# coding=utf-8 # Copyright 2025 Meituan and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """LongCat Flash model configuration""" from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation class LongcatFlashConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LongcatFlashModel`]. It is used to instantiate a LongCat Flash model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LongCat Flash architecture. e.g. [meituan-longcat/LongCat-Flash-Chat](https://huggingface.co/meituan-longcat/LongCat-Flash-Chat) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 131072): Vocabulary size of the LongCat Flash model. Defines the number of different tokens that can be represented by the `input_ids` passed when calling [`LongcatFlashModel`] hidden_size (`int`, *optional*, defaults to 6144): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 56): Number of hidden layers in the Transformer decoder. num_layers (`int`, *optional*, defaults to 28): number of layers, each with 2 sublayers. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting from a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 131072): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon value used by the RMS normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie input and output embeddings. rope_theta (`float`, *optional*, defaults to 10000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimension of the MLP representations. q_lora_rank (`int`, *optional*, defaults to 1536): The rank of the query LoRA projection in MLA (Multi-head Latent Attention). kv_lora_rank (`int`, *optional*, defaults to 512): The rank of the key-value LoRA projection in MLA. qk_nope_head_dim (`int`, *optional*, defaults to 128): The dimension of the non-position encoding part of query/key heads. qk_rope_head_dim (`int`, *optional*, defaults to 64): The dimension of the RoPE part of query/key heads. head_dim (`int`, *optional*, defaults to 64): Standard dimension of qk heads, unused except for CI. v_head_dim (`int`, *optional*, defaults to 128): The dimension of value heads. qk_head_dim (`int`, *optional*): The total dimension of query/key heads. If not specified, set to `qk_nope_head_dim + qk_rope_head_dim`. moe_topk (`int`, *optional*, defaults to 12): Number of experts to route to for each token in the MoE layer. n_routed_experts (`int`, *optional*, defaults to 512): Number of routed experts in the MoE layer. zero_expert_num (`int`, *optional*, defaults to 256): Number of zero experts (identity function) to add to the expert pool. expert_ffn_hidden_size (`int`, *optional*, defaults to 2048): Hidden size of individual expert FFN layers. routed_scaling_factor (`float`, *optional*, defaults to 6.0): Scaling factor applied to the routing weights. ```python >>> from transformers import LongcatFlashModel, LongcatFlashConfig >>> # Initializing a LongCat Flash style configuration >>> configuration = LongcatFlashConfig() >>> # Initializing a model from the configuration >>> model = LongcatFlashModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "longcat_flash" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.*.q_b_proj": "colwise", "layers.*.self_attn.*.kv_b_proj": "colwise", "layers.*.self_attn.*.o_proj": "rowwise", "layers.*.mlps.*.gate_proj": "colwise", "layers.*.mlps.*.up_proj": "colwise", "layers.*.mlps.*.down_proj": "rowwise", "layers.*.mlp.experts.*.gate_proj": "colwise", "layers.*.mlp.experts.*.up_proj": "colwise", "layers.*.mlp.experts.*.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=131072, hidden_size=6144, num_hidden_layers=56, num_layers=28, num_attention_heads=64, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, ffn_hidden_size=12288, q_lora_rank=1536, kv_lora_rank=512, qk_nope_head_dim=128, qk_rope_head_dim=64, head_dim=64, v_head_dim=128, qk_head_dim=None, moe_topk=12, n_routed_experts=512, zero_expert_num=256, expert_ffn_hidden_size=2048, routed_scaling_factor=6.0, **kwargs, ): if num_key_value_heads is None: num_key_value_heads = num_attention_heads if qk_head_dim is None: qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_layers = num_layers self.num_hidden_layers = num_hidden_layers self.num_attention_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.attention_bias = attention_bias self.attention_dropout = attention_dropout self.ffn_hidden_size = ffn_hidden_size 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.qk_head_dim = qk_head_dim self.head_dim = head_dim self.moe_topk = moe_topk self.n_routed_experts = n_routed_experts self.zero_expert_num = zero_expert_num self.expert_ffn_hidden_size = expert_ffn_hidden_size self.routed_scaling_factor = routed_scaling_factor if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] if self.rope_scaling is not None: for key in ["beta_fast", "beta_slow", "factor"]: if key in self.rope_scaling: self.rope_scaling[key] = float(self.rope_scaling[key]) rope_config_validation(self) 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, ) __all__ = ["LongcatFlashConfig"]