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_bias_linear_function_node.py |
unit
|
python | This PyTest unit test verifies the LinearFunctionHandler’s sharding strategy implementation for linear operations with bias in distributed environments. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py |
unit
|
python | This PyTest unit test verifies binary elementwise operation handling and sharding strategies in ColossalAI’s auto-parallel system |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py |
unit
|
python | This PyTest unit test verifies the ADDMM operation handler’s sharding strategies and numerical correctness in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py |
unit
|
python | This PyTest unit test verifies the LinearModule handler’s sharding strategies and operation data mapping in ColossalAI’s auto-parallel system |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py |
unit
|
python | This PyTest unit test verifies BMM handler functionality and sharding strategies in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py |
unit
|
python | This pytest unit test verifies the GetAttr handler’s functionality for tensor sharding strategies in ColossalAI’s auto-parallel system. |
| tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py |
unit
|
python | This PyTorch unit test verifies the NormPoolingHandler’s tensor sharding strategies and meta tensor operations in distributed computing environments. |
| applications/ColossalChat/tests/test_lora.py |
unit
|
python | This PyTorch unit test verifies LoRA adaptation functionality including linear layers, embeddings, and model conversion accuracy in neural networks. |
| applications/ColossalQA/tests/test_memory.py |
unit
|
python | This Python unit test verifies memory management and conversation buffer functionality in ColossalQA’s document-aware chat system |
| applications/ColossalQA/tests/test_text_splitter.py |
unit
|
python | This Python unit test verifies the Chinese text splitting functionality with specific chunk size parameters while maintaining sentence integrity. |