# 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 transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation from transformers.utils import logging logger = logging.get_logger(__name__) 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. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE attention_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers during self-attention. 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" keys_to_ignore_at_inference = ["past_key_values"] 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, attention_bias=True, **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.partial_rotary_factor = rotary_pct self.rotary_emb_base = rotary_emb_base self.rope_theta = 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.attention_bias = attention_bias # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. if self.rope_scaling is not None and "type" in self.rope_scaling: self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self) 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!" )