sthui/SimpleSeg

1from transformers.configuration_utils import PretrainedConfig2from transformers.utils import logging3from typing import Optional, Union4 5logger = logging.get_logger(__name__)6 7DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}8 9 10class DeepseekV3Config(PretrainedConfig):11    r"""12    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek13    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the14    defaults will yield a similar configuration to that of the DeepSeek-V3.15 16    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the17    documentation from [`PretrainedConfig`] for more information.18 19    Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py20 21    Args:22        vocab_size (`int`, *optional*, defaults to 129280):23            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the24            `inputs_ids` passed when calling [`DeepseekV3Model`]25        hidden_size (`int`, *optional*, defaults to 4096):26            Dimension of the hidden representations.27        intermediate_size (`int`, *optional*, defaults to 11008):28            Dimension of the MLP representations.29        moe_intermediate_size (`int`, *optional*, defaults to 1407):30            Dimension of the MoE representations.31        num_hidden_layers (`int`, *optional*, defaults to 32):32            Number of hidden layers in the Transformer decoder.33        num_nextn_predict_layers (`int`, *optional*, defaults to 1):34            Number of nextn predict layers in the DeepSeekV3 Model.35        num_attention_heads (`int`, *optional*, defaults to 32):36            Number of attention heads for each attention layer in the Transformer decoder.37        n_shared_experts (`int`, *optional*, defaults to None):38            Number of shared experts, None means dense model.39        n_routed_experts (`int`, *optional*, defaults to None):40            Number of routed experts, None means dense model.41        routed_scaling_factor (`float`, *optional*, defaults to 1.0):42            Scaling factor or routed experts.43        topk_method (`str`, *optional*, defaults to `gready`):44            Topk method used in routed gate.45        n_group (`int`, *optional*, defaults to None):46            Number of groups for routed experts.47        topk_group (`int`, *optional*, defaults to None):48            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).49        num_experts_per_tok (`int`, *optional*, defaults to None):50            Number of selected experts, None means dense model.51        moe_layer_freq (`int`, *optional*, defaults to 1):52            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.53        first_k_dense_replace (`int`, *optional*, defaults to 0):54            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).55                                                            \--k dense layers--/56        norm_topk_prob (`bool`, *optional*, defaults to False):57            Whether to normalize the weights of the routed experts.58        scoring_func (`str`, *optional*, defaults to 'softmax'):59            Method of computing expert weights.60        aux_loss_alpha (`float`, *optional*, defaults to 0.001):61            Auxiliary loss weight coefficient.62        seq_aux = (`bool`, *optional*, defaults to True):63            Whether to compute the auxiliary loss for each individual sample.64        num_key_value_heads (`int`, *optional*):65            This is the number of key_value heads that should be used to implement Grouped Query Attention. If66            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if67            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When68            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed69            by meanpooling all the original heads within that group. For more details checkout [this70            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to71            `num_attention_heads`.72        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):73            The non-linear activation function (function or string) in the decoder.74        max_position_embeddings (`int`, *optional*, defaults to 2048):75            The maximum sequence length that this model might ever be used with.76        initializer_range (`float`, *optional*, defaults to 0.02):77            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.78        rms_norm_eps (`float`, *optional*, defaults to 1e-06):79            The epsilon used by the rms normalization layers.80        use_cache (`bool`, *optional*, defaults to `True`):81            Whether or not the model should return the last key/values attentions (not used by all models). Only82            relevant if `config.is_decoder=True`.83        pad_token_id (`int`, *optional*):84            Padding token id.85        bos_token_id (`int`, *optional*, defaults to 1):86            Beginning of stream token id.87        eos_token_id (`int`, *optional*, defaults to 2):88            End of stream token id.89        pretraining_tp (`int`, *optional*, defaults to 1):90            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this91            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is92            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this93            issue](https://github.com/pytorch/pytorch/issues/76232).94        tie_word_embeddings (`bool`, *optional*, defaults to `False`):95            Whether to tie weight embeddings96        rope_theta (`float`, *optional*, defaults to 10000.0):97            The base period of the RoPE embeddings.98        rope_scaling (`Dict`, *optional*):99            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling100            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is101            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update102            `max_position_embeddings` to the expected new maximum.103        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):104            Whether to use a bias in the query, key, value and output projection layers during self-attention.105        attention_dropout (`float`, *optional*, defaults to 0.0):106            The dropout ratio for the attention probabilities.107 108    ```python109    >>> from transformers import DeepseekV3Model, DeepseekV3Config110 111    >>> # Initializing a Deepseek-V3 style configuration112    >>> configuration = DeepseekV3Config()113 114    >>> # Accessing the model configuration115    >>> configuration = model.config116    ```"""117 118    model_type = "deepseek_v3"119    keys_to_ignore_at_inference = ["past_key_values"]120 121    def __init__(122        self,123        vocab_size=129280,124        hidden_size=7168,125        intermediate_size=18432,126        moe_intermediate_size=2048,127        num_hidden_layers=61,128        num_nextn_predict_layers=1,129        num_attention_heads=128,130        num_key_value_heads=128,131        n_shared_experts=1,132        n_routed_experts=256,133        ep_size=1,134        routed_scaling_factor=2.5,135        kv_lora_rank=512,136        q_lora_rank=1536,137        qk_rope_head_dim=64,138        v_head_dim=128,139        qk_nope_head_dim=128,140        topk_method="noaux_tc",141        n_group=8,142        topk_group=4,143        num_experts_per_tok=8,144        moe_layer_freq=1,145        first_k_dense_replace=3,146        norm_topk_prob=True,147        scoring_func="sigmoid",148        aux_loss_alpha=0.001,149        seq_aux=True,150        hidden_act="silu",151        max_position_embeddings=4096,152        initializer_range=0.02,153        rms_norm_eps=1e-6,154        use_cache=True,155        pad_token_id=None,156        bos_token_id=0,157        eos_token_id=1,158        pretraining_tp=1,159        tie_word_embeddings=False,160        rope_theta=10000.0,161        rope_scaling=None,162        attention_bias=False,163        attention_dropout=0.0,164        **kwargs,165    ):166        self.vocab_size = vocab_size167        self.max_position_embeddings = max_position_embeddings168        self.hidden_size = hidden_size169        self.intermediate_size = intermediate_size170        self.moe_intermediate_size = moe_intermediate_size171        self.num_hidden_layers = num_hidden_layers172        self.num_nextn_predict_layers = num_nextn_predict_layers173        self.num_attention_heads = num_attention_heads174        self.n_shared_experts = n_shared_experts175        self.n_routed_experts = n_routed_experts176        self.ep_size = ep_size177        self.routed_scaling_factor = routed_scaling_factor178        self.kv_lora_rank = kv_lora_rank179        self.q_lora_rank = q_lora_rank180        self.qk_rope_head_dim = qk_rope_head_dim181        self.v_head_dim = v_head_dim182        self.qk_nope_head_dim = qk_nope_head_dim183        self.topk_method = topk_method184        self.n_group = n_group185        self.topk_group = topk_group186        self.num_experts_per_tok = num_experts_per_tok187        self.moe_layer_freq = moe_layer_freq188        self.first_k_dense_replace = first_k_dense_replace189        self.norm_topk_prob = norm_topk_prob190        self.scoring_func = scoring_func191        self.aux_loss_alpha = aux_loss_alpha192        self.seq_aux = seq_aux193        # for backward compatibility194        if num_key_value_heads is None:195            num_key_value_heads = num_attention_heads196 197        self.num_key_value_heads = num_key_value_heads198        self.hidden_act = hidden_act199        self.initializer_range = initializer_range200        self.rms_norm_eps = rms_norm_eps201        self.pretraining_tp = pretraining_tp202        self.use_cache = use_cache203        self.rope_theta = rope_theta204        self.rope_scaling = rope_scaling205        self.attention_bias = attention_bias206        self.attention_dropout = attention_dropout207 208        super().__init__(209            pad_token_id=pad_token_id,210            bos_token_id=bos_token_id,211            eos_token_id=eos_token_id,212            tie_word_embeddings=tie_word_embeddings,213            **kwargs,214        )215 216 217class MoonViTConfig(PretrainedConfig):218    model_type = "moonvit"219 220    def __init__(221        self,222        patch_size: int = 14,223        init_pos_emb_height: int = 64,224        init_pos_emb_width: int = 64,225        num_attention_heads: int = 16,226        num_hidden_layers: int = 27,227        hidden_size: int = 1152,228        intermediate_size: int = 4304,229        merge_kernel_size: tuple[int, int] = (2, 2),230        **kwargs,231    ):232        super().__init__(**kwargs)233        self.patch_size = patch_size234        # Positional embedding config235        self.init_pos_emb_height = init_pos_emb_height236        self.init_pos_emb_width = init_pos_emb_width237        # Transformer config238        self.num_hidden_layers = num_hidden_layers239        self.num_attention_heads = num_attention_heads240        self.hidden_size = hidden_size241        self.intermediate_size = intermediate_size242        # Patch merger config243        self.merge_kernel_size = merge_kernel_size244 245 246class KimiVLConfig(PretrainedConfig):247    model_type = "kimi_vl"248 249    def __init__(250        self,251        vision_config: Optional[Union[dict, MoonViTConfig]] = None,252        text_config: Optional[Union[dict, DeepseekV3Config]] = None,253        ignore_index: int = -100,254        media_placeholder_token_id: int = 163605,255        pad_token_id: int = 0,256        **kwargs,257    ):258        if vision_config is None:259            vision_config = MoonViTConfig()260        elif isinstance(vision_config, dict):261            vision_config = MoonViTConfig(**vision_config)262        self.vision_config = vision_config263 264        if text_config is None:265            text_config = DeepseekV3Config()266        elif isinstance(text_config, dict):267            text_config = DeepseekV3Config(**text_config)268        self.text_config = text_config269 270        self.ignore_index = ignore_index271        self.media_placeholder_token_id = media_placeholder_token_id272 273        attn_implementation = kwargs.get("attn_implementation")274        if attn_implementation is not None:275            if attn_implementation in ["eager", "flash_attention_2"]:276                self._attn_implementation = attn_implementation277                self.vision_config._attn_implementation = attn_implementation278                self.text_config._attn_implementation = attn_implementation279            else:280                raise ValueError(281                    f"Invalid attention implementation: {attn_implementation}"282                )283 284        super().__init__(pad_token_id=pad_token_id, **kwargs)285