xautodl/lib/xlayers/super_module.py
2021-03-19 15:17:49 +08:00

72 lines
2.1 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.01 #
#####################################################
import abc
import torch.nn as nn
from enum import Enum
import spaces
class SuperRunMode(Enum):
"""This class defines the enumerations for Super Model Running Mode."""
FullModel = "fullmodel"
Candidate = "candidate"
Default = "fullmodel"
class SuperModule(abc.ABC, nn.Module):
"""This class equips the nn.Module class with the ability to apply AutoDL."""
def __init__(self):
super(SuperModule, self).__init__()
self._super_run_type = SuperRunMode.Default
self._abstract_child = None
def set_super_run_type(self, super_run_type):
def _reset_super_run(m):
if isinstance(m, SuperModule):
m._super_run_type = super_run_type
self.apply(_reset_super_run)
def apply_candiate(self, abstract_child):
if not isinstance(abstract_child, spaces.VirtualNode):
raise ValueError(
"Invalid abstract child program: {:}".format(abstract_child)
)
self._abstract_child = abstract_child
@property
def abstract_search_space(self):
raise NotImplementedError
@property
def super_run_type(self):
return self._super_run_type
@property
def abstract_child(self):
return self._abstract_child
@abc.abstractmethod
def forward_raw(self, *inputs):
"""Use the largest candidate for forward. Similar to the original PyTorch model."""
raise NotImplementedError
@abc.abstractmethod
def forward_candidate(self, *inputs):
raise NotImplementedError
def forward(self, *inputs):
if self.super_run_type == SuperRunMode.FullModel:
return self.forward_raw(*inputs)
elif self.super_run_type == SuperRunMode.Candidate:
return self.forward_candidate(*inputs)
else:
raise ModeError(
"Unknown Super Model Run Mode: {:}".format(self.super_run_type)
)