Subchapter 24.19

Rlaif Builtin

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Code Output Guidereferences

Dataset Transformation Code

Sagemaker Dataset Formats

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references/finetuning/code_templates/rlaif_builtin.py

Python169 lines6 KB

1# RLAIF (Reinforcement Learning from AI Feedback) Template — Builtin Reward Prompt
2
3# Cell 0 [markdown]: Fine-Tuning
4
5# Cell 1: Install Dependencies
6
7# %pip install --upgrade 'sagemaker>=3.7.1,<4.0' boto3 -q  # NOTEBOOK_ONLY
8
9# Cell 2: Setup & Credentials
10
11import json
12from pathlib import Path
13
14import boto3
15from botocore.exceptions import ClientError

rlaif_builtin.py on GitHub

from sagemaker.ai_registry.dataset import DataSet

from sagemaker.core import Attribution, set_attribution

from sagemaker.core.helper.session_helper import Session, get_execution_role

from sagemaker.core.resources import ModelPackageGroup

from sagemaker.train.common import TrainingType

from sagemaker.train.rlaif_trainer import RLAIFTrainer

set_attribution(Attribution.SAGEMAKER_AGENT_PLUGIN)

# Setup

sm_client = boto3.Session().client("sagemaker")

sagemaker_session = Session(sagemaker_client=sm_client)

bucket = sagemaker_session.default_bucket()

# Configuration - USER please fill in these fields with your information:

BASE_MODEL = "" # Sagemaker Hub model id

TRAINING_DATA_S3 = "" # S3 path

S3_OUTPUT_PATH = f"s3://{bucket}/finetuning-output/"

ROLE_ARN = get_execution_role() # You can change this to a specific role

ACCEPT_EULA = (

False # Set to True to accept the base model's End-User License Agreement (OSS models only)

)

MODEL_PACKAGE_GROUP_NAME = "" # Auto-generated based on use case

# Reward model — the Bedrock LLM used as judge

# Available models and regions: see references/rlaif_guide.md

REWARD_MODEL_ID = ""

# Builtin reward prompt value — choose one that matches your use case:

# "Builtin.Summarize", "Builtin.Faithfulness", "Builtin.ChainOfThought", "Builtin.Evaluation"

REWARD_PROMPT_VALUE = ""

# Cell 3: Create Dataset and Model Package Group

# Create Model Package Group

try:

model_package_group = ModelPackageGroup.create(

model_package_group_name=MODEL_PACKAGE_GROUP_NAME,

model_package_group_description="",

)

print(f"Created new model package group named {MODEL_PACKAGE_GROUP_NAME}")

except ClientError as e:

if e.response["Error"]["Code"] in ("ResourceInUse", "ValidationException"):

model_package_group = ModelPackageGroup.get(

model_package_group_name=MODEL_PACKAGE_GROUP_NAME

)

print(

f"There is already a model package group with the name {MODEL_PACKAGE_GROUP_NAME}.\nIf you want to save your finetuned model under a different name, change the value of MODEL_PACKAGE_GROUP_NAME in the previous cell."

)

else:

raise

# Create Dataset

dataset = DataSet.create(name=MODEL_PACKAGE_GROUP_NAME, source=TRAINING_DATA_S3, wait=True)

TRAINING_DATASET_ARN = dataset.arn

print(f"Here is your model package group ARN: {model_package_group.model_package_group_arn}\n")

print(f"Here is your training dataset ARN: {dataset.arn}")

# Cell 4: Configure Trainer

trainer = RLAIFTrainer(

model=BASE_MODEL,

model_package_group=model_package_group,

reward_model_id=REWARD_MODEL_ID,

reward_prompt=REWARD_PROMPT_VALUE,

training_dataset=TRAINING_DATASET_ARN,

s3_output_path=S3_OUTPUT_PATH,

sagemaker_session=sagemaker_session,

# accept_eula=ACCEPT_EULA, # Uncomment for Meta models

role=ROLE_ARN,

)

print("Here are the recommended hyperparameters for the current training job:")

print(f"Batch size: {trainer.hyperparameters.global_batch_size}")

print(f"Learning rate: {trainer.hyperparameters.learning_rate}")

print(f"Epochs: {trainer.hyperparameters.max_epochs}")

# Cell 5: Hyperparameter Overrides

# To change a hyperparameter, uncomment its corresponding line, and set the value you want.

# Note: If the value you choose is not supported for your model, you will get an error indicating the allowed range.

# Uncomment the following line to change the learning rate

# trainer.hyperparameters.learning_rate = 0.0002

# Uncomment the following line to change the batch size

# trainer.hyperparameters.global_batch_size = 16

# Uncomment the following line to change the number of epochs

# trainer.hyperparameters.max_epochs = 5

# Cell 6: Start Training

# Start training

training_job = trainer.train(wait=True)

print(f"Training Job Name: {training_job.training_job_name}")

print(f"Training Status: {training_job.training_job_status}")

# Save manifest

manifest_dir = Path("[PROJECT_DIR]") / "manifests"

manifest_dir.mkdir(parents=True, exist_ok=True)

manifest_path = manifest_dir / f"training-{training_job.training_job_name}.json"

manifest_path.write_text(

json.dumps(

{

"training_job_name": training_job.training_job_name,

"model_package_group_name": MODEL_PACKAGE_GROUP_NAME,

},

indent=2,

)

print(f"Manifest saved: {manifest_path}")

# Cell 7: Plot and Display Metrics # NOTEBOOK_ONLY_SECTION

import matplotlib.pyplot as plt

import mlflow

from mlflow.tracking import MlflowClient

run_id = training_job.mlflow_details.mlflow_run_id

mlflow.set_tracking_uri(training_job.mlflow_config.mlflow_resource_arn)

client = MlflowClient()

metrics = [

"critic/rewards/mean",

"response_length/mean",

"actor/entropy_loss",

"actor/grad_norm",

"critic/advantages/mean",

]

fig, axes = plt.subplots(1, len(metrics), figsize=(4 * len(metrics), 3))

for idx, metric in enumerate(metrics):

history = client.get_metric_history(run_id, metric)

if history:

axes[idx].plot(

[h.step for h in history],

[h.value for h in history],

linewidth=2,

marker="o",

markersize=4,

)

axes[idx].set_xlabel("Step")

axes[idx].set_ylabel(metric.split("/")[-1])

axes[idx].set_title(metric, fontweight="bold")

axes[idx].grid(True, alpha=0.3)

plt.suptitle(f"Training Metrics: {training_job.training_job_name}", fontweight="bold")

plt.tight_layout()

plt.show()