| |
| 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, |
| num_nextn_predict_layers: int = 0, |
| linear_attn_config: Optional[dict] = None, |
| **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.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 |
|
|
| 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"] |
| ) |
|
|
