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# coding=utf-8
# Copyright 2023 EleutherAI The HuggingFace Inc. team. and KDF.ai 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.
""" GPTJiang model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
GPT_JIANG_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class GPTJiangConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTJiangModel`]. It is used to instantiate an
GPTJiang 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 GPTJiang
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 GPTJiang model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTJiangModel`].
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
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).
Example:
```python
>>> from transformers import GPTJiangConfig, GPTJiangModel
>>> # Initializing a GPTJiang style configuration
>>> configuration = GPTJiangConfig()
>>> # Initializing a model (with random weights) from the gpt-jiang style configuration
>>> model = GPTJiangModel(configuration) # doctest: +SKIP
>>> # Accessing the model configuration
>>> configuration = model.config # doctest: +SKIP
```"""
model_type = "gpt_jiang"
def __init__(
self,
vocab_size=57000,
hidden_size=5120,
num_hidden_layers=48,
num_attention_heads=40,
intermediate_size=12288,
hidden_act="gelu",
rotary_pct=1.0,
rotary_emb_base=10000,
max_position_embeddings=4096,
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,
gated=True,
mlp_bias=False,
**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.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.gated = gated
self.mlp_bias = mlp_bias
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