[cleanup] assign todos, faster bart-cnn test (#7835)

* 2 beam output

* unassign/remove TODOs

* remove one more

examples/lightning_base.py

@@ -291,7 +291,8 @@ class LoggingCallback(pl.Callback):
 
 
 def add_generic_args(parser, root_dir) -> None:
-    #  TODO(SS): allow all pl args? parser = pl.Trainer.add_argparse_args(parser)
+    #  To allow all pl args uncomment the following line
+    #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir",
         default=None,

examples/seq2seq/test_bash_script.py

@@ -21,7 +21,6 @@ from utils import load_json
 
 
 MODEL_NAME = MBART_TINY
-# TODO(SS): MODEL_NAME = "sshleifer/student_mbart_en_ro_1_1"
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
@@ -99,7 +98,7 @@ def test_train_mbart_cc25_enro_script():
     assert expected_key in ckpt["state_dict"]
     assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
 
-    # TODO(SS): turn on args.do_predict when PL bug fixed.
+    # TODO: turn on args.do_predict when PL bug fixed.
     if args.do_predict:
         contents = {os.path.basename(p) for p in contents}
         assert "test_generations.txt" in contents
@@ -178,7 +177,7 @@ def test_opus_mt_distill_script():
     assert expected_key in ckpt["state_dict"]
     assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.float32
 
-    # TODO(SS): turn on args.do_predict when PL bug fixed.
+    # TODO: turn on args.do_predict when PL bug fixed.
     if args.do_predict:
         contents = {os.path.basename(p) for p in contents}
         assert "test_generations.txt" in contents

examples/seq2seq/test_finetune_trainer.py

@@ -25,7 +25,6 @@ def test_finetune_trainer():
 
 @slow
 def test_finetune_trainer_slow():
-    # TODO(SS): This will fail on devices with more than 1 GPU.
     # There is a missing call to __init__process_group somewhere
     output_dir = run_trainer(eval_steps=2, max_len="128", model_name=MARIAN_MODEL, num_train_epochs=3)
 

examples/seq2seq/test_seq2seq_examples.py

@@ -52,7 +52,7 @@ CHEAP_ARGS = {
     "student_decoder_layers": 1,
     "val_check_interval": 1.0,
     "output_dir": "",
-    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
+    "fp16": False,  # TODO: set this to CUDA_AVAILABLE if ci installs apex or start using native amp
     "no_teacher": False,
     "fp16_opt_level": "O1",
     "gpus": 1 if CUDA_AVAILABLE else 0,

src/transformers/convert_pegasus_tf_to_pytorch.py

@@ -54,8 +54,6 @@ def rename_state_dict_key(k):
 
 # See appendix C of paper for all hyperparams
 
-# TODO(SS): one constant
-
 
 def convert_pegasus(tf_weights: dict, cfg_updates: dict) -> PegasusForConditionalGeneration:
     cfg_kwargs = DEFAULTS.copy()

src/transformers/tokenization_pegasus.py

@@ -154,11 +154,8 @@ class PegasusTokenizer(ReformerTokenizer):
             return model_inputs
         if max_target_length is not None:
             tokenizer_kwargs["max_length"] = max_target_length
-        # TODO(@sshleifer): maybe tgt_texts = [self.pad_token + t for t in tgt_texts]  # add decoder_start_token_id
         labels: BatchEncoding = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
         model_inputs["labels"] = labels
-        # for k, v in decoder_inputs.items():
-        #    model_inputs[f"decoder_{k}"] = v
         return model_inputs
 
 
@@ -169,10 +166,6 @@ class PegasusTokenizerFast(ReformerTokenizerFast):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     slow_tokenizer_class = PegasusTokenizer
 
-    # def num_special_tokens_to_add(self, pair=False):
-    #     """Just EOS"""
-    #     return 1
-
     def _special_token_mask(self, seq):
         all_special_ids = set(self.all_special_ids)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
@@ -236,9 +229,6 @@ class PegasusTokenizerFast(ReformerTokenizerFast):
             return model_inputs
         if max_target_length is not None:
             tokenizer_kwargs["max_length"] = max_target_length
-        # TODO(@sshleifer): maybe tgt_texts = [self.pad_token + t for t in tgt_texts]  # add decoder_start_token_id
         labels: BatchEncoding = self(tgt_texts, **tokenizer_kwargs)["input_ids"]
         model_inputs["labels"] = labels
-        # for k, v in decoder_inputs.items():
-        #    model_inputs[f"decoder_{k}"] = v
         return model_inputs

tests/test_modeling_fsmt.py

@@ -125,12 +125,9 @@ class FSMTModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else ()
     all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else ()
     is_encoder_decoder = True
-    # TODO(SS): fix the below in a separate PR
     test_pruning = False
-    test_torchscript = True
     test_head_masking = False
-    test_resize_embeddings = True  # This requires inputs_dict['input_ids']
+    test_missing_keys = False
-    test_missing_keys = False  # because FSMTForConditionalGeneration and FSMTModel now have identical state_dict
 
     def setUp(self):
         self.model_tester = ModelTester(self)
@@ -326,7 +323,6 @@ class FSMTHeadTests(unittest.TestCase):
             max_length=max_length,
         )
         self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length))
-        # TODO(SS): uneven length batches, empty inputs
 
     def test_shift_tokens_right(self):
         input_ids = torch.Tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]).long()