codingutf8 2022 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 yoso model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername yosopretrainedconfigarchivemap uwmadisonyoso4096 https huggingface couwmadisonyoso4096resolvemainconfig json see all yoso models at https huggingface comodels filteryoso class yosoconfigpretrainedconfig r this is the configuration class to store the configuration of a yosomodel it is used to instantiate an yoso 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 yoso uwmadisonyoso4096https huggingface couwmadisonyoso4096 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 50265 vocabulary size of the yoso model defines the number of different tokens that can be represented by the inputsids passed when calling yosomodel 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 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 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 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 yosomodel 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 positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery useexpectation bool optional defaults to true whether or not to use yoso expectation overrides any effect of numhash hashcodelen int optional defaults to 9 the length of hashes generated by the hash functions numhash int optional defaults to 64 number of hash functions used in yososelfattention convwindow int optional kernel size of depthwise convolution usefasthash bool optional defaults to false whether or not to use custom cuda kernels which perform fast random projection via hadamard transform lshbackward bool optional defaults to true whether or not to perform backpropagation using locality sensitive hashing example python from transformers import yosoconfig yosomodel initializing a yoso uwmadisonyoso4096 style configuration configuration yosoconfig initializing a model with random weights from the uwmadisonyoso4096 style configuration model yosomodelconfiguration accessing the model configuration configuration model config modeltype yoso def init self vocabsize50265 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings4096 typevocabsize1 initializerrange0 02 layernormeps1e12 positionembeddingtypeabsolute useexpectationtrue hashcodelen9 numhash64 convwindownone usefasthashtrue lshbackwardtrue padtokenid1 bostokenid0 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid 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 positionembeddingtype positionembeddingtype self useexpectation useexpectation self hashcodelen hashcodelen self numhash numhash self convwindow convwindow self usefasthash usefasthash self lshbackward lshbackward coding utf 8 2022 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 yoso model configuration see all yoso models at https huggingface co models filter yoso this is the configuration class to store the configuration of a yosomodel it is used to instantiate an yoso 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 yoso uw madison yoso 4096 https huggingface co uw madison yoso 4096 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 50265 vocabulary size of the yoso model defines the number of different tokens that can be represented by the inputs_ids passed when calling yosomodel 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 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 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 yosomodel 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 position_embedding_type str optional defaults to absolute type of position embedding choose one of absolute relative_key relative_key_query use_expectation bool optional defaults to true whether or not to use yoso expectation overrides any effect of num_hash hash_code_len int optional defaults to 9 the length of hashes generated by the hash functions num_hash int optional defaults to 64 number of hash functions used in yososelfattention conv_window int optional kernel size of depth wise convolution use_fast_hash bool optional defaults to false whether or not to use custom cuda kernels which perform fast random projection via hadamard transform lsh_backward bool optional defaults to true whether or not to perform backpropagation using locality sensitive hashing example python from transformers import yosoconfig yosomodel initializing a yoso uw madison yoso 4096 style configuration configuration yosoconfig initializing a model with random weights from the uw madison yoso 4096 style configuration model yosomodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    
}


class YosoConfig(PretrainedConfig):
    r

    model_type = "yoso"

    def __init__(
        self,
        vocab_size=50265,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=4096,
        type_vocab_size=1,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        position_embedding_type="absolute",
        use_expectation=True,
        hash_code_len=9,
        num_hash=64,
        conv_window=None,
        use_fast_hash=True,
        lsh_backward=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **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.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.position_embedding_type = position_embedding_type
        self.use_expectation = use_expectation
        self.hash_code_len = hash_code_len
        self.num_hash = num_hash
        self.conv_window = conv_window
        self.use_fast_hash = use_fast_hash
        self.lsh_backward = lsh_backward