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[FusedDense] Allow Row/ColumnParallelLinear to have uneven split

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This commit is contained in:
Tri Dao 2023-08-16 23:43:35 -07:00
parent bcfa7c9751
commit cb0daccc41
1 changed files with 19 additions and 9 deletions
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28
flash_attn/ops/fused_dense.py
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@ -170,16 +170,21 @@ class ColumnParallelLinear(nn.Linear):
process_group: ProcessGroup, process_group: ProcessGroup,
bias: bool = True, bias: bool = True,
sequence_parallel=True, sequence_parallel=True,
multiple_of=1,
device=None, device=None,
dtype=None, dtype=None,
) -> None: ) -> None:
world_size = torch.distributed.get_world_size(process_group) world_size = torch.distributed.get_world_size(process_group)
if out_features % world_size != 0: if out_features % multiple_of:
raise ValueError( raise ValueError(f"out_features ({out_features}) must be a multiple of {multiple_of}")
f"out_features ({out_features}) must be divisible by " f"world_size ({world_size})" multiple = out_features // multiple_of
) # We want to split @multiple across world_size, but it could be an uneven split
div = multiple // world_size
mod = multiple % world_size
# The first @mod ranks get @div + 1 copies, the rest get @div copies
local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
super().__init__( super().__init__(
in_features, out_features // world_size, bias=bias, device=device, dtype=dtype in_features, local_multiple * multiple_of, bias=bias, device=device, dtype=dtype
) )
self.process_group = process_group self.process_group = process_group
self.sequence_parallel = sequence_parallel self.sequence_parallel = sequence_parallel
@ -205,15 +210,20 @@ class RowParallelLinear(nn.Linear):
process_group: ProcessGroup, process_group: ProcessGroup,
bias: bool = True, bias: bool = True,
sequence_parallel=True, sequence_parallel=True,
multiple_of=1,
device=None, device=None,
dtype=None, dtype=None,
) -> None: ) -> None:
world_size = torch.distributed.get_world_size(process_group) world_size = torch.distributed.get_world_size(process_group)
rank = torch.distributed.get_rank(process_group) rank = torch.distributed.get_rank(process_group)
if in_features % world_size != 0: if in_features % multiple_of:
raise ValueError( raise ValueError(f"in_features ({in_features}) must be a multiple of {multiple_of}")
f"in_features ({in_features}) must be divisible by " f"world_size ({world_size})" multiple = in_features // multiple_of
) # We want to split @multiple across world_size, but it could be an uneven split
div = multiple // world_size
mod = multiple % world_size
# The first @mod ranks get @div + 1 copies, the rest get @div copies
local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
# Only rank 0 will have bias # Only rank 0 will have bias
super().__init__( super().__init__(
in_features // world_size, in_features // world_size,