# Copyright © 2024 BAAI. All rights reserved. # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2022 EleutherAI 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. """ Aquila model configuration""" # modified from https://github.com/PaddlePaddle/PaddleNLP/blob/7947bca07f0dfb37172a4c0040defd0cdbbc10a0/paddlenlp/transformers/llama/configuration.py from paddlenlp.transformers.configuration_utils import PretrainedConfig __all__ = [ "AQUILA_PRETRAINED_INIT_CONFIGURATION", "AquilaConfig", "AQUILA_PRETRAINED_RESOURCE_FILES_MAP", ] AQUILA_PRETRAINED_INIT_CONFIGURATION = { } AQUILA_PRETRAINED_RESOURCE_FILES_MAP = { "model_state": { }, } class AquilaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`~AquilaModel`]. It is used to instantiate an Aquila 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 Llama-7B. 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 Aquila model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~AquilaModel`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension 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 encoder. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. 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-12): The epsilon used by the rms 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`. tie_word_embeddings(`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings Enable rope fusion or not. 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`. ```""" model_type = "aquila" attribute_map = { "n_positions": "max_position_embeddings", "n_embd": "hidden_size", "n_layer": "num_hidden_layers", "n_head": "num_attention_heads", "n_inner": "intermediate_size", "activation_function": "hidden_act", } pretrained_init_configuration = AQUILA_PRETRAINED_INIT_CONFIGURATION def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, max_position_embeddings=2048, seq_length=2048, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, initializer_range=0.02, rms_norm_eps=1e-6, rope_theta=10000.0, use_cache=True, fuse_attention_qkv=False, fuse_attention_ffn=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, alibi=False, rope_scaling_factor=1.0, rope_scaling_type=None, long_sequence_strategy_type=None, long_sequence_strategy_name=None, long_sequence_init_args=None, use_long_sequence_strategies=False, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.seq_length = seq_length 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 self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.rope_theta = rope_theta self.use_cache = use_cache self.fuse_attention_qkv = fuse_attention_qkv self.fuse_attention_ffn = fuse_attention_ffn self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.alibi = alibi self.rope_scaling_factor = rope_scaling_factor self.rope_scaling_type = rope_scaling_type self.long_sequence_strategy_type = long_sequence_strategy_type self.long_sequence_strategy_name = long_sequence_strategy_name self.long_sequence_init_args = {} if long_sequence_init_args is None else long_sequence_init_args self.use_long_sequence_strategies = use_long_sequence_strategies super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) @property def rope(self): return not self.alibi