Fix _merge_input_ids_with_image_features for llava model

src/transformers/models/llava/modeling_llava.py

@@ -276,9 +276,7 @@ def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_m
         self.vocab_size = model_embeds.num_embeddings
         return model_embeds
 
-    def _merge_input_ids_with_image_features(
-        self, image_features, inputs_embeds, input_ids, attention_mask, position_ids
-    ):
+    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
         num_images, num_image_patches, embed_dim = image_features.shape
         batch_size, sequence_length = input_ids.shape
         left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
@@ -307,6 +305,10 @@ def _merge_input_ids_with_image_features(
         final_attention_mask = torch.zeros(
             batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
         )
+        if labels is not None:
+            final_labels = torch.full(
+                (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
+            )
         # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
         # set the corresponding tensors into their correct target device.
         target_device = inputs_embeds.device
@@ -321,6 +323,8 @@ def _merge_input_ids_with_image_features(
         # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
         final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
         final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
+        if labels is not None:
+            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
 
         # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
         image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
@@ -335,7 +339,11 @@ def _merge_input_ids_with_image_features(
         final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
         final_attention_mask |= image_to_overwrite
         position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
-        return final_embedding, final_attention_mask, position_ids
+
+        if labels is None:
+            final_labels = None
+
+        return final_embedding, final_attention_mask, final_labels, position_ids
 
     @add_start_docstrings_to_model_forward(LLAVA_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=LlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
@@ -420,8 +428,8 @@ def forward(
                     )
 
                 image_features = self.multi_modal_projector(selected_image_feature)
-                inputs_embeds, attention_mask, position_ids = self._merge_input_ids_with_image_features(
-                    image_features, inputs_embeds, input_ids, attention_mask, position_ids
+                inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
+                    image_features, inputs_embeds, input_ids, attention_mask, labels
                 )
                 if labels is None:
                     labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long)

src/transformers/models/vipllava/modeling_vipllava.py

@@ -284,9 +284,7 @@ def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_m
         self.vocab_size = model_embeds.num_embeddings
         return model_embeds
 
-    def _merge_input_ids_with_image_features(
-        self, image_features, inputs_embeds, input_ids, attention_mask, position_ids
-    ):
+    def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
         num_images, num_image_patches, embed_dim = image_features.shape
         batch_size, sequence_length = input_ids.shape
         left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id))
@@ -315,6 +313,10 @@ def _merge_input_ids_with_image_features(
         final_attention_mask = torch.zeros(
             batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
         )
+        if labels is not None:
+            final_labels = torch.full(
+                (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device
+            )
         # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
         # set the corresponding tensors into their correct target device.
         target_device = inputs_embeds.device
@@ -329,6 +331,8 @@ def _merge_input_ids_with_image_features(
         # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
         final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
         final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
+        if labels is not None:
+            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
 
         # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
         image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
@@ -343,7 +347,11 @@ def _merge_input_ids_with_image_features(
         final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
         final_attention_mask |= image_to_overwrite
         position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
-        return final_embedding, final_attention_mask, position_ids
+
+        if labels is None:
+            final_labels = None
+
+        return final_embedding, final_attention_mask, final_labels, position_ids
 
     @add_start_docstrings_to_model_forward(VIPLLAVA_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=VipLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
@@ -419,8 +427,8 @@ def forward(
                 image_features = torch.cat(image_features, dim=-1)
 
                 image_features = self.multi_modal_projector(image_features)
-                inputs_embeds, attention_mask, position_ids = self._merge_input_ids_with_image_features(
-                    image_features, inputs_embeds, input_ids, attention_mask, position_ids
+                inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
+                    image_features, inputs_embeds, input_ids, attention_mask, labels
                 )
                 if labels is None:
                     labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long)

tests/models/llava/test_modeling_llava.py

@@ -26,7 +26,7 @@
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import require_bitsandbytes, require_torch, slow, torch_device
+from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
@@ -332,3 +332,41 @@ def test_llava_index_error_bug(self):
 
         # Make sure that `generate` works
         _ = model.generate(**inputs, max_new_tokens=20)
+
+    @slow
+    @require_torch_gpu
+    def test_llava_merge_inputs_error_bug(self):
+        # This is a reproducer of https://github.com/huggingface/transformers/pull/28333 and makes sure it does not happen anymore
+        model_id = "llava-hf/llava-1.5-7b-hf"
+        model = LlavaForConditionalGeneration.from_pretrained(
+            model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
+        ).to(torch_device)
+
+        # Simulate some user inputs
+        pixel_values = torch.randn(
+            (2, 3, 336, 336),
+            dtype=torch.float,
+            device=torch_device,
+        )
+        input_ids = torch.tensor(
+            [
+                [32001, 32001, 1, 15043, 7084, 32000, 29871, 13, 7900],
+                [1, 15043, 7084, 29901, 29871, 32000, 29871, 13, 7900],
+            ],
+            dtype=torch.long,
+            device=torch_device,
+        )
+        attention_mask = torch.tensor(
+            [[0, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]],
+            dtype=torch.long,
+            device=torch_device,
+        )
+
+        # Make sure that the loss is properly computed
+        loss = model(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=input_ids,
+        ).loss
+        loss.backward()

tests/models/vipllava/test_modeling_vipllava.py

@@ -26,7 +26,7 @@
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import require_bitsandbytes, require_torch, slow, torch_device
+from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
@@ -214,3 +214,41 @@ def test_small_model_integration_test(self):
 
         EXPECTED_OUTPUT = "USER: <image> \nCan you please describe this image?\nASSISTANT: The image features a brown and white cat sitting on"
         self.assertEqual(processor.decode(outputs[0], skip_special_tokens=True), EXPECTED_OUTPUT)
+
+    @slow
+    @require_torch_gpu
+    def test_vipllava_merge_inputs_error_bug(self):
+        # This is a reproducer of https://github.com/huggingface/transformers/pull/28333 and makes sure it does not happen anymore
+        model_id = "llava-hf/vip-llava-7b-hf"
+        model = VipLlavaForConditionalGeneration.from_pretrained(
+            model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
+        ).to(torch_device)
+
+        # Simulate some user inputs
+        pixel_values = torch.randn(
+            (2, 3, 336, 336),
+            dtype=torch.float,
+            device=torch_device,
+        )
+        input_ids = torch.tensor(
+            [
+                [32001, 32001, 1, 15043, 7084, 32000, 29871, 13, 7900],
+                [1, 15043, 7084, 29901, 29871, 32000, 29871, 13, 7900],
+            ],
+            dtype=torch.long,
+            device=torch_device,
+        )
+        attention_mask = torch.tensor(
+            [[0, 0, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]],
+            dtype=torch.long,
+            device=torch_device,
+        )
+
+        # Make sure that the loss is properly computed
+        loss = model(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=input_ids,
+        ).loss
+        loss.backward()