Remove more Variable() calls (#198)

fairseq/models/fconv.py

@@ -633,6 +633,7 @@ def fconv_lm_dauphin_wikitext103(args):
     args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,20000,200000')
     base_lm_architecture(args)
 
+
 @register_model_architecture('fconv_lm', 'fconv_lm_dauphin_gbw')
 def fconv_lm_dauphin_gbw(args):
     layers = '[(512, 5)]'

interactive.py

@@ -11,7 +11,6 @@ import numpy as np
 import sys
 
 import torch
-from torch.autograd import Variable
 
 from fairseq import data, options, tasks, tokenizer, utils
 from fairseq.sequence_generator import SequenceGenerator
@@ -131,8 +130,8 @@ def main(args):
             lengths = lengths.cuda()
 
         translations = translator.generate(
-            Variable(tokens),
-            Variable(lengths),
+            tokens,
+            lengths,
             maxlen=int(args.max_len_a * tokens.size(1) + args.max_len_b),
         )
 

tests/test_convtbc.py

@@ -9,7 +9,6 @@ import torch
 import unittest
 from fairseq.modules import ConvTBC
 import torch.nn as nn
-from torch.autograd import Variable
 
 
 class TestConvTBC(unittest.TestCase):
@@ -23,8 +22,9 @@ class TestConvTBC(unittest.TestCase):
         conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2))
         conv_tbc.bias.data.copy_(conv1d.bias.data)
 
-        input_tbc = Variable(torch.randn(7, 2, 4), requires_grad=True)
-        input1d = Variable(input_tbc.data.transpose(0, 1).transpose(1, 2), requires_grad=True)
+        input_tbc = torch.randn(7, 2, 4, requires_grad=True)
+        input1d = input_tbc.data.transpose(0, 1).transpose(1, 2)
+        input1d.requires_grad = True
 
         output_tbc = conv_tbc(input_tbc)
         output1d = conv1d(input1d)

tests/test_dictionary.py

@@ -11,7 +11,7 @@ import unittest
 import torch
 
 from fairseq.data import Dictionary
-from fairseq.tokenizer import Tokenizer, tokenize_line
+from fairseq.tokenizer import Tokenizer
 
 
 class TestDictionary(unittest.TestCase):

tests/test_sequence_generator.py

@@ -9,7 +9,6 @@ import argparse
 import unittest
 
 import torch
-from torch.autograd import Variable
 
 from fairseq.sequence_generator import SequenceGenerator
 
@@ -29,11 +28,11 @@ class TestSequenceGenerator(unittest.TestCase):
         self.w2 = 5
 
         # construct source data
-        self.src_tokens = Variable(torch.LongTensor([
+        self.src_tokens = torch.LongTensor([
             [self.w1, self.w2, self.eos],
             [self.w1, self.w2, self.eos],
-        ]))
-        self.src_lengths = Variable(torch.LongTensor([2, 2]))
+        ])
+        self.src_lengths = torch.LongTensor([2, 2])
 
         args = argparse.Namespace()
         unk = 0.

tests/test_utils.py

@@ -8,7 +8,6 @@
 import unittest
 
 import torch
-from torch.autograd import Variable
 
 from fairseq import utils
 

tests/utils.py

@@ -6,7 +6,6 @@
 # can be found in the PATENTS file in the same directory.
 
 import torch
-from torch.autograd import Variable
 
 from fairseq import utils
 from fairseq.data import Dictionary
@@ -156,7 +155,7 @@ class TestIncrementalDecoder(FairseqIncrementalDecoder):
         # random attention
         attn = torch.rand(bbsz, tgt_len, src_len)
 
-        return Variable(probs), Variable(attn)
+        return probs, attn
 
     def get_normalized_probs(self, net_output, log_probs, _):
         # the decoder returns probabilities directly