149 lines
5.3 KiB
Python
149 lines
5.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers.configuration_utils import PretrainedConfig
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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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_parameters=None,
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tie_word_embeddings=False,
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moe_intermediate_size: int | None = 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: int | None = None,
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num_experts_per_token: int | None = 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: int | None = None,
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kv_lora_rank: int | None = None,
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qk_nope_head_dim: int | None = None,
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qk_rope_head_dim: int | None = None,
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v_head_dim: int | None = None,
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mla_use_nope: bool | None = False,
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num_nextn_predict_layers: int = 0,
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linear_attn_config: dict | None = 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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# for backward compatibility
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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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# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
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rope_scaling = kwargs.pop("rope_scaling", None)
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rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
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rope_theta = kwargs.pop("rope_theta", 10000.0)
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if "rope_theta" not in rope_parameters:
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rope_parameters["rope_theta"] = rope_theta
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self.rope_parameters = rope_parameters
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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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# moe config
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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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