[data] fix internvl plugin (#7817)

src/llamafactory/data/mm_plugin.py

@@ -21,7 +21,7 @@ import re
 from copy import deepcopy
 from dataclasses import dataclass
 from io import BytesIO
-from typing import TYPE_CHECKING, Any, BinaryIO, Literal, Optional, TypedDict, Union
+from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
 
 import numpy as np
 import torch
@@ -86,20 +86,6 @@ if TYPE_CHECKING:
             pass
 
 
-def _concatenate_list(input_list: list[Any]) -> Union[list[Any], "NDArray", "torch.Tensor"]:
-    r"""Concatenate a list of lists, numpy arrays or torch tensors.
-
-    Returns:
-        a list of numpy arrays or torch tensors.
-    """
-    if isinstance(input_list[0], list):
-        return [item for sublist in input_list for item in sublist]
-    elif isinstance(input_list[0], np.ndarray):
-        return np.concatenate(input_list, axis=0)
-    elif isinstance(input_list[0], torch.Tensor):
-        return torch.cat(input_list, dim=0)
-
-
 def _get_paligemma_token_type_ids(imglens: list[int], seqlens: list[int], processor: "MMProcessor") -> list[list[int]]:
     r"""Get paligemma token type ids for computing loss.
 
@@ -496,8 +482,15 @@ class InternVLPlugin(BasePlugin):
         **kwargs,
     ) -> dict[str, "torch.Tensor"]:
         image_processor: BaseImageProcessor = getattr(processor, "image_processor")
-        attributes = ["crop_to_patches", "min_patches", "max_patches"]  # need for image processor
-        image_kwargs = {attr: getattr(image_processor, attr, None) for attr in attributes}
+        image_processor_kwargs = {}
+        if getattr(processor, "crop_to_patches", False):
+            image_processor_kwargs.update(
+                {
+                    "crop_to_patches": True,
+                    "max_patches": 12,
+                    "min_patches": 1,
+                }
+            )
 
         mm_inputs = {}
         image_video_patches = []
@@ -520,7 +513,7 @@ class InternVLPlugin(BasePlugin):
 
         if len(images) != 0:
             images = make_flat_list_of_images(images)
-            image_inputs = image_processor(images=images, **image_kwargs)
+            image_inputs = image_processor(images=images, return_tensors="pt", **image_processor_kwargs)
             image_num_patches = image_inputs.pop("num_patches")
             image_pixel_values = image_inputs.pop("pixel_values")
             image_num_patches_indices = np.cumsum(image_num_patches)
@@ -529,8 +522,8 @@ class InternVLPlugin(BasePlugin):
             videos = make_batched_videos(videos)
             num_frames_per_video = [len(video) for video in videos]
             patch_indices = np.cumsum(num_frames_per_video)
-            image_kwargs["crop_to_patches"] = False
-            video_inputs = image_processor(images=videos, **image_kwargs)
+            image_processor_kwargs["crop_to_patches"] = False
+            video_inputs = image_processor(images=videos, return_tensors="pt", **image_processor_kwargs)
             video_num_patches = video_inputs.pop("num_patches")
             video_pixel_values = video_inputs.pop("pixel_values")
             video_num_patches_indices = np.cumsum(video_num_patches)
@@ -543,18 +536,16 @@ class InternVLPlugin(BasePlugin):
                 image_video_patches.append(image_pixel_values[start_index:end_index])
 
         if len(videos) != 0 and video_pixel_values is not None:
+            patch_indices_with_prefix = [0] + list(patch_indices)
             for i in range(len(videos)):
-                current_patch_index = patch_indices[i - 1] if i > 0 else 0
-                end_patch_index = patch_indices[i]
-                start_index = video_num_patches_indices[current_patch_index] if i > 0 else 0
+                current_patch_index = patch_indices_with_prefix[i]
+                end_patch_index = patch_indices_with_prefix[i + 1]
+                start_index = video_num_patches_indices[current_patch_index - 1] if i > 0 else 0
                 end_index = video_num_patches_indices[end_patch_index - 1]
                 image_video_patches.append(video_pixel_values[start_index:end_index])
 
         if len(images) != 0 or len(videos) != 0:
-            pixel_values_list = _concatenate_list(image_video_patches)
-            # in the latest version of transformers,
-            # the pixel_values is a list of tensors not ndarray
-            mm_inputs["pixel_values"] = torch.stack(pixel_values_list)
+            mm_inputs["pixel_values"] = torch.cat(image_video_patches, dim=0)
 
         if len(images) != 0:
             mm_inputs.update({"image_num_patches": image_num_patches})

src/llamafactory/hparams/model_args.py

@@ -231,6 +231,10 @@ class ProcessorArguments:
         default=False,
         metadata={"help": "Use pan and scan to process image for gemma3."},
     )
+    crop_to_patches: bool = field(
+        default=False,
+        metadata={"help": "Whether to crop the image to patches for internvl."},
+    )
     use_audio_in_video: bool = field(
         default=False,
         metadata={"help": "Whether or not to use audio in video inputs."},

src/llamafactory/model/patcher.py

@@ -80,6 +80,7 @@ def patch_processor(
     setattr(processor, "image_max_pixels", model_args.image_max_pixels)
     setattr(processor, "image_min_pixels", model_args.image_min_pixels)
     setattr(processor, "image_do_pan_and_scan", model_args.image_do_pan_and_scan)
+    setattr(processor, "crop_to_patches", model_args.crop_to_patches)
     setattr(processor, "video_max_pixels", model_args.video_max_pixels)
     setattr(processor, "video_min_pixels", model_args.video_min_pixels)
     setattr(processor, "video_fps", model_args.video_fps)

tests/version.txt

@@ -1,2 +1,2 @@
 # change if test fails or cache is outdated
-0.9.3.103
+0.9.3.104