CPM-9G-8B/inference.py

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Python
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2024-11-03 20:04:38 +08:00
import json, torch, re, sys, subprocess
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel, StoppingCriteria
device = "cuda" # the device to load the model onto
from tqdm import tqdm
def exec_code(test):
with open("test_case.json", "r") as f:
test_cases = json.load(f)
right_num = 0
all_num = 0
package = "import os, sys, math, re, json, random\n"
for item, test_case in zip(test, test_cases):
if "```python\n" in item["raw_outputs"]:
matches = re.findall('```python(.*?)```', item["raw_outputs"], re.DOTALL)
if len(matches) == 1:
item["raw_outputs"] = matches[0]
else:
matches = re.findall('```python(.*?)assert', item["raw_outputs"], re.DOTALL)
if len(matches) == 1:
item["raw_outputs"] = matches[0]
else:
item["raw_outputs"] = item["raw_outputs"][item["raw_outputs"].index("python\n") + len("python\n"):]
print(item)
#break
code = item["raw_outputs"].replace("<|im_end|>", "").replace("</s>", "").replace("```", "").strip().rstrip("\n")
raw_code = code
codes = raw_code.split("\n")
last_line = 0
for index, line in enumerate(codes):
if " return" in line:
last_line = index
code = "\n".join(codes[:last_line+1])
'''
if raw_code != code:
print("\n--------------------------------------------------------\n", [raw_code], "\n--------------------------------------------------------\n")
print("clean:\n", [code], "\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n\n")
'''
with open('code_.py', 'w') as fout:
fout.write(package + code + "\n" + "\n".join(test_case["test_case"]))
batcmd = 'timeout 3 ' + sys.executable + ' code_.py'
try:
shell_output = subprocess.check_output(batcmd, shell=True).decode('utf8')
right_num += 1
item["result"] = "True"
except Exception as e:
print("++++++++++++++++++++++++++++++++++++++++++++++++++++\n", raw_code, "\n-----------------------------------------\n\n\n", package + code + "\n--------------------------\n" + "\n".join(test_case["test_case"]))
print("--------------------------------------------------------\n\n\nitem:", item)
print("e: ", e, "\n================================================\n")#, e, )
item["result"] = "False"
all_num += 1
item["raw_outputs"] = [code]
print(len(test), right_num, all_num, right_num / all_num)
with open(f'wjf_{model_path.replace("/", "-")}{right_num / all_num}.json', "w") as f:
json.dump(test, f, indent=4)
return test, right_num / all_num
def get_result(model, tokenizer):
test = []
with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
#test = json.load(f)
for line in f:
test.append(json.loads(line))
all_score = 0
all_num = 0
test_num = 1000
from tqdm import tqdm
for example in tqdm(test[:]):
#print(example["question"])
example["question"] = example["question"].replace("'''", '"""')
ai_prefix = ""
if example["question"].split(" ")[0] == "Write":
question = example["question"][:example["question"].index("\n")].strip().rstrip()
test_case = example["question"][example["question"].index("\n"):].split("\n")
print("test_case: ", test_case)
function_name = test_case[1].split(" ")[1].split("(")[0]
ai_prefix = "def " + function_name
messages = [
{"role": "user", "content": question + "\n\n" + ("\n".join(test_case))}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
text += ai_prefix
example["test_case"] = test_case
else:
tmp = re.findall(r'"""(.*?)"""', example["question"], flags=re.DOTALL)[0].split("\n")
question = ""
for line in tmp:
line = line.strip().rstrip()
if len(line) == 0:
continue
#if "xample" in line and len(line) < 20:
# break
question += line + " "
code = re.sub(r'"""(.*?)"""', '', example["question"], flags=re.DOTALL).strip().rstrip()
ai_prefix = code
messages = [
{"role": "user", "content": question}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
text += ai_prefix
example["prompt"] = text
print("text: " , [text])
input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
output = model.generate(input_ids,
#top_p=1.0,
max_new_tokens=600,
#repetition_penalty=1.1 + t*0.01,
temperature=0.1,
#no_repeat_ngram_size = 5,
).squeeze()
output_str = tokenizer.decode(output[input_ids.shape[1]:])
output_str = ai_prefix + output_str
print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
return test
def get_result_1(model, tokenizer):
test = []
with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
#test = json.load(f)
for line in f:
test.append(json.loads(line))
all_score = 0
all_num = 0
test_num = 1000
from tqdm import tqdm
for example in tqdm(test[:]):
#print(example["question"])
messages = [
{"role": "user", "content": example["question"]}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
example["prompt"] = text
print("text: " , [text])
input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
output = model.generate(input_ids,
#top_p=1.0,
max_new_tokens=600,
#repetition_penalty=1.1 + t*0.01,
temperature=0.1,
#no_repeat_ngram_size = 5,
).squeeze()
output_str = tokenizer.decode(output[input_ids.shape[1]:])
print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
return test
answers = {}
for model_path in [
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all/TACO/",
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_2/CodeNet4Repair/",
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_1/CodeExercise-Python-27k/",
]:
print("model_path: ", model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype="auto",
device_map=device,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
test = get_result(model, tokenizer)
test, score = exec_code(test)
answers[score] = test
test = get_result_1(model, tokenizer)
test, score = exec_code(test)
answers[score] = test
'''
import os
for path in os.listdir("./"):
if "home-wujianfeng" in path:
with open(path, "r") as f:
test = json.load(f)
answers[float(path.split(".")[-2].split("-")[-1])] = test
'''
answers = list(dict(sorted(answers.items())).values())
print("answers: ", answers)
right = 0
jiuge_right = 0
merge = []
for i in range(len(answers[0])):
#for i in range(2):
flag = 0
for answer in answers:
if answer[i]["result"] == "True":
right += 1
jiuge_right += 1
flag = 1
merge.append(answer[i])
break
if flag == 0:
merge.append(answers[0][i])
print(right / len(answers[0]), jiuge_right / len(answers[0]))
with open("wjf_jiuge.jsonl", "w") as f:
for item in merge:
item.pop("result")
f.write(json.dumps(item, ensure_ascii=False) + '\n')