# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/gemma3/modular_gemma3.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_gemma3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Google Inc. 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 typing import Any, Literal from ...configuration_utils import PreTrainedConfig, layer_type_validation from ...modeling_rope_utils import RopeParameters from ...utils import logging from ..siglip import SiglipVisionConfig logger = logging.get_logger(__name__) class Gemma3TextConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Gemma3TextModel`]. It is used to instantiate an Gemma3Text 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 Gemma3Text-7B. e.g. [google/gemma3_text-7b](https://huggingface.co/google/gemma3_text-7b) 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 262208): Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Gemma3TextModel`] hidden_size (`int`, *optional*, defaults to 2304): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 9216): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 26): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 4): 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`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. 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 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*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. 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. query_pre_attn_scalar (`float`, *optional*, defaults to 256): Scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): In Gemma3Text, every other layer uses sliding window attention. This is the size of the sliding window. layer_types (`list`, *optional*): Attention pattern for each layer. final_logit_softcapping (`float`, *optional*): Scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*): Scaling factor when applying tanh softcapping on the attention scores. rope_parameters (`dict`, *optional*): Dictionary mapping attention patterns (`"full_attention"`, `"sliding_attention"`) to `RopeParameters`. Each value should be a dictionary containing `rope_type` and optional scaling parameters. use_bidirectional_attention (`bool`, *optional*, defaults to `False`): If True, the model will attend to all text tokens instead of using a causal mask. This does not change behavior for vision tokens. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings ```python >>> from transformers import Gemma3TextModel, Gemma3TextConfig >>> # Initializing a Gemma3Text gemma3_text-7b style configuration >>> configuration = Gemma3TextConfig() >>> # Initializing a model from the gemma3_text-7b style configuration >>> model = Gemma3TextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "gemma3_text" 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_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.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"]), } default_theta = {"global": 1_000_000.0, "local": 10_000.0} def __init__( self, vocab_size: int | None = 262_208, hidden_size: int | None = 2304, intermediate_size: int | None = 9216, num_hidden_layers: int | None = 26, num_attention_heads: int | None = 8, num_key_value_heads: int | None = 4, head_dim: int | None = 256, hidden_activation: str | None = "gelu_pytorch_tanh", max_position_embeddings: int | None = 131_072, initializer_range: float | None = 0.02, rms_norm_eps: int | None = 1e-6, use_cache: bool | None = True, pad_token_id: int | None = 0, eos_token_id: int | None = 1, bos_token_id: int | None = 2, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, query_pre_attn_scalar: int | None = 256, sliding_window: int | None = 4096, layer_types: list[str] | None = None, final_logit_softcapping: float | None = None, attn_logit_softcapping: float | None = None, rope_parameters: dict[Literal["full_attention", "sliding_attention"], RopeParameters] | None = None, use_bidirectional_attention: bool | None = False, tie_word_embeddings: bool | None = True, **kwargs, ): self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.tie_word_embeddings = tie_word_embeddings 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.head_dim = head_dim self.num_key_value_heads = num_key_value_heads self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.hidden_activation = hidden_activation self.query_pre_attn_scalar = query_pre_attn_scalar self.sliding_window = sliding_window self.final_logit_softcapping = final_logit_softcapping self.attn_logit_softcapping = attn_logit_softcapping self.layer_types = layer_types self.use_bidirectional_attention = use_bidirectional_attention if use_bidirectional_attention: self.sliding_window = (self.sliding_window // 2) + 1 # due to fa we set exclusive bounds # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6) if self.layer_types is None: self.layer_types = [ "sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types, self.num_hidden_layers) self.rope_parameters = rope_parameters super().__init__(**kwargs) def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs): rope_scaling = kwargs.pop("rope_scaling", None) # Try to set `rope_scaling` if available, otherwise use `rope_parameters`. If we find `rope_parameters` # as arg in the inputs, we can safely assume that it is in the new format. New naming used -> new format default_rope_params = { "sliding_attention": {"rope_type": "default"}, "full_attention": {"rope_type": "default"}, } self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else default_rope_params if rope_scaling is not None: self.rope_parameters["full_attention"].update(rope_scaling) # Set default values if not present if self.rope_parameters.get("full_attention") is None: self.rope_parameters["full_attention"] = {"rope_type": "default"} self.rope_parameters["full_attention"].setdefault( "rope_theta", kwargs.pop("rope_theta", self.default_theta["global"]) ) if self.rope_parameters.get("sliding_attention") is None: self.rope_parameters["sliding_attention"] = {"rope_type": "default"} self.rope_parameters["sliding_attention"].setdefault( "rope_theta", kwargs.pop("rope_local_base_freq", self.default_theta["local"]) ) # Standardize and validate the correctness of rotary position embeddings parameters self.standardize_rope_params() self.validate_rope(ignore_keys=ignore_keys_at_rope_validation) return kwargs class Gemma3Config(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Gemma3ForConditionalGeneration`]. It is used to instantiate an Gemma3ForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PaliGemma-2B. e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b) 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[Gemma3TextConfig, dict]`, *optional*): The config object of the text backbone. vision_config (`Union[AutoConfig, dict]`, *optional*): Custom vision config or dict. mm_tokens_per_image (`int`, *optional*, defaults to 256): The number of tokens per image embedding. boi_token_index (`int`, *optional*, defaults to 255999): The begin-of-image token index to wrap the image prompt. eoi_token_index (`int`, *optional*, defaults to 256000): The end-of-image token index to wrap the image prompt. image_token_index (`int`, *optional*, defaults to 262144): The image token index to encode the image prompt. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig >>> # Initializing a Siglip-like vision config >>> vision_config = SiglipVisionConfig() >>> # Initializing a Gemma3 Text config >>> text_config = Gemma3TextConfig() >>> # Initializing a Gemma3 gemma-3-4b style configuration >>> configuration = Gemma3Config(vision_config, text_config) >>> # Initializing a model from the gemma-3-4b style configuration >>> model = Gemma3TextConfig(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gemma3" attribute_map = { "image_token_id": "image_token_index", "boi_token_id": "boi_token_index", "eoi_token_id": "eoi_token_index", } sub_configs = { "text_config": Gemma3TextConfig, "vision_config": SiglipVisionConfig, } def __init__( self, text_config: Gemma3TextConfig | dict[str, Any] | None = None, vision_config: SiglipVisionConfig | dict[str, Any] | None = None, mm_tokens_per_image: int | None = 256, boi_token_index: int | None = 255_999, eoi_token_index: int | None = 256_000, image_token_index: int | None = 262_144, initializer_range: float | None = 0.02, tie_word_embeddings: bool | None = True, **kwargs, ): if text_config is None: text_config = Gemma3TextConfig() logger.info("text_config is None, using default Gemma3TextConfig text config.") elif isinstance(text_config, dict): text_config = Gemma3TextConfig(**text_config) if isinstance(vision_config, dict): vision_config = SiglipVisionConfig(**vision_config) elif vision_config is None: vision_config = SiglipVisionConfig() logger.info("vision_config is None, using default SiglipVisionConfig vision config.") self.text_config = text_config self.vision_config = vision_config self.mm_tokens_per_image = mm_tokens_per_image self.boi_token_index = boi_token_index self.eoi_token_index = eoi_token_index self.image_token_index = image_token_index self.initializer_range = initializer_range self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["Gemma3Config", "Gemma3TextConfig"]