feat: lora manager

[JamesBrianD]

Motivation

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

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[gemini-code-assist]

Summary of Changes

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This pull request establishes the foundational components for integrating LoRA (Low-Rank Adaptation) capabilities into the sgl-jax framework. It introduces a structured approach to manage LoRA adapters and their associated weights, with a strong emphasis on JAX compatibility, including pytree registration and sharding for distributed environments. The current implementation focuses on setting up the core architecture and memory management, with the actual loading of LoRA weights from external files and their application during model inference deferred to a subsequent development phase.

Highlights

• New LoRA Management Classes: Introduced core classes for LoRA (Low-Rank Adaptation) support, including LoRAAdapter for handling individual LoRA configurations and weights, LoRAManager for orchestrating LoRA operations, and LoRAMemoryPool for efficient GPU memory management of LoRA weights.
• JAX-Native Memory Pool: The LoRAMemoryPool is designed to be JAX-native, supporting pytree registration for JIT compilation and utilizing JAX sharding for distributed inference. It manages GPU buffers for LoRA A and B matrices.
• Placeholder Implementation (Phase 3): This pull request establishes the foundational architecture for LoRA, explicitly marking it as a 'Phase 3 placeholder.' While the structure for managing LoRA adapters and memory is in place, the actual loading of weights from disk and their application during model inference are planned for a future 'Phase 4' development.
• Dynamic LoRA Batch Preparation: The LoRAManager includes functionality to prepare LoRA batches dynamically, loading required adapters into the memory pool based on the current inference requests, and handling buffer slot allocation.

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[pathfinder-pf]

same as line 187