허깅페이스와 LoRA를 사용하여 단일 Amazon SageMaker GPU에서 대규모 언어 모델(LLM) 훈련하기

import sagemaker
import boto3
sess = sagemaker.Session()
# sagemaker session bucket -> used for uploading data, models and logs
# sagemaker will automatically create this bucket if it does not exist
sagemaker_session_bucket=None
if sagemaker_session_bucket is None and sess is not None:
    # set to default bucket if a bucket name is not given
    sagemaker_session_bucket = sess.default_bucket()

try:
    role = sagemaker.get_execution_role()
except ValueError:
    iam = boto3.client('iam')
    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']

sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)

print(f"sagemaker role arn: {role}")
print(f"sagemaker bucket: {sess.default_bucket()}")
print(f"sagemaker session region: {sess.boto_region_name}")

{
  "id": "13818513",
  "summary": "Amanda baked cookies and will bring Jerry some tomorrow.",
  "dialogue": "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"
}

from transformers import AutoTokenizer

model_id="bigscience/bloomz-7b1"

# Load tokenizer of BLOOMZ
tokenized = AutoTokenizer.from_pretrained(model_id)
tokenizer.model_max_length = 2048 # overwrite wrong value

from random import randint
from itertools import chain
from functools import partial

# custom instruct prompt start
prompt_template = f"Summarize the chat dialogue:\n{{dialogue}}\n---\nSummary:\n{{summary}}{{eos_token}}"

# template dataset to add prompt to each sample
def template_dataset(sample):
    sample["text"] = prompt_template.format(dialogue=sample["dialogue"],
                                            summary=sample["summary"],
                                            eos_token=tokenizer.eos_token)
    return sample


# apply prompt template per sample
dataset = dataset.map(template_dataset, remove_columns=list(dataset.features))

print(dataset[randint(0, len(dataset))]["text"])

# empty list to save remainder from batches to use in next batch
remainder = {"input_ids": [], "attention_mask": []}


def chunk(sample, chunk_length=2048):
    # define global remainder variable to save remainder from batches to use in next batch
    global remainder
    # Concatenate all texts and add remainder from previous batch
    concatenated_examples = {k: list(chain(*sample[k])) for k in sample.keys()}
    concatenated_examples = {k: remainder[k] + concatenated_examples[k] for k in concatenated_examples.keys()}
    # get total number of tokens for batch
    batch_total_length = len(concatenated_examples[list(sample.keys())[0]])

    # get max number of chunks for batch
    if batch_total_length >= chunk_length:
        batch_chunk_length = (batch_total_length // chunk_length) * chunk_length

    # Split by chunks of max_len.
    result = {
        k: [t[i : i + chunk_length] for i in range(0, batch_chunk_length, chunk_length)]
        for k, t in concatenated_examples.items()
    }
    # add remainder to global variable for next batch
    remainder = {k: concatenated_examples[k][batch_chunk_length:] for k in concatenated_examples.keys()}
    # prepare labels
    result["labels"] = result["input_ids"].copy()
    return result


# tokenize and chunk dataset
lm_dataset = dataset.map(
    lambda sample: tokenizer(sample["text"]), batched=True, remove_columns=list(dataset.features)
).map(
    partial(chunk, chunk_length=2048),
    batched=True,
)

# Print total number of samples
print(f"Total number of samples: {len(lm_dataset)}")

# save train_dataset to s3
training_input_path = f's3://{sess.default_bucket()}/processed/samsum-sagemaker/train'
lm_dataset.save_to_disk(training_input_path)

print("uploaded data to:")
print(f"training dataset to: {training_input_path}")

In [ ]:
training_input_path="s3://sagemaker-us-east-1-558105141721/processed/samsum-sagemaker/train"

def create_peft_config(model):
    from peft import (
        get_peft_model,
        LoraConfig,
        TaskType,
        prepare_model_for_int8_training,
    )

    peft_config = LoraConfig(
        task_type=TaskType.CAUSAL_LM,
        inference_mode=False,
        r=8, # Lora attention dimension.
        lora_alpha=32, # the alpha parameter for Lora scaling.
        lora_dropout=0.05, # the dropout probability for Lora layers.
        target_modules=["query_key_value"],
    )

    # prepare int-8 model for training
    model = prepare_model_for_int8_training(model)
    model = get_peft_model(model, peft_config)
    model.print_trainable_parameters()
    return model

import time
# define Training Job Name 
job_name = f'huggingface-peft-{time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())}'

from sagemaker.huggingface import HuggingFace

# hyperparameters, which are passed into the training job
hyperparameters ={
  'model_id': model_id,                                # pre-trained model
  'dataset_path': '/opt/ml/input/data/training', # path where sagemaker will save training dataset
  'epochs': 3,                                         # number of training epochs
  'per_device_train_batch_size': 1,                    # batch size for training
  'lr': 2e-4,                                          # learning rate used during training
}

# create the Estimator
huggingface_estimator = HuggingFace(
    entry_point          = 'run_clm.py',      # train script
    source_dir           = 'scripts',         # directory which includes all the files needed for training
    instance_type        = 'ml.g5.2xlarge', # instances type used for the training job
    instance_count       = 1,                 # the number of instances used for training
    base_job_name        = job_name,          # the name of the training job
    role                 = role,              # IAM role used in training job to access AWS resources, e.g. S3
    volume_size          = 300,               # the size of the EBS volume in GB
    transformers_version = '4.26',            # the transformers version used in the training job
    pytorch_version      = '1.13',            # the pytorch_version version used in the training job
    py_version           = 'py39',            # the python version used in the training job
    hyperparameters      =  hyperparameters
)

# define a data input dictionary with our uploaded s3 uris
data = {'training': training_input_path}

# starting the train job with our uploaded datasets as inputs
huggingface_estimator.fit(data, wait=True)

from sagemaker.huggingface import HuggingFaceModel

# 1. create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=huggingface_estimator.model_data,
   #model_data="s3://hf-sagemaker-inference/model.tar.gz",  # Change to your model path
   role=role, 
   transformers_version="4.26", 
   pytorch_version="1.13", 
   py_version="py39",
   model_server_workers=1
)

# 2. deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
   initial_instance_count=1,
   instance_type= "ml.g5.4xlarge"
)

from random import randint
from datasets import load_dataset

# 1. Load dataset from the hub
test_dataset = load_dataset("samsum", split="test")

# 2. select a random test sample
sample = test_dataset[randint(0,len(test_dataset))]

# 3. format the sample
prompt_template = f"Summarize the chat dialogue:\n{{dialogue}}\n---\nSummary:\n"

fomatted_sample = {
  "inputs": prompt_template.format(dialogue=sample["dialogue"]),
  "parameters": {
    "do_sample": True, # sample output predicted probabilities
    "top_p": 0.9, # sampling technique Fan et. al (2018)
    "temperature": 0.1, # increasing the likelihood of high probability words and decreasing the likelihood of low probability words
    "max_new_tokens": 100, # 
  }
}

# 4. Invoke the SageMaker endpoint with the formatted sample
res = predictor.predict(fomatted_sample)


# 5. Print the model output
print(res[0]["generated_text"].split("Summary:")[-1])
# Sample model output: Kirsten and Alex are going bowling this Friday at 7 pm. They will meet up and then go together.

print(sample["summary"])
# Sample model input: Kirsten reminds Alex that the youth group meets this Friday at 7 pm to go bowling.

predictor.delete_model()
predictor.delete_endpoint()