# Copyright (c) SkyworkAI and the HuggingFace Inc. team. All rights reserved. # This code is built upon Huggingface's transformers repository. from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) Skywork_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class SkyworkConfig(PretrainedConfig): model_type = "skywork" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, pretraining_tp=1, tie_word_embeddings=False, rope_scaling=None, rope_theta=10000.0, attention_bias=False, use_flash_attention=False, **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 # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_scaling = rope_scaling self.rope_theta = rope_theta self.attention_bias = attention_bias self.use_flash_attention = use_flash_attention if self.use_flash_attention: try: from flash_attn.flash_attn_interface import flash_attn_varlen_func from einops import rearrange except: raise ValueError("`use_flash_attention` requires Flash Attention 2+ and einops.\nTry `pip install einops` and installing Flash Attention from from https://github.com/Dao-AILab/flash-attention") 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, )