codingutf8 2021 google 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 pegasus model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername pegasuspretrainedconfigarchivemap googlepegasuslarge https huggingface cogooglepegasuslargeresolvemainconfig json see all pegasus models at https huggingface comodels filterpegasus class pegasusconfigpretrainedconfig r this is the configuration class to store the configuration of a pegasusmodel it is used to instantiate an pegasus 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 pegasus googlepegasuslargehttps huggingface cogooglepegasuslarge 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 pegasus model defines the number of different tokens that can be represented by the inputsids passed when calling pegasusmodel or tfpegasusmodel dmodel int optional defaults to 1024 dimensionality of the layers and the pooler layer encoderlayers int optional defaults to 12 number of encoder layers decoderlayers int optional defaults to 12 number of decoder layers encoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder decoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder encoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder activationfunction str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer 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 initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices encoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details decoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details scaleembedding bool optional defaults to false 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 forcedeostokenid int optional defaults to 1 the id of the token to force as the last generated token when maxlength is reached usually set to eostokenid example python from transformers import pegasusconfig pegasusmodel initializing a pegasus googlepegasuslarge style configuration configuration pegasusconfig initializing a model with random weights from the googlepegasuslarge style configuration model pegasusmodelconfiguration accessing the model configuration configuration model config modeltype pegasus keystoignoreatinference pastkeyvalues attributemap numattentionheads encoderattentionheads hiddensize dmodel def init self vocabsize50265 maxpositionembeddings1024 encoderlayers12 encoderffndim4096 encoderattentionheads16 decoderlayers12 decoderffndim4096 decoderattentionheads16 encoderlayerdrop0 0 decoderlayerdrop0 0 usecachetrue isencoderdecodertrue activationfunctiongelu dmodel1024 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 decoderstarttokenid0 scaleembeddingfalse padtokenid0 eostokenid1 forcedeostokenid1 kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self dmodel dmodel self encoderffndim encoderffndim self encoderlayers encoderlayers self encoderattentionheads encoderattentionheads self decoderffndim decoderffndim self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self activationfunction activationfunction self initstd initstd self encoderlayerdrop encoderlayerdrop self decoderlayerdrop decoderlayerdrop self usecache usecache self numhiddenlayers encoderlayers self scaleembedding scaleembedding scale factor will be sqrtdmodel if true super init padtokenidpadtokenid eostokenideostokenid isencoderdecoderisencoderdecoder decoderstarttokeniddecoderstarttokenid forcedeostokenidforcedeostokenid kwargs property def numattentionheadsself int return self encoderattentionheads property def hiddensizeself int return self dmodel coding utf 8 2021 google 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 pegasus model configuration see all pegasus models at https huggingface co models filter pegasus this is the configuration class to store the configuration of a pegasusmodel it is used to instantiate an pegasus 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 pegasus google pegasus large https huggingface co google pegasus large 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 pegasus model defines the number of different tokens that can be represented by the inputs_ids passed when calling pegasusmodel or tfpegasusmodel d_model int optional defaults to 1024 dimensionality of the layers and the pooler layer encoder_layers int optional defaults to 12 number of encoder layers decoder_layers int optional defaults to 12 number of decoder layers encoder_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder decoder_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoder_ffn_dim int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in decoder encoder_ffn_dim int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in decoder activation_function str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu silu and gelu_new are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activation_dropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer 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 init_std float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices encoder_layerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop paper see https arxiv org abs 1909 11556 for more details decoder_layerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop paper see https arxiv org abs 1909 11556 for more details scale_embedding bool optional defaults to false 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 forced_eos_token_id int optional defaults to 1 the id of the token to force as the last generated token when max_length is reached usually set to eos_token_id example python from transformers import pegasusconfig pegasusmodel initializing a pegasus google pegasus large style configuration configuration pegasusconfig initializing a model with random weights from the google pegasus large style configuration model pegasusmodel configuration accessing the model configuration configuration model config scale factor will be sqrt d_model if true
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    
}


class PegasusConfig(PretrainedConfig):
    r

    model_type = "pegasus"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(
        self,
        vocab_size=50265,
        max_position_embeddings=1024,
        encoder_layers=12,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=12,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=0,
        scale_embedding=False,
        pad_token_id=0,
        eos_token_id=1,
        forced_eos_token_id=1,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  
        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            forced_eos_token_id=forced_eos_token_id,
            **kwargs,
        )

    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads

    @property
    def hidden_size(self) -> int:
        return self.d_model