[Bugfix] Fix precision loss in LoRA-wrapped RowParallelLinear by fusing bias into GEMM

[prashanth058]

Issue:
LoRA-wrapped RowParallelLinear was adding bias as a separate bfloat16 operation instead of fusing it into the GEMM kernel like the unwrapped layer does. This caused precision loss because the fused kernel can accumulate in higher precision (FP32) before converting to bfloat16, while separate addition incurs additional rounding errors. The discrepancy appeared even with zero LoRA weights when comparing LoRA-wrapped vs merged weight results.

Fix:
Pass bias to apply() only on rank 0 (or when skip_bias_add=False), allowing the quantization method to fuse bias addition with matrix multiplication in the GEMM kernel. This matches the unwrapped layer's behavior and eliminates precision discrepancies.

[gemini-code-assist]

Code Review

This pull request addresses a precision loss issue in LoRA-wrapped RowParallelLinear by fusing the bias addition into the GEMM kernel, which aligns its behavior with the non-LoRA equivalent layer. The changes correctly pass the bias to the apply method only on rank 0 to prevent redundant additions in tensor-parallel setups, and the refactoring of the bias handling logic improves code clarity. The fix appears correct and well-implemented. I have no major concerns with this change.

[jeejeelee]

over LGTM, could you please address CI failure first?