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from transformers.configuration_utils import PretrainedConfig
class FM4BioConfig(PretrainedConfig):
model_type = "fm4bio"
def __init__(
self,
vocab_size=128,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="swiglu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=2048,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-05,
pad_token_id=0,
add_linear_bias=True,
position_embedding_type="rope",
normalization_type="RMSNorm",
use_cache=True,
rotary_percent=1.0,
seq_len_interpolation_factor=None,
moe=False,
num_experts=0,
experts_per_token=0,
use_lm_head=True,
tie_word_embeddings=True,
output_vocab_size: int = None, # when set, the output vocab size is different from the input vocab size
gradient_checkpointing=False, # << Pan: Gradient checkpoint for memory saving
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.add_linear_bias = add_linear_bias
assert normalization_type in [
"RMSNorm",
"LayerNorm",
], "normalization_type must be 'RMSNorm' or 'LayerNorm'"
self.normalization_type = normalization_type
self.rotary_percent = rotary_percent
self.seq_len_interpolation_factor = seq_len_interpolation_factor
self.moe = moe
self.num_experts = num_experts
self.experts_per_token = experts_per_token
self.use_lm_head = use_lm_head
self.tie_word_embeddings = tie_word_embeddings
self.output_vocab_size = output_vocab_size
self.gradient_checkpointing = gradient_checkpointing # << Pan: Gradient checkpoint for memory saving |