# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/glm4v_moe/modular_glm4v_moe.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_glm4v_moe.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The ZhipuAI Inc. 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 from ...modeling_rope_utils import rope_config_validation class Glm4vMoeVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vMoeVisionModel`]. It is used to instantiate an Glm4vMoeVisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking). Args: hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. depth (`int`, *optional*, defaults to 24): Number of layers (depth) in the model. attention_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the queries, keys and values. intermediate_size (`int`, *optional*, defaults to 13696): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"selu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for attention weights. projection_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the projection layer. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. image_size (`int` or `list[int]`, *optional*, defaults to `[336, 336]`): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to `14`): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. out_hidden_size (`int`, *optional*, defaults to 4096): The output hidden size of the vision model. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. spatial_merge_size (`int`, *optional*, defaults to 2): The size used for merging spatial dimensions. temporal_patch_size (`int`, *optional*, defaults to 2): The size used for patches along the temporal dimension. Example: ```python >>> from transformers import Glm4vMoeVisionConfig, Glm4vMoeVisionModel >>> # Initializing a Glm4vMoeVisionConfig GLM-4.1V-9B style configuration >>> configuration = Glm4vMoeVisionConfig() >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration >>> model = Glm4vMoeVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm4v_moe" base_config_key = "vision_config" def __init__( self, depth=24, hidden_size=1536, hidden_act="silu", attention_bias=False, attention_dropout=0.0, num_heads=12, in_channels=3, image_size=336, patch_size=14, rms_norm_eps=1e-05, spatial_merge_size=2, temporal_patch_size=2, out_hidden_size=4096, intermediate_size=13696, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.num_heads = num_heads self.in_channels = in_channels self.image_size = image_size self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.out_hidden_size = out_hidden_size self.intermediate_size = intermediate_size self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.attention_bias = attention_bias self.attention_dropout = attention_dropout class Glm4vMoeTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vMoeModel`]. It is used to instantiate a GLM-4.5V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.5V [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V). 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 151424): Vocabulary size of the Glm4vMoe model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Glm4vMoeModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 10944): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 46): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 96): Number of attention heads for each attention layer in the Transformer encoder. partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position. num_key_value_heads (`int`, *optional*, defaults to 8): 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 checkout [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. 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 65536): 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 1e-05): 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 the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. attention_bias (`bool`, defaults to `True`, *optional*, defaults to `True`): 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. moe_intermediate_size (`int`, *optional*, defaults to 1408): Intermediate size of the routed expert. num_experts_per_tok (`int`, *optional*, defaults to 8): number of experts per token. n_shared_experts (`int`, *optional*, defaults to 1): Number of shared experts. n_routed_experts (`int`, *optional*, defaults to 128): Number of routed experts. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor or routed experts. n_group (`int`, *optional*, defaults to 1): Number of groups for routed experts. topk_group (`int`, *optional*, defaults to 1): Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups). first_k_dense_replace (`int`, *optional*, defaults to 1): Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). \--k dense layers--/ norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize the topk probabilities. ```python >>> from transformers import Glm4vMoeTextModel, Glm4vMoeConfig >>> # Initializing a GLM-4.5V style configuration >>> configuration = Glm4vMoeConfig() >>> # Initializing a model from the GLM-4.5V style configuration >>> model = Glm4vMoeTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "Glm4vMoe_text" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `Glm4vMoe` 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"]), } base_config_key = "text_config" def __init__( self, vocab_size=151424, hidden_size=4096, intermediate_size=10944, num_hidden_layers=46, num_attention_heads=96, partial_rotary_factor=0.5, num_key_value_heads=8, hidden_act="silu", max_position_embeddings=65536, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=True, attention_dropout=0.0, moe_intermediate_size=1408, num_experts_per_tok=8, n_shared_experts=1, n_routed_experts=128, routed_scaling_factor=1.0, n_group=1, topk_group=1, first_k_dense_replace=1, norm_topk_prob=True, **kwargs, ): super().__init__(tie_word_embeddings=tie_word_embeddings, **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.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 # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move 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"] rope_config_validation(self, ignore_keys={"mrope_section"}) # MoE arguments self.moe_intermediate_size = moe_intermediate_size self.num_experts_per_tok = num_experts_per_tok self.n_group = n_group self.topk_group = topk_group self.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.routed_scaling_factor = routed_scaling_factor self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob class Glm4vMoeConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vMoeModel`]. It is used to instantiate a GLM-4.5V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.5V [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vMoeTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vMoeVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151363): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151364): The video token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 151339): The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 151340): The image end token index to encode the end of image. video_start_token_id (`int`, *optional*, defaults to 151341): The video start token index to encode the start of video. video_end_token_id (`int`, *optional*, defaults to 151342): The video end token index to encode the end of video. ```python >>> from transformers import Glm4vMoeForConditionalGeneration, Glm4vMoeConfig >>> # Initializing a GLM-4.5V style configuration >>> configuration = Glm4vMoeConfig() >>> # Initializing a model from the GLM-4.5V style configuration >>> model = Glm4vMoeForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm4v_moe" sub_configs = {"vision_config": Glm4vMoeVisionConfig, "text_config": Glm4vMoeTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=151363, video_token_id=151364, image_start_token_id=151339, image_end_token_id=151340, video_start_token_id=151341, video_end_token_id=151342, **kwargs, ): super().__init__(**kwargs) if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: # For BC use all kwargs to init `TextConfig` self.text_config = self.sub_configs["text_config"](**kwargs) self.image_token_id = image_token_id self.video_token_id = video_token_id self.video_start_token_id = video_start_token_id self.video_end_token_id = video_end_token_id self.image_start_token_id = image_start_token_id self.image_end_token_id = image_end_token_id __all__ = ["Glm4vMoeConfig", "Glm4vMoeTextConfig"]