# coding=utf-8 # Copied from # https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/blob/main/configuration_exaone.py # Copyright 2021 The LG AI Research EXAONE Lab. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Exaone model configuration""" from typing import Dict from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: Dict[str, str] = {} class ExaoneConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class: `~transformers.ExaoneModel`. It is used to instantiate a GPT Lingvo model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Exaone Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from : class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50257): Vocabulary size of the GPT Lingvo model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.ExaoneModel`. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class: `~transformers.EXAONEModel`. hidden_size (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. num_layers (:obj:`int`, `optional`, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. rotary_pct (`float`, *optional*, defaults to 0.25): percentage of hidden dimensions to allocate to rotary embeddings intermediate_size (:obj:`int`, `optional`, defaults to 8192): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. embed_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`int`, `optional`, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.EXAONEModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): The epsilon used by the layer normalization layers. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if ``config.is_decoder=True``. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. Example:: >>> from transformers import ExoneModel, ExaoneConfig >>> # Initializing a EXAONE configuration >>> configuration = ExaoneConfig() >>> # Initializing a model from configuration >>> model = ExoneModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "exaone" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_hidden_layers": "num_layers"} def __init__( self, vocab_size=102400, max_position_embeddings=2048, hidden_size=2048, num_layers=32, num_attention_heads=32, num_key_value_heads=None, intermediate_size=None, activation_function="silu", rotary_pct=0.25, resid_dropout=0.0, embed_dropout=0.0, attention_dropout=0.0, layer_norm_epsilon=1e-6, initializer_range=0.02, use_cache=True, bos_token_id=0, eos_token_id=2, tie_word_embeddings=True, **kwargs, ): super().__init__( 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.num_layers = num_layers self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_layers if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads if intermediate_size: self.intermediate_size = intermediate_size else: self.intermediate_size = hidden_size * 4 self.activation_function = activation_function self.resid_dropout = resid_dropout self.embed_dropout = embed_dropout self.attention_dropout = attention_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.use_cache = use_cache self.rotary_pct = rotary_pct self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.use_logit_cap = kwargs.pop("use_logit_cap", False) self.ln_no_scale = kwargs.pop("ln_no_scale", False) self.use_gated = kwargs.pop("use_gated", False) self.use_emb_norm = kwargs.pop("use_emb_norm", False) self.use_rotary_pos = kwargs.pop("use_rotary_pos", False) self.rotary_type = kwargs.pop("rotary_type", None) self.scaling_factor = kwargs.pop("scaling_factor", 1) self.use_absolute_pos = kwargs.pop("use_absolute_pos", True) self.use_extra_logit = kwargs.pop("use_extra_logit", True) self.rotary_expand_length = kwargs.pop("rotary_expand_length", None) self.rotary_base = kwargs.pop("rotary_base", 10000.0) self.use_qkv_fuse = kwargs.pop("use_qkv_fuse", False) self.rescale_before_lm_head = kwargs.pop("rescale_before_lm_head", (rotary_pct == 0.25)) if self.use_rotary_pos: self.use_absolute_pos = False