autodl-projects/xautodl/xlayers/super_linear.py
2021-05-19 13:00:33 +08:00

320 lines
12 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional, Callable
from xautodl import spaces
from .super_module import SuperModule
from .super_module import IntSpaceType
from .super_module import BoolSpaceType
class SuperLinear(SuperModule):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
def __init__(
self,
in_features: IntSpaceType,
out_features: IntSpaceType,
bias: BoolSpaceType = True,
) -> None:
super(SuperLinear, self).__init__()
# the raw input args
self._in_features = in_features
self._out_features = out_features
self._bias = bias
# weights to be optimized
self.register_parameter(
"_super_weight",
torch.nn.Parameter(torch.Tensor(self.out_features, self.in_features)),
)
if self.bias:
self.register_parameter(
"_super_bias", torch.nn.Parameter(torch.Tensor(self.out_features))
)
else:
self.register_parameter("_super_bias", None)
self.reset_parameters()
@property
def in_features(self):
return spaces.get_max(self._in_features)
@property
def out_features(self):
return spaces.get_max(self._out_features)
@property
def bias(self):
return spaces.has_categorical(self._bias, True)
@property
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
if not spaces.is_determined(self._in_features):
root_node.append(
"_in_features", self._in_features.abstract(reuse_last=True)
)
if not spaces.is_determined(self._out_features):
root_node.append(
"_out_features", self._out_features.abstract(reuse_last=True)
)
if not spaces.is_determined(self._bias):
root_node.append("_bias", self._bias.abstract(reuse_last=True))
return root_node
def reset_parameters(self) -> None:
nn.init.kaiming_uniform_(self._super_weight, a=math.sqrt(5))
if self.bias:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._super_weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self._super_bias, -bound, bound)
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
# check inputs ->
if not spaces.is_determined(self._in_features):
expected_input_dim = self.abstract_child["_in_features"].value
else:
expected_input_dim = spaces.get_determined_value(self._in_features)
if input.size(-1) != expected_input_dim:
raise ValueError(
"Expect the input dim of {:} instead of {:}".format(
expected_input_dim, input.size(-1)
)
)
# create the weight matrix
if not spaces.is_determined(self._out_features):
out_dim = self.abstract_child["_out_features"].value
else:
out_dim = spaces.get_determined_value(self._out_features)
candidate_weight = self._super_weight[:out_dim, :expected_input_dim]
# create the bias matrix
if not spaces.is_determined(self._bias):
if self.abstract_child["_bias"].value:
candidate_bias = self._super_bias[:out_dim]
else:
candidate_bias = None
else:
if spaces.get_determined_value(self._bias):
candidate_bias = self._super_bias[:out_dim]
else:
candidate_bias = None
return F.linear(input, candidate_weight, candidate_bias)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
return F.linear(input, self._super_weight, self._super_bias)
def extra_repr(self) -> str:
return "in_features={:}, out_features={:}, bias={:}".format(
self._in_features, self._out_features, self._bias
)
def forward_with_container(self, input, container, prefix=[]):
super_weight_name = ".".join(prefix + ["_super_weight"])
super_weight = container.query(super_weight_name)
super_bias_name = ".".join(prefix + ["_super_bias"])
if container.has(super_bias_name):
super_bias = container.query(super_bias_name)
else:
super_bias = None
return F.linear(input, super_weight, super_bias)
class SuperMLPv1(SuperModule):
"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
def __init__(
self,
in_features: IntSpaceType,
hidden_features: IntSpaceType,
out_features: IntSpaceType,
act_layer: Callable[[], nn.Module] = nn.GELU,
drop: Optional[float] = None,
):
super(SuperMLPv1, self).__init__()
self._in_features = in_features
self._hidden_features = hidden_features
self._out_features = out_features
self._drop_rate = drop
self.fc1 = SuperLinear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = SuperLinear(hidden_features, out_features)
self.drop = nn.Dropout(drop or 0.0)
@property
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
space_fc1 = self.fc1.abstract_search_space
space_fc2 = self.fc2.abstract_search_space
if not spaces.is_determined(space_fc1):
root_node.append("fc1", space_fc1)
if not spaces.is_determined(space_fc2):
root_node.append("fc2", space_fc2)
return root_node
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperMLPv1, self).apply_candidate(abstract_child)
if "fc1" in abstract_child:
self.fc1.apply_candidate(abstract_child["fc1"])
if "fc2" in abstract_child:
self.fc2.apply_candidate(abstract_child["fc2"])
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
return self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
x = self.fc1(input)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def extra_repr(self) -> str:
return "in_features={:}, hidden_features={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
self._in_features,
self._hidden_features,
self._out_features,
self._drop_rate,
)
class SuperMLPv2(SuperModule):
"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
def __init__(
self,
in_features: IntSpaceType,
hidden_multiplier: IntSpaceType,
out_features: IntSpaceType,
act_layer: Callable[[], nn.Module] = nn.GELU,
drop: Optional[float] = None,
):
super(SuperMLPv2, self).__init__()
self._in_features = in_features
self._hidden_multiplier = hidden_multiplier
self._out_features = out_features
self._drop_rate = drop
self._params = nn.ParameterDict({})
self._create_linear(
"fc1", self.in_features, int(self.in_features * self.hidden_multiplier)
)
self._create_linear(
"fc2", int(self.in_features * self.hidden_multiplier), self.out_features
)
self.act = act_layer()
self.drop = nn.Dropout(drop or 0.0)
self.reset_parameters()
@property
def in_features(self):
return spaces.get_max(self._in_features)
@property
def hidden_multiplier(self):
return spaces.get_max(self._hidden_multiplier)
@property
def out_features(self):
return spaces.get_max(self._out_features)
def _create_linear(self, name, inC, outC):
self._params["{:}_super_weight".format(name)] = torch.nn.Parameter(
torch.Tensor(outC, inC)
)
self._params["{:}_super_bias".format(name)] = torch.nn.Parameter(
torch.Tensor(outC)
)
def reset_parameters(self) -> None:
nn.init.kaiming_uniform_(self._params["fc1_super_weight"], a=math.sqrt(5))
nn.init.kaiming_uniform_(self._params["fc2_super_weight"], a=math.sqrt(5))
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
self._params["fc1_super_weight"]
)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self._params["fc1_super_bias"], -bound, bound)
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
self._params["fc2_super_weight"]
)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self._params["fc2_super_bias"], -bound, bound)
@property
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
if not spaces.is_determined(self._in_features):
root_node.append(
"_in_features", self._in_features.abstract(reuse_last=True)
)
if not spaces.is_determined(self._hidden_multiplier):
root_node.append(
"_hidden_multiplier", self._hidden_multiplier.abstract(reuse_last=True)
)
if not spaces.is_determined(self._out_features):
root_node.append(
"_out_features", self._out_features.abstract(reuse_last=True)
)
return root_node
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
# check inputs ->
if not spaces.is_determined(self._in_features):
expected_input_dim = self.abstract_child["_in_features"].value
else:
expected_input_dim = spaces.get_determined_value(self._in_features)
if input.size(-1) != expected_input_dim:
raise ValueError(
"Expect the input dim of {:} instead of {:}".format(
expected_input_dim, input.size(-1)
)
)
# create the weight and bias matrix for fc1
if not spaces.is_determined(self._hidden_multiplier):
hmul = self.abstract_child["_hidden_multiplier"].value * expected_input_dim
else:
hmul = spaces.get_determined_value(self._hidden_multiplier)
hidden_dim = int(expected_input_dim * hmul)
_fc1_weight = self._params["fc1_super_weight"][:hidden_dim, :expected_input_dim]
_fc1_bias = self._params["fc1_super_bias"][:hidden_dim]
x = F.linear(input, _fc1_weight, _fc1_bias)
x = self.act(x)
x = self.drop(x)
# create the weight and bias matrix for fc2
if not spaces.is_determined(self._out_features):
out_dim = self.abstract_child["_out_features"].value
else:
out_dim = spaces.get_determined_value(self._out_features)
_fc2_weight = self._params["fc2_super_weight"][:out_dim, :hidden_dim]
_fc2_bias = self._params["fc2_super_bias"][:out_dim]
x = F.linear(x, _fc2_weight, _fc2_bias)
x = self.drop(x)
return x
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
x = F.linear(
input, self._params["fc1_super_weight"], self._params["fc1_super_bias"]
)
x = self.act(x)
x = self.drop(x)
x = F.linear(
x, self._params["fc2_super_weight"], self._params["fc2_super_bias"]
)
x = self.drop(x)
return x
def extra_repr(self) -> str:
return "in_features={:}, hidden_multiplier={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
self._in_features,
self._hidden_multiplier,
self._out_features,
self._drop_rate,
)