[Core] Split LoRA layers

[jeejeelee]

Purpose

Split LoRA layers into separate files to facilitate maintenance and improve readability.

Test Plan

Test Result

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request refactors the LoRA layers by splitting a large layers.py file into a more organized package structure. This is a great improvement for maintainability. I've found a critical issue that could break support for Marlin quantized models, and a high-severity typo in a new module's name that should be corrected.

[gemini-code-assist]

The _get_lora_device function is missing a check for the B attribute, which is used by Marlin quantized models. This can cause a ValueError when using LoRA with Marlin quantization, as the function would fail to determine the correct device. The weight property in BaseLinearLayerWithLoRA correctly handles this case, and this function should be consistent with it to avoid a regression.

Suggested change

	def _get_lora_device(base_layer: nn.Module) -> torch.device:
	# code borrowed from https://github.com/fmmoret/vllm/blob/fm-support-lora-on-quantized-models/vllm/lora/layers.py#L34
	"""Returns the device for where to place the LoRA tensors."""
	# unquantizedLinear
	if hasattr(base_layer, "weight"):
	return base_layer.weight.device
	# Compressed Tensor
	elif hasattr(base_layer, "weight_packed"):
	return base_layer.weight_packed.device
	# GPTQ/AWQ
	elif hasattr(base_layer, "qweight"):
	return base_layer.qweight.device
	# HQQ marlin
	elif hasattr(base_layer, "W_q"):
	return base_layer.W_q.device
	else:
	raise ValueError(f"Unsupported base layer: {base_layer}")
	def _get_lora_device(base_layer: nn.Module) -> torch.device:
	# code borrowed from https://github.com/fmmoret/vllm/blob/fm-support-lora-on-quantized-models/vllm/lora/layers.py#L34
	"""Returns the device for where to place the LoRA tensors."""
	# unquantizedLinear
	if hasattr(base_layer, "weight"):
	return base_layer.weight.device
	# Compressed Tensor
	elif hasattr(base_layer, "weight_packed"):
	return base_layer.weight_packed.device
	# GPTQ/AWQ
	elif hasattr(base_layer, "qweight"):
	return base_layer.qweight.device
	# marlin
	elif hasattr(base_layer, "B"):
	return base_layer.B.device
	# HQQ marlin
	elif hasattr(base_layer, "W_q"):
	return base_layer.W_q.device
	else:
	raise ValueError(f"Unsupported base layer: {base_layer}")

[gemini-code-assist]

There appears to be a typo in the module name vocal_parallel_embedding. It should likely be vocab_parallel_embedding to be consistent with the class name VocabParallelEmbeddingWithLoRA and the general terminology. This should be corrected in the filename (vllm/lora/layers/vocal_parallel_embedding.py) and here in the import statement for better code clarity and maintainability.

Suggested change

	from vllm.lora.layers.vocal_parallel_embedding import (
	VocabParallelEmbeddingWithLoRA,
	)
	from vllm.lora.layers.vocab_parallel_embedding import (
	VocabParallelEmbeddingWithLoRA,
	)

[Isotr0py]

Should we also break this file's implementations into column_parallel_linear.py and row_parallel_linear.py etc to keep them in same file with parent class?

[jeejeelee]

Makes sense

[Isotr0py]

Otherwise LGTM!