1.为element测试添加torch标签 2.解决torch版本导致的int张量无法对整数使用除法的问题

fastNLP/modules/torch/generator/seq2seq_generator.py

@@ -368,13 +368,13 @@ def _beam_search_generate(decoder: Seq2SeqDecoder, tokens=None, state=None, max_
             next_scores, ids = _scores.topk(2 * num_beams, dim=1, largest=True, sorted=True)
             _tokens = _tokens.view(batch_size, num_beams * (num_beams + 1))
             next_tokens = _tokens.gather(dim=1, index=ids)  # (batch_size, 2*num_beams)
-            from_which_beam = torch.floor(ids / (num_beams + 1)).long()  # (batch_size, 2*num_beams)
+            from_which_beam = torch.floor(ids.float() / (num_beams + 1)).long()  # (batch_size, 2*num_beams)
         else:
             scores = F.log_softmax(scores, dim=-1)  # (batch_size * num_beams, vocab_size)
             _scores = scores + beam_scores[:, None]  # (batch_size * num_beams, vocab_size)
             _scores = _scores.view(batch_size, -1)  # (batch_size, num_beams*vocab_size)
             next_scores, ids = torch.topk(_scores, 2 * num_beams, dim=1, largest=True, sorted=True)  # (bsz, 2*num_beams)
-            from_which_beam = torch.floor(ids / vocab_size).long()  # (batch_size, 2*num_beams)
+            from_which_beam = torch.floor(ids.float() / vocab_size).long()  # (batch_size, 2*num_beams)
             next_tokens = ids % vocab_size  # (batch_size, 2*num_beams)
 
         #  接下来需要组装下一个batch的结果。

tests/core/metrics/test_element_cal_element.py

@@ -27,6 +27,7 @@ class MyMetric(Metric):
 
 class TestElemnt:
 
+    @pytest.mark.torch
     def test_case_v1(self):
         pred = torch.tensor([1, 1, 1, 1])
         metric = MyMetric()