# coding=utf-8 # Copyright 2025 The GLM4 & ZhipuAI team and 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 ...configuration_utils import PretrainedConfig class Glm4Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4Model`]. It is used to instantiate an Glm4 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 Glm4-4-9b-chat. e.g. [THUDM/GLM-4-9B-0414](https://huggingface.co/THUDM/GLM-4-9B-0414) 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 151552): Vocabulary size of the Glm4 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Glm4Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 13696): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 2): 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 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, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position. head_dim (`int`, *optional*, defaults to 128): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The legacy activation function. It is overwritten by the `hidden_activation`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. 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 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1.5625e-07): 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. pad_token_id (`int`, *optional*, defaults to 151329): Padding token id. eos_token_id (`int` | `list`, *optional*, defaults to `[151329, 151336, 151338]`): End of stream token id. bos_token_id (`int`, *optional*): Beginning of stream token id. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers during self-attention. ```python >>> from transformers import Glm4Model, Glm4Config >>> # Initializing a Glm4 glm4-4-9b-chat style configuration >>> configuration = Glm4Config() >>> # Initializing a model from the glm4-4-9b-chat style configuration >>> model = Glm4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm4" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_up_proj": "colwise_rep", # we need to replicate here due to the `chunk` operation "layers.*.mlp.down_proj": "rowwise_rep", # we need to replicate here due to the `chunk` operation } 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=151552, hidden_size=4096, intermediate_size=13696, num_hidden_layers=40, num_attention_heads=32, num_key_value_heads=2, partial_rotary_factor=0.5, head_dim=128, hidden_act="silu", attention_dropout=0.0, max_position_embeddings=131072, initializer_range=0.02, rms_norm_eps=0.00000015625, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, pad_token_id=151329, eos_token_id=[151329, 151336, 151338], bos_token_id=None, attention_bias=True, **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.partial_rotary_factor = partial_rotary_factor self.head_dim = head_dim 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.attention_bias = attention_bias self.attention_dropout = attention_dropout 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__ = ["Glm4Config"]