[UT for RL] Add UT to cover release/resume memory case for moe model#8803
[UT for RL] Add UT to cover release/resume memory case for moe model#8803zhaochenyang20 merged 3 commits intosgl-project:mainfrom
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Summary of Changes
Hello @ryang-max, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
I've added a new unit test to cover the critical workflow of releasing, resuming, and updating memory for Mixture-of-Experts (MoE) models. This change aims to prevent potential breaking changes that could impact reinforcement learning (RL) workflows, where such memory management and model update operations are common. The new test ensures that these complex memory operations function correctly and that model weights can be updated seamlessly after memory adjustments.
Highlights
- New Unit Test for MoE Memory Management: I've introduced a new unit test,
test_moe_model_release_and_resume, specifically designed to validate the memory release, memory resume, and weight update workflow for Mixture-of-Experts (MoE) models. This test is critical for ensuring the stability and correctness of memory management operations in MoE models, especially in scenarios like reinforcement learning (RL) where dynamic model updates might occur. - Refactoring of MoE Model Test Constants: To support the new MoE model tests, I've refactored the test utility constants by renaming
DEFAULT_SMALL_MOE_MODEL_NAME_FOR_TESTtoDEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST_BASEand addingDEFAULT_SMALL_MOE_MODEL_NAME_FOR_TEST_CHAT. This provides clearer distinctions between base and chat-tuned MoE models used in testing. - Enhanced Engine Setup for Expert Parallelism: I've updated the
_setup_enginemethod intest_release_memory_occupation.pyto accept anep_sizeparameter. This allows for explicit configuration of expert parallel size during engine initialization, which is essential for properly testing MoE models in distributed or parallel environments.
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Code Review
This pull request adds a unit test to cover the memory release and resume workflow for MoE models, which is important for reinforcement learning use cases. The changes are well-structured, introducing new test parameters and a dedicated test case for MoE models. I have one suggestion to improve code consistency by using an existing helper function for engine setup.
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Motivation
Add unit test for MOE model release memory - resume memory - update weights workflow, to avoid break change that may have effect on RL workflow.
Modifications
Add a unit test case
Accuracy Test
Benchmark & Profiling
Checklist