# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """ LLaMA model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from transformers import LlamaConfig logger = logging.get_logger(__name__) LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class CLEXLlamaConfig(LlamaConfig): r""" This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA 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 LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LlamaModel`] 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. 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`. pretraining_tp (`int`, *optional*, defaults to `1`): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). 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., 512 or 1024 or 2048). 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 rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions. Example: ```python >>> from transformers import LlamaModel, LlamaConfig >>> # Initializing a LLaMA llama-7b style configuration >>> configuration = LlamaConfig() >>> # Initializing a model from the llama-7b style configuration >>> model = LlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "llama" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, rope_scaling=None, use_flashattn=True, log_scale=True, **kwargs, ): super().__init__( **kwargs, ) self.use_flashattn = use_flashattn self.log_scale = log_scale self.rope_theta = 10000 self.max_position_embeddings = 4096 self.data_length = 4096 self.rope_scaling = rope_scaling self._rope_scaling_validation() def _rope_scaling_validation(self): """ Validate the `rope_scaling` configuration. """ if self.rope_scaling is None: return # if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: # raise ValueError( # "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, " # f"got {self.rope_scaling}" # ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_max_factor = self.rope_scaling.get("max_factor", None) rope_scaling_param_factor = self.rope_scaling.get("param_factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "clex"]: raise ValueError( f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'clex'], got {rope_scaling_type}" ) # if rope_scaling_max_factor is None or not isinstance(rope_scaling_max_factor, float) or rope_scaling_max_factor <= 1.0: # raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_max_factor}") # if rope_scaling_param_factor is None or not isinstance(rope_scaling_param_factor, float) or rope_scaling_param_factor <= 1.0: # raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_param_factor}")