codingutf8 2020 the huggingface 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 mobilebert 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 mobilebertpretrainedconfigarchivemap googlemobilebertuncased https huggingface cogooglemobilebertuncasedresolvemainconfig json class mobilebertconfigpretrainedconfig r this is the configuration class to store the configuration of a mobilebertmodel or a tfmobilebertmodel it is used to instantiate a mobilebert 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 mobilebert googlemobilebertuncasedhttps huggingface cogooglemobilebertuncased 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 mobilebert model defines the number of different tokens that can be represented by the inputsids passed when calling mobilebertmodel or tfmobilebertmodel hiddensize int optional defaults to 512 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 24 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 512 dimensionality of the intermediate often named feedforward layer in the transformer encoder hiddenact str or function optional defaults to relu 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 0 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 mobilebertmodel or tfmobilebertmodel 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 padtokenid int optional defaults to 0 the id of the token in the word embedding to use as padding embeddingsize int optional defaults to 128 the dimension of the word embedding vectors trigraminput bool optional defaults to true use a convolution of trigram as input usebottleneck bool optional defaults to true whether to use bottleneck in bert intrabottlenecksize int optional defaults to 128 size of bottleneck layer output usebottleneckattention bool optional defaults to false whether to use attention inputs from the bottleneck transformation keyquerysharedbottleneck bool optional defaults to true whether to use the same linear transformation for querykey in the bottleneck numfeedforwardnetworks int optional defaults to 4 number of ffns in a block normalizationtype str optional defaults to nonorm the normalization type in mobilebert classifierdropout float optional the dropout ratio for the classification head examples python from transformers import mobilebertconfig mobilebertmodel initializing a mobilebert configuration configuration mobilebertconfig initializing a model with random weights from the configuration above model mobilebertmodelconfiguration accessing the model configuration configuration model config attributes pretrainedconfigarchivemap dictstr str a dictionary containing all the available pretrained checkpoints copied from transformers models bert configurationbert bertonnxconfig with bertmobilebert coding utf 8 2020 the huggingface 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 mobilebert model configuration this is the configuration class to store the configuration of a mobilebertmodel or a tfmobilebertmodel it is used to instantiate a mobilebert 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 mobilebert google mobilebert uncased https huggingface co google mobilebert uncased 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 mobilebert model defines the number of different tokens that can be represented by the inputs_ids passed when calling mobilebertmodel or tfmobilebertmodel hidden_size int optional defaults to 512 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 24 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 512 dimensionality of the intermediate often named feed forward layer in the transformer encoder hidden_act str or function optional defaults to relu 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 0 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 mobilebertmodel or tfmobilebertmodel 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 pad_token_id int optional defaults to 0 the id of the token in the word embedding to use as padding embedding_size int optional defaults to 128 the dimension of the word embedding vectors trigram_input bool optional defaults to true use a convolution of trigram as input use_bottleneck bool optional defaults to true whether to use bottleneck in bert intra_bottleneck_size int optional defaults to 128 size of bottleneck layer output use_bottleneck_attention bool optional defaults to false whether to use attention inputs from the bottleneck transformation key_query_shared_bottleneck bool optional defaults to true whether to use the same linear transformation for query key in the bottleneck num_feedforward_networks int optional defaults to 4 number of ffns in a block normalization_type str optional defaults to no_norm the normalization type in mobilebert classifier_dropout float optional the dropout ratio for the classification head examples python from transformers import mobilebertconfig mobilebertmodel initializing a mobilebert configuration configuration mobilebertconfig initializing a model with random weights from the configuration above model mobilebertmodel configuration accessing the model configuration configuration model config attributes pretrained_config_archive_map dict str str a dictionary containing all the available pre trained checkpoints copied from transformers models bert configuration_bert bertonnxconfig with bert mobilebert
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__)

MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}


class MobileBertConfig(PretrainedConfig):
    r

    pretrained_config_archive_map = MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    model_type = "mobilebert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=512,
        num_hidden_layers=24,
        num_attention_heads=4,
        intermediate_size=512,
        hidden_act="relu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        embedding_size=128,
        trigram_input=True,
        use_bottleneck=True,
        intra_bottleneck_size=128,
        use_bottleneck_attention=False,
        key_query_shared_bottleneck=True,
        num_feedforward_networks=4,
        normalization_type="no_norm",
        classifier_activation=True,
        classifier_dropout=None,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        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.embedding_size = embedding_size
        self.trigram_input = trigram_input
        self.use_bottleneck = use_bottleneck
        self.intra_bottleneck_size = intra_bottleneck_size
        self.use_bottleneck_attention = use_bottleneck_attention
        self.key_query_shared_bottleneck = key_query_shared_bottleneck
        self.num_feedforward_networks = num_feedforward_networks
        self.normalization_type = normalization_type
        self.classifier_activation = classifier_activation

        if self.use_bottleneck:
            self.true_hidden_size = intra_bottleneck_size
        else:
            self.true_hidden_size = hidden_size

        self.classifier_dropout = classifier_dropout



class MobileBertOnnxConfig(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),
            ]
        )