[Model] Fix Phi-3.5-vision-instruct 'num_crops' issue (#7710)

docs/source/models/supported_models.rst

@@ -225,9 +225,9 @@ Multimodal Language Models
     - :code:`google/paligemma-3b-pt-224`, :code:`google/paligemma-3b-mix-224`, etc.
     - 
   * - :code:`Phi3VForCausalLM`
-    - Phi-3-Vision
+    - Phi-3-Vision, Phi-3.5-Vision
     - Image
-    - :code:`microsoft/Phi-3-vision-128k-instruct`, etc.
+    - :code:`microsoft/Phi-3-vision-128k-instruct`, :code:`microsoft/Phi-3.5-vision-instruct` etc.
     -
   * - :code:`MiniCPMV`
     - MiniCPM-V

tests/models/test_phi3v.py

@@ -21,7 +21,7 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n",
 })
 
-models = ["microsoft/Phi-3-vision-128k-instruct"]
+models = ["microsoft/Phi-3.5-vision-instruct"]
 
 
 def vllm_to_hf_output(vllm_output: Tuple[List[int], str,

vllm/config.py

@@ -13,7 +13,9 @@ from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
 from vllm.tracing import is_otel_available, otel_import_error_traceback
-from vllm.transformers_utils.config import get_config, get_hf_text_config
+from vllm.transformers_utils.config import (get_config,
+                                            get_hf_image_processor_config,
+                                            get_hf_text_config)
 from vllm.utils import (STR_NOT_IMPL_ENC_DEC_CUDAGRAPH, GiB_bytes,
                         cuda_device_count_stateless, get_cpu_memory, is_cpu,
                         is_hip, is_neuron, is_openvino, is_xpu,
@@ -167,6 +169,8 @@ class ModelConfig:
         self.hf_config = get_config(self.model, trust_remote_code, revision,
                                     code_revision, rope_scaling, rope_theta)
         self.hf_text_config = get_hf_text_config(self.hf_config)
+        self.hf_image_processor_config = get_hf_image_processor_config(
+            self.model, revision)
         self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
 
         # Choose a default enforce_eager value if the user did not specify

vllm/inputs/registry.py

@@ -2,8 +2,8 @@ import functools
 from array import array
 from collections import UserDict
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Callable, Dict, Mapping, Optional, Protocol,
-                    Tuple, Type)
+from typing import (TYPE_CHECKING, Any, Callable, Dict, Mapping, Optional,
+                    Protocol, Tuple, Type)
 
 from torch import nn
 from transformers import PretrainedConfig
@@ -55,6 +55,13 @@ class InputContext:
 
         return hf_config
 
+    def get_hf_image_processor_config(self) -> Dict[str, Any]:
+        """
+        Get the HuggingFace image processor configuration of the model.
+        """
+
+        return self.model_config.hf_image_processor_config
+
 
 N = TypeVar("N", bound=Type[nn.Module])
 

vllm/model_executor/models/phi3v.py

@@ -15,8 +15,8 @@
 # limitations under the License.
 import re
 from functools import lru_cache
-from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
-                    TypedDict, Union)
+from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional,
+                    Tuple, TypedDict, Union)
 
 import numpy as np
 import torch
@@ -324,12 +324,12 @@ def _calc_hd_transform_size(*, width: int, height: int, hd_num: int = 16):
 
 # Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L181
 def get_phi3v_image_feature_size(
-    hf_config: PretrainedConfig,
+    hf_config: Dict[str, Any],
     *,
     input_height: int,
     input_width: int,
 ) -> int:
-    num_crops = getattr(hf_config, "num_crops", 16)
+    num_crops = hf_config.get("num_crops", 16)
     new_width, new_height = _calc_hd_transform_size(width=input_width,
                                                     height=input_height,
                                                     hd_num=num_crops)
@@ -341,7 +341,7 @@ def get_phi3v_image_feature_size(
 def get_max_phi3v_image_tokens(ctx: InputContext):
 
     return get_phi3v_image_feature_size(
-        ctx.get_hf_config(),
+        ctx.get_hf_image_processor_config(),
         input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
         input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
     )
@@ -395,7 +395,7 @@ def input_processor_for_phi3v(ctx: InputContext, llm_inputs: LLMInputs):
         return llm_inputs
 
     model_config = ctx.model_config
-    hf_config = ctx.get_hf_config()
+    hf_config = ctx.get_hf_image_processor_config()
 
     image_data = multi_modal_data["image"]
     if isinstance(image_data, Image.Image):

vllm/transformers_utils/config.py

 import contextlib
 from pathlib import Path
-from typing import Dict, Optional, Type, Union
+from typing import Any, Dict, Optional, Type, Union
 
 from transformers import GenerationConfig, PretrainedConfig
+from transformers.models.auto.image_processing_auto import (
+    get_image_processor_config)
 from transformers.models.auto.modeling_auto import (
     MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
 
@@ -98,6 +100,17 @@ def get_config(
     return config
 
 
+def get_hf_image_processor_config(
+    model: Union[str, Path],
+    revision: Optional[str] = None,
+    **kwargs,
+) -> Dict[str, Any]:
+    # Separate model folder from file path for GGUF models
+    if Path(model).is_file() and Path(model).suffix == ".gguf":
+        model = Path(model).parent
+    return get_image_processor_config(model, revision=revision, **kwargs)
+
+
 def get_hf_text_config(config: PretrainedConfig):
     """Get the "sub" config relevant to llm for multi modal models.
         No op for pure text models.