[Model] Port deepseek-vl2 processor and remove deepseek_vl2 dependency

.buildkite/test-pipeline.yaml

@@ -52,7 +52,6 @@ steps:
   - tests/worker
   - tests/standalone_tests/lazy_torch_compile.py
   commands:
-  - pip install git+https://github.com/Isotr0py/DeepSeek-VL2.git  # Used by multimoda processing test
   - python3 standalone_tests/lazy_torch_compile.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
   - pytest -v -s async_engine # AsyncLLMEngine

docs/source/models/supported_models.md

@@ -767,16 +767,10 @@ See [this page](#generative-models) for more information on how to use generativ
 <sup>E</sup> Pre-computed embeddings can be inputted for this modality.  
 <sup>+</sup> Multiple items can be inputted per text prompt for this modality.
 
-````{note}
-To use `DeepSeek-VL2` series models, you need to install a fork version `deepseek_vl2` package:
-
-```shell
-pip install git+https://github.com/Isotr0py/DeepSeek-VL2.git
+```{note}
+To use `DeepSeek-VL2` series models, you have to pass `--hf_overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}'` when running vLLM.
 ```
 
-Besides, to run `DeepSeek-VL2` series models, you have to pass `--hf_overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}'` when running vLLM.
-````
-
 ```{note}
 To use `TIGER-Lab/Mantis-8B-siglip-llama3`, you have to pass `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'` when running vLLM.
 ```

examples/offline_inference/vision_language_multi_image.py

@@ -393,7 +393,7 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
 
 model_example_map = {
     "aria": load_aria,
-    "deepseek_vl2": load_deepseek_vl2,
+    "deepseek_vl_v2": load_deepseek_vl2,
     "h2ovl_chat": load_h2onvl,
     "idefics3": load_idefics3,
     "internvl_chat": load_internvl,

tests/models/decoder_only/vision_language/test_models.py

@@ -190,7 +190,7 @@
         dtype="bfloat16",
     ),
     "deepseek_vl_v2": VLMTestInfo(
-        models=["deepseek-ai/deepseek-vl2-tiny"],
+        models=["Isotr0py/deepseek-vl2-tiny"], # model repo using dynamic module
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         prompt_formatter=lambda img_prompt: f"<|User|>: {img_prompt}\n\n<|Assistant|>: ", # noqa: E501
         max_model_len=4096,

tests/models/multimodal/processing/test_common.py

@@ -22,6 +22,8 @@ def _test_processing_correctness(
 ):
     if model_id == "TIGER-Lab/Mantis-8B-siglip-llama3":
         hf_overrides = {"architectures": ["MantisForConditionalGeneration"]}
+    elif model_id == "deepseek-ai/deepseek-vl2-tiny":
+        hf_overrides = {"architectures": ["DeepseekVLV2ForCausalLM"]}
     else:
         hf_overrides = {}
 
@@ -139,6 +141,7 @@ def _test_processing_correctness(
     ("rhymes-ai/Aria", {"image": True}),
     ("Salesforce/blip2-opt-2.7b", {"image": False}),
     ("facebook/chameleon-7b", {"image": False}),
+    ("deepseek-ai/deepseek-vl2-tiny", {"image": True}),
     ("adept/fuyu-8b", {"image": False}),
     ("llava-hf/llava-1.5-7b-hf", {"image": True}),
     ("llava-hf/llava-v1.6-mistral-7b-hf", {"image": True}),

vllm/model_executor/models/deepseek_vl2.py

@@ -1,15 +1,15 @@
 # adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
 """Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
 import math
-from functools import cached_property, partial
+from functools import cached_property
 from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
                     TypedDict, Union)
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange, repeat
-from transformers import AutoProcessor, BatchFeature, ProcessorMixin
+from transformers import BatchFeature
 
 from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
@@ -31,6 +31,8 @@
 from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
                                                           MlpProjectorConfig,
                                                           VisionEncoderConfig)
+from vllm.transformers_utils.processors.deepseek_vl2 import (
+    DeepseekVLV2Processor)
 from vllm.utils import is_list_of
 
 from .interfaces import SupportsMultiModal, SupportsPP
@@ -129,25 +131,8 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
     def get_hf_config(self):
         return self.ctx.get_hf_config(DeepseekVLV2Config)
 
-    def get_hf_processor(self) -> ProcessorMixin:
-        # TODO(Isotr0py): we should get rid of dependency on deepseek_vl2
-        # in the future, because it's flasky and lack of maintenance.
-        try:
-            from deepseek_vl2.models.processing_deepseek_vl_v2 import (
-                DeepseekVLV2Processor, select_best_resolution)
-            AutoProcessor.register("DeepseekVLV2Processor",
-                                   DeepseekVLV2Processor)
-        except ModuleNotFoundError as exc:
-            raise ModuleNotFoundError(
-                "You need to `pip install "
-                "git+https://github.com/deepseek-ai/DeepSeek-VL2.git` "
-                "to use this model") from exc
-
-        processor = self.ctx.get_hf_processor(DeepseekVLV2Processor)
-        processor.select_best_resolution = partial(
-            select_best_resolution,
-            candidate_resolutions=processor.candidate_resolutions)
-        return processor
+    def get_hf_processor(self) -> DeepseekVLV2Processor:
+        return self.ctx.get_hf_processor(DeepseekVLV2Processor)
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": None}
@@ -224,31 +209,21 @@ def _call_hf_processor(
         mm_kwargs: Mapping[str, object],
     ) -> BatchFeature:
         if mm_data:
-            outputs = self.info.ctx.call_hf_processor(
+            processed_outputs = self.info.ctx.call_hf_processor(
                 self.info.get_hf_processor(**mm_kwargs),
                 dict(prompt=prompt, **mm_data),
                 mm_kwargs,
             )
-
-            # Deepseek-vl2 processor don't return BatchFeature,
-            # we need to manually create it
-            processed_outputs = dict(input_ids=outputs["input_ids"])
-            processed_outputs = BatchFeature(data=dict(processed_outputs),
-                                             tensor_type="pt")
-
-            # Remove batch dimension from processor outputs,
-            # because we will try batch to create NestedTensors
             target_dtype = self.info.ctx.model_config.dtype
-            pixel_values = outputs["images"].to(target_dtype).squeeze(0)
-            images_spatial_crop = outputs["images_spatial_crop"].squeeze(0)
+            pixel_values = processed_outputs.pop("pixel_values").to(
+                target_dtype)
+            # split pixel values into patches corresponding to each image
+            images_spatial_crop = processed_outputs["images_spatial_crop"]
             patches_per_image = [
                 x.prod().item() + 1 for x in images_spatial_crop
             ]
-
-            # Rename `images` -> `pixel_values` to avoid confusion
-            processed_outputs["pixel_values"] = list(
-                pixel_values.split(patches_per_image))
-            processed_outputs["images_spatial_crop"] = images_spatial_crop
+            pixel_values = pixel_values.split(patches_per_image)
+            processed_outputs["pixel_values"] = pixel_values
         else:
             tokenizer = self.info.get_tokenizer()
             processed_outputs = tokenizer(prompt,

vllm/transformers_utils/processors/__init__.py

@@ -0,0 +1,4 @@
+from vllm.transformers_utils.processors.deepseek_vl2 import (
+    DeepseekVLV2Processor)
+
+__all__ = ["DeepseekVLV2Processor"]