configuration_viscpmchatbee.py

# coding=utf-8
# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team. All rights reserved.
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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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" CpmBee model configuration"""

from typing import List, Optional, Tuple, Union

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)

CPMBEE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "openbmb/viscpmchat-bee-10b": "https://huggingface.co/openbmb/VisCPM-Chat/resolve/main/config.json",
    # See all VisCpmBee models at https://huggingface.co/models?filter=viscpmbee
}


class VisCpmChatBeeConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`CpmBeeModel`]. It is used to instbeeiate an
    CPMBee model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the CPMBee
    [openbmb/cpm-bee-10b](https://huggingface.co/openbmb/cpm-bee-10b) architecture.

    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 30720):
            Vocabulary size of the CPMBee model. Defines the number of different tokens that can be represented by the
            `input` passed when calling [`CpmBeeModel`].
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the encoder layers.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads in the Transformer encoder.
        dim_head (`int`, *optional*, defaults to 128):
            Dimension of attention heads for each attention layer in the Transformer encoder.
        dim_ff (`int`, *optional*, defaults to 10240):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 48):
            Number of layers of the Transformer encoder.
        dropout_p (`float`, *optional*, defaults to 0.1):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder.
        position_bias_num_buckets (`int`, *optional*, defaults to 512):
            The number of position_bias buckets.
        position_bias_num_segment_buckets (`int`, *optional*, defaults to 32):
            The number of segment buckets.
        position_bias_max_distance (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        eps (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the layer normalization layers.
        init_std (`float`, *optional*, defaults to 1.0):
            Initialize parameters with std = init_std.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether to use cache.
        distance_scale (`float` or `int`, *optional*, defaults to 16):
            Scale the rotary embedding.
        mask_modules (`list` or `tuple`, *optional*, defaults to None):
            Decides which feedforward block or attention block is pruned.
        half (`bool`, *optional*, defaults to `False`):
            Decides the model parameters are half-precision or not.

    Example:

    ```python
    >>> from transformers import CpmBeeModel, CpmBeeConfig

    >>> # Initializing a CPMBee cpm-bee-10b style configuration
    >>> configuration = CpmBeeConfig()

    >>> # Initializing a model from the cpm-bee-10b style configuration
    >>> model = CpmBeeModel(configuration)

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

    def __init__(
        self,
        vocab_size: int = 30720,
        hidden_size: int = 4096,
        num_attention_heads: int = 64,
        dim_head: int = 64,
        dim_ff: int = 10240,
        num_hidden_layers: int = 32,
        dropout_p: int = 0.0,
        position_bias_num_buckets: int = 256,
        position_bias_num_segment_buckets: int = 32,
        position_bias_max_distance: int = 2048,
        eps: int = 1e-6,
        init_std: float = 1.0,
        use_cache: bool = True,
        distance_scale: Union[int, float] = 16,
        mask_modules: Optional[Union[List, Tuple]] = None,
        half: bool = False,
        vision_dim: int = 1024,
        query_num: int = 64,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.position_bias_num_segment_buckets = position_bias_num_segment_buckets
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.dim_head = dim_head
        self.dim_ff = dim_ff
        self.num_hidden_layers = num_hidden_layers
        self.position_bias_num_buckets = position_bias_num_buckets
        self.position_bias_max_distance = position_bias_max_distance
        self.dropout_p = dropout_p
        self.eps = eps
        self.use_cache = use_cache
        self.vocab_size = vocab_size
        self.init_std = init_std
        self.distance_scale = distance_scale
        self.half = half
        self.mask_modules = mask_modules
        self.vision_dim = vision_dim
        self.query_num = query_num