import torch
from . import nccl
from torch._utils import _accumulate, _take_tensors, _flatten_dense_tensors, \
_flatten_sparse_tensors, _unflatten_dense_tensors, \
_unflatten_sparse_tensors, _reorder_tensors_as
[docs]def broadcast(tensor, devices):
"""Broadcasts a tensor to a number of GPUs.
Arguments:
tensor (Tensor): tensor to broadcast.
devices (Iterable): an iterable of devices among which to broadcast.
Note that it should be like (src, dst1, dst2, ...), the first element
of which is the source device to broadcast from.
Returns:
A tuple containing copies of the ``tensor``, placed on devices
corresponding to indices from ``devices``.
"""
return torch._C._broadcast(tensor, devices)
[docs]def broadcast_coalesced(tensors, devices, buffer_size=10485760):
"""Broadcasts a sequence tensors to the specified GPUs.
Small tensors are first coalesced into a buffer to reduce the number
of synchronizations.
Arguments:
tensors (sequence): tensors to broadcast.
devices (Iterable): an iterable of devices among which to broadcast.
Note that it should be like (src, dst1, dst2, ...), the first element
of which is the source device to broadcast from.
buffer_size (int): maximum size of the buffer used for coalescing
Returns:
A tuple containing copies of the ``tensor``, placed on devices
corresponding to indices from ``devices``.
"""
return torch._C._broadcast_coalesced(tensors, devices, buffer_size)
[docs]def reduce_add(inputs, destination=None):
"""Sums tensors from multiple GPUs.
All inputs should have matching shapes.
Arguments:
inputs (Iterable[Tensor]): an iterable of tensors to add.
destination (int, optional): a device on which the output will be
placed (default: current device).
Returns:
A tensor containing an elementwise sum of all inputs, placed on the
``destination`` device.
"""
# TODO: try to find an input on another gpu, copy it,
# and accumulate into the copy
if destination is None:
destination = torch.cuda.current_device()
input_size = inputs[0].size()
nccl_root = None
for i, inp in enumerate(inputs):
assert inp.is_cuda, "reduce_add expects all inputs to be on GPUs"
if inp.get_device() == destination:
nccl_root = i
if inp.size() != input_size:
got = 'x'.join(str(x) for x in inp.size())
expected = 'x'.join(str(x) for x in input_size)
raise ValueError("input {} has invalid size: got {}, but expected "
"{}".format(i, got, expected))
if nccl_root is None:
raise RuntimeError("reduce_add expects destination to be on the same GPU with one of the tensors")
result = inp.new(device=destination).resize_as_(inp).zero_()
if nccl.is_available(inputs) and inputs[0].get_device() == destination:
outputs = [result] + [t.new(t.size()) for t in inputs[1:]]
nccl.reduce(inputs, outputs, root=nccl_root)
return result
for inp in inputs:
input_correct_gpu = inp.cuda(result.get_device())
result.add_(input_correct_gpu)
return result
def reduce_add_coalesced(inputs, destination=None, buffer_size=10485760):
"""Sums tensors from multiple GPUs.
Small tensors are first coalesced into a buffer to reduce the number
of synchronizations.
Arguments:
inputs (Iterable[Iterable[Tensor]]): iterable of iterables that
contain tensors from a single device.
destination (int, optional): a device on which the output will be
placed (default: current device).
buffer_size (int): maximum size of the buffer used for coalescing
Returns:
A tuple of tensors containing an elementwise sum of each group of
inputs, placed on the ``destination`` device.
"""
dense_tensors = [[] for _ in inputs] # shape (num_gpus, num_tensors)
output = []
ref_order = []
# process sparse ones first since they may have different sizes on different gpus
for tensor_at_gpus in zip(*inputs):
if all(t.is_sparse for t in tensor_at_gpus):
result = reduce_add(tensor_at_gpus, destination)
output.append(result)
ref_order.append(tensor_at_gpus[0])
else:
for coll, t in zip(dense_tensors, tensor_at_gpus):
coll.append(t.to_dense() if t.is_sparse else t)
ref_order.append(dense_tensors[0][-1])
itrs = [_take_tensors(tensors, buffer_size) for tensors in dense_tensors]
# now the dense ones, which have consistent sizes
for chunks in zip(*itrs):
flat_tensors = [_flatten_dense_tensors(chunk) for chunk in chunks]
flat_result = reduce_add(flat_tensors, destination)
output.extend(_unflatten_dense_tensors(flat_result, chunks[0]))
return tuple(_reorder_tensors_as(output, ref_order))
[docs]def scatter(tensor, devices, chunk_sizes=None, dim=0, streams=None):
"""Scatters tensor across multiple GPUs.
Arguments:
tensor (Tensor): tensor to scatter.
devices (Iterable[int]): iterable of ints, specifying among which
devices the tensor should be scattered.
chunk_sizes (Iterable[int], optional): sizes of chunks to be placed on
each device. It should match ``devices`` in length and sum to
``tensor.size(dim)``. If not specified, the tensor will be divided
into equal chunks.
dim (int, optional): A dimension along which to chunk the tensor.
Returns:
A tuple containing chunks of the ``tensor``, spread across given
``devices``.
"""
if chunk_sizes is None:
chunks = tensor.chunk(len(devices), dim)
else:
assert sum(chunk_sizes) == tensor.size(dim), "given chunk sizes " \
"don't sum up to the tensor's size (sum(chunk_sizes) == {}, but " \
"expected {})".format(sum(chunk_sizes), tensor.size(dim))
assert min(chunk_sizes) > 0, "got a negative chunk_size"
chunks = [tensor.narrow(dim, start - size, size)
for start, size in zip(_accumulate(chunk_sizes), chunk_sizes)]
chunks = tuple(chunk.contiguous() for chunk in chunks)
# TODO: copy to a pinned buffer first (if copying from CPU)
if streams is None:
streams = [None] * len(devices)
outputs = []
for device, chunk, stream in zip(devices, chunks, streams):
with torch.cuda.device(device), torch.cuda.stream(stream):
outputs.append(chunk.cuda(device, non_blocking=True))
return tuple(outputs)
[docs]def gather(tensors, dim=0, destination=None):
"""Gathers tensors from multiple GPUs.
Tensor sizes in all dimension different than ``dim`` have to match.
Arguments:
tensors (Iterable[Tensor]): iterable of tensors to gather.
dim (int): a dimension along which the tensors will be concatenated.
destination (int, optional): output device (-1 means CPU, default:
current device)
Returns:
A tensor located on ``destination`` device, that is a result of
concatenating ``tensors`` along ``dim``.
"""
total_size = 0
expected_size = list(tensors[0].size())
for tensor in tensors:
assert tensor.is_cuda, "gather expects all inputs to be on GPUs"
expected_size[dim] = tensor.size(dim)
if list(tensor.size()) != expected_size:
got = 'x'.join(str(x) for x in tensor.size())
expected = 'x'.join(str(x) for x in expected_size)
raise ValueError("gather got an input of invalid size: got {}, "
"but expected {}".format(got, expected))
total_size += tensor.size(dim)
expected_size[dim] = total_size
expected_size = torch.Size(expected_size)
if destination is None:
destination = torch.cuda.current_device()
if destination == -1:
result = tensors[0].new().cpu().resize_(expected_size)
else:
result = tensors[0].new(expected_size, device=destination)
chunk_start = 0
# TODO: if copying to CPU, allocate a pinned buffer, do async copies to it,
# and copy it to regular memory
for tensor in tensors:
result.narrow(dim, chunk_start, tensor.size(dim)).copy_(tensor, True)
chunk_start += tensor.size(dim)
return result