[s2s] add BartTranslationDistiller for distilling mBART (#6363)

examples/seq2seq/distillation.py

@@ -10,20 +10,40 @@ from torch import nn
 from torch.nn import functional as F
 
 from lightning_base import generic_train
-from transformers import AdamW, BartConfig, BartForConditionalGeneration, T5Config, T5ForConditionalGeneration
+from transformers import (
+    AdamW,
+    BartConfig,
+    BartForConditionalGeneration,
+    MBartTokenizer,
+    T5Config,
+    T5ForConditionalGeneration,
+)
 
 
 try:
-    from .finetune import SummarizationModule
-    from .finetune import main as ft_main
+    from .finetune import SummarizationModule, TranslationModule
     from .initialization_utils import init_student, copy_layers
-    from .utils import use_task_specific_params, pickle_load, freeze_params, assert_all_frozen, any_requires_grad
-
+    from .utils import (
+        use_task_specific_params,
+        pickle_load,
+        freeze_params,
+        assert_all_frozen,
+        any_requires_grad,
+        calculate_bleu_score,
+    )
+    from .finetune import main as ft_main
 except ImportError:
-    from finetune import SummarizationModule
+    from finetune import SummarizationModule, TranslationModule
     from finetune import main as ft_main
     from initialization_utils import init_student, copy_layers
-    from utils import use_task_specific_params, pickle_load, freeze_params, assert_all_frozen, any_requires_grad
+    from utils import (
+        use_task_specific_params,
+        pickle_load,
+        freeze_params,
+        assert_all_frozen,
+        any_requires_grad,
+        calculate_bleu_score,
+    )
 
 
 class BartSummarizationDistiller(SummarizationModule):
@@ -159,17 +179,7 @@ class BartSummarizationDistiller(SummarizationModule):
     @staticmethod
     def add_model_specific_args(parser, root_dir):
         SummarizationModule.add_model_specific_args(parser, root_dir)
-        parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
-        parser.add_argument("--alpha_ce", default=0.8, type=float)
-        parser.add_argument("--alpha_mlm", default=0.2, type=float)
-        # parser.add_argument("--alpha_cos", default=0.0, type=float)
-        parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
-        parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
-        parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
-        parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
-        parser.add_argument("--no_teacher", action="store_true", default=False)
-        parser.add_argument("--length_penalty", type=float, default=-1)
-
+        add_distill_args(parser)
         return parser
 
     def _step(self, batch):
@@ -247,6 +257,44 @@ class BartSummarizationDistiller(SummarizationModule):
         return sum(hidden_losses)
 
 
+def add_distill_args(parser):
+    parser.add_argument("--teacher", default="facebook/bart-large-cnn", type=str)
+    parser.add_argument("--alpha_ce", default=0.8, type=float)
+    parser.add_argument("--alpha_mlm", default=0.2, type=float)
+    parser.add_argument("--alpha_encoder_loss", default=0.0, type=float)
+    parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
+    parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
+    parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
+    parser.add_argument("--no_teacher", action="store_true", default=False)
+    parser.add_argument("--length_penalty", type=float, default=-1)
+
+
+class BartTranslationDistiller(BartSummarizationDistiller):
+    mode = "translation"
+    loss_names = ["loss"]
+    metric_names = ["bleu"]
+    val_metric = "bleu"
+
+    def __init__(self, hparams, **kwargs):
+        super().__init__(hparams, **kwargs)
+        assert isinstance(self.tokenizer, MBartTokenizer)
+        assert hparams.src_lang is not None
+        assert hparams.tgt_lang is not None
+        self.dataset_kwargs["src_lang"] = hparams.src_lang
+        self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
+        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
+            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
+
+    def calc_generative_metrics(self, preds, target) -> dict:
+        return calculate_bleu_score(preds, target)
+
+    @staticmethod
+    def add_model_specific_args(parser, root_dir):
+        TranslationModule.add_model_specific_args(parser, root_dir)
+        add_distill_args(parser)
+        return parser
+
+
 class T5SummarizationDistiller(BartSummarizationDistiller):
     def pre_init(self, hparams):
         raise NotImplementedError("T5 Distillation does not work yet")
@@ -364,15 +412,14 @@ class T5SummarizationDistiller(BartSummarizationDistiller):
 def create_module(args):
     t5 = "t5" in args.model_name_or_path
     if args.no_teacher:
-        assert not args.enc_only
-        module_cls = SummarizationModule
-    elif t5:
+        module_cls = TranslationModule if "translation" in args.task else SummarizationModule
+    elif t5:  # DISTILL T5 WITH TEACHER FOR SUMMARIZATION
+        assert "translation" not in args.task, "t5 translation distillation not supported"
         module_cls = T5SummarizationDistiller
-    elif args.enc_only:
-        raise ValueError("Deleted that")
-    else:
-        module_cls = BartSummarizationDistiller
+    else:  # DISTILL WITH TEACHER
+        module_cls = BartTranslationDistiller if "translation" in args.task else BartSummarizationDistiller
     args.setup_cls: str = module_cls.__name__
+    print(f"using module {args.setup_cls}")
     model = module_cls(args)
     return model
 

examples/seq2seq/test_seq2seq_examples.py

@@ -166,6 +166,31 @@ class TestSummarizationDistiller(unittest.TestCase):
             # TODO: understand why this breaks
             self.assertEqual(nll_loss, model_computed_loss)
 
+    def test_distill_mbart(self):
+        updates = dict(
+            student_encoder_layers=2,
+            student_decoder_layers=1,
+            num_train_epochs=4,
+            val_check_interval=0.25,
+            alpha_hid=2.0,
+            task="translation",
+            model_name_or_path="IGNORE_THIS_IT_DOESNT_GET_USED",
+            tokenizer_name=MBART_TINY,
+            teacher=MBART_TINY,
+            src_lang="en_XX",
+            tgt_lang="ro_RO",
+        )
+        model = self._test_distiller_cli(updates, check_contents=False)
+
+        ckpts = list(Path(model.output_dir).glob("*.ckpt"))
+        self.assertEqual(1, len(ckpts))
+        transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
+        all_files = list(Path(model.output_dir).glob("best_tfmr/*"))
+        assert len(all_files) > 2
+        self.assertEqual(len(transformer_ckpts), 2)
+
+        evaluate_checkpoint(ckpts[0], dest_dir=Path(tempfile.mkdtemp()))
+
     @unittest.skip("T5 distillation is broken at the moment")
     def test_distill_t5(self):
         updates = dict(
@@ -180,7 +205,7 @@ class TestSummarizationDistiller(unittest.TestCase):
 
     def _test_distiller_cli(self, updates, check_contents=True):
         default_updates = dict(
-            label_smoothing_eps=0.0,
+            label_smoothing=0.0,
             early_stopping_patience=-1,
             train_batch_size=1,
             eval_batch_size=2,