Error in finetuning

#1
by ashk72 - opened

import os
import sys
from typing import List
import re

import fire
import torch
import torch.nn as nn
import bitsandbytes as bnb
from datasets import load_dataset
import transformers
from peft import LoraConfig, PeftModel

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from peft import (
prepare_model_for_int8_training,
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
)

def train(
# model/data params
base_model: str = "", # the only required argument
data_path: str = "./alpaca_data_cleaned.json",
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 = 512,
val_set_size: int = 0,
# 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
group_by_length: bool = True, # faster, but produces an odd training loss curve
# other
mask: bool = False,
):
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"group_by_length: {group_by_length}\n"
)
assert (
base_model
), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
gradient_accumulation_steps = batch_size // micro_batch_size

#quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True,load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
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 = AutoModelForCausalLM.from_pretrained(
    base_model,
    return_dict=True,
    torch_dtype=torch.float32,
    device_map=device_map
)

tokenizer = AutoTokenizer.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

    if mask:
        print('Masking Obersevation')
        tokenizer.mask_token = "~"

        # Split the input text into lines
        lines = prompt.split('\n')

        # Initialize an empty list to store the modified lines
        masked_lines = []

        # Initialize a flag to indicate if we are between "Observation:" and "Thought:"
        between_observation_and_thought = False

        # Iterate through each line
        for line in lines:
            if "Observation:" in line:
                between_observation_and_thought = True
                #split the line and mask all but the first word
                line = line.split()
                line[1:] = [tokenizer.mask_token] * len(line[1:])
                line = " ".join(line)
                masked_lines.append(line)  # Add the line as-is
            else:
                masked_lines.append(line)  # Add the line as-is

        # Concatenate the modified lines to form the masked text
        masked_text = '\n'.join(masked_lines)

        prompt = masked_text

    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)
    else:
        if len(result["input_ids"]) >= cutoff_len:
            print("WARNING: input too long, truncating")

    masked_token_id = tokenizer.mask_token_id
    ids = [-100 if token_id == 3695 else token_id for token_id in result["input_ids"]]

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

    return result

def generate_and_tokenize_prompt(data_point):
    full_prompt = generate_prompt(data_point)
    tokenized_full_prompt = tokenize(full_prompt)
    if not train_on_inputs:
        user_prompt = generate_prompt({**data_point, "output": ""})
        tokenized_user_prompt = tokenize(user_prompt, add_eos_token=False)
        user_prompt_len = len(tokenized_user_prompt["input_ids"])

        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)

data = load_dataset("json", data_files=data_path)

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

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,
        logging_steps=10,
        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,
    ),
    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()

model.save_pretrained(output_dir)

merge_model = AutoModelForCausalLM.from_pretrained(
base_model,
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
device_map=device_map,
)
model = PeftModel.from_pretrained(merge_model, output_dir)
model = model.merge_and_unload()

# Reload tokenizer to save it
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

model.push_to_hub(output_dir, use_temp_dir=False)
tokenizer.push_to_hub(output_dir, use_temp_dir=False)

print("\n If there's a warning about missing keys above, please disregard :)")
while True:
  prompt = input("Enter a prompt: ")
  if prompt=="exit":
      break
  pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
  result = pipe(f"<s>[INST] {prompt} [/INST]")
  print(result[0]['generated_text'])
  print("You can end the session by typing exit")

def generate_prompt(data_point):
print(data_point)
# sorry about the formatting disaster gotta move fast
if data_point["input"]:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

Instruction:

{data_point["instruction"]}

Input:

{data_point["input"]}

Response:

{data_point["output"]}"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

Instruction:

{data_point["instruction"]}

Response:

{data_point["output"]}"""

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

Above is my finetuning code. I am trying to finetune meta-llama/Llama-2-13b-chat-hf, but running into error:
self.is_model_parallel = self.args.device != torch.device(devices[0])
IndexError: list index out of range

When I remove device_map="auto", there are other error line NotImplementedError: Cannot copy out of meta tensor; no data!

And when I set device_map completely on GPU, using {"":0}, I am getting error:RuntimeError: No CUDA GPUs are available

Is there any particular solution around it as I am new to all this. I am running this in gcp g2-standard-12 VM with nvidia L4 GPU. GPU memory is maybe around 24gb

Sign up or log in to comment