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_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py |
unit
|
python | This pytest unit test verifies consistency between Python and C solver implementations for checkpoint optimization in ColossalAI. |
| tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py |
unit
|
python | This pytest unit test verifies checkpoint solver functionality for torchvision models in ColossalAI, ensuring proper activation checkpointing and gradient computation. |
| tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py |
unit
|
python | This pytest unit test verifies the linearization and checkpoint solving functionality in ColossalAI’s automatic parallel system. |
| tests/test_auto_parallel/test_offload/test_perf.py |
unit
|
python | This pytest unit test verifies memory optimization and offloading performance in ColossalAI for large model training scenarios. |
| tests/test_auto_parallel/test_pass/test_node_converting_pass.py |
unit
|
python | This PyTorch unit test verifies node argument converting functionality in distributed tensor operations with shape transformations. |
| tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py |
unit
|
python | This PyTest unit test verifies bias addition operations in linear and convolutional layers across distributed tensor sharding implementations in ColossalAI. |
| tests/test_auto_parallel/test_tensor_shard/test_broadcast.py |
unit
|
python | This PyTorch unit test verifies tensor broadcasting operations and sharding specification handling in distributed tensor computations. |
| tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py |
unit
|
python | This pytest unit test verifies the compatibility between tensor sharding and Distributed Data Parallel (DDP) in a multi-GPU environment. |
| tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py |
unit
|
python | This PyTest unit test verifies the compatibility between automatic parallel tensor sharding and Gemini optimization in distributed training scenarios. |
| tests/test_auto_parallel/test_tensor_shard/test_gpt/gpt_modules.py |
unit
|
python | This PyTorch unit test verifies GPT model components functionality and distributed computing compatibility in ColossalAI’s automatic parallel system. |