Source code for immuneML.ml_methods.generative_models.progen.ProGenConfig

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# Modified configuration implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/configuration_gptj.py
# Modified for ImmuneML. Original code from https://github.com/salesforce/progen

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


[docs] class ProGenConfig(PretrainedConfig): model_type = "progen" def __init__( self, vocab_size=50400, n_positions=2048, n_ctx=2048, n_embd=4096, n_layer=28, n_head=16, rotary_dim=64, n_inner=None, activation_function="gelu_new", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs ): super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.n_ctx = n_ctx self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.rotary_dim = rotary_dim self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.gradient_checkpointing = gradient_checkpointing self.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id @property def max_position_embeddings(self): return self.n_positions @property def hidden_size(self): return self.n_embd @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return self.n_layer