ColossalAI Testing: Distributed GPU Computing and Model Optimization Validation
The ColossalAI testing framework implements a comprehensive suite of unit tests using pytest, focusing on verifying critical distributed computing and model optimization functionalities. With 179 test cases, the framework thoroughly validates components like FP8 operations, bias additions, and distributed GPU communications, ensuring the reliability of ColossalAI's large-scale AI training capabilities. Qodo Tests Hub provides developers with detailed insights into ColossalAI's testing patterns, making it easier to understand how to implement robust tests for distributed AI systems. Through interactive exploration of real test implementations, developers can learn best practices for testing complex operations like model sharding, precision formats, and multi-GPU communications – essential knowledge for building reliable AI infrastructure.
| Path | Test Type | Language | Description |
|---|---|---|---|
| tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py |
unit
|
python | This PyTest unit test verifies automatic memory chunking functionality for EvoformerBlock in ColossalAI’s AlphaFold implementation. |
| tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py |
unit
|
python | This pytest unit test verifies the ExtraMSA Block component’s functionality and memory management in ColossalAI’s AlphaFold implementation. |
| tests/test_autochunk/test_autochunk_diffuser/benchmark_autochunk_diffuser.py |
unit
|
python | This Python unit test verifies the memory optimization capabilities of ColossalAI’s AutoChunk feature for UNet models through performance benchmarking. |
| tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py |
unit
|
python | This pytest unit test verifies automatic chunking functionality for UNet2D models with varying memory constraints in ColossalAI. |
| tests/test_autochunk/test_autochunk_transformer/benchmark_autochunk_transformer.py |
unit
|
python | This PyTorch benchmark test verifies automatic memory chunking optimization for transformer models in ColossalAI’s GPT implementation. |
| tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py |
unit
|
python | This PyTorch unit test verifies automatic chunking optimization for transformer models in ColossalAI, ensuring correct memory management and execution consistency. |
| tests/test_booster/test_accelerator.py |
unit
|
python | This PyTorch unit test verifies the Accelerator component’s ability to configure models for different compute devices in ColossalAI. |
| tests/test_booster/test_plugin/test_3d_plugin.py |
unit
|
python | This PyUnit test verifies 3D hybrid parallel training functionality including tensor, pipeline, and data parallelism in the ColossalAI framework |
| tests/test_booster/test_plugin/test_dp_plugin_base.py |
unit
|
python | This PyTorch unit test verifies data parallel plugin functionality and dataloader sharding in distributed training environments. |
| tests/test_booster/test_plugin/test_gemini_plugin.py |
unit
|
python | This Python unit test verifies the GeminiPlugin’s functionality across various deep learning models in a distributed training environment. |