"""TPU Gemma2 model configuration""" from transformers.configuration_utils import PretrainedConfig class TPUGemma2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2 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 Gemma2-7B. e.g. [google/gemma2-7b](https://huggingface.co/google/gemma2-7b) 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 Gemma2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Gemma2Model`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): 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://arxiv.org/pdf/2305.13245.pdf). 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 224): scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the size of the sliding window. 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. cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`. ```python >>> from transformers import Gemma2Model, Gemma2Config >>> # Initializing a Gemma2 gemma2-7b style configuration >>> configuration = Gemma2Config() >>> # Initializing a model from the gemma2-7b style configuration >>> model = Gemma2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "tpu_gemma2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=256000, hidden_size=3072, intermediate_size=24576, num_hidden_layers=28, num_attention_heads=16, num_key_value_heads=16, 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=224, sliding_window=4096, final_logit_softcapping=30.0, attn_logit_softcapping=50.0, cache_implementation="hybrid", expand_input_ids=False, # Transformers-native PyTorch generation support expand_input_ids_maxlen=None, expand_input_ids_vocab_size=None, expand_input_ids_dict=None, **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.cache_implementation = cache_implementation self.expand_input_ids = expand_input_ids self.expand_input_ids_maxlen = expand_input_ids_maxlen self.expand_input_ids_vocab_size = expand_input_ids_vocab_size self.expand_input_ids_dict = expand_input_ids_dict