introduce device_context to simplify code.

[ykaitao]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @ykaitao, 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 refactors device-specific operations by introducing a DeviceContext class. This new abstraction centralizes logic for handling different GPU types (CUDA, HIP, XPU), including retrieving device properties and managing GPU cache. The change significantly simplifies the codebase by eliminating redundant conditional checks and promoting a more modular design for device interaction.

Highlights

• Centralized Device Management: Introduced a new DeviceContext class in unsloth/device_type.py to encapsulate and centralize device-specific operations for CUDA, HIP, and XPU. This class handles retrieving device properties, toolkit versions, and managing GPU cache.
• Code Simplification and Readability: Replaced repetitive conditional logic for device statistics gathering and GPU cache management with calls to a singleton instance of the DeviceContext. This significantly reduces boilerplate code in unsloth/models/llama.py and improves overall readability.
• Improved Maintainability: By consolidating device-related functions into a single class, future additions or modifications to device support (e.g., new GPU types or toolkit versions) will be more straightforward and require changes in fewer places.

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[gemini-code-assist]

Code Review

This pull request introduces a DeviceContext class to encapsulate and simplify device-specific operations, which is a great refactoring for code clarity and maintainability. The changes in unsloth/models/llama.py correctly adopt this new abstraction.

My review includes a critical fix for a typo that would cause a NameError and a suggestion to improve exception handling for better robustness. Overall, this is a positive change that improves the codebase structure.

[ykaitao]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a DeviceContext class to encapsulate device-specific logic, which significantly simplifies the code in unsloth/models/llama.py. The refactoring is well-done and improves maintainability by centralizing device-related information and operations. I have a couple of suggestions to further improve the code's clarity and robustness.

[mmathew23]

Hi @ykaitao, there is similar statistics logic in vision.py. Would you be able to add your simplifications there as well? Thank you!

[ykaitao]

Hi @ykaitao, there is similar statistics logic in vision.py. Would you be able to add your simplifications there as well? Thank you!

Hi @mmathew23 , yes I can do it later today.

[ykaitao]

/gemini review

[ykaitao]

Hi @ykaitao, there is similar statistics logic in vision.py. Would you be able to add your simplifications there as well? Thank you!

Hi @mmathew23 , I have handled the similar statistics logic in vision.py. Please review again.

[gemini-code-assist]

Code Review

This pull request introduces a DeviceContext class to encapsulate device-specific logic, which is an excellent refactoring. It significantly simplifies the code in unsloth/models/llama.py and unsloth/models/vision.py by removing duplicated device-checking logic and centralizing it. This greatly improves code clarity and maintainability. My review includes a few suggestions to further enhance the new DeviceContext class for better code quality and to improve exception handling in the model files.

[ykaitao]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a DeviceContext class to encapsulate device-specific logic, which is a great improvement. This refactoring simplifies the code in unsloth/models/llama.py and unsloth/models/vision.py by removing duplicated if/elif/else blocks for handling different device types (CUDA, HIP, XPU), enhancing code readability and maintainability. I've found one critical issue regarding module loading that could break the library for users without an XPU-enabled PyTorch build, and I've provided a suggestion to fix it.

[ykaitao]

Hi Team, I have addressed all the comments. Can this PR be merged? Or do you have additional comments? @mmathew23 @Datta0 @danielhanchen