# 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.
import torch.nn as nn
import torch
from typing import Dict, List, Set, Callable
from dlk.core.base_module import SimpleModule, BaseModuleConfig
from . import encoder_register, encoder_config_register
from dlk.core.modules import module_config_register, module_register
from dlk.utils.logger import Logger
logger = Logger.get_logger()
[docs]@encoder_config_register("lstm")
class LSTMConfig(BaseModuleConfig):
"""Config for LSTM
Config Example:
>>> {
>>> module: {
>>> _base: "lstm",
>>> },
>>> config: {
>>> input_map: {},
>>> output_map: {},
>>> input_size: *@*,
>>> output_size: "*@*",
>>> num_layers: 1,
>>> dropout: "*@*", // dropout between layers
>>> },
>>> _link: {
>>> config.input_size: [module.config.input_size],
>>> config.output_size: [module.config.output_size],
>>> config.dropout: [module.config.dropout],
>>> },
>>> _name: "lstm",
>>> }
"""
def __init__(self, config: Dict):
super(LSTMConfig, self).__init__(config)
self.lstm_config = config["module"]
assert self.lstm_config['_name'] == "lstm"
self.post_check(config['config'], used=[
"input_size",
"output_size",
"num_layers",
"return_logits",
"dropout",
])
[docs]@encoder_register("lstm")
class LSTM(SimpleModule):
"""Wrap for torch.nn.LSTM
"""
def __init__(self, config: LSTMConfig):
super(LSTM, self).__init__(config)
self._provide_keys = {'embedding'}
self._required_keys = {'embedding', 'attention_mask'}
self._provided_keys = set()
self.config = config
self.lstm = module_register.get('lstm')(module_config_register.get('lstm')(config.lstm_config))
[docs] def init_weight(self, method: Callable):
"""init the weight of submodules by 'method'
Args:
method: init method
Returns:
None
"""
self.lstm.init_weight(method)
[docs] def forward(self, inputs: Dict[str, torch.Tensor])->Dict[str, torch.Tensor]:
"""All step do this
Args:
inputs: one mini-batch inputs
Returns:
one mini-batch outputs
"""
inputs[self.get_output_name('embedding')] = self.lstm(inputs[self.get_input_name('embedding')], inputs[self.get_input_name('attention_mask')])
if self._logits_gather.layer_map:
inputs.update(self._logits_gather([inputs[self.get_output_name('embedding')]]))
return inputs