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import torch
import torch.distributed as dist
from torch import Tensor
from torch.distributed import ProcessGroup
def _pad_tensor(x: Tensor, dim: int, padding_size: int, padding_value: int = 0) -> Tensor:
shape = list(x.shape)
shape[dim] = padding_size
pad = torch.full(shape, padding_value, dtype=x.dtype, device=x.device)
return torch.cat([x, pad], dim=dim)
def _unpad_tensor(x: Tensor, dim: int, padding_size: int) -> Tensor:
slc = [slice(None)] * len(x.shape)
slc[dim] = slice(0, -padding_size)
return x[tuple(slc)]
def _all_to_all_single(
x: Tensor,
scatter_dim: int,
gather_dim: int,
group: Optional[dist.ProcessGroup] = None,
async_op: bool = False,
):
group = group or dist.group.WORLD
sp_world_size = dist.get_world_size(group)
assert scatter_dim <= 1, "scatter_dim must be 0 or 1 when using all_to_all_single!"
assert gather_dim <= 1, "gather_dim must be 0 or 1 when using all_to_all_single!"
if scatter_dim != 0:
gather_dim_bef = x.shape[gather_dim]
scatter_dim_bef = x.shape[scatter_dim]
x = (
x.reshape(
[gather_dim_bef, sp_world_size, scatter_dim_bef // sp_world_size]
+ list(x.shape[2:])
)
.transpose(0, 1)
.reshape(
[gather_dim_bef * sp_world_size, scatter_dim_bef // sp_world_size]
+ list(x.shape[2:])
)
.contiguous()
)
output = torch.empty_like(x)
comm = dist.all_to_all_single(output, x.contiguous(), group=group, async_op=async_op)
if async_op:
def wait():
comm.wait()
if scatter_dim == 0:
return torch.cat(output.split(x.size(0) // sp_world_size), dim=gather_dim)
else:
return output
return wait
if scatter_dim == 0:
output = torch.cat(output.split(x.size(0) // sp_world_size), dim=gather_dim)
return output
def _all_to_all(
local_input: Tensor,
scatter_dim: int,
gather_dim: int,
group: Optional[dist.ProcessGroup] = None,
async_op: bool = False,
):
group = group or dist.group.WORLD
seq_world_size = dist.get_world_size(group)
input_list = [
t.contiguous()
for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)
]
output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)]
comm = dist.all_to_all(output_list, input_list, group=group, async_op=async_op)
if async_op:
def wait():
comm.wait()
return torch.cat(output_list, dim=gather_dim).contiguous()
return wait
return torch.cat(output_list, dim=gather_dim).contiguous()
def _all_to_all_tensor(
x: Tensor,
scatter_dim: int,
gather_dim: int,
group: dist.ProcessGroup,
async_op: bool = False,
):
if scatter_dim <= 1 and gather_dim <= 1:
return _all_to_all_single(x, scatter_dim, gather_dim, group, async_op)
return _all_to_all(x, scatter_dim, gather_dim, group, async_op)
class _SeqAllToAll(torch.autograd.Function):
@staticmethod
def forward(
ctx: Any,
group: dist.ProcessGroup,
local_input: Tensor,
scatter_dim: int,
gather_dim: int,
async_op: bool,
) -> Tensor:
ctx.group = group
ctx.scatter_dim = scatter_dim
ctx.gather_dim = gather_dim
ctx.async_op = async_op
return _all_to_all_tensor(local_input, scatter_dim, gather_dim, group, async_op)
@staticmethod
def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None, None]:
if ctx.async_op:
input_t = torch.cat(grad_output[1:], dim=ctx.gather_dim).contiguous()
else:
input_t = grad_output[0]
return (
None,
_all_to_all_tensor(
input_t, ctx.gather_dim, ctx.scatter_dim, ctx.group, False
),
None,
None,
None,
)
def gather_seq_scatter_heads_qkv(
qkv_tensor: Tensor,
seq_dim: int,
unpadded_dim_size: Optional[int] = None,
restore_shape: bool = True,
async_op: bool = False,
group: Optional[ProcessGroup] = None,
) -> Tensor:
group = group or dist.group.WORLD
if not group:
return qkv_tensor
sp_world = dist.get_world_size(group)
orig_shape = qkv_tensor.shape
scatter_dim = qkv_tensor.dim()
bef_all2all_shape = list(orig_shape)
qkv_proj_dim = bef_all2all_shape[-1]
bef_all2all_shape = bef_all2all_shape[:-1] + [3, qkv_proj_dim // 3]
qkv_tensor = qkv_tensor.view(bef_all2all_shape)
if async_op:
return _SeqAllToAll.apply(group, qkv_tensor, scatter_dim, seq_dim, async_op)
qkv_tensor = _SeqAllToAll.apply(group, qkv_tensor, scatter_dim, seq_dim, async_op)
if restore_shape:
out_shape = list(orig_shape)
out_shape[seq_dim] *= sp_world
out_shape[-1] = qkv_proj_dim // sp_world
qkv_tensor = qkv_tensor.view(out_shape)
if unpadded_dim_size and unpadded_dim_size % sp_world != 0:
padding_size = qkv_tensor.size(seq_dim) - unpadded_dim_size
qkv_tensor = _unpad_tensor(qkv_tensor, seq_dim, padding_size)
return qkv_tensor
def solution(
qkv_tensor: torch.Tensor,
seq_dim: int,
group: Optional[ProcessGroup] = None,
unpadded_dim_size: Optional[int] = None,
restore_shape: bool = True,
) -> torch.Tensor:
group = group or dist.group.WORLD
return gather_seq_scatter_heads_qkv(
qkv_tensor,
seq_dim=seq_dim,
unpadded_dim_size=unpadded_dim_size or 0,
restore_shape=restore_shape,
async_op=False,
group=group,
)
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