<h2 dir="auto">🐛 Bug</h2>
<p dir="auto">I noticed that my generated JIT was creating two variables with the exact same parameters.</p>
<p dir="auto">Also: Simply deleting the duplicated lines makes everything faster.</p>
<h2 dir="auto">To Reproduce</h2>
<p dir="auto">Python:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="for (x, l, c) in zip(sources, self.loc, self.conf):
        conf.append(c(x).permute(0, 2, 3, 1).contiguous().view(c(x).size(0), -1, self.num_classes))
        loc.append(l(x).permute(0, 2, 3, 1).contiguous().view(l(x).size(0), -1, 4))"><pre class="notranslate"><span class="pl-k">for</span> (<span class="pl-s1">x</span>, <span class="pl-s1">l</span>, <span class="pl-s1">c</span>) <span class="pl-c1">in</span> <span class="pl-en">zip</span>(<span class="pl-s1">sources</span>, <span class="pl-s1">self</span>.<span class="pl-s1">loc</span>, <span class="pl-s1">self</span>.<span class="pl-s1">conf</span>):
        <span class="pl-s1">conf</span>.<span class="pl-en">append</span>(<span class="pl-en">c</span>(<span class="pl-s1">x</span>).<span class="pl-en">permute</span>(<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>).<span class="pl-en">contiguous</span>().<span class="pl-en">view</span>(<span class="pl-en">c</span>(<span class="pl-s1">x</span>).<span class="pl-en">size</span>(<span class="pl-c1">0</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-s1">self</span>.<span class="pl-s1">num_classes</span>))
        <span class="pl-s1">loc</span>.<span class="pl-en">append</span>(<span class="pl-en">l</span>(<span class="pl-s1">x</span>).<span class="pl-en">permute</span>(<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>).<span class="pl-en">contiguous</span>().<span class="pl-en">view</span>(<span class="pl-en">l</span>(<span class="pl-s1">x</span>).<span class="pl-en">size</span>(<span class="pl-c1">0</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">4</span>))</pre></div>
<p dir="auto">JIT:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="  _78 = torch._convolution(input64, weight56, _66, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _79 = torch._convolution(input64, weight56, _66, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _80 = ops.prim.NumToTensor(torch.size(_79, 0))
  _81 = torch.contiguous(torch.permute(_78, [0, 2, 3, 1]))
  _82 = torch.view(_81, [int(_80), -1, 2])
  _83 = torch._convolution(input64, weight53, _59, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _84 = torch._convolution(input64, weight53, _59, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _85 = ops.prim.NumToTensor(torch.size(_84, 0))
  _86 = torch.contiguous(torch.permute(_83, [0, 2, 3, 1]))
  _87 = torch.view(_86, [int(_85), -1, 4])
  _88 = torch._convolution(input70, weight57, _68, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _89 = torch._convolution(input70, weight57, _68, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _90 = ops.prim.NumToTensor(torch.size(_89, 0))
  _91 = torch.contiguous(torch.permute(_88, [0, 2, 3, 1]))
  _92 = torch.view(_91, [int(_90), -1, 2])
  _93 = torch._convolution(input70, weight54, _61, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _94 = torch._convolution(input70, weight54, _61, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _95 = ops.prim.NumToTensor(torch.size(_94, 0))
  _96 = torch.contiguous(torch.permute(_93, [0, 2, 3, 1]))
  _97 = torch.view(_96, [int(_95), -1, 4])
  _98 = torch._convolution(input76, weight58, _70, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _99 = torch._convolution(input76, weight58, _70, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _100 = ops.prim.NumToTensor(torch.size(_99, 0))
  _101 = torch.contiguous(torch.permute(_98, [0, 2, 3, 1]))
  _102 = torch.view(_101, [int(_100), -1, 2])
  _103 = torch._convolution(input76, weight55, _63, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _104 = torch._convolution(input76, weight55, _63, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _105 = ops.prim.NumToTensor(torch.size(_104, 0))
  _106 = torch.contiguous(torch.permute(_103, [0, 2, 3, 1]))
  _107 = [_87, _97, torch.view(_106, [int(_105), -1, 4])]"><pre class="notranslate">  <span class="pl-s1">_78</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input64</span>, <span class="pl-s1">weight56</span>, <span class="pl-s1">_66</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_79</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input64</span>, <span class="pl-s1">weight56</span>, <span class="pl-s1">_66</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_80</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_79</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_81</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_78</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_82</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_81</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_80</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">2</span>])
  <span class="pl-s1">_83</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input64</span>, <span class="pl-s1">weight53</span>, <span class="pl-s1">_59</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_84</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input64</span>, <span class="pl-s1">weight53</span>, <span class="pl-s1">_59</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_85</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_84</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_86</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_83</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_87</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_86</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_85</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">4</span>])
  <span class="pl-s1">_88</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input70</span>, <span class="pl-s1">weight57</span>, <span class="pl-s1">_68</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_89</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input70</span>, <span class="pl-s1">weight57</span>, <span class="pl-s1">_68</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_90</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_89</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_91</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_88</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_92</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_91</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_90</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">2</span>])
  <span class="pl-s1">_93</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input70</span>, <span class="pl-s1">weight54</span>, <span class="pl-s1">_61</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_94</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input70</span>, <span class="pl-s1">weight54</span>, <span class="pl-s1">_61</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_95</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_94</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_96</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_93</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_97</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_96</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_95</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">4</span>])
  <span class="pl-s1">_98</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input76</span>, <span class="pl-s1">weight58</span>, <span class="pl-s1">_70</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_99</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input76</span>, <span class="pl-s1">weight58</span>, <span class="pl-s1">_70</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_100</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_99</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_101</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_98</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_102</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_101</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_100</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">2</span>])
  <span class="pl-s1">_103</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input76</span>, <span class="pl-s1">weight55</span>, <span class="pl-s1">_63</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_104</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">_convolution</span>(<span class="pl-s1">input76</span>, <span class="pl-s1">weight55</span>, <span class="pl-s1">_63</span>, [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], [<span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-c1">False</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">0</span>], <span class="pl-c1">1</span>, <span class="pl-c1">True</span>, <span class="pl-c1">False</span>, <span class="pl-c1">True</span>)
  <span class="pl-s1">_105</span> <span class="pl-c1">=</span> <span class="pl-s1">ops</span>.<span class="pl-s1">prim</span>.<span class="pl-v">NumToTensor</span>(<span class="pl-s1">torch</span>.<span class="pl-en">size</span>(<span class="pl-s1">_104</span>, <span class="pl-c1">0</span>))
  <span class="pl-s1">_106</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">contiguous</span>(<span class="pl-s1">torch</span>.<span class="pl-en">permute</span>(<span class="pl-s1">_103</span>, [<span class="pl-c1">0</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">1</span>]))
  <span class="pl-s1">_107</span> <span class="pl-c1">=</span> [<span class="pl-s1">_87</span>, <span class="pl-s1">_97</span>, <span class="pl-s1">torch</span>.<span class="pl-en">view</span>(<span class="pl-s1">_106</span>, [<span class="pl-en">int</span>(<span class="pl-s1">_105</span>), <span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">4</span>])]</pre></div>
<p dir="auto">See _78, _79 and _83,_84 for example.</p>
<p dir="auto">Simplified code:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="  _78 = torch._convolution(input64, weight56, _66, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _batch = int(ops.prim.NumToTensor(torch.size(_78, 0)))
  _81 = torch.contiguous(torch.permute(_78, [0, 2, 3, 1]))
  _82 = torch.view(_81, [_batch, -1, 2])
  _83 = torch._convolution(input64, weight53, _59, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _86 = torch.contiguous(torch.permute(_83, [0, 2, 3, 1]))
  _87 = torch.view(_86, [_batch, -1, 4])
  _88 = torch._convolution(input70, weight57, _68, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _91 = torch.contiguous(torch.permute(_88, [0, 2, 3, 1]))
  _92 = torch.view(_91, [_batch, -1, 2])
  _93 = torch._convolution(input70, weight54, _61, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _96 = torch.contiguous(torch.permute(_93, [0, 2, 3, 1]))
  _97 = torch.view(_96, [_batch, -1, 4])
  _98 = torch._convolution(input76, weight58, _70, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _101 = torch.contiguous(torch.permute(_98, [0, 2, 3, 1]))
  _102 = torch.view(_101, [_batch, -1, 2])
  _103 = torch._convolution(input76, weight55, _63, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _106 = torch.contiguous(torch.permute(_103, [0, 2, 3, 1]))
  _107 = [_87, _97, torch.view(_106, [_batch, -1, 4])]"><pre class="notranslate"><code class="notranslate">  _78 = torch._convolution(input64, weight56, _66, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _batch = int(ops.prim.NumToTensor(torch.size(_78, 0)))
  _81 = torch.contiguous(torch.permute(_78, [0, 2, 3, 1]))
  _82 = torch.view(_81, [_batch, -1, 2])
  _83 = torch._convolution(input64, weight53, _59, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _86 = torch.contiguous(torch.permute(_83, [0, 2, 3, 1]))
  _87 = torch.view(_86, [_batch, -1, 4])
  _88 = torch._convolution(input70, weight57, _68, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _91 = torch.contiguous(torch.permute(_88, [0, 2, 3, 1]))
  _92 = torch.view(_91, [_batch, -1, 2])
  _93 = torch._convolution(input70, weight54, _61, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _96 = torch.contiguous(torch.permute(_93, [0, 2, 3, 1]))
  _97 = torch.view(_96, [_batch, -1, 4])
  _98 = torch._convolution(input76, weight58, _70, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _101 = torch.contiguous(torch.permute(_98, [0, 2, 3, 1]))
  _102 = torch.view(_101, [_batch, -1, 2])
  _103 = torch._convolution(input76, weight55, _63, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, True, False, True)
  _106 = torch.contiguous(torch.permute(_103, [0, 2, 3, 1]))
  _107 = [_87, _97, torch.view(_106, [_batch, -1, 4])]
</code></pre></div>
<h2 dir="auto">Expected behavior</h2>
<p dir="auto">Only a single convolution</p>
<h2 dir="auto">Environment</h2>
<p dir="auto">Please copy and paste the output from our<br>
<a href="https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py" rel="nofollow">environment collection script</a><br>
(or fill out the checklist below manually).</p>
<p dir="auto">You can get the script and run it with:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py"><pre class="notranslate"><code class="notranslate">wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
</code></pre></div>
<ul dir="auto">
<li>PyTorch Version (e.g., 1.0): 1.2.0</li>
<li>OS (e.g., Linux): Linux</li>
<li>How you installed PyTorch (<code class="notranslate">conda</code>, <code class="notranslate">pip</code>, source): pip</li>
<li>Build command you used (if compiling from source): N/A</li>
<li>Python version: 3.6</li>
<li>CUDA/cuDNN version: 10.0/7.5</li>
<li>GPU models and configuration: Titan V</li>
<li>Any other relevant information:</li>
</ul>
<h2 dir="auto"><g-emoji class="g-emoji" alias="question" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/2753.png">❓</g-emoji> Questions and Help</h2>
<h3 dir="auto">Please note that this issue tracker is not a help form and this issue will be closed.</h3>
<p dir="auto">We have a set of <a href="https://pytorch.org/resources" rel="nofollow">listed resources available on the website</a>. Our primary means of support is our discussion forum:</p>
<ul dir="auto">
<li><a href="https://discuss.pytorch.org/" rel="nofollow">Discussion Forum</a></li>
</ul>