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_tensor_shard/test_node_handler/test_placeholder_handler.py |
unit
|
python | This pytest unit test verifies PlaceholderHandler’s sharding strategy implementation for both distributed and replicated configurations in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py |
unit
|
python | This PyTest unit test verifies the SoftmaxHandler’s tensor sharding strategies and operation handling in distributed training scenarios. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py |
unit
|
python | This PyTest unit test verifies tensor splitting operations and sharding strategies in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py |
unit
|
python | This pytest unit test verifies the SumHandler’s strategy generation and tensor sharding behavior in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py |
unit
|
python | This PyTorch unit test verifies tensor constructor handling and sharding strategies in auto-parallel processing. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py |
unit
|
python | This PyTest unit test verifies the ViewHandler’s ability to manage tensor reshaping operations in distributed environments within ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py |
unit
|
python | This PyTest unit test verifies the WhereHandler component’s tensor sharding and strategy generation capabilities in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/utils.py |
unit
|
python | This PyUnit test verifies tensor sharding strategies and numerical accuracy in ColossalAI’s auto-parallel system |
| tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py |
unit
|
python | This Python unit test verifies tensor sharding solver functionality using ResNet50 model in ColossalAI’s auto-parallel system. |
| tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py |
unit
|
python | This PyTorch unit test verifies the performance and memory optimization of EvoFormer stack implementation using AutoChunk functionality in ColossalAI. |