy_loss
        self.do_copy = do_copy

        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)


__all__ = ["V1Config"]