# coding=utf-8 # Copyright 2022 ABEJA, Inc. 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 Japanese model configuration""" from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation from ...utils import logging logger = logging.get_logger(__name__) class GPTNeoXJapaneseConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`GPTNeoXModelJapanese`]. It is used to instantiate a 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 GPTNeoXJapanese [abeja/gpt-neox-japanese-2.7b](https://huggingface.co/abeja/gpt-neox-japanese-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Default configs is set as 2.7B model Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the GPTNeoXJapanese model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GPTNeoXJapanese`]. hidden_size (`int`, *optional*, defaults to 2560): Dimension of the encoder layers and the pooler layer. 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. intermediate_multiple_size (`int`, *optional*, defaults to 4): Dimension of the "intermediate" layer in the Transformer encoder is calculated by hidden_size * intermediate_multiple_size. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. rotary_pct (`float`, *optional*, defaults to 1.00): percentage of hidden dimensions to allocate to rotary embeddings rotary_emb_base (`int`, *optional*, defaults to 10000) base for computing rotary embeddings frequency max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-5): 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`. 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_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the hidden layer. Example: ```python >>> from transformers import GPTNeoXJapaneseConfig, GPTNeoXJapaneseModel >>> # Initializing a GPTNeoXJapanese gpt-neox-japanese-2.7b style configuration >>> configuration = GPTNeoXJapaneseConfig() >>> # Initializing a model (with random weights) from the gpt-neox-japanese-2.7b style configuration >>> model = GPTNeoXJapaneseModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gpt_neox_japanese" def __init__( self, vocab_size=32000, hidden_size=2560, num_hidden_layers=32, num_attention_heads=32, intermediate_multiple_size=4, hidden_act="gelu", rotary_pct=1.00, rotary_emb_base=10000, max_position_embeddings=2048, initializer_range=0.02, layer_norm_eps=1e-5, use_cache=True, bos_token_id=31996, eos_token_id=31999, rope_scaling=None, attention_dropout=0.1, hidden_dropout=0.0, **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_multiple_size = intermediate_multiple_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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.rope_scaling = rope_scaling self.attention_dropout = attention_dropout self.hidden_dropout = hidden_dropout # 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)