add some

Former-commit-id: 771cc802941cf1953b32e5102c817c6a3090b5ce

src/llamafactory/data/collator.py

@@ -19,8 +19,8 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence
 
 import torch
-from torch.nn.utils.rnn import pad_sequence
 import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
 from transformers import DataCollatorForSeq2Seq
 
 from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER
@@ -106,7 +106,9 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             batch_vidlens.append(len(videos))
             batch_input_ids.append(feature["input_ids"])
 
-        if self.processor is not None and sum(batch_imglens) == 0:  # avoid process hanging in zero3/fsdp case
+        if (
+            self.processor is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
+        ):  # avoid process hanging in zero3/fsdp case
             fake_messages = [{"role": "user", "content": IMAGE_PLACEHOLDER}]
             fake_images = [Image.new("RGB", (64, 64), (255, 255, 255))]
             fake_messages = self.template.mm_plugin.process_messages(fake_messages, fake_images, [], self.processor)
@@ -157,10 +159,14 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
 
         if "image_bound" in features:  # for minicpmv inputs
             features["position_ids"] = [torch.arange(input_ids.size(0)).long() for input_ids in features["input_ids"]]
-            features["input_ids"] = pad(features["input_ids"],)
+            features["input_ids"] = pad(
+                features["input_ids"],
+            )
             features["position_ids"] = pad(features["position_ids"])
             features["labels"] = pad(features["labels"], padding_value=-100)
-            features["attention_mask"] = pad(features["attention_mask"],)
+            features["attention_mask"] = pad(
+                features["attention_mask"],
+            )
             new_features = {}
             new_features.update({"data": features})
             new_features.update(features)

src/llamafactory/data/mm_plugin.py

@@ -265,8 +265,19 @@ class CpmOPlugin(BasePlugin):
     ) -> List[Dict[str, str]]:
         self._validate_input(images, videos)
         num_image_tokens = 0
+        num_video_tokens = 0
         messages = deepcopy(messages)
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+        mm_inputs = {}
+
+        if len(videos) != 0:
+            assert len(images) == 0, "Only support video and image sft seperately"
+            max_slice_nums = 2
+            use_image_id = False
+            mm_inputs = self._get_mm_inputs([], videos, processor)
+        else:
+            max_slice_nums = image_processor.max_slice_nums
+            use_image_id = image_processor.use_image_id
 
         for message in messages:
             content = message["content"]
@@ -274,15 +285,21 @@ class CpmOPlugin(BasePlugin):
                 num_image_tokens += 1
                 content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
 
+            while VIDEO_PLACEHOLDER in content:
+                num_video_tokens += 1
+                content = content.replace(
+                    VIDEO_PLACEHOLDER, "{{image}}" * len(mm_inputs["pixel_values"][num_video_tokens - 1]), 1
+                )
+
             message["content"] = content.replace("{{image}}", "(<image>./</image>)")
 
         if num_image_tokens > 0:
-            mm_inputs = self._get_mm_inputs(images, videos, processor)
+            mm_inputs = self._get_mm_inputs(images, [], processor)
 
+        if mm_inputs:
             pattern = "(<image>./</image>)"
-            images, image_sizes = mm_inputs["pixel_values"], mm_inputs["image_sizes"]
+            image_sizes = mm_inputs["image_sizes"]
 
-            image_index = 0
             for index, message in enumerate(messages):
                 text = message["content"]
                 image_tags = re.findall(pattern, text)
@@ -293,19 +310,21 @@ class CpmOPlugin(BasePlugin):
                         final_text
                         + text_chunks[i]
                         + image_processor.get_slice_image_placeholder(
-                            image_sizes[image_index][i],
+                            image_sizes[0][i],
                             i,
-                            image_processor.max_slice_nums,
+                            max_slice_nums,
-                            image_processor.use_image_id,
+                            use_image_id,
                         )
                     )
-                    image_index += 1
                 final_text += text_chunks[-1]
                 messages[index]["content"] = final_text
 
         if len(images) != num_image_tokens:
             raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
 
+        if len(videos) != num_video_tokens:
+            raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
+
         return messages
 
     @override
@@ -333,7 +352,7 @@ class CpmOPlugin(BasePlugin):
                     new_images.append(images[idx : idx + valid_image_nums])
                     idx += valid_image_nums
                 images = new_images
-                
+
             image_inputs = image_processor(
                 images, do_pad=True, max_slice_nums=image_processor.max_slice_nums, return_tensors="pt"
             )
@@ -346,6 +365,8 @@ class CpmOPlugin(BasePlugin):
                 video_fps=getattr(processor, "video_fps", 2.0),
                 video_maxlen=getattr(processor, "video_maxlen", 64),
             )
+            video_inputs = image_processor(videos, do_pad=True, max_slice_nums=2, return_tensors="pt")
+            mm_inputs.update(video_inputs)
 
         return mm_inputs
 
@@ -380,12 +401,9 @@ class CpmOPlugin(BasePlugin):
                 ]
             )
             image_bounds_list.append(image_bounds)
+
         mm_inputs = self._get_mm_inputs(images, videos, processor, valid_image_nums_ls=valid_image_nums_ls)
-        mm_inputs.update(
+        mm_inputs.update({"image_bound": image_bounds_list})
-            {
-                "image_bound": image_bounds_list,
-            }
-        )
         return mm_inputs