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mainfunc.py
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mainfunc.py
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import argparse
import os
import json
from inference import run_main
from evaluation import (
eval_recall,
eval_question_answering,
eval_fact_checking,
eval_dialogue_system
)
def readfiles(infile):
if infile.endswith('json'):
lines = json.load(open(infile, 'r', encoding='utf8'))
elif infile.endswith('jsonl'):
lines = open(infile, 'r', encoding='utf8').readlines()
lines = [json.loads(l) for l in lines]
else:
raise NotImplementedError
if len(lines[0]) == 1 and lines[0].get('prompt'):
lines = lines[1:] ## skip prompt line
return lines
def step1(dataset, datatype, split, max_tokens, engine, prompt, pid, n, temp):
inputfile = f'indatasets/{dataset}/{dataset}-{split}.jsonl'
inlines = readfiles(inputfile)
if (temp is None) or (temp == 0):
outputfolder = f'backgrounds-greedy-{engine}/{dataset}'
else: # tempature > 0
outputfolder = f'backgrounds-sample(n={n},temp={temp})-{engine}/{dataset}'
os.makedirs(outputfolder, exist_ok=True)
outputfile = f'{outputfolder}/{dataset}-{split}-p{pid}.jsonl'
run_main(inlines, outputfile, engine, prompt, max_tokens, n, temp)
if datatype == 'question answering': ## Eval Recall@K score
recallfile = f'{outputfolder}/{dataset}-recall@k.jsonl'
with open(recallfile, 'a') as recallout:
recall, length = eval_recall(outputfile)
outmetrics = {
'outputfile': outputfile,
'prompt': prompt,
'recall@k': recall,
'length': length,
}
print(f'Recall@k: {recall}; Avg.Length: {length}')
recallout.write(json.dumps(outmetrics) + '\n')
def step2(dataset, datatype, split, max_tokens, engine, prompt, pid):
inputfile = f'backgrounds-greedy-{engine}/{dataset}/{dataset}-{split}-p{pid}.jsonl'
inlines = readfiles(inputfile)
outputfolder = f'finaloutput-greedy-{engine}/{dataset}'
os.makedirs(outputfolder, exist_ok=True)
outputfile = f'{outputfolder}/{dataset}-{split}-p{pid}.jsonl'
run_main(inlines, outputfile, engine, prompt, max_tokens)
if datatype == 'question answering': ## Eval Exact Match
evalfile = f'{outputfolder}/{dataset}-metrics.jsonl'
with open(evalfile, 'a') as evalout:
emscore, length = eval_question_answering(outputfile)
outmetrics = {
'outputfile': outputfile,
'prompt': prompt,
'exact match': emscore,
'length': length,
}
print(f'Exact Match: {emscore}; Avg.Length: {length}')
evalout.write(json.dumps(outmetrics) + '\n')
elif datatype == 'fact checking': ## Eval Accuracy
evalfile = f'{outputfolder}/{dataset}-metrics.jsonl'
with open(evalfile, 'a') as evalout:
accuracy, length = eval_fact_checking(outputfile)
outmetrics = {
'outputfile': outputfile,
'prompt': prompt,
'accuracy': accuracy,
'length': length,
}
print(f'Accuracy: {accuracy}; Avg.Length: {length}')
evalout.write(json.dumps(outmetrics) + '\n')
elif datatype == 'dialogue system': ## Eval F1 and Rouge
evalfile = f'{outputfolder}/{dataset}-metrics.jsonl'
with open(evalfile, 'a') as evalout:
f1score, rougel, length = eval_dialogue_system(outputfile)
outmetrics = {
'outputfile': outputfile,
'prompt': prompt,
'f1-score': f1score,
'rouge-l': rougel,
'length': length,
}
print(f'F1-score: {f1score}; Rouge-L: {rougel}; Avg.Length: {length}')
evalout.write(json.dumps(outmetrics) + '\n')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--dataset", default=None, type=str, required=True,
help="dataset name: [nq, tqa, webq, wizard, fever, fm2]",
)
parser.add_argument("--task", default=None, type=str, required=True,
help="task name: [step1, step2], should be either 1 or 2",
)
parser.add_argument("--split", default=None, type=str, required=True,
help="dataset split: [train, dev, test]",
)
parser.add_argument("--clustering", action='store_true',
help="if clustering prompt, we enumerate each prompts in the prompt file",
)
parser.add_argument("--engine", default='text-davinci-002', type=str, required=False,
help="text-davinci-002 (used in our experiments), code-davinci-002",
)
parser.add_argument("--num_sequence", default=1, type=int, required=False)
parser.add_argument("--temperature", default=0, type=float, required=False)
args = parser.parse_args()
if args.dataset in ['nq', 'webq', 'tqa', 'twiki']:
datatype = 'question answering'
elif args.dataset in ['fever', 'fm2']:
datatype = 'fact checking'
elif args.dataset in ['wizard']:
datatype = 'dialogue system'
else: # other task type?
raise NotImplementedError
if args.task == 'step1':
max_tokens = 300
elif args.task == 'step2':
if datatype == 'dialogue system':
max_tokens = 50
else: # QA and Fact ...
max_tokens = 10
promptfile = 'cluster' if args.clustering else 'regular'
promptlines = open(f'inprompts/{promptfile}.jsonl', 'r').readlines()
for line in promptlines:
line = json.loads(line)
if args.clustering and args.dataset != line.get('dataset'):
continue ## for clustering, each dataset has own prompts
if line['type'] == datatype and line['task'] == args.task:
prompt = line['prompt']
pid = line['pid']
if args.task == 'step1':
outputs = step1(args.dataset, datatype, args.split, max_tokens, args.engine,
prompt, pid, args.num_sequence, args.temperature)
elif args.task == 'step2':
outputs = step2(args.dataset, datatype, args.split,
max_tokens, args.engine, prompt, pid)
else: ## should be either 1 or 2
raise NotImplementedError
if promptfile == 'regular':
break ## only use the first prompt