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ai-modelscope
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sample_finetune.py
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sample_finetune.py
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import sys
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import logging
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import datasets
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from datasets import load_dataset
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from peft import LoraConfig
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import torch
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import transformers
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from trl import SFTTrainer
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
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"""
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A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
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a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
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This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
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script can be run on V100 or later generation GPUs. Here are some suggestions on
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futher reducing memory consumption:
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- reduce batch size
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- decrease lora dimension
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- restrict lora target modules
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Please follow these steps to run the script:
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1. Install dependencies:
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conda install -c conda-forge accelerate
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pip3 install -i https://pypi.org/simple/ bitsandbytes
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pip3 install peft transformers trl datasets
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pip3 install deepspeed
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2. Setup accelerate and deepspeed config based on the machine used:
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accelerate config
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Here is a sample config for deepspeed zero3:
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compute_environment: LOCAL_MACHINE
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debug: false
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deepspeed_config:
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gradient_accumulation_steps: 1
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offload_optimizer_device: none
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offload_param_device: none
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zero3_init_flag: true
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zero3_save_16bit_model: true
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zero_stage: 3
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distributed_type: DEEPSPEED
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downcast_bf16: 'no'
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enable_cpu_affinity: false
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machine_rank: 0
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main_training_function: main
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mixed_precision: bf16
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num_machines: 1
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num_processes: 4
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rdzv_backend: static
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same_network: true
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tpu_env: []
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tpu_use_cluster: false
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tpu_use_sudo: false
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use_cpu: false
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3. check accelerate config:
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accelerate env
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4. Run the code:
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accelerate launch sample_finetune.py
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"""
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logger = logging.getLogger(__name__)
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###################
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# Hyper-parameters
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###################
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training_config = {
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"bf16": True,
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"do_eval": False,
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"learning_rate": 5.0e-06,
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"log_level": "info",
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"logging_steps": 20,
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"logging_strategy": "steps",
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"lr_scheduler_type": "cosine",
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"num_train_epochs": 1,
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"max_steps": -1,
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"output_dir": "./checkpoint_dir",
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"overwrite_output_dir": True,
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"per_device_eval_batch_size": 4,
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"per_device_train_batch_size": 4,
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"remove_unused_columns": True,
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"save_steps": 100,
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"save_total_limit": 1,
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"seed": 0,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs":{"use_reentrant": False},
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"gradient_accumulation_steps": 1,
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"warmup_ratio": 0.2,
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}
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peft_config = {
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"r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"bias": "none",
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"task_type": "CAUSAL_LM",
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"target_modules": "all-linear",
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"modules_to_save": None,
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}
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train_conf = TrainingArguments(**training_config)
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peft_conf = LoraConfig(**peft_config)
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###############
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# Setup logging
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###############
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = train_conf.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process a small summary
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logger.warning(
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f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
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+ f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
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)
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logger.info(f"Training/evaluation parameters {train_conf}")
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logger.info(f"PEFT parameters {peft_conf}")
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################
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# Modle Loading
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################
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checkpoint_path = "microsoft/Phi-3-medium-4k-instruct"
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# checkpoint_path = "microsoft/Phi-3-medium-128k-instruct"
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model_kwargs = dict(
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use_cache=False,
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trust_remote_code=True,
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attn_implementation="flash_attention_2", # loading the model with flash-attenstion support
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torch_dtype=torch.bfloat16,
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device_map=None
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)
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model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
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tokenizer.model_max_length = 2048
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tokenizer.pad_token = tokenizer.unk_token # use unk rather than eos token to prevent endless generation
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tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
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tokenizer.padding_side = 'right'
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##################
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# Data Processing
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##################
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def apply_chat_template(
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example,
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tokenizer,
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):
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messages = example["messages"]
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example["text"] = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=False)
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return example
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raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
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train_dataset = raw_dataset["train_sft"]
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test_dataset = raw_dataset["test_sft"]
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column_names = list(train_dataset.features)
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processed_train_dataset = train_dataset.map(
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apply_chat_template,
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fn_kwargs={"tokenizer": tokenizer},
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num_proc=10,
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remove_columns=column_names,
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desc="Applying chat template to train_sft",
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)
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processed_test_dataset = test_dataset.map(
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apply_chat_template,
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fn_kwargs={"tokenizer": tokenizer},
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num_proc=10,
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remove_columns=column_names,
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desc="Applying chat template to test_sft",
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)
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###########
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# Training
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###########
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trainer = SFTTrainer(
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model=model,
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args=train_conf,
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peft_config=peft_conf,
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train_dataset=processed_train_dataset,
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eval_dataset=processed_test_dataset,
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max_seq_length=2048,
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dataset_text_field="text",
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tokenizer=tokenizer,
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packing=True
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)
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train_result = trainer.train()
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metrics = train_result.metrics
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trainer.log_metrics("train", metrics)
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trainer.save_metrics("train", metrics)
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trainer.save_state()
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#############
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# Evaluation
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#############
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tokenizer.padding_side = 'left'
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metrics = trainer.evaluate()
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metrics["eval_samples"] = len(processed_test_dataset)
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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# ############
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# # Save model
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# ############
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trainer.save_model(train_conf.output_dir)
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