from collections import OrderedDict from typing import Any, List, Mapping, Optional from transformers import PreTrainedTokenizer, TensorType, is_torch_available from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class ShrinkConfig(PretrainedConfig): model_type = "shrink" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "hidden_size", "max_position_embeddings": "max_position_embeddings", "num_attention_heads": "num_attention_heads", "num_hidden_layers": "num_hidden_layers", } def __init__( self, vocab_size=32000, max_position_embeddings=2048, hidden_size_0=8192, hidden_size=768, qk_hidden_size=None, # in case you want to use cross-attention num_hidden_layers=10, num_attention_heads=12, intermediate_size=None, activation_function="silu", layer_norm_epsilon=1e-6, initializer_range=0.02, scale_attn_weights=True, use_cache=True, bos_token_id=1, eos_token_id=2, combined_qkv=True, use_bias=False, projection_bias=True, lm_head_bias=False, **kwargs, ): self.qk_hidden_size = qk_hidden_size self.lm_head_bias = lm_head_bias self.projection_bias = projection_bias self.use_bias = use_bias self.hidden_size_0 = hidden_size_0 self.combined_qkv = combined_qkv self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = ( intermediate_size if intermediate_size is not None else hidden_size * 4 ) self.activation_function = activation_function self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)