# coding=utf-8 # Copyright 2024 JetMoe AI and the 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. """JetMoe model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class JetMoeConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a configuration of the JetMoe-4B. [jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b) 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 32000): Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`JetMoeModel`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 16): Number of attention heads for each key and value in the Transformer encoder. kv_channels (`int`, *optional*, defaults to 128): Defines the number of channels for the key and value tensors. intermediate_size (`int`, *optional*, defaults to 5632): Dimension of the MLP representations. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of up to 4096 tokens. activation_function (`string`, *optional*, defaults to `"silu"`): Defines the activation function for MLP experts. num_local_experts (`int`, *optional*, defaults to 8): Defines the number of experts in the MoE and MoA. num_experts_per_tok (`int, *optional*, defaults to 2): The number of experts to route per-token and for MoE and MoA. output_router_logits (`bool`, *optional*, defaults to `False`): Whether or not the router logits should be returned by the model. Enabeling this will also allow the model to output the auxiliary loss. aux_loss_coef (`float`, *optional*, defaults to 0.01): The coefficient for the auxiliary loss. 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): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. initializer_range (`float`, *optional*, defaults to 0.01): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import JetMoeModel, JetMoeConfig >>> # Initializing a JetMoe 4B style configuration >>> configuration = JetMoeConfig() >>> # Initializing a model from the JetMoe 4B style configuration >>> model = JetMoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "jetmoe" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=2048, num_hidden_layers=12, num_key_value_heads=16, kv_channels=128, intermediate_size=5632, max_position_embeddings=4096, activation_function="silu", num_local_experts=8, num_experts_per_tok=2, output_router_logits=False, aux_loss_coef=0.01, use_cache=True, bos_token_id=1, eos_token_id=2, tie_word_embeddings=True, rope_theta=10000.0, rms_norm_eps=1e-6, initializer_range=0.01, attention_dropout=0.0, **kwargs, ): if num_experts_per_tok > num_local_experts: raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`") self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_key_value_heads * num_experts_per_tok self.num_key_value_heads = num_key_value_heads self.kv_channels = kv_channels self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.activation_function = activation_function self.num_local_experts = num_local_experts self.num_experts_per_tok = num_experts_per_tok self.output_router_logits = output_router_logits self.aux_loss_coef = aux_loss_coef self.use_cache = use_cache self.initializer_range = initializer_range self.attention_dropout = attention_dropout self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.rope_theta = rope_theta self.rms_norm_eps = rms_norm_eps super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs ) __all__ = ["JetMoeConfig"]