jinja2.exceptions.UndefinedError: dict object has no element 0

[LLLL114]

I followed the training steps to train the llama2 model, but encountered the following error. I searched a lot, but still couldn't solve it.

UndefinedError  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 678, in _next_data
: dict object has no element 0
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/hs/hl/Medusa/medusa/train/train_legacy.py", line 278, in __getitem__
    ret = preprocess([self.raw_data[i]], self.tokenizer)
  File "/home/hs/hl/Medusa/medusa/train/train_legacy.py", line 183, in preprocess
    prompt = tokenizer.apply_chat_template(conversation, tokenize=False)
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1833, in apply_chat_template
    rendered_chat = compiled_template.render(
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/jinja2/environment.py", line 1304, in render
    self.environment.handle_exception()
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/jinja2/environment.py", line 939, in handle_exception
    raise rewrite_traceback_stack(source=source)
  File "<template>", line 1, in top-level template code
  File "/home/hs/anaconda3/envs/onebit/lib/python3.10/site-packages/jinja2/sandbox.py", line 304, in getitem
    return obj[argument]
jinja2.exceptions.UndefinedError: dict object has no element 0
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My training script is as follows：

ModelPath=/data/hl/model/llama2-7b-hf
DataSetPath=/data/hl/dataset/sharegpt/ShareGPT_V4.3_unfiltered_cleaned_split.json
WANDB_MODE=offline torchrun --nproc_per_node=3 medusa/train/train_legacy.py --model_name_or_path $ModelPath
--data_path $DataSetPath
--bf16 True
--output_dir test
--num_train_epochs 2
--per_device_train_batch_size 1
--per_device_eval_batch_size 1
--gradient_accumulation_steps 4
--evaluation_strategy "no"
--save_strategy "no"
--learning_rate 1e-3
--weight_decay 0.0
--warmup_ratio 0.1
--lr_scheduler_type "cosine"
--logging_steps 1
--tf32 True
--model_max_length 2048
--lazy_preprocess True
--medusa_num_heads 3
--medusa_num_layers 1

my pip list：

accelerate 0.33.0
aiohttp 3.9.5
aiosignal 1.3.1
annotated-types 0.7.0
anyio 4.4.0
async-timeout 4.0.3
attrs 23.2.0
certifi 2024.7.4
charset-normalizer 3.3.2
click 8.1.7
cmake 3.30.1
dnspython 2.6.1
docker-pycreds 0.4.0
email_validator 2.2.0
exceptiongroup 1.2.2
fastapi 0.111.1
fastapi-cli 0.0.4
filelock 3.15.4
frozenlist 1.4.1
fschat 0.2.36
fsspec 2024.6.1
gitdb 4.0.11
GitPython 3.1.43
h11 0.14.0
httpcore 1.0.5
httptools 0.6.1
httpx 0.27.0
huggingface-hub 0.24.3
idna 3.7
Jinja2 3.1.4
latex2mathml 3.77.0
lit 18.1.8
markdown-it-py 3.0.0
markdown2 2.5.0
MarkupSafe 2.1.5
mdurl 0.1.2
medusa-llm 1.0 /home/shixl/hl/Medusa
mpmath 1.3.0
multidict 6.0.5
networkx 3.3
nh3 0.2.18
numpy 1.26.4
packaging 24.1
pip 24.0
platformdirs 4.2.2
prompt_toolkit 3.0.47
protobuf 5.27.2
psutil 6.0.0
pydantic 2.8.2
pydantic_core 2.20.1
Pygments 2.18.0
python-dotenv 1.0.1
python-multipart 0.0.9
PyYAML 6.0.1
regex 2024.7.24
requests 2.32.3
rich 13.7.1
safetensors 0.4.3
sentencepiece 0.2.0
sentry-sdk 2.11.0
setproctitle 1.3.3
setuptools 69.5.1
shellingham 1.5.4
shortuuid 1.0.13
six 1.16.0
smmap 5.0.1
sniffio 1.3.1
starlette 0.37.2
svgwrite 1.4.3
sympy 1.13.1
tiktoken 0.7.0
tokenizers 0.19.1
torch 2.0.1+cu117
tqdm 4.66.4
transformers 4.43.3
triton 2.0.0
typer 0.12.3
typing_extensions 4.12.2
urllib3 2.2.2
uvicorn 0.30.3
uvloop 0.19.0
wandb 0.17.5
watchfiles 0.22.0
wavedrom 2.0.3.post3
wcwidth 0.2.13
websockets 12.0
wheel 0.43.0
yarl 1.9.4

I am considering whether it is because I need to perform the following operation on the shareGPT dataset, but I think this step is optional：
python create_data.py --input-filename ShareGPT_Vicuna_unfiltered/ShareGPT_V4.3_unfiltered_cleaned_split.json --output-filename mistral.json

[Camellia1110]

Hi, have you solved this problem?

[xhjcxxl]

i find some error in code, you should change dataset process like qwen2，then you will fix it:

def preprocess_qwen(
    messages,
    tokenizer: transformers.PreTrainedTokenizer,
    max_len: int,
) -> Dict:
    """Preprocesses the data for supervised fine-tuning."""

    texts = []
    for i, msg in enumerate(messages):
        texts.append(
            tokenizer.apply_chat_template(
                msg,
                chat_template=TEMPLATE,
                tokenize=True,
                add_generation_prompt=False,
                padding="max_length",
                max_length=max_len,
                truncation=True,
            )
        )
    input_ids = torch.tensor(texts, dtype=torch.int)
    target_ids = input_ids.clone()
    target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID
    attention_mask = input_ids.ne(tokenizer.pad_token_id)

    return dict(
        input_ids=input_ids, target_ids=target_ids, attention_mask=attention_mask
    )

and fix load data like qwen2:

ret = preprocess_qwen([self.raw_data[i]["messages"]], self.tokenizer, 4096)