# coding=utf-8 # Copyright 2023 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. """ LongLLaMA model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) LONGLLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = { "syzymon/long_llama_3b": "https://huggingface.co/syzymon/long_llama_3b/resolve/main/config.json", } class LongLlamaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LongLlamaModel`]. It is used to instantiate an LongLLaMA 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 LongLLaMA-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 LongLLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LongLlamaModel`] 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. 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 mem_layers (`List[int]`, defaults to `[]`): Layers with memory mem_positionals (`bool`, *optional*, defaults to `True`): Whether to use positional embeddings in memory layers mem_dtype (`str`, *optional*, defaults to `"bfloat16"`): Type for keys and values stored in memory mem_attention_grouping (`Tuple[int, int]`, *optional*, defaults to `None`): One can trade speed for memory by performing attention in memory layers sequentially. When equal to `(4, 2048)` the memory layers will process at most 4 heads and 2048 queries from each head at once. That is at most 4*2048 queries at once. torch_attention (`bool`, *optional*, defaults to `False`): Whether to use torch scaled_dot_product_attention gradient_checkpoint_every_ith (`int`, *optional*, defaults to `1`): When gradient checkpointing is enabled checkpoint every ith layer Example: ```python >>> from transformers import LongLlamaModel, LongLlamaConfig >>> # Initializing a LongLLaMA longllama-7b style configuration >>> configuration = LongLlamaConfig() >>> # Initializing a model from the longllama-7b style configuration >>> model = LongLlamaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "longllama" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=11008, num_hidden_layers=32, num_attention_heads=32, hidden_act="silu", max_position_embeddings=2048, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, last_context_length=1024, mem_layers=[], mem_positionals=True, mem_dtype="bfloat16", mem_attention_grouping=None, torch_attention=False, gradient_checkpoint_every_ith=1, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.last_context_length = last_context_length self.mem_layers = mem_layers self.mem_positionals = mem_positionals self.mem_dtype = mem_dtype self.mem_attention_grouping = mem_attention_grouping self.torch_attention = torch_attention self.gradient_checkpoint_every_ith = gradient_checkpoint_every_ith 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, )