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Testing HuggingFace ALBERT Model Integration in ColossalAI

This test suite validates the functionality of ALBERT models from Hugging Face within the ColossalAI framework. It focuses on ensuring proper model tracing and output comparison for various ALBERT model configurations, while handling version compatibility and memory management.

Test Coverage Overview

The test suite covers ALBERT model implementations from the Hugging Face transformers library, excluding AlbertForPreTraining.

Key areas tested include:

Model output consistency after tracing
Version compatibility with PyTorch ≥ 1.12.0
Different ALBERT model variants from the model zoo
Memory management through cache clearing

Implementation Analysis

The testing approach employs a systematic verification of ALBERT models using the trace_model_and_compare_output utility.

Technical patterns include:

Dynamic model instantiation from registry
Selective model testing with exclusion logic
Automated batch processing with fixed sequence lengths
Version-aware test execution

Technical Details

Testing infrastructure includes:

PyTest framework for test organization
Custom trace_model_and_compare_output utility
Model zoo registry for test cases
Cache clearing decorator for memory management
Version parsing for compatibility checks
Batch size of 2 and sequence length of 16 for testing

Best Practices Demonstrated

The test implementation showcases several testing best practices in deep learning model validation.

Notable practices include:

Explicit version dependency management
Memory cleanup between test runs
Modular test case organization
Systematic model exclusion handling
Reusable test utilities
Clear test boundaries and scope definition

hpcaitech/colossalai

tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py

            
import pytest
import torch
from hf_tracer_utils import trace_model_and_compare_output
from packaging import version

from colossalai.testing import clear_cache_before_run
from tests.kit.model_zoo import model_zoo

BATCH_SIZE = 2
SEQ_LENGTH = 16


@pytest.mark.skipif(version.parse(torch.__version__) < version.parse("1.12.0"), reason="torch version < 12")
@clear_cache_before_run()
def test_albert():
    sub_registry = model_zoo.get_sub_registry("transformers_albert")

    for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items():
        model = model_fn()
        # TODO: support the following models
        # 1. "AlbertForPreTraining"
        # as they are not supported, let's skip them
        if model.__class__.__name__ in ["AlbertForPreTraining"]:
            continue
        trace_model_and_compare_output(model, data_gen_fn)


if __name__ == "__main__":
    test_albert()