# coding=utf-8 # 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 transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class ExaoneConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ExaoneModel`]. It is used to instantiate a EXAONE 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-3.0-7.8B-Instruct [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 102400): Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ExaoneModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of [`ExaoneModel`]. max_position_embeddings (`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). hidden_size (`int`, *optional*, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. num_layers (`int`, *optional*, defaults to 32): 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`. intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. activation_function (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE embed_dropout (`float`, *optional*, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `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``. bos_token_id (`int`, *optional*, defaults to 0): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. Example: ```python >>> from transformers import EXAONEModel, ExaoneConfig >>> # Initializing a EXAONE configuration >>> configuration = ExaoneConfig() >>> # Initializing a model from configuration >>> model = EXAONEModel(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", rope_theta=10000.0, rope_scaling=None, embed_dropout=0.0, attention_dropout=0.0, layer_norm_epsilon=1e-5, initializer_range=0.02, use_cache=True, bos_token_id=0, eos_token_id=2, **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_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.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.rope_theta = rope_theta self.rope_scaling = rope_scaling 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)