license:mit region:us
---
license: mit
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---
# Dataset Card for MultiPL-C2C

## Dataset Description

- **Repository:**  https://github.com/PootieT/explain-then-translate
- **Paper:** https://openreview.net/forum?id=uyUO80sbm0
- **Point of Contact:** zilutang@bu.edu

## Dataset Summary

MultiPL-C2C is a dataset for evaluating large language models for code
translation between any of the 19 programming languages. It is an extension
from MultiPL-E dataset, which takes the OpenAI "HumanEval" and the MBPP 
Python benchmarks and uses little compilers to translate them to other 
languages. On top of MultiPL-E, it is easy to add support for new languages 
and benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.

## Subsets

To evaluate program translation with MultiPL-C2C, you need mainly three 
arguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension 
for each language to identify the language, e.g., "py" for Python, "cpp" for 
C++, "lua" for Lua, and so on. *EXP* specifies what type of prompting is done for
the translation: `direct` is direct translation. {`exp`, `exp-lbl`, `exp-lbl-d`}
are other explain-then-translate method. See paper for detailed description.

Our paper ["explain-then-translate"](https://openreview.net/forum?id=uyUO80sbm0)
explores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot
translation accuracy. We also provide a few other variations:

- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we
  used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,
  we cached Python-Java intermediate generations (explanations), and reused them
  in other Python-to-X directions. 

- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for
  our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all
  intermediate generations (explanations) in the prompts. Different from 
  *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as `Lua-Python` will have
  explanations generated by GPT-3.5

Note that we did not experiment with MBPP even thought it is available with
MultiPL-E. Feel free to PR and we can include them to further improve 
code-translation datasets.

## Example

The following script uses the Salesforce/codegen model to translate Python-Lua
and MultiPL-E to produce a script with unit tests for luaunit.

```python
import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM

SRC_LANG = "py"
TGT_LANG = "lua"
EXP = "direct"
MODEL_NAME = "Salesforce/codegen-350M-multi"
TOFILL_TOKEN = "{{{TOFILL}}}"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
problems = datasets.load_dataset("zilu-peter-tang/MultiPL-C2C", f"humaneval-{SRC_LANG}-{TGT_LANG}-{EXP}")

def stop_at_stop_token(decoded_string, problem):
    """
    Truncates the output at stop tokens, taking care to skip the prompt
    which may have stop tokens.
    """
    min_stop_index = len(decoded_string)
    for stop_token in problem["stop_tokens"]:
        stop_index = decoded_string.find(stop_token, len(problem["translation_prompt"]))
        if stop_index != -1 and stop_index < min_stop_index:
            min_stop_index = stop_index
    return decoded_string[:min_stop_index]


def generate(problem, tokenizer, model):
    input_ids = tokenizer(
        problem["translation_prompt"],
        return_tensors="pt",
    ).input_ids.cuda()
    generated_ids = model.generate(
        input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
    )
    truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
    return truncated_string


for problem in problems["test"]:

    # generate each intermediate steps (explanation) sequentially, if any
    stop = problem["stop_tokens"]
    prompt = problem["translation_prompt"]
    while TOFILL_TOKEN in problem["translation_prompt"]:
        incomplete_prompt = prompt[:prompt.find(TOFILL_TOKEN)]
        problem["stop_tokens"] = ["\n#"]
        problem["translation_prompt"] = incomplete_prompt
        intermediate_completion = generate(problem, tokenizer, model).replace(incomplete_prompt, "")
        prompt = prompt.replace(TOFILL_TOKEN, intermediate_completion, 1)

    # finally generate translation
    problem["translation_prompt"] = prompt
    problem["stop_tokens"] = stop
    completion = generate(problem, tokenizer, model).replace(prompt, "")

    filename = problem["name"] + "-" + SRC_LANG + "-" + TGT_LANG + "." + TGT_LANG
    with open(filename, "w") as f:
        print(f"Created {filename}")
        f.write(problem["prompt"])
        f.write(completion)
        f.write("\n")
        f.write(problem["tests"])
```

Depending on the quality of the model, you may or may not find some extraneous natural langauge
or program commands at the end of the generated functions. To trim them using programmatic rules,
see our [repo](https://github.com/PootieT/explain-then-translate/blob/main/MultiPL-C2C/inference/__main__.py)
for a more detailed inference script that takes care of this in post-processing. 

For few shot examples checkout our all other translation prompts used [here](https://github.com/PootieT/explain-then-translate/tree/main/MultiPL-C2C/translation_prompts/translation_prompts_chatgpt_with_explanation)