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from typing import Optional |
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from transformers.configuration_utils import PretrainedConfig |
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class KimiLinearConfig(PretrainedConfig): |
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model_type = "kimi_linear" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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model_type="kimi_linear", |
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vocab_size=163840, |
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hidden_size=4096, |
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head_dim=None, |
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intermediate_size=11008, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=None, |
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hidden_act="silu", |
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initializer_range=0.02, |
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rms_norm_eps=1e-6, |
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use_cache=True, |
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pad_token_id=0, |
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bos_token_id=1, |
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eos_token_id=2, |
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rope_theta=10000.0, |
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rope_scaling=None, |
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tie_word_embeddings=False, |
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moe_intermediate_size: Optional[int] = None, |
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moe_renormalize: bool = True, |
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moe_router_activation_func: str = "sigmoid", |
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num_experts: Optional[int] = None, |
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num_experts_per_token: Optional[int] = None, |
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num_shared_experts: int = 0, |
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routed_scaling_factor: float = 1.0, |
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first_k_dense_replace: int = 0, |
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moe_layer_freq: int = 1, |
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use_grouped_topk: bool = True, |
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num_expert_group: int = 1, |
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topk_group: int = 1, |
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q_lora_rank: Optional[int] = None, |
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kv_lora_rank: Optional[int] = None, |
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qk_nope_head_dim: Optional[int] = None, |
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qk_rope_head_dim: Optional[int] = None, |
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v_head_dim: Optional[int] = None, |
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mla_use_nope: Optional[bool] = False, |
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num_nextn_predict_layers: int = 0, |
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linear_attn_config: Optional[dict] = None, |
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**kwargs, |
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): |
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self.model_type = model_type |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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self.head_dim = ( |
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head_dim if head_dim is not None else hidden_size // num_attention_heads |
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) |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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if num_key_value_heads is None: |
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num_key_value_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.hidden_act = hidden_act |
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self.initializer_range = initializer_range |
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self.rms_norm_eps = rms_norm_eps |
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self.use_cache = use_cache |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.q_lora_rank = q_lora_rank |
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self.kv_lora_rank = kv_lora_rank |
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self.qk_nope_head_dim = qk_nope_head_dim |
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self.qk_rope_head_dim = qk_rope_head_dim |
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self.v_head_dim = v_head_dim |
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self.mla_use_nope = mla_use_nope |
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self.num_experts = num_experts |
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self.num_experts_per_token = num_experts_per_token |
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self.moe_renormalize = moe_renormalize |
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self.num_shared_experts = num_shared_experts |
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self.routed_scaling_factor = routed_scaling_factor |
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self.moe_router_activation_func = moe_router_activation_func |
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assert self.moe_router_activation_func in ("softmax", "sigmoid") |
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self.moe_intermediate_size = moe_intermediate_size |
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self.first_k_dense_replace = first_k_dense_replace |
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self.moe_layer_freq = moe_layer_freq |
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self.use_grouped_topk = use_grouped_topk |
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self.num_expert_group = num_expert_group |
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self.topk_group = topk_group |
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self.num_nextn_predict_layers = num_nextn_predict_layers |
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if linear_attn_config is not None: |
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assert linear_attn_config["kda_layers"] is not None |
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assert linear_attn_config["full_attn_layers"] is not None |
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self.linear_attn_config = linear_attn_config |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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@property |
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def is_mla(self): |
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return ( |
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self.q_lora_rank is not None |
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or self.kv_lora_rank is not None |
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or self.qk_nope_head_dim is not None |
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or self.qk_rope_head_dim is not None |
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or self.v_head_dim is not None |
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or self.mla_use_nope is True |
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) |
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@property |
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def is_moe(self): |
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return self.num_experts is not None |
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@property |
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def is_linear_attn(self) -> bool: |
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return not ( |
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self.linear_attn_config is None |
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or ( |
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isinstance(self.linear_attn_config, dict) |
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and self.linear_attn_config["kda_layers"] is not None |
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and len(self.linear_attn_config["kda_layers"]) == 0 |
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) |
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) |
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def is_kda_layer(self, layer_idx: int): |
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return ( |
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self.linear_attn_config is not None |
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and (layer_idx + 1) in self.linear_attn_config["kda_layers"] |
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) |
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