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# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Common NHNet/Bert2Bert configuration."""
from typing import List, Text
import dataclasses
from official.modeling.hyperparams import base_config
@dataclasses.dataclass
class BERT2BERTConfig(base_config.Config):
"""High-level configurations for BERT2BERT model.
These include parameters that are not directly related to the experiment,
e.g. encoder, decoder, prediction, training, etc.
"""
vocab_size: int = 30522
hidden_size: int = 768
num_hidden_layers: int = 12
num_attention_heads: int = 12
intermediate_size: int = 3072
hidden_act: str = "gelu"
hidden_dropout_prob: float = 0.1
attention_probs_dropout_prob: float = 0.1
max_position_embeddings: int = 512
type_vocab_size: int = 2
initializer_range: float = 0.02
decoder_intermediate_size: int = 3072
num_decoder_attn_heads: int = 12
num_decoder_layers: int = 12
label_smoothing: float = 0.1
learning_rate: float = 0.05
learning_rate_warmup_steps: int = 20000
optimizer: str = "Adam"
adam_beta1: float = 0.9
adam_beta2: float = 0.997
adam_epsilon: float = 1e-09
# predict params
beam_size: int = 5
alpha: float = 0.6
initializer_gain: float = 1.0
use_cache: bool = True
# input params
input_sharding: bool = False
input_data_not_padded: bool = False
pad_token_id: int = 0
end_token_id: int = 102
start_token_id: int = 101
@dataclasses.dataclass
class NHNetConfig(BERT2BERTConfig):
"""High-level configurations for NHNet model.
These include parameters that are not directly related to the experiment,
e.g. encoder, decoder, prediction, training, etc.
"""
multi_channel_cross_attention: bool = True
passage_list: List[Text] = dataclasses.field(
default_factory=lambda: [chr(ord("b") + i) for i in range(5)])
# Initialization method.
# If init_from_bert2bert is false, we assume the checkpoint is from BERT
# pretraining and only encoder and self-attention variables are initialized.
init_from_bert2bert: bool = True
UNITTEST_CONFIG = {
"attention_probs_dropout_prob": 0.0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 16,
"initializer_range": 0.02,
"intermediate_size": 32,
"max_position_embeddings": 128,
"num_attention_heads": 2,
"num_hidden_layers": 1,
"type_vocab_size": 2,
"vocab_size": 30522,
"initializer_gain": 1.0,
"decoder_intermediate_size": 32,
"num_decoder_attn_heads": 2,
"num_decoder_layers": 1,
"use_cache": True,
"input_data_not_padded": False,
"pad_token_id": 0,
"end_token_id": 102,
"start_token_id": 101,
}
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