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 albert model configuration from collections import ordereddict from typing import mapping from configurationutils import pretrainedconfig from onnx import onnxconfig albertpretrainedconfigarchivemap albertbasev1 https huggingface coalbertbasev1resolvemainconfig json albertlargev1 https huggingface coalbertlargev1resolvemainconfig json albertxlargev1 https huggingface coalbertxlargev1resolvemainconfig json albertxxlargev1 https huggingface coalbertxxlargev1resolvemainconfig json albertbasev2 https huggingface coalbertbasev2resolvemainconfig json albertlargev2 https huggingface coalbertlargev2resolvemainconfig json albertxlargev2 https huggingface coalbertxlargev2resolvemainconfig json albertxxlargev2 https huggingface coalbertxxlargev2resolvemainconfig json class albertconfigpretrainedconfig r this is the configuration class to store the configuration of a albertmodel or a tfalbertmodel it is used to instantiate an albert 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 albert albertxxlargev2https huggingface coalbertxxlargev2 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 30000 vocabulary size of the albert model defines the number of different tokens that can be represented by the inputsids passed when calling albertmodel or tfalbertmodel embeddingsize int optional defaults to 128 dimensionality of vocabulary embeddings hiddensize int optional defaults to 4096 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numhiddengroups int optional defaults to 1 number of groups for the hidden layers parameters in the same group are shared numattentionheads int optional defaults to 64 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 16384 the dimensionality of the intermediate often named feedforward layer in the transformer encoder innergroupnum int optional defaults to 1 the number of inner repetition of attention and ffn hiddenact str or callable optional defaults to gelunew 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 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 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 e g 512 or 1024 or 2048 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling albertmodel or tfalbertmodel 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 classifierdropoutprob float optional defaults to 0 1 the dropout ratio for attached classifiers 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 padtokenid int optional defaults to 0 padding token id bostokenid int optional defaults to 2 beginning of stream token id eostokenid int optional defaults to 3 end of stream token id examples python from transformers import albertconfig albertmodel initializing an albertxxlarge style configuration albertxxlargeconfiguration albertconfig initializing an albertbase style configuration albertbaseconfiguration albertconfig hiddensize768 numattentionheads12 intermediatesize3072 initializing a model with random weights from the albertbase style configuration model albertmodelalbertxxlargeconfiguration accessing the model configuration configuration model config modeltype albert def init self vocabsize30000 embeddingsize128 hiddensize4096 numhiddenlayers12 numhiddengroups1 numattentionheads64 intermediatesize16384 innergroupnum1 hiddenactgelunew hiddendropoutprob0 attentionprobsdropoutprob0 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 classifierdropoutprob0 1 positionembeddingtypeabsolute padtokenid0 bostokenid2 eostokenid3 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self embeddingsize embeddingsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numhiddengroups numhiddengroups self numattentionheads numattentionheads self innergroupnum innergroupnum self hiddenact hiddenact self intermediatesize intermediatesize self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self classifierdropoutprob classifierdropoutprob self positionembeddingtype positionembeddingtype copied from transformers models bert configurationbert bertonnxconfig with robertaalbert class albertonnxconfigonnxconfig 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 albert model configuration this is the configuration class to store the configuration of a albertmodel or a tfalbertmodel it is used to instantiate an albert 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 albert albert xxlarge v2 https huggingface co albert xxlarge v2 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 30000 vocabulary size of the albert model defines the number of different tokens that can be represented by the inputs_ids passed when calling albertmodel or tfalbertmodel embedding_size int optional defaults to 128 dimensionality of vocabulary embeddings hidden_size int optional defaults to 4096 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_hidden_groups int optional defaults to 1 number of groups for the hidden layers parameters in the same group are shared num_attention_heads int optional defaults to 64 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 16384 the dimensionality of the intermediate often named feed forward layer in the transformer encoder inner_group_num int optional defaults to 1 the number of inner repetition of attention and ffn hidden_act str or callable optional defaults to gelu_new 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 the dropout probability 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 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 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 albertmodel or tfalbertmodel 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 classifier_dropout_prob float optional defaults to 0 1 the dropout ratio for attached classifiers 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 pad_token_id int optional defaults to 0 padding token id bos_token_id int optional defaults to 2 beginning of stream token id eos_token_id int optional defaults to 3 end of stream token id examples python from transformers import albertconfig albertmodel initializing an albert xxlarge style configuration albert_xxlarge_configuration albertconfig initializing an albert base style configuration albert_base_configuration albertconfig hidden_size 768 num_attention_heads 12 intermediate_size 3072 initializing a model with random weights from the albert base style configuration model albertmodel albert_xxlarge_configuration accessing the model configuration configuration model config copied from transformers models bert configuration_bert bertonnxconfig with roberta albert
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}


class AlbertConfig(PretrainedConfig):
    r

    model_type = "albert"

    def __init__(
        self,
        vocab_size=30000,
        embedding_size=128,
        hidden_size=4096,
        num_hidden_layers=12,
        num_hidden_groups=1,
        num_attention_heads=64,
        intermediate_size=16384,
        inner_group_num=1,
        hidden_act="gelu_new",
        hidden_dropout_prob=0,
        attention_probs_dropout_prob=0,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        classifier_dropout_prob=0.1,
        position_embedding_type="absolute",
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=3,
        **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.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_hidden_groups = num_hidden_groups
        self.num_attention_heads = num_attention_heads
        self.inner_group_num = inner_group_num
        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.classifier_dropout_prob = classifier_dropout_prob
        self.position_embedding_type = position_embedding_type



class AlbertOnnxConfig(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),
            ]
        )