# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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 from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import auto_docstring @auto_docstring(checkpoint="google/t5_gemma_module-7b") @strict class T5GemmaModuleConfig(PreTrainedConfig): r""" query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores 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"]), } vocab_size: int = 256000 hidden_size: int = 2304 intermediate_size: int = 9216 num_hidden_layers: int = 26 num_attention_heads: int = 8 num_key_value_heads: int = 4 head_dim: int = 256 hidden_activation: str = "gelu_pytorch_tanh" max_position_embeddings: int = 8192 initializer_range: float = 0.02 rms_norm_eps: float = 1e-6 use_cache: bool = True pad_token_id: int | None = 0 eos_token_id: int | list[int] | None = 1 bos_token_id: int | None = 2 tie_word_embeddings: bool = True rope_parameters: RopeParameters | dict | None = None attention_bias: bool = False attention_dropout: int | float | None = 0.0 query_pre_attn_scalar: int = 256 sliding_window: int | None = 4096 layer_types: list[str] | None = None final_logit_softcapping: float | None = 30.0 attn_logit_softcapping: float | None = 50.0 is_decoder: bool = False def __post_init__(self, **kwargs): 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) ] super().__post_init__(**kwargs) def validate_architecture(self): """Part of `@strict`-powered validation. Validates the architecture of the config.""" if self.hidden_size % self.num_attention_heads != 0: raise ValueError( f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " f"heads ({self.num_attention_heads})." ) @auto_docstring(checkpoint="google/t5_gemma_module-7b") @strict class T5GemmaConfig(PreTrainedConfig): r""" encoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*): Configuration for the encoder. decoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*): Configuration for the decoder. Example: ```python >>> from transformers import T5GemmaConfig, T5GemmaModel >>> t5gemma_config = T5GemmaConfig.from_pretrained("google/t5gemma-2b-2b-prefixlm-it") >>> model = T5GemmaModel(t5gemma_config) ```""" model_type = "t5gemma" keys_to_ignore_at_inference = ["past_key_values"] sub_configs = {"encoder": T5GemmaModuleConfig, "decoder": T5GemmaModuleConfig} encoder: T5GemmaModuleConfig | dict[Any, Any] | None = None decoder: T5GemmaModuleConfig | dict[Any, Any] | None = None is_encoder_decoder: bool = True dropout_rate: int | float = 0.0 classifier_dropout_rate: int | float = 0.0 attention_dropout: float | int = 0.0 tie_word_embeddings: bool = True vocab_size: int = 256000 def __post_init__(self, **kwargs): if isinstance(self.encoder, dict): self.encoder = T5GemmaModuleConfig(**self.encoder) elif self.encoder is None: self.encoder = T5GemmaModuleConfig() if isinstance(self.decoder, dict): self.decoder = T5GemmaModuleConfig(**self.decoder) elif self.decoder is None: self.decoder = T5GemmaModuleConfig() self.encoder.is_decoder = False self.encoder.dropout_rate = self.dropout_rate self.encoder.attention_dropout = self.attention_dropout self.decoder.is_decoder = True self.decoder.use_cache = True self.decoder.dropout_rate = self.dropout_rate self.decoder.attention_dropout = self.attention_dropout self.decoder.cross_attention_hidden_size = self.encoder.hidden_size self.initializer_range = kwargs.pop("initializer_range", self.decoder.initializer_range) 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(self.decoder, special_token_key) super().__post_init__(**kwargs) __all__ = ["T5GemmaConfig", "T5GemmaModuleConfig"]