Fixes token type ids for folded sequences

CHANGELOG.md

@@ -12,6 +12,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `TaskSuite` base class and command line functionality for running [`checklist`](https://github.com/marcotcr/checklist) test suites, along with implementations for `SentimentAnalysisSuite`, `QuestionAnsweringSuite`, and `TextualEntailmentSuite`. These can be found in the `allennlp.sanity_checks.task_checklists` module.
 
 
+## Unreleased
+
+### Fixed
+
+- When `PretrainedTransformerIndexer` folds long sequences, it no longer loses the information from token type ids.
+
+
 ## [v2.4.0](https://github.com/allenai/allennlp/releases/tag/v2.4.0) - 2021-04-22
 
 ### Added

allennlp/data/token_indexers/pretrained_transformer_indexer.py

@@ -154,25 +154,52 @@ def _postprocess_output(self, output: IndexedTokenList) -> IndexedTokenList:
             # TODO(zhaofengw): we aren't respecting word boundaries when segmenting wordpieces.
 
             indices = output["token_ids"]
+            type_ids = output.get("type_ids", [0] * len(indices))
+
             # Strips original special tokens
             indices = indices[
                 self._num_added_start_tokens : len(indices) - self._num_added_end_tokens
             ]
+            type_ids = type_ids[
+                self._num_added_start_tokens : len(type_ids) - self._num_added_end_tokens
+            ]
+
             # Folds indices
             folded_indices = [
                 indices[i : i + self._effective_max_length]
                 for i in range(0, len(indices), self._effective_max_length)
             ]
+            folded_type_ids = [
+                type_ids[i : i + self._effective_max_length]
+                for i in range(0, len(type_ids), self._effective_max_length)
+            ]
+
             # Adds special tokens to each segment
             folded_indices = [
                 self._tokenizer.build_inputs_with_special_tokens(segment)
                 for segment in folded_indices
             ]
+            single_sequence_start_type_ids = [
+                t.type_id for t in self._allennlp_tokenizer.single_sequence_start_tokens
+            ]
+            single_sequence_end_type_ids = [
+                t.type_id for t in self._allennlp_tokenizer.single_sequence_end_tokens
+            ]
+            folded_type_ids = [
+                single_sequence_start_type_ids + segment + single_sequence_end_type_ids
+                for segment in folded_type_ids
+            ]
+            assert all(
+                len(segment_indices) == len(segment_type_ids)
+                for segment_indices, segment_type_ids in zip(folded_indices, folded_type_ids)
+            )
+
             # Flattens
             indices = [i for segment in folded_indices for i in segment]
+            type_ids = [i for segment in folded_type_ids for i in segment]
 
             output["token_ids"] = indices
-            output["type_ids"] = [0] * len(indices)
+            output["type_ids"] = type_ids
             output["segment_concat_mask"] = [True] * len(indices)
 
         return output

tests/data/token_indexers/pretrained_transformer_indexer_test.py

@@ -163,6 +163,22 @@ def test_long_sequence_splitting(self):
         assert indexed["segment_concat_mask"] == [True] * len(expected_ids)
         assert indexed["mask"] == [True] * 7  # original length
 
+    def test_type_ids_when_folding(self):
+        allennlp_tokenizer = PretrainedTransformerTokenizer(
+            "bert-base-uncased", add_special_tokens=False
+        )
+        indexer = PretrainedTransformerIndexer(model_name="bert-base-uncased", max_length=6)
+        first_string = "How do trees get online?"
+        second_string = "They log in!"
+
+        tokens = allennlp_tokenizer.add_special_tokens(
+            allennlp_tokenizer.tokenize(first_string), allennlp_tokenizer.tokenize(second_string)
+        )
+        vocab = Vocabulary()
+        indexed = indexer.tokens_to_indices(tokens, vocab)
+        assert min(indexed["type_ids"]) == 0
+        assert max(indexed["type_ids"]) == 1
+
     @staticmethod
     def _assert_tokens_equal(expected_tokens, actual_tokens):
         for expected, actual in zip(expected_tokens, actual_tokens):

tests/modules/token_embedders/pretrained_transformer_embedder_test.py

@@ -3,7 +3,7 @@
 import torch
 
 from allennlp.common import Params
-from allennlp.common.testing import AllenNlpTestCase
+from allennlp.common.testing import AllenNlpTestCase, requires_gpu
 from allennlp.data import Vocabulary
 from allennlp.data.batch import Batch
 from allennlp.data.fields import TextField
@@ -15,14 +15,15 @@
 
 
 class TestPretrainedTransformerEmbedder(AllenNlpTestCase):
+    @requires_gpu
     def test_forward_runs_when_initialized_from_params(self):
         # This code just passes things off to `transformers`, so we only have a very simple
         # test.
         params = Params({"model_name": "bert-base-uncased"})
-        embedder = PretrainedTransformerEmbedder.from_params(params)
+        embedder = PretrainedTransformerEmbedder.from_params(params).cuda()
         token_ids = torch.randint(0, 100, (1, 4))
         mask = torch.randint(0, 2, (1, 4)).bool()
-        output = embedder(token_ids=token_ids, mask=mask)
+        output = embedder(token_ids=token_ids.cuda(), mask=mask.cuda())
         assert tuple(output.size()) == (1, 4, 768)
 
     @pytest.mark.parametrize(
@@ -169,22 +170,24 @@ def test_end_to_end_t5(
         assert bert_vectors.size() == (2, 8, 64)
         assert bert_vectors.requires_grad == (train_parameters or not last_layer_only)
 
+    @requires_gpu
     def test_big_token_type_ids(self):
-        token_embedder = PretrainedTransformerEmbedder("roberta-base")
+        token_embedder = PretrainedTransformerEmbedder("roberta-base").cuda()
         token_ids = torch.LongTensor([[1, 2, 3], [2, 3, 4]])
         mask = torch.ones_like(token_ids).bool()
         type_ids = torch.zeros_like(token_ids)
         type_ids[1, 1] = 1
         with pytest.raises(ValueError):
-            token_embedder(token_ids, mask, type_ids)
+            token_embedder(token_ids.cuda(), mask.cuda(), type_ids.cuda())
 
+    @requires_gpu
     def test_xlnet_token_type_ids(self):
-        token_embedder = PretrainedTransformerEmbedder("xlnet-base-cased")
+        token_embedder = PretrainedTransformerEmbedder("xlnet-base-cased").cuda()
         token_ids = torch.LongTensor([[1, 2, 3], [2, 3, 4]])
         mask = torch.ones_like(token_ids).bool()
         type_ids = torch.zeros_like(token_ids)
         type_ids[1, 1] = 1
-        token_embedder(token_ids, mask, type_ids)
+        token_embedder(token_ids.cuda(), mask.cuda(), type_ids.cuda())
 
     def test_long_sequence_splitting_end_to_end(self):
         # Mostly the same as the end_to_end test (except for adding max_length=4),
@@ -310,11 +313,14 @@ def test_unfold_long_sequences(self):
         )
         assert (unfolded_embeddings_out == unfolded_embeddings).all()
 
+    @requires_gpu
     def test_encoder_decoder_model(self):
-        token_embedder = PretrainedTransformerEmbedder("facebook/bart-large", sub_module="encoder")
+        token_embedder = PretrainedTransformerEmbedder(
+            "facebook/bart-large", sub_module="encoder"
+        ).cuda()
         token_ids = torch.LongTensor([[1, 2, 3], [2, 3, 4]])
         mask = torch.ones_like(token_ids).bool()
-        token_embedder(token_ids, mask)
+        token_embedder(token_ids.cuda(), mask.cuda())
 
     def test_embeddings_resize(self):
         regular_token_embedder = PretrainedTransformerEmbedder("bert-base-cased")