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| Author | SHA1 | Date |
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 p83651209
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9abc3a1123 |
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29
README.md
29
README.md
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@ -1,27 +1,2 @@
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夸克网盘 docker链接：https://pan.quark.cn/s/4cda395f13e8
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(没有会员请联系我下载)
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1.使用llama-factory对九格模型进行全参数微调。数据集见dataset
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2.训练和推理都已验证无误，在A100*8卡机器上。
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docker 启动：sudo docker run -it --runtime=nvidia --gpus all --shm-size=256g wjf:train
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推理：python inference.py
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训练：
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cd training
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sh training.sh
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3.推理使用多checkpoint、多次推理融合。
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4.所有资料都已打包进docker，只需要docker即可。
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5.启动训练时将覆盖提交的checkpoint。
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6.docker卡在数据处理可能是机器的问题，尝试docker中输入：
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export NCCL_DEBUG=INFO
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export NCCL_SHM_DISABLE=1
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export NCCL_P2P_DISABLE=1
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由于需要保存多个checkpoint，请务必保证磁盘空间足够，大于500G。
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7.提交不易，请有问题是及时联系我（电话：13121813131）
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训练代码：
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LLaMA-Factory
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278
inference.py
278
inference.py
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@ -1,278 +0,0 @@
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import json, torch, re, sys, subprocess
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel, StoppingCriteria
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device = "cuda" # the device to load the model onto
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from tqdm import tqdm
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def exec_code(test):
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with open("test_case.json", "r") as f:
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test_cases = json.load(f)
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right_num = 0
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all_num = 0
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package = "import os, sys, math, re, json, random\n"
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for item, test_case in zip(test, test_cases):
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if "```python\n" in item["raw_outputs"]:
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matches = re.findall('```python(.*?)```', item["raw_outputs"], re.DOTALL)
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if len(matches) == 1:
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item["raw_outputs"] = matches[0]
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else:
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matches = re.findall('```python(.*?)assert', item["raw_outputs"], re.DOTALL)
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if len(matches) == 1:
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item["raw_outputs"] = matches[0]
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else:
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item["raw_outputs"] = item["raw_outputs"][item["raw_outputs"].index("python\n") + len("python\n"):]
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print(item)
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#break
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code = item["raw_outputs"].replace("<|im_end|>", "").replace("</s>", "").replace("```", "").strip().rstrip("\n")
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raw_code = code
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codes = raw_code.split("\n")
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last_line = 0
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for index, line in enumerate(codes):
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if " return" in line:
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last_line = index
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code = "\n".join(codes[:last_line+1])
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'''
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if raw_code != code:
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print("\n--------------------------------------------------------\n", [raw_code], "\n--------------------------------------------------------\n")
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print("clean:\n", [code], "\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n\n")
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'''
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with open('code_.py', 'w') as fout:
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fout.write(package + code + "\n" + "\n".join(test_case["test_case"]))
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batcmd = 'timeout 3 ' + sys.executable + ' code_.py'
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try:
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shell_output = subprocess.check_output(batcmd, shell=True).decode('utf8')
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right_num += 1
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item["result"] = "True"
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except Exception as e:
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print("++++++++++++++++++++++++++++++++++++++++++++++++++++\n", raw_code, "\n-----------------------------------------\n\n\n", package + code + "\n--------------------------\n" + "\n".join(test_case["test_case"]))
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print("--------------------------------------------------------\n\n\nitem:", item)
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print("e: ", e, "\n================================================\n")#, e, )
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item["result"] = "False"
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all_num += 1
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item["raw_outputs"] = [code]
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print(len(test), right_num, all_num, right_num / all_num)
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with open(f'wjf_{model_path.replace("/", "-")}{right_num / all_num}.json', "w") as f:
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json.dump(test, f, indent=4)
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return test, right_num / all_num
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def get_result(model, tokenizer):
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test = []
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with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
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#test = json.load(f)
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for line in f:
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test.append(json.loads(line))
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all_score = 0
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all_num = 0
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test_num = 1000
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from tqdm import tqdm
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for example in tqdm(test[:]):
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#print(example["question"])
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example["question"] = example["question"].replace("'''", '"""')
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ai_prefix = ""
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if example["question"].split(" ")[0] == "Write":
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question = example["question"][:example["question"].index("\n")].strip().rstrip()
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test_case = example["question"][example["question"].index("\n"):].split("\n")
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print("test_case: ", test_case)
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function_name = test_case[1].split(" ")[1].split("(")[0]
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ai_prefix = "def " + function_name
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messages = [
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{"role": "user", "content": question + "\n\n" + ("\n".join(test_case))}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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text += ai_prefix
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example["test_case"] = test_case
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else:
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tmp = re.findall(r'"""(.*?)"""', example["question"], flags=re.DOTALL)[0].split("\n")
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question = ""
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for line in tmp:
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line = line.strip().rstrip()
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if len(line) == 0:
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continue
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#if "xample" in line and len(line) < 20:
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# break
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question += line + " "
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code = re.sub(r'"""(.*?)"""', '', example["question"], flags=re.DOTALL).strip().rstrip()
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ai_prefix = code
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messages = [
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{"role": "user", "content": question}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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text += ai_prefix
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example["prompt"] = text
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print("text: " , [text])
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input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
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output = model.generate(input_ids,
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#top_p=1.0,
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max_new_tokens=600,
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#repetition_penalty=1.1 + t*0.01,
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temperature=0.1,
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#no_repeat_ngram_size = 5,
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).squeeze()
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output_str = tokenizer.decode(output[input_ids.shape[1]:])
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output_str = ai_prefix + output_str
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print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
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example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
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return test
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def get_result_1(model, tokenizer):
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test = []
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with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
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#test = json.load(f)
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for line in f:
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test.append(json.loads(line))
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all_score = 0
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all_num = 0
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test_num = 1000
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from tqdm import tqdm
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for example in tqdm(test[:]):
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#print(example["question"])
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messages = [
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{"role": "user", "content": example["question"]}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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example["prompt"] = text
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print("text: " , [text])
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input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
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output = model.generate(input_ids,
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#top_p=1.0,
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max_new_tokens=600,
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#repetition_penalty=1.1 + t*0.01,
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temperature=0.1,
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#no_repeat_ngram_size = 5,
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).squeeze()
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output_str = tokenizer.decode(output[input_ids.shape[1]:])
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print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
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example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
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return test
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answers = {}
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for model_path in [
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"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all/TACO/",
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"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_2/CodeNet4Repair/",
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"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_1/CodeExercise-Python-27k/",
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]:
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print("model_path: ", model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype="auto",
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device_map=device,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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test = get_result(model, tokenizer)
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test, score = exec_code(test)
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answers[score] = test
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test = get_result_1(model, tokenizer)
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test, score = exec_code(test)
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answers[score] = test
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'''
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import os
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for path in os.listdir("./"):
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if "home-wujianfeng" in path:
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with open(path, "r") as f:
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test = json.load(f)
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answers[float(path.split(".")[-2].split("-")[-1])] = test
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'''
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answers = list(dict(sorted(answers.items())).values())
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print("answers: ", answers)
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right = 0
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jiuge_right = 0
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merge = []
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for i in range(len(answers[0])):
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#for i in range(2):
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flag = 0
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for answer in answers:
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if answer[i]["result"] == "True":
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right += 1
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jiuge_right += 1
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flag = 1
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merge.append(answer[i])
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break
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if flag == 0:
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merge.append(answers[0][i])
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print(right / len(answers[0]), jiuge_right / len(answers[0]))
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with open("wjf_jiuge.jsonl", "w") as f:
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for item in merge:
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item.pop("result")
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f.write(json.dumps(item, ensure_ascii=False) + '\n')
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@ -1,14 +0,0 @@
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model_wight:通过百度网盘分享的文件：
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链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
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提取码：6666
|
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#https://www.alipan.com/s/FTPWUSBuz7s
|
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|
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docker:
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链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
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提取码：6666
|
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#https://www.alipan.com/s/FTPWUSBuz7s
|
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train_data:
|
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链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
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提取码：6666
|
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#https://www.alipan.com/s/FTPWUSBuz7s
|
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4099
test_case.json
4099
test_case.json
File diff suppressed because it is too large
Load Diff
154
train.sh
154
train.sh
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@ -1,154 +0,0 @@
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#!/bin/bash
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deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
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--stage sft \
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--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
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--do_train \
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--dataset TACO \
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--template jiuge \
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--finetuning_type full \
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--output_dir TACO \
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--per_device_train_batch_size 14 \
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--gradient_accumulation_steps 6 \
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--lr_scheduler_type cosine \
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--logging_step 1 \
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--save_steps 300 \
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--lr_scheduler_type cosine_with_restarts \
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--warmup_ratio 0.001 \
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--optim adamw_torch \
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--learning_rate 2e-5 \
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--num_train_epochs 2.0 \
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--plot_loss \
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--bf16 \
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--gradient_checkpointing \
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--report_to tensorboard \
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--deepspeed deepspeed_configs/zero2.json \
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--cutoff_len 2048
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deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
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--stage sft \
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--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
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--do_train \
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--dataset Tested-143k-Python-Alpaca \
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--template jiuge \
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--finetuning_type full \
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--output_dir Tested-143k-Python-Alpaca \
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--per_device_train_batch_size 14 \
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--gradient_accumulation_steps 6 \
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--lr_scheduler_type cosine \
|
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--logging_step 1 \
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--save_steps 300 \
|
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--lr_scheduler_type cosine_with_restarts \
|
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--warmup_ratio 0.001 \
|
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--optim adamw_torch \
|
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--learning_rate 2e-5 \
|
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--num_train_epochs 2.0 \
|
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--plot_loss \
|
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--bf16 \
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--gradient_checkpointing \
|
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--report_to tensorboard \
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--deepspeed deepspeed_configs/zero2.json \
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--cutoff_len 2048
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|
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deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
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--stage sft \
|
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--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
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--do_train \
|
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--dataset UltraInteract_sft \
|
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--template jiuge \
|
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--finetuning_type full \
|
||||
--output_dir UltraInteract_sft \
|
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--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
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--dataset code_instructions_120k_alpaca \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir code_instructions_120k_alpaca \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset CodeExercise-Python-27k \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir CodeExercise-Python-27k \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset CodeNet4Repair \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir CodeNet4Repair \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
Loading…
Reference in New Issue