226 lines
12 KiB
Python
226 lines
12 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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""" PEER model configuration """
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# modified the path according to the structure in my directory csssl_4_15/cssl/ and its env
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from transformers.configuration_utils import PretrainedConfig
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from modelscope.utils import logger as logging
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logger = logging.get_logger()
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class PeerConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.PeerModel` or a
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:class:`~transformers.TFPeerModel`. It is used to instantiate a PEER model according to the specified
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arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar
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configuration to that of the PEER `google/peer-small-discriminator
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<https://huggingface.co/google/peer-small-discriminator>`__ 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`, `onal`, defaults to 30522)
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Vocabulary size of the PEER 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.PeerModel` or
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:class:`~transformers.TFPeerModel`.
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embedding_size (:obj:`int`, `onal`, defaults to 128)
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Dimensionality of the encoder layers and the pooler layer.
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hidden_size (:obj:`int`, `onal`, defaults to 256)
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (:obj:`int`, `onal`, defaults to 12)
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`int`, `onal`, defaults to 4)
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (:obj:`int`, `onal`, defaults to 1024)
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (:obj:`str` or :obj:`Callable`, `onal`, 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`, `onal`, 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`, `onal`, defaults to 0.1)
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The dropout ratio for the attention probabilities.
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max_position_embeddings (:obj:`int`, `onal`, 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`, `onal`, defaults to 2)
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The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.PeerModel` or
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:class:`~transformers.TFPeerModel`.
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initializer_range (:obj:`float`, `onal`, 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`, `onal`, defaults to 1e-12)
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The epsilon used by the layer normalization layers.
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summary_type (:obj:`str`, `onal`, defaults to :obj:`"first"`)
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Has to be one of the following ons
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- :obj:`"last"`: Take the last token hidden state (like XLNet).
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- :obj:`"first"`: Take the first token hidden state (like BERT).
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- :obj:`"mean"`: Take the mean of all tokens hidden states.
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- :obj:`"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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- :obj:`"attn"`: Not implemented now, use multi-head attention.
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summary_use_proj (:obj:`bool`, `onal`, defaults to :obj:`True`)
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Whether or not to add a projection after the vector extraction.
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summary_activation (:obj:`str`, `onal`)
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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Pass :obj:`"gelu"` for a gelu activation to the output, any other value will result in no activation.
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summary_last_dropout (:obj:`float`, `onal`, defaults to 0.0)
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Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
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The dropout ratio to be used after the projection and activation.
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position_embedding_type (:obj:`str`, `onal`, 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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Examples::
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>>> from transformers import PeerModel, PeerConfig
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>>> # Initializing a PEER peer-base-uncased style configuration
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>>> configuration = PeerConfig()
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>>> # Initializing a model from the peer-base-uncased style configuration
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>>> model = PeerModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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"""
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model_type = 'peer'
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def __init__(self,
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vocab_size=30522,
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embedding_size=128,
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hidden_size=256,
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num_hidden_layers=12,
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num_hidden_layers_shared=3,
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num_hidden_layers_gen=6,
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num_attention_heads=4,
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intermediate_size=1024,
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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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summary_type='first',
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summary_use_proj=True,
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summary_activation='gelu',
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summary_last_dropout=0.1,
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pad_token_id=0,
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position_embedding_type='absolute',
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gen_weight=1,
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dis_weight=50,
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dis_weight_scheduler=1,
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augmentation_copies=1,
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augmentation_temperature=1,
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absolute_position_embedding=1,
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relative_position_embedding=32,
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seq_side_info_embeddings=0,
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cold_start_epochs=1.25,
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debug_config=dict(),
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rtd_levels=2,
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rtd_level_thresholds='',
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ranking_start_epoch=1.0,
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real_token_rank_for_good_estimate=5,
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rank_sampl_prop=0.3,
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rank_sampl_range=100,
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rank_delta_factor=0.0,
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rank_level_compare_method=0,
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weight_loss_low_levels=1.0,
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weight_loss_low_levels_setting='1.0-1.0',
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weight_loss_low_levels_scheduler=0,
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weight_loss_level_compos=1,
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mask_da=0,
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mask_da_start_epoch=0.0,
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mask_da_mlm_topk_val=0,
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mask_ratio_setting='0.15-0.15',
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mask_ratio_scheduler=0,
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mask_ratio_stage1_epochs=0.0,
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**kwargs):
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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.embedding_size = embedding_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_hidden_layers_shared = num_hidden_layers_shared
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self.num_hidden_layers_gen = num_hidden_layers_gen
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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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.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_last_dropout = summary_last_dropout
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if type(position_embedding_type) == str:
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position_embedding_type = position_embedding_type.split('+')
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self.position_embedding_type = position_embedding_type
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self.augmentation_temperature = augmentation_temperature
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self.gen_weight = gen_weight
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self.dis_weight = dis_weight
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self.dis_weight_scheduler = dis_weight_scheduler
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self.augmentation_copies = augmentation_copies
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self.absolute_position_embedding = absolute_position_embedding
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self.relative_position_embedding = relative_position_embedding
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self.seq_side_info_embeddings = seq_side_info_embeddings
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self.cold_start_epochs = cold_start_epochs
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self.debug_config = debug_config
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self.rtd_levels = rtd_levels
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self.rtd_level_thresholds = rtd_level_thresholds
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self.ranking_start_epoch = ranking_start_epoch
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self.real_token_rank_for_good_estimate = real_token_rank_for_good_estimate
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self.rank_sampl_prop = rank_sampl_prop
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self.rank_sampl_range = rank_sampl_range
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self.rank_delta_factor = rank_delta_factor
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self.rank_level_compare_method = rank_level_compare_method
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self.weight_loss_low_levels = weight_loss_low_levels
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self.weight_loss_low_levels_setting = weight_loss_low_levels_setting
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self.weight_loss_low_levels_scheduler = weight_loss_low_levels_scheduler
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self.weight_loss_level_compos = weight_loss_level_compos
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self.mask_da = mask_da
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self.mask_da_start_epoch = mask_da_start_epoch
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self.mask_da_mlm_topk_val = mask_da_mlm_topk_val
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self.mask_ratio_setting = mask_ratio_setting
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self.mask_ratio_scheduler = mask_ratio_scheduler
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self.mask_ratio_stage1_epochs = mask_ratio_stage1_epochs
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