Datasets:
File size: 2,309 Bytes
453129d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | from typing import List, Optional
import torch
import torch.distributed as dist
def _local_pth_sum(grad_tensors: List[torch.Tensor], p: float) -> torch.Tensor:
dev = None
acc = None
for g in grad_tensors:
if g is None:
continue
g_local = g
if dev is None:
dev = g_local.device
acc = torch.tensor(0.0, device=dev, dtype=torch.float32)
gn = torch.norm(g_local.detach().to(torch.float32), p=p)
acc = acc + (gn ** p)
if acc is None:
acc = torch.tensor(
0.0,
device=next((t.device for t in grad_tensors if t is not None), torch.device("cuda", 0)),
dtype=torch.float32,
)
return acc
def _fsdp2_reduce_group(
grad_tensors: List[torch.Tensor],
norm_type: float,
reduce_groups: List[tuple],
) -> torch.Tensor:
p = float(norm_type)
val = _local_pth_sum(grad_tensors, p)
for _, group in reduce_groups:
if group is not None:
dist.all_reduce(val, op=dist.ReduceOp.SUM, group=group)
return val
def solution(
non_ep_grad_tensors: List[torch.Tensor],
ep_grad_tensors: List[torch.Tensor],
max_norm: float,
norm_type: float = 2.0,
ep_size: int = 1,
fsdp_group: Optional[dist.ProcessGroup] = None,
ep_fsdp_group: Optional[dist.ProcessGroup] = None,
ep_group: Optional[dist.ProcessGroup] = None,
) -> torch.Tensor:
if ep_size > 1 and ep_grad_tensors:
scale = 1.0 / float(ep_size)
for t in ep_grad_tensors:
if t is not None:
t.detach().mul_(scale)
non_ep_total = _fsdp2_reduce_group(
non_ep_grad_tensors,
norm_type=norm_type,
reduce_groups=[("fsdp", fsdp_group)],
)
ep_total = _fsdp2_reduce_group(
ep_grad_tensors,
norm_type=norm_type,
reduce_groups=[("ep_fsdp", ep_fsdp_group), ("ep", ep_group)],
)
total_norm = (non_ep_total + ep_total) ** (1.0 / float(norm_type))
if total_norm > max_norm:
coef = (max_norm / total_norm)
for t in non_ep_grad_tensors:
if t is not None:
t.mul_(coef.to(t.device))
for t in ep_grad_tensors:
if t is not None:
t.mul_(coef.to(t.device))
return total_norm
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