codingutf8 2022 the fairseq s 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 mvp model configuration import warnings from configurationutils import pretrainedconfig from utils import logging logger logging getloggername mvppretrainedconfigarchivemap rucaiboxmvp https huggingface corucaiboxmvpresolvemainconfig json class mvpconfigpretrainedconfig r this is the configuration class to store the configuration of a mvpmodel it is used to instantiate a mvp 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 mvp rucaiboxmvphttps huggingface corucaiboxmvp 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 50267 vocabulary size of the mvp model defines the number of different tokens that can be represented by the inputsids passed when calling mvpmodel 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 classifierdropout float optional defaults to 0 0 the dropout ratio for classifier 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 2 the id of the token to force as the last generated token when maxlength is reached usually set to eostokenid useprompt bool optional defaults to false whether or not to use prompt promptlength int optional defaults to 100 the length of prompt promptmiddim int optional defaults to 800 dimensionality of the intermediate layer in prompt example python from transformers import mvpconfig mvpmodel initializing a mvp rucaiboxmvp style configuration configuration mvpconfig initializing a model with random weights from the rucaiboxmvp style configuration model mvpmodelconfiguration accessing the model configuration configuration model config modeltype mvp keystoignoreatinference pastkeyvalues attributemap numattentionheads encoderattentionheads hiddensize dmodel def init self vocabsize50267 maxpositionembeddings1024 encoderlayers12 encoderffndim4096 encoderattentionheads16 decoderlayers12 decoderffndim4096 decoderattentionheads16 encoderlayerdrop0 0 decoderlayerdrop0 0 activationfunctiongelu dmodel1024 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 classifierdropout0 0 scaleembeddingfalse usecachetrue padtokenid1 bostokenid0 eostokenid2 isencoderdecodertrue decoderstarttokenid2 forcedeostokenid2 usepromptfalse promptlength100 promptmiddim800 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 classifierdropout classifierdropout self usecache usecache self numhiddenlayers encoderlayers self scaleembedding scaleembedding scale factor will be sqrtdmodel if true self useprompt useprompt self promptlength promptlength self promptmiddim promptmiddim super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid isencoderdecoderisencoderdecoder decoderstarttokeniddecoderstarttokenid forcedeostokenidforcedeostokenid kwargs if self forcedbostokenid is none and kwargs getforcebostokentobegenerated false self forcedbostokenid self bostokenid warnings warn fplease make sure the config includes forcedbostokenidself bostokenid in future versions the config can simply be saved and uploaded again to be fixed coding utf 8 2022 the fairseq s 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 mvp model configuration this is the configuration class to store the configuration of a mvpmodel it is used to instantiate a mvp 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 mvp rucaibox mvp https huggingface co rucaibox mvp 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 50267 vocabulary size of the mvp model defines the number of different tokens that can be represented by the inputs_ids passed when calling mvpmodel 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 classifier_dropout float optional defaults to 0 0 the dropout ratio for classifier 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 2 the id of the token to force as the last generated token when max_length is reached usually set to eos_token_id use_prompt bool optional defaults to false whether or not to use prompt prompt_length int optional defaults to 100 the length of prompt prompt_mid_dim int optional defaults to 800 dimensionality of the intermediate layer in prompt example python from transformers import mvpconfig mvpmodel initializing a mvp rucaibox mvp style configuration configuration mvpconfig initializing a model with random weights from the rucaibox mvp style configuration model mvpmodel configuration accessing the model configuration configuration model config scale factor will be sqrt d_model if true
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MVP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}


class MvpConfig(PretrainedConfig):
    r

    model_type = "mvp"
    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=50267,
        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,
        activation_function="gelu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        classifier_dropout=0.0,
        scale_embedding=False,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        is_encoder_decoder=True,
        decoder_start_token_id=2,
        forced_eos_token_id=2,
        use_prompt=False,
        prompt_length=100,
        prompt_mid_dim=800,
        **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.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  
        self.use_prompt = use_prompt
        self.prompt_length = prompt_length
        self.prompt_mid_dim = prompt_mid_dim

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_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,
        )

        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
            self.forced_bos_token_id = self.bos_token_id
            warnings.warn(
                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                "The config can simply be saved and uploaded again to be fixed."
            )