GLM-130B/tasks/cot/task.py

import os
import json
import re
from typing import Union, List, Dict, Callable
from datetime import datetime
from evaluation.tasks import GenerationTask, GenerationTaskDataset, GenerationTaskConfig
from evaluation.utils import print_rank_0
from dataclasses import dataclass


@dataclass
class ChainOfThoughtConfig(GenerationTaskConfig):
    prompt_path: str = None
    chain_of_thought: bool = True
    prompt_type: str = None


def read_examples(prompt_path):
    examples = []
    item = {"question": None, "answer": None}
    with open(prompt_path) as file:
        for line in file:
            line = line.strip()
            if line.startswith("Q:"):
                question = line[3:]
                item["question"] = question
            elif line.startswith("A:"):
                answer = line[3:]
                item["answer"] = answer
                examples.append(item)
                item = {"question": None, "answer": None}
            else:
                raise NotImplementedError
    return examples


def build_prompt(examples, task_name, chain_of_thought=True, prompt_type=None):
    prompts = []
    for i, item in enumerate(examples):
        question, answer = item["question"], item["answer"]
        if not chain_of_thought:
            answer = extract_answer(answer, task_name)
        if prompt_type == "number":
            prompts.append(f"{i+1}. Question: {question} Answer: {answer}")
        else:
            prompts.append(f"Question: {question} Answer: {answer}")
    if prompt_type == "return":
        prompt = " <n>".join(prompts)
    else:
        prompt = " ".join(prompts)
    return prompt


def extract_answer(prediction, task_name, chain_of_thought=True):
    if task_name.startswith("gsm8k"):
        prediction = prediction.lower()
        if chain_of_thought:
            pattern = r'(?<=the answer is )\d+'
        else:
            pattern = r'\d+'
        match = re.search(pattern, prediction)
        if match:
            answer = match.group(0)
        else:
            answer = ""
    elif task_name.startswith("sports"):
        prediction = prediction.lower()
        if chain_of_thought:
            pattern = r'(?<=the answer is )(yes|no)'
        else:
            pattern = r'yes|no'
        match = re.search(pattern, prediction)
        if match:
            answer = match.group(0)
        else:
            answer = "no"
    else:
        raise NotImplementedError(task_name)
    return answer


class ChainOfThoughtDataset(GenerationTaskDataset):
    config: ChainOfThoughtConfig

    def __init__(self, path: Union[str, List[str]], config: ChainOfThoughtConfig):
        self.labeled_examples = read_examples(config.prompt_path)
        self.labeled_prompt = build_prompt(self.labeled_examples, config.name, chain_of_thought=config.chain_of_thought,
                                           prompt_type=config.prompt_type)
        print_rank_0(self.labeled_prompt)
        self.printed_count = 0
        super().__init__(path, config)

    def process_single_item(self, item, **kwargs):
        question, targets = item["question"], item["targets"]
        if self.config.prompt_type == "number":
            text = self.labeled_prompt + f" {len(self.labeled_examples) + 1}. Question: {question} Answer:"
        elif self.config.prompt_type == "return":
            text = self.labeled_prompt + f" <n>Question: {question} Answer:"
        else:
            text = self.labeled_prompt + f" Question: {question} Answer:"
        text, targets = self.tokenizer.tokenize(text), self.tokenizer.tokenize(targets)
        if len(text) + self.config.max_gen_length + 2 > self.config.max_seq_length:
            text_length = self.config.max_seq_length - self.config.max_gen_length - 2
            text = text[len(text) - text_length: len(text)]
        if self.printed_count < 3:
            print_rank_0(self.tokenizer.detokenize(text))
            self.printed_count += 1
        return [{"text": text, "targets": targets, **kwargs}]


class GSM8KDataset(ChainOfThoughtDataset):
    def process_single_item(self, item, **kwargs):
        item["targets"] = item["answer"].split("####")[1].strip()
        return super().process_single_item(item)


class SportsDataset(ChainOfThoughtDataset):
    def process_single_file(self, path):
        with open(path) as file:
            dataset = json.load(file)
        for item in dataset["examples"]:
            sentence = item["input"]
            item["question"] = f'Is the following sentence plausible? \"{sentence}.\"'
            if item["target_scores"]["plausible"] == 1:
                item["targets"] = "yes"
            else:
                item["targets"] = "no"
            self.data.extend(self.process_single_item(item))


class ChainOfThoughtTask(GenerationTask):
    config: ChainOfThoughtConfig

    @classmethod
    def config_class(cls):
        return ChainOfThoughtConfig

    @property
    def metrics(self) -> Dict[str, Callable]:
        return {'acuracy': self.extracted_accuracy_metric}

    def extracted_accuracy_metric(self, predictions, examples):
        count = 0
        num_predictions = max(len(predictions), 1)
        assert len(predictions) == len(examples)
        for prediction, example in zip(predictions, examples):
            output = self.tokenizer.detokenize(prediction)
            prediction = extract_answer(output, self.config.name, self.config.chain_of_thought).strip()
            target = self.tokenizer.detokenize(example["targets"]).strip()
            count += prediction == target
        return count * 100.0 / num_predictions

    def build_dataset(self, relative_path, split):
        if self.config.name.startswith("gsm8k"):
            return GSM8KDataset(os.path.join(self.config.path, relative_path), self.config)
        elif self.config.name.startswith("sports"):
            return SportsDataset(os.path.join(self.config.path, relative_path), self.config)
        else:
            raise NotImplementedError

    def save_prediction_to_file(self, file, predictions, data):
        results = []
        for output, item in zip(predictions, data):
            output = self.tokenizer.detokenize(output)
            prediction = extract_answer(output, self.config.name, self.config.chain_of_thought)
            target = self.tokenizer.detokenize(item["targets"])
            results.append({"output": output, "prediction": prediction, "answer": target})
        file_name = file.split(".")[0]
        if not os.path.exists("outputs"):
            os.mkdir("outputs")
        with open("outputs/" + self.config.name + "_" + datetime.now().strftime(
                '%m-%d-%H-%M_') + file_name + ".json", "w") as output:
            for result in results:
                output.write(json.dumps(result) + "\n")