src/parse.py

from transformers import (
    pipeline,
)
import torch
import json
from transformers.pipelines.pt_utils import KeyDataset
from tqdm import tqdm
from datasets import Dataset
from argparse import ArgumentParser
from typing import Dict, List

# Reddit CSV dump parser script

def make_prompt_mistral(data: Dict[str, List]) -> Dict[str, List]:
    prompt_template = "### Instruction:\n{instruct}:\n\n### Input:\n{input}\n\n### Response:\n"
    result = []
    for doc in data["joke"]:
        prompt = prompt_template.format(
            instruct="For the following joke, add a | separator between intro part and the punchline. "
            "Do not change the sentence, only add a separator. "
            "Full sencence should be considered a punchline. "
            "A question is a full intro section, everything following a question must be considered punchline. "
            "Do not repeat the punchline, do not change words in the sentence.",
            input=doc,
        )
        result.append(prompt)
    return {"prompt": result}


def make_prompt_llama3(data: Dict[str, List]) -> Dict[str, List]:
    prompt_template = "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{instruct}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{input}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
    result = []
    for doc in data["joke"]:
        prompt = prompt_template.format(
            instruct="For the following joke, add a | separator between intro part and the punchline. "
            "Do not change the sentence, only add a separator. "
            "Full sencence should be considered a punchline. "
            "A question is a full intro section, everything following a question must be considered punchline. "
            "Do not repeat the punchline, do not change words in the sentence. "
            "Do not repeat this instruction, only output the result."
            "Do not tell you're an assistant.",
            input=doc[:256],
        )
        result.append(prompt)
    return {"prompt": result}


if __name__ == "__main__":
    parser = ArgumentParser(prog="batch_split", description="dadjokes")
    parser.add_argument("--data", action="store", help="path to reddit.csv", required=True)
    parser.add_argument("--out", action="store", help="path to out file", required=True)
    args = parser.parse_args()
    print(args)

    dataset = Dataset.from_csv(args.data, split="train")
    generator = pipeline(
        task="text-generation",
        # model="mistralai/Mistral-7B-Instruct-v0.3",
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        torch_dtype=torch.bfloat16,
        device_map="auto",
    )
    generator.tokenizer.pad_token_id = 128009

    prompts = KeyDataset(dataset.map(function=make_prompt_llama3, batched=True), "prompt")
    with open(args.out, "w") as f:
        for result in tqdm(
            generator(
                prompts,
                return_full_text=False,
                max_new_tokens=128,
                num_return_sequences=1,
                batch_size=24,
            ),
            total=len(prompts),
        ):
            raw_text = result[0]["generated_text"].split("\n")
            joke = raw_text[-1]
            if "|" in joke:
                tokens = joke.split("|")
                if len(tokens) == 2:
                    intro = tokens[0].strip()
                    punch = tokens[1].strip()
                    if len(intro) > 10 and len(punch) > 5:
                        f.write(json.dumps({"input": intro, "output": punch}) + "\n")