codingutf8 2022 the huggingface team and microsoft research ai4science 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 biogpt model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername biogptpretrainedconfigarchivemap microsoftbiogpt https huggingface comicrosoftbiogptresolvemainconfig json see all biogpt models at https huggingface comodels filterbiogpt class biogptconfigpretrainedconfig r this is the configuration class to store the configuration of a biogptmodel it is used to instantiate an biogpt 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 biogpt microsoftbiogpthttps huggingface comicrosoftbiogpt 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 42384 vocabulary size of the biogpt model defines the number of different tokens that can be represented by the inputsids passed when calling biogptmodel hiddensize int optional defaults to 1024 dimension 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 16 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 4096 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 1024 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 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 scaleembedding bool optional defaults to true scale embeddings by diving by sqrtdmodel 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 layerdrop float optional defaults to 0 0 please refer to the paper about layerdrop https arxiv orgabs1909 11556 for further details activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer padtokenid int optional defaults to 1 padding token id bostokenid int optional defaults to 0 beginning of stream token id eostokenid int optional defaults to 2 end of stream token id example python from transformers import biogptmodel biogptconfig initializing a biogpt microsoftbiogpt style configuration configuration biogptconfig initializing a model from the microsoftbiogpt style configuration model biogptmodelconfiguration accessing the model configuration configuration model config modeltype biogpt def init self vocabsize42384 hiddensize1024 numhiddenlayers24 numattentionheads16 intermediatesize4096 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings1024 initializerrange0 02 layernormeps1e12 scaleembeddingtrue usecachetrue layerdrop0 0 activationdropout0 0 padtokenid1 bostokenid0 eostokenid2 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 layernormeps layernormeps self scaleembedding scaleembedding self usecache usecache self layerdrop layerdrop self activationdropout activationdropout super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs coding utf 8 2022 the huggingface team and microsoft research ai4science 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 biogpt model configuration see all biogpt models at https huggingface co models filter biogpt this is the configuration class to store the configuration of a biogptmodel it is used to instantiate an biogpt 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 biogpt microsoft biogpt https huggingface co microsoft biogpt 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 42384 vocabulary size of the biogpt model defines the number of different tokens that can be represented by the inputs_ids passed when calling biogptmodel hidden_size int optional defaults to 1024 dimension 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 16 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 4096 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 1024 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 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 scale_embedding bool optional defaults to true scale embeddings by diving by sqrt d_model 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 layerdrop float optional defaults to 0 0 please refer to the paper about layerdrop https arxiv org abs 1909 11556 for further details activation_dropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer pad_token_id int optional defaults to 1 padding token id bos_token_id int optional defaults to 0 beginning of stream token id eos_token_id int optional defaults to 2 end of stream token id example python from transformers import biogptmodel biogptconfig initializing a biogpt microsoft biogpt style configuration configuration biogptconfig initializing a model from the microsoft biogpt style configuration model biogptmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    
}


class BioGptConfig(PretrainedConfig):
    r

    model_type = "biogpt"

    def __init__(
        self,
        vocab_size=42384,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1024,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        scale_embedding=True,
        use_cache=True,
        layerdrop=0.0,
        activation_dropout=0.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **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.layer_norm_eps = layer_norm_eps
        self.scale_embedding = scale_embedding
        self.use_cache = use_cache
        self.layerdrop = layerdrop
        self.activation_dropout = activation_dropout
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)