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_booster/test_plugin/test_low_level_zero_plugin.py |
unit
|
python | This Python unit test verifies LowLevelZeroPlugin functionality and LoRA integration across various model architectures in distributed training scenarios. |
| tests/test_booster/test_plugin/test_torch_ddp_plugin.py |
unit
|
python | This PyTorch unit test verifies TorchDDPPlugin functionality for distributed training in ColossalAI, including gradient synchronization and model optimization. |
| tests/test_checkpoint_io/test_gemini_checkpoint_io.py |
unit
|
python | This PyTest unit test verifies checkpoint I/O operations for the Gemini plugin in distributed training environments with various model and optimizer configurations. |
| tests/test_checkpoint_io/test_gemini_torch_compability.py |
unit
|
python | This PyTest unit test verifies checkpoint I/O compatibility between Gemini and PyTorch implementations in distributed training scenarios. |
| tests/test_checkpoint_io/test_general_checkpoint_io.py |
unit
|
python | This pytest unit test verifies checkpoint I/O operations for model states, optimizers, and schedulers in ColossalAI. |
| tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py |
unit
|
python | This PyTest unit test verifies checkpoint I/O functionality in hybrid parallel environments using ColossalAI’s HybridParallelPlugin. |
| tests/test_checkpoint_io/test_low_level_zero_checkpoint_io.py |
unit
|
python | This PyTorch unit test verifies checkpoint I/O operations for low-level ZeRO optimization in distributed training scenarios. |
| tests/test_checkpoint_io/test_plugins_huggingface_compatibility.py |
unit
|
python | This PyTest unit test verifies Hugging Face model checkpoint compatibility with ColossalAI’s distributed training plugins. |
| tests/test_checkpoint_io/test_safetensors_async_io.py |
unit
|
python | This PyTorch unit test verifies safetensors asynchronous I/O operations for model and optimizer state management in ColossalAI. |
| tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py |
unit
|
python | This PyTorch unit test verifies FSDP checkpoint I/O operations in distributed training scenarios. |