Update modelling_hat.py

modelling_hat.py

@@ -319,116 +319,119 @@ class SentenceClassifierOutput(ModelOutput):
     sentence_attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
-class HATConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.HAT`.
-    It is used to instantiate a HAT 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 HAT `kiddothe2b/hat-base-4096 <https://huggingface.co/kiddothe2b/hat-base-4096>`__ architecture.
-
-    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 BERT model. Defines the number of different tokens that can be represented by the
-            :obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or
-            :class:`~transformers.TFBertModel`.
-        max_sentences (:obj:`int`, `optional`, defaults to 64):
-            The maximum number of sentences that this model might ever be used with.
-        max_sentence_size (:obj:`int`, `optional`, defaults to 128):
-            The maximum sentence length that this model might ever be used with.
-        model_max_length (:obj:`int`, `optional`, defaults to 8192):
-            The maximum  sequence length (max_sentences * max_sentence_size) that this model might ever be used with
-        encoder_layout (:obj:`Dict`):
-            The sentence/document encoder layout.
-        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.BertModel` or
-            :class:`~transformers.TFBertModel`.
-        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>`__.
-        use_cache (:obj:`bool`, `optional`, defaults to :obj:`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 (:obj:`float`, `optional`):
-            The dropout ratio for the classification head.
-    """
-    model_type = "hierarchical-transformer"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        max_sentences=64,
-        max_sentence_size=128,
-        model_max_length=8192,
-        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",
-        encoder_layout=None,
-        use_cache=True,
-        classifier_dropout=None,
-        **kwargs
-    ):
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.max_sentences = max_sentences
-        self.max_sentence_size = max_sentence_size
-        self.model_max_length = model_max_length
-        self.encoder_layout = encoder_layout
-        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
-
+# class HATConfig(PretrainedConfig):
+#     r"""
+#     This is the configuration class to store the configuration of a :class:`~transformers.HAT`.
+#     It is used to instantiate a HAT 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 HAT `kiddothe2b/hat-base-4096 <https://huggingface.co/kiddothe2b/hat-base-4096>`__ architecture.
+
+#     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 BERT model. Defines the number of different tokens that can be represented by the
+#             :obj:`inputs_ids` passed when calling :class:`~transformers.BertModel` or
+#             :class:`~transformers.TFBertModel`.
+#         max_sentences (:obj:`int`, `optional`, defaults to 64):
+#             The maximum number of sentences that this model might ever be used with.
+#         max_sentence_size (:obj:`int`, `optional`, defaults to 128):
+#             The maximum sentence length that this model might ever be used with.
+#         model_max_length (:obj:`int`, `optional`, defaults to 8192):
+#             The maximum  sequence length (max_sentences * max_sentence_size) that this model might ever be used with
+#         encoder_layout (:obj:`Dict`):
+#             The sentence/document encoder layout.
+#         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.BertModel` or
+#             :class:`~transformers.TFBertModel`.
+#         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>`__.
+#         use_cache (:obj:`bool`, `optional`, defaults to :obj:`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 (:obj:`float`, `optional`):
+#             The dropout ratio for the classification head.
+#     """
+#     model_type = "hierarchical-transformer"
+
+#     def __init__(
+#         self,
+#         vocab_size=30522,
+#         hidden_size=768,
+#         max_sentences=64,
+#         max_sentence_size=128,
+#         model_max_length=8192,
+#         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",
+#         encoder_layout=None,
+#         use_cache=True,
+#         classifier_dropout=None,
+#         **kwargs
+#     ):
+#         super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+#         self.vocab_size = vocab_size
+#         self.hidden_size = hidden_size
+#         self.max_sentences = max_sentences
+#         self.max_sentence_size = max_sentence_size
+#         self.model_max_length = model_max_length
+#         self.encoder_layout = encoder_layout
+#         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
+
+
+
+from configuration_hat import HATConfig
 
 class HATEmbeddings(nn.Module):
     """