Add loss for BridgeTowerForMaskedLM and BridgeTowerForImageAndTextRetrieval (#21684)

* Add loss for BridgeTowerForMaskedLM and BridgeTowerForImageAndTextRetrieval

* minor fix return_dict

* implement test for loss computation

---------

Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>

src/transformers/models/bridgetower/modeling_bridgetower.py

@@ -22,6 +22,7 @@ from typing import List, Optional, Tuple, Union
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN, QuickGELUActivation
 from ...modeling_outputs import (
@@ -1535,8 +1536,10 @@ class BridgeTowerForMaskedLM(BridgeTowerPreTrainedModel):
         labels: Optional[torch.LongTensor] = None,
     ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
         r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels are currently not supported.
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
         Returns:
 
         Examples:
@@ -1580,11 +1583,17 @@ class BridgeTowerForMaskedLM(BridgeTowerPreTrainedModel):
         )
 
         mlm_logits = self.mlm_score(outputs.text_features if return_dict else outputs[0])
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(mlm_logits.view(-1, self.config.text_config.vocab_size), labels.view(-1))
 
         if not return_dict:
-            return tuple(mlm_logits)
+            output = tuple(mlm_logits)
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
 
         return MaskedLMOutput(
+            loss=masked_lm_loss,
             logits=mlm_logits,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
@@ -1627,8 +1636,9 @@ class BridgeTowerForImageAndTextRetrieval(BridgeTowerPreTrainedModel):
         labels: Optional[torch.LongTensor] = None,
     ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
         r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+        labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
-            Labels are currently not supported.
+            Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match.
+            The pairs with 0 will be skipped for calculation.
         Returns:
 
         Examples:
@@ -1673,11 +1683,17 @@ class BridgeTowerForImageAndTextRetrieval(BridgeTowerPreTrainedModel):
 
         logits = self.itm_score(pooler_output)
 
+        itm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            itm_loss = loss_fct(logits, labels)
+
         if not return_dict:
-            return tuple(logits)
+            output = tuple(logits)
+            return ((itm_loss,) + output) if itm_loss is not None else output
 
         return SequenceClassifierOutput(
-            loss=None,
+            loss=itm_loss,
             logits=logits,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,

tests/models/bridgetower/test_modeling_bridgetower.py

@@ -392,6 +392,13 @@ class BridgeTowerModelIntegrationTest(unittest.TestCase):
         self.assertEqual(outputs.logits.shape, expected_shape)
         self.assertTrue(outputs.logits[0, 1].item() > outputs.logits[0, 0].item())
 
+        # verify loss
+        inputs["labels"] = torch.ones(1, dtype=torch.long, device=torch_device)
+        inputs = inputs.to(torch_device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        self.assertAlmostEqual(outputs.loss.item(), 0.5108, places=4)
+
     @slow
     def test_masked_language_modeling(self):
         model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(torch_device)
@@ -412,3 +419,62 @@ class BridgeTowerModelIntegrationTest(unittest.TestCase):
         # verify predicted word
         predicted_id = outputs.logits.argmax(dim=-1).squeeze(0).tolist()[4]
         self.assertTrue(processor.decode([predicted_id]) == " cats")
+
+        # verify loss
+        inputs["labels"] = inputs["input_ids"].clone()
+        inputs = inputs.to(torch_device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        self.assertAlmostEqual(outputs.loss.item(), 5.7373, places=4)
+
+
+@require_torch
+@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
+class BridgeTowerModelTrainingTest(unittest.TestCase):
+    all_training_supported_model_classes = (
+        (BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM) if is_torch_available() else ()
+    )
+
+    def setUp(self):
+        self.model_tester = BridgeTowerModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=50265)
+
+    def _prepare_inputs_for_training(self, model_class):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        if model_class == BridgeTowerForMaskedLM:
+            inputs_dict["labels"] = inputs_dict["input_ids"]
+        elif model_class == BridgeTowerForImageAndTextRetrieval:
+            inputs_dict["labels"] = ids_tensor([1], 2)
+        return config, inputs_dict
+
+    def _get_non_used_layer_names(self, model_class):
+        non_used_layer_names = ["text_model.pooler"]
+        if model_class == BridgeTowerForMaskedLM:
+            non_used_layer_names = non_used_layer_names + [
+                "cross_modal_image_layers.5",
+                "cross_modal_image_pooler",
+                "cross_modal_text_pooler",
+            ]
+        return non_used_layer_names
+
+    def _is_layer_used(self, model_class, layer_name):
+        non_used_layer_names = self._get_non_used_layer_names(model_class)
+        for non_used_layer_name in non_used_layer_names:
+            if non_used_layer_name in layer_name:
+                return False
+        return True
+
+    def test_training(self):
+        for model_class in self.all_training_supported_model_classes:
+            config, inputs_dict = self._prepare_inputs_for_training(model_class)
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+
+            loss = model(**inputs_dict).loss
+            loss.backward()
+
+            # verify the gradients of used layers' weight are not None
+            for name, param in model.named_parameters():
+                if self._is_layer_used(model_class, name):
+                    self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}")