Comments (2)
Thanks for the reply! It works!!!
from vilt.
I can not figure out what causes the hanging now, but all_gathering in set_task
seems to be the source of the problem.
You can make it a non-distributed version by patching set_task
function as below:
def set_task(pl_module):
if not pl_module.training:
pl_module.current_tasks = [
k for k, v in pl_module.hparams.config["loss_names"].items() if v >= 1
]
return
if not check_non_acc_grad(pl_module):
return
sampling_pools = list()
for k, v in pl_module.hparams.config["loss_names"].items():
sampling_pools.extend([k] * int(v))
g = torch.Generator()
g.manual_seed(pl_module.global_step)
idx = torch.randperm(len(sampling_pools), generator=g)[0]
pl_module.current_tasks = sampling_pools[idx]
I think there is no need for all gathering in the first place if we use the code above.
See if the snippet works and please let me know if it handles the issue.
:)
from vilt.
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