[Tutorial] Phi-3.5 Fine-tuning
Import packagesimport 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, BitsAndBytesConfigA simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. Fora more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. Thescript can be run on V100 or later generation GPUs. Here are some suggestions onfuther reducing memory consumption:- reduce batch size- decrease lora dimension- restrict lora target modulesPlease follow these steps to run the script:Install dependencies:conda install -c conda-forge acceleratepip3 install -iSimple indexbitsandbytespip3 install peft transformers trl datasetspip3 install deepspeedSetup accelerate and deepspeed config based on the machine used:accelerate configHere is a sample config for deepspeed zero3:compute_environment: LOCAL_MACHINEdebug: falsedeepspeed_config:gradient_accumulation_steps: 1offload_optimizer_device: noneoffload_param_device: nonezero3_init_flag: truezero3_save_16bit_model: truezero_stage: 3distributed_type: DEEPSPEEDdowncast_bf16: ‘no’enable_cpu_affinity: falsemachine_rank: 0main_training_function: mainmixed_precision: bf16num_machines: 1num_processes: 4rdzv_backend: staticsame_network: truetpu_env:tpu_use_cluster: falsetpu_use_sudo: falseuse_cpu: falsecheck accelerate config:accelerate envRun the code:accelerate launch sample_finetune.pyCreate Loggerlogger = logging.getLogger(__name__)Hyper-parameterstraining_config = {
    "bf16": True,
    "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": 4,
    "per_device_train_batch_size": 4,
    "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 logginglogging.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 summarylogger.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 Loadingcheckpoint_path = "microsoft/Phi-3.5-mini-instruct"
model_kwargs = dict(
    use_cache=False,
    trust_remote_code=True,
    attn_implementation="flash_attention_2",  # loading the model with flash-attenstion support
    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.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'Data Processingdef apply_chat_template(
    example,
    tokenizer,
):
    messages = example["messages"]
    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)Trainprocessed_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",
)Testprocessed_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",
)Trainingtrainer = 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()Evaluationtokenizer.padding_side = 'left'
metrics = trainer.evaluate()
metrics["eval_samples"] = len(processed_test_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)Save modeltrainer.save_model(train_conf.output_dir)The above tutorial is inspired by the officialPhi-3.5model sample fine-tuning