codingutf8 2021 google research and the huggingface inc team 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 bigbird 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 bigbirdpretrainedconfigarchivemap googlebigbirdrobertabase https huggingface cogooglebigbirdrobertabaseresolvemainconfig json googlebigbirdrobertalarge https huggingface cogooglebigbirdrobertalargeresolvemainconfig json googlebigbirdbasetriviaitc https huggingface cogooglebigbirdbasetriviaitcresolvemainconfig json see all bigbird models at https huggingface comodels filterbigbird class bigbirdconfigpretrainedconfig r this is the configuration class to store the configuration of a bigbirdmodel it is used to instantiate an bigbird 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 bigbird googlebigbirdrobertabasehttps huggingface cogooglebigbirdrobertabase 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 50358 vocabulary size of the bigbird model defines the number of different tokens that can be represented by the inputsids passed when calling bigbirdmodel hiddensize int optional defaults to 768 dimension 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 12 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 3072 dimension of the intermediate i e feedforward layer in the transformer encoder hiddenact str or function optional defaults to gelunew the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported hiddendropoutprob float optional defaults to 0 1 the dropout probabilitiy 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 4096 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 1024 or 2048 or 4096 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling bigbirdmodel 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 isdecoder bool optional defaults to false whether the model is used as a decoder or not if false the model is used as an encoder 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 attentiontype str optional defaults to blocksparse whether to use block sparse attention with n complexity as introduced in paper or original attention layer with n2 complexity possible values are originalfull and blocksparse usebias bool optional defaults to true whether to use bias in query key value rescaleembeddings bool optional defaults to false whether to rescale embeddings with hiddensize 0 5 blocksize int optional defaults to 64 size of each block useful only when attentiontype blocksparse numrandomblocks int optional defaults to 3 each query is going to attend these many number of random blocks useful only when attentiontype blocksparse classifierdropout float optional the dropout ratio for the classification head example python from transformers import bigbirdconfig bigbirdmodel initializing a bigbird googlebigbirdrobertabase style configuration configuration bigbirdconfig initializing a model with random weights from the googlebigbirdrobertabase style configuration model bigbirdmodelconfiguration accessing the model configuration configuration model config modeltype bigbird def init self vocabsize50358 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelunew hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings4096 typevocabsize2 initializerrange0 02 layernormeps1e12 usecachetrue padtokenid0 bostokenid1 eostokenid2 septokenid66 attentiontypeblocksparse usebiastrue rescaleembeddingsfalse blocksize64 numrandomblocks3 classifierdropoutnone kwargs super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid septokenidseptokenid kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self typevocabsize typevocabsize self layernormeps layernormeps self usecache usecache self rescaleembeddings rescaleembeddings self attentiontype attentiontype self usebias usebias self blocksize blocksize self numrandomblocks numrandomblocks self classifierdropout classifierdropout class bigbirdonnxconfigonnxconfig 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 coding utf 8 2021 google research and the huggingface inc team 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 bigbird model configuration see all bigbird models at https huggingface co models filter big_bird this is the configuration class to store the configuration of a bigbirdmodel it is used to instantiate an bigbird 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 bigbird google bigbird roberta base https huggingface co google bigbird roberta base 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 50358 vocabulary size of the bigbird model defines the number of different tokens that can be represented by the inputs_ids passed when calling bigbirdmodel hidden_size int optional defaults to 768 dimension 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 12 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 3072 dimension of the intermediate i e feed forward layer in the transformer encoder hidden_act str or function optional defaults to gelu_new the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new are supported hidden_dropout_prob float optional defaults to 0 1 the dropout probabilitiy 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 4096 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 1024 or 2048 or 4096 type_vocab_size int optional defaults to 2 the vocabulary size of the token_type_ids passed when calling bigbirdmodel 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 is_decoder bool optional defaults to false whether the model is used as a decoder or not if false the model is used as an encoder 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 attention_type str optional defaults to block_sparse whether to use block sparse attention with n complexity as introduced in paper or original attention layer with n 2 complexity possible values are original_full and block_sparse use_bias bool optional defaults to true whether to use bias in query key value rescale_embeddings bool optional defaults to false whether to rescale embeddings with hidden_size 0 5 block_size int optional defaults to 64 size of each block useful only when attention_type block_sparse num_random_blocks int optional defaults to 3 each query is going to attend these many number of random blocks useful only when attention_type block_sparse classifier_dropout float optional the dropout ratio for the classification head example python from transformers import bigbirdconfig bigbirdmodel initializing a bigbird google bigbird roberta base style configuration configuration bigbirdconfig initializing a model with random weights from the google bigbird roberta base style configuration model bigbirdmodel 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__)

BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    
}


class BigBirdConfig(PretrainedConfig):
    r

    model_type = "big_bird"

    def __init__(
        self,
        vocab_size=50358,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu_new",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=4096,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        sep_token_id=66,
        attention_type="block_sparse",
        use_bias=True,
        rescale_embeddings=False,
        block_size=64,
        num_random_blocks=3,
        classifier_dropout=None,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            sep_token_id=sep_token_id,
            **kwargs,
        )

        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        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.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache

        self.rescale_embeddings = rescale_embeddings
        self.attention_type = attention_type
        self.use_bias = use_bias
        self.block_size = block_size
        self.num_random_blocks = num_random_blocks
        self.classifier_dropout = classifier_dropout


class BigBirdOnnxConfig(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),
            ]
        )