141 lines
5.9 KiB
Python
141 lines
5.9 KiB
Python
# coding=utf-8
|
|
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
|
|
#
|
|
# 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.
|
|
""" DistilBERT model configuration"""
|
|
from collections import OrderedDict
|
|
from typing import Mapping
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...onnx import OnnxConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
from ..deprecated._archive_maps import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
class DistilBertConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It
|
|
is used to instantiate a DistilBERT 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 DistilBERT
|
|
[distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) architecture.
|
|
|
|
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 30522):
|
|
Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by
|
|
the `inputs_ids` passed when calling [`DistilBertModel`] or [`TFDistilBertModel`].
|
|
max_position_embeddings (`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).
|
|
sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`):
|
|
Whether to use sinusoidal positional embeddings.
|
|
n_layers (`int`, *optional*, defaults to 6):
|
|
Number of hidden layers in the Transformer encoder.
|
|
n_heads (`int`, *optional*, defaults to 12):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
dim (`int`, *optional*, defaults to 768):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
hidden_dim (`int`, *optional*, defaults to 3072):
|
|
The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
|
|
dropout (`float`, *optional*, defaults to 0.1):
|
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
|
attention_dropout (`float`, *optional*, defaults to 0.1):
|
|
The dropout ratio for the attention probabilities.
|
|
activation (`str` or `Callable`, *optional*, defaults to `"gelu"`):
|
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
|
`"relu"`, `"silu"` and `"gelu_new"` are supported.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
qa_dropout (`float`, *optional*, defaults to 0.1):
|
|
The dropout probabilities used in the question answering model [`DistilBertForQuestionAnswering`].
|
|
seq_classif_dropout (`float`, *optional*, defaults to 0.2):
|
|
The dropout probabilities used in the sequence classification and the multiple choice model
|
|
[`DistilBertForSequenceClassification`].
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import DistilBertConfig, DistilBertModel
|
|
|
|
>>> # Initializing a DistilBERT configuration
|
|
>>> configuration = DistilBertConfig()
|
|
|
|
>>> # Initializing a model (with random weights) from the configuration
|
|
>>> model = DistilBertModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "distilbert"
|
|
attribute_map = {
|
|
"hidden_size": "dim",
|
|
"num_attention_heads": "n_heads",
|
|
"num_hidden_layers": "n_layers",
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=30522,
|
|
max_position_embeddings=512,
|
|
sinusoidal_pos_embds=False,
|
|
n_layers=6,
|
|
n_heads=12,
|
|
dim=768,
|
|
hidden_dim=4 * 768,
|
|
dropout=0.1,
|
|
attention_dropout=0.1,
|
|
activation="gelu",
|
|
initializer_range=0.02,
|
|
qa_dropout=0.1,
|
|
seq_classif_dropout=0.2,
|
|
pad_token_id=0,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.sinusoidal_pos_embds = sinusoidal_pos_embds
|
|
self.n_layers = n_layers
|
|
self.n_heads = n_heads
|
|
self.dim = dim
|
|
self.hidden_dim = hidden_dim
|
|
self.dropout = dropout
|
|
self.attention_dropout = attention_dropout
|
|
self.activation = activation
|
|
self.initializer_range = initializer_range
|
|
self.qa_dropout = qa_dropout
|
|
self.seq_classif_dropout = seq_classif_dropout
|
|
super().__init__(**kwargs, pad_token_id=pad_token_id)
|
|
|
|
|
|
class DistilBertOnnxConfig(OnnxConfig):
|
|
@property
|
|
def inputs(self) -> Mapping[str, Mapping[int, str]]:
|
|
if self.task == "multiple-choice":
|
|
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
|
|
else:
|
|
dynamic_axis = {0: "batch", 1: "sequence"}
|
|
return OrderedDict(
|
|
[
|
|
("input_ids", dynamic_axis),
|
|
("attention_mask", dynamic_axis),
|
|
]
|
|
)
|