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import torch
import pdb
from transformers import AutoTokenizer, HfArgumentParser, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments
from datasets import load_dataset
from peft import LoraConfig, PeftModel
from trl import SFTTrainer
import os
import random
def sft(ScriptArguments, model_id, formatting_func, datasets, save_path):
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4"
)
# Load model
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization_config,
torch_dtype=torch.float32,
attn_implementation="sdpa" if not script_args.use_flash_attention_2 else "flash_attention_2"
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
lora_config = LoraConfig(
r=script_args.lora_r,
target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
bias="none",
task_type="CAUSAL_LM",
lora_alpha=script_args.lora_alpha,
lora_dropout=script_args.lora_dropout
)
train_dataset = load_dataset('json', data_files={'train': datasets['train'], 'test': datasets['valid']}, split='train')
training_arguments = TrainingArguments(
output_dir=save_path,
per_device_train_batch_size=script_args.per_device_train_batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
optim=script_args.optim,
save_steps=script_args.save_steps,
logging_steps=script_args.logging_steps,
learning_rate=script_args.learning_rate,
max_grad_norm=script_args.max_grad_norm,
max_steps=script_args.max_steps,
warmup_ratio=script_args.warmup_ratio,
lr_scheduler_type=script_args.lr_scheduler_type,
gradient_checkpointing=script_args.gradient_checkpointing,
fp16=script_args.fp16,
bf16=script_args.bf16,
)
trainer = SFTTrainer(
model=model,
args=training_arguments,
train_dataset=train_dataset,
peft_config=lora_config,
packing=False,
tokenizer=tokenizer,
max_seq_length=script_args.max_seq_length,
formatting_func=formatting_func,
)
trainer.train()
# merge
base_model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=False,
torch_dtype=torch.float32,
device_map={"": "cuda:0"},
)
lora_model = PeftModel.from_pretrained(
base_model,
os.path.join(save_path, "checkpoint-{}".format(script_args.max_steps)),
device_map={"": "cuda:0"},
torch_dtype=torch.float32,
)
model = lora_model.merge_and_unload()
lora_model.train(False)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model.save_pretrained(os.path.join(save_path, "merged_model"))
tokenizer.save_pretrained(os.path.join(save_path, "merged_model")) |