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| # coding=utf-8 | |
| # Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao, | |
| # Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team. | |
| # Copyright (c) 20121, 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. | |
| """ I-BERT configuration """ | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json", | |
| "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json", | |
| "kssteven/ibert-roberta-large-mnli": "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json", | |
| } | |
| class IBertConfig(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of a :class:`~transformers.IBertModel`. It is used to | |
| instantiate a I-BERT model according to the specified arguments, | |
| Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model | |
| outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. | |
| Args: | |
| vocab_size (:obj:`int`, `optional`, defaults to 30522): | |
| Vocabulary size of the I-BERT model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.IBertModel` | |
| hidden_size (:obj:`int`, `optional`, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (:obj:`int`, `optional`, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (:obj:`int`, `optional`, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (:obj:`int`, `optional`, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, | |
| :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. | |
| hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 2): | |
| The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.IBertModel` | |
| initializer_range (:obj:`float`, `optional`, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`): | |
| Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`, | |
| :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on | |
| :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.) | |
| <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"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>`__. | |
| quant_mode (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether to quantize the model or not. | |
| force_dequant (:obj:`str`, `optional`, defaults to :obj:`"none"`): | |
| Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision. | |
| :obj:`"none"`, :obj:`"gelu"`, :obj:`"softmax"`, :obj:`"layernorm"` and :obj:`"nonlinear"` are supported. As | |
| deafult, it is set as :obj:`"none"`, which does not dequantize any layers. Please specify :obj:`"gelu"`, | |
| :obj:`"softmax"`, or :obj:`"layernorm"` to dequantize GELU, Softmax, or LayerNorm, respectively. | |
| :obj:`"nonlinear"` will dequantize all nonlinear layers, i.e., GELU, Softmax, and LayerNorm. | |
| """ | |
| model_type = "ibert" | |
| 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", | |
| quant_mode=False, | |
| force_dequant="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.quant_mode = quant_mode | |
| self.force_dequant = force_dequant | |