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""" HAT configuration""" |
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from collections import OrderedDict |
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from typing import Mapping |
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from transformers.onnx import OnnxConfig |
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from transformers.utils import logging |
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from transformers import PretrainedConfig |
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logger = logging.get_logger(__name__) |
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HAT_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"kiddothe2b/hierarchical-transformer-base-4096": "https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096/resolve/main/config.json", |
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"kiddothe2b/adhoc-hierarchical-transformer-base-4096": "https://huggingface.co/kiddothe2b/adhoc-hierarchical-transformer-base-4096/resolve/main/config.json", |
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} |
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class HATConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a :class:`~transformers.HAT`. |
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It is used to instantiate a HAT model according to the specified arguments, |
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defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration |
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to that of the HAT `kiddothe2b/hierarchical-transformer-base-4096 |
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<https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096>`__ architecture. |
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model |
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outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. |
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Args: |
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vocab_size (:obj:`int`, `optional`, defaults to 30522): |
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Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the |
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:obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or |
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:class:`~transformers.TFBertModel`. |
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max_sentences (:obj:`int`, `optional`, defaults to 64): |
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The maximum number of sentences that this model might ever be used with. |
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max_sentence_size (:obj:`int`, `optional`, defaults to 128): |
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The maximum sentence length that this model might ever be used with. |
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model_max_length (:obj:`int`, `optional`, defaults to 8192): |
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The maximum sequence length (max_sentences * max_sentence_size) that this model might ever be used with |
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encoder_layout (:obj:`Dict`): |
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The sentence/document encoder layout. |
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hidden_size (:obj:`int`, `optional`, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (:obj:`int`, `optional`, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (:obj:`int`, `optional`, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (:obj:`int`, `optional`, defaults to 3072): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
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hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, |
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:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. |
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hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (:obj:`int`, `optional`, defaults to 512): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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type_vocab_size (:obj:`int`, `optional`, defaults to 2): |
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The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or |
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:class:`~transformers.TFBertModel`. |
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initializer_range (:obj:`float`, `optional`, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`): |
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Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`, |
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:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on |
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:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.) |
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<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to |
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`Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.) |
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<https://arxiv.org/abs/2009.13658>`__. |
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use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if ``config.is_decoder=True``. |
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classifier_dropout (:obj:`float`, `optional`): |
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The dropout ratio for the classification head. |
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""" |
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model_type = "hierarchical-transformer" |
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def __init__( |
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self, |
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vocab_size=30522, |
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hidden_size=768, |
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max_sentences=64, |
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max_sentence_size=128, |
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model_max_length=8192, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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max_position_embeddings=512, |
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type_vocab_size=2, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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pad_token_id=0, |
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position_embedding_type="absolute", |
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encoder_layout=None, |
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use_cache=True, |
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classifier_dropout=None, |
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**kwargs |
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): |
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super().__init__(pad_token_id=pad_token_id, **kwargs) |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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self.max_sentences = max_sentences |
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self.max_sentence_size = max_sentence_size |
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self.model_max_length = model_max_length |
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self.encoder_layout = encoder_layout |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.hidden_act = hidden_act |
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self.intermediate_size = intermediate_size |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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self.position_embedding_type = position_embedding_type |
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self.use_cache = use_cache |
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self.classifier_dropout = classifier_dropout |
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class HATOnnxConfig(OnnxConfig): |
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@property |
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def inputs(self) -> Mapping[str, Mapping[int, str]]: |
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return OrderedDict( |
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[ |
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("input_ids", {0: "batch", 1: "sequence"}), |
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("attention_mask", {0: "batch", 1: "sequence"}), |
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] |
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) |
