cleanup deltas

src/transformers/modeling_bart.py

@@ -640,9 +640,8 @@ class SelfAttention(nn.Module):
             reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool)
             attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-        attn_weights_float = F.softmax(attn_weights, dim=-1)
-        attn_probs = F.dropout(attn_weights_float, p=self.dropout, training=self.training,)
-        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training,)
 
         assert v is not None
         attn_output = torch.bmm(attn_probs, v)

tests/test_modeling_bart.py

@@ -243,15 +243,15 @@ class BartHeadTests(unittest.TestCase):
             decoder_ffn_dim=32,
             max_position_embeddings=48,
         )
-        lm_model = BartForMaskedLM(config).to(torch_device)
-        context = _long_tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]])
-        summary = _long_tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]])
+        lm_model = BartForMaskedLM(config)
+        context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long()
+        summary = torch.Tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]).long()
         logits, enc_features = lm_model.forward(input_ids=context, decoder_input_ids=summary)
         expected_shape = (*summary.shape, config.vocab_size)
         self.assertEqual(logits.shape, expected_shape)
 
     def test_generate_beam_search(self):
-        input_ids = _long_tensor([[71, 82, 2], [68, 34, 2]])
+        input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long()
         config = BartConfig(
             vocab_size=self.vocab_size,
             d_model=24,
@@ -264,7 +264,7 @@ class BartHeadTests(unittest.TestCase):
             max_position_embeddings=48,
             output_past=True,
         )
-        lm_model = BartForMaskedLM(config).to(torch_device)
+        lm_model = BartForMaskedLM(config)
         lm_model.eval()
 
         new_input_ids = lm_model.generate(