configuration_rembert.py

# coding=utf-8
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""" RemBERT model configuration"""
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__)


class RemBertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`RemBertModel`]. It is used to instantiate an
    RemBERT 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 RemBERT
    [google/rembert](https://huggingface.co/google/rembert) 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 250300):
            Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`]. Vocabulary size of the model.
            Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of
            [`RemBertModel`].
        hidden_size (`int`, *optional*, defaults to 1152):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 18):
            Number of attention heads for each attention layer in the Transformer encoder.
        input_embedding_size (`int`, *optional*, defaults to 256):
            Dimensionality of the input embeddings.
        output_embedding_size (`int`, *optional*, defaults to 1664):
            Dimensionality of the output embeddings.
        intermediate_size (`int`, *optional*, defaults to 4608):
            Dimensionality 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):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the classifier layer when fine-tuning.
        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 [`RemBertModel`] or [`TFRemBertModel`].
        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`.

    Example:

    ```python
    >>> from transformers import RemBertModel, RemBertConfig

    >>> # Initializing a RemBERT rembert style configuration
    >>> configuration = RemBertConfig()

    >>> # Initializing a model from the rembert style configuration
    >>> model = RemBertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "rembert"

    def __init__(
        self,
        vocab_size=250300,
        hidden_size=1152,
        num_hidden_layers=32,
        num_attention_heads=18,
        input_embedding_size=256,
        output_embedding_size=1664,
        intermediate_size=4608,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        classifier_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=312,
        eos_token_id=313,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.input_embedding_size = input_embedding_size
        self.output_embedding_size = output_embedding_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.classifier_dropout_prob = classifier_dropout_prob
        self.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache
        self.tie_word_embeddings = False


class RemBertOnnxConfig(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"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]
        )

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