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import argparse
import os
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, logging, set_seed
from trl import SFTTrainer
from trl.trainer import ConstantLengthDataset
"""
Fine-Tune Llama-7b on SE paired dataset
"""
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="")
parser.add_argument("--dataset_name", type=str, default="lvwerra/stack-exchange-paired")
parser.add_argument("--subset", type=str, default="data/finetune")
parser.add_argument("--split", type=str, default="train")
parser.add_argument("--size_valid_set", type=int, default=4000)
parser.add_argument("--streaming", action="store_true")
parser.add_argument("--shuffle_buffer", type=int, default=5000)
parser.add_argument("--seq_length", type=int, default=1024)
parser.add_argument("--max_steps", type=int, default=10000)
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--eos_token_id", type=int, default=49152)
parser.add_argument("--learning_rate", type=float, default=1e-4)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
parser.add_argument("--num_warmup_steps", type=int, default=100)
parser.add_argument("--weight_decay", type=float, default=0.05)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--no_fp16", action="store_false")
parser.add_argument("--bf16", action="store_true", default=False)
parser.add_argument("--no_gradient_checkpointing", action="store_false", default=False)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=None)
parser.add_argument("--output_dir", type=str, default="./checkpoints")
parser.add_argument("--log_freq", default=1, type=int)
parser.add_argument("--eval_freq", default=1000, type=int)
parser.add_argument("--save_freq", default=1000, type=int)
return parser.parse_args()
def chars_token_ratio(dataset, tokenizer, nb_examples=400):
"""
Estimate the average number of characters per token in the dataset.
"""
total_characters, total_tokens = 0, 0
for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
text = prepare_sample_text(example)
total_characters += len(text)
if tokenizer.is_fast:
total_tokens += len(tokenizer(text).tokens())
else:
total_tokens += len(tokenizer.tokenize(text))
return total_characters / total_tokens
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
def prepare_sample_text(example):
"""Prepare the text from a sample of the dataset."""
text = f"Question: {example['question']}\n\nAnswer: {example['response_j']}"
return text
def create_datasets(tokenizer, args):
dataset = load_dataset(
args.dataset_name,
data_dir=args.subset,
split=args.split,
use_auth_token=True,
num_proc=args.num_workers if not args.streaming else None,
streaming=args.streaming,
)
if args.streaming:
print("Loading the dataset in streaming mode")
valid_data = dataset.take(args.size_valid_set)
train_data = dataset.skip(args.size_valid_set)
train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
else:
dataset = dataset.train_test_split(test_size=0.005, seed=args.seed)
train_data = dataset["train"]
valid_data = dataset["test"]
print(f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}")
chars_per_token = chars_token_ratio(train_data, tokenizer)
print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
formatting_func=prepare_sample_text,
infinite=True,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
formatting_func=prepare_sample_text,
infinite=False,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
return train_dataset, valid_dataset
def run_training(args, train_data, val_data):
print("Loading the model")
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
train_data.start_iteration = 0
print("Starting main loop")
training_args = TrainingArguments(
output_dir=args.output_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=args.max_steps,
eval_steps=args.eval_freq,
save_steps=args.save_freq,
logging_steps=args.log_freq,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
lr_scheduler_type=args.lr_scheduler_type,
warmup_steps=args.num_warmup_steps,
gradient_accumulation_steps=args.gradient_accumulation_steps,
gradient_checkpointing=not args.no_gradient_checkpointing,
fp16=not args.no_fp16,
bf16=args.bf16,
weight_decay=args.weight_decay,
run_name="llama-7b-finetuned",
report_to="wandb",
ddp_find_unused_parameters=False,
)
model = AutoModelForCausalLM.from_pretrained(
args.model_path, load_in_8bit=True, device_map={"": Accelerator().process_index}
)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=train_data,
eval_dataset=val_data,
peft_config=lora_config,
packing=True,
)
print_trainable_parameters(trainer.model)
print("Training...")
trainer.train()
print("Saving last checkpoint of the model")
trainer.model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
train_dataset, eval_dataset = create_datasets(tokenizer, args)
run_training(args, train_dataset, eval_dataset)
if __name__ == "__main__":
args = get_args()
assert args.model_path != "", "Please provide the llama model path"
set_seed(args.seed)
os.makedirs(args.output_dir, exist_ok=True)
logging.set_verbosity_error()
main(args)
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