Add mbart-large-cc25, support translation finetuning (#5129)

improve unittests for finetuning, especially w.r.t testing frozen parameters
fix freeze_embeds for T5
add streamlit setup.cfg

examples/seq2seq/test_seq2seq_examples.py

@@ -223,10 +223,30 @@ def test_finetune(model):
         output_dir=output_dir,
         do_predict=True,
         task=task,
+        src_lang="en_XX",
+        tgt_lang="ro_RO",
+        freeze_encoder=True,
+        freeze_embeds=True,
     )
     assert "n_train" in args_d
     args = argparse.Namespace(**args_d)
-    main(args)
+    module = main(args)
+
+    input_embeds = module.model.get_input_embeddings()
+    assert not input_embeds.weight.requires_grad
+    if model == T5_TINY:
+        lm_head = module.model.lm_head
+        assert not lm_head.weight.requires_grad
+        assert (lm_head.weight == input_embeds.weight).all().item()
+
+    else:
+        bart = module.model.model
+        embed_pos = bart.decoder.embed_positions
+        assert not embed_pos.weight.requires_grad
+        assert not bart.shared.weight.requires_grad
+        # check that embeds are the same
+        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
+        assert bart.decoder.embed_tokens == bart.shared
 
 
 @pytest.mark.parametrize(
@@ -239,7 +259,12 @@ def test_dataset(tok):
     max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
     trunc_target = 4
     train_dataset = SummarizationDataset(
-        tokenizer, data_dir=tmp_dir, type_path="train", max_source_length=20, max_target_length=trunc_target,
+        tokenizer,
+        data_dir=tmp_dir,
+        type_path="train",
+        max_source_length=20,
+        max_target_length=trunc_target,
+        tgt_lang="ro_RO",
     )
     dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
     for batch in dataloader: