Merge branch 'master' of https://github.com/huggingface/pytorch-pretrained-BERT

modeling_pytorch.py

@@ -494,9 +494,22 @@ class BertForQuestionAnswering(nn.Module):
         start_logits, end_logits = logits.split(1, dim=-1)
 
         if start_positions is not None and end_positions is not None:
-            loss_fct = CrossEntropyLoss()
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
+            #loss_fct = CrossEntropyLoss()
+            #start_loss = loss_fct(start_logits, start_positions)
+            #end_loss = loss_fct(end_logits, end_positions)
+            batch_size, seq_length = input_ids.size()
+            
+            def compute_loss(logits, positions):
+                max_position = positions.max().item()
+                one_hot = torch.FloatTensor(batch_size, max(max_position, seq_length) +1).zero_()
+                one_hot = one_hot.scatter(1, positions.cpu(), 1) # Second argument need to be LongTensor and not cuda.LongTensor
+                one_hot = one_hot[:, :seq_length].to(input_ids.device)
+                log_probs = nn.functional.log_softmax(logits, dim = -1).view(batch_size, seq_length)
+                loss = -torch.mean(torch.sum(one_hot*log_probs), dim = -1)
+                return loss
+            
+            start_loss = compute_loss(start_logits, start_positions)
+            end_loss = compute_loss(end_logits, end_positions)
             total_loss = (start_loss + end_loss) / 2
             return total_loss, (start_logits, end_logits)
         else:

optimization_test_pytorch.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import optimization_pytorch as optimization
+import torch
+import unittest
+
+
+class OptimizationTest(unittest.TestCase):
+
+    def assertListAlmostEqual(self, list1, list2, tol):
+        self.assertEqual(len(list1), len(list2))
+        for a, b in zip(list1, list2):
+            self.assertAlmostEqual(a, b, delta=tol)
+
+    def test_adam(self):
+        w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
+        x = torch.tensor([0.4, 0.2, -0.5])
+        criterion = torch.nn.MSELoss(reduction='elementwise_mean')
+        optimizer = optimization.BERTAdam(params={w}, lr=0.2, schedule='warmup_linear', warmup=0.1, t_total=100)
+        for _ in range(100):
+            # TODO Solve: reduction='elementwise_mean'=True not taken into account so division by x.size(0) is necessary
+            loss = criterion(x, w) / x.size(0)
+            loss.backward()
+            optimizer.step()
+        self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
+
+
+if __name__ == "__main__":
+    unittest.main()