autodl-projects/lib/xlayers/super_norm.py
2021-05-12 20:32:50 +08:00

225 lines
7.7 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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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)