Delete sample_finetune.py

sample_finetune.py

@@ -1,214 +0,0 @@
-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
-
-"""
-A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
-a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
-This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
-script can be run on V100 or later generation GPUs. Here are some suggestions on 
-futher reducing memory consumption:
-    - reduce batch size
-    - decrease lora dimension
-    - restrict lora target modules
-Please follow these steps to run the script:
-1. Install dependencies: 
-    conda install -c conda-forge accelerate
-    pip3 install -i https://pypi.org/simple/ bitsandbytes
-    pip3 install peft transformers trl datasets
-    pip3 install deepspeed
-2. Setup accelerate and deepspeed config based on the machine used:
-    accelerate config
-Here is a sample config for deepspeed zero3:
-    compute_environment: LOCAL_MACHINE
-    debug: false
-    deepspeed_config:
-      gradient_accumulation_steps: 1
-      offload_optimizer_device: none
-      offload_param_device: none
-      zero3_init_flag: true
-      zero3_save_16bit_model: true
-      zero_stage: 3
-    distributed_type: DEEPSPEED
-    downcast_bf16: 'no'
-    enable_cpu_affinity: false
-    machine_rank: 0
-    main_training_function: main
-    mixed_precision: bf16
-    num_machines: 1
-    num_processes: 4
-    rdzv_backend: static
-    same_network: true
-    tpu_env: []
-    tpu_use_cluster: false
-    tpu_use_sudo: false
-    use_cpu: false
-3. check accelerate config:
-    accelerate env
-4. Run the code:
-    accelerate launch sample_finetune.py
-"""
-
-logger = logging.getLogger(__name__)
-
-
-###################
-# Hyper-parameters
-###################
-training_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 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-3.5-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 Processing
-##################
-def 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)
-
-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)