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| """Checkpoint converter for Mobilebert."""
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| import copy
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| import json
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|
|
| import tensorflow.compat.v1 as tf
|
|
|
| from official.modeling import tf_utils
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| from official.nlp.modeling import layers
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| from official.nlp.modeling import models
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| from official.nlp.modeling import networks
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|
|
|
|
| class BertConfig(object):
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| """Configuration for `BertModel`."""
|
|
|
| def __init__(self,
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| vocab_size,
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| hidden_size=768,
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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=16,
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| initializer_range=0.02,
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| embedding_size=None,
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| trigram_input=False,
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| use_bottleneck=False,
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| intra_bottleneck_size=None,
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| use_bottleneck_attention=False,
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| key_query_shared_bottleneck=False,
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| num_feedforward_networks=1,
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| normalization_type="layer_norm",
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| classifier_activation=True):
|
| """Constructs BertConfig.
|
|
|
| Args:
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| vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
|
| 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
|
| 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
|
| encoder and pooler.
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| hidden_dropout_prob: The dropout probability for all fully connected
|
| layers in the embeddings, encoder, and pooler.
|
| attention_probs_dropout_prob: The dropout ratio for the attention
|
| probabilities.
|
| max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
|
| `BertModel`.
|
| initializer_range: The stdev of the truncated_normal_initializer for
|
| initializing all weight matrices.
|
| embedding_size: The size of the token embedding.
|
| trigram_input: Use a convolution of trigram as input.
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| use_bottleneck: Use the bottleneck/inverted-bottleneck structure in BERT.
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| intra_bottleneck_size: The hidden size in the bottleneck.
|
| use_bottleneck_attention: Use attention inputs from the bottleneck
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| transformation.
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| key_query_shared_bottleneck: Use the same linear transformation for
|
| query&key in the bottleneck.
|
| num_feedforward_networks: Number of FFNs in a block.
|
| normalization_type: The normalization type in BERT.
|
| classifier_activation: Using the tanh activation for the final
|
| representation of the [CLS] token in fine-tuning.
|
| """
|
| self.vocab_size = vocab_size
|
| self.hidden_size = hidden_size
|
| self.num_hidden_layers = num_hidden_layers
|
| self.num_attention_heads = num_attention_heads
|
| self.hidden_act = hidden_act
|
| 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
|
| self.type_vocab_size = type_vocab_size
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| self.initializer_range = initializer_range
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| self.embedding_size = embedding_size
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| self.trigram_input = trigram_input
|
| self.use_bottleneck = use_bottleneck
|
| self.intra_bottleneck_size = intra_bottleneck_size
|
| self.use_bottleneck_attention = use_bottleneck_attention
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| self.key_query_shared_bottleneck = key_query_shared_bottleneck
|
| self.num_feedforward_networks = num_feedforward_networks
|
| self.normalization_type = normalization_type
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| self.classifier_activation = classifier_activation
|
|
|
| @classmethod
|
| def from_dict(cls, json_object):
|
| """Constructs a `BertConfig` from a Python dictionary of parameters."""
|
| config = BertConfig(vocab_size=None)
|
| for (key, value) in json_object.items():
|
| config.__dict__[key] = value
|
| if config.embedding_size is None:
|
| config.embedding_size = config.hidden_size
|
| if config.intra_bottleneck_size is None:
|
| config.intra_bottleneck_size = config.hidden_size
|
| return config
|
|
|
| @classmethod
|
| def from_json_file(cls, json_file):
|
| """Constructs a `BertConfig` from a json file of parameters."""
|
| with tf.gfile.GFile(json_file, "r") as reader:
|
| text = reader.read()
|
| return cls.from_dict(json.loads(text))
|
|
|
| def to_dict(self):
|
| """Serializes this instance to a Python dictionary."""
|
| output = copy.deepcopy(self.__dict__)
|
| return output
|
|
|
| def to_json_string(self):
|
| """Serializes this instance to a JSON string."""
|
| return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
|
|
|
|
|
| def create_mobilebert_pretrainer(bert_config):
|
| """Creates a BertPretrainerV2 that wraps MobileBERTEncoder model."""
|
| mobilebert_encoder = networks.MobileBERTEncoder(
|
| word_vocab_size=bert_config.vocab_size,
|
| word_embed_size=bert_config.embedding_size,
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| type_vocab_size=bert_config.type_vocab_size,
|
| max_sequence_length=bert_config.max_position_embeddings,
|
| num_blocks=bert_config.num_hidden_layers,
|
| hidden_size=bert_config.hidden_size,
|
| num_attention_heads=bert_config.num_attention_heads,
|
| intermediate_size=bert_config.intermediate_size,
|
| intermediate_act_fn=tf_utils.get_activation(bert_config.hidden_act),
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| hidden_dropout_prob=bert_config.hidden_dropout_prob,
|
| attention_probs_dropout_prob=bert_config.attention_probs_dropout_prob,
|
| intra_bottleneck_size=bert_config.intra_bottleneck_size,
|
| initializer_range=bert_config.initializer_range,
|
| use_bottleneck_attention=bert_config.use_bottleneck_attention,
|
| key_query_shared_bottleneck=bert_config.key_query_shared_bottleneck,
|
| num_feedforward_networks=bert_config.num_feedforward_networks,
|
| normalization_type=bert_config.normalization_type,
|
| classifier_activation=bert_config.classifier_activation)
|
|
|
| masked_lm = layers.MobileBertMaskedLM(
|
| embedding_table=mobilebert_encoder.get_embedding_table(),
|
| activation=tf_utils.get_activation(bert_config.hidden_act),
|
| initializer=tf_keras.initializers.TruncatedNormal(
|
| stddev=bert_config.initializer_range),
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| name="cls/predictions")
|
|
|
| pretrainer = models.BertPretrainerV2(
|
| encoder_network=mobilebert_encoder, customized_masked_lm=masked_lm)
|
|
|
| _ = pretrainer(pretrainer.inputs)
|
| return pretrainer
|
|
|
