[VLM] Update compatibility with transformers 4.49

Author: DarkLight1337

docs/source/models/supported_models.md

@@ -883,8 +883,7 @@ For more details, please see: <gh-pr:4087#issuecomment-2250397630>
 :::
 
 :::{note}
-The chat template for Pixtral-HF is incorrect (see [discussion](https://huggingface.co/mistral-community/pixtral-12b/discussions/22)).
-A corrected version is available at <gh-file:examples/template_pixtral_hf.jinja>.
+`mistral-community/pixtral-12b` does not support V1 yet.
 :::
 
 :::{note}

examples/template_pixtral_hf.jinja

@@ -761,7 +761,6 @@ def test_resolve_content_format_hf_defined(model, expected_format):
      ("template_falcon.jinja", "string"),
      ("template_inkbot.jinja", "string"),
      ("template_llava.jinja", "string"),
-     ("template_pixtral_hf.jinja", "openai"),
      ("template_vlm2vec.jinja", "openai"),
      ("tool_chat_template_granite_20b_fc.jinja", "string"),
      ("tool_chat_template_hermes.jinja", "string"),

tests/entrypoints/test_chat_utils.py

@@ -224,7 +224,7 @@
         marks=[
             pytest.mark.skipif(
                 Version(TRANSFORMERS_VERSION) >= Version("4.48"),
-                reason="HF model is not compatible with transformers>=4.48.0",
+                reason="HF model is not compatible with transformers>=4.48",
             )
         ],
     ),
@@ -359,7 +359,7 @@
         marks=[
             pytest.mark.skipif(
                 Version(TRANSFORMERS_VERSION) >= Version("4.48"),
-                reason="HF model is not compatible with transformers>=4.48.0",
+                reason="HF model is not compatible with transformers>=4.48",
             )
         ],
     ),

tests/models/decoder_only/vision_language/test_models.py

@@ -4,7 +4,6 @@
 
 import pytest
 import torch.nn.functional as F
-import transformers
 from transformers import AutoModelForVision2Seq
 
 from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
@@ -57,6 +56,10 @@ def _run_test(
 
     with hf_runner(model, dtype=dtype,
                    auto_cls=AutoModelForVision2Seq) as hf_model:
+        # Patch the issue where generation_config.json is missing
+        hf_model.processor.patch_size = \
+            hf_model.model.config.vision_config.patch_size
+
         # Patch the issue where image_token_id
         # exceeds the maximum allowed vocab size
         hf_model.model.resize_token_embeddings(
@@ -88,8 +91,6 @@ def _run_test(
     )
 
 
-@pytest.mark.skipif(transformers.__version__ >= "4.46",
-                    reason="Model broken with changes in transformers 4.46")
 @pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])

tests/models/embedding/vision_language/test_llava_next.py

@@ -293,16 +293,29 @@ def _call_hf_processor(
 
         pixel_values = processed_outputs.get("pixel_values")
         if pixel_values is not None:
-            images = mm_data["images"]
-            assert isinstance(images, list)
-
-            # Original output: (1, num_images, C, H, W)
-            # New output: (num_images, C, H, W)
-            assert (isinstance(pixel_values, list) and len(pixel_values) == 1)
-            assert (isinstance(pixel_values[0], list)
-                    and len(pixel_values[0]) == len(images))
-
-            processed_outputs["pixel_values"] = pixel_values[0]
+            # Before/after https://github.com/huggingface/transformers/pull/35122
+            if Version(TRANSFORMERS_VERSION) <= Version("4.48.2"):
+                images = mm_data["images"]
+                assert isinstance(images, list)
+
+                # Original output: (1, num_images, C, H, W)
+                # New output: (num_images, C, H, W)
+                assert (isinstance(pixel_values, list)
+                        and len(pixel_values) == 1)
+                assert (isinstance(pixel_values[0], list)
+                        and len(pixel_values[0]) == len(images))
+
+                processed_outputs["pixel_values"] = pixel_values[0]
+            else:
+                # Avoid padding since we need the output for each image to be
+                # independent of other images for the cache to work correctly
+                image_sizes = processed_outputs["image_sizes"]
+                assert len(pixel_values) == len(image_sizes)
+
+                processed_outputs["pixel_values"] = [
+                    p[:, :h, :w]
+                    for p, (h, w) in zip(pixel_values, image_sizes)
+                ]
 
         return processed_outputs
 

vllm/model_executor/models/llava.py

@@ -73,7 +73,15 @@ def get_hf_config(self) -> LlavaNextLikeConfig:
         return self.ctx.get_hf_config(LlavaNextConfig)
 
     def get_hf_processor(self):
-        return self.ctx.get_hf_processor(LlavaNextProcessor)
+        hf_processor = self.ctx.get_hf_processor(LlavaNextProcessor)
+
+        # In case patch_size is omitted from `processor_config.json`
+        # e.g. for E5-V: https://huggingface.co/royokong/e5-v
+        if hf_processor.patch_size is None:
+            patch_size = self.get_vision_encoder_info().get_patch_size()
+            hf_processor.patch_size = patch_size
+
+        return hf_processor
 
     # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L113
     def get_num_image_tokens(

vllm/model_executor/models/llava_next.py

@@ -342,6 +342,15 @@ def get_hf_processor(
         **kwargs: object,
     ):
         hf_processor = self.ctx.get_hf_processor()
+
+        # NumPy arrays are considered as Iterable but not Sequence in
+        # https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L428
+        image_processor = hf_processor.image_processor  # type: ignore
+        for attr in ("mean", "std"):
+            val = getattr(image_processor, attr)
+            if isinstance(val, np.ndarray):
+                setattr(image_processor, attr, val.tolist())
+
         return hf_processor
 
     def get_image_processor(self):

vllm/model_executor/models/minicpmv.py

@@ -141,9 +141,9 @@ class PlaceholderRange(TypedDict):
 def nested_tensors_equal(a: NestedTensors, b: NestedTensors) -> bool:
     """Equality check between :data:`NestedTensors` objects."""
     if isinstance(a, torch.Tensor):
-        return isinstance(b, torch.Tensor) and bool((a == b).all().item())
+        return isinstance(b, torch.Tensor) and torch.equal(a, b)
     elif isinstance(b, torch.Tensor):
-        return isinstance(a, torch.Tensor) and bool((b == a).all().item())
+        return isinstance(a, torch.Tensor) and torch.equal(b, a)
 
     if isinstance(a, list):
         return (isinstance(b, list)