from typing import List, Optional, Set, Callable, get_type_hints
from torch_geometric.typing import Adj, Size
import os
import re
import inspect
import os.path as osp
from uuid import uuid1
from itertools import chain
from inspect import Parameter
from collections import OrderedDict
import torch
from torch import Tensor
from jinja2 import Template
from torch.utils.hooks import RemovableHandle
from torch_sparse import SparseTensor
from torch_scatter import gather_csr, scatter, segment_csr
from .utils.helpers import expand_left
from .utils.jit import class_from_module_repr
from .utils.typing import (sanitize, split_types_repr, parse_types,
resolve_types)
from .utils.inspector import Inspector, func_header_repr, func_body_repr
[docs]class MessagePassing(torch.nn.Module):
r"""Base class for creating message passing layers of the form
.. math::
\mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i,
\square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}}
\left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{j,i}\right) \right),
where :math:`\square` denotes a differentiable, permutation invariant
function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}`
and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as
MLPs.
See `here <https://pytorch-geometric.readthedocs.io/en/latest/notes/
create_gnn.html>`__ for the accompanying tutorial.
Args:
aggr (string, optional): The aggregation scheme to use
(:obj:`"add"`, :obj:`"mean"`, :obj:`"max"` or :obj:`None`).
(default: :obj:`"add"`)
flow (string, optional): The flow direction of message passing
(:obj:`"source_to_target"` or :obj:`"target_to_source"`).
(default: :obj:`"source_to_target"`)
node_dim (int, optional): The axis along which to propagate.
(default: :obj:`-2`)
decomposed_layers (int, optional): The number of feature decomposition
layers, as introduced in the `"Optimizing Memory Efficiency of
Graph Neural Networks on Edge Computing Platforms"
<https://arxiv.org/abs/2104.03058>`_ paper.
Feature decomposition reduces the peak memory usage by slicing
the feature dimensions into separated feature decomposition layers
during GNN aggregation.
This method can accelerate GNN execution on CPU-based platforms
(*e.g.*, 2-3x speedup on the
:class:`~torch_geometric.datasets.Reddit` dataset) for common GNN
models such as :class:`~torch_geometric.nn.models.GCN`,
:class:`~torch_geometric.nn.models.GraphSAGE`,
:class:`~torch_geometric.nn.models.GIN`, etc.
However, this method is not applicable to all GNN operators
available, in particular for operators in which message computation
can not easily be decomposed, *e.g.* in attention-based GNNs.
The selection of the optimal value of :obj:`decomposed_layers`
depends both on the specific graph dataset and available hardware
resources.
A value of :obj:`2` is suitable in most cases.
Although the peak memory usage is directly associated with the
granularity of feature decomposition, the same is not necessarily
true for execution speedups. (default: :obj:`1`)
"""
special_args: Set[str] = {
'edge_index', 'adj_t', 'edge_index_i', 'edge_index_j', 'size',
'size_i', 'size_j', 'ptr', 'index', 'dim_size'
}
def __init__(self, aggr: Optional[str] = "add",
flow: str = "source_to_target", node_dim: int = -2,
decomposed_layers: int = 1):
super().__init__()
self.aggr = aggr
assert self.aggr in ['add', 'mean', 'max', None]
self.flow = flow
assert self.flow in ['source_to_target', 'target_to_source']
self.node_dim = node_dim
self.decomposed_layers = decomposed_layers
self.inspector = Inspector(self)
self.inspector.inspect(self.message)
self.inspector.inspect(self.aggregate, pop_first=True)
self.inspector.inspect(self.message_and_aggregate, pop_first=True)
self.inspector.inspect(self.update, pop_first=True)
self.inspector.inspect(self.edge_update)
self.__user_args__ = self.inspector.keys(
['message', 'aggregate', 'update']).difference(self.special_args)
self.__fused_user_args__ = self.inspector.keys(
['message_and_aggregate', 'update']).difference(self.special_args)
self.__edge_user_args__ = self.inspector.keys(
['edge_update']).difference(self.special_args)
# Support for "fused" message passing.
self.fuse = self.inspector.implements('message_and_aggregate')
# Support for GNNExplainer.
self.__explain__ = False
self.__edge_mask__ = None
self.__loop_mask__ = None
# Hooks:
self._propagate_forward_pre_hooks = OrderedDict()
self._propagate_forward_hooks = OrderedDict()
self._message_forward_pre_hooks = OrderedDict()
self._message_forward_hooks = OrderedDict()
self._aggregate_forward_pre_hooks = OrderedDict()
self._aggregate_forward_hooks = OrderedDict()
self._message_and_aggregate_forward_pre_hooks = OrderedDict()
self._message_and_aggregate_forward_hooks = OrderedDict()
self._edge_update_forward_pre_hooks = OrderedDict()
self._edge_update_forward_hooks = OrderedDict()
def __check_input__(self, edge_index, size):
the_size: List[Optional[int]] = [None, None]
if isinstance(edge_index, Tensor):
assert edge_index.dtype == torch.long
assert edge_index.dim() == 2
assert edge_index.size(0) == 2
if size is not None:
the_size[0] = size[0]
the_size[1] = size[1]
return the_size
elif isinstance(edge_index, SparseTensor):
if self.flow == 'target_to_source':
raise ValueError(
('Flow direction "target_to_source" is invalid for '
'message propagation via `torch_sparse.SparseTensor`. If '
'you really want to make use of a reverse message '
'passing flow, pass in the transposed sparse tensor to '
'the message passing module, e.g., `adj_t.t()`.'))
the_size[0] = edge_index.sparse_size(1)
the_size[1] = edge_index.sparse_size(0)
return the_size
raise ValueError(
('`MessagePassing.propagate` only supports `torch.LongTensor` of '
'shape `[2, num_messages]` or `torch_sparse.SparseTensor` for '
'argument `edge_index`.'))
def __set_size__(self, size: List[Optional[int]], dim: int, src: Tensor):
the_size = size[dim]
if the_size is None:
size[dim] = src.size(self.node_dim)
elif the_size != src.size(self.node_dim):
raise ValueError(
(f'Encountered tensor with size {src.size(self.node_dim)} in '
f'dimension {self.node_dim}, but expected size {the_size}.'))
def __lift__(self, src, edge_index, dim):
if isinstance(edge_index, Tensor):
index = edge_index[dim]
return src.index_select(self.node_dim, index)
elif isinstance(edge_index, SparseTensor):
if dim == 1:
rowptr = edge_index.storage.rowptr()
rowptr = expand_left(rowptr, dim=self.node_dim, dims=src.dim())
return gather_csr(src, rowptr)
elif dim == 0:
col = edge_index.storage.col()
return src.index_select(self.node_dim, col)
raise ValueError
def __collect__(self, args, edge_index, size, kwargs):
i, j = (1, 0) if self.flow == 'source_to_target' else (0, 1)
out = {}
for arg in args:
if arg[-2:] not in ['_i', '_j']:
out[arg] = kwargs.get(arg, Parameter.empty)
else:
dim = 0 if arg[-2:] == '_j' else 1
data = kwargs.get(arg[:-2], Parameter.empty)
if isinstance(data, (tuple, list)):
assert len(data) == 2
if isinstance(data[1 - dim], Tensor):
self.__set_size__(size, 1 - dim, data[1 - dim])
data = data[dim]
if isinstance(data, Tensor):
self.__set_size__(size, dim, data)
data = self.__lift__(data, edge_index,
j if arg[-2:] == '_j' else i)
out[arg] = data
if isinstance(edge_index, Tensor):
out['adj_t'] = None
out['edge_index'] = edge_index
out['edge_index_i'] = edge_index[i]
out['edge_index_j'] = edge_index[j]
out['ptr'] = None
elif isinstance(edge_index, SparseTensor):
out['adj_t'] = edge_index
out['edge_index'] = None
out['edge_index_i'] = edge_index.storage.row()
out['edge_index_j'] = edge_index.storage.col()
out['ptr'] = edge_index.storage.rowptr()
if out.get('edge_weight', None) is None:
out['edge_weight'] = edge_index.storage.value()
if out.get('edge_attr', None) is None:
out['edge_attr'] = edge_index.storage.value()
if out.get('edge_type', None) is None:
out['edge_type'] = edge_index.storage.value()
out['index'] = out['edge_index_i']
out['size'] = size
out['size_i'] = size[1] or size[0]
out['size_j'] = size[0] or size[1]
out['dim_size'] = out['size_i']
return out
[docs] def propagate(self, edge_index: Adj, size: Size = None, **kwargs):
r"""The initial call to start propagating messages.
Args:
edge_index (Tensor or SparseTensor): A :obj:`torch.LongTensor` or a
:obj:`torch_sparse.SparseTensor` that defines the underlying
graph connectivity/message passing flow.
:obj:`edge_index` holds the indices of a general (sparse)
assignment matrix of shape :obj:`[N, M]`.
If :obj:`edge_index` is of type :obj:`torch.LongTensor`, its
shape must be defined as :obj:`[2, num_messages]`, where
messages from nodes in :obj:`edge_index[0]` are sent to
nodes in :obj:`edge_index[1]`
(in case :obj:`flow="source_to_target"`).
If :obj:`edge_index` is of type
:obj:`torch_sparse.SparseTensor`, its sparse indices
:obj:`(row, col)` should relate to :obj:`row = edge_index[1]`
and :obj:`col = edge_index[0]`.
The major difference between both formats is that we need to
input the *transposed* sparse adjacency matrix into
:func:`propagate`.
size (tuple, optional): The size :obj:`(N, M)` of the assignment
matrix in case :obj:`edge_index` is a :obj:`LongTensor`.
If set to :obj:`None`, the size will be automatically inferred
and assumed to be quadratic.
This argument is ignored in case :obj:`edge_index` is a
:obj:`torch_sparse.SparseTensor`. (default: :obj:`None`)
**kwargs: Any additional data which is needed to construct and
aggregate messages, and to update node embeddings.
"""
decomposed_layers = 1 if self.__explain__ else self.decomposed_layers
for hook in self._propagate_forward_pre_hooks.values():
res = hook(self, (edge_index, size, kwargs))
if res is not None:
edge_index, size, kwargs = res
size = self.__check_input__(edge_index, size)
# Run "fused" message and aggregation (if applicable).
if (isinstance(edge_index, SparseTensor) and self.fuse
and not self.__explain__):
coll_dict = self.__collect__(self.__fused_user_args__, edge_index,
size, kwargs)
msg_aggr_kwargs = self.inspector.distribute(
'message_and_aggregate', coll_dict)
for hook in self._message_and_aggregate_forward_pre_hooks.values():
res = hook(self, (edge_index, msg_aggr_kwargs))
if res is not None:
edge_index, msg_aggr_kwargs = res
out = self.message_and_aggregate(edge_index, **msg_aggr_kwargs)
for hook in self._message_and_aggregate_forward_hooks.values():
res = hook(self, (edge_index, msg_aggr_kwargs), out)
if res is not None:
out = res
update_kwargs = self.inspector.distribute('update', coll_dict)
out = self.update(out, **update_kwargs)
# Otherwise, run both functions in separation.
elif isinstance(edge_index, Tensor) or not self.fuse:
if decomposed_layers > 1:
user_args = self.__user_args__
decomp_args = {a[:-2] for a in user_args if a[-2:] == '_j'}
decomp_kwargs = {
a: kwargs[a].chunk(decomposed_layers, -1)
for a in decomp_args
}
decomp_out = []
for i in range(decomposed_layers):
if decomposed_layers > 1:
for arg in decomp_args:
kwargs[arg] = decomp_kwargs[arg][i]
coll_dict = self.__collect__(self.__user_args__, edge_index,
size, kwargs)
msg_kwargs = self.inspector.distribute('message', coll_dict)
for hook in self._message_forward_pre_hooks.values():
res = hook(self, (msg_kwargs, ))
if res is not None:
msg_kwargs = res[0] if isinstance(res, tuple) else res
out = self.message(**msg_kwargs)
for hook in self._message_forward_hooks.values():
res = hook(self, (msg_kwargs, ), out)
if res is not None:
out = res
# For `GNNExplainer`, we require a separate message and
# aggregate procedure since this allows us to inject the
# `edge_mask` into the message passing computation scheme.
if self.__explain__:
edge_mask = self.__edge_mask__.sigmoid()
# Some ops add self-loops to `edge_index`. We need to do
# the same for `edge_mask` (but do not train those).
if out.size(self.node_dim) != edge_mask.size(0):
edge_mask = edge_mask[self.__loop_mask__]
loop = edge_mask.new_ones(size[0])
edge_mask = torch.cat([edge_mask, loop], dim=0)
assert out.size(self.node_dim) == edge_mask.size(0)
out = out * edge_mask.view([-1] + [1] * (out.dim() - 1))
aggr_kwargs = self.inspector.distribute('aggregate', coll_dict)
for hook in self._aggregate_forward_pre_hooks.values():
res = hook(self, (aggr_kwargs, ))
if res is not None:
aggr_kwargs = res[0] if isinstance(res, tuple) else res
out = self.aggregate(out, **aggr_kwargs)
for hook in self._aggregate_forward_hooks.values():
res = hook(self, (aggr_kwargs, ), out)
if res is not None:
out = res
update_kwargs = self.inspector.distribute('update', coll_dict)
out = self.update(out, **update_kwargs)
if decomposed_layers > 1:
decomp_out.append(out)
if decomposed_layers > 1:
out = torch.cat(decomp_out, dim=-1)
for hook in self._propagate_forward_hooks.values():
res = hook(self, (edge_index, size, kwargs), out)
if res is not None:
out = res
return out
[docs] def edge_updater(self, edge_index: Adj, **kwargs):
r"""The initial call to compute or update features for each edge in the
graph.
Args:
edge_index (Tensor or SparseTensor): A :obj:`torch.LongTensor` or a
:obj:`torch_sparse.SparseTensor` that defines the underlying
graph connectivity/message passing flow.
See :meth:`propagate` for more information.
**kwargs: Any additional data which is needed to compute or update
features for each edge in the graph.
"""
for hook in self._edge_update_forward_pre_hooks.values():
res = hook(self, (edge_index, kwargs))
if res is not None:
edge_index, kwargs = res
size = self.__check_input__(edge_index, size=None)
coll_dict = self.__collect__(self.__edge_user_args__, edge_index, size,
kwargs)
edge_kwargs = self.inspector.distribute('edge_update', coll_dict)
out = self.edge_update(**edge_kwargs)
for hook in self._edge_update_forward_hooks.values():
res = hook(self, (edge_index, kwargs), out)
if res is not None:
out = res
return out
[docs] def message(self, x_j: Tensor) -> Tensor:
r"""Constructs messages from node :math:`j` to node :math:`i`
in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in
:obj:`edge_index`.
This function can take any argument as input which was initially
passed to :meth:`propagate`.
Furthermore, tensors passed to :meth:`propagate` can be mapped to the
respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or
:obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.
"""
return x_j
[docs] def aggregate(self, inputs: Tensor, index: Tensor,
ptr: Optional[Tensor] = None,
dim_size: Optional[int] = None) -> Tensor:
r"""Aggregates messages from neighbors as
:math:`\square_{j \in \mathcal{N}(i)}`.
Takes in the output of message computation as first argument and any
argument which was initially passed to :meth:`propagate`.
By default, this function will delegate its call to scatter functions
that support "add", "mean" and "max" operations as specified in
:meth:`__init__` by the :obj:`aggr` argument.
"""
if ptr is not None:
ptr = expand_left(ptr, dim=self.node_dim, dims=inputs.dim())
return segment_csr(inputs, ptr, reduce=self.aggr)
else:
return scatter(inputs, index, dim=self.node_dim, dim_size=dim_size,
reduce=self.aggr)
[docs] def message_and_aggregate(self, adj_t: SparseTensor) -> Tensor:
r"""Fuses computations of :func:`message` and :func:`aggregate` into a
single function.
If applicable, this saves both time and memory since messages do not
explicitly need to be materialized.
This function will only gets called in case it is implemented and
propagation takes place based on a :obj:`torch_sparse.SparseTensor`.
"""
raise NotImplementedError
[docs] def update(self, inputs: Tensor) -> Tensor:
r"""Updates node embeddings in analogy to
:math:`\gamma_{\mathbf{\Theta}}` for each node
:math:`i \in \mathcal{V}`.
Takes in the output of aggregation as first argument and any argument
which was initially passed to :meth:`propagate`.
"""
return inputs
[docs] def edge_update(self) -> Tensor:
r"""Computes or updates features for each edge in the graph.
This function can take any argument as input which was initially passed
to :meth:`edge_updater`.
Furthermore, tensors passed to :meth:`edge_updater` can be mapped to
the respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or
:obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.
"""
raise NotImplementedError
[docs] def register_propagate_forward_pre_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward pre-hook on the module.
The hook will be called every time before :meth:`propagate` is invoked.
It should have the following signature:
.. code-block:: python
hook(module, inputs) -> None or modified input
The hook can modify the input.
Input keyword arguments are passed to the hook as a dictionary in
:obj:`inputs[-1]`.
Returns a :class:`torch.utils.hooks.RemovableHandle` that can be used
to remove the added hook by calling :obj:`handle.remove()`.
"""
handle = RemovableHandle(self._propagate_forward_pre_hooks)
self._propagate_forward_pre_hooks[handle.id] = hook
return handle
[docs] def register_propagate_forward_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward hook on the module.
The hook will be called every time after :meth:`propagate` has computed
an output.
It should have the following signature:
.. code-block:: python
hook(module, inputs, output) -> None or modified output
The hook can modify the output.
Input keyword arguments are passed to the hook as a dictionary in
:obj:`inputs[-1]`.
Returns a :class:`torch.utils.hooks.RemovableHandle` that can be used
to remove the added hook by calling :obj:`handle.remove()`.
"""
handle = RemovableHandle(self._propagate_forward_hooks)
self._propagate_forward_hooks[handle.id] = hook
return handle
[docs] def register_message_forward_pre_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward pre-hook on the module.
The hook will be called every time before :meth:`message` is invoked.
See :meth:`register_propagate_forward_pre_hook` for more information.
"""
handle = RemovableHandle(self._message_forward_pre_hooks)
self._message_forward_pre_hooks[handle.id] = hook
return handle
[docs] def register_message_forward_hook(self, hook: Callable) -> RemovableHandle:
r"""Registers a forward hook on the module.
The hook will be called every time after :meth:`message` has computed
an output.
See :meth:`register_propagate_forward_hook` for more information.
"""
handle = RemovableHandle(self._message_forward_hooks)
self._message_forward_hooks[handle.id] = hook
return handle
[docs] def register_aggregate_forward_pre_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward pre-hook on the module.
The hook will be called every time before :meth:`aggregate` is invoked.
See :meth:`register_propagate_forward_pre_hook` for more information.
"""
handle = RemovableHandle(self._aggregate_forward_pre_hooks)
self._aggregate_forward_pre_hooks[handle.id] = hook
return handle
[docs] def register_aggregate_forward_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward hook on the module.
The hook will be called every time after :meth:`aggregate` has computed
an output.
See :meth:`register_propagate_forward_hook` for more information.
"""
handle = RemovableHandle(self._aggregate_forward_hooks)
self._aggregate_forward_hooks[handle.id] = hook
return handle
[docs] def register_message_and_aggregate_forward_pre_hook(
self, hook: Callable) -> RemovableHandle:
r"""Registers a forward pre-hook on the module.
The hook will be called every time before :meth:`message_and_aggregate`
is invoked.
See :meth:`register_propagate_forward_pre_hook` for more information.
"""
handle = RemovableHandle(self._message_and_aggregate_forward_pre_hooks)
self._message_and_aggregate_forward_pre_hooks[handle.id] = hook
return handle
[docs] def register_message_and_aggregate_forward_hook(
self, hook: Callable) -> RemovableHandle:
r"""Registers a forward hook on the module.
The hook will be called every time after :meth:`message_and_aggregate`
has computed an output.
See :meth:`register_propagate_forward_hook` for more information.
"""
handle = RemovableHandle(self._message_and_aggregate_forward_hooks)
self._message_and_aggregate_forward_hooks[handle.id] = hook
return handle
[docs] def register_edge_update_forward_pre_hook(
self, hook: Callable) -> RemovableHandle:
r"""Registers a forward pre-hook on the module.
The hook will be called every time before :meth:`edge_update` is
invoked. See :meth:`register_propagate_forward_pre_hook` for more
information.
"""
handle = RemovableHandle(self._edge_update_forward_pre_hooks)
self._edge_update_forward_pre_hooks[handle.id] = hook
return handle
[docs] def register_edge_update_forward_hook(self,
hook: Callable) -> RemovableHandle:
r"""Registers a forward hook on the module.
The hook will be called every time after :meth:`edge_update` has
computed an output.
See :meth:`register_propagate_forward_hook` for more information.
"""
handle = RemovableHandle(self._edge_update_forward_hooks)
self._edge_update_forward_hooks[handle.id] = hook
return handle
[docs] @torch.jit.unused
def jittable(self, typing: Optional[str] = None):
r"""Analyzes the :class:`MessagePassing` instance and produces a new
jittable module.
Args:
typing (string, optional): If given, will generate a concrete
instance with :meth:`forward` types based on :obj:`typing`,
*e.g.*: :obj:`"(Tensor, Optional[Tensor]) -> Tensor"`.
"""
# Find and parse `propagate()` types to format `{arg1: type1, ...}`.
if hasattr(self, 'propagate_type'):
prop_types = {
k: sanitize(str(v))
for k, v in self.propagate_type.items()
}
else:
source = inspect.getsource(self.__class__)
match = re.search(r'#\s*propagate_type:\s*\((.*)\)', source)
if match is None:
raise TypeError(
'TorchScript support requires the definition of the types '
'passed to `propagate()`. Please specificy them via\n\n'
'propagate_type = {"arg1": type1, "arg2": type2, ... }\n\n'
'or via\n\n'
'# propagate_type: (arg1: type1, arg2: type2, ...)\n\n'
'inside the `MessagePassing` module.')
prop_types = split_types_repr(match.group(1))
prop_types = dict([re.split(r'\s*:\s*', t) for t in prop_types])
type_hints = get_type_hints(self.__class__.update)
prop_return_type = type_hints.get('return', 'Tensor')
if str(prop_return_type)[:6] == '<class':
prop_return_type = prop_return_type.__name__
# Parse `__collect__()` types to format `{arg:1, type1, ...}`.
collect_types = self.inspector.types(
['message', 'aggregate', 'update'])
# Collect `forward()` header, body and @overload types.
forward_types = parse_types(self.forward)
forward_types = [resolve_types(*types) for types in forward_types]
forward_types = list(chain.from_iterable(forward_types))
keep_annotation = len(forward_types) < 2
forward_header = func_header_repr(self.forward, keep_annotation)
forward_body = func_body_repr(self.forward, keep_annotation)
if keep_annotation:
forward_types = []
elif typing is not None:
forward_types = []
forward_body = 8 * ' ' + f'# type: {typing}\n{forward_body}'
root = os.path.dirname(osp.realpath(__file__))
with open(osp.join(root, 'message_passing.jinja'), 'r') as f:
template = Template(f.read())
uid = uuid1().hex[:6]
cls_name = f'{self.__class__.__name__}Jittable_{uid}'
jit_module_repr = template.render(
uid=uid,
module=str(self.__class__.__module__),
cls_name=cls_name,
parent_cls_name=self.__class__.__name__,
prop_types=prop_types,
prop_return_type=prop_return_type,
fuse=self.fuse,
collect_types=collect_types,
user_args=self.__user_args__,
forward_header=forward_header,
forward_types=forward_types,
forward_body=forward_body,
msg_args=self.inspector.keys(['message']),
aggr_args=self.inspector.keys(['aggregate']),
msg_and_aggr_args=self.inspector.keys(['message_and_aggregate']),
update_args=self.inspector.keys(['update']),
check_input=inspect.getsource(self.__check_input__)[:-1],
lift=inspect.getsource(self.__lift__)[:-1],
)
# Instantiate a class from the rendered JIT module representation.
cls = class_from_module_repr(cls_name, jit_module_repr)
module = cls.__new__(cls)
module.__dict__ = self.__dict__.copy()
module.jittable = None
return module
def __repr__(self) -> str:
if hasattr(self, 'in_channels') and hasattr(self, 'out_channels'):
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels})')
return f'{self.__class__.__name__}()'