Fix MLM loss ignore idx

requirements-test.txt

@@ -8,3 +8,8 @@ awscli==1.33.33
 nbval==0.11.0
 # For NvFaidx equivalence tests
 pyfaidx==0.8.1.3
+
+# Temporary pin for pytorch-lightning until megatron callbacks in ProgressPrinter can get fixed.
+# See https://nvidia.slack.com/archives/C02A7LYGHK8/p1734727482697309
+pytorch-lightning<2.5.0
+lightning<2.5.0

sub-packages/bionemo-esm2/src/bionemo/esm2/model/finetune/finetune_token_classifier.py

@@ -27,6 +27,7 @@
 
 from bionemo.esm2.api import ESM2GenericConfig, ESM2Model
 from bionemo.esm2.data import tokenizer
+from bionemo.llm.data.collate import MLM_LOSS_IGNORE_INDEX
 from bionemo.llm.data.label2id_tokenizer import Label2IDTokenizer
 from bionemo.llm.data.types import BertSample
 from bionemo.llm.model.biobert.model import BioBertOutput
@@ -230,6 +231,7 @@ def __init__(
         self.tokenizer = tokenizer
         label_tokenizer = Label2IDTokenizer()
         self.label_tokenizer = label_tokenizer.build_vocab("CHE")
+        self.label_cls_eos_id = MLM_LOSS_IGNORE_INDEX
 
     def __len__(self) -> int:
         """Length of dataset."""
@@ -257,13 +259,13 @@ def _tokenize_labels(self, labels_sequence: str) -> Tensor:
 
         # # for multi-label classification with BCEWithLogitsLoss
         # tokenized_labels = torch.nn.functional.one_hot(label_ids, num_classes=self.label_tokenizer.vocab_size)
-        # cls_eos = torch.full((1, self.label_tokenizer.vocab_size), -1, dtype=tokenized_labels.dtype)
+        # cls_eos = torch.full((1, self.label_tokenizer.vocab_size), self.label_cls_eos_id, dtype=tokenized_labels.dtype)
 
         # for multi-class (mutually exclusive) classification with CrossEntropyLoss
         tokenized_labels = label_ids
-        cls_eos = torch.tensor([-1], dtype=tokenized_labels.dtype)
+        cls_eos = torch.tensor([self.label_cls_eos_id], dtype=tokenized_labels.dtype)
 
-        # add cls / eos labels with padding value -1 to have the same shape as tokenized_sequence
+        # add cls / eos label ids with padding value -100 to have the same shape as tokenized_sequence
         labels = torch.cat((cls_eos, tokenized_labels, cls_eos))
         return labels
 

sub-packages/bionemo-llm/src/bionemo/llm/data/collate.py

@@ -28,6 +28,9 @@
 _warned_once: bool = False
 
 
+MLM_LOSS_IGNORE_INDEX = -100  # This should match the masked value used in the MLM loss mask.
+
+
 def padding_collate_fn(
     batch: Sequence[_T],
     padding_values: dict[str, int],
@@ -105,7 +108,7 @@ def bert_padding_collate_fn(
         "text": padding_value,
         "types": 0,
         "attention_mask": False,
-        "labels": -100,  # This should match the masked value used in the MLM loss mask.
+        "labels": MLM_LOSS_IGNORE_INDEX,  # This should match the masked value used in the MLM loss mask.
         "loss_mask": False,
         "is_random": 0,
     }