mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
f2d7e94948
See issue #20 The torch.Size class is a tuple subclass which distinguishes sizes from other tuples so that torch.Tensor(size) is interpreted as size instead of data.
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
import torch
|
|
import torch.cuda.nccl as nccl
|
|
from torch._utils import _accumulate
|
|
|
|
# TODO: sync streams when implemented
|
|
|
|
def broadcast(tensor, devices):
|
|
"Broadcasts a tensor to a number of GPUs"
|
|
if nccl.is_available([tensor]) and len(set(devices)) == len(devices):
|
|
tensors = [tensor]
|
|
for device in devices[1:]:
|
|
with torch.cuda.device(device):
|
|
tensors.append(type(tensor)(tensor.size()))
|
|
nccl.broadcast(tensors)
|
|
return tuple(tensors)
|
|
|
|
# TODO: copy to a pinned buffer first (if copy is from CPU)
|
|
return tuple(tensor.cuda(gpu, async=True) for gpu in devices)
|
|
|
|
|
|
def reduce_add(inputs, destination=None):
|
|
"Reduces tensors from multiple GPUs and returns a result a specified device"
|
|
# TODO: try to find an input on another gpu, copy it,
|
|
# and accumulate into the copy
|
|
input_size = inputs[0].size()
|
|
for i, inp in enumerate(inputs):
|
|
assert inp.is_cuda, "reduce_add expects all inputs to be on GPUs"
|
|
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 destination is None:
|
|
destination = torch.cuda.current_device()
|
|
with torch.cuda.device(destination):
|
|
result = type(inp)(input_size).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)
|
|
return result
|
|
|
|
for inp in inputs:
|
|
input_correct_gpu = inp.cuda(result.get_device())
|
|
result.add_(input_correct_gpu)
|
|
return result
|
|
|
|
|
|
def scatter(tensor, devices, chunk_sizes=None, dim=0):
|
|
"Scatters tensor across multiple GPUs"
|
|
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)]
|
|
# TODO: copy to a pinned buffer first (if copying from CPU)
|
|
return tuple(chunk.cuda(gpu_id, async=chunk.is_contiguous())
|
|
for gpu_id, chunk in zip(devices, chunks))
|
|
|
|
|
|
def gather(tensors, dim=0, destination=None):
|
|
"""Gathers tensors from multiple GPUs (destination == -1, places the result
|
|
on CPU)
|
|
"""
|
|
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 = getattr(torch, type(tensors[0]).__name__)(expected_size)
|
|
else:
|
|
with torch.cuda.device(destination):
|
|
result = type(tensors[0])(expected_size)
|
|
|
|
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
|