wehub-resource-sync
186 lines
8.4 KiB
Python
186 lines
8.4 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. 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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""" MobileBert model configuration"""
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from __future__ import annotations
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from ..configuration_utils import PretrainedConfig
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__all__ = ["MOBILEBERT_PRETRAINED_INIT_CONFIGURATION", "MobileBertConfig", "MOBILEBERT_PRETRAINED_RESOURCE_FILES_MAP"]
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MOBILEBERT_PRETRAINED_INIT_CONFIGURATION = {
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"mobilebert-uncased": {
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"attention_probs_dropout_prob": 0.1,
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"classifier_activation": False,
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"embedding_size": 128,
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"hidden_act": "relu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"intra_bottleneck_size": 128,
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"key_query_shared_bottleneck": True,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "mobilebert",
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"normalization_type": "no_norm",
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"num_attention_heads": 4,
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"num_feedforward_networks": 4,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"transformers_version": "4.6.0.dev0",
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"trigram_input": True,
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"true_hidden_size": 128,
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"type_vocab_size": 2,
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"use_bottleneck": True,
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"use_bottleneck_attention": False,
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"vocab_size": 30522,
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}
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}
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MOBILEBERT_PRETRAINED_RESOURCE_FILES_MAP = {
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"model_state": {
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"mobilebert-uncased": "https://bj.bcebos.com/paddlenlp/models/transformers/mobilebert/mobilebert-uncased/model_state.pdparams"
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}
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}
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class MobileBertConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~paddlenlp.transformers.MobileBertModel`.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the MobileBERT model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`MobileBertModel`].
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hidden_size (`int`, *optional*, defaults to 512):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, 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 (`int`, *optional*, defaults to 512):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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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 (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`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 (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`MobileBertModel`].
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initializer_range (`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 (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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pad_token_id (`int`, *optional*, defaults to 0):
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The ID of the token in the word embedding to use as padding.
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embedding_size (`int`, *optional*, defaults to 128):
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The dimension of the word embedding vectors.
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trigram_input (`bool`, *optional*, defaults to `True`):
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Use a convolution of trigram as input.
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use_bottleneck (`bool`, *optional*, defaults to `True`):
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Whether to use bottleneck in BERT.
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intra_bottleneck_size (`int`, *optional*, defaults to 128):
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Size of bottleneck layer output.
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use_bottleneck_attention (`bool`, *optional*, defaults to `False`):
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Whether to use attention inputs from the bottleneck transformation.
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key_query_shared_bottleneck (`bool`, *optional*, defaults to `True`):
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Whether to use the same linear transformation for query&key in the bottleneck.
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num_feedforward_networks (`int`, *optional*, defaults to 4):
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Number of FFNs in a block.
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normalization_type (`str`, *optional*, defaults to `"no_norm"`):
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The normalization type in MobileBERT.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Examples:
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```python
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>>> from paddlenlp.transformers import MobileBertConfig, MobileBertModel
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>>> # Initializing a MobileBERT configuration
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>>> configuration = MobileBertConfig()
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>>> # Initializing a model (with random weights) from the configuration above
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>>> model = MobileBertModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "mobilebert"
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pretrained_init_configuration = MOBILEBERT_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = MOBILEBERT_PRETRAINED_RESOURCE_FILES_MAP
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keys_to_ignore_at_inference = ["pooled_output"]
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def __init__(
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self,
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vocab_size=30522,
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hidden_size=512,
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num_hidden_layers=24,
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num_attention_heads=4,
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intermediate_size=512,
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hidden_act="relu",
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hidden_dropout_prob=0.0,
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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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embedding_size=128,
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true_hidden_size=128,
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normalization_type="no_norm",
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use_bottleneck=True,
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use_bottleneck_attention=False,
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intra_bottleneck_size=128,
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key_query_shared_bottleneck=True,
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num_feedforward_networks=4,
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trigram_input=True,
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classifier_activation=False,
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classifier_dropout=None,
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add_pooling_layer=True,
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**kwargs
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_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_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.pad_token_id = pad_token_id
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self.embedding_size = embedding_size
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self.true_hidden_size = true_hidden_size
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self.normalization_type = normalization_type
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self.use_bottleneck = use_bottleneck
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self.use_bottleneck_attention = use_bottleneck_attention
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self.intra_bottleneck_size = intra_bottleneck_size
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self.key_query_shared_bottleneck = key_query_shared_bottleneck
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self.num_feedforward_networks = num_feedforward_networks
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self.trigram_input = trigram_input
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self.classifier_activation = classifier_activation
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if self.use_bottleneck:
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self.true_hidden_size = intra_bottleneck_size
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else:
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self.true_hidden_size = hidden_size
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self.classifier_dropout = classifier_dropout
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self.add_pooling_layer = add_pooling_layer
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