models/Kimi-VL-A3B/configuration_kimi_vl.py

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from typing import Optional, Union

logger = logging.get_logger(__name__)

DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


class DeepseekV3Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
    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 DeepSeek-V3.

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

    Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py

    Args:
        vocab_size (`int`, *optional*, defaults to 129280):
            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`DeepseekV3Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        moe_intermediate_size (`int`, *optional*, defaults to 1407):
            Dimension of the MoE representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
            Number of nextn predict layers in the DeepSeekV3 Model.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        n_shared_experts (`int`, *optional*, defaults to None):
            Number of shared experts, None means dense model.
        n_routed_experts (`int`, *optional*, defaults to None):
            Number of routed experts, None means dense model.
        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor or routed experts.
        topk_method (`str`, *optional*, defaults to `gready`):
            Topk method used in routed gate.
        n_group (`int`, *optional*, defaults to None):
            Number of groups for routed experts.
        topk_group (`int`, *optional*, defaults to None):
            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
        num_experts_per_tok (`int`, *optional*, defaults to None):
            Number of selected experts, None means dense model.
        moe_layer_freq (`int`, *optional*, defaults to 1):
            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
        first_k_dense_replace (`int`, *optional*, defaults to 0):
            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                            \--k dense layers--/
        norm_topk_prob (`bool`, *optional*, defaults to False):
            Whether to normalize the weights of the routed experts.
        scoring_func (`str`, *optional*, defaults to 'softmax'):
            Method of computing expert weights.
        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
            Auxiliary loss weight coefficient.
        seq_aux = (`bool`, *optional*, defaults to True):
            Whether to compute the auxiliary loss for each individual sample.
        num_key_value_heads (`int`, *optional*):
            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
            `num_attention_heads`.
        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 2048):
            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-06):
            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`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232).
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from transformers import DeepseekV3Model, DeepseekV3Config

    >>> # Initializing a Deepseek-V3 style configuration
    >>> configuration = DeepseekV3Config()

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

    model_type = "deepseek_v3"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=129280,
        hidden_size=7168,
        intermediate_size=18432,
        moe_intermediate_size=2048,
        num_hidden_layers=61,
        num_nextn_predict_layers=1,
        num_attention_heads=128,
        num_key_value_heads=128,
        n_shared_experts=1,
        n_routed_experts=256,
        ep_size=1,
        routed_scaling_factor=2.5,
        kv_lora_rank=512,
        q_lora_rank=1536,
        qk_rope_head_dim=64,
        v_head_dim=128,
        qk_nope_head_dim=128,
        topk_method="noaux_tc",
        n_group=8,
        topk_group=4,
        num_experts_per_tok=8,
        moe_layer_freq=1,
        first_k_dense_replace=3,
        norm_topk_prob=True,
        scoring_func="sigmoid",
        aux_loss_alpha=0.001,
        seq_aux=True,
        hidden_act="silu",
        max_position_embeddings=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=0,
        eos_token_id=1,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.moe_intermediate_size = moe_intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_nextn_predict_layers = num_nextn_predict_layers
        self.num_attention_heads = num_attention_heads
        self.n_shared_experts = n_shared_experts
        self.n_routed_experts = n_routed_experts
        self.ep_size = ep_size
        self.routed_scaling_factor = routed_scaling_factor
        self.kv_lora_rank = kv_lora_rank
        self.q_lora_rank = q_lora_rank
        self.qk_rope_head_dim = qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.qk_nope_head_dim = qk_nope_head_dim
        self.topk_method = topk_method
        self.n_group = n_group
        self.topk_group = topk_group
        self.num_experts_per_tok = num_experts_per_tok
        self.moe_layer_freq = moe_layer_freq
        self.first_k_dense_replace = first_k_dense_replace
        self.norm_topk_prob = norm_topk_prob
        self.scoring_func = scoring_func
        self.aux_loss_alpha = aux_loss_alpha
        self.seq_aux = seq_aux
        # 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.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,
        )


class MoonViTConfig(PretrainedConfig):
    model_type = "moonvit"

    def __init__(
        self,
        patch_size: int = 14,
        init_pos_emb_height: int = 64,
        init_pos_emb_width: int = 64,
        num_attention_heads: int = 16,
        num_hidden_layers: int = 27,
        hidden_size: int = 1152,
        intermediate_size: int = 4304,
        merge_kernel_size: tuple[int, int] = (2, 2),
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.patch_size = patch_size
        # Positional embedding config
        self.init_pos_emb_height = init_pos_emb_height
        self.init_pos_emb_width = init_pos_emb_width
        # Transformer config
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        # Patch merger config
        self.merge_kernel_size = merge_kernel_size


class KimiVLConfig(PretrainedConfig):
    model_type = "kimi_vl"

    def __init__(
        self,
        vision_config: Optional[Union[dict, MoonViTConfig]] = None,
        text_config: Optional[Union[dict, DeepseekV3Config]] = None,
        ignore_index: int = -100,
        media_placeholder_token_id: int = 163605,
        pad_token_id: int = 0,
        **kwargs,
    ):
        if vision_config is None:
            vision_config = MoonViTConfig()
        elif isinstance(vision_config, dict):
            vision_config = MoonViTConfig(**vision_config)
        self.vision_config = vision_config

        if text_config is None:
            text_config = DeepseekV3Config()
        elif isinstance(text_config, dict):
            text_config = DeepseekV3Config(**text_config)
        self.text_config = text_config

        self.ignore_index = ignore_index
        self.media_placeholder_token_id = media_placeholder_token_id

        attn_implementation = kwargs.get("attn_implementation")
        if attn_implementation is not None:
            if attn_implementation in ["eager", "flash_attention_2"]:
                self._attn_implementation = attn_implementation
                self.vision_config._attn_implementation = attn_implementation
                self.text_config._attn_implementation = attn_implementation
            else:
                raise ValueError(
                    f"Invalid attention implementation: {attn_implementation}"
                )

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