import torch
import importlib
from collections import defaultdict
def _type(self, new_type=None, async=False):
"""Returns the type if `new_type` is not provided, else casts this object to
the specified type.
If this is already of the correct type, no copy is performed and the
original object is returned.
Args:
new_type (type or string): The desired type
async (bool): If ``True``, and the source is in pinned memory and
destination is on the GPU or vice versa, the copy is
performed asynchronously with respect to the host.
Otherwise, the argument has no effect.
"""
if new_type is None:
return self.__module__ + '.' + self.__class__.__name__
if isinstance(new_type, str):
new_type = _import_dotted_name(new_type)
if new_type == type(self):
return self
if self.is_sparse:
if not new_type.is_sparse:
raise RuntimeError("Cannot cast sparse tensor to dense tensor")
new_module_name = new_type.__module__.replace('.sparse', '')
new_values_type_name = new_module_name + '.' + new_type.__name__
new_values = self._values().type(new_values_type_name, async)
new_indices_type_name = new_module_name + '.LongTensor'
new_indices = self._indices().type(new_indices_type_name, async)
return new_type(new_indices, new_values, self.size())
if new_type.is_sparse:
raise RuntimeError("Cannot cast dense tensor to sparse tensor")
return new_type(self.size()).copy_(self, async)
def _cuda(self, device=None, async=False):
"""Returns a copy of this object in CUDA memory.
If this object is already in CUDA memory and on the correct device, then
no copy is performed and the original object is returned.
Args:
device (int): The destination GPU id. Defaults to the current device.
async (bool): If ``True`` and the source is in pinned memory, the copy will
be asynchronous with respect to the host. Otherwise, the
argument has no effect.
"""
if self.is_cuda:
if device is None:
device = torch.cuda.current_device()
if self.get_device() == device:
return self
else:
if device is None:
device = -1
with torch.cuda.device(device):
if self.is_sparse:
new_type = getattr(torch.cuda.sparse, self.__class__.__name__)
indices = self._indices().cuda(device, async)
values = self._values().cuda(device, async)
return new_type(indices, values, self.size())
else:
new_type = getattr(torch.cuda, self.__class__.__name__)
return new_type(self.size()).copy_(self, async)
def _rebuild_tensor(storage, storage_offset, size, stride):
class_name = storage.__class__.__name__.replace('Storage', 'Tensor')
module = importlib.import_module(storage.__module__)
tensor_class = getattr(module, class_name)
return tensor_class().set_(storage, storage_offset, size, stride)
def _range(*args, **kwargs):
return __builtins__['range'](*args, **kwargs)
def _import_dotted_name(name):
components = name.split('.')
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj
# Taken from python 3.5 docs
def _accumulate(iterable, fn=lambda x, y: x + y):
'Return running totals'
# _accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# _accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = fn(total, element)
yield total
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise operation on this buffer will be equivalent to
operating individually.
Arguments:
tensors (Iterable[Tensor]): dense tensors to flatten.
Returns:
A contiguous 1D buffer containing input tensors.
"""
if len(tensors) == 1:
return tensors[0].contiguous().view(-1)
numels = [tensor.numel() for tensor in tensors]
size = sum(numels)
offset = 0
flat = tensors[0].new(size)
for tensor, numel in zip(tensors, numels):
flat.narrow(0, offset, numel).copy_(tensor, broadcast=False)
offset += numel
return flat
def _flatten_sparse_tensors(tensors):
"""Flatten sparse tensors into two contiguous 1D buffers, one of indices and
one of values. Assume tensors are of same sparse type.
Arguments:
tensors (Iterable[Tensor]): sparse tensors to flatten.
Returns:
A tuple of two contiguous 1D buffers, one containing input tensors'
indices and the other containing the values.
"""
flat_indices = _flatten_dense_tensors([t._indices() for t in tensors])
flat_values = _flatten_dense_tensors([t._values() for t in tensors])
return flat_indices, flat_values
def _unflatten_dense_tensors(flat, tensors):
"""View a flat buffer using the sizes of tensors. Assume that tensors are of
same dense type, and that flat is given by _flatten_dense_tensors.
Arguments:
flat (Tensor): flattened dense tensors to unflatten.
tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened dense tensors with sizes same as tensors and values from
flat.
"""
outputs = []
offset = 0
for tensor in tensors:
numel = tensor.numel()
outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
offset += numel
return tuple(outputs)
def _unflatten_sparse_tensors(flat, tensors):
"""View flat buffer (containing indices and values) using the sizes of
tensors. Assume that tensors are of same sparse type, and that flat is given
by _flatten_sparse_tensors.
Arguments:
flat (tuple(Tensor, Tensor)): flattened indices and values of sparse
tensors to unflatten.
tensors (Iterable[Tensor]): sparse tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened sparse tensors with sizes same as tensors and values from
flat.
"""
flat_indices, flat_values = flat
indices = _unflatten_dense_tensors(flat_indices, [t._indices() for t in tensors])
values = _unflatten_dense_tensors(flat_values, [t._values() for t in tensors])
outputs = []
for t, i, v in zip(tensors, indices, values):
outputs.append(t.new(i, v, t.size()))
return tuple(outputs)
def _reorder_tensors_as(tensors, ordered_tensors):
"""Assume that tensors are of same order as ordered_tensors within their
types, e.g., from _take_tensors. Reorder them to be of same order as
ordered_tensors.
Arguments:
tensors (Iterable[Tensor]): tensors to be reordered. They should be of
the same order as ordered_tensors within their own types.
ordered_tensors (Iterable[Tensor]): tensors whose order will be the
reference.
Returns:
Ordered tuple of tensors with contents from tensors and order of
ordered_tensors.
"""
type_dict = defaultdict(list)
for tensor in tensors:
type_dict[type(tensor)].append(tensor)
type_dict = {t: iter(coll) for t, coll in type_dict.items()}
return tuple(next(type_dict[type(tensor)]) for tensor in ordered_tensors)
def _take_tensors(tensors, size_limit):
"""Group tensors into chunks. This generator yields a chunk at each time,
each containing tensors of same type up to certain byte limit in total size.
Args:
tensors (Sequence): A sequence of tensors to be separated into chunks.
size_limit (int): The limit of each chunk in bytes.
Yields:
Blocks of tensors of same type and within size_limit. The yielded
tensors are only ordered as the original sequence within its types.
"""
buf_dict = defaultdict(lambda: [[], 0])
for tensor in tensors:
t = type(tensor)
if tensor.is_sparse:
indices = tensor._indices()
values = tensor._values()
size = indices.numel() * indices.element_size() + values.numel() * values.element_size()
else:
size = tensor.numel() * tensor.element_size()
buf_and_size = buf_dict[t]
if buf_and_size[1] + size > size_limit and buf_and_size[1] > 0:
yield buf_and_size[0]
buf_and_size = buf_dict[t] = [[], 0]
buf_and_size[0].append(tensor)
buf_and_size[1] += size
for buf, _ in buf_dict.values():
if len(buf) > 0:
yield buf