feat: Add LoRA (Low-Rank Adaptation) support for efficient model fine-tuning #108
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- Add LoRA module infrastructure with configurable rank, alpha, dropout - Implement LoRALinear wrapper for seamless integration with Linear layers - Support tensor parallelism via LoRAParallelLinear - Add LoRAModel utility for managing multiple LoRA layers - Integrate LoRA configuration and utilities - Add GPT2 example demonstrating LoRA fine-tuning - Include comprehensive usage documentation and test suite Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Added LoRA (Low-Rank Adaptation) support for parameter-efficient fine-tuning. This feature significantly reduces the number of trainable parameters through low-rank decomposition, enabling efficient fine-tuning of large models.
Changes
New Features
LoRA Infrastructure (
infini_train/include/nn/lora/):lora_config.h/cc- LoRA configuration (rank, alpha, dropout)lora_linear.h/cc- LoRA linear layer wrapperlora_model.h/cc- Multi-LoRA layer managementlora_parallel_linear.h/cc- Tensor parallelism supportlora_utils.h/cc- Utility functionsTests:
test/lora/test_lora.cc- Unit testsDocumentation:
docs/lora_usage.md- Usage documentationExamples:
example/gpt2/main.cc- Added LoRA training exampleBuild:
CMakeLists.txt- Added test_lora build targetTechnical Details
LoRA adds trainable low-rank adapters to frozen model weights: