| | |
| | 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"] |
| | ) |
| |
|
