# coding=utf-8 # Copyright 2023 The Meta AI Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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 # # 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. """X-MOD 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 XmodConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD 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 [facebook/xmod-base](https://huggingface.co/facebook/xmod-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 30522): Vocabulary size of the X-MOD model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XmodModel`]. 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 [`XmodModel`]. 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. pre_norm (`bool`, *optional*, defaults to `False`): Whether to apply layer normalization before each block. adapter_reduction_factor (`int` or `float`, *optional*, defaults to 2): The factor by which the dimensionality of the adapter is reduced relative to `hidden_size`. adapter_layer_norm (`bool`, *optional*, defaults to `False`): Whether to apply a new layer normalization before the adapter modules (shared across all adapters). adapter_reuse_layer_norm (`bool`, *optional*, defaults to `True`): Whether to reuse the second layer normalization and apply it before the adapter modules as well. ln_before_adapter (`bool`, *optional*, defaults to `True`): Whether to apply the layer normalization before the residual connection around the adapter module. languages (`Iterable[str]`, *optional*, defaults to `["en_XX"]`): An iterable of language codes for which adapter modules should be initialized. default_language (`str`, *optional*): Language code of a default language. It will be assumed that the input is in this language if no language codes are explicitly passed to the forward method. Examples: ```python >>> from transformers import XmodConfig, XmodModel >>> # Initializing an X-MOD facebook/xmod-base style configuration >>> configuration = XmodConfig() >>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration >>> model = XmodModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xmod" 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=1, bos_token_id=0, eos_token_id=2, position_embedding_type="absolute", use_cache=True, classifier_dropout=None, pre_norm=False, adapter_reduction_factor=2, adapter_layer_norm=False, adapter_reuse_layer_norm=True, ln_before_adapter=True, languages=("en_XX",), default_language=None, **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.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.pre_norm = pre_norm self.adapter_reduction_factor = adapter_reduction_factor self.adapter_layer_norm = adapter_layer_norm self.adapter_reuse_layer_norm = adapter_reuse_layer_norm self.ln_before_adapter = ln_before_adapter self.languages = list(languages) self.default_language = default_language # Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Xmod class XmodOnnxConfig(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), ] ) __all__ = ["XmodConfig", "XmodOnnxConfig"]