"""Base types and utilities for Graph Bolt."""
from collections import deque
from dataclasses import dataclass
import torch
from torch.utils.data import functional_datapipe
from torchdata.datapipes.iter import IterDataPipe
from ..utils import recursive_apply
__all__ = [
"CANONICAL_ETYPE_DELIMITER",
"ORIGINAL_EDGE_ID",
"etype_str_to_tuple",
"etype_tuple_to_str",
"CopyTo",
"FutureWaiter",
"Waiter",
"Bufferer",
"EndMarker",
"isin",
"index_select",
"expand_indptr",
"CSCFormatBase",
"seed",
]
CANONICAL_ETYPE_DELIMITER = ":"
ORIGINAL_EDGE_ID = "_ORIGINAL_EDGE_ID"
[docs]def seed(val):
"""Set the random seed of Graphbolt.
Parameters
----------
val : int
The seed.
"""
torch.ops.graphbolt.set_seed(val)
[docs]def isin(elements, test_elements):
"""Tests if each element of elements is in test_elements. Returns a boolean
tensor of the same shape as elements that is True for elements in
test_elements and False otherwise.
Parameters
----------
elements : torch.Tensor
A 1D tensor represents the input elements.
test_elements : torch.Tensor
A 1D tensor represents the values to test against for each input.
Examples
--------
>>> isin(torch.tensor([1, 2, 3, 4]), torch.tensor([2, 3]))
tensor([[False, True, True, False]])
"""
assert elements.dim() == 1, "Elements should be 1D tensor."
assert test_elements.dim() == 1, "Test_elements should be 1D tensor."
return torch.ops.graphbolt.isin(elements, test_elements)
[docs]def expand_indptr(indptr, dtype=None, node_ids=None, output_size=None):
"""Converts a given indptr offset tensor to a COO format tensor. If
node_ids is not given, it is assumed to be equal to
torch.arange(indptr.size(0) - 1, dtype=dtype, device=indptr.device).
This is equivalent to
.. code:: python
if node_ids is None:
node_ids = torch.arange(len(indptr) - 1, dtype=dtype, device=indptr.device)
return node_ids.to(dtype).repeat_interleave(indptr.diff())
Parameters
----------
indptr : torch.Tensor
A 1D tensor represents the csc_indptr tensor.
dtype : Optional[torch.dtype]
The dtype of the returned output tensor.
node_ids : Optional[torch.Tensor]
A 1D tensor represents the column node ids that the returned tensor will
be populated with.
output_size : Optional[int]
The size of the output tensor. Should be equal to indptr[-1]. Using this
argument avoids a stream synchronization to calculate the output shape.
Returns
-------
torch.Tensor
The converted COO tensor with values from node_ids.
"""
assert indptr.dim() == 1, "Indptr should be 1D tensor."
assert not (
node_ids is None and dtype is None
), "One of node_ids or dtype must be given."
assert (
node_ids is None or node_ids.dim() == 1
), "Node_ids should be 1D tensor."
if dtype is None:
dtype = node_ids.dtype
return torch.ops.graphbolt.expand_indptr(
indptr, dtype, node_ids, output_size
)
[docs]def index_select(tensor, index):
"""Returns a new tensor which indexes the input tensor along dimension dim
using the entries in index.
The returned tensor has the same number of dimensions as the original tensor
(tensor). The first dimension has the same size as the length of index;
other dimensions have the same size as in the original tensor.
When tensor is a pinned tensor and index.is_cuda is True, the operation runs
on the CUDA device and the returned tensor will also be on CUDA.
Parameters
----------
tensor : torch.Tensor
The input tensor.
index : torch.Tensor
The 1-D tensor containing the indices to index.
Returns
-------
torch.Tensor
The indexed input tensor, equivalent to tensor[index].
"""
assert index.dim() == 1, "Index should be 1D tensor."
return torch.ops.graphbolt.index_select(tensor, index)
[docs]def etype_tuple_to_str(c_etype):
"""Convert canonical etype from tuple to string.
Examples
--------
>>> c_etype = ("user", "like", "item")
>>> c_etype_str = _etype_tuple_to_str(c_etype)
>>> print(c_etype_str)
"user:like:item"
"""
assert isinstance(c_etype, tuple) and len(c_etype) == 3, (
"Passed-in canonical etype should be in format of (str, str, str). "
f"But got {c_etype}."
)
return CANONICAL_ETYPE_DELIMITER.join(c_etype)
[docs]def etype_str_to_tuple(c_etype):
"""Convert canonical etype from string to tuple.
Examples
--------
>>> c_etype_str = "user:like:item"
>>> c_etype = _etype_str_to_tuple(c_etype_str)
>>> print(c_etype)
("user", "like", "item")
"""
if isinstance(c_etype, tuple):
return c_etype
ret = tuple(c_etype.split(CANONICAL_ETYPE_DELIMITER))
assert len(ret) == 3, (
"Passed-in canonical etype should be in format of 'str:str:str'. "
f"But got {c_etype}."
)
return ret
def apply_to(x, device):
"""Apply `to` function to object x only if it has `to`."""
return x.to(device) if hasattr(x, "to") else x
[docs]@functional_datapipe("copy_to")
class CopyTo(IterDataPipe):
"""DataPipe that transfers each element yielded from the previous DataPipe
to the given device. For MiniBatch, only the related attributes
(automatically inferred) will be transferred by default. If you want to
transfer any other attributes, indicate them in the ``extra_attrs``.
Functional name: :obj:`copy_to`.
When ``data`` has ``to`` method implemented, ``CopyTo`` will be equivalent
to
.. code:: python
for data in datapipe:
yield data.to(device)
For :class:`~dgl.graphbolt.MiniBatch`, only a part of attributes will be
transferred to accelerate the process by default:
- When ``seed_nodes`` is not None and ``node_pairs`` is None, node related
task is inferred. Only ``labels``, ``sampled_subgraphs``, ``node_features``
and ``edge_features`` will be transferred.
- When ``node_pairs`` is not None and ``seed_nodes`` is None, edge/link
related task is inferred. Only ``labels``, ``compacted_node_pairs``,
``compacted_negative_srcs``, ``compacted_negative_dsts``,
``sampled_subgraphs``, ``node_features`` and ``edge_features`` will be
transferred.
- Otherwise, all attributes will be transferred.
- If you want some other attributes to be transferred as well, please
specify the name in the ``extra_attrs``. For instance, the following code
will copy ``seed_nodes`` to the GPU as well:
.. code:: python
datapipe = datapipe.copy_to(device="cuda", extra_attrs=["seed_nodes"])
Parameters
----------
datapipe : DataPipe
The DataPipe.
device : torch.device
The PyTorch CUDA device.
extra_attrs: List[string]
The extra attributes of the data in the DataPipe you want to be carried
to the specific device. The attributes specified in the ``extra_attrs``
will be transferred regardless of the task inferred. It could also be
applied to classes other than :class:`~dgl.graphbolt.MiniBatch`.
"""
def __init__(self, datapipe, device, extra_attrs=None):
super().__init__()
self.datapipe = datapipe
self.device = device
self.extra_attrs = extra_attrs
def __iter__(self):
for data in self.datapipe:
data = recursive_apply(data, apply_to, self.device)
if self.extra_attrs is not None:
for attr in self.extra_attrs:
setattr(
data,
attr,
recursive_apply(
getattr(data, attr), apply_to, self.device
),
)
yield data
@functional_datapipe("mark_end")
class EndMarker(IterDataPipe):
"""Used to mark the end of a datapipe and is a no-op."""
def __init__(self, datapipe):
self.datapipe = datapipe
def __iter__(self):
yield from self.datapipe
@functional_datapipe("buffer")
class Bufferer(IterDataPipe):
"""Buffers items before yielding them.
Parameters
----------
datapipe : DataPipe
The data pipeline.
buffer_size : int, optional
The size of the buffer which stores the fetched samples. If data coming
from datapipe has latency spikes, consider setting to a higher value.
Default is 1.
"""
def __init__(self, datapipe, buffer_size=1):
self.datapipe = datapipe
if buffer_size <= 0:
raise ValueError(
"'buffer_size' is required to be a positive integer."
)
self.buffer = deque(maxlen=buffer_size)
def __iter__(self):
for data in self.datapipe:
if len(self.buffer) < self.buffer.maxlen:
self.buffer.append(data)
else:
return_data = self.buffer.popleft()
self.buffer.append(data)
yield return_data
while len(self.buffer) > 0:
yield self.buffer.popleft()
@functional_datapipe("wait")
class Waiter(IterDataPipe):
"""Calls the wait function of all items."""
def __init__(self, datapipe):
self.datapipe = datapipe
def __iter__(self):
for data in self.datapipe:
data.wait()
yield data
@functional_datapipe("wait_future")
class FutureWaiter(IterDataPipe):
"""Calls the result function of all items and returns their results."""
def __init__(self, datapipe):
self.datapipe = datapipe
def __iter__(self):
for data in self.datapipe:
yield data.result()
@dataclass
class CSCFormatBase:
r"""Basic class representing data in Compressed Sparse Column (CSC) format.
Examples
--------
>>> indptr = torch.tensor([0, 1, 3])
>>> indices = torch.tensor([1, 4, 2])
>>> csc_foramt_base = CSCFormatBase(indptr=indptr, indices=indices)
>>> print(csc_format_base.indptr)
... torch.tensor([0, 1, 3])
>>> print(csc_foramt_base)
... torch.tensor([1, 4, 2])
"""
indptr: torch.Tensor = None
indices: torch.Tensor = None
def __init__(self, indptr: torch.Tensor, indices: torch.Tensor):
self.indptr = indptr
self.indices = indices
if not indptr.is_cuda:
assert self.indptr[-1] == len(
self.indices
), "The last element of indptr should be the same as the length of indices."
def __repr__(self) -> str:
return _csc_format_base_str(self)
def to(self, device: torch.device) -> None: # pylint: disable=invalid-name
"""Copy `CSCFormatBase` to the specified device using reflection."""
for attr in dir(self):
# Only copy member variables.
if not callable(getattr(self, attr)) and not attr.startswith("__"):
setattr(
self,
attr,
recursive_apply(
getattr(self, attr), lambda x: apply_to(x, device)
),
)
return self
def _csc_format_base_str(csc_format_base: CSCFormatBase) -> str:
final_str = "CSCFormatBase("
def _add_indent(_str, indent):
lines = _str.split("\n")
lines = [lines[0]] + [" " * indent + line for line in lines[1:]]
return "\n".join(lines)
final_str += (
f"indptr={_add_indent(str(csc_format_base.indptr), 21)},\n" + " " * 14
)
final_str += (
f"indices={_add_indent(str(csc_format_base.indices), 22)},\n" + ")"
)
return final_str