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import sys
import logging
import datasets
from datasets import load_dataset
import torch
import transformers
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
from typing import Dict, List
logger = logging.getLogger(__name__)
"""
# multi-gpu training
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch --gradient_clipping=1.0 --multi_gpu --num_processes=8 --num_machines=1 --mixed_precision=bf16 --zero_stage=3 sft_fast.py
# single-gpu training
CUDA_VISIBLE_DEVICES=0 accelerate launch --gradient_clipping=1.0 --mixed_precision=bf16 --zero_stage=3 sft.py
# use tmux to train it in the background
tmux new -d -s training "CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch --gradient_clipping=1.0 --multi_gpu --num_processes=8 --num_machines=1 --mixed_precision=bf16 --zero_stage=3 sft.py"
"""
###################
# Hyper-parameters
###################
# NOTICE: the global batch size has to be at least 512. When you using different number of GPUs, please adjust the gradient accumulation steps
# the global batch size = gradient_accumulation_steps * num_gpus * per_device_train_batch_size
training_config = {
"bf16": True,
"do_eval": False,
"learning_rate": 5e-06,
"log_level": "info",
"logging_steps": 20,
"logging_strategy": "steps",
"lr_scheduler_type": "cosine",
"num_train_epochs": 3.0,
"max_steps": -1,
"output_dir": "./share_gpt_sft",
"overwrite_output_dir": True,
"per_device_eval_batch_size": 1,
"per_device_train_batch_size": 1,
"remove_unused_columns": True,
"save_steps": 1000,
"save_total_limit": 1,
"seed": 0,
"gradient_checkpointing": True,
"gradient_checkpointing_kwargs":{"use_reentrant": False},
"gradient_accumulation_steps": 4,
"warmup_ratio": 0.03,
"ddp_find_unused_parameters": True,
}
train_conf = TrainingArguments(**training_config)
###############
# Setup logging
###############
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = train_conf.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process a small summary
logger.warning(
f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
+ f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
)
logger.info(f"Training/evaluation parameters {train_conf}")
################
# Model Loading
################
checkpoint_path = "./"
model_kwargs = dict(
use_cache=False,
trust_remote_code=True,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
device_map=None
)
model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
tokenizer.model_max_length = 2048
tokenizer.pad_token = tokenizer.eos_token # use unk rather than eos token to prevent endless generation
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.padding_side = 'right'
##################
# Data Processing
##################
def load_sharegpt_dataset(file_path: str):
dataset = load_dataset('json', data_files=file_path)
return dataset
def apply_chat_template(
example: Dict,
tokenizer: AutoTokenizer,
max_length: int = None
) -> Dict:
messages = example["conversations"]
converted_messages = []
role_mapping = {
'human': 'user',
'gpt': 'assistant'
}
for message in messages:
role = message['from']
content = message['value']
role = role_mapping.get(role, role)
converted_messages.append({
'content': content,
'role': role
})
example["text"] = tokenizer.apply_chat_template(
converted_messages,
tokenize=False,
add_generation_prompt=False
)
return example
def process_dataset(
dataset_path: str,
tokenizer: AutoTokenizer,
num_proc: int = 64,
max_length: int = None
):
dataset = load_sharegpt_dataset(dataset_path)
column_names = list(dataset['train'].features)
processed_dataset = dataset['train'].map(
apply_chat_template,
fn_kwargs={
"tokenizer": tokenizer,
"max_length": max_length
},
num_proc=num_proc,
remove_columns=column_names,
desc="Applying chat template"
)
return processed_dataset
processed_dataset = process_dataset(
dataset_path="./ShareGPT_40k.json",
tokenizer=tokenizer,
num_proc=64
)
###########
# Training
###########
trainer = SFTTrainer(
model=model,
args=train_conf,
peft_config=None,
train_dataset=processed_dataset,
eval_dataset=None,
max_seq_length=2048,
dataset_text_field="text",
tokenizer=tokenizer,
packing=False
)
train_result = trainer.train()
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# ############
# # Save model
# ############
trainer.save_model(train_conf.output_dir) |