codingutf8 2022 uwmadison 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 nystromformer model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername nystromformerpretrainedconfigarchivemap uwmadisonnystromformer512 https huggingface couwmadisonnystromformer512resolvemainconfig json see all nystromformer models at https huggingface comodels filternystromformer class nystromformerconfigpretrainedconfig r this is the configuration class to store the configuration of a nystromformermodel it is used to instantiate an nystromformer 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 nystromformer uwmadisonnystromformer512https huggingface couwmadisonnystromformer512 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 nystromformer model defines the number of different tokens that can be represented by the inputsids passed when calling nystromformermodel 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 nystromformermodel segmentmeansseqlen int optional defaults to 64 sequence length used in segmentmeans numlandmarks int optional defaults to 64 the number of landmark or nystrom points to use in nystrom approximation of the softmax selfattention matrix convkernelsize int optional defaults to 65 the kernel size of depthwise convolution used in nystrom approximation invcoeffinitoption bool optional defaults to false whether or not to use exact coefficient computation for the initial values for the iterative method of calculating the moorepenrose inverse of a matrix 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 example python from transformers import nystromformermodel nystromformerconfig initializing a nystromformer uwmadisonnystromformer512 style configuration configuration nystromformerconfig initializing a model from the uwmadisonnystromformer512 style configuration model nystromformermodelconfiguration accessing the model configuration configuration model config modeltype nystromformer def init self vocabsize30000 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelunew hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings510 typevocabsize2 segmentmeansseqlen64 numlandmarks64 convkernelsize65 invcoeffinitoptionfalse initializerrange0 02 layernormeps1e5 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 typevocabsize typevocabsize self segmentmeansseqlen segmentmeansseqlen self numlandmarks numlandmarks self convkernelsize convkernelsize self invcoeffinitoption invcoeffinitoption self layernormeps layernormeps super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs coding utf 8 2022 uw madison 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 nystromformer model configuration see all nystromformer models at https huggingface co models filter nystromformer this is the configuration class to store the configuration of a nystromformermodel it is used to instantiate an nystromformer 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 nystromformer uw madison nystromformer 512 https huggingface co uw madison nystromformer 512 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 nystromformer model defines the number of different tokens that can be represented by the inputs_ids passed when calling nystromformermodel 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 nystromformermodel segment_means_seq_len int optional defaults to 64 sequence length used in segment means num_landmarks int optional defaults to 64 the number of landmark or nystrom points to use in nystrom approximation of the softmax self attention matrix conv_kernel_size int optional defaults to 65 the kernel size of depthwise convolution used in nystrom approximation inv_coeff_init_option bool optional defaults to false whether or not to use exact coefficient computation for the initial values for the iterative method of calculating the moore penrose inverse of a matrix 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 example python from transformers import nystromformermodel nystromformerconfig initializing a nystromformer uw madison nystromformer 512 style configuration configuration nystromformerconfig initializing a model from the uw madison nystromformer 512 style configuration model nystromformermodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    
}


class NystromformerConfig(PretrainedConfig):
    r

    model_type = "nystromformer"

    def __init__(
        self,
        vocab_size=30000,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu_new",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=510,
        type_vocab_size=2,
        segment_means_seq_len=64,
        num_landmarks=64,
        conv_kernel_size=65,
        inv_coeff_init_option=False,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        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.type_vocab_size = type_vocab_size
        self.segment_means_seq_len = segment_means_seq_len
        self.num_landmarks = num_landmarks
        self.conv_kernel_size = conv_kernel_size
        self.inv_coeff_init_option = inv_coeff_init_option
        self.layer_norm_eps = layer_norm_eps
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)