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configuration_gpt_neox_japanese.py
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configuration_gpt_neox_japanese.py
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# 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 ...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`.
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,
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.rotary_emb_base = rotary_emb_base
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.attention_dropout = attention_dropout
self.hidden_dropout = hidden_dropout