codingutf8 2021 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 roformer model configuration from collections import ordereddict from typing import mapping from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername roformerpretrainedconfigarchivemap junnyuroformerchinesesmall https huggingface cojunnyuroformerchinesesmallresolvemainconfig json junnyuroformerchinesebase https huggingface cojunnyuroformerchinesebaseresolvemainconfig json junnyuroformerchinesecharsmall https huggingface cojunnyuroformerchinesecharsmallresolvemainconfig json junnyuroformerchinesecharbase https huggingface cojunnyuroformerchinesecharbaseresolvemainconfig json junnyuroformersmalldiscriminator https huggingface cojunnyuroformersmalldiscriminatorresolvemainconfig json junnyuroformersmallgenerator https huggingface cojunnyuroformersmallgeneratorresolvemainconfig json see all roformer models at https huggingface comodels filterroformer class roformerconfigpretrainedconfig r this is the configuration class to store the configuration of a roformermodel it is used to instantiate an roformer 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 roformer junnyuroformerchinesebasehttps huggingface cojunnyuroformerchinesebase 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 50000 vocabulary size of the roformer model defines the number of different tokens that can be represented by the inputsids passed when calling roformermodel or tfroformermodel embeddingsize int optional defaults to none dimensionality of the encoder layers and the pooler layer defaults to the hiddensize if not provided 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 1536 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 1536 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling roformermodel or tfroformermodel 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 isdecoder bool optional defaults to false whether the model is used as a decoder or not if false the model is used as an encoder 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 rotaryvalue bool optional defaults to false whether or not apply rotary position embeddings on value layer example python from transformers import roformermodel roformerconfig initializing a roformer junnyuroformerchinesebase style configuration configuration roformerconfig initializing a model with random weights from the junnyuroformerchinesebase style configuration model roformermodelconfiguration accessing the model configuration configuration model config modeltype roformer def init self vocabsize50000 embeddingsizenone hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings1536 typevocabsize2 initializerrange0 02 layernormeps1e12 padtokenid0 rotaryvaluefalse usecachetrue kwargs super initpadtokenidpadtokenid kwargs self vocabsize vocabsize self embeddingsize hiddensize if embeddingsize is none else embeddingsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self hiddenact hiddenact self intermediatesize intermediatesize self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self rotaryvalue rotaryvalue self usecache usecache class roformeronnxconfigonnxconfig property def inputsself mappingstr mappingint str if self task multiplechoice dynamicaxis 0 batch 1 choice 2 sequence else dynamicaxis 0 batch 1 sequence dynamicaxis 0 batch 1 sequence return ordereddict inputids dynamicaxis attentionmask dynamicaxis tokentypeids dynamicaxis coding utf 8 2021 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 roformer model configuration see all roformer models at https huggingface co models filter roformer this is the configuration class to store the configuration of a roformermodel it is used to instantiate an roformer 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 roformer junnyu roformer_chinese_base https huggingface co junnyu roformer_chinese_base 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 50000 vocabulary size of the roformer model defines the number of different tokens that can be represented by the inputs_ids passed when calling roformermodel or tfroformermodel embedding_size int optional defaults to none dimensionality of the encoder layers and the pooler layer defaults to the hidden_size if not provided 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 1536 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 1536 type_vocab_size int optional defaults to 2 the vocabulary size of the token_type_ids passed when calling roformermodel or tfroformermodel 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 is_decoder bool optional defaults to false whether the model is used as a decoder or not if false the model is used as an encoder 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 rotary_value bool optional defaults to false whether or not apply rotary position embeddings on value layer example python from transformers import roformermodel roformerconfig initializing a roformer junnyu roformer_chinese_base style configuration configuration roformerconfig initializing a model with random weights from the junnyu roformer_chinese_base style configuration model roformermodel configuration accessing the model configuration configuration model config
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    
}


class RoFormerConfig(PretrainedConfig):
    r

    model_type = "roformer"

    def __init__(
        self,
        vocab_size=50000,
        embedding_size=None,
        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=1536,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        rotary_value=False,
        use_cache=True,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.embedding_size = hidden_size if embedding_size is None else embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.rotary_value = rotary_value
        self.use_cache = use_cache


class RoFormerOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        dynamic_axis = {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]
        )