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D-X-Y
319
xautodl/xlayers/super_mlp.py
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319
xautodl/xlayers/super_mlp.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Callable
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from xautodl import spaces
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from .super_module import SuperModule
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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class SuperLinear(SuperModule):
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"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
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def __init__(
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self,
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in_features: IntSpaceType,
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out_features: IntSpaceType,
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bias: BoolSpaceType = True,
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) -> None:
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super(SuperLinear, self).__init__()
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# the raw input args
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self._in_features = in_features
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self._out_features = out_features
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self._bias = bias
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# weights to be optimized
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self.register_parameter(
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"_super_weight",
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torch.nn.Parameter(torch.Tensor(self.out_features, self.in_features)),
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)
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if self.bias:
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self.register_parameter(
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"_super_bias", torch.nn.Parameter(torch.Tensor(self.out_features))
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)
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else:
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self.register_parameter("_super_bias", None)
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self.reset_parameters()
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@property
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def in_features(self):
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return spaces.get_max(self._in_features)
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@property
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def out_features(self):
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return spaces.get_max(self._out_features)
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@property
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def bias(self):
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return spaces.has_categorical(self._bias, True)
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@property
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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if not spaces.is_determined(self._in_features):
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root_node.append(
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"_in_features", self._in_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._out_features):
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root_node.append(
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"_out_features", self._out_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._bias):
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root_node.append("_bias", self._bias.abstract(reuse_last=True))
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return root_node
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def reset_parameters(self) -> None:
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nn.init.kaiming_uniform_(self._super_weight, a=math.sqrt(5))
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if self.bias:
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._super_weight)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._super_bias, -bound, bound)
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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# check inputs ->
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if not spaces.is_determined(self._in_features):
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expected_input_dim = self.abstract_child["_in_features"].value
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else:
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expected_input_dim = spaces.get_determined_value(self._in_features)
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if input.size(-1) != expected_input_dim:
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raise ValueError(
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"Expect the input dim of {:} instead of {:}".format(
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expected_input_dim, input.size(-1)
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)
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)
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# create the weight matrix
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if not spaces.is_determined(self._out_features):
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out_dim = self.abstract_child["_out_features"].value
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else:
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out_dim = spaces.get_determined_value(self._out_features)
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candidate_weight = self._super_weight[:out_dim, :expected_input_dim]
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# create the bias matrix
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if not spaces.is_determined(self._bias):
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if self.abstract_child["_bias"].value:
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candidate_bias = self._super_bias[:out_dim]
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else:
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candidate_bias = None
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else:
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if spaces.get_determined_value(self._bias):
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candidate_bias = self._super_bias[:out_dim]
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else:
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candidate_bias = None
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return F.linear(input, candidate_weight, candidate_bias)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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return F.linear(input, self._super_weight, self._super_bias)
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def extra_repr(self) -> str:
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return "in_features={:}, out_features={:}, bias={:}".format(
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self._in_features, self._out_features, self._bias
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)
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def forward_with_container(self, input, container, prefix=[]):
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super_weight_name = ".".join(prefix + ["_super_weight"])
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super_weight = container.query(super_weight_name)
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super_bias_name = ".".join(prefix + ["_super_bias"])
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if container.has(super_bias_name):
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super_bias = container.query(super_bias_name)
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else:
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super_bias = None
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return F.linear(input, super_weight, super_bias)
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class SuperMLPv1(SuperModule):
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"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
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def __init__(
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self,
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in_features: IntSpaceType,
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hidden_features: IntSpaceType,
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out_features: IntSpaceType,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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drop: Optional[float] = None,
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):
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super(SuperMLPv1, self).__init__()
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self._in_features = in_features
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self._hidden_features = hidden_features
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self._out_features = out_features
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self._drop_rate = drop
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self.fc1 = SuperLinear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = SuperLinear(hidden_features, out_features)
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self.drop = nn.Dropout(drop or 0.0)
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@property
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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space_fc1 = self.fc1.abstract_search_space
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space_fc2 = self.fc2.abstract_search_space
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if not spaces.is_determined(space_fc1):
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root_node.append("fc1", space_fc1)
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if not spaces.is_determined(space_fc2):
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root_node.append("fc2", space_fc2)
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return root_node
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def apply_candidate(self, abstract_child: spaces.VirtualNode):
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super(SuperMLPv1, self).apply_candidate(abstract_child)
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if "fc1" in abstract_child:
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self.fc1.apply_candidate(abstract_child["fc1"])
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if "fc2" in abstract_child:
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self.fc2.apply_candidate(abstract_child["fc2"])
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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return self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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x = self.fc1(input)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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def extra_repr(self) -> str:
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return "in_features={:}, hidden_features={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
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self._in_features,
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self._hidden_features,
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self._out_features,
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self._drop_rate,
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)
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class SuperMLPv2(SuperModule):
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"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
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def __init__(
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self,
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in_features: IntSpaceType,
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hidden_multiplier: IntSpaceType,
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out_features: IntSpaceType,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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drop: Optional[float] = None,
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):
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super(SuperMLPv2, self).__init__()
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self._in_features = in_features
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self._hidden_multiplier = hidden_multiplier
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self._out_features = out_features
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self._drop_rate = drop
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self._params = nn.ParameterDict({})
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self._create_linear(
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"fc1", self.in_features, int(self.in_features * self.hidden_multiplier)
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)
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self._create_linear(
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"fc2", int(self.in_features * self.hidden_multiplier), self.out_features
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)
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self.act = act_layer()
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self.drop = nn.Dropout(drop or 0.0)
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self.reset_parameters()
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@property
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def in_features(self):
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return spaces.get_max(self._in_features)
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@property
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def hidden_multiplier(self):
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return spaces.get_max(self._hidden_multiplier)
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@property
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def out_features(self):
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return spaces.get_max(self._out_features)
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def _create_linear(self, name, inC, outC):
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self._params["{:}_super_weight".format(name)] = torch.nn.Parameter(
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torch.Tensor(outC, inC)
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)
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self._params["{:}_super_bias".format(name)] = torch.nn.Parameter(
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torch.Tensor(outC)
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)
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def reset_parameters(self) -> None:
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nn.init.kaiming_uniform_(self._params["fc1_super_weight"], a=math.sqrt(5))
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nn.init.kaiming_uniform_(self._params["fc2_super_weight"], a=math.sqrt(5))
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
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self._params["fc1_super_weight"]
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)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._params["fc1_super_bias"], -bound, bound)
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
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self._params["fc2_super_weight"]
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)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._params["fc2_super_bias"], -bound, bound)
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@property
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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if not spaces.is_determined(self._in_features):
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root_node.append(
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"_in_features", self._in_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._hidden_multiplier):
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root_node.append(
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"_hidden_multiplier", self._hidden_multiplier.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._out_features):
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root_node.append(
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"_out_features", self._out_features.abstract(reuse_last=True)
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)
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return root_node
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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# check inputs ->
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if not spaces.is_determined(self._in_features):
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expected_input_dim = self.abstract_child["_in_features"].value
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else:
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expected_input_dim = spaces.get_determined_value(self._in_features)
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if input.size(-1) != expected_input_dim:
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raise ValueError(
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"Expect the input dim of {:} instead of {:}".format(
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expected_input_dim, input.size(-1)
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)
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)
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# create the weight and bias matrix for fc1
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if not spaces.is_determined(self._hidden_multiplier):
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hmul = self.abstract_child["_hidden_multiplier"].value * expected_input_dim
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else:
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hmul = spaces.get_determined_value(self._hidden_multiplier)
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hidden_dim = int(expected_input_dim * hmul)
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_fc1_weight = self._params["fc1_super_weight"][:hidden_dim, :expected_input_dim]
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_fc1_bias = self._params["fc1_super_bias"][:hidden_dim]
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x = F.linear(input, _fc1_weight, _fc1_bias)
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x = self.act(x)
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x = self.drop(x)
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# create the weight and bias matrix for fc2
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if not spaces.is_determined(self._out_features):
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out_dim = self.abstract_child["_out_features"].value
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else:
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out_dim = spaces.get_determined_value(self._out_features)
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_fc2_weight = self._params["fc2_super_weight"][:out_dim, :hidden_dim]
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_fc2_bias = self._params["fc2_super_bias"][:out_dim]
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x = F.linear(x, _fc2_weight, _fc2_bias)
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x = self.drop(x)
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return x
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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x = F.linear(
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input, self._params["fc1_super_weight"], self._params["fc1_super_bias"]
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)
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x = self.act(x)
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x = self.drop(x)
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x = F.linear(
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x, self._params["fc2_super_weight"], self._params["fc2_super_bias"]
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)
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x = self.drop(x)
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return x
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def extra_repr(self) -> str:
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return "in_features={:}, hidden_multiplier={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
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self._in_features,
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self._hidden_multiplier,
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self._out_features,
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self._drop_rate,
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)
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