i want to know how to not to use the wandb tool at the finetune.py

[BBaekdabang]

when i modify the finetune.py code as below code, but wandb tool continuously executed. so the wandb sslerror occured. please tell me which row of the code makes the wandb worked.

``import os import sys from typing import List

import fire import torch import transformers from datasets import load_dataset

""" Unused imports: import torch.nn as nn import bitsandbytes as bnb """

from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) from transformers import LlamaForCausalLM, LlamaTokenizer

from utils.prompter import Prompter

import os os.environ['WANDB_DISABLE'] = 'True'

import ssl

ssl._create_default_https_context = ssl._create_unverified_context

def train(

model/data params

base_model: str = "",  # the only required argument
data_path: str = "yahma/alpaca-cleaned",
output_dir: str = "./lora-alpaca",
# training hyperparams
batch_size: int = 128,
micro_batch_size: int = 4,
num_epochs: int = 3,
learning_rate: float = 3e-4,
cutoff_len: int = 256,
val_set_size: int = 2000,
# lora hyperparams
lora_r: int = 8,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
lora_target_modules: List[str] = [
    "q_proj",
    "v_proj",
],
# llm hyperparams
train_on_inputs: bool = True,  # if False, masks out inputs in loss
add_eos_token: bool = False,
group_by_length: bool = False,  # faster, but produces an odd training loss curve

wandb params

wandb_project: str = "",

wandb_run_name: str = "",

wandb_watch: str = "", # options: false | gradients | all

wandb_log_model: str = "", # options: false | true

resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.

): use_wandb=False os.environ['WANDB_DISABLE'] = 'True' if int(os.environ.get("LOCAL_RANK", 0)) == 0: print( f"Training Alpaca-LoRA model with params:\n" f"base_model: {base_model}\n" f"data_path: {data_path}\n" f"output_dir: {output_dir}\n" f"batch_size: {batch_size}\n" f"micro_batch_size: {micro_batch_size}\n" f"num_epochs: {num_epochs}\n" f"learning_rate: {learning_rate}\n" f"cutoff_len: {cutoff_len}\n" f"val_set_size: {val_set_size}\n" f"lora_r: {lora_r}\n" f"lora_alpha: {lora_alpha}\n" f"lora_dropout: {lora_dropout}\n" f"lora_target_modules: {lora_target_modules}\n" f"train_on_inputs: {train_on_inputs}\n" f"add_eos_token: {add_eos_token}\n" f"group_by_length: {group_by_length}\n" f"resume_from_checkpoint: {resume_from_checkpoint or False}\n" f"prompt template: {prompt_template_name}\n" ) assert ( base_model ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'" gradient_accumulation_steps = batch_size // micro_batch_size

prompter = Prompter(prompt_template_name)

device_map = "auto"
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
if ddp:
    device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
    gradient_accumulation_steps = gradient_accumulation_steps // world_size

model = LlamaForCausalLM.from_pretrained(
    base_model,
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map=device_map,
)

tokenizer = LlamaTokenizer.from_pretrained(base_model)

tokenizer.pad_token_id = (
    0  # unk. we want this to be different from the eos token
)
tokenizer.padding_side = "left"  # Allow batched inference

def tokenize(prompt, add_eos_token=True):
    # there's probably a way to do this with the tokenizer settings
    # but again, gotta move fast
    result = tokenizer(
        prompt,
        truncation=True,
        max_length=cutoff_len,
        padding=False,
        return_tensors=None,
    )
    if (
        result["input_ids"][-1] != tokenizer.eos_token_id
        and len(result["input_ids"]) < cutoff_len
        and add_eos_token
    ):
        result["input_ids"].append(tokenizer.eos_token_id)
        result["attention_mask"].append(1)

    result["labels"] = result["input_ids"].copy()

    return result

def generate_and_tokenize_prompt(data_point):
    full_prompt = prompter.generate_prompt(
        data_point["instruction"],
        data_point["input"],
        data_point["output"],
    )
    tokenized_full_prompt = tokenize(full_prompt)
    if not train_on_inputs:
        user_prompt = prompter.generate_prompt(
            data_point["instruction"], data_point["input"]
        )
        tokenized_user_prompt = tokenize(
            user_prompt, add_eos_token=add_eos_token
        )
        user_prompt_len = len(tokenized_user_prompt["input_ids"])

        if add_eos_token:
            user_prompt_len -= 1

        tokenized_full_prompt["labels"] = [
            -100
        ] * user_prompt_len + tokenized_full_prompt["labels"][
            user_prompt_len:
        ]  # could be sped up, probably
    return tokenized_full_prompt

model = prepare_model_for_int8_training(model)

config = LoraConfig(
    r=lora_r,
    lora_alpha=lora_alpha,
    target_modules=lora_target_modules,
    lora_dropout=lora_dropout,
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)

if data_path.endswith(".json") or data_path.endswith(".jsonl"):
    data = load_dataset("json", data_files=data_path)
else:
    data = load_dataset(data_path)

if resume_from_checkpoint:
    # Check the available weights and load them
    checkpoint_name = os.path.join(
        resume_from_checkpoint, "pytorch_model.bin"
    )  # Full checkpoint
    if not os.path.exists(checkpoint_name):
        checkpoint_name = os.path.join(
            resume_from_checkpoint, "adapter_model.bin"
        )  # only LoRA model - LoRA config above has to fit
        resume_from_checkpoint = (
            False  # So the trainer won't try loading its state
        )
    # The two files above have a different name depending on how they were saved, but are actually the same.
    if os.path.exists(checkpoint_name):
        print(f"Restarting from {checkpoint_name}")
        adapters_weights = torch.load(checkpoint_name)
        set_peft_model_state_dict(model, adapters_weights)
    else:
        print(f"Checkpoint {checkpoint_name} not found")

model.print_trainable_parameters()  # Be more transparent about the % of trainable params.

if val_set_size > 0:
    train_val = data["train"].train_test_split(
        test_size=val_set_size, shuffle=True, seed=42
    )
    train_data = (
        train_val["train"].shuffle().map(generate_and_tokenize_prompt)
    )
    val_data = (
        train_val["test"].shuffle().map(generate_and_tokenize_prompt)
    )
else:
    train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
    val_data = None

if not ddp and torch.cuda.device_count() > 1:
    # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
    model.is_parallelizable = True
    model.model_parallel = True

trainer = transformers.Trainer(
    model=model,
    train_dataset=train_data,
    eval_dataset=val_data,
    args=transformers.TrainingArguments(
        per_device_train_batch_size=micro_batch_size,
        gradient_accumulation_steps=gradient_accumulation_steps,
        warmup_steps=100,
        num_train_epochs=num_epochs,
        learning_rate=learning_rate,
        fp16=True,
        logging_steps=10,
        optim="adamw_torch",
        evaluation_strategy="steps" if val_set_size > 0 else "no",
        save_strategy="steps",
        eval_steps=200 if val_set_size > 0 else None,
        save_steps=200,
        output_dir=output_dir,
        save_total_limit=3,
        load_best_model_at_end=True if val_set_size > 0 else False,
        ddp_find_unused_parameters=False if ddp else None,
        group_by_length=group_by_length,
        report_to=None ,
        run_name=None ,
    ),
    data_collator=transformers.DataCollatorForSeq2Seq(
        tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
    ),
)
model.config.use_cache = False

if torch.__version__ >= "2" and sys.platform != "win32":
    model = torch.compile(model)

trainer.train(resume_from_checkpoint=resume_from_checkpoint)

model.save_pretrained(output_dir)

print(
    "\n If there's a warning about missing keys above, please disregard :)"
)

if name == "main": fire.Fire(train)