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""" MEGATRON_BERT model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
[docs]class MegatronBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.MegatronBertModel`. It is
used to instantiate a MEGATRON_BERT 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 MEGATRON_BERT
`megatron-bert-uncased-345m <https://huggingface.co/nvidia/megatron-bert-uncased-345m>`__ 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 29056):
Vocabulary size of the MEGATRON_BERT model. Defines the number of different tokens that can be represented
by the :obj:`inputs_ids` passed when calling :class:`~transformers.MegatronBertModel`.
hidden_size (:obj:`int`, `optional`, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_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 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
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.MegatronBertModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
`Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
<https://arxiv.org/abs/2009.13658>`__.
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``.
Examples::
>>> from transformers import MegatronBertModel, MegatronBertConfig
>>> # Initializing a MEGATRON_BERT bert-base-uncased style configuration
>>> configuration = MegatronBertConfig()
>>> # Initializing a model from the bert-base-uncased style configuration
>>> model = MegatronBertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "megatron-bert"
def __init__(
self,
vocab_size=29056,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
gradient_checkpointing=False,
position_embedding_type="absolute",
use_cache=True,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.gradient_checkpointing = gradient_checkpointing
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache