# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/exaone4/modular_exaone4.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_exaone4.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The LG AI Research and HuggingFace Inc. team. 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. from transformers.configuration_utils import PretrainedConfig, layer_type_validation from transformers.utils import logging logger = logging.get_logger(__name__) def check_is_sliding(config, layer_idx): """ Check if the current layer is a sliding window attention (local attention) layer. """ if config.sliding_window is None: return False if config.layer_types is not None: return config.layer_types[layer_idx] == "sliding_attention" if isinstance(config.sliding_window_pattern, int): return ((layer_idx + 1) % config.sliding_window_pattern) != 0 elif isinstance(config.sliding_window_pattern, str): assert isinstance(config.sliding_window, int), ( f"Sliding window must be positive integer, but got {config.sliding_window}" ) return ( layer_idx != config.num_hidden_layers - 1 and config.sliding_window_pattern[layer_idx % len(config.sliding_window_pattern)] == "L" ) else: logger.warning_once( "Sliding window is set, but none of `sliding_window_pattern` or `layer_types` is set. " "Defaulting to use 'full_attention' for all layers." ) return False class Exaone4Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Exaone4Model`]. It is used to instantiate a EXAONE 4.0 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-4.0-Instruct [LGAI-EXAONE/EXAONE-4.0-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-Instruct) NOTE: `EXAONE-4.0-Instruct` is a placeholder model ID. The exact model ID will be updated in the future. 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 4.0 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Exaone4Model`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`): Dimensionality of the MLP representations. num_hidden_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`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. 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., 32768 for EXAONE 3.5). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. 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. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings 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 attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. sliding_window (`int`, *optional*): The size of the sliding window for the sliding window attention. sliding_window_pattern (`str`, *optional*): The pattern to use for sliding window attention. Can be one of: - `None`: No sliding window attention is used - `int`: Every `sliding_window` layers, use global attention, else use local attention. - `str`: A sequence of "L" (local attention) and "G" (global attention) characters that defines the attention pattern. The pattern starts from layer 0 and repeats every `sliding_window` layers. The final layer always uses global attention regardless of the pattern. For instance, sliding_window_pattern="LLLG" same as sliding_window=4, which means: - Layer 0, 1, 2: local attention, - Layer 3: global attention, ...(repeated) layer_types (`list`, *optional*): Attention pattern for each layer. Prioritized over `sliding_window_pattern`. Example: ```python >>> from transformers import Exaone4Model, Exaone4Config >>> # Initializing a EXAONE configuration >>> configuration = Exaone4Config() >>> # Initializing a model from configuration >>> model = Exaone4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "exaone4" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `LlamaModel` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=102400, hidden_size=4096, intermediate_size=None, 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-5, use_cache=True, bos_token_id=0, eos_token_id=2, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_dropout=0.0, sliding_window=None, sliding_window_pattern=None, layer_types=None, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads 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.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_dropout = attention_dropout self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.sliding_window = sliding_window self.sliding_window_pattern = sliding_window_pattern self.layer_types = layer_types if self.layer_types is None: self.layer_types = [ "sliding_attention" if check_is_sliding(self, i) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs ) __all__ = ["Exaone4Config"]