[Bugfix] Add phi3v resize for dynamic shape and fix torchvision requirement (#5772)

requirements-cpu.txt

@@ -3,4 +3,5 @@
 
 # Dependencies for x86_64 CPUs
 torch == 2.3.1+cpu
+torchvision == 0.18.1+cpu   # required for the image processor of phi3v, this must be updated alongside torch
 triton >= 2.2.0  # FIXME(woosuk): This is a hack to avoid import error.

requirements-cuda.txt

@@ -5,5 +5,7 @@
 ray >= 2.9
 nvidia-ml-py # for pynvml package
 torch == 2.3.0
+# These must be updated alongside torch
+torchvision == 0.18.0   # Required for phi3v processor, also see https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 xformers == 0.0.26.post1  # Requires PyTorch 2.3.0
 vllm-flash-attn == 2.5.9  # Requires PyTorch 2.3.0

requirements-test.txt

@@ -14,7 +14,6 @@ peft
 requests
 ray
 sentence-transformers # required for embedding
-torchvision # required for the image processor of phi3v
 
 # Benchmarking
 aiohttp

tests/models/test_phi3v.py

@@ -22,6 +22,7 @@ assert len(HF_IMAGE_PROMPTS) == len(IMAGE_FILES)
 def iter_phi3v_configs(model_name: str):
     image_hw_to_feature_size = {
         (1008, 1344): 1921,
+        (2016, 2688): 1933,
     }
 
     for (h, w), f in image_hw_to_feature_size.items():
@@ -75,6 +76,9 @@ if is_cpu():
 # TODO: Add test for `tensor_parallel_size` [ref: PR #3883]
 # Since we use _attn_implementation="eager" for hf_runner, here is
 # numeric difference for longer context and test can't pass
+@pytest.mark.xfail(
+    reason="Inconsistent image processor being used due to lack "
+    "of support for dynamic image token replacement")
 @pytest.mark.parametrize("model_and_config", model_and_vl_config)
 @pytest.mark.parametrize("dtype", [target_dtype])
 @pytest.mark.parametrize("max_tokens", [128])

vllm/model_executor/models/phi3v.py

@@ -13,14 +13,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
+from typing import Dict, Iterable, List, Literal, Optional, Tuple, TypedDict
 
+import numpy as np
 import torch
 import torch.nn as nn
+from PIL import Image
 from transformers import CLIPVisionConfig, PretrainedConfig
 
 from vllm.attention import AttentionMetadata
-from vllm.config import CacheConfig, VisionLanguageConfig
+from vllm.config import CacheConfig, ModelConfig, VisionLanguageConfig
+from vllm.logger import init_logger
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
@@ -32,9 +35,11 @@ from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.models.vlm_base import VisionLanguageModelBase
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.image import get_dummy_image_data
+from vllm.multimodal.image import ImagePixelData, get_dummy_image_data
 from vllm.sequence import SamplerOutput
 
+logger = init_logger(__name__)
+
 _KEYS_TO_MODIFY_MAPPING = {
     "model.vision_embed_tokens": "vision_embed_tokens",
 }
@@ -268,7 +273,63 @@ class Phi3VImagePixelInputs(TypedDict):
     """Shape: (batch_size, 2)"""
 
 
-@MULTIMODAL_REGISTRY.register_image_pixel_input()
+# FIXME(Isotr0py): Remove these after dynamic num_img_tokens is supported
+# copied from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py
+def calc_padded_size(width, height, padding_unit=336):
+    target_height = int(np.ceil(height / padding_unit) * padding_unit)
+    top_padding = int((target_height - height) / 2)
+    bottom_padding = target_height - height - top_padding
+    padded_width = width
+    padded_height = height + top_padding + bottom_padding
+    return padded_width, padded_height
+
+
+# copied from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py
+def calc_hd_transform_size(width, height, hd_num=16):
+    transposed = False
+    if width < height:
+        width, height = height, width
+        transposed = True
+
+    ratio = width / height
+    scale = 1
+    while scale * np.ceil(scale / ratio) <= hd_num:
+        scale += 1
+    scale -= 1
+
+    new_width = int(scale * 336)
+    new_height = int(new_width / ratio)
+
+    padded_width, padded_height = calc_padded_size(new_width, new_height)
+
+    if transposed:
+        padded_width, padded_height = padded_height, padded_width
+
+    return padded_width, padded_height
+
+
+def _image_processor(
+    data: ImagePixelData,
+    model_config: ModelConfig,
+    vlm_config: VisionLanguageConfig,
+) -> Dict[str, torch.Tensor]:
+    image = data.image
+
+    if isinstance(image, Image.Image):
+        # Temporary patch before dynamic number of image tokens is supported
+        _, _, h, w = vlm_config.image_input_shape
+        if (w, h) != calc_hd_transform_size(image.width, image.height):
+            logger.warning(
+                "Dynamic image shape is currently not supported. "
+                "Resizing input image to (%d, %d).", w, h)
+
+            data.image = image.resize((w, h))
+
+    return MULTIMODAL_REGISTRY._get_plugin_for_data_type(ImagePixelData) \
+            ._default_input_processor(data, model_config, vlm_config)
+
+
+@MULTIMODAL_REGISTRY.register_image_pixel_input(_image_processor)
 @MULTIMODAL_REGISTRY.register_dummy_data(get_dummy_image_data)
 class Phi3VForCausalLM(VisionLanguageModelBase):