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# coding=utf-8 | |
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. | |
# Copyright (c) 2018, NVIDIA CORPORATION. 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. | |
""" ALBERT model configuration""" | |
from collections import OrderedDict | |
from typing import Mapping | |
from ...configuration_utils import PretrainedConfig | |
from ...onnx import OnnxConfig | |
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json", | |
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json", | |
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json", | |
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json", | |
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json", | |
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json", | |
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json", | |
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json", | |
} | |
class AlbertConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used | |
to instantiate an ALBERT 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 ALBERT | |
[albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) 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 30000): | |
Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`]. | |
embedding_size (`int`, *optional*, defaults to 128): | |
Dimensionality of vocabulary embeddings. | |
hidden_size (`int`, *optional*, defaults to 4096): | |
Dimensionality of the encoder layers and the pooler layer. | |
num_hidden_layers (`int`, *optional*, defaults to 12): | |
Number of hidden layers in the Transformer encoder. | |
num_hidden_groups (`int`, *optional*, defaults to 1): | |
Number of groups for the hidden layers, parameters in the same group are shared. | |
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 16384): | |
The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
inner_group_num (`int`, *optional*, defaults to 1): | |
The number of inner repetition of attention and ffn. | |
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu_new"`): | |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
`"relu"`, `"silu"` and `"gelu_new"` are supported. | |
hidden_dropout_prob (`float`, *optional*, defaults to 0): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0): | |
The dropout ratio for the attention probabilities. | |
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 | |
(e.g., 512 or 1024 or 2048). | |
type_vocab_size (`int`, *optional*, defaults to 2): | |
The vocabulary size of the `token_type_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`]. | |
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-12): | |
The epsilon used by the layer normalization layers. | |
classifier_dropout_prob (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for attached classifiers. | |
position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | |
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For | |
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to | |
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). | |
For more information on `"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). | |
pad_token_id (`int`, *optional*, defaults to 0): | |
Padding token id. | |
bos_token_id (`int`, *optional*, defaults to 2): | |
Beginning of stream token id. | |
eos_token_id (`int`, *optional*, defaults to 3): | |
End of stream token id. | |
Examples: | |
```python | |
>>> from transformers import AlbertConfig, AlbertModel | |
>>> # Initializing an ALBERT-xxlarge style configuration | |
>>> albert_xxlarge_configuration = AlbertConfig() | |
>>> # Initializing an ALBERT-base style configuration | |
>>> albert_base_configuration = AlbertConfig( | |
... hidden_size=768, | |
... num_attention_heads=12, | |
... intermediate_size=3072, | |
... ) | |
>>> # Initializing a model (with random weights) from the ALBERT-base style configuration | |
>>> model = AlbertModel(albert_xxlarge_configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "albert" | |
def __init__( | |
self, | |
vocab_size=30000, | |
embedding_size=128, | |
hidden_size=4096, | |
num_hidden_layers=12, | |
num_hidden_groups=1, | |
num_attention_heads=64, | |
intermediate_size=16384, | |
inner_group_num=1, | |
hidden_act="gelu_new", | |
hidden_dropout_prob=0, | |
attention_probs_dropout_prob=0, | |
max_position_embeddings=512, | |
type_vocab_size=2, | |
initializer_range=0.02, | |
layer_norm_eps=1e-12, | |
classifier_dropout_prob=0.1, | |
position_embedding_type="absolute", | |
pad_token_id=0, | |
bos_token_id=2, | |
eos_token_id=3, | |
**kwargs, | |
): | |
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
self.vocab_size = vocab_size | |
self.embedding_size = embedding_size | |
self.hidden_size = hidden_size | |
self.num_hidden_layers = num_hidden_layers | |
self.num_hidden_groups = num_hidden_groups | |
self.num_attention_heads = num_attention_heads | |
self.inner_group_num = inner_group_num | |
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.classifier_dropout_prob = classifier_dropout_prob | |
self.position_embedding_type = position_embedding_type | |
# Copied from transformers.models.bert.configuration_bert.BertOnnxConfig with Roberta->Albert | |
class AlbertOnnxConfig(OnnxConfig): | |
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), | |
("token_type_ids", dynamic_axis), | |
] | |
) | |