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from dataclasses import dataclass, field
from typing import Dict, Optional, Sequence
import logging
import os, sys
import copy
import torch
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer
from torch.utils.data import Dataset
from transformers import Trainer
sys.path.append(os.path.dirname(__file__))
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from utils.special_tok_llama2 import (
B_CODE,
E_CODE,
B_RESULT,
E_RESULT,
B_INST,
E_INST,
B_SYS,
E_SYS,
DEFAULT_PAD_TOKEN,
DEFAULT_BOS_TOKEN,
DEFAULT_EOS_TOKEN,
DEFAULT_UNK_TOKEN,
IGNORE_INDEX,
)
from conversation_template import json_to_code_result_tok_temp
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="./ckpt/llama-2-13b-chat")
peft: bool = field(default=False)
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data."}
)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=4096,
metadata={
"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
def create_peft_config(model):
from peft import (
get_peft_model,
LoraConfig,
TaskType,
prepare_model_for_int8_training,
)
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,
r=8,
lora_alpha=16,
lora_dropout=0.05,
target_modules=["q_proj", "v_proj"],
)
# prepare int-8 model for training
model = prepare_model_for_int8_training(model)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
print(f"Using Peft")
return model, peft_config
def _tokenize_fn(
strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer
) -> Dict:
"""Tokenize a list of strings."""
tokenized_list = [
tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
)
for text in strings
]
input_ids = [tokenized.input_ids[0] for tokenized in tokenized_list]
input_ids_lens = [
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
for tokenized in tokenized_list
]
return dict(
input_ids=input_ids,
input_ids_lens=input_ids_lens,
)
def find_all_sublist_end(main_list, sublist):
"""Find all the ending indices of a sublist in a main list."""
sublist_len = len(sublist)
main_list = main_list.tolist()
indices = []
for index in (i for i, e in enumerate(main_list) if e == sublist[0]):
if main_list[index : index + sublist_len] == sublist:
indices.append(index + sublist_len)
return indices
def find_all_sublist_start(main_list, sublist):
"""Find all the starting indices of a sublist in a main list."""
sublist_len = len(sublist)
main_list = main_list.tolist()
indices = []
for index in (i for i, e in enumerate(main_list) if e == sublist[0]):
if main_list[index : index + sublist_len] == sublist:
indices.append(index)
return indices
def preprocess(
trajs: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:
INST_START_INDEX = tokenizer.encode(f"{B_INST}")[-1]
INST_END_INDEX = tokenizer.encode(f"{E_INST}")[-1]
RESULT_START_INDEX = tokenizer.encode(f"{B_RESULT}")[-1]
RESULT_END_INDEX = tokenizer.encode(f"{E_RESULT}")[-1]
"""Preprocess the data by tokenizing."""
examples_tokenized = _tokenize_fn(trajs, tokenizer)
input_ids_lens = examples_tokenized["input_ids_lens"]
input_ids = examples_tokenized["input_ids"] # [torch.tensor , torch.tensor , ...]
labels = copy.deepcopy(input_ids)
# IGNORE INDEX SET
for i, label in enumerate(labels):
user_start_inds = find_all_sublist_start(label, [INST_START_INDEX])
assistant_start_inds = find_all_sublist_end(label, [INST_END_INDEX])
result_start_inds = find_all_sublist_start(label, [RESULT_START_INDEX])
result_end_inds = find_all_sublist_end(label, [RESULT_END_INDEX])
# for debug
# for len_i, ind in enumerate(label):
# print(f'{len_i}|{ind} -> "{tokenizer.decode(ind)}"')
assert len(user_start_inds) == len(
assistant_start_inds
), f"User and Assistant pair should be equal :: \n\tUser [{user_start_inds}]/\n\tAssistant [{assistant_start_inds}]\n\n Text : \n{trajs[i]}"
assert len(result_start_inds) == len(
result_end_inds
), f"Start and End indices pairs do not match.: : \nText : \n{trajs[i]}"
for user_start_ind, assistant_start_ind in zip(
user_start_inds, assistant_start_inds
):
label[user_start_ind + 1 : assistant_start_ind - 1] = IGNORE_INDEX
for start, end in zip(result_start_inds, result_end_inds):
label[start + 1 : end - 1] = IGNORE_INDEX
# cut max length
input_ids = [i[:1500] for i in input_ids]
labels = [i[:1500] for i in labels]
return dict(input_ids=input_ids, labels=labels)
class SupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer):
super(SupervisedDataset, self).__init__()
logging.warning(f"Loading data from data path : {data_path}")
all_json = os.listdir(data_path)
trajs = list()
for json_file_name in all_json:
traj = json_to_code_result_tok_temp(json_file_name=json_file_name)
trajs.append(traj)
logging.warning("Tokenizing inputs... This may take some time...")
data_dict = preprocess(trajs, tokenizer)
self.input_ids = data_dict["input_ids"]
self.labels = data_dict["labels"]
def __len__(self):
return len(self.input_ids)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
return dict(input_ids=self.input_ids[i], labels=self.labels[i])
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids, labels = tuple(
[instance[key] for instance in instances] for key in ("input_ids", "labels")
)
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = torch.nn.utils.rnn.pad_sequence(
labels, batch_first=True, padding_value=IGNORE_INDEX
)
return dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
)
def make_supervised_data_module(
tokenizer: transformers.PreTrainedTokenizer, data_args
) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_dataset = SupervisedDataset(
tokenizer=tokenizer, data_path=data_args.data_path
)
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
return dict(
train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator
)
def build_model_from_hf_path(
hf_model_path: str = "./ckpt/llama-2-13b-chat", peft: bool = False
):
# build tokenizer
tokenizer = LlamaTokenizer.from_pretrained(
hf_model_path,
padding_side="right",
use_fast=False,
)
special_tokens_dict = dict()
if tokenizer.pad_token is None:
special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN # 32000
if tokenizer.eos_token is None:
special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN # 2
if tokenizer.bos_token is None:
special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN # 1
if tokenizer.unk_token is None:
special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
tokenizer.add_special_tokens(special_tokens_dict)
tokenizer.add_tokens(
[
B_CODE, # 32001
E_CODE, # 32002
B_RESULT, # 32003
E_RESULT, # 32004
B_INST,
E_INST,
B_SYS,
E_SYS, # 32008
],
special_tokens=True,
)
# build model
if peft:
model = LlamaForCausalLM.from_pretrained(
hf_model_path,
load_in_8bit=True,
device_map="auto",
ignore_mismatched_sizes=True,
torch_dtype=torch.float16,
)
else:
# for llama
# model = LlamaForCausalLM.from_pretrained(
# hf_model_path, ignore_mismatched_sizes=True
# )
# for codellama
from codellama_wrapper import CodeLlamaForCausalLM
model = CodeLlamaForCausalLM.from_pretrained(hf_model_path)
model.resize_token_embeddings(len(tokenizer))
return {"tokenizer": tokenizer, "model": model}
def train():
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments)
)
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model_dict = build_model_from_hf_path(
hf_model_path=model_args.model_name_or_path, peft=model_args.peft
)
model, tokenizer = model_dict["model"], model_dict["tokenizer"]
# peft setting
model.train()
if model_args.peft:
model, lora_config = create_peft_config(model)
# make dataset
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
trainer = Trainer(
model=model, tokenizer=tokenizer, args=training_args, **data_module
)
# train
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
if __name__ == "__main__":
train()
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