Manually set autograd params for reduced memory

benchmark.py

@@ -55,15 +55,15 @@ def iteration(input_data):
  target_size = torch.IntTensor(batch_size).fill_(int((seconds * 100) / 2))
  input_percentages = torch.IntTensor(batch_size).fill_(1)
 
- inputs = Variable(input_data)
- target_sizes = Variable(target_size)
- targets = Variable(target)
+ inputs = Variable(input_data, requires_grad=False)
+ target_sizes = Variable(target_size requires_grad=False)
+ targets = Variable(target requires_grad=False)
  start = time.time()
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
 
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int() requires_grad=False)
  loss = criterion(out, targets, sizes, target_sizes)
  loss = loss / inputs.size(0) # average the loss by minibatch
  # compute gradient

predict.py

@@ -26,7 +26,7 @@
  parser = SpectrogramParser(audio_conf, normalize=True)
  spect = parser.parse_audio(args.audio_path).contiguous()
  spect = spect.view(1, 1, spect.size(0), spect.size(1))
- out = model(Variable(spect))
+ out = model(Variable(spect, volatile=True))
  out = out.transpose(0, 1) # TxNxH
  decoded_output = decoder.decode(out.data)
  print(decoded_output[0])

test.py

@@ -34,7 +34,7 @@
  for i, (data) in enumerate(test_loader):
  inputs, targets, input_percentages, target_sizes = data
 
- inputs = Variable(inputs)
+ inputs = Variable(inputs, volatile=True)
 
  # unflatten targets
  split_targets = []
@@ -49,7 +49,7 @@
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), volatile=True)
 
  decoded_output = decoder.decode(out.data, sizes)
  target_strings = decoder.process_strings(decoder.convert_to_strings(split_targets))

train.py

@@ -177,9 +177,9 @@ def main():
  inputs, targets, input_percentages, target_sizes = data
  # measure data loading time
  data_time.update(time.time() - end)
- inputs = Variable(inputs)
- target_sizes = Variable(target_sizes)
- targets = Variable(targets)
+ inputs = Variable(inputs, requires_grad=False)
+ target_sizes = Variable(target_sizes, requires_grad=False)
+ targets = Variable(targets. reqiores_grad=False)
 
  if args.cuda:
  inputs = inputs.cuda()
@@ -188,7 +188,7 @@ def main():
  out = out.transpose(0, 1) # TxNxH
 
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
 
  loss = criterion(out, targets, sizes, target_sizes)
  loss = loss / inputs.size(0) # average the loss by minibatch
@@ -243,7 +243,7 @@ def main():
  for i, (data) in enumerate(test_loader): # test
  inputs, targets, input_percentages, target_sizes = data
 
- inputs = Variable(inputs)
+ inputs = Variable(inputs, volatile=True)
 
  # unflatten targets
  split_targets = []
@@ -258,7 +258,7 @@ def main():
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), volatile=True)
 
  decoded_output = decoder.decode(out.data, sizes)
  target_strings = decoder.process_strings(decoder.convert_to_strings(split_targets))