import copy
import math
from typing import Any, Optional
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn.parameter import Parameter
from torch_geometric.nn import inits
try:
from pyg_lib.ops import segment_matmul # noqa
_WITH_PYG_LIB = True
except ImportError:
_WITH_PYG_LIB = False
def segment_matmul(inputs: Tensor, ptr: Tensor, other: Tensor) -> Tensor:
raise NotImplementedError
def is_uninitialized_parameter(x: Any) -> bool:
if not hasattr(nn.parameter, 'UninitializedParameter'):
return False
return isinstance(x, nn.parameter.UninitializedParameter)
def reset_weight_(weight: Tensor, in_channels: int,
initializer: Optional[str] = None) -> Tensor:
if in_channels <= 0:
pass
elif initializer == 'glorot':
inits.glorot(weight)
elif initializer == 'uniform':
bound = 1.0 / math.sqrt(in_channels)
torch.nn.init.uniform_(weight.data, -bound, bound)
elif initializer == 'kaiming_uniform':
inits.kaiming_uniform(weight, fan=in_channels, a=math.sqrt(5))
elif initializer is None:
inits.kaiming_uniform(weight, fan=in_channels, a=math.sqrt(5))
else:
raise RuntimeError(f"Weight initializer '{initializer}' not supported")
return weight
def reset_bias_(bias: Optional[Tensor], in_channels: int,
initializer: Optional[str] = None) -> Optional[Tensor]:
if bias is None or in_channels <= 0:
pass
elif initializer == 'zeros':
inits.zeros(bias)
elif initializer is None:
inits.uniform(in_channels, bias)
else:
raise RuntimeError(f"Bias initializer '{initializer}' not supported")
return bias
[docs]class Linear(torch.nn.Module):
r"""Applies a linear tranformation to the incoming data
.. math::
\mathbf{x}^{\prime} = \mathbf{x} \mathbf{W}^{\top} + \mathbf{b}
similar to :class:`torch.nn.Linear`.
It supports lazy initialization and customizable weight and bias
initialization.
Args:
in_channels (int): Size of each input sample. Will be initialized
lazily in case it is given as :obj:`-1`.
out_channels (int): Size of each output sample.
bias (bool, optional): If set to :obj:`False`, the layer will not learn
an additive bias. (default: :obj:`True`)
weight_initializer (str, optional): The initializer for the weight
matrix (:obj:`"glorot"`, :obj:`"uniform"`, :obj:`"kaiming_uniform"`
or :obj:`None`).
If set to :obj:`None`, will match default weight initialization of
:class:`torch.nn.Linear`. (default: :obj:`None`)
bias_initializer (str, optional): The initializer for the bias vector
(:obj:`"zeros"` or :obj:`None`).
If set to :obj:`None`, will match default bias initialization of
:class:`torch.nn.Linear`. (default: :obj:`None`)
Shapes:
- **input:** features :math:`(*, F_{in})`
- **output:** features :math:`(*, F_{out})`
"""
def __init__(self, in_channels: int, out_channels: int, bias: bool = True,
weight_initializer: Optional[str] = None,
bias_initializer: Optional[str] = None):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.weight_initializer = weight_initializer
self.bias_initializer = bias_initializer
if in_channels > 0:
self.weight = Parameter(torch.Tensor(out_channels, in_channels))
else:
self.weight = nn.parameter.UninitializedParameter()
self._hook = self.register_forward_pre_hook(
self.initialize_parameters)
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self._load_hook = self._register_load_state_dict_pre_hook(
self._lazy_load_hook)
self.reset_parameters()
def __deepcopy__(self, memo):
out = Linear(self.in_channels, self.out_channels, self.bias
is not None, self.weight_initializer,
self.bias_initializer)
if self.in_channels > 0:
out.weight = copy.deepcopy(self.weight, memo)
if self.bias is not None:
out.bias = copy.deepcopy(self.bias, memo)
return out
[docs] def reset_parameters(self):
reset_weight_(self.weight, self.in_channels, self.weight_initializer)
reset_bias_(self.bias, self.in_channels, self.bias_initializer)
[docs] def forward(self, x: Tensor) -> Tensor:
r"""
Args:
x (Tensor): The features.
"""
return F.linear(x, self.weight, self.bias)
@torch.no_grad()
def initialize_parameters(self, module, input):
if is_uninitialized_parameter(self.weight):
self.in_channels = input[0].size(-1)
self.weight.materialize((self.out_channels, self.in_channels))
self.reset_parameters()
self._hook.remove()
delattr(self, '_hook')
def _save_to_state_dict(self, destination, prefix, keep_vars):
if (is_uninitialized_parameter(self.weight)
or torch.onnx.is_in_onnx_export()):
destination[prefix + 'weight'] = self.weight
else:
destination[prefix + 'weight'] = self.weight.detach()
if self.bias is not None:
if torch.onnx.is_in_onnx_export():
destination[prefix + 'bias'] = self.bias
else:
destination[prefix + 'bias'] = self.bias.detach()
def _lazy_load_hook(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
weight = state_dict.get(prefix + 'weight', None)
if weight is not None and is_uninitialized_parameter(weight):
self.in_channels = -1
self.weight = nn.parameter.UninitializedParameter()
if not hasattr(self, '_hook'):
self._hook = self.register_forward_pre_hook(
self.initialize_parameters)
elif weight is not None and is_uninitialized_parameter(self.weight):
self.in_channels = weight.size(-1)
self.weight.materialize((self.out_channels, self.in_channels))
if hasattr(self, '_hook'):
self._hook.remove()
delattr(self, '_hook')
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels}, bias={self.bias is not None})')
[docs]class HeteroLinear(torch.nn.Module):
r"""Applies separate linear tranformations to the incoming data according
to types
.. math::
\mathbf{x}^{\prime}_{\kappa} = \mathbf{x}_{\kappa}
\mathbf{W}^{\top}_{\kappa} + \mathbf{b}_{\kappa}
for type :math:`\kappa`.
It supports lazy initialization and customizable weight and bias
initialization.
Args:
in_channels (int): Size of each input sample. Will be initialized
lazily in case it is given as :obj:`-1`.
out_channels (int): Size of each output sample.
num_types (int): The number of types.
is_sorted (bool, optional): If set to :obj:`True`, assumes that
:obj:`type_vec` is sorted. This avoids internal re-sorting of the
data and can improve runtime and memory efficiency.
(default: :obj:`False`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.Linear`.
Shapes:
- **input:**
features :math:`(*, F_{in})`,
type vector :math:`(*)`
- **output:** features :math:`(*, F_{out})`
"""
def __init__(self, in_channels: int, out_channels: int, num_types: int,
is_sorted: bool = False, **kwargs):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.num_types = num_types
self.is_sorted = is_sorted
self.kwargs = kwargs
self._WITH_PYG_LIB = _WITH_PYG_LIB
if self._WITH_PYG_LIB:
self.lins = None
self.weight = torch.nn.Parameter(
torch.Tensor(num_types, in_channels, out_channels))
if kwargs.get('bias', True):
self.bias = Parameter(torch.Tensor(num_types, out_channels))
else:
self.register_parameter('bias', None)
else:
self.lins = torch.nn.ModuleList([
Linear(in_channels, out_channels, **kwargs)
for _ in range(num_types)
])
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.reset_parameters()
[docs] def reset_parameters(self):
if self._WITH_PYG_LIB:
reset_weight_(self.weight, self.in_channels,
self.kwargs.get('weight_initializer', None))
reset_weight_(self.bias, self.in_channels,
self.kwargs.get('bias_initializer', None))
else:
for lin in self.lins:
lin.reset_parameters()
[docs] def forward(self, x: Tensor, type_vec: Tensor) -> Tensor:
r"""
Args:
x (Tensor): The input features.
type_vec (LongTensor): A vector that maps each entry to a type.
"""
if self._WITH_PYG_LIB:
assert self.weight is not None
if not self.is_sorted:
if (type_vec[1:] < type_vec[:-1]).any():
type_vec, perm = type_vec.sort()
x = x[perm]
type_vec_ptr = torch.ops.torch_sparse.ind2ptr(
type_vec, self.num_types)
out = segment_matmul(x, type_vec_ptr, self.weight)
if self.bias is not None:
out += self.bias[type_vec]
else:
assert self.lins is not None
out = x.new_empty(x.size(0), self.out_channels)
for i, lin in enumerate(self.lins):
mask = type_vec == i
out[mask] = lin(x[mask])
return out
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels}, num_types={self.num_types}, '
f'bias={self.kwargs.get("bias", True)})')