188 lines
9.0 KiB
Python
188 lines
9.0 KiB
Python
# 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.
|
|
""" ELECTRA 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 ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
class ElectraConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`ElectraModel`] or a [`TFElectraModel`]. It is
|
|
used to instantiate a ELECTRA 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 ELECTRA
|
|
[google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) 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 ELECTRA model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`ElectraModel`] or [`TFElectraModel`].
|
|
embedding_size (`int`, *optional*, defaults to 128):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
hidden_size (`int`, *optional*, defaults to 256):
|
|
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_attention_heads (`int`, *optional*, defaults to 4):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
intermediate_size (`int`, *optional*, defaults to 1024):
|
|
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
|
hidden_act (`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.
|
|
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
|
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
|
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
|
|
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
|
|
just in case (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 [`ElectraModel`] or [`TFElectraModel`].
|
|
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.
|
|
summary_type (`str`, *optional*, defaults to `"first"`):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Has to be one of the following options:
|
|
|
|
- `"last"`: Take the last token hidden state (like XLNet).
|
|
- `"first"`: Take the first token hidden state (like BERT).
|
|
- `"mean"`: Take the mean of all tokens hidden states.
|
|
- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
|
|
- `"attn"`: Not implemented now, use multi-head attention.
|
|
summary_use_proj (`bool`, *optional*, defaults to `True`):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Whether or not to add a projection after the vector extraction.
|
|
summary_activation (`str`, *optional*):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Pass `"gelu"` for a gelu activation to the output, any other value will result in no activation.
|
|
summary_last_dropout (`float`, *optional*, defaults to 0.0):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
The dropout ratio to be used after the projection and activation.
|
|
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).
|
|
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`.
|
|
classifier_dropout (`float`, *optional*):
|
|
The dropout ratio for the classification head.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ElectraConfig, ElectraModel
|
|
|
|
>>> # Initializing a ELECTRA electra-base-uncased style configuration
|
|
>>> configuration = ElectraConfig()
|
|
|
|
>>> # Initializing a model (with random weights) from the electra-base-uncased style configuration
|
|
>>> model = ElectraModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "electra"
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=30522,
|
|
embedding_size=128,
|
|
hidden_size=256,
|
|
num_hidden_layers=12,
|
|
num_attention_heads=4,
|
|
intermediate_size=1024,
|
|
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,
|
|
summary_type="first",
|
|
summary_use_proj=True,
|
|
summary_activation="gelu",
|
|
summary_last_dropout=0.1,
|
|
pad_token_id=0,
|
|
position_embedding_type="absolute",
|
|
use_cache=True,
|
|
classifier_dropout=None,
|
|
**kwargs,
|
|
):
|
|
super().__init__(pad_token_id=pad_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_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_act = hidden_act
|
|
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.summary_type = summary_type
|
|
self.summary_use_proj = summary_use_proj
|
|
self.summary_activation = summary_activation
|
|
self.summary_last_dropout = summary_last_dropout
|
|
self.position_embedding_type = position_embedding_type
|
|
self.use_cache = use_cache
|
|
self.classifier_dropout = classifier_dropout
|
|
|
|
|
|
class ElectraOnnxConfig(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),
|
|
("token_type_ids", dynamic_axis),
|
|
]
|
|
)
|