mirror of
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120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2010, The T5 Authors and HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" T5 model configuration """
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from __future__ import absolute_import, division, print_function, unicode_literals
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import json
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import logging
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import sys
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import six
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from io import open
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",
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't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-config.json",
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't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-config.json",
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't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-config.json",
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't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-config.json",
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}
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class T5Config(PretrainedConfig):
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r"""
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:class:`~transformers.T5Config` is the configuration class to store the configuration of a
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`T5Model`.
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Arguments:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
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hidden_size: Size of the encoder layers and the pooler layer.
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num_hidden_layers: Number of hidden layers in the Transformer encoder.
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num_attention_heads: Number of attention heads for each attention layer in
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the Transformer encoder.
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intermediate_size: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
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hidden_dropout_prob: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into
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`T5Model`.
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initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).
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layer_norm_eps: The epsilon used by LayerNorm.
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"""
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pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=32128,
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n_positions=512,
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d_model=512,
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d_kv=64,
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d_ff=2048,
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num_layers=6,
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num_heads=8,
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relative_attention_num_buckets=32,
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dropout_rate=0.1,
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layer_norm_epsilon=1e-6,
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initializer_factor=1.0,
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**kwargs):
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super(T5Config, self).__init__(**kwargs)
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self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
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self.n_positions = n_positions
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self.d_model = d_model
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self.d_kv = d_kv
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self.d_ff = d_ff
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_factor = initializer_factor
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if isinstance(vocab_size_or_config_json_file, six.string_types):
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with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif not isinstance(vocab_size_or_config_json_file, int):
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raise ValueError(
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"First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)"
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)
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@property
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def max_position_embeddings(self):
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return self.n_positions
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@property
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def hidden_size(self):
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return self.d_model
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@property
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def num_attention_heads(self):
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return self.num_heads
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@property
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def num_hidden_layers(self):
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return self.num_layers
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