codingutf8 2018 the google ai language team s and the huggingface inc team c 2018 nvidia corporation 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 orglicenseslicense2 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 configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername electrapretrainedconfigarchivemap googleelectrasmallgenerator https huggingface cogoogleelectrasmallgeneratorresolvemainconfig json googleelectrabasegenerator https huggingface cogoogleelectrabasegeneratorresolvemainconfig json googleelectralargegenerator https huggingface cogoogleelectralargegeneratorresolvemainconfig json googleelectrasmalldiscriminator https huggingface cogoogleelectrasmalldiscriminatorresolvemainconfig json googleelectrabasediscriminator https huggingface cogoogleelectrabasediscriminatorresolvemainconfig json googleelectralargediscriminator https huggingface cogoogleelectralargediscriminatorresolvemainconfig json class electraconfigpretrainedconfig 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 googleelectrasmalldiscriminatorhttps huggingface cogoogleelectrasmalldiscriminator architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 30522 vocabulary size of the electra model defines the number of different tokens that can be represented by the inputsids passed when calling electramodel or tfelectramodel embeddingsize int optional defaults to 128 dimensionality of the encoder layers and the pooler layer hiddensize int optional defaults to 256 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 4 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 1024 dimensionality of the intermediate i e feedforward layer in the transformer encoder hiddenact str or callable optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported hiddendropoutprob float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 1 the dropout ratio for the attention probabilities maxpositionembeddings 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 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling electramodel or tfelectramodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers summarytype 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 clsindex supply a tensor of classification token position like gptgpt2 attn not implemented now use multihead attention summaryuseproj 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 summaryactivation 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 summarylastdropout 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 positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery for positional embeddings use absolute for more information on relativekey please refer to selfattention with relative position representations shaw et al https arxiv orgabs1803 02155 for more information on relativekeyquery please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv orgabs2009 13658 usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue classifierdropout float optional the dropout ratio for the classification head examples python from transformers import electraconfig electramodel initializing a electra electrabaseuncased style configuration configuration electraconfig initializing a model with random weights from the electrabaseuncased style configuration model electramodelconfiguration accessing the model configuration configuration model config modeltype electra def init self vocabsize30522 embeddingsize128 hiddensize256 numhiddenlayers12 numattentionheads4 intermediatesize1024 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 summarytypefirst summaryuseprojtrue summaryactivationgelu summarylastdropout0 1 padtokenid0 positionembeddingtypeabsolute usecachetrue classifierdropoutnone kwargs super initpadtokenidpadtokenid kwargs self vocabsize vocabsize self embeddingsize embeddingsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self summarytype summarytype self summaryuseproj summaryuseproj self summaryactivation summaryactivation self summarylastdropout summarylastdropout self positionembeddingtype positionembeddingtype self usecache usecache self classifierdropout classifierdropout class electraonnxconfigonnxconfig property def inputsself mappingstr mappingint str if self task multiplechoice dynamicaxis 0 batch 1 choice 2 sequence else dynamicaxis 0 batch 1 sequence return ordereddict inputids dynamicaxis attentionmask dynamicaxis tokentypeids dynamicaxis coding utf 8 2018 the google ai language team s and the huggingface inc team c 2018 nvidia corporation 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 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
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__)

ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}


class ElectraConfig(PretrainedConfig):
    r

    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),
            ]
        )