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Testing ALBERT Model Splitting and Validation in ColossalAI

This test suite validates the functionality of ALBERT model variants in the ColossalAI framework, specifically focusing on model splitting and output comparison. It tests multiple ALBERT configurations including base model, pre-training, masked language modeling, and classification tasks.

Test Coverage Overview

The test suite provides comprehensive coverage of ALBERT model variants and their transformations.

Tests multiple ALBERT model configurations
Validates model splitting functionality
Verifies output consistency across different architectures
Covers key model variants including pre-training and classification tasks

Implementation Analysis

The implementation uses pytest framework with a systematic approach to model testing. It employs a data generation pattern for consistent input tensors and leverages transformers library configurations.

Utilizes custom data generation for controlled testing
Implements model splitting verification
Employs configuration-based model initialization

Technical Details

PyTest framework for test organization
Transformers library for ALBERT models
PyTorch tensor operations
Custom utility function split_model_and_compare_output
Configurable batch size and sequence length
Specific ALBERT configuration parameters

Best Practices Demonstrated

The test implementation showcases several testing best practices for deep learning models.

Modular test structure with clear separation of concerns
Consistent input data generation
Comprehensive model variant coverage
Configurable model parameters
Systematic output validation

hpcaitech/colossalai

tests/test_fx/test_pipeline/test_hf_model/test_albert.py

            
import pytest
import torch
import transformers
from hf_utils import split_model_and_compare_output

BATCH_SIZE = 2
SEQ_LENGHT = 16


@pytest.mark.skip("balance split v2 is not ready")
def test_single_sentence_albert():
    MODEL_LIST = [
        transformers.AlbertModel,
        transformers.AlbertForPreTraining,
        transformers.AlbertForMaskedLM,
        transformers.AlbertForSequenceClassification,
        transformers.AlbertForTokenClassification,
    ]

    config = transformers.AlbertConfig(
        vocab_size=100,
        embedding_size=128,
        hidden_size=128,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=256,
    )

    def data_gen():
        input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
        token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
        attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64)
        meta_args = dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
        return meta_args

    for model_cls in MODEL_LIST:
        model = model_cls(config=config)
        split_model_and_compare_output(model, data_gen)


if __name__ == "__main__":
    test_single_sentence_albert()