codingutf8 2020 the allen institute for ai team 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 longformer configuration from collections import ordereddict from typing import typechecking any list mapping optional union from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import tensortype logging if typechecking from onnx config import patchingspec from tokenizationutilsbase import pretrainedtokenizerbase logger logging getloggername longformerpretrainedconfigarchivemap allenailongformerbase4096 https huggingface coallenailongformerbase4096resolvemainconfig json allenailongformerlarge4096 https huggingface coallenailongformerlarge4096resolvemainconfig json allenailongformerlarge4096finetunedtriviaqa https huggingface coallenailongformerlarge4096finetunedtriviaqaresolvemainconfig json allenailongformerbase4096extra pos embd only https huggingface coallenailongformerbase4096extra pos embd onlyresolvemainconfig json allenailongformerlarge4096extra pos embd only https huggingface coallenailongformerlarge4096extra pos embd onlyresolvemainconfig json class longformerconfigpretrainedconfig r this is the configuration class to store the configuration of a longformermodel or a tflongformermodel it is used to instantiate a longformer model according to the specified arguments defining the model architecture this is the configuration class to store the configuration of a longformermodel it is used to instantiate an longformer 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 longformer allenailongformerbase4096https huggingface coallenailongformerbase4096 architecture with a sequence length 4 096 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 30522 vocabulary size of the longformer model defines the number of different tokens that can be represented by the inputsids passed when calling longformermodel or tflongformermodel hiddensize int optional defaults to 768 dimensionality 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 dimensionality of the intermediate often named feedforward layer in the transformer encoder hiddenact str or callable 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 hiddendropoutprob float optional defaults to 0 1 the dropout probability 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 longformermodel or tflongformermodel 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 attentionwindow int or listint optional defaults to 512 size of an attention window around each token if an int use the same size for all layers to specify a different window size for each layer use a listint where lenattentionwindow numhiddenlayers example python from transformers import longformerconfig longformermodel initializing a longformer configuration configuration longformerconfig initializing a model from the configuration model longformermodelconfiguration accessing the model configuration configuration model config modeltype longformer def init self attentionwindow unionlistint int 512 septokenid int 2 padtokenid int 1 bostokenid int 0 eostokenid int 2 vocabsize int 30522 hiddensize int 768 numhiddenlayers int 12 numattentionheads int 12 intermediatesize int 3072 hiddenact str gelu hiddendropoutprob float 0 1 attentionprobsdropoutprob float 0 1 maxpositionembeddings int 512 typevocabsize int 2 initializerrange float 0 02 layernormeps float 1e12 onnxexport bool false kwargs what absolute tolerance value to use during model conversion validation returns float absolute tolerance value needs to be 14 to support tril operator for some reason replacing this code by inputsglobalattentionmask torch randint2 inputsinputids shape dtypetorch int64 makes the export fail randomly make every second token global coding utf 8 2020 the allen institute for ai team 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 longformer configuration this is the configuration class to store the configuration of a longformermodel or a tflongformermodel it is used to instantiate a longformer model according to the specified arguments defining the model architecture this is the configuration class to store the configuration of a longformermodel it is used to instantiate an longformer 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 longformer allenai longformer base 4096 https huggingface co allenai longformer base 4096 architecture with a sequence length 4 096 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 30522 vocabulary size of the longformer model defines the number of different tokens that can be represented by the inputs_ids passed when calling longformermodel or tflongformermodel hidden_size int optional defaults to 768 dimensionality 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 dimensionality of the intermediate often named feed forward layer in the transformer encoder hidden_act str or callable 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 hidden_dropout_prob float optional defaults to 0 1 the dropout probability 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 longformermodel or tflongformermodel 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 attention_window int or list int optional defaults to 512 size of an attention window around each token if an int use the same size for all layers to specify a different window size for each layer use a list int where len attention_window num_hidden_layers example python from transformers import longformerconfig longformermodel initializing a longformer configuration configuration longformerconfig initializing a model from the configuration model longformermodel configuration accessing the model configuration configuration model config constructs longformerconfig what absolute tolerance value to use during model conversion validation returns float absolute tolerance value needs to be 14 to support tril operator for some reason replacing this code by inputs global_attention_mask torch randint 2 inputs input_ids shape dtype torch int64 makes the export fail randomly make every second token global
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ...onnx.config import PatchingSpec
    from ...tokenization_utils_base import PreTrainedTokenizerBase


logger = logging.get_logger(__name__)

LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}


class LongformerConfig(PretrainedConfig):
    r

    model_type = "longformer"

    def __init__(
        self,
        attention_window: Union[List[int], int] = 512,
        sep_token_id: int = 2,
        pad_token_id: int = 1,
        bos_token_id: int = 0,
        eos_token_id: int = 2,
        vocab_size: int = 30522,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        hidden_act: str = "gelu",
        hidden_dropout_prob: float = 0.1,
        attention_probs_dropout_prob: float = 0.1,
        max_position_embeddings: int = 512,
        type_vocab_size: int = 2,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        onnx_export: bool = False,
        **kwargs,
    ):
        
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.attention_window = attention_window
        self.sep_token_id = sep_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.vocab_size = vocab_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.onnx_export = onnx_export


class LongformerOnnxConfig(OnnxConfig):
    def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: "List[PatchingSpec]" = None):
        super().__init__(config, task, patching_specs)
        config.onnx_export = True

    @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"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("global_attention_mask", dynamic_axis),
            ]
        )

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        outputs = super().outputs
        if self.task == "default":
            outputs["pooler_output"] = {0: "batch"}
        return outputs

    @property
    def atol_for_validation(self) -> float:
        
        return 1e-4

    @property
    def default_onnx_opset(self) -> int:
        
        return max(super().default_onnx_opset, 14)

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        inputs = super().generate_dummy_inputs(
            preprocessor=tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )
        import torch

        
        
        inputs["global_attention_mask"] = torch.zeros_like(inputs["input_ids"])
        
        inputs["global_attention_mask"][:, ::2] = 1

        return inputs