Fix division by zero when there are zero-length spans in MismatchedEm…

…bedder. (#4615)

* Implment MattG's fix for NaN gradients in MismatchedEmbedder.

Fix `clamp_min` on embeddings.

Implment MattG's fix for NaN gradients in MismatchedEmbedder.

* Fix NaN gradients caused by weird tokens in MismatchedEmbedder.

Fixed division by zero error when there are zero-length spans in the input to a
mismatched embedder.

* Add changelog message.

* Re-run `black` to get code formatting right.

* combine fixed sections after merging with master

Co-authored-by: Matt Gardner <mattg@allenai.org>

CHANGELOG.md

@@ -11,12 +11,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Fixed handling of some edge cases when constructing classes with `FromParams` where the class
   accepts `**kwargs`.
+- Fixed division by zero error when there are zero-length spans in the input to a
+  `PretrainedTransformerMismatchedIndexer`.
 
 ### Added
 
 - `Predictor.capture_model_internals()` now accepts a regex specifying
   which modules to capture
 
+
 ## [v1.1.0rc4](https://github.com/allenai/allennlp/releases/tag/v1.1.0rc4) - 2020-08-20
 
 ### Added
@@ -63,7 +66,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Added the option to specify `requires_grad: false` within an optimizer's parameter groups.
 - Added the `file-friendly-logging` flag back to the `train` command. Also added this flag to the `predict`, `evaluate`, and `find-learning-rate` commands.
-- Added an `EpochCallback` to track current epoch as a model class member. 
+- Added an `EpochCallback` to track current epoch as a model class member.
 - Added the option to enable or disable gradient checkpointing for transformer token embedders via boolean parameter `gradient_checkpointing`.
 
 ### Removed

allennlp/modules/token_embedders/pretrained_transformer_mismatched_embedder.py

@@ -105,7 +105,7 @@ def forward(
         span_embeddings_sum = span_embeddings.sum(2)
         span_embeddings_len = span_mask.sum(2)
         # Shape: (batch_size, num_orig_tokens, embedding_size)
-        orig_embeddings = span_embeddings_sum / span_embeddings_len
+        orig_embeddings = span_embeddings_sum / torch.clamp_min(span_embeddings_len, 1)
 
         # All the places where the span length is zero, write in zeros.
         orig_embeddings[(span_embeddings_len == 0).expand(orig_embeddings.shape)] = 0

tests/modules/token_embedders/pretrained_transformer_mismatched_embedder_test.py

@@ -8,6 +8,7 @@
 from allennlp.data.instance import Instance
 from allennlp.data.token_indexers import PretrainedTransformerMismatchedIndexer
 from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
+from allennlp.modules.token_embedders import PretrainedTransformerMismatchedEmbedder
 from allennlp.common.testing import AllenNlpTestCase
 
 
@@ -143,3 +144,36 @@ def test_token_without_wordpieces(self):
         assert not torch.isnan(bert_vectors).any()
         assert all(bert_vectors[0, 1] == 0)
         assert all(bert_vectors[1, 1] == 0)
+
+    def test_exotic_tokens_no_nan_grads(self):
+        token_indexer = PretrainedTransformerMismatchedIndexer("bert-base-uncased")
+
+        sentence1 = ["A", "", "AllenNLP", "sentence", "."]
+        sentence2 = ["A", "\uf732\uf730\uf730\uf733", "AllenNLP", "sentence", "."]
+
+        tokens1 = [Token(word) for word in sentence1]
+        tokens2 = [Token(word) for word in sentence2]
+        vocab = Vocabulary()
+
+        token_embedder = BasicTextFieldEmbedder(
+            {"bert": PretrainedTransformerMismatchedEmbedder("bert-base-uncased")}
+        )
+
+        instance1 = Instance({"tokens": TextField(tokens1, {"bert": token_indexer})})
+        instance2 = Instance({"tokens": TextField(tokens2, {"bert": token_indexer})})
+
+        batch = Batch([instance1, instance2])
+        batch.index_instances(vocab)
+
+        padding_lengths = batch.get_padding_lengths()
+        tensor_dict = batch.as_tensor_dict(padding_lengths)
+        tokens = tensor_dict["tokens"]
+
+        bert_vectors = token_embedder(tokens)
+        test_loss = bert_vectors.mean()
+
+        test_loss.backward()
+
+        for name, param in token_embedder.named_parameters():
+            grad = param.grad
+            assert (grad is None) or (not torch.any(torch.isnan(grad)).item())