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Validating Reward Model Loading and Conversion in LAION-AI Open-Assistant

This test suite validates the loading and conversion functionality of reward models in the Open-Assistant project, focusing on GPTNeoX model handling and reward scoring capabilities.

Test Coverage Overview

The test suite covers two main areas: model conversion and reward model loading functionality.

Key functionality tested includes:

Converting pretrained models to GPTNeoXRewardModel format
Loading and tokenization of reward models
Reward scoring for conversation snippets

Integration points include interactions with HuggingFace’s transformers library and custom model architectures.

Implementation Analysis

The testing approach implements direct functional validation of model operations using pythia-70m-deduped and oasst-rm-1-pythia-1b models. The implementation utilizes PyTorch tensors for input processing and employs AutoModel classes for flexible model loading.

Key patterns include:

Namespace configuration for model parameters
Tokenizer synchronization with models
Automated model conversion workflows

Technical Details

Testing tools and components:

transformers library for model handling
Custom GPTNeoXRewardModel implementation
AutoTokenizer and AutoModelForSequenceClassification
PyTorch tensor operations
Local cache directory management

Best Practices Demonstrated

The test suite demonstrates robust testing practices including proper model initialization, error handling, and resource management.

Notable practices:

Configurable cache and output directories
Explicit type checking and verification
Modular test function design
Clear separation of model conversion and loading logic

laion-ai/open-assistant

model/model_training/tests/test_rm_loading.py

            
from argparse import Namespace

import model_training.models.reward_model  # noqa: F401
from model_training.models.reward_model import GPTNeoXRewardModel
from model_training.utils.utils import get_tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer


def test_convert_model(
    model_name: str = "EleutherAI/pythia-70m-deduped",
    cache_dir: str = ".cache",
    output_dir: str = ".saved_models_rm/debug",
):
    training_conf = Namespace(
        cache_dir=cache_dir,
        model_name=model_name,
    )
    tokenizer = get_tokenizer(training_conf)
    model = GPTNeoXRewardModel.from_pretrained(model_name, cache_dir=cache_dir)
    print("model", type(model))
    print("tokenizer", type(tokenizer))
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)


def test_load_reward_model(model_name: str = "andreaskoepf/oasst-rm-1-pythia-1b", cache_dir: str = ".cache"):
    tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
    rm = AutoModelForSequenceClassification.from_pretrained(model_name, cache_dir=cache_dir)
    print("auto", type(rm))
    print("auto.config", type(rm.config))
    question = "<|prompter|>Hi how are you?<|endoftext|><|assistant|>Hi, I am Open-Assistant a large open-source language model trained by LAION AI. How can I help you today?<|endoftext|>"
    inputs = tokenizer(question, return_tensors="pt")
    print(inputs)
    score = rm(**inputs).logits[0].cpu().detach()
    print(score)


if __name__ == "__main__":
    # test_load_reward_model("../.saved_models_rm/oasst-rm-1-pythia-1b/")
    test_load_reward_model("andreaskoepf/oasst-rm-1-pythia-1b")