225 lines
7.7 KiB
Python
225 lines
7.7 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
|
|
|
|
import spaces
|
|
from .super_module import SuperModule
|
|
from .super_module import IntSpaceType
|
|
from .super_module import BoolSpaceType
|
|
|
|
|
|
class SuperLayerNorm1D(SuperModule):
|
|
"""Super Layer Norm."""
|
|
|
|
def __init__(
|
|
self, dim: IntSpaceType, eps: float = 1e-6, elementwise_affine: bool = True
|
|
) -> None:
|
|
super(SuperLayerNorm1D, self).__init__()
|
|
self._in_dim = dim
|
|
self._eps = eps
|
|
self._elementwise_affine = elementwise_affine
|
|
if self._elementwise_affine:
|
|
self.register_parameter("weight", nn.Parameter(torch.Tensor(self.in_dim)))
|
|
self.register_parameter("bias", nn.Parameter(torch.Tensor(self.in_dim)))
|
|
else:
|
|
self.register_parameter("weight", None)
|
|
self.register_parameter("bias", None)
|
|
self.reset_parameters()
|
|
|
|
@property
|
|
def in_dim(self):
|
|
return spaces.get_max(self._in_dim)
|
|
|
|
@property
|
|
def eps(self):
|
|
return self._eps
|
|
|
|
def reset_parameters(self) -> None:
|
|
if self._elementwise_affine:
|
|
nn.init.ones_(self.weight)
|
|
nn.init.zeros_(self.bias)
|
|
|
|
@property
|
|
def abstract_search_space(self):
|
|
root_node = spaces.VirtualNode(id(self))
|
|
if not spaces.is_determined(self._in_dim):
|
|
root_node.append("_in_dim", self._in_dim.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_dim):
|
|
expected_input_dim = self.abstract_child["_in_dim"].value
|
|
else:
|
|
expected_input_dim = spaces.get_determined_value(self._in_dim)
|
|
if input.size(-1) != expected_input_dim:
|
|
raise ValueError(
|
|
"Expect the input dim of {:} instead of {:}".format(
|
|
expected_input_dim, input.size(-1)
|
|
)
|
|
)
|
|
if self._elementwise_affine:
|
|
weight = self.weight[:expected_input_dim]
|
|
bias = self.bias[:expected_input_dim]
|
|
else:
|
|
weight, bias = None, None
|
|
return F.layer_norm(input, (expected_input_dim,), weight, bias, self.eps)
|
|
|
|
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
|
return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
|
|
|
|
def forward_with_container(self, input, container, prefix=[]):
|
|
super_weight_name = ".".join(prefix + ["weight"])
|
|
if container.has(super_weight_name):
|
|
weight = container.query(super_weight_name)
|
|
else:
|
|
weight = None
|
|
super_bias_name = ".".join(prefix + ["bias"])
|
|
if container.has(super_bias_name):
|
|
bias = container.query(super_bias_name)
|
|
else:
|
|
bias = None
|
|
return F.layer_norm(input, (self.in_dim,), weight, bias, self.eps)
|
|
|
|
def extra_repr(self) -> str:
|
|
return (
|
|
"shape={in_dim}, eps={eps}, elementwise_affine={elementwise_affine}".format(
|
|
in_dim=self._in_dim,
|
|
eps=self._eps,
|
|
elementwise_affine=self._elementwise_affine,
|
|
)
|
|
)
|
|
|
|
|
|
class SuperSimpleNorm(SuperModule):
|
|
"""Super simple normalization."""
|
|
|
|
def __init__(self, mean, std, inplace=False) -> None:
|
|
super(SuperSimpleNorm, self).__init__()
|
|
self.register_buffer("_mean", torch.tensor(mean, dtype=torch.float))
|
|
self.register_buffer("_std", torch.tensor(std, dtype=torch.float))
|
|
self._inplace = inplace
|
|
|
|
@property
|
|
def abstract_search_space(self):
|
|
return spaces.VirtualNode(id(self))
|
|
|
|
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
|
# check inputs ->
|
|
return self.forward_raw(input)
|
|
|
|
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
|
if not self._inplace:
|
|
tensor = input.clone()
|
|
else:
|
|
tensor = input
|
|
mean = torch.as_tensor(self._mean, dtype=tensor.dtype, device=tensor.device)
|
|
std = torch.as_tensor(self._std, dtype=tensor.dtype, device=tensor.device)
|
|
if (std == 0).any():
|
|
raise ValueError(
|
|
"std evaluated to zero after conversion to {}, leading to division by zero.".format(
|
|
tensor.dtype
|
|
)
|
|
)
|
|
while mean.ndim < tensor.ndim:
|
|
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
|
|
return tensor.sub_(mean).div_(std)
|
|
|
|
def extra_repr(self) -> str:
|
|
return "mean={mean}, std={std}, inplace={inplace}".format(
|
|
mean=self._mean.item(), std=self._std.item(), inplace=self._inplace
|
|
)
|
|
|
|
|
|
class SuperSimpleLearnableNorm(SuperModule):
|
|
"""Super simple normalization."""
|
|
|
|
def __init__(self, mean=0, std=1, eps=1e-6, inplace=False) -> None:
|
|
super(SuperSimpleLearnableNorm, self).__init__()
|
|
self.register_parameter(
|
|
"_mean", nn.Parameter(torch.tensor(mean, dtype=torch.float))
|
|
)
|
|
self.register_parameter(
|
|
"_std", nn.Parameter(torch.tensor(std, dtype=torch.float))
|
|
)
|
|
self._eps = eps
|
|
self._inplace = inplace
|
|
|
|
@property
|
|
def abstract_search_space(self):
|
|
return spaces.VirtualNode(id(self))
|
|
|
|
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
|
# check inputs ->
|
|
return self.forward_raw(input)
|
|
|
|
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
|
if not self._inplace:
|
|
tensor = input.clone()
|
|
else:
|
|
tensor = input
|
|
mean, std = (
|
|
self._mean.to(tensor.device),
|
|
torch.abs(self._std.to(tensor.device)) + self._eps,
|
|
)
|
|
if (std == 0).any():
|
|
raise ValueError("std leads to division by zero.")
|
|
while mean.ndim < tensor.ndim:
|
|
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
|
|
return tensor.sub_(mean).div_(std)
|
|
|
|
def forward_with_container(self, input, container, prefix=[]):
|
|
if not self._inplace:
|
|
tensor = input.clone()
|
|
else:
|
|
tensor = input
|
|
mean_name = ".".join(prefix + ["_mean"])
|
|
std_name = ".".join(prefix + ["_std"])
|
|
mean, std = (
|
|
container.query(mean_name).to(tensor.device),
|
|
torch.abs(container.query(std_name).to(tensor.device)) + self._eps,
|
|
)
|
|
while mean.ndim < tensor.ndim:
|
|
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
|
|
return tensor.sub_(mean).div_(std)
|
|
|
|
def extra_repr(self) -> str:
|
|
return "mean={mean}, std={std}, inplace={inplace}".format(
|
|
mean=self._mean.item(), std=self._std.item(), inplace=self._inplace
|
|
)
|
|
|
|
|
|
class SuperIdentity(SuperModule):
|
|
"""Super identity mapping layer."""
|
|
|
|
def __init__(self, inplace=False, **kwargs) -> None:
|
|
super(SuperIdentity, self).__init__()
|
|
self._inplace = inplace
|
|
|
|
@property
|
|
def abstract_search_space(self):
|
|
return spaces.VirtualNode(id(self))
|
|
|
|
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
|
# check inputs ->
|
|
return self.forward_raw(input)
|
|
|
|
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
|
if not self._inplace:
|
|
tensor = input.clone()
|
|
else:
|
|
tensor = input
|
|
return tensor
|
|
|
|
def extra_repr(self) -> str:
|
|
return "inplace={inplace}".format(inplace=self._inplace)
|
|
|
|
def forward_with_container(self, input, container, prefix=[]):
|
|
return self.forward_raw(input)
|