Correct llava mask & fix missing setter for vocab_size (#29389)

* correct llava mask

* fix vipllava as wlel

* mask out embedding for padding tokens

* add test

* fix style

* add setter

* fix test on suggestion

src/transformers/models/llava/configuration_llava.py

@@ -147,6 +147,10 @@ def vocab_size(self):
         )
         return self._vocab_size
 
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self._vocab_size = value
+
     def to_dict(self):
         output = super().to_dict()
         output.pop("_vocab_size", None)

src/transformers/models/llava/modeling_llava.py

@@ -344,6 +344,12 @@ def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, in
         final_attention_mask |= image_to_overwrite
         position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
 
+        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
+        batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
+        indices_to_mask = new_token_positions[batch_indices, pad_indices]
+
+        final_embedding[batch_indices, indices_to_mask] = 0
+
         if labels is None:
             final_labels = None
 
@@ -449,10 +455,11 @@ def forward(
                 batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
 
                 # Get the target length
-                target_seqlen = first_layer_past_key_value.shape[-1] + 1
+                target_length = input_ids.shape[1]
+                past_length = first_layer_past_key_value.shape[-1]
 
                 extended_attention_mask = torch.ones(
-                    (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
+                    (attention_mask.shape[0], past_length),
                     dtype=attention_mask.dtype,
                     device=attention_mask.device,
                 )
@@ -467,7 +474,7 @@ def forward(
                 # Zero-out the places where we don't need to attend
                 extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
 
-                attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
+                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
                 position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
 
         outputs = self.language_model(

src/transformers/models/llava_next/modeling_llava_next.py

@@ -356,7 +356,6 @@ def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_m
     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))
         # 1. Create a mask to know where special image tokens are
         special_image_token_mask = input_ids == self.config.image_token_index
@@ -418,6 +417,12 @@ def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, in
         final_attention_mask |= image_to_overwrite
         position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
 
+        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
+        batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
+        indices_to_mask = new_token_positions[batch_indices, pad_indices]
+
+        final_embedding[batch_indices, indices_to_mask] = 0
+
         if labels is None:
             final_labels = None
 

src/transformers/models/vipllava/modeling_vipllava.py

@@ -347,6 +347,12 @@ def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, in
         final_attention_mask |= image_to_overwrite
         position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
 
+        # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
+        batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
+        indices_to_mask = new_token_positions[batch_indices, pad_indices]
+
+        final_embedding[batch_indices, indices_to_mask] = 0
+
         if labels is None:
             final_labels = None
 
@@ -442,11 +448,11 @@ def forward(
                     # Sum all dimensions of head_dim (-1) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                     batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-1) == 0)
 
-                    # Get the target length
-                    target_seqlen = first_layer_past_key_value.shape[-2] + 1
+                    target_length = input_ids.shape[1]
+                    past_length = first_layer_past_key_value.shape[-1]
 
                     extended_attention_mask = torch.ones(
-                        (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
+                        (attention_mask.shape[0], past_length),
                         dtype=attention_mask.dtype,
                         device=attention_mask.device,
                     )
@@ -461,7 +467,7 @@ def forward(
                     # Zero-out the places where we don't need to attend
                     extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
 
-                    attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
+                    attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
                     position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
 
         outputs = self.language_model(

tests/models/llava/test_modeling_llava.py

@@ -27,7 +27,14 @@
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, slow, torch_device
+from transformers.testing_utils import (
+    require_bitsandbytes,
+    require_torch,
+    require_torch_gpu,
+    require_vision,
+    slow,
+    torch_device,
+)
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
@@ -470,10 +477,45 @@ def test_small_model_integration_test_llama_batched_regression(self):
 
         output = model.generate(**inputs, max_new_tokens=20)
 
-        EXPECTED_DECODED_TEXT = ['USER:  \nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT: When visiting this serene location, one should be cautious about the weather conditions and potential', 'USER:  \nWhat is this?\nASSISTANT: Two cats lying on a bed!\nUSER:  \nAnd this?\nASSISTANT: A cat sleeping on a bed.']  # fmt: skip
+        EXPECTED_DECODED_TEXT = ['USER:  \nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT: When visiting this place, which appears to be a dock or pier extending over a body of water', 'USER:  \nWhat is this?\nASSISTANT: Two cats lying on a bed!\nUSER:  \nAnd this?\nASSISTANT: A cat sleeping on a bed.']  # fmt: skip
 
         self.assertEqual(processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
 
+    @slow
+    @require_torch
+    @require_vision
+    def test_batched_generation(self):
+        model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf").to(torch_device)
+
+        processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+        prompt1 = "<image>\n<image>\nUSER: What's the the difference of two images?\nASSISTANT:"
+        prompt2 = "<image>\nUSER: Describe the image.\nASSISTANT:"
+        prompt3 = "<image>\nUSER: Describe the image.\nASSISTANT:"
+        url1 = "https://images.unsplash.com/photo-1552053831-71594a27632d?q=80&w=3062&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
+        url2 = "https://images.unsplash.com/photo-1617258683320-61900b281ced?q=80&w=3087&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
+        image1 = Image.open(requests.get(url1, stream=True).raw)
+        image2 = Image.open(requests.get(url2, stream=True).raw)
+
+        inputs = processor(
+            text=[prompt1, prompt2, prompt3],
+            images=[image1, image2, image1, image2],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device)
+
+        model = model.eval()
+
+        EXPECTED_OUTPUT = [
+            "\n \nUSER: What's the the difference of two images?\nASSISTANT: In the two images, the primary difference is the presence of a small dog holding a flower in one",
+            "\nUSER: Describe the image.\nASSISTANT: The image features a small, fluffy dog sitting on a sidewalk. The dog is holding",
+            "\nUSER: Describe the image.\nASSISTANT: The image features a lone, adult llama standing on a grassy hill. The llama",
+        ]
+
+        generate_ids = model.generate(**inputs, max_new_tokens=20)
+        outputs = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        self.assertEqual(outputs, EXPECTED_OUTPUT)
+
     @slow
     @require_bitsandbytes
     def test_llava_index_error_bug(self):