85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
#####################################################
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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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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 SuperLayerNorm1D(SuperModule):
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"""Super Layer Norm."""
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def __init__(
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self, dim: IntSpaceType, eps: float = 1e-5, elementwise_affine: bool = True
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) -> None:
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super(SuperLayerNorm1D, self).__init__()
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self._in_dim = dim
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self._eps = eps
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self._elementwise_affine = elementwise_affine
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if self._elementwise_affine:
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self.weight = nn.Parameter(torch.Tensor(self.in_dim))
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self.bias = nn.Parameter(torch.Tensor(self.in_dim))
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else:
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self.register_parameter("weight", None)
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self.register_parameter("bias", None)
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self.reset_parameters()
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@property
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def in_dim(self):
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return spaces.get_max(self._in_dim)
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@property
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def eps(self):
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return self._eps
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def reset_parameters(self) -> None:
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if self._elementwise_affine:
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nn.init.ones_(self.weight)
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nn.init.zeros_(self.bias)
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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_dim):
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root_node.append("_in_dim", self._in_dim.abstract(reuse_last=True))
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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_dim):
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expected_input_dim = self.abstract_child["_in_dim"].value
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else:
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expected_input_dim = spaces.get_determined_value(self._in_dim)
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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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if self._elementwise_affine:
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weight = self.weight[:expected_input_dim]
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bias = self.bias[:expected_input_dim]
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else:
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weight, bias = None, None
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return F.layer_norm(input, (expected_input_dim,), weight, bias, self.eps)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
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def extra_repr(self) -> str:
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return (
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"shape={in_dim}, eps={eps}, elementwise_affine={elementwise_affine}".format(
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in_dim=self._in_dim,
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eps=self._eps,
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elementwise_affine=self._elementwise_affine,
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)
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)
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