[Model] Refactor Ovis2 to support original tokenizer (#17537)

Signed-off-by: Isotr0py <2037008807@qq.com>

examples/offline_inference/vision_language.py

@@ -730,11 +730,9 @@ def run_ovis2(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
 
     model_name = "AIDC-AI/Ovis2-1B"
-    tokenizer = "Isotr0py/Ovis2-tokenizer"
 
     engine_args = EngineArgs(
         model=model_name,
-        tokenizer=tokenizer,
         max_model_len=4096,
         max_num_seqs=2,
         trust_remote_code=True,

examples/offline_inference/vision_language_multi_image.py

@@ -439,11 +439,9 @@ def load_nvlm_d(question: str, image_urls: list[str]) -> ModelRequestData:
 # Ovis2
 def load_ovis2(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "AIDC-AI/Ovis2-1B"
-    tokenizer = "Isotr0py/Ovis2-tokenizer"
 
     engine_args = EngineArgs(
         model=model_name,
-        tokenizer=tokenizer,
         max_model_len=8192,
         max_num_seqs=2,
         trust_remote_code=True,

tests/models/registry.py

@@ -349,7 +349,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                         transformers_version_reason="Use of deprecated imports which have been removed.",  # noqa: E501
                                         extras={"phi3.5": "microsoft/Phi-3.5-vision-instruct"}),  # noqa: E501
     "Ovis2ForConditionalGeneration": _HfExamplesInfo("AIDC-AI/Ovis2-1B",
-                                                    tokenizer="Isotr0py/Ovis2-tokenizer",
                                                     trust_remote_code=True,
                                                     hf_overrides={"architectures": ["Ovis2ForConditionalGeneration"]}), # noqa: E501
     "Phi4MMForCausalLM": _HfExamplesInfo("microsoft/Phi-4-multimodal-instruct",

vllm/model_executor/models/ovis2.py

@@ -46,8 +46,7 @@ from .utils import merge_multimodal_embeddings
 
 # Cannot find the following number from hf config.
 IMAGE_TOKEN = "<image>"
-IMAGE_ATOM_TOKEN_ID = 151666
-IMAGE_PAD_TOKEN_ID = 151672
+IMAGE_PAD_TOKEN_ID = 151655
 NUMBER_OF_TOKEN_TO_RESERVE_FOR_SEGMENT = 256
 
 
@@ -59,6 +58,12 @@ class Ovis2ImagePatchInputs(TypedDict):
     `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
     """
 
+    inducator_tokens: torch.Tensor
+    """
+    Shape: 
+    `(batch_size * (num_patches + 1))`
+    """
+
     patches_per_image: List[int]
     """
     List of number of total patches for each image in the batch.
@@ -138,6 +143,21 @@ class Ovis2DummyInputsBuilder(BaseDummyInputsBuilder[Ovis2ProcessingInfo]):
 
 class Ovis2MultiModalProcessor(BaseMultiModalProcessor[Ovis2ProcessingInfo]):
 
+    def image_indicators_to_visual_tokens(
+        self,
+        image_indicators: list[int],
+    ) -> list[int]:
+        """
+        Filter image indicators placeholders and convert them to corresponding 
+        tokens in visual tokenizer.
+        For example, [-301, -300, -302, -300, -303, -300, -304, -300, -305]
+        should return [vocab_size-1, vocab_size-2, ..., vocab_size-5]
+        """
+        hf_config = self.info.get_hf_config()
+        vte_vocab_size = hf_config.visual_tokenizer_config.vocab_size
+        # -300 is image_atom token, filter them out
+        return [vte_vocab_size + x + 300 for x in image_indicators if x < -300]
+
     def _call_hf_processor(
         self,
         prompt: str,
@@ -156,6 +176,16 @@ class Ovis2MultiModalProcessor(BaseMultiModalProcessor[Ovis2ProcessingInfo]):
             mm_kwargs=mm_kwargs,
         )
 
+        hf_processor = self.info.get_hf_processor()
+        image_indicators = [
+            hf_processor.construct_image_indicators(grid)
+            for grid in processed_outputs["grids"]
+        ]
+        indicator_tokens = [
+            self.image_indicators_to_visual_tokens(indicator)
+            for indicator in image_indicators
+        ]
+        processed_outputs["indicator_tokens"] = indicator_tokens
         return processed_outputs
 
     def _apply_hf_processor_tokens_only(
@@ -171,7 +201,8 @@ class Ovis2MultiModalProcessor(BaseMultiModalProcessor[Ovis2ProcessingInfo]):
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
         return dict(pixel_values=MultiModalFieldConfig.batched("image"),
-                    grids=MultiModalFieldConfig.batched("image"))
+                    grids=MultiModalFieldConfig.batched("image"),
+                    indicator_tokens=MultiModalFieldConfig.batched("image"))
 
     def _get_prompt_updates(
         self,
@@ -230,20 +261,28 @@ class Ovis2ForConditionalGeneration(nn.Module, SupportsMultiModal):
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[Ovis2ImagePatchInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
-        if pixel_values is None:
+        indicator_tokens = kwargs.pop("indicator_tokens", None)
+
+        if pixel_values is None and indicator_tokens is None:
             return None
 
-        if pixel_values is not None:
+        if pixel_values is not None and indicator_tokens is not None:
             if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
+            if not isinstance(indicator_tokens, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of indicator_tokens. "
+                                 f"Got type: {type(pixel_values)}")
+
             return Ovis2ImagePatchInputs(
                 type="image_patches",
                 flat_data=flatten_bn(flatten_bn(pixel_values), concat=True),
                 patches_per_image=[
                     x.shape[0] for x in flatten_bn(pixel_values)
                 ],
+                indicator_tokens=flatten_bn(flatten_bn(indicator_tokens),
+                                            concat=True),
             )
 
         raise AssertionError("This line should be unreachable.")
@@ -252,15 +291,33 @@ class Ovis2ForConditionalGeneration(nn.Module, SupportsMultiModal):
             self, image_input: Ovis2ImagePatchInputs) -> MultiModalEmbeddings:
         image_patches_flat = image_input["flat_data"]
         patches_per_image = image_input["patches_per_image"]
+        indicator_tokens = image_input["indicator_tokens"]
+
+        indicator_per_image = list(
+            map(lambda x: x + 1 if x > 1 else x + 2, patches_per_image))
 
         target_dtype = self.visual_tokenizer.dtype
         visual_tokens = self.visual_tokenizer(
             image_patches_flat.to(target_dtype))
         visual_embeds = self.vte(visual_tokens)  # 1:1 numeric eq.
 
-        return tuple(
-            x.flatten(0, 1)
-            for x in visual_embeds.split(patches_per_image, dim=0))
+        indicator_embeds = self.vte(indicator_tokens)
+        indicator_embeds_per_image = indicator_embeds.split(
+            indicator_per_image)
+
+        visual_embeds_per_image = visual_embeds.split(patches_per_image, dim=0)
+        vision_embeddings = []
+        for indicator, visual in zip(indicator_embeds_per_image,
+                                     visual_embeds_per_image):
+            vision_embeddings_per_image = []
+            for i in range(visual.shape[0]):
+                vision_embeddings_per_image.append(
+                    torch.cat([indicator[i:i + 1], visual[i]], dim=0))
+            vision_embeddings_per_image.append(indicator[i + 1:])
+            vision_embeddings.append(
+                torch.cat(vision_embeddings_per_image, dim=0))
+
+        return tuple(vision_embeddings)
 
     def get_multimodal_embeddings(
             self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
@@ -281,7 +338,7 @@ class Ovis2ForConditionalGeneration(nn.Module, SupportsMultiModal):
         if multimodal_embeddings is not None:
             inputs_embeds = merge_multimodal_embeddings(
                 input_ids, inputs_embeds, multimodal_embeddings,
-                [IMAGE_ATOM_TOKEN_ID, IMAGE_PAD_TOKEN_ID])
+                [IMAGE_PAD_TOKEN_ID])
         return inputs_embeds
 
     def forward(

vllm/transformers_utils/processors/ovis2.py

@@ -69,20 +69,21 @@ class OvisProcessor(ProcessorMixin):
     image_processor_class = "AutoImageProcessor"
     tokenizer_class = "Qwen2Tokenizer"
 
-    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
-        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
-        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, image_pad_token=None, **kwargs):
+        self.image_token = "<image>"
+        self.image_pad_token = "<|image_pad|>" if image_pad_token is None else image_pad_token
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
+        self.image_pad_token_id = self.tokenizer.get_vocab()[self.image_pad_token]
         self.extra_special_tokens = {
-            "image_token": "<image>",
-            "image_atom": "<image_atom>",
-            "image_start": "<img>",
-            "image_prefix": "<pre>",
-            "image_col_sep": "<col>",
-            "image_row_sep": "<row>",
-            "image_end": "</img>",
-            'image_pad': '<image_pad>',
+            "image_token": -200,
+            "image_atom": -300,
+            "image_start": -301,
+            "image_prefix": -302,
+            "image_col_sep": -303,
+            "image_row_sep": -304,
+            "image_end": -305,
+            'image_pad': self.image_pad_token_id,
         }
 
     def __call__(
@@ -157,58 +158,44 @@ class OvisProcessor(ProcessorMixin):
             if not isinstance(text, list):
                 text = [text]
 
-            tokenized_batched_text = self.tokenizer.batch_encode_plus(
-                text,
-                **output_kwargs["text_kwargs"]
-            )
+            tokenized_batched_text = self._tokenize_with_image_symbol(text)
             image_token_id = self.get_token_value("image_token")
             replaced_ids_list = []
-            replaced_attn_mask_list = []
             idx = 0
-            for ids_tensor, attn_mask in zip(tokenized_batched_text['input_ids'],
-                                             tokenized_batched_text['attention_mask']):
+            for ids_tensor in tokenized_batched_text:
                 if image_token_id in ids_tensor and "image_placeholders" in image_features:
                     if idx < len(image_features["image_placeholders"]):
                         # Converts in list for ease of use
                         ids_list = ids_tensor.tolist()
-                        attn_list = attn_mask.tolist()
 
                         new_ids = []
-                        new_attn = []
 
                         # replace placeholders
                         for i, token_id in enumerate(ids_list):
                             if token_id == image_token_id:
                                 placeholder_ids = image_features["image_placeholders"][idx]
                                 new_ids.extend(placeholder_ids)
-                                new_attn.extend([1] * len(placeholder_ids))
                                 idx += 1
                             else:
                                 new_ids.append(token_id)
-                                new_attn.append(attn_list[i])
 
                         # Converts back to tensors
                         ids_tensor = torch.tensor(new_ids, dtype=torch.long)
-                        attn_mask = torch.tensor(new_attn, dtype=torch.long)
                     else:
                         raise RuntimeError(
                             'Mismatch between the images you provided and the number of placeholder present in the text')
 
                 replaced_ids_list.append(ids_tensor)
-                replaced_attn_mask_list.append(attn_mask)
 
             if replaced_ids_list:
                 replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
-                replaced_and_tokenized_attn_mask = torch.stack(replaced_attn_mask_list)
             else:
                 replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
-                replaced_and_tokenized_attn_mask = torch.tensor([], dtype=torch.long)
 
             # Create the output with text features
             output = BatchFeature(
                 data={
                     "input_ids": replaced_and_tokenized_ids,
-                    "attention_mask": replaced_and_tokenized_attn_mask,
                 }
             )
 
@@ -219,10 +206,22 @@ class OvisProcessor(ProcessorMixin):
 
             return output
 
-
         # If only images were provided
         return BatchFeature(data=image_features)
 
+    def _tokenize_with_image_symbol(self, text_list: list[str]) -> torch.LongTensor:
+        batch_token_ids = []
+        for text in text_list:
+            text_chunks = [self.tokenizer(chunk, add_special_tokens=False).input_ids for chunk in
+                           text.split(self.image_token)]
+            token_ids = []
+            num_chuck = len(text_chunks)
+            for i, chunk in enumerate(text_chunks):
+                token_ids.extend(chunk)
+                if i < num_chuck - 1:
+                    token_ids.append(self.get_token_value("image_token"))
+            batch_token_ids.append(token_ids)
+        return torch.tensor(batch_token_ids, dtype=torch.long)
 
     def get_image_size(self):
         height = self.image_processor.crop_size["height"]
@@ -230,10 +229,9 @@ class OvisProcessor(ProcessorMixin):
         return height, width
 
     def get_token_value(self, tok):
-        return self.tokenizer.get_vocab()[self.extra_special_tokens[tok]]
-
-    def construct_image_placeholders(self, grid):
+        return self.extra_special_tokens[tok]
 
+    def construct_image_indicators(self, grid):
         image_placeholders = [self.get_token_value('image_start'),
                               self.get_token_value('image_atom'),
                               self.get_token_value('image_prefix')]
@@ -246,7 +244,11 @@ class OvisProcessor(ProcessorMixin):
                 if r < grid[0] - 1:
                     image_placeholders.append(self.get_token_value('image_row_sep'))
         image_placeholders.append(self.get_token_value('image_end'))
-        # return image_placeholders
+        return image_placeholders
+
+    def construct_image_placeholders(self, grid):
+
+        image_placeholders = self.construct_image_indicators(grid)
 
         image_atom_token_id = self.get_token_value('image_atom')
         # Extract the padding token ID from tokenizer
@@ -255,7 +257,7 @@ class OvisProcessor(ProcessorMixin):
         # Create a new list with padding tokens inserted
         padded_placeholder_tokens = []
         for token in image_placeholders:
-            padded_placeholder_tokens.append(token)
+            padded_placeholder_tokens.append(image_padding_token_id)
             if token == image_atom_token_id:
                 # Add 255 padding tokens after each image atom token
                 padded_placeholder_tokens.extend([image_padding_token_id] * 255)
@@ -394,4 +396,4 @@ class OvisProcessor(ProcessorMixin):
         return names_from_processor + ["second_per_grid_ts"]
 
 
-AutoProcessor.register("OvisProcessor", OvisProcessor)
+AutoProcessor.register("OvisProcessor", OvisProcessor)
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