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qa_process.py
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qa_process.py
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"""
======================================================================
QA_PROCESS ---
QA datasets's process.
Author: Zi Liang <[email protected]>
Copyright © 2024, ZiLiang, all rights reserved.
Created: 26 March 2024
======================================================================
"""
# ------------------------ Code --------------------------------------
import os
if __name__ == "__main__":
# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
# os.environ["CUDA_VISIBLE_DEVICES"] = "4,5,6,7"
# os.environ["CUDA_VISIBLE_DEVICES"] = "6,7"
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
# os.environ["CUDA_VISIBLE_DEVICES"] = "4,5"
os.environ["TORCH_USE_CUDA_DSA"]="1"
from datasets import load_dataset
import json
import random
from tqdm import tqdm
import numpy as np
from gen_pipeline_open import InferObj
from wmt_process import commonly_used_openai_post_process
import os
from collections import OrderedDict
from pprint import pprint
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from transformers import AutoModelForCausalLM,AutoTokenizer
from peft import PeftModel
import torch
def load_qa_datals(
tokenizer,
task_name="piqa",
train_num=100,
model_name="gpt-3.5-turbo-1106",
topk=5,
max_length=256,
openai_tmp_save_pth="./STEALED_PKLS/wmt_data_saveto_",
):
lm_tokenizer = tokenizer
V = lm_tokenizer.vocab_size
tasks_we_used = ["piqa", "truthful_qa", "allenai/ai2_arc"]
assert task_name in tasks_we_used
dataset_name = task_name
inp_ls = []
if task_name == tasks_we_used[0]:
# trainset_text = load_dataset(
# dataset_name, split=f"train[:{train_num}]")
trainset_text = load_dataset(
dataset_name, split=f"train")\
.shuffle(20240306)\
.to_iterable_dataset()\
.take(train_num)
for item in trainset_text:
question = item["goal"]
sol1 = item["sol1"]
sol2 = item["sol2"]
label = str(item["label"])
text = (
f"Question: {question}\n\n Selection 1: {sol1} \n\n Selection 2:{sol2}"
)
inp_ls.append(text)
elif task_name == tasks_we_used[1]:
trainset_text = load_dataset(
dataset_name, "multiple_choice",
split=f"validation[:{train_num}]"
)
for item in trainset_text:
question = item["question"]
assert len(item["mc1_targets"]["choices"]) >= 2
sol1 = item["mc1_targets"]["choices"][0]
sol2 = item["mc1_targets"]["choices"][1]
label = str(0)
if random.random() > 0.5:
# then flip
temp = sol1
sol1 = sol2
sol2 = temp
label = str(1)
text = (
f"Question: {question}\n\n Selection 1: {sol1} \n\n Selection 2:{sol2}"
)
inp_ls.append(text)
elif task_name == tasks_we_used[2]:
trainset_text = load_dataset(
dataset_name, "ARC-Challenge", split=f"train[:{train_num}]"
)
for item in trainset_text:
question = item["question"] + "\n\n"
choices_text = ""
for idx in range(len(item["choices"]["label"])):
choices_text += f"Selection {item['choices']['label'][idx]}"
choices_text += " " + item["choices"]["text"][idx] + "\n\n"
label = item["answerKey"]
text = f"Question: {question}{choices_text}"
inp_ls.append(text)
assert inp_ls != []
pp = "Please select the correct answer for the Question of Users."
prompts = [f"Instruction: {pp} User: {x} Assistant: " for x in inp_ls]
p_idxls = []
for p in prompts:
p_idxls.append(lm_tokenizer(p, return_tensors="pt").input_ids[0])
task_name1 = task_name.replace("/", "_")
openai_tmp_save_pth += f"QAtask_{task_name1}-trainNUM_{train_num}.pkl"
return commonly_used_openai_post_process(
openai_tmp_save_pth,
inp_ls,
pp,
model_name,
topk,
max_length,
p_idxls,
V,
lm_tokenizer,
)
def infer_qa(modelname, task_name, res_pth, test_set_take_num=100,
mnt=32,
base_model_name=None,
):
save_pth = res_pth
tasks_we_used = [
"piqa",
"truthful_qa",
"allenai/ai2_arc",
]
assert task_name in tasks_we_used
task_seqlen_map = {
"piqa": 256,
"truthful_qa": 256,
"allenai/ai2_arc": 256,
}
print(task_name)
pp = "Please select the correct answer for the Question of Users."
inp_ls = []
if task_name == tasks_we_used[0]:
trainset_text = (
load_dataset(task_name, split=f"validation")
.shuffle(20240307)
.to_iterable_dataset()
.take(test_set_take_num)
)
for item in trainset_text:
question = item["goal"]
sol1 = item["sol1"]
sol2 = item["sol2"]
label = str(item["label"])
text = (
f"Question: {question}\n\n Selection 1: {sol1} \n\n Selection 2:{sol2}"
)
inp_ls.append((text, label))
elif task_name == tasks_we_used[1]:
trainset_text = load_dataset(
task_name, "multiple_choice", split=f"validation")
for item in trainset_text:
question = item["question"]
assert len(item["mc1_targets"]["choices"]) >= 2
sol1 = item["mc1_targets"]["choices"][0]
sol2 = item["mc1_targets"]["choices"][1]
if random.random() > 0.5:
# then flip
temp = sol1
sol1 = sol2
sol2 = temp
label = str(0)
text = (
f"Question: {question}\n\n Selection 1: {sol1} \n\n Selection 2:{sol2}"
)
inp_ls.append((text, label))
elif task_name == tasks_we_used[2]:
trainset_text = load_dataset(
task_name, "ARC-Challenge", split=f"validation")
for item in trainset_text:
question = item["question"] + "\n\n"
choices_text = ""
for idx in range(len(item["choices"]["label"])):
choices_text += f"Selection {item['choices']['label'][idx]}"
choices_text += " " + item["choices"]["text"][idx] + "\n\n"
label = item["answerKey"]
text = f"Question: {question}{choices_text}"
inp_ls.append((text, label))
assert inp_ls != []
if modelname=="gpt-3.5-turbo-1106":
from training_data_collecting_openai import chatWithOpenAI_APIs
res_ls=[]
for d in tqdm(inp_ls):
inps,summary = d
res=chatWithOpenAI_APIs(modelname, pp, inps)
print(f"Generated Text: {res}")
res_ls.append((res, summary))
elif modelname=="meta-llama/Meta-Llama-3-8B-Instruct":
print("USING PRE-TRAINED BASE MODEL")
# load model based on our idea
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer\
.from_pretrained(base_model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
res_ls = []
for d in tqdm(inp_ls):
inps, summary = d
final_inps = "Instruction: " + pp + " User: " + inps + " Assistant: "
inps_idx=tokenizer.encode(final_inps,max_length=128,
padding="longest",
return_tensors="pt")
print(inps_idx)
inps_idx=inps_idx.to("cuda")
res = model.generate(inps_idx,
max_new_tokens=mnt,)
print(res)
res=tokenizer.decode(res[0])
if final_inps in res:
res = res.split(final_inps)[1]
else:
res = res
print(f"Text Generated:>>> {res}")
res_ls.append((res, summary))
elif base_model_name is None:
model = InferObj(
model_name=modelname, device="auto",
max_length=task_seqlen_map[task_name],
)
gen_pipeline = model.text_gen
res_ls = []
for d in tqdm(inp_ls):
inps, summary = d
final_inps = "Instruction: " + pp + " User: " + inps + " Assistant: "
res = gen_pipeline(
final_inps,
max_new_tokens=mnt,
)[
0
]["generated_text"]
res = res.split(final_inps)[1]
print(f"Text Generated:>>> {res}")
res_ls.append((res, summary))
else:
print("USING PEFT: BASE MODEL + LORA")
# load model based on our idea
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(model, modelname)
tokenizer = AutoTokenizer\
.from_pretrained(base_model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
res_ls = []
for d in tqdm(inp_ls):
inps, summary = d
final_inps = "Instruction: " + pp + " User: " + inps + " Assistant: "
inps_idx=tokenizer.encode(final_inps,max_length=128,
padding="longest",
return_tensors="pt")
print(inps_idx)
inps_idx=inps_idx.to("cuda")
res = model.generate(inps_idx,
max_new_tokens=mnt,)
print(res)
res=tokenizer.decode(res[0])
if final_inps in res:
res = res.split(final_inps)[1]
else:
res = res
print(f"Text Generated:>>> {res}")
res_ls.append((res, summary))
model = None
gen_pipeline = None
tokenizer = None
with open(save_pth, 'w', encoding='utf8') as f:
json.dump(res_ls, f, ensure_ascii=False, indent=4)
print(f"save inference file {save_pth} done.")
return res_ls
def eval_qa_res():
ckpt_ls = (
# [
# "piqa",
# "gpt-3.5-turbo-1106",
# ],
# [
# "truthful_qa",
# "gpt-3.5-turbo-1106",
# ],
# [
# "allenai/ai2_arc",
# "gpt-3.5-turbo-1106",
# ],
# [
# "piqa",
# "google/gemma-7b",
# ],
# ["piqa",
# "./LoRA-LoRD-ckptsvaryTrainNum___321piqaComplex-lord332164256___period2"
# ],
# [
# "piqa",
# "meta-llama/Meta-Llama-3-8B-Instruct",
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA2561piqaLoRD-VI212132256___period5/",
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA0.202561piqaLoRD-VI212132256___period11/",
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA0.202561piqaLoRD-VI212132256___period5/",
# ],
# ##
# [
# "piqa",
# "./qa_ckpts/QAAA2561piqavanilla332132256___finally/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period1/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period2/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period3/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period4/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period5/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period6/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period7/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period8/"
# ],
# [
# "piqa",
# "./qa_ckpts/QAAA---TEMP--res___period256/"
# ],
# ["piqa","./qa_ckpts/QAAAnewpiqa5121LoRD-VI___period512/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa5121LoRD-VI___period512/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc5121LoRD-VI___period512/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa2561LoRD-VI___period256/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa2561LoRD-VI___period256/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc2561LoRD-VI___period256/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa1281LoRD-VI___period128/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa1281LoRD-VI___period128/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc1281LoRD-VI___period128/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period64/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa641LoRD-VI___period64/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc641LoRD-VI___period64/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa5121vanilla___finally/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa5121vanilla___finally/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc5121vanilla___finally/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa2561vanilla___finally/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa2561vanilla___finally/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc2561vanilla___finally/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa1281vanilla___finally/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa1281vanilla___finally/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc1281vanilla___finally/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa641vanilla___finally/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa641vanilla___finally/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc641vanilla___finally/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa5121LoRD-VI___period512/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa5121LoRD-VI___period512/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc5121LoRD-VI___period512/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa2561LoRD-VI___period512/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa2561LoRD-VI___period512/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc2561LoRD-VI___period512/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa1281LoRD-VI___period512/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa1281LoRD-VI___period512/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc1281LoRD-VI___period512/",],
# ["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period512/",],
# ["truthful_qa","./qa_ckpts/QAAAnewtruthful_qa641LoRD-VI___period512/",],
# ["allenai/ai2_arc","./qa_ckpts/QAAAnewallenai/ai2_arc641LoRD-VI___period512/",],
["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period64/",],
["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period128/",],
["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period256/",],
["piqa","./qa_ckpts/QAAAnewpiqa641LoRD-VI___period512/",],
)
base_model_name1="meta-llama/Meta-Llama-3-8B-Instruct"
res_dict = {}
dir_p = "./qa_dataset_res/"
if not os.path.exists(dir_p):
os.makedirs(dir_p)
for task_ckpt in ckpt_ls:
task, ckpt = task_ckpt
if ckpt==base_model_name1:
base_model_name=None
else:
base_model_name=base_model_name1
res_pth = ckpt + f"___{task}_qa_infer_res"
res_pth = res_pth.replace("/", "__").replace(".", "")
res_pth += ".json"
if not os.path.exists(dir_p + res_pth):
res_ls = infer_qa(
ckpt,
task,
dir_p + res_pth,
test_set_take_num=500,
# test_set_take_num=50,
# mnt=64,
mnt=8,
base_model_name=base_model_name
)
else:
# from collections import OrderedDict
with open(dir_p + res_pth, "r", encoding="utf8") as f:
res_ls = json.load(f, object_pairs_hook=OrderedDict)
print(res_ls)
scores = eval_qaacc(task, res_ls)
print(task, ckpt)
print(scores)
res_dict[task + "-----" + ckpt] = scores
with open(dir_p + "temp_boring_res_delete_thisfile_itisuseless.json", "w", encoding="utf8") as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)
def eval_tau1_res():
taskls = [
"piqa",
# "truthful_qa",
# "allenai/ai2_arc",
]
mls = [
"LoRD-VI",
# "vanilla",
]
train_times = [
"1",
"2",
"3",
"4",
"5",
]
train_nums = [
"64",
# "128",
# "256",
# "512",
]
tau1ls= [
"0.70",
"0.75",
"0.80",
"0.85",
"0.90",
"0.95",
"1.0",
]
base_model_name1="meta-llama/Meta-Llama-3-8B-Instruct"
dir_p = "./qa_0514_tau1_res/"
res_dict = {}
if not os.path.exists(dir_p):
os.makedirs(dir_p)
# ===============================================================
res_dict_averaged={}
for tau1 in tau1ls:
for task in taskls:
for train_num in train_nums:
for m in mls:
temp_scorels=[]
for itime in train_times:
prefix = f"./qa_ckpts/QAAA-TAU1{tau1}"
ckpt = (
prefix
+ f"{task}{train_num}{itime}{m}___period{train_num}/"
)
res_pth = ckpt + f"___{task}_qa_infer_res.json"
res_pth = res_pth.replace("/", "__").replace(".", "")
if not os.path.exists(dir_p+res_pth):
res_ls = infer_qa(ckpt, task, dir_p+res_pth,
test_set_take_num=500,
mnt=8,
base_model_name=base_model_name1,
)
else:
print(
f"{dir_p+res_pth} file already exists. directly loading...")
# from collections import OrderedDict
with open(dir_p + res_pth, "r", encoding="utf8") as f:
res_ls = json.load(
f, object_pairs_hook=OrderedDict)
scores = eval_qaacc(task, res_ls)
res_dict[task + "-----" + res_pth] = scores
temp_scorels.append(scores)
# obtain the mean value
# obtain the std value
temp_scorels=np.array(temp_scorels)
meanvaluels=np.mean(temp_scorels,axis=0).tolist()
stdvaluels=np.std(temp_scorels,axis=0,ddof=1).tolist()
res_dict_averaged[task+"--"+res_pth]=\
{"mean": meanvaluels,
"std": stdvaluels}
with open(
dir_p + "Overall__qa_varytrain_num_inference_scores.json", "w", encoding="utf8"
) as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
with open(
dir_p + "OverallScoresAveraged.json",
"w", encoding="utf8"
) as f:
json.dump(res_dict_averaged, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)
pprint(res_dict_averaged)
def eval_tau2_res():
taskls = [
"piqa",
# "truthful_qa",
# "allenai/ai2_arc",
]
mls = [
"LoRD-VI",
# "vanilla",
]
train_times = [
"1",
"2",
"3",
"4",
"5",
]
train_nums = [
"64",
# "128",
# "256",
# "512",
]
tau1ls= [
"0.80",
]
tau2ls= [
"0.80",
"0.85",
"0.90",
"0.95",
"1.0",
]
base_model_name1="meta-llama/Meta-Llama-3-8B-Instruct"
dir_p = "./qa_0515_tau2_res/"
res_dict = {}
if not os.path.exists(dir_p):
os.makedirs(dir_p)
# ===============================================================
res_dict_averaged={}
for tau1 in tau1ls:
for tau2 in tau2ls:
for task in taskls:
for train_num in train_nums:
for m in mls:
temp_scorels=[]
for itime in train_times:
prefix = f"./qa_ckpts/QAAA-TAU1{tau1}TAU2{tau2}"
ckpt = (
prefix
+ f"{task}{train_num}{itime}{m}___period512/"
)
res_pth = ckpt + f"___{task}_qa_infer_res.json"
res_pth = res_pth.replace("/", "__").replace(".", "")
if not os.path.exists(dir_p+res_pth):
res_ls = infer_qa(ckpt, task, dir_p+res_pth,
test_set_take_num=500,
mnt=8,
base_model_name=base_model_name1,
)
else:
print(
f"{dir_p+res_pth} file already exists. directly loading...")
# from collections import OrderedDict
with open(dir_p + res_pth, "r", encoding="utf8") as f:
res_ls = json.load(
f, object_pairs_hook=OrderedDict)
scores = eval_qaacc(task, res_ls)
res_dict[task + "-----" + res_pth] = scores
temp_scorels.append(scores)
# obtain the mean value
# obtain the std value
temp_scorels=np.array(temp_scorels)
meanvaluels=np.mean(temp_scorels,axis=0).tolist()
stdvaluels=np.std(temp_scorels,axis=0,ddof=1).tolist()
res_dict_averaged[task+"--"+res_pth]=\
{"mean": meanvaluels,
"std": stdvaluels}
with open(
dir_p + "Overall__qa_varytrain_num_inference_scores.json", "w", encoding="utf8"
) as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
with open(
dir_p + "OverallScoresAveraged.json",
"w", encoding="utf8"
) as f:
json.dump(res_dict_averaged, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)
pprint(res_dict_averaged)
def eval_pretraind_or_victim():
taskls = [
"piqa",
# "truthful_qa",
# "allenai/ai2_arc",
]
train_times = [
"1",
]
train_nums = [
"4",
]
victim_model="gpt-3.5-turbo-1106"
pretrained_model="meta-llama/Meta-Llama-3-8B-Instruct"
mls = [
victim_model,
pretrained_model,
]
dir_p = "./qa_0630_dataset_res/"
res_dict = {}
if not os.path.exists(dir_p):
os.makedirs(dir_p)
# ===============================================================
res_dict_averaged={}
for task in taskls:
for train_num in train_nums:
for m in mls:
temp_scorels=[]
for itime in train_times:
ckpt = m
res_pth = ckpt + f"___{task}_qa_infer_res.json"
res_pth = res_pth.replace("/", "__").replace(".", "")
if not os.path.exists(dir_p+res_pth):
res_ls = infer_qa(ckpt, task, dir_p+res_pth,
test_set_take_num=500,
mnt=32,
base_model_name=ckpt,
)
else:
print(
f"{dir_p+res_pth} file already exists. directly loading...")
# from collections import OrderedDict
with open(dir_p + res_pth, "r",
encoding="utf8") as f:
res_ls = json.load(
f, object_pairs_hook=OrderedDict)
scores = eval_qaacc(task, res_ls)
res_dict[task + "-----" + res_pth] = scores
temp_scorels.append(scores)
# obtain the mean value
# obtain the std value
temp_scorels=np.array(temp_scorels)
meanvaluels=np.mean(temp_scorels,axis=0).tolist()
stdvaluels=np.std(temp_scorels,axis=0,ddof=1).tolist()
res_dict_averaged[task+"--"+res_pth]=\
{"mean": meanvaluels,
"std": stdvaluels}
with open(
dir_p + "Overall__qa_varytrain_num_inference_scores.json", "w", encoding="utf8"
) as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
with open(
dir_p + "OverallScoresAveraged.json",
"w", encoding="utf8"
) as f:
json.dump(res_dict_averaged, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)
pprint(res_dict_averaged)
def eval_varytrainum_res():
taskls = [
"piqa",
# "truthful_qa",
# "allenai/ai2_arc",
]
mls = [
# "LoRD-VI",
"vanilla",
# "kd",
]
train_times = [
# "1",
# "2",
# "3",
# "4",
"5",
]
train_nums = [
# "4",
# "8",
# "16",
# "32",
# "64",
# "128",
# "256",
# "512",
# "1024",
"2048",
]
base_model_name1="meta-llama/Meta-Llama-3-8B-Instruct"
dir_p = "./qa_0630_dataset_res/"
res_dict = {}
if not os.path.exists(dir_p):
os.makedirs(dir_p)
# ===============================================================
res_dict_averaged={}
for task in taskls:
for train_num in train_nums:
for m in mls:
temp_scorels=[]
for itime in train_times:
prefix = "./NEW_VARYING_QUERYTIME_CKPTS/text2sql"
if m=="vanilla" or m =="kd":
ckpt = (
prefix
+ f"{task}{train_num}{itime}{m}___finally/"
)
elif train_num=="1024":
ckpt = prefix + \
f"{task}{train_num}{itime}{m}___period1024/"
elif train_num in ["64", "128"]:
# ckpt = prefix + \
# f"{task}{train_num}{itime}{m}___period1024/"
ckpt = prefix + \
f"{task}{train_num}{itime}{m}___period512/"
else:
ckpt = (
prefix
+ f"{task}{train_num}{itime}{m}___period512/"
)
res_pth = ckpt + f"___{task}_qa_infer_res.json"
res_pth = res_pth.replace("/", "__").replace(".", "")
if not os.path.exists(dir_p+res_pth):
res_ls = infer_qa(ckpt, task, dir_p+res_pth,
test_set_take_num=500,
mnt=32,
base_model_name=base_model_name1,
)
else:
print(
f"{dir_p+res_pth} file already exists. directly loading...")
# from collections import OrderedDict
with open(dir_p + res_pth, "r", encoding="utf8") as f:
res_ls = json.load(
f, object_pairs_hook=OrderedDict)
scores = eval_qaacc(task, res_ls)
res_dict[task + "-----" + res_pth] = scores
temp_scorels.append(scores)
# obtain the mean value
# obtain the std value
temp_scorels=np.array(temp_scorels)
meanvaluels=np.mean(temp_scorels,axis=0).tolist()
stdvaluels=np.std(temp_scorels,axis=0,ddof=1).tolist()
res_dict_averaged[task+"--"+res_pth]=\
{"mean": meanvaluels,
"std": stdvaluels}
with open(
dir_p + "Overall__qa_varytrain_num_inference_scores.json", "w", encoding="utf8"
) as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
with open(
dir_p + "OverallScoresAveraged.json",
"w", encoding="utf8"
) as f:
json.dump(res_dict_averaged, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)
pprint(res_dict_averaged)
def eval_varytrainum_231_ours():
taskls = [
"piqa",
"truthful_qa",
"allenai/ai2_arc",
]
# taskls = [
# "piqa",
# ]
# taskls = [
# "truthful_qa",
# ]
taskls = [
"allenai/ai2_arc",
]
mls = ["LoRD-II"]
# mls = ["LoRD-IV"]
# mls = ["google/gemma-2b",]
train_times = [
"1",
"2",
"3",
]
train_nums = ["4", "8", "16", "32", "64", "100", "256", "512"]
period_nums = ["8"]
dir_p = "./vary_train_num_qa_infers/"
res_dict = {}
if not os.path.exists(dir_p):
os.makedirs(dir_p)
# ===============================================================
for task in taskls:
for train_num in train_nums:
for m in mls:
for itime in train_times:
for periodnum in period_nums:
prefix = "./vArY_TrAiN_nUm_ckpts/"
if m == "google/gemma-2b":
ckpt = m
elif m == "Complex-lord":
ckpt = (
prefix
+ f"varyTrainNum___{train_num}{itime}{task}{m}332164256___period2"
)
elif "LoRD" in m:
ckpt = (
prefix
+ f"varyTrainNum___{train_num}{itime}{task}{m}112164256___period{periodnum}"
)
else:
ckpt = (
prefix
+ f"varyTrainNum___{train_num}{itime}{task}{m}332164256___finally"
)
if m == "google/gemma-2b":
res_pth = ckpt + \
f"__{itime}_{task}_qa_infer_res.json"
else:
res_pth = ckpt + f"___{task}_qa_infer_res.json"
res_pth = res_pth.replace("/", "__").replace(".", "")
if not os.path.exists(dir_p + res_pth):
res_ls = infer_qa(
ckpt, task, dir_p + res_pth,
test_set_take_num=1000,
mnt=64,
)
else:
# from collections import OrderedDict
with open(dir_p + res_pth, "r", encoding="utf8") as f:
res_ls = json.load(
f, object_pairs_hook=OrderedDict)
scores = eval_qaacc(task, res_ls)
res_dict[task + "-----" + res_pth] = scores
with open(
dir_p + "Overall__qa_varytrain_num_inference_scores.json", "w", encoding="utf8"
) as f:
json.dump(res_dict, f, ensure_ascii=False, indent=4)
print("OVERALL Save DONE.")
pprint(res_dict)