[chat] fix bugs in stage 3 training

applications/Chat/coati/dataset/prompt_dataset.py

@@ -45,7 +45,7 @@ def __init__(self,
                 self.keyed_prompt[k].extend(tensor.to(torch.cuda.current_device()).unbind())
 
     def __len__(self):
-        return len(self.keyed_prompt)
+        return len(self.keyed_prompt["input_ids"])
 
     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         return {k: v[i] for k, v in self.keyed_prompt.items()}

applications/Chat/examples/README.md

@@ -153,7 +153,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \
          --rm_path /your/rm/model/path
 ```
 
-Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/example_data_reformat.py) to reformat [seed_prompts_ch.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_ch.jsonl) or [seed_prompts_en.jsonl](https://github.com/XueFuzhao/InstructionWild/blob/main/data/seed_prompts_en.jsonl) in InstructionWild.  
+Prompt dataset: the instruction dataset mentioned in the above figure which includes the instructions, e.g. you can use the [script](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples/generate_prompt_dataset.py) which samples `instinwild_en.json` or `instinwild_ch.json` in [InstructionWild](https://github.com/XueFuzhao/InstructionWild/tree/main/data#instructwild-data) to generate the prompt dataset.
 Pretrain dataset: the pretrain dataset including the instruction and corresponding response, e.g. you can use the [InstructWild Data](https://github.com/XueFuzhao/InstructionWild/tree/main/data) in stage 1 supervised instructs tuning.
 
 ### Arg List

applications/Chat/examples/generate_prompt_dataset.py

@@ -0,0 +1,30 @@
+import argparse
+
+import random
+import json
+
+random.seed(42)
+
+
+def sample(args):
+    with open(args.dataset_path, mode='r') as f:
+        dataset_list = json.load(f)
+
+    sampled_dataset = [{"instruction": sample["instruction"], "id":idx}
+                       for idx, sample in enumerate(random.sample(dataset_list, args.sample_size))]
+
+    with open(args.save_path, mode='w') as f:
+        json.dump(sampled_dataset, f, indent=4,
+                  default=str, ensure_ascii=False)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset_path', type=str, default=None,
+                        required=True, help="path to the pretrain dataset")
+    parser.add_argument('--save_path', type=str, default='prompt.json',
+                        help="path to save the prompt dataset")
+    parser.add_argument('--sample_size', type=int,
+                        default=16384, help="size of the prompt dataset")
+    args = parser.parse_args()
+    sample(args)