[misc] fix script (#6977)

Former-commit-id: cc8c7e762b9c873ef79529152465bbed9231053c

scripts/vllm_infer.py

@@ -46,13 +46,14 @@ def vllm_infer(
     max_new_tokens: int = 1024,
     repetition_penalty: float = 1.0,
     pipeline_parallel_size: int = 1,
-    image_resolution: int = 512 * 512,
+    image_max_pixels: int = 768 * 768,
+    image_min_pixels: int = 32 * 32,
 ):
     r"""
     Performs batch generation using vLLM engine, which supports tensor parallelism.
     Usage: python vllm_infer.py --model_name_or_path meta-llama/Llama-2-7b-hf --template llama --dataset alpaca_en_demo
     """
-    check_version("vllm>=0.4.3,<=0.6.5")
+    check_version("vllm>=0.4.3,<=0.7.2")
     if pipeline_parallel_size > get_device_count():
         raise ValueError("Pipeline parallel size should be smaller than the number of gpus.")
 
@@ -86,7 +87,9 @@ def vllm_infer(
     for sample in dataset_module["train_dataset"]:
         if sample["images"]:
             multi_modal_data = {
-                "image": template_obj.mm_plugin._regularize_images(sample["images"], image_resolution=image_resolution)
+                "image": template_obj.mm_plugin._regularize_images(
+                    sample["images"], image_max_pixels=image_max_pixels, image_min_pixels=image_min_pixels
+                )
             }
         else:
             multi_modal_data = None

src/llamafactory/chat/vllm_engine.py

@@ -21,18 +21,13 @@ from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
 from ..extras.misc import get_device_count
-from ..extras.packages import is_pillow_available, is_vllm_available
+from ..extras.packages import is_vllm_available
 from ..model import load_config, load_tokenizer
 from ..model.model_utils.quantization import QuantizationMethod
 from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
 from .base_engine import BaseEngine, Response
 
 
-if is_pillow_available():
-    from PIL import Image
-    from PIL.Image import Image as ImageObject
-
-
 if is_vllm_available():
     from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
     from vllm.lora.request import LoRARequest
@@ -54,6 +49,7 @@ class VllmEngine(BaseEngine):
         finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
     ) -> None:
+        self.model_args = model_args
         config = load_config(model_args)  # may download model from ms hub
         if getattr(config, "quantization_config", None):  # gptq models should use float16
             quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
@@ -180,15 +176,13 @@ class VllmEngine(BaseEngine):
         )
 
         if images is not None:  # add image features
-            multi_modal_data = {"image": []}
-            for image in images:
-                if not isinstance(image, (str, ImageObject)):
-                    raise ValueError(f"Expected image input is a path or PIL.Image, but got {type(image)}.")
-
-                if isinstance(image, str):
-                    image = Image.open(image).convert("RGB")
-
-                multi_modal_data["image"].append(image)
+            multi_modal_data = {
+                "image": self.template.mm_plugin._regularize_images(
+                    images,
+                    image_max_pixels=self.model_args.image_max_pixels,
+                    image_min_pixels=self.model_args.image_min_pixels,
+                )
+            }
         else:
             multi_modal_data = None
 

src/llamafactory/data/mm_plugin.py

@@ -1112,9 +1112,13 @@ class Qwen2vlPlugin(BasePlugin):
         self._validate_input(images, videos, audios)
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
         merge_length: int = getattr(image_processor, "merge_size") ** 2
+        if self.expand_mm_tokens:
             mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
             image_grid_thw = mm_inputs.get("image_grid_thw", [])
             video_grid_thw = mm_inputs.get("video_grid_thw", [])
+        else:
+            image_grid_thw = [None] * len(images)
+            video_grid_thw = [None] * len(videos)
 
         num_image_tokens, num_video_tokens = 0, 0
         messages = deepcopy(messages)