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_device/test_search_logical_device_mesh.py |
unit
|
python | This pytest unit test verifies the AlphaBetaProfiler’s logical device mesh search functionality in a distributed computing environment. |
| tests/test_fp8/test_fp8_cast.py |
unit
|
python | This PyTorch unit test verifies FP8 casting operations and numerical accuracy across different data types and formats in ColossalAI. |
| tests/test_fp8/test_fp8_reduce_scatter.py |
unit
|
python | This PyTorch unit test verifies FP8 reduce scatter operations against native PyTorch implementation in a distributed GPU environment. |
| tests/test_fx/test_comm_size_compute.py |
unit
|
python | This PyTorch unit test verifies communication size computation between pipeline stages in a distributed MLP model using ColossalAI’s FX transformation framework. |
| tests/test_fx/test_meta/test_aten.py |
unit
|
python | This PyTest unit test verifies the compatibility and correctness of PyTorch meta tensor operations in ColossalAI’s neural network implementations. |
| tests/test_fx/test_meta/test_meta_trace.py |
unit
|
python | This PyTest unit test verifies meta tracing functionality across multiple vision model architectures in the ColossalAI framework. |
| tests/test_fx/test_pipeline/test_hf_model/test_albert.py |
unit
|
python | This pytest unit test verifies ALBERT model splitting and output consistency across different model variants in ColossalAI. |
| tests/test_fx/test_pipeline/test_hf_model/test_bert.py |
unit
|
python | This pytest unit test verifies BERT model splitting and output consistency across multiple BERT variants in the ColossalAI framework. |
| tests/test_fx/test_pipeline/test_hf_model/test_t5.py |
unit
|
python | This PyTest unit test verifies the functionality and output consistency of T5 transformer models after splitting operations in ColossalAI. |
| tests/test_fx/test_pipeline/test_hf_model/test_gpt.py |
unit
|
python | This pytest unit test verifies GPT-2 model variant implementations and their pipeline splitting functionality in ColossalAI. |