🏷️ Account for token_type_ids in DataCollatorForVisionLanguageModeling

tests/test_sft_trainer.py

@@ -1333,6 +1333,38 @@ def test_train_vlm_prompt_completion(self):
             new_param = trainer.model.get_parameter(n)
             assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"
 
+    # Special case for Gemma, as it uses token_type_ids, and we need to ensure they are properly in the collator.
+    @require_vision
+    def test_train_vlm_prompt_completion_gemma(self):
+        # Get the dataset
+        dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_completion", split="train")
+
+        # Initialize the trainer
+        training_args = SFTConfig(
+            output_dir=self.tmp_dir,
+            max_length=None,  # For VLMs, truncating can remove image tokens, leading to errors
+            report_to="none",
+        )
+        trainer = SFTTrainer(
+            model="trl-internal-testing/tiny-Gemma3ForConditionalGeneration",
+            args=training_args,
+            train_dataset=dataset,
+        )
+
+        # Save the initial parameters to compare them later
+        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+        # Train the model
+        trainer.train()
+
+        # Check that the training loss is not None
+        self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+
+        # Check the params have changed
+        for n, param in previous_trainable_params.items():
+            new_param = trainer.model.get_parameter(n)
+            self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated")
+
     # Gemma 3n uses a timm encoder, making it difficult to create a smaller variant for testing.
     # To ensure coverage, we run tests on the full model but mark them as slow to exclude from default runs.
     @pytest.mark.slow

trl/trainer/sft_trainer.py

@@ -418,15 +418,26 @@ def _collate_prompt_completion(self, examples: list[dict[str, Any]]) -> dict[str
         input_ids = torch.cat((prompt_ids, completion_ids), dim=1)
         attention_mask = torch.cat((prompt_mask, completion_mask), dim=1)
         completion_mask = torch.cat((torch.zeros_like(prompt_mask), completion_mask), dim=1)
+        if "token_type_ids" in processed_prompts:  # special case for Gemma
+            prompt_token_type_ids = processed_prompts["token_type_ids"]
+            completion_token_type_ids = processed_completions["token_type_ids"]
+            token_type_ids = torch.cat((prompt_token_type_ids, completion_token_type_ids), dim=1)
 
         # Flush left to reduce padding
-        attention_mask, input_ids, completion_mask = flush_left(attention_mask, input_ids, completion_mask)
+        if "token_type_ids" in processed_prompts:
+            attention_mask, input_ids, completion_mask, token_type_ids = flush_left(
+                attention_mask, input_ids, completion_mask, token_type_ids
+            )
+        else:
+            attention_mask, input_ids, completion_mask = flush_left(attention_mask, input_ids, completion_mask)
 
         # Truncate if necessary
         if self.max_length is not None:
             input_ids = input_ids[:, : self.max_length]
             attention_mask = attention_mask[:, : self.max_length]
             completion_mask = completion_mask[:, : self.max_length]
+            if "token_type_ids" in processed_prompts:
+                token_type_ids = token_type_ids[:, : self.max_length]
 
         # Create labels and mask padding tokens
         labels = input_ids.clone()
@@ -439,6 +450,8 @@ def _collate_prompt_completion(self, examples: list[dict[str, Any]]) -> dict[str
         output["input_ids"] = input_ids
         output["attention_mask"] = attention_mask
         output["labels"] = labels
+        if "token_type_ids" in processed_prompts:
+            output["token_type_ids"] = token_type_ids
         return output