fix: filter overlong prompts should also consider multi modal inputs

verl/utils/dataset/rl_dataset.py

@@ -121,19 +121,19 @@ def _read_files_and_tokenize(self):
         dataframes = []
         for parquet_file in self.data_files:
             # read parquet files and cache
-            dataframe = datasets.load_dataset("parquet", data_files=parquet_file)["train"]
+            if os.path.isdir(parquet_file):
+                dataframe = datasets.load_dataset("parquet", data_dir=parquet_file, split="train")
+            else:
+                dataframe = datasets.load_dataset("parquet", data_files=parquet_file, split="train")
             dataframes.append(dataframe)
         self.dataframe: datasets.Dataset = datasets.concatenate_datasets(dataframes)
 
         print(f'dataset len: {len(self.dataframe)}')
 
         # filter out too long prompts
         if self.filter_overlong_prompts:
-            tokenizer = self.tokenizer
-            prompt_key = self.prompt_key
             self.dataframe = self.dataframe.filter(
-                lambda doc: len(tokenizer.apply_chat_template(doc[prompt_key], add_generation_prompt=True)
-                               ) <= self.max_prompt_length,
+                self._filter_overlong_prompts,
                 num_proc=self.num_workers,
                 desc=f"Filtering prompts longer than {self.max_prompt_length} tokens")
 
@@ -148,6 +148,34 @@ def resume_dataset_state(self):
         else:
             print(r'old dataloader ckpt file is used, please train from scratch for better ckpt performance')
 
+    def _filter_overlong_prompts(self, example: dict) -> bool:
+        chat_messsages = example[self.prompt_key]
+        prompt_with_chat_template = self.tokenizer.apply_chat_template(chat_messsages,
+                                                                       add_generation_prompt=True,
+                                                                       tokenize=False)
+        if self.image_key in example and self.processor is not None:
+            # for multi modal, we need to process the prompt_with_chat_template
+            images = [process_image(image) for image in example[self.image_key]]
+            image_inputs = self.processor.image_processor(images, return_tensors='pt')
+            image_grid_thw = image_inputs['image_grid_thw']
+
+            if image_grid_thw is not None:
+                merge_length = self.processor.image_processor.merge_size**2
+                index = 0
+                while '<image>' in prompt_with_chat_template:
+                    prompt_with_chat_template = prompt_with_chat_template.replace(
+                        '<image>',
+                        '<|vision_start|>' + '<|placeholder|>' * (image_grid_thw[index].prod() // merge_length) +
+                        '<|vision_end|>',
+                        1,
+                    )
+                    index += 1
+
+                prompt_with_chat_template = prompt_with_chat_template.replace('<|placeholder|>',
+                                                                              self.processor.image_token)
+        input_ids = self.tokenizer.encode(prompt_with_chat_template, add_special_tokens=False)
+        return len(input_ids) <= self.max_prompt_length
+
     def __len__(self):
         return len(self.dataframe)