Support deprecation of volatile Variables in latest PyTorch

fairseq/multiprocessing_trainer.py

@@ -227,6 +227,7 @@ class MultiprocessingTrainer(MultiprocessingEventLoop):
             self.model.train()
             self.optimizer.zero_grad()
 
+        with utils.maybe_no_grad(eval):
             sample_size, logging_output, oom = 0, {}, False
             if self._sample is not None:
                 try:

fairseq/utils.py

@@ -6,6 +6,7 @@
 # can be found in the PATENTS file in the same directory.
 #
 
+import contextlib
 import logging
 import os
 import torch
@@ -244,3 +245,10 @@ def rstrip_pad(tensor, pad):
     if strip > 0:
         return tensor[:-strip]
     return tensor
+
+
+def maybe_no_grad(condition):
+    if hasattr(torch, 'no_grad') and condition:
+        return torch.no_grad()
+    # no-op context manager
+    return contextlib.ExitStack()

generate.py

@@ -35,6 +35,8 @@ def main():
     print(args)
 
     use_cuda = torch.cuda.is_available() and not args.cpu
+    if hasattr(torch, 'set_grad_enabled'):
+        torch.set_grad_enabled(False)
 
     # Load dataset
     if args.replace_unk is None: