| from typing import Optional |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class KimiLinearConfig(PretrainedConfig): |
| model_type = "kimi_linear" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| model_type="kimi_linear", |
| vocab_size=163840, |
| hidden_size=4096, |
| head_dim=None, |
| intermediate_size=11008, |
| num_hidden_layers=32, |
| num_attention_heads=32, |
| num_key_value_heads=None, |
| hidden_act="silu", |
| initializer_range=0.02, |
| rms_norm_eps=1e-6, |
| use_cache=True, |
| pad_token_id=0, |
| bos_token_id=1, |
| eos_token_id=2, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| tie_word_embeddings=False, |
| moe_intermediate_size: Optional[int] = None, |
| moe_renormalize: bool = True, |
| moe_router_activation_func: str = "sigmoid", |
| num_experts: Optional[int] = None, |
| num_experts_per_token: Optional[int] = None, |
| num_shared_experts: int = 0, |
| routed_scaling_factor: float = 1.0, |
| first_k_dense_replace: int = 0, |
| moe_layer_freq: int = 1, |
| use_grouped_topk: bool = True, |
| num_expert_group: int = 1, |
| topk_group: int = 1, |
| q_lora_rank: Optional[int] = None, |
| kv_lora_rank: Optional[int] = None, |
| qk_nope_head_dim: Optional[int] = None, |
| qk_rope_head_dim: Optional[int] = None, |
| v_head_dim: Optional[int] = None, |
| mla_use_nope: Optional[bool] = False, |
| mla_use_output_gate: Optional[bool] = False, |
| num_nextn_predict_layers: int = 0, |
| linear_attn_config: Optional[dict] = None, |
| attn_res_block_size: Optional[int] = None, |
| latent_moe_use_norm: bool = False, |
| activation_situ_beta: Optional[float] = None, |
| activation_situ_linear_beta: Optional[float] = None, |
| max_position_embeddings: int = 4096, |
| routed_expert_hidden_size: Optional[int] = None, |
| topk_method: str = "noaux_tc", |
| **kwargs, |
| ): |
| self.model_type = model_type |
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.head_dim = ( |
| head_dim if head_dim is not None else hidden_size // num_attention_heads |
| ) |
| self.intermediate_size = intermediate_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
|
|
| |
| 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.rope_scaling = rope_scaling |
|
|
| self.q_lora_rank = q_lora_rank |
| self.kv_lora_rank = kv_lora_rank |
| self.qk_nope_head_dim = qk_nope_head_dim |
| self.qk_rope_head_dim = qk_rope_head_dim |
| self.v_head_dim = v_head_dim |
| self.mla_use_nope = mla_use_nope |
| self.mla_use_output_gate = mla_use_output_gate |
| |
| self.num_experts = num_experts |
| self.num_experts_per_token = num_experts_per_token |
| self.moe_renormalize = moe_renormalize |
| self.num_shared_experts = num_shared_experts |
| self.routed_scaling_factor = routed_scaling_factor |
| self.moe_router_activation_func = moe_router_activation_func |
| assert self.moe_router_activation_func in ("softmax", "sigmoid") |
| self.moe_intermediate_size = moe_intermediate_size |
| self.first_k_dense_replace = first_k_dense_replace |
| self.moe_layer_freq = moe_layer_freq |
| self.use_grouped_topk = use_grouped_topk |
| self.num_expert_group = num_expert_group |
| self.topk_group = topk_group |
| self.num_nextn_predict_layers = num_nextn_predict_layers |
|
|
| self.attn_res_block_size = attn_res_block_size |
| self.latent_moe_use_norm = latent_moe_use_norm |
| self.activation_situ_beta = activation_situ_beta |
| self.activation_situ_linear_beta = activation_situ_linear_beta |
| self.max_position_embeddings = max_position_embeddings |
| self.routed_expert_hidden_size = routed_expert_hidden_size |
| self.topk_method = topk_method |
|
|
| if linear_attn_config is not None: |
| assert linear_attn_config["kda_layers"] is not None |
| assert linear_attn_config["full_attn_layers"] is not None |
| self.linear_attn_config = linear_attn_config |
|
|
| 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, |
| ) |
|
|
| @property |
| def is_mla(self): |
| return ( |
| self.q_lora_rank is not None |
| or self.kv_lora_rank is not None |
| or self.qk_nope_head_dim is not None |
| or self.qk_rope_head_dim is not None |
| or self.v_head_dim is not None |
| or self.mla_use_nope is True |
| ) |
|
|
| @property |
| def is_moe(self): |
| return self.num_experts is not None |
|
|
| @property |
| def is_linear_attn(self) -> bool: |
| return not ( |
| self.linear_attn_config is None |
| or ( |
| isinstance(self.linear_attn_config, dict) |
| and self.linear_attn_config["kda_layers"] is not None |
| and len(self.linear_attn_config["kda_layers"]) == 0 |
| ) |
| ) |
|
|
| def is_kda_layer(self, layer_idx: int): |
| return ( |
| self.linear_attn_config is not None |
| and (layer_idx + 1) in self.linear_attn_config["kda_layers"] |
| ) |
|
|
|
|
| class KimiK3VisionConfig(PretrainedConfig): |
|
|
| def __init__( |
| self, |
| patch_size: int = 14, |
| init_pos_emb_height: int = 64, |
| init_pos_emb_width: int = 64, |
| init_pos_emb_time: int = 4, |
| pos_emb_type: str = 'divided_fixed', |
| vt_num_attention_heads: int = 12, |
| vt_num_hidden_layers: int = 27, |
| vt_hidden_size: int = 1024, |
| vt_intermediate_size: int = 4096, |
| merge_kernel_size: tuple = (2, 2), |
| merge_type: str = 'sd2_tpool', |
| _attn_implementation: str = 'flash_attention_2', |
| |
| mm_projector_type: str = 'patchmergerv2', |
| mm_hidden_size: int | None = None, |
| projector_hidden_act: str = "gelu", |
| projector_ln_eps: float = 1e-5, |
| |
| qkv_hidden_size: int = 1536, |
| norm_type: str = 'rmsnorm', |
| attn_bias: bool = False, |
| patch_embed_proj_bias: bool = False, |
| mlp_type: str = 'mlp2', |
| linear_bias: bool = False, |
| activation_func: str = 'gelu_pytorch_tanh', |
| pos_emb_interpolation_mode: str = 'bilinear', |
| |
| ignore_index: int = -100, |
| media_placeholder_token_id: int = 163605, |
| pad_token_id: int = 0, |
| text_hidden_size=7168, |
| **kwargs): |
|
|
| self.patch_size = patch_size |
| self.init_pos_emb_height = init_pos_emb_height |
| self.init_pos_emb_width = init_pos_emb_width |
| self.init_pos_emb_time = init_pos_emb_time |
| self.pos_emb_type = pos_emb_type |
| self.vt_num_attention_heads = vt_num_attention_heads |
| self.vt_num_hidden_layers = vt_num_hidden_layers |
| self.vt_hidden_size = vt_hidden_size |
| self.vt_intermediate_size = vt_intermediate_size |
| self.merge_kernel_size = merge_kernel_size |
| self.merge_type = merge_type |
| self._attn_implementation = _attn_implementation |
|
|
| |
| self.mm_projector_type = mm_projector_type |
| self.mm_hidden_size = mm_hidden_size if mm_hidden_size is not None else vt_hidden_size |
| self.projector_hidden_act = projector_hidden_act |
| self.projector_ln_eps = projector_ln_eps |
| self.text_hidden_size = text_hidden_size |
|
|
| |
| self.qkv_hidden_size = qkv_hidden_size |
| self.norm_type = norm_type |
| self.attn_bias = attn_bias |
| self.patch_embed_proj_bias = patch_embed_proj_bias |
| self.mlp_type = mlp_type |
| self.linear_bias = linear_bias |
| self.activation_func = activation_func |
| self.pos_emb_interpolation_mode = pos_emb_interpolation_mode |
|
|
| super().__init__(**kwargs) |
|
|
|
|
| class KimiK3Config(PretrainedConfig): |
| """Kimi-K3 model configuration. |
| |
| Args: |
| text_config (dict | KimiLinearConfig): Configuration for the text model. |
| |
| Vision Tower Parameters (from MoonViT3dConfig): |
| patch_size (int): Patch size for vision tower. |
| init_pos_emb_height (int): Initial position embedding height. |
| init_pos_emb_width (int): Initial position embedding width. |
| init_pos_emb_time (int): Initial position embedding time dimension. |
| pos_emb_type (str): Type of position embedding. |
| vt_num_attention_heads (int): Number of attention heads in vision tower. |
| vt_num_hidden_layers (int): Number of hidden layers in vision tower. |
| vt_hidden_size (int): Hidden size of vision tower. |
| vt_intermediate_size (int): Intermediate size in vision tower FFN. |
| merge_kernel_size (tuple): Kernel size for patch merging. |
| merge_type (str): Type of merge operation. |
| _attn_implementation (str): Attention implementation type. |
| |
| MM Projector Parameters (from MultiModalProjectorConfig): |
| mm_projector_type (str): Type of multimodal projector. |
| mm_hidden_size (int): Hidden size from vision tower (should match vt_hidden_size). |
| projector_hidden_act (str): Activation function for projector. |
| projector_ln_eps (float): Layer norm epsilon for projector. |
| |
| Other Parameters: |
| ignore_index (int): The ignore index for the loss function. |
| media_placeholder_token_id (int): The token ID to use for media placeholders. |
| pad_token_id (int): The token ID to use for padding. |
| """ |
|
|
| model_type = "kimi_k3" |
|
|
| def __init__( |
| self, |
| text_config: dict | KimiLinearConfig = None, |
| vision_config: dict | KimiK3VisionConfig = None, |
| |
| ignore_index: int = -100, |
| media_placeholder_token_id: int = 163605, |
| pad_token_id: int = 0, |
| **kwargs, |
| ): |
| if isinstance(text_config, dict): |
| text_config = KimiLinearConfig(**text_config) |
| if isinstance(vision_config, dict): |
| vision_config = KimiK3VisionConfig(**vision_config) |
| self.text_config = text_config |
| self.vision_config = vision_config |
| |
| self.ignore_index = ignore_index |
| self.media_placeholder_token_id = media_placeholder_token_id |
| if getattr(self.text_config, "quantization_config", None) is not None: |
| self.quantization_config = self.text_config.quantization_config |
|
|
| super().__init__(pad_token_id=pad_token_id, **kwargs) |
|
|
