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import torch
import torch.distributed as dist
from torch.autograd import Function
# The two imports below are not always available depending on the
# USE_DISTRIBUTED compile flag. Make sure they raise import error
# if we're trying to use them.
from torch.distributed import group, ReduceOp
def broadcast(tensor, src, group=group.WORLD):
"""
Broadcasts the tensor to the whole group.
``tensor`` must have the same number of elements in all processes
participating in the collective.
Arguments:
tensor (Tensor): Data to be sent if ``src`` is the rank of current
process.
src (int): Source rank.
group (ProcessGroup, optional): The process group to work on.
Returns:
Tensor: Received tensor from the broadcast op.
"""
return _Broadcast.apply(src, group, tensor)
def gather(tensor, dst=0, group=group.WORLD):
"""
Gathers a list of tensors in a single process.
Arguments:
tensor (Tensor): Input tensor.
dst (int, optional): Destination rank (default is 0).
group (ProcessGroup, optional): The process group to work on.
Returns:
tuple[Tensor]: List of appropriately-sized tensors with the gathered data.
"""
return _Gather.apply(dst, group, tensor)
def scatter(tensors, src=0, group=group.WORLD):
"""
Scatters a list of tensors to all processes in a group.
Each process will receive exactly one tensor and store its data in the
``tensor`` argument.
Arguments:
tensors (list[Tensor]): List of tensors to scatter on the source rank.
Receivers must pass ``None`.
src (int, optional): Source rank (default is 0).
group (ProcessGroup, optional): The process group to work on.
Returns:
Tensor: Output tensor from the scatter operation.
"""
return _Scatter.apply(src, group, *tensors)
def reduce(tensor, dst, op=ReduceOp.SUM, group=group.WORLD):
"""
Reduces the tensor data across all machines.
Only the process with rank ``dst`` is going to receive the final result.
Arguments:
tensor (Tensor): Input of the collective.
dst (int): Destination rank.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on.
Returns:
Tensor: Output of the collective.
"""
return _Reduce.apply(dst, op, group, tensor)
def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=group.WORLD):
"""
Reduces, then scatters a list of tensors to all processes in a group.
Arguments:
output (Tensor): Output tensor.
input_list (list[Tensor]): List of tensors to reduce and scatter.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on.
Returns:
Tensor: Output of the collective.
"""
return _Reduce_Scatter.apply(op, group, output, *input_list)
def all_gather(tensor, group=group.WORLD):
"""
Gathers tensors from the whole group in a list.
Arguments:
tensor (Tensor): Tensor to be broadcast from current process.
group (ProcessGroup, optional): The process group to work on.
Returns:
tuple([Tensor]): Output of the collective.
"""
return _AllGather.apply(group, tensor)
def _all_gather_base(output_tensor, input_tensor, group=group.WORLD):
"""
Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
Args:
output_tensor (Tensor): Output tensor. It should contain
correctly-sized tensors to be used for output of the collective.
input_tensor (Tensor): Tensor to be broadcast from current process.
group (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
Examples:
>>> # All tensors below are of torch.int64 dtype.
>>> # We have 2 process groups, 2 ranks.
>>> # xdoctest: +SKIP("incorrect want text")
>>> output_tensor = torch.zeros(2, dtype=torch.int64)
>>> output_tensor
[tensor([0, 0])] # Rank 0 and 1
>>> tensor = torch.arange(1, dtype=torch.int64) + 1 + rank
>>> tensor
tensor([1]) # Rank 0
tensor([2]) # Rank 1
>>> dist.all_gather_base(output_tensor, tensor)
>>> output_tensor
tensor([1,2]) # Rank 0
tensor([1,2]) # Rank 1
.. warning::
`_all_gather_base` is experimental and subject to change.
It is the caller's responsibility to ensure the output_tensor
is correctly sized.
"""
return _AllGatherBase.apply(output_tensor, input_tensor, group)
def all_to_all(output_tensor_list, input_tensor_list, group=group.WORLD):
"""
Each process scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
Arguments:
output_tensor_list (list[Tensor]): list of tensors to gather one per rank.
input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
group (ProcessGroup, optional): The process group to work on.
Returns:
tuple([Tensor]): Output of the collective.
"""
return _AlltoAll.apply(group, output_tensor_list, *input_tensor_list)
def all_to_all_single(
output,
input,
output_split_sizes=None,
input_split_sizes=None,
group=group.WORLD,
):
"""
Each process splits input tensor and then scatters the split list to all processes in a group.
Then concatenate the received tensors from all the processes in the group and return single output tensor.
Arguments:
output (Tensor): Gathered concatenated output tensor.
input (Tensor): Input tensor to scatter.
output_split_sizes: (list[Int], optional): Output split sizes for dim 0
if specified None or empty, dim 0 of ``output`` tensor must divide
equally by ``world_size``.
input_split_sizes: (list[Int], optional): Input split sizes for dim 0
if specified None or empty, dim 0 of ``input`` tensor must divide
equally by ``world_size``.
Returns:
Tensor: Output of the collective.
"""
return _AlltoAllSingle.apply(
group, output, output_split_sizes, input_split_sizes, input
)
def all_reduce(tensor, op=ReduceOp.SUM, group=group.WORLD):
"""
Reduces the tensor data across all machines in such a way that all get the final result.
After the call the returned tensor is going to be bitwise
identical in all processes.
Arguments:
tensor (Tensor): Input of the collective.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on.
Returns:
Tensor: Output of the collective
"""
return _AllReduce.apply(op, group, tensor)
class _Broadcast(Function):
@staticmethod
def forward(ctx, src, group, tensor):
ctx.src = src
ctx.group = group
ctx.rank = dist.get_rank(group=group)
# torch.distributed makes all the calls in place
# we allocate new tensors to avoid this
tensor = tensor.clone()
dist.broadcast(tensor, src, group=group)
return tensor
@staticmethod
def backward(ctx, grad_output):
gx = _Reduce.apply(ctx.src, ReduceOp.SUM, ctx.group, grad_output)
if ctx.src != ctx.rank:
gx.zero_()
return (None, None, gx)
class _Gather(Function):
@staticmethod
def forward(ctx, dst, group, tensor):
ctx.dst = dst
ctx.group = group
# Need to create a list of tensors here to do the
# aggregation, get it from the group size
# tensor should be correctly sized for the method
# gathering
tensor_list = [
torch.zeros_like(tensor) for i in range(dist.get_world_size(group=group))
]
tensor = tensor.contiguous()
if dist.get_rank(group=group) == dst:
dist.gather(tensor, tensor_list, dst, group=group)
else:
dist.gather(tensor, None, dst, group=group)
return tuple(tensor_list)
@staticmethod
def backward(ctx, *grad_outputs):
return (None, None) + (_Scatter.apply(ctx.dst, ctx.group, *grad_outputs),)
class _Scatter(Function):
@staticmethod
def forward(ctx, src, group, *tensors):
ctx.src = src
ctx.group = group
assert all(t.size() == tensors[0].size() for t in tensors)
output = torch.zeros_like(tensors[0])
if dist.get_rank(group=group) == src:
dist.scatter(output, list(tensors), src, group=group)
else:
dist.scatter(output, None, src, group=group)
return output
@staticmethod
def backward(ctx, grad_output):
return (None, None) + _Gather.apply(ctx.src, ctx.group, grad_output)
class _Reduce(Function):
@staticmethod
def forward(ctx, src, op, group, tensor):
ctx.src = src
ctx.group = group
tensor = tensor.clone()
dist.reduce(tensor, src, op=op, group=group)
return tensor
@staticmethod
def backward(ctx, grad_output):
return (None, None, None) + (_Broadcast.apply(ctx.src, ctx.group, grad_output),)
class _Reduce_Scatter(Function):
@staticmethod
def forward(ctx, op, group, tensor, *input_tensor_list):
ctx.group = group
# Need contiguous tensors for collectives.
tensor = tensor.contiguous()
input_tensor_list = tuple(t.contiguous() for t in input_tensor_list)
dist.reduce_scatter(tensor, list(input_tensor_list), op=op, group=group)
return tensor
@staticmethod
def backward(ctx, grad_output):
return (None, None, None) + _AllGather.apply(ctx.group, grad_output)
class _AllGather(Function):
@staticmethod
def forward(ctx, group, tensor):
# Need contiguous tensors for collectives.
tensor = tensor.contiguous()
ctx.group = group
out_tensor_list = [
torch.empty_like(tensor) for _ in range(dist.get_world_size(group=group))
]
dist.all_gather(out_tensor_list, tensor, group=group)
return tuple(out_tensor_list)
@staticmethod
def backward(ctx, *grad_outputs):
if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
rank = dist.get_rank(group=ctx.group)
gx = torch.empty_like(grad_outputs[rank])
gx = _Reduce_Scatter.apply(ReduceOp.SUM, ctx.group, gx, *grad_outputs)
else:
# As many backends doesn't support ReduceScatter, we use AlltoAll with .sum()
# to emulate the ReduceScatter behavior
tensor_list = [torch.empty_like(tensor) for tensor in grad_outputs]
gxs = _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
gx = torch.sum(torch.stack(gxs), dim=0)
return (None, gx)
class _AllGatherBase(Function):
@staticmethod
def forward(ctx, output_tensor, input_tensor, group):
ctx.group = group
dist._all_gather_base(output_tensor, input_tensor.contiguous(), group=group)
return output_tensor
@staticmethod
def backward(ctx, grad_output):
if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
world_size = dist.get_world_size(group=ctx.group)
out_size = list(grad_output.size())
if out_size[0] % world_size != 0:
raise RuntimeError(
f'Tensor with dimensions: {out_size} does '
f'not have first dimension divisible by world_size: {world_size}'
)
out_size[0] = out_size[0] // dist.get_world_size(group=ctx.group)
gx = torch.empty(out_size, device=grad_output.device, dtype=grad_output.dtype)
dist._reduce_scatter_base(gx, grad_output, ReduceOp.SUM, ctx.group)
else:
raise RuntimeError("Backend not supported!")
return (None, gx, None)
class _AlltoAll(Function):
@staticmethod
def forward(ctx, group, out_tensor_list, *tensors):
ctx.group = group
ctx.input_tensor_size_list = [
tensors[i].size() for i in range(dist.get_world_size(group=group))
]
my_rank = dist.get_rank(group=group)
tensors = tuple(t.contiguous() for t in tensors)
# Implement it on means of scatter/gather, send/recv async operations have issues
if dist.get_backend(group=group) is dist.Backend.GLOO:
for i in range(dist.get_world_size(group=group)):
to_send = None
if i == my_rank:
to_send = list(tensors)
dist.scatter(out_tensor_list[i], to_send, i, group=group)
else:
dist.all_to_all(
out_tensor_list,
list(tensors),
group=group,
)
return tuple(out_tensor_list)
@staticmethod
def backward(ctx, *grad_outputs):
tensor_list = [
torch.empty(size, device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
for size in ctx.input_tensor_size_list
]
return (None, None) + _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
class _AlltoAllSingle(Function):
@staticmethod
def forward(ctx, group, output, output_split_sizes, input_split_sizes, input):
ctx.group = group
ctx.input_size = input.size()
ctx.output_split_sizes = input_split_sizes
ctx.input_split_sizes = output_split_sizes
dist.all_to_all_single(
output,
input,
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=group,
)
return output
@staticmethod
def backward(ctx, grad_output):
tensor = torch.empty(ctx.input_size, device=grad_output.device, dtype=grad_output.dtype)
return (None, None, None, None) + (
_AlltoAllSingle.apply(
ctx.group,
tensor,
ctx.output_split_sizes,
ctx.input_split_sizes,
grad_output.contiguous(),
),
)
class _AllReduce(Function):
@staticmethod
def forward(ctx, op, group, tensor):
ctx.group = group
ctx.op = op
tensor = tensor.clone()
dist.all_reduce(tensor, op=op, group=group)
return tensor
@staticmethod
def backward(ctx, grad_output):
return (None, None) + (_AllReduce.apply(ctx.op, ctx.group, grad_output),)
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