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Zero
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import torch
import torch.distributed as dist
# ====================
# All-To-All
# ====================
def _all_to_all(
input_: torch.Tensor,
world_size: int,
group: dist.ProcessGroup,
scatter_dim: int,
gather_dim: int,
):
input_list = [t.contiguous() for t in torch.tensor_split(input_, world_size, scatter_dim)]
output_list = [torch.empty_like(input_list[0]) for _ in range(world_size)]
dist.all_to_all(output_list, input_list, group=group)
return torch.cat(output_list, dim=gather_dim).contiguous()
class _AllToAll(torch.autograd.Function):
"""All-to-all communication.
Args:
input_: input matrix
process_group: communication group
scatter_dim: scatter dimension
gather_dim: gather dimension
"""
@staticmethod
def forward(ctx, input_, process_group, scatter_dim, gather_dim):
ctx.process_group = process_group
ctx.scatter_dim = scatter_dim
ctx.gather_dim = gather_dim
ctx.world_size = dist.get_world_size(process_group)
output = _all_to_all(input_, ctx.world_size, process_group, scatter_dim, gather_dim)
return output
@staticmethod
def backward(ctx, grad_output):
grad_output = _all_to_all(
grad_output,
ctx.world_size,
ctx.process_group,
ctx.gather_dim,
ctx.scatter_dim,
)
return (
grad_output,
None,
None,
None,
)
def all_to_all(
input_: torch.Tensor,
process_group: dist.ProcessGroup,
scatter_dim: int = 2,
gather_dim: int = 1,
):
return _AllToAll.apply(input_, process_group, scatter_dim, gather_dim)
def _gather(
input_: torch.Tensor,
world_size: int,
group: dist.ProcessGroup,
gather_dim: int,
):
if gather_list is None:
gather_list = [torch.empty_like(input_) for _ in range(world_size)]
dist.gather(input_, gather_list, group=group, gather_dim=gather_dim)
return gather_list
# ====================
# Gather-Split
# ====================
def _split(input_, pg: dist.ProcessGroup, dim=-1):
# skip if only one rank involved
world_size = dist.get_world_size(pg)
rank = dist.get_rank(pg)
if world_size == 1:
return input_
# Split along last dimension.
dim_size = input_.size(dim)
assert dim_size % world_size == 0, (
f"The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), "
f"cannot split tensor evenly"
)
tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
output = tensor_list[rank].contiguous()
return output
def _gather(input_, pg: dist.ProcessGroup, dim=-1):
# skip if only one rank involved
input_ = input_.contiguous()
world_size = dist.get_world_size(pg)
dist.get_rank(pg)
if world_size == 1:
return input_
# all gather
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
assert input_.device.type == "cuda"
torch.distributed.all_gather(tensor_list, input_, group=pg)
# concat
output = torch.cat(tensor_list, dim=dim).contiguous()
return output
class _GatherForwardSplitBackward(torch.autograd.Function):
"""Gather the input from model parallel region and concatenate.
Args:
input_: input matrix.
process_group: parallel mode.
dim: dimension
"""
@staticmethod
def symbolic(graph, input_):
return _gather(input_)
@staticmethod
def forward(ctx, input_, process_group, dim, grad_scale):
ctx.mode = process_group
ctx.dim = dim
ctx.grad_scale = grad_scale
return _gather(input_, process_group, dim)
@staticmethod
def backward(ctx, grad_output):
if ctx.grad_scale == "up":
grad_output = grad_output * dist.get_world_size(ctx.mode)
elif ctx.grad_scale == "down":
grad_output = grad_output / dist.get_world_size(ctx.mode)
return _split(grad_output, ctx.mode, ctx.dim), None, None, None
class _SplitForwardGatherBackward(torch.autograd.Function):
"""
Split the input and keep only the corresponding chuck to the rank.
Args:
input_: input matrix.
process_group: parallel mode.
dim: dimension
"""
@staticmethod
def symbolic(graph, input_):
return _split(input_)
@staticmethod
def forward(ctx, input_, process_group, dim, grad_scale):
ctx.mode = process_group
ctx.dim = dim
ctx.grad_scale = grad_scale
return _split(input_, process_group, dim)
@staticmethod
def backward(ctx, grad_output):
if ctx.grad_scale == "up":
grad_output = grad_output * dist.get_world_size(ctx.mode)
elif ctx.grad_scale == "down":
grad_output = grad_output / dist.get_world_size(ctx.mode)
return _gather(grad_output, ctx.mode, ctx.dim), None, None, None
def split_forward_gather_backward(input_, process_group, dim, grad_scale=1.0):
return _SplitForwardGatherBackward.apply(input_, process_group, dim, grad_scale)
def gather_forward_split_backward(input_, process_group, dim, grad_scale=None):
return _GatherForwardSplitBackward.apply(input_, process_group, dim, grad_scale)
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