Fix call ordering to ATen addmm and sum (#22)

fairseq/clib/temporal_convolution_tbc/temporal_convolution_tbc.cpp

@@ -67,7 +67,7 @@ extern "C" void TemporalConvolutionTBC_forward(
       auto W = weight[k];
       auto I = input.narrow(0, iShift, t).view({t * batchSize, inputPlanes});
       auto O = output.narrow(0, oShift, t).view({t * batchSize, outputPlanes});
-      at::addmm_out(1, O, 1, I, W, O);
+      O.addmm_(I, W);
     }
   }
 }
@@ -108,7 +108,7 @@ extern "C" void TemporalConvolutionTBC_backward(
     if (t > 0) {
       auto dO = dOutput.narrow(0, oShift, t).view({t * batchSize, outputPlanes});
       auto dI = dInput.narrow(0, iShift, t).view({t * batchSize, inputPlanes});
-      at::addmm_out(1, dI, 1, dO, weight[k].t(), dI);
+      dI.addmm_(dO, weight[k].t());
     }
   }
 
@@ -121,10 +121,10 @@ extern "C" void TemporalConvolutionTBC_backward(
       auto dW = dWeight[k];
       auto dO = dOutput.narrow(0, oShift, t).view({t * batchSize, outputPlanes});
       auto I = input.narrow(0, iShift, t).view({t * batchSize, inputPlanes}).t();
-      at::addmm_out(1, dW, 1, I, dO, dW);
+      dW.addmm_(I, dO);
     }
   }
 
   auto tmp = dOutput.sum(0, false);
-  at::sum_out(tmp, 0, dBias);
+  dBias.assign_(tmp.sum(0));
 }

tests/test_convtbc.py

+# Copyright (c) 2017-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the LICENSE file in
+# the root directory of this source tree. An additional grant of patent rights
+# can be found in the PATENTS file in the same directory.
+#
+
+import torch
+import unittest
+from fairseq.modules import ConvTBC
+import torch.nn as nn
+from torch.autograd import Variable, gradcheck
+
+
+class TestConvTBC(unittest.TestCase):
+
+    def test_convtbc(self):
+        # ksz, in_channels, out_channels
+        conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1)
+        # out_channels, in_channels, ksz
+        conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1)
+
+        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)
+
+        output_tbc = conv_tbc(input_tbc)
+        output1d = conv1d(input1d)
+
+        self.assertAlmostEqual(output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data)
+        
+        grad_tbc = torch.randn(output_tbc.size())
+        grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous()
+
+        output_tbc.backward(grad_tbc)
+        output1d.backward(grad1d)
+
+        self.assertAlmostEqual(conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data)
+        self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data)
+        self.assertAlmostEqual(input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data)
+
+    def assertAlmostEqual(self, t1, t2):
+        self.assertEqual(t1.size(), t2.size(), "size mismatch")
+        self.assertLess((t1 - t2).abs().max(), 1e-4)
+
+
+if __name__ == '__main__':
+    unittest.main()