# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/t5gemma/modular_t5gemma.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_t5gemma.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # 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, Optional, Union from ...configuration_utils import PretrainedConfig, layer_type_validation class T5GemmaModuleConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`T5GemmaModuleModel`]. It is used to instantiate an T5GemmaModule 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 T5GemmaModule-7B. e.g. [google/t5_gemma_module-7b](https://huggingface.co/google/t5_gemma_module-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 256000): Vocabulary size of the T5GemmaModule model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`T5GemmaModuleModel`] 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 8192): 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. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE 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. query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in T5GemmaModule, 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*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores. ```python >>> from transformers import T5GemmaModuleModel, T5GemmaModuleConfig >>> # Initializing a T5GemmaModule t5_gemma_module-7b style configuration >>> configuration = T5GemmaModuleConfig() >>> # Initializing a model from the t5_gemma_module-7b style configuration >>> model = T5GemmaModuleModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "t5_gemma_module" 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"]), } def __init__( self, vocab_size=256000, hidden_size=2304, intermediate_size=9216, num_hidden_layers=26, num_attention_heads=8, num_key_value_heads=4, head_dim=256, hidden_activation="gelu_pytorch_tanh", max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, eos_token_id=1, bos_token_id=2, tie_word_embeddings=True, rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, query_pre_attn_scalar=256, sliding_window=4096, layer_types=None, final_logit_softcapping=30.0, attn_logit_softcapping=50.0, **kwargs, ): 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, ) 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.rope_theta = rope_theta 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 if self.layer_types is None: self.layer_types = [ "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) class T5GemmaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`T5GemmaModel`]. It is used to instantiate an T5Gemma model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to a hypothetical balanced Gemma2 encoder-decoder model. e.g. [google/t5gemma-2b-2b-prefixlm-it](https://huggingface.co/google/t5gemma-2b-2b-prefixlm-it) ```python >>> from transformers import T5GemmaConfig, T5GemmaModel >>> t5gemma_config = T5GemmaConfig.from_pretrained("google/t5gemma-2b-2b-prefixlm-it") >>> model = T5GemmaModel(t5gemma_config) ``` Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information. Args: encoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*): Configuration for the encoder. decoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*): Configuration for the decoder. is_encoder_decoder (bool, optional, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. dropout_rate (`float`, *optional*, defaults to 0.0): The ratio for all dropout layers (following T5). classifier_dropout_rate (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier (following T5). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for attention. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether tie input and output embeddings. vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the T5Gemma model (the same as Gemma 2). kwargs (additional keyword arguments, optional, *optional*): Will be passed to the PretrainedConfig base class. """ model_type = "t5gemma" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { # encoder "encoder.layers.*.self_attn.q_proj": "colwise", "encoder.layers.*.self_attn.k_proj": "colwise", "encoder.layers.*.self_attn.v_proj": "colwise", "encoder.layers.*.self_attn.o_proj": "rowwise", "encoder.layers.*.mlp.gate_proj": "colwise", "encoder.layers.*.mlp.up_proj": "colwise", "encoder.layers.*.mlp.down_proj": "rowwise", # decoder "decoder.layers.*.self_attn.q_proj": "colwise", "decoder.layers.*.self_attn.k_proj": "colwise", "decoder.layers.*.self_attn.v_proj": "colwise", "decoder.layers.*.self_attn.o_proj": "rowwise", "decoder.layers.*.cross_attn.q_proj": "colwise", "decoder.layers.*.cross_attn.k_proj": "colwise", "decoder.layers.*.cross_attn.v_proj": "colwise", "decoder.layers.*.cross_attn.o_proj": "rowwise", "decoder.layers.*.mlp.gate_proj": "colwise", "decoder.layers.*.mlp.up_proj": "colwise", "decoder.layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { # encoder "encoder.embed_tokens": (["input_ids"], ["inputs_embeds"]), "encoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "encoder.norm": (["hidden_states"], ["hidden_states"]), # decoder "decoder.embed_tokens": (["input_ids"], ["inputs_embeds"]), "decoder.layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "decoder.norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, encoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None, decoder: Optional[Union[T5GemmaModuleConfig, dict[Any, Any]]] = None, is_encoder_decoder: bool = True, dropout_rate: float = 0.0, classifier_dropout_rate: float = 0.0, attention_dropout: float = 0.0, tie_word_embeddings: bool = True, vocab_size: int = 256000, **kwargs, ): if isinstance(encoder, dict): encoder = T5GemmaModuleConfig(**encoder) elif encoder is None: encoder = T5GemmaModuleConfig() else: assert isinstance(encoder, T5GemmaModuleConfig), f"{type(encoder)} is not supported." if isinstance(decoder, dict): decoder = T5GemmaModuleConfig(**decoder) elif decoder is None: decoder = encoder else: assert isinstance(decoder, T5GemmaModuleConfig), f"{type(decoder)} is not supported." encoder = T5GemmaModuleConfig(**encoder.to_dict()) decoder = T5GemmaModuleConfig(**decoder.to_dict()) encoder.is_decoder = False encoder.dropout_rate = dropout_rate encoder.attention_dropout = attention_dropout self.encoder = encoder decoder.is_decoder = True decoder.use_cache = True decoder.dropout_rate = dropout_rate decoder.attention_dropout = attention_dropout decoder.cross_attention_hidden_size = encoder.hidden_size self.decoder = decoder for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id"]: if special_token_key not in kwargs: kwargs[special_token_key] = getattr(decoder, special_token_key) super().__init__(**kwargs) self.is_encoder_decoder = is_encoder_decoder self.use_cache = kwargs.get("use_cache", decoder.use_cache) self.initializer_range = kwargs.get("initializer_range", decoder.initializer_range) self.dropout_rate = dropout_rate self.attention_dropout = attention_dropout self.classifier_dropout_rate = classifier_dropout_rate self.tie_word_embeddings = tie_word_embeddings # Used in pipeline generation. self.vocab_size = vocab_size def __setattr__(self, key, value): shared_attr_with_submodules = [ "output_hidden_states", "output_attentions", "_attn_implementation", "dropout_rate", "attention_dropout", "vocab_size", ] if key in shared_attr_with_submodules: setattr(self.encoder, key, value) setattr(self.decoder, key, value) super().__setattr__(key, value) def get_text_config(self, decoder=False): # Always return self, regardless of the decoder option. del decoder return self __all__ = ["T5GemmaConfig", "T5GemmaModuleConfig"]