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"""TPU Gemma3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class TPUGemma3Config(PretrainedConfig):
model_type = "tpu_gemma3"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=262_208,
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=131_072,
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=1_000_000.0,
attention_bias=False,
attention_dropout=0.0,
query_pre_attn_scalar=256,
sliding_window=4096,
final_logit_softcapping=None,
attn_logit_softcapping=None,
cache_implementation="hybrid",
rope_scaling=None,
rope_local_base_freq=10_000.0,
sliding_window_pattern=6,
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,
project_mode=None, # latent projection args
previous_hidden_size=None,
skip_out_norm=False,
**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.rope_local_base_freq = rope_local_base_freq
# For configuring HybridCache to work with 5:1 attention pattern
self.sliding_window_pattern = sliding_window_pattern
self.rope_scaling = rope_scaling
rope_config_validation(self)
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
self.project_mode = project_mode
self.previous_hidden_size = previous_hidden_size
self.skip_out_norm = skip_out_norm |