[step 1]refine code to support all devices in torch#1879
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Pull request overview
This PR refactors device handling by introducing a unified DeviceManager abstraction and updating existing utilities to use it, with the intent of reducing scattered backend-specific (cuda/xpu/hpu) branching across the codebase.
Changes:
- Added
auto_round/utils/device_manager.pyto centralize backend discovery and runtime ops (sync/cache/memory queries). - Updated
auto_round/utils/device.pyto route device counting, selection, memory clearing, and memory queries through the new device manager APIs. - Updated
auto_round/auto_scheme/delta_loss.pyto synchronize via the active device manager and broaden “non-CPU device” checks.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| auto_round/utils/device.py | Replaces backend-specific device/memory logic with DeviceManager calls. |
| auto_round/utils/device_manager.py | New unified device backend abstraction (discovery + runtime + memory APIs). |
| auto_round/auto_scheme/delta_loss.py | Uses DeviceManager for synchronization and generalized non-CPU device checks. |
Contributor
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/azp run Unit-Test-CUDA-AutoRound |
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Azure Pipelines successfully started running 1 pipeline(s). |
…into refine_device_1 # Conflicts: # auto_round/utils/device_manager.py
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/azp run Unit-Test-CUDA-AutoRound |
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Azure Pipelines successfully started running 1 pipeline(s). |
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…into refine_device_1
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…into refine_device_1
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…into refine_device_1
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…into refine_device_1 # Conflicts: # auto_round/utils/device_manager.py
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/azp run Unit-Test-CUDA-AutoRound |
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Azure Pipelines successfully started running 1 pipeline(s). |
…into refine_device_1
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wenhuach21
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/azp run Unit-Test-CUDA-AutoRound |
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Azure Pipelines successfully started running 1 pipeline(s). |
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Description
Please briefly describe your main changes, the motivation.
Type of Change
Bug fix
Related Issues
Fixes or relates to #
Checklist Before Submitting
/azp run Unit-Test-CUDA-AutoRound.