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import sys
import logging
import datasets
from datasets import load_dataset
from peft import LoraConfig
import torch
import transformers
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
logger = logging.getLogger(__name__)
###################
# Hyper-parameters
###################
training_config = {
"bf16": False,
"do_eval": False,
"learning_rate": 5.0e-06,
"log_level": "info",
"logging_steps": 20,
"logging_strategy": "steps",
"lr_scheduler_type": "cosine",
"num_train_epochs": 1,
"max_steps": -1,
"output_dir": "./checkpoint_dir",
"overwrite_output_dir": True,
"per_device_eval_batch_size": 2, # Reduce batch size to lower memory usage
"per_device_train_batch_size": 2, # Reduce batch size to lower memory usage
"remove_unused_columns": True,
"save_steps": 100,
"save_total_limit": 1,
"seed": 0,
"gradient_checkpointing": True,
"gradient_checkpointing_kwargs":{"use_reentrant": False},
"gradient_accumulation_steps": 1,
"warmup_ratio": 0.2,
}
peft_config = {
"r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"bias": "none",
"task_type": "CAUSAL_LM",
"target_modules": "all-linear",
"modules_to_save": None,
}
train_conf = TrainingArguments(**training_config)
peft_conf = LoraConfig(**peft_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}")
logger.info(f"PEFT parameters {peft_conf}")
################
# Model Loading
################
checkpoint_path = "microsoft/Phi-3-mini-4k-instruct"
# checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"
model_kwargs = dict(
use_cache=False,
trust_remote_code=True,
attn_implementation="flash_attention_2", # loading the model with flash-attention support
torch_dtype=torch.float16, # Changed to float16
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.unk_token # use unk rather than eos token to prevent endless generation
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = 'right'
# Move the model to GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
##################
# Data Processing
##################
def apply_chat_template(example, tokenizer):
messages = example["messages"]
# Add an empty system message if there is none
if messages[0]["role"] != "system":
messages.insert(0, {"role": "system", "content": ""})
example["text"] = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=False)
return example
raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
train_dataset = raw_dataset["train_sft"]
test_dataset = raw_dataset["test_sft"]
column_names = list(train_dataset.features)
processed_train_dataset = train_dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": tokenizer},
num_proc=10,
remove_columns=column_names,
desc="Applying chat template to train_sft",
)
processed_test_dataset = test_dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": tokenizer},
num_proc=10,
remove_columns=column_names,
desc="Applying chat template to test_sft",
)
###########
# Training
###########
trainer = SFTTrainer(
model=model,
args=train_conf,
peft_config=peft_conf,
train_dataset=processed_train_dataset,
eval_dataset=processed_test_dataset,
max_seq_length=2048,
dataset_text_field="text",
tokenizer=tokenizer,
packing=True
)
train_result = trainer.train()
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
#############
# Evaluation
#############
tokenizer.padding_side = 'left'
metrics = trainer.evaluate()
metrics["eval_samples"] = len(processed_test_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
############
# Save model
############
trainer.save_model(train_conf.output_dir)
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