# 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 ...modeling_rope_utils import RopeParameters from ...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. 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 c;assifier head. 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_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. 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"] base_model_tp_plan = { "layers.*.attention.query_key_value": "colwise", "layers.*.attention.dense": "rowwise", "layers.*.mlp.dense_h_to_4h": "colwise", "layers.*.mlp.dense_4h_to_h": "rowwise", } base_model_pp_plan = { "embed_in": (["input_ids"], ["inputs_embeds"]), "emb_dropout": (["inputs_embeds"], ["hidden_states"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "final_layer_norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size: int | None = 50432, hidden_size: int | None = 6144, num_hidden_layers: int | None = 44, num_attention_heads: int | None = 64, intermediate_size: int | None = 24576, hidden_act: str | None = "gelu", attention_dropout: float | None = 0.0, hidden_dropout: float | None = 0.0, classifier_dropout: float | None = 0.1, max_position_embeddings: int | None = 2048, initializer_range: float | None = 0.02, layer_norm_eps: int | None = 1e-5, use_cache: bool | None = True, bos_token_id: int | None = 0, eos_token_id: int | None = 2, pad_token_id: int | None = None, tie_word_embeddings: bool | None = False, use_parallel_residual: bool | None = True, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, attention_bias: bool | None = True, is_decoder: bool | None = False, **kwargs, ): self.is_decoder = is_decoder self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id 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.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.use_parallel_residual = use_parallel_residual self.attention_bias = attention_bias self.rope_parameters = rope_parameters self.tie_word_embeddings = tie_word_embeddings if self.hidden_size % self.num_attention_heads != 0: raise ValueError( "The hidden size is not divisible by the number of attention heads! Make sure to update them!" ) super().__init__(**kwargs) def convert_rope_params_to_dict(self, ignore_keys_at_rope_validation=None, **kwargs): rope_scaling = kwargs.pop("rope_scaling", None) self.rope_parameters = rope_scaling or self.rope_parameters self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {} # Standardize and validate the correctness of rotary position embeddings parameters # Model uses non-standard naming for rope params, overwrite! self.rope_parameters.setdefault("rope_theta", kwargs.pop("rotary_emb_base", self.default_theta)) self.rope_parameters["partial_rotary_factor"] = kwargs.pop("rotary_pct", 0.25) self.standardize_rope_params() self.validate_rope(ignore_keys=ignore_keys_at_rope_validation) return kwargs __all__ = ["GPTNeoXConfig"]