# Copyright 2021 cstsunfu. 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.
from transformers.models.bert.modeling_bert import BertModel
from transformers.models.bert.configuration_bert import BertConfig
import json
import os
import torch.nn as nn
import torch
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from typing import Dict
from . import module_register, module_config_register, Module
from dlk.utils.config import BaseConfig
from dlk.utils.io import open
[docs]@module_config_register("bert")
class BertWrapConfig(BaseConfig):
"""Config for BertWrap
Config Example:
>>> {
>>> "config": {
>>> "pretrained_model_path": "*@*",
>>> "from_pretrain": true,
>>> "freeze": false,
>>> "dropout": 0.0,
>>> },
>>> "_name": "bert",
>>> }
"""
def __init__(self, config: Dict):
super(BertWrapConfig, self).__init__(config)
self.pretrained_model_path = config['config']['pretrained_model_path']
self.from_pretrain = config['config']['from_pretrain']
self.freeze = config['config']['freeze']
self.dropout = config['config']['dropout']
if os.path.isdir(self.pretrained_model_path):
if os.path.exists(os.path.join(self.pretrained_model_path, 'config.json')):
with open(os.path.join(self.pretrained_model_path, 'config.json'), 'r') as f:
self.bert_config = BertConfig(**json.load(f))
else:
raise PermissionError(f"config.json must in the dir {self.pretrained_model_path}")
else:
if os.path.isfile(self.pretrained_model_path):
try:
with open(self.pretrained_model_path, 'r') as f:
self.bert_config = BertConfig(**json.load(f))
except:
raise PermissionError(f"You must provide the pretrained model dir or the config file path.")
self.post_check(config['config'], used=['pretrained_model_path', 'from_pretrain', 'freeze', 'dropout'])
[docs]@module_register("bert")
class BertWrap(Module):
"""Bert wrap"""
def __init__(self, config: BertWrapConfig):
super(BertWrap, self).__init__()
self.config = config
self.bert = BertModel(config.bert_config, add_pooling_layer=False)
self.dropout = nn.Dropout(float(self.config.dropout))
[docs] def init_weight(self, method):
"""init the weight of model by 'bert.init_weight()' or from_pretrain
Args:
method: init method, no use for pretrained_transformers
Returns:
None
"""
if self.config.from_pretrain:
self.from_pretrained()
else:
self.bert.init_weights()
[docs] def from_pretrained(self):
"""init the model from pretrained_model_path
"""
self.bert = BertModel.from_pretrained(self.config.pretrained_model_path)
[docs] def forward(self, inputs: Dict):
"""do forward on a mini batch
Args:
batch: a mini batch inputs
Returns:
sequence_output, all_hidden_states, all_self_attentions
"""
if self.config.freeze:
with torch.no_grad():
outputs = self.bert(
input_ids = inputs.get("input_ids", None),
attention_mask = inputs.get("attention_mask", None),
token_type_ids = inputs.get("token_type_ids", None),
position_ids = inputs.get("position_ids", None),
head_mask = inputs.get("head_mask", None),
inputs_embeds = inputs.get("inputs_embeds", None),
encoder_hidden_states = inputs.get("encoder_hidden_states", None),
encoder_attention_mask = inputs.get("encoder_attention_mask", None),
past_key_values = inputs.get("past_key_values", None),
use_cache = None,
output_attentions = True,
output_hidden_states = True,
return_dict = False
)
else:
outputs = self.bert(
input_ids = inputs.get("input_ids", None),
attention_mask = inputs.get("attention_mask", None),
token_type_ids = inputs.get("token_type_ids", None),
position_ids = inputs.get("position_ids", None),
head_mask = inputs.get("head_mask", None),
inputs_embeds = inputs.get("inputs_embeds", None),
encoder_hidden_states = inputs.get("encoder_hidden_states", None),
encoder_attention_mask = inputs.get("encoder_attention_mask", None),
past_key_values = inputs.get("past_key_values", None),
use_cache = None,
output_attentions = True,
output_hidden_states = True,
return_dict = False
)
assert len(outputs) == 4, f"Please check transformers version, the len(outputs) is 4 in version == 4.12|4.15, or check your config and remove the 'add_cross_attention'"
sequence_output, all_hidden_states, all_self_attentions = outputs[0], outputs[2], outputs[3]
sequence_output = self.dropout(sequence_output)
return sequence_output, all_hidden_states, all_self_attentions