configuration_bert.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
# Copyright (c) 2023 Jina AI GmbH. All rights reserved.
#
# 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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""" BERT model configuration"""
from collections import OrderedDict
from typing import Mapping
import warnings

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

try:
    from optimum.exporters.onnx.model_configs import BertOnnxConfig
    OPTIMUM_INSTALLED = True
except ImportError:
    warnings.warn("optimum is not installed. To use OnnxConfig and BertOnnxConfig, make sure that `optimum` package is installed")
    OPTIMUM_INSTALLED = False


logger = logging.get_logger(__name__)


class JinaBertConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`JinaBertModel`]. It is used to
    instantiate a BERT 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 BERT
    [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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 30522):
            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
        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 [`BertModel`] or [`TFBertModel`].
        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.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        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`.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.
        feed_forward_type (`str`, *optional*, defaults to `"original"`):
            The type of feed forward layer to use in the bert layers.
            Can be one of GLU variants, e.g. `"reglu"`, `"geglu"`
        emb_pooler (`str`, *optional*, defaults to `None`):
            The function to use for pooling the last layer embeddings to get the sentence embeddings.
            Should be one of `None`, `"mean"`.
        attn_implementation (`str`, *optional*, defaults to `"torch"`):
            The implementation of the self-attention layer. Can be one of:
            - `None` for the original implementation,
            - `torch` for the PyTorch SDPA implementation,

    Examples:

    ```python
    >>> from transformers import JinaBertConfig, JinaBertModel

    >>> # Initializing a JinaBert configuration
    >>> configuration = JinaBertConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = JinaBertModel(configuration)

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

    >>> # Encode text inputs
    >>> embeddings = model.encode(text_inputs)
    ```"""
    model_type = "bert"

    def __init__(
        self,
        vocab_size=30522,
        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=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        classifier_dropout=None,
        feed_forward_type="original",
        emb_pooler=None,
        attn_implementation=None,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        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.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout
        self.feed_forward_type = feed_forward_type
        self.emb_pooler = emb_pooler
        self.attn_implementation = attn_implementation

if OPTIMUM_INSTALLED:

    class JinaBertOnnxConfig(BertOnnxConfig):

        @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),
                ]
            )