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[fix] fix evaluation loop to allow inference #2

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[fix] fix evaluation loop to allow inference #2

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2 changes: 1 addition & 1 deletion mmf/trainers/core/evaluation_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ def prediction_loop(self, dataset_type: str) -> None:

for batch in tqdm.tqdm(dataloader):
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, torch.device("cuda"))
prepared_batch = to_device(prepared_batch, self.device)
with torch.cuda.amp.autocast(enabled=self.training_config.fp16):
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
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16 changes: 8 additions & 8 deletions mmf/utils/distributed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@

import torch
from torch import distributed as dist
try:
try:
import torch_xla.core.xla_model as xm
except ImportError:
xm = None
Expand Down Expand Up @@ -82,10 +82,10 @@ def broadcast_tensor(tensor, src=0):
if is_xla():
tensor = xm.all_to_all(
tensor.repeat([world_size,1]),
split_dimension=0,
concat_dimension=0,
split_count=world_size)[0]
else:
split_dimension=0,
concat_dimension=0,
split_count=world_size)[0]
else:
dist.broadcast(tensor, src=0)

return tensor
Expand Down Expand Up @@ -128,9 +128,9 @@ def gather_tensor(tensor):
if is_xla():
tensor_list = xm.all_gather(tensor)
tensor_list = tensor_list.view(world_size, *tensor.size())
else:
else:
dist.all_gather(tensor_list, tensor)
tensor_list = torch.stack(tensor_list, dim=0)
tensor_list = torch.stack(tuple(tensor_list), dim=0)
return tensor_list


Expand All @@ -151,7 +151,7 @@ def reduce_dict(dictionary):
[values],
scale=1.0/world_size
)[0]
else:
else:
dist.reduce(values, dst=0)
if dist.get_rank() == 0:
# only main process gets accumulated, so only divide by
Expand Down