# coding=utf-8 # Copyright 2025 Tencent Youtu Lab 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. from transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation Youtu_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class YoutuConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`YoutuModel`]. It is used to instantiate an Youtu 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 Youtu-LLM-2B. e.g. [tencent/Youtu-LLM-2B](https://huggingface.co/tencent/Youtu-LLM-2B) 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 128256): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`YoutuModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 6144): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): In MLA, num_key_value_heads=num_attention_heads. kv_lora_rank (`int`, *optional*, defaults to 512): Rank of the LoRA matrices for key and value projections. q_lora_rank (`int`, *optional*, defaults to 1536): Rank of the LoRA matrices for query projections. qk_rope_head_dim (`int`, *optional*, defaults to 64): Dimension of the query/key heads that use rotary position embeddings. v_head_dim (`int`, *optional*, defaults to 128): Dimension of the value heads. qk_nope_head_dim (`int`, *optional*, defaults to 128): Dimension of the query/key heads that don't use rotary position embeddings. 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. initializer_range (`float`, *optional*, defaults to None): The standard deviation of the truncated_normal_initializer for initializing all weight matrices, except embedding matrices. embedding_initializer_range (`float`, *optional*, defaults to None): The standard deviation of the truncated_normal_initializer for initializing all embedding matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon 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 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 1600000): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*, defaults to `None`): 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}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. rope_interleave (`bool`, *optional*, defaults to `True`): Whether to interleave the rotary position embeddings. attention_bias (`bool`, defaults to `False`, *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. ```python >>> from transformers import YoutuModel, YoutuConfig >>> # Initializing a Youtu-LLM-2B style configuration >>> configuration = YoutuConfig() >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "youtu_llm" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.mlp.gate_proj": "local_colwise", "layers.*.mlp.up_proj": "local_colwise", "layers.*.mlp.down_proj": "local_rowwise", "layers.*.mlp": "gather", # This is the only moment where results are gathered } 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=128256, hidden_size=2048, intermediate_size=6144, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=16, kv_lora_rank=512, q_lora_rank=1536, qk_rope_head_dim=64, v_head_dim=128, qk_nope_head_dim=128, hidden_act="silu", max_position_embeddings=131072, initializer_range=None, embedding_initializer_range=None, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, bos_token_id=128000, eos_token_id=128001, tie_word_embeddings=True, rope_theta=1600000, rope_scaling=None, rope_interleave=True, attention_bias=False, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.head_dim = qk_rope_head_dim self.rope_interleave = rope_interleave # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.mlp_bias = False self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act # if initializer_range is None, set it to 2.0 / (5.0 * self.hidden_size) ** 0.5 self.initializer_range = (2.0 / (5.0 * self.hidden_size)) ** 0.5 if initializer_range is None else initializer_range # if embedding_initializer_range is None, set it to 2.0 * self.initializer_range self.embedding_initializer_range = self.initializer_range * 2.0 if embedding_initializer_range is None else embedding_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 # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, copy it it to 'rope_type'. 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__ = ["YoutuConfig"]