eval_qa.py

import os
import json
import logging
import datasets
import random
from typing import List
from accelerate import Accelerator
from torch.utils.data import DataLoader
from transformers import HfArgumentParser
from dataclasses import dataclass, field, asdict

from src.lm import (
    LM, 
    LMArgs,
    GenerationArgs
)
from src.retrieval import (
    RetrievalArgs, 
    RetrievalMetric,
)
from src.utils.util import makedirs, remove_eos, normalize_text, DefaultDataCollator, DatasetProcessFn, FileLogger
from .eval_retrieval import main as retrieval_main

logger = logging.getLogger(__name__)


@dataclass
class QAArgs(LMArgs, RetrievalArgs):
    output_dir: str = field(
        default="data/results/qa/",
    )
    eval_data: str = field(
        default="llm-embedder:qa/nq/test.json",
        metadata={'help': 'Path to the test file.'}
    )
    lm_batch_size: int = field(
        default=4,
        metadata={'help': 'Evaluation batch size.'},
    )
    
    few_shot: int = field(
        default=10,
        metadata={'help': 'How many few shot train samples?'},
    )
    train_data: str = field(
        default="llm-embedder:qa/nq/dev.json",
        metadata={'help': 'Path to the file containing training examples.'}
    )

    hits: int = field(
        default=10,
        metadata={'help': 'How many hits per query?'},
    )
    key_num: int = field(
        default=3,
        metadata={'help': 'How many docs to provide in prompt?'},
    )
    corpus: str = field(
        default="llm-embedder:qa/nq/corpus.json",
        metadata={'help': 'Corpus path for retrieval.'}
    )    
    key_template: str = field(
        default="{title} {text}",
        metadata={'help': 'How to concatenate columns in the corpus to form one key?'}
    )
    query_max_length: int = field(
        default=32, 
        metadata={'help': 'How many tokens at maximum in a query.'}
    )
    key_max_length: int = field(
        default=128,
        metadata={'help': 'How many tokens at maximum in a key.'}
    )
    metrics: List[str] = field(
        default_factory=lambda: ["collate_key"],
    )
    save_to_output: bool = field(
        default=True,
        metadata={'help': 'Save the result/key/negative to output_dir? If not true, they will be saved next to the eval_data.'}
    )

    log_path: str = field(
        default="data/results/qa/qa.log",
        metadata={'help': 'Path to the file for logging.'}
    )


@dataclass
class GenerationArgs(GenerationArgs):
    max_new_tokens: int = field(
        default=32,
        metadata={'help': 'Maximum new tokens to generate.'}
    )
    eos_token_id: int = 13


def process_qa(tokenizer, context_max_length=2048, key_num=3, few_shot=0, train_data=None, cache_dir=None, is_encoder_decoder=False):
    test = tokenizer("test", return_special_tokens_mask=True)["special_tokens_mask"]
    has_bos = has_eos = False
    if test[0] == 1:
        has_bos = True
    if test[-1] == 1:
        has_eos = True

    if few_shot > 0:
        assert train_data is not None
        train_dataset = datasets.load_dataset("json", data_files=train_data, cache_dir=cache_dir, split="train")
        sample_indices = random.sample(range(len(train_dataset)), few_shot)
        train_dataset = train_dataset.select(sample_indices)

    def _prepare_sample(query, answers=None, **kwds):
        sample = f"Question: {query}\nAnswer:"
        if answers is not None:
            sample = sample + " " + random.choice(answers)
        return sample

    def _prepare_retrieval(keys):
        if keys is not None:
            keys = keys[:key_num]
            keys = "\n".join(keys)
            keys = f"Knowledge: {keys}"
        else:
            keys = ""
        return keys

    @DatasetProcessFn()
    def _process(query, query_id, key=None, **kwds):
        """Yield keys and query with a prompt template"""
        output = {}
        query = query.strip()

        knowledge = _prepare_retrieval(key)

        train_samples_max_length = context_max_length - len(tokenizer.encode("\n\n" if len(knowledge) else "" + _prepare_sample(query), add_special_tokens=False)) - int(has_bos)

        if few_shot > 0:
            train_samples = ""
            train_samples_length = 0

            for i in range(few_shot):
                train_sample = train_dataset[i]
                train_sample = _prepare_sample(**train_sample) + "\n\n"
                if train_samples_length + len(tokenizer.encode(train_sample)) > train_samples_max_length:
                    break
                else:                    
                    train_samples += train_sample
                    train_samples_length += len(tokenizer.encode(train_sample))
        else:
            train_samples = ""

        left = knowledge
        # \n\n to split retrieved knowledge
        right = "\n\n" + train_samples + _prepare_sample(query)

        pair = tokenizer.encode(left, right, add_special_tokens=False, truncation="only_first", max_length=context_max_length - int(has_bos) - int(has_eos))

        # strip spaces and \n in the head (when there is no retrieved passage)
        seq = tokenizer.decode(pair).strip()
        inputs = tokenizer(seq, return_token_type_ids=False)

        if has_eos and not is_encoder_decoder:
            inputs = remove_eos(inputs, tokenizer.eos_token_id)

        inputs["query_id"] = query_id

        for k, v in inputs.items():
            output[k] = v
        return output
    return _process


def evaluate_qa(eval_data, save_path, **kwds):
    def compute_metric(eval_preds):
        makedirs(save_path)
        
        samples = {}
        with open(eval_data) as f:
            for line in f:
                sample = json.loads(line.strip())
                samples[sample["query_id"]] = sample

        exact_match = 0
        with open(save_path, "w") as f:
            for query_id, generation in zip(*eval_preds):
                sample = samples[query_id]
                em = max(normalize_text(generation) == normalize_text(answer) for answer in sample["answers"])
                exact_match += int(em)

                sample["output"] = generation
                f.write(json.dumps(sample, ensure_ascii=False) + "\n")

        exact_match /= len(eval_preds[0])
        return {"exact_match": exact_match}
    return compute_metric


def main():
    parser = HfArgumentParser([QAArgs, GenerationArgs])
    args, generation_args = parser.parse_args_into_dataclasses()
    
    accelerator = Accelerator(cpu=args.cpu)
    
    # modify the output_dir for retrieval
    if args.retrieval_method == "dense":
        output_dir = os.path.join(args.output_dir, args.query_encoder.strip(os.sep).replace(os.sep, "--"))
    else:
        output_dir = os.path.join(args.output_dir, args.retrieval_method)
    args.output_dir = output_dir

    if args.retrieval_method != "no":
        retrieval_main(args=args, accelerator=accelerator, log=False)
        eval_data = RetrievalMetric._get_save_path(args.eval_data, args.output_dir, field="key", save_name=args.save_name)
    else:
        eval_data = args.eval_data

    llm = LM(
        model_name_or_path=args.model_name_or_path,
        dtype=args.lm_dtype,
        device_map=args.lm_device_map,
        padding_side=args.padding_side,
        cache_dir=args.model_cache_dir,
        accelerator=accelerator,
        generation_args=asdict(generation_args)
    )

    tokenizer = llm.tokenizer
    
    logging.info(f"Loading data from {eval_data}...")

    with accelerator.main_process_first():
        dataset = datasets.load_dataset("json", data_files=eval_data, split="train", cache_dir=args.dataset_cache_dir)
        dataset = dataset.map(process_qa(
            tokenizer, 
            context_max_length=args.context_max_length, 
            key_num=args.key_num,
            few_shot=args.few_shot,
            train_data=args.train_data,
            cache_dir=args.dataset_cache_dir,
            is_encoder_decoder=llm.model.config.is_encoder_decoder
        ), remove_columns=dataset.column_names, batched=True, num_proc=32)

    data_collator = DefaultDataCollator(tokenizer=tokenizer, add_position_ids=args.add_position_ids)
    dataloader = DataLoader(
        dataset, 
        batch_size=args.lm_batch_size, 
        collate_fn=data_collator,
        pin_memory=True,
    )
    dataloader = accelerator.prepare(dataloader)

    results = llm.generate(dataloader)

    if accelerator.process_index == 0:
        file_logger = FileLogger(makedirs(args.log_path))
        result_path = os.path.join(args.output_dir, args.model_name_or_path.strip(os.sep).replace(os.sep, "--") + ".json")
        metrics = evaluate_qa(eval_data, result_path)(results)
        file_logger.log(metrics, Args=asdict(args))


if __name__ == "__main__":
    main()