codingutf8 the huggingface team 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 rembert 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 rembertpretrainedconfigarchivemap googlerembert https huggingface cogooglerembertresolvemainconfig json see all rembert models at https huggingface comodels filterrembert class rembertconfigpretrainedconfig r this is the configuration class to store the configuration of a rembertmodel it is used to instantiate an rembert 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 rembert googleremberthttps huggingface cogooglerembert 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 250300 vocabulary size of the rembert model defines the number of different tokens that can be represented by the inputsids passed when calling rembertmodel or tfrembertmodel vocabulary size of the model defines the different tokens that can be represented by the inputsids passed to the forward method of rembertmodel hiddensize int optional defaults to 1152 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 32 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 18 number of attention heads for each attention layer in the transformer encoder inputembeddingsize int optional defaults to 256 dimensionality of the input embeddings outputembeddingsize int optional defaults to 1664 dimensionality of the output embeddings intermediatesize int optional defaults to 4608 dimensionality of the intermediate i e feedforward layer in the transformer encoder hiddenact str or function optional defaults to gelu 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 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 the dropout ratio for the attention probabilities classifierdropoutprob float optional defaults to 0 1 the dropout ratio for the classifier layer when finetuning 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 rembertmodel or tfrembertmodel 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 example python from transformers import rembertmodel rembertconfig initializing a rembert rembert style configuration configuration rembertconfig initializing a model from the rembert style configuration model rembertmodelconfiguration accessing the model configuration configuration model config modeltype rembert def init self vocabsize250300 hiddensize1152 numhiddenlayers32 numattentionheads18 inputembeddingsize256 outputembeddingsize1664 intermediatesize4608 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 classifierdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 usecachetrue padtokenid0 bostokenid312 eostokenid313 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self inputembeddingsize inputembeddingsize self outputembeddingsize outputembeddingsize 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 classifierdropoutprob classifierdropoutprob self initializerrange initializerrange self typevocabsize typevocabsize self layernormeps layernormeps self usecache usecache self tiewordembeddings false class rembertonnxconfigonnxconfig 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 property def atolforvalidationself float return 1e4 coding utf 8 the huggingface team 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 rembert model configuration see all rembert models at https huggingface co models filter rembert this is the configuration class to store the configuration of a rembertmodel it is used to instantiate an rembert 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 rembert google rembert https huggingface co google rembert 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 250300 vocabulary size of the rembert model defines the number of different tokens that can be represented by the inputs_ids passed when calling rembertmodel or tfrembertmodel vocabulary size of the model defines the different tokens that can be represented by the inputs_ids passed to the forward method of rembertmodel hidden_size int optional defaults to 1152 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 32 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 18 number of attention heads for each attention layer in the transformer encoder input_embedding_size int optional defaults to 256 dimensionality of the input embeddings output_embedding_size int optional defaults to 1664 dimensionality of the output embeddings intermediate_size int optional defaults to 4608 dimensionality of the intermediate i e feed forward layer in the transformer encoder hidden_act str or function optional defaults to gelu 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 the dropout probabilitiy 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 classifier_dropout_prob float optional defaults to 0 1 the dropout ratio for the classifier layer when fine tuning 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 rembertmodel or tfrembertmodel 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 example python from transformers import rembertmodel rembertconfig initializing a rembert rembert style configuration configuration rembertconfig initializing a model from the rembert style configuration model rembertmodel 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__)

REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    
}


class RemBertConfig(PretrainedConfig):
    r

    model_type = "rembert"

    def __init__(
        self,
        vocab_size=250300,
        hidden_size=1152,
        num_hidden_layers=32,
        num_attention_heads=18,
        input_embedding_size=256,
        output_embedding_size=1664,
        intermediate_size=4608,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=312,
        eos_token_id=313,
        **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.input_embedding_size = input_embedding_size
        self.output_embedding_size = output_embedding_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.classifier_dropout_prob = classifier_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.tie_word_embeddings = False


class RemBertOnnxConfig(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),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4