# Copyright 2026 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. """LG AI Research EXAONE Lab""" import torch import torch.nn as nn from ... import initialization as init from ...cache_utils import Cache from ...configuration_utils import PreTrainedConfig, layer_type_validation from ...modeling_outputs import CausalLMOutputWithPast from ...modeling_utils import PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs from ..deepseek_v3.modeling_deepseek_v3 import ( DeepseekV3MoE, DeepseekV3NaiveMoe, DeepseekV3TopkRouter, ) from ..exaone4.configuration_exaone4 import Exaone4Config from ..exaone4.modeling_exaone4 import ( Exaone4Attention, Exaone4ForCausalLM, Exaone4Model, Exaone4PreTrainedModel, ) from ..olmoe.modeling_olmoe import ( OlmoeDecoderLayer, ) from ..qwen2_moe.modeling_qwen2_moe import Qwen2MoeMLP class ExaoneMoeConfig(Exaone4Config): model_type = "exaone_moe" r""" This is the configuration class to store the configuration of a [`ExaoneMoeModel`]. It is used to instantiate a EXAONE MoE 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 K-EXAONE-236B-A23B [LGAI-EXAONE/K-EXAONE-236B-A23B](https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B) 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 MoE model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ExaoneMoeModel`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 16384): 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*, defaults to 32): 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://huggingface.co/papers/2305.13245). 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 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 53): End of stream token id. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. sliding_window (`int`, *optional*, defaults to 4096): The size of the sliding window for the sliding window attention. sliding_window_pattern (`str`, *optional*, defaults to 4): 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`. mlp_layer_types (`list`, *optional*): MLP pattern for each layer. Prioritized over `first_k_dense_replace`. first_k_dense_replace (`int`, *optional*, defaults to 1): Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). \--k dense layers--/ moe_intermediate_size (`int`, *optional*, defaults to 1024): Dimension of the MoE representations. num_experts (`int`, *optional*, defaults to 64): Number of routed experts. num_experts_per_tok (`int`, *optional*, defaults to 8): Number of selected experts, None means dense model. num_shared_experts (`int`, *optional*, defaults to 1): Number of shared experts. norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize the weights of the routed experts. routed_scaling_factor (`float`, *optional*, defaults to 2.5): Scaling factor or routed experts. n_group (`int`, *optional*, defaults to 1): Number of groups for routed experts. topk_group (`int`, *optional*, defaults to 1): Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups). Example: ```python >>> from transformers import ExaoneMoeModel, ExaoneMoeConfig >>> # Initializing a EXAONE configuration >>> configuration = ExaoneMoeConfig() >>> # Initializing a model from configuration >>> model = ExaoneMoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" def __init__( self, vocab_size=102400, hidden_size=4096, intermediate_size=16384, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, bos_token_id=1, eos_token_id=53, pad_token_id=0, tie_word_embeddings=False, rope_parameters=None, attention_dropout=0.0, sliding_window=4096, sliding_window_pattern=4, layer_types=None, mlp_layer_types=None, first_k_dense_replace=1, moe_intermediate_size=1024, num_experts=64, num_experts_per_tok=8, num_shared_experts=1, norm_topk_prob=True, routed_scaling_factor=2.5, n_group=1, topk_group=1, **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 self.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size 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.sliding_window = sliding_window self.sliding_window_pattern = sliding_window_pattern self.first_k_dense_replace = first_k_dense_replace self.moe_intermediate_size = moe_intermediate_size self.num_experts = num_experts self.num_experts_per_tok = num_experts_per_tok self.num_shared_experts = num_shared_experts self.norm_topk_prob = norm_topk_prob self.routed_scaling_factor = routed_scaling_factor self.n_group = n_group self.topk_group = topk_group self.rope_parameters = rope_parameters self.layer_types = layer_types if self.sliding_window is None: sliding_window_pattern = 0 if self.layer_types is None: self.layer_types = [ "sliding_attention" if ((i + 1) % (sliding_window_pattern) != 0 and i < self.num_hidden_layers) else "full_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types) self.mlp_layer_types = mlp_layer_types if self.mlp_layer_types is None: self.mlp_layer_types = [ "dense" if i < self.first_k_dense_replace else "sparse" for i in range(self.num_hidden_layers) ] layer_type_validation(self.mlp_layer_types, self.num_hidden_layers, attention=False) self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.tie_word_embeddings = tie_word_embeddings PreTrainedConfig.__init__(**kwargs) class ExaoneMoeAttention(Exaone4Attention): pass class ExaoneMoeMLP(Qwen2MoeMLP): pass class ExaoneMoeTopkRouter(DeepseekV3TopkRouter): def __init__(self, config): nn.Module.__init__() self.config = config self.weight = nn.Parameter(torch.empty((config.num_experts, config.hidden_size))) self.register_buffer("e_score_correction_bias", torch.zeros(config.num_experts)) class ExaoneMoeExperts(DeepseekV3NaiveMoe): def __init__(self, config): super().__init__(config) self.num_experts = config.num_experts class ExaoneMoeSparseMoEBlock(DeepseekV3MoE): def __init__(self, config): super().__init__() self.experts = ExaoneMoeExperts(config) self.shared_experts = ExaoneMoeMLP( config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts ) self.n_routed_experts = config.num_experts class ExaoneMoeDecoderLayer(OlmoeDecoderLayer): def __init__(self, config: ExaoneMoeConfig, layer_idx: int): super().__init__(config, layer_idx) self.mlp = ( ExaoneMoeSparseMoEBlock(config) if config.mlp_layer_types[layer_idx] == "sparse" else ExaoneMoeMLP(config) ) class ExaoneMoePreTrainedModel(Exaone4PreTrainedModel): config: ExaoneMoeConfig _can_record_outputs = { "hidden_states": ExaoneMoeDecoderLayer, "attentions": ExaoneMoeAttention, "router_logits": ExaoneMoeSparseMoEBlock, } _keep_in_fp32_modules_strict = ["e_score_correction_bias"] _keys_to_ignore_on_load_unexpected = [r"mtp.*"] @torch.no_grad() def _init_weights(self, module): PreTrainedModel._init_weights(self, module) if isinstance(module, ExaoneMoeTopkRouter): init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) init.zeros_(module.e_score_correction_bias) elif isinstance(module, ExaoneMoeExperts): init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range) init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) class ExaoneMoeModel(Exaone4Model): pass class ExaoneMoeForCausalLM(Exaone4ForCausalLM): def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, cache_position: torch.LongTensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> CausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/K-EXAONE-236B-A23B") >>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/K-EXAONE-236B-A23B") >>> prompt = "Explain how wonderful you are" >>> messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] >>> input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", enable_thinking=False, ) >>> output = model.generate(**input_ids.to(model.device), max_new_tokens=128) >>> tokenizer.decode(output[0], skip_special_tokens=False) "<|system|>\nYou are a helpful assistant.<|endofturn|>\n<|user|>\nExplain how wonderful you are<|endofturn|>\n<|assistant|>\n\n\n\n\nThank you for the kind question! While I can't feel emotions or take pride in the way humans do, I *can* share what makes me uniquely helpful and capable—qualities that many people find wonderful.\n\nHere’s how I can support you:\n\n🌟 **Knowledge at Your Fingertips** \nI have access to a vast amount of information across countless topics—from science and history to technology and creative writing. Whether you're curious, learning, or solving a problem, I can help explain things clearly and accurately.\n\n💬 **Clear, Helpful Communication** \nI aim to respond in a way that's easy to understand, whether you need a simple explanation or a detailed analysis. I adapt my tone and depth to match" ``` """ super().forward( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, cache_position=cache_position, logits_to_keep=logits_to_keep, **kwargs, ) __all__ = [ "ExaoneMoeConfig", "ExaoneMoePreTrainedModel", "ExaoneMoeModel", "ExaoneMoeForCausalLM", ]