# coding=utf-8 # 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. # """ GPTNeoX model configuration""" # from ...configuration_utils import PretrainedConfig # from ...utils import logging # logger = logging.get_logger(__name__) # GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP = { # "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json", # # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox # } # class GPTNeoXConfig(PretrainedConfig): # r""" # This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an # GPTNeoX 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 GPTNeoX # [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture. # 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 50432): # Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the # `inputs_ids` passed when calling [`GPTNeoXModel`]. # hidden_size (`int`, *optional*, defaults to 6144): # Dimension of the encoder layers and the pooler layer. # num_hidden_layers (`int`, *optional*, defaults to 44): # Number of hidden layers in the Transformer encoder. # num_attention_heads (`int`, *optional*, defaults to 64): # Number of attention heads for each attention layer in the Transformer encoder. # intermediate_size (`int`, *optional*, defaults to 24576): # Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. # hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): # The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, # `"relu"`, `"selu"` and `"gelu_new"` are supported. # rotary_pct (`float`, *optional*, defaults to 0.25): # percentage of hidden dimensions to allocate to rotary embeddings # rotary_emb_base (`int`, *optional*, defaults to 10000) # base for computing rotary embeddings frequency # attention_dropout (`float`, *optional*, defaults to 0.0): # The dropout ratio probability of the attention score. # hidden_dropout (`float`, *optional*, defaults to 0.0): # The dropout ratio of (1) the word embeddings, (2) the post-attention hidden states, and (3) the post-mlp # hidden states. # classifier_dropout (`float`, *optional*, defaults to 0.1): # Argument used when doing token classification, used in the model [`GPTNeoXForTokenClassification`]. # The dropout ratio for the hidden layer. # 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 1e-5): # The standard deviation of the truncated_normal_initializer for initializing all weight matrices. # layer_norm_eps (`float`, *optional*, defaults to 1e-12): # 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`. # use_parallel_residual (`bool`, *optional*, defaults to `True`): # Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training # speedup at large scales (e.g. 20B). # rope_scaling (`Dict`, *optional*): # Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two 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 GPTNeoXConfig, GPTNeoXModel # >>> # Initializing a GPTNeoX gpt-neox-20b style configuration # >>> configuration = GPTNeoXConfig() # >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration # >>> model = GPTNeoXModel(configuration) # doctest: +SKIP # >>> # Accessing the model configuration # >>> configuration = model.config # doctest: +SKIP # ```""" # model_type = "gpt_neox" from transformers import PretrainedConfig class CustomConfig4(PretrainedConfig): model_type = "custom4" def __init__( self, vocab_size=50432, hidden_size=6144, num_hidden_layers=44, num_attention_heads=64, intermediate_size=24576, hidden_act="gelu", rotary_pct=0.25, rotary_emb_base=10000, attention_dropout=0.0, hidden_dropout=0.0, classifier_dropout=0.1, max_position_embeddings=2048, initializer_range=0.02, layer_norm_eps=1e-5, use_cache=True, bos_token_id=0, eos_token_id=2, tie_word_embeddings=False, use_parallel_residual=True, rope_scaling=None, **kwargs, ): super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.rotary_pct = rotary_pct self.rotary_emb_base = rotary_emb_base self.attention_dropout = attention_dropout self.hidden_dropout = hidden_dropout self.classifier_dropout = classifier_dropout self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.tie_word_embeddings = tie_word_embeddings self.use_parallel_residual = use_parallel_residual self.rope_scaling = rope_scaling self._rope_scaling_validation() if self.hidden_size % self.num_attention_heads != 0: raise ValueError( "The hidden size is not divisble by the number of attention heads! Make sure to update them!" ) # Copied from transformers.models.llama.configuration_llama.LlamaConfig._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, `type` and `factor`, " f"got {self.rope_scaling}" ) rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")