Add merge() method to LoRALinear for zero-cost inference deployment

[derekxu]

Summary:
Implements W_merged = W + (alpha/rank) * B @ A, allowing LoRA weights
to be folded into the base linear layer at deployment time, eliminating
additional inference latency per the LoRA paper (arxiv 2106.09685).

Differential Revision: D97174451

[pytorch-bot]

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❌ 1 New Failure, 2 Unrelated Failures

As of commit 388cd45 with merge base b2f0a5a ():

NEW FAILURE - The following job has failed:

• pull / unittest-editable / macos / macos-job (gh)
  backends/xnnpack/test/ops/test_conv2d.py::TestConv2d::test_fp16_conv2d

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / unittest-editable / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.

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[meta-codesync]

@derekxu has exported this pull request. If you are a Meta employee, you can view the originating Diff in D97174451.

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