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Summary of ChangesHello @HwVanICI, 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! This pull request significantly enhances support for Ascend NPUs by refining the NPU platform initialization and providing a comprehensive, ready-to-use example configuration. The new example streamlines the setup for Generative Reinforcement Learning with Policy Optimization (GRPO) on NPUs, pre-configuring it with Highlights
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Code Review
This pull request introduces a new example configuration for running RLVR on Ascend NPUs and removes a redundant operation during platform initialization. The changes look good. The removal of the unused import in areal/platforms/__init__.py is a nice cleanup. The new gsm8k_grpo_npu.yaml file is well-structured for NPU-specific runs. I've added a few suggestions to improve the configuration file's maintainability and to clarify some performance-related settings.
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Hi @garrett4wade, it would be great if we can have this YAML merged to the main branch to keep the NPU example consistent with the rest in case there is a breaking change like in #789. Thanks! |
Description
This PR is only relevant to Ascend NPUs. It removes a redundant operation and cherry-picks the NPU example YAML file into the main branch so it can evolve alongside upstream changes to the expected YAML format.
The core differences between
gsm8k_grpo_npu.yamlandgsm8k_grpo.yamlare: (1) the rollout engine is set tovllmby default, and (2) NPU-specific envvars are included to allow the configuration to be used out of the box.Related Issue
#747
Type of Change
work as expected)
Checklist
jb build docs/gemini review)Breaking Change Details (if applicable):
Additional Context
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