# 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
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
[docs]@encoder_config_register("linear")
class LinearConfig(BaseModuleConfig):
"""Config for Linear
Config Example:
>>> {
>>> "module": {
>>> "_base": "linear",
>>> },
>>> "config": {
>>> "input_size": "*@*",
>>> "output_size": "*@*",
>>> "pool": null,
>>> "dropout": 0.0,
>>> "output_map": {},
>>> "input_map": {}, // required_key: provide_key
>>> },
>>> "_link":{
>>> "config.input_size": ["module.config.input_size"],
>>> "config.output_size": ["module.config.output_size"],
>>> "config.pool": ["module.config.pool"],
>>> },
>>> "_name": "linear",
>>> }
"""
def __init__(self, config: Dict):
super(LinearConfig, self).__init__(config)
self.linear_config = config["module"]
self.post_check(config['config'], used=[
"input_size",
"output_size",
"return_logits",
"pool",
"dropout",
])
[docs]@encoder_register("linear")
class Linear(SimpleModule):
"""wrap for torch.nn.Linear
"""
def __init__(self, config: LinearConfig):
super(Linear, self).__init__(config)
self._provide_keys = {'embedding'}
self._required_keys = {'embedding'}
self._provided_keys = set()
self.config = config
self.linear = module_register.get('linear')(module_config_register.get('linear')(config.linear_config))
[docs] def init_weight(self, method: Callable):
"""init the weight of submodules by 'method'
Args:
method: init method
Returns:
None
"""
self.linear.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.linear(inputs[self.get_input_name('embedding')])
if self._logits_gather.layer_map:
inputs.update(self._logits_gather([inputs[self.get_output_name('embedding')]]))
return inputs