# coding=utf-8
# Copyright 2021 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.
""" GPT Neo model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
[docs]class GPTNeoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.GPTNeoModel`. It is used to
instantiate a GPT Neo 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 GPTNeo `gpt-neo-1.3B
<https://huggingface.co/EleutherAI/gpt-neo-1.3B>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 50257):
Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.GPTNeoModel`. Vocabulary size of the model.
Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
:class:`~transformers.GPTNeoModel`.
attention_types (:obj:`List`, `optional`, defaults to :obj:`[[["global", "local"], 12]]`):
The type of attention for each layer in a :obj:`List` of the following format :obj:`[[["attention_type"],
num_layerss]]` e.g. for a 24 layer model :obj:`[[["global"], 24]]` or :obj:`[[["global", "local"], 12]]`
Choose the value of ``attention_type`` from :obj:`["global", "local"]`
hidden_size (:obj:`int`, `optional`, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (:obj:`int`, `optional`, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_heads (:obj:`int`, `optional`, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 8192):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
embed_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`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).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.GPTNeoModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
The epsilon used by the layer normalization layers.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`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``.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
Example::
>>> from transformers import GPTNeoModel, GPTNeoConfig
>>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration
>>> configuration = GPTNeoConfig()
>>> # Initializing a model from the EleutherAI/gpt-neo-1.3B style configuration
>>> model = GPTNeoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "gpt_neo"
def __init__(
self,
vocab_size=50257,
max_position_embeddings=2048,
hidden_size=2048,
num_layers=24,
attention_types=[[["global", "local"], 12]],
num_heads=16,
intermediate_size=None,
window_size=256,
activation_function="gelu_new",
resid_dropout=0.0,
embed_dropout=0.0,
attention_dropout=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
gradient_checkpointing=False,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
**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_layers = num_layers
self.num_heads = num_heads
self.intermediate_size = intermediate_size
self.window_size = window_size
self.activation_function = activation_function
self.resid_dropout = resid_dropout
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
self.gradient_checkpointing = gradient_checkpointing
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.attention_types = attention_types
self.attention_layers = self.expand_attention_types_params(attention_types)
if len(self.attention_layers) != self.num_layers:
raise ValueError(
"Configuration for convolutional module is incorrect."
"It is required that `len(config.attention_layers)` == `config.num_layers`"
f"but is `len(config.attention_layers) = {len(self.attention_layers)}`,"
f"`config.num_layers = {self.num_layers}`."
"`config.attention_layers` is prepared using `config.attention_types`."
"Please verify the value of `config.attention_types` argument."
)
@staticmethod
def expand_attention_types_params(attention_types):
attentions = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
@property
def num_attention_heads(self):
return self.num_heads
@property
def num_hidden_layers(self):
return self.num_layers