Update LoRA.py

[srossitto79]

Add ggml lora support for llama.cpp gguf/ggml models (convert any lora with llama.cpp/convert-lora-to-ggml.py )

Checklist:

• I have read the Contributing guidelines.

[srossitto79]

Hi, I didn't test it with different params yet. I still evaluating it...

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On Fri, Oct 6, 2023, 17:32 Forkoz ***@***.***> wrote: Does this work for Kquants + offloading yet? Because when I tried it, the model required to be F16 and all on CPU. — Reply to this email directly, view it on GitHub <#4184 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAWVIYZTOP7L3KP25JYUYETX6AQATAVCNFSM6AAAAAA5ULWC2SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONJQHEYTMNJTGQ> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[Touch-Night]

Hmmm, I did the same work before I saw this pr.

[Touch-Night]

That makes 3 of us.. I added it where it loads the llama.cpp model originally and then just reload to add or remove the lora. In this way you don't have to move all of those parameters to lora.py.

Unfortunately I was met with the message of having to use FP16 model or not use GPU offloading, that made it very useless. Have had better luck merging the lora directly into Kquants after conversions. The models don't increase perplexity very much and can be fully offloaded.

Mine is the same approach, using the llama.cpp's --lora parameter and reload. I don't know how to merge it directly into Kquants.

[oobabooga]

Is there a way of doing it without reloading the entire model?

[srossitto79]

In theory yes, but I tried and didn't work to me. Maybe you can retry if the version of llama.cpp has changed. It should be in the code as commented.

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On Sun, Oct 22, 2023, 21:08 oobabooga ***@***.***> wrote: Is there a way of doing it without reloading the entire model? — Reply to this email directly, view it on GitHub <#4184 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAWVIY4QWBVEDUJ3LCXDCA3YAVVMDAVCNFSM6AAAAAA5ULWC2SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZUGE3TINBUGE> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[Jamim]

Hello @srossitto79,
I have a few suggestions here:

• you can just check lora_names with not
• while you already have return there, you don't need to have the else block. You can just remove an else and decrease the indentation accordingly
• using an additional variable for the lora_name might be reasonable
• ', '.join([lora_names[0]]) makes no sense because the result will be equal to lora_names[0] since there is only one element in the list
• the return at the end is also redundant.

Suggested change

	if len(lora_names) == 0:
	shared.lora_names = []
	return
	else:
	if len(lora_names) > 1:
	logger.warning('Llama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
	lora_path = get_lora_path(lora_names[0])
	lora_adapter_path = str(lora_path / "ggml-adapter-model.bin")
	logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
	if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
	tensor_split_list = None
	else:
	tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
	params = {
	'model_path': str(shared.model.model.model_path),
	'lora_path': str(lora_adapter_path),
	'n_ctx': shared.args.n_ctx,
	'seed': int(shared.args.llama_cpp_seed),
	'n_threads': shared.args.threads or None,
	'n_threads_batch': shared.args.threads_batch or None,
	'n_batch': shared.args.n_batch,
	'use_mmap': not shared.args.no_mmap,
	'use_mlock': shared.args.mlock,
	'mul_mat_q': shared.args.mul_mat_q,
	'numa': shared.args.numa,
	'n_gpu_layers': shared.args.n_gpu_layers,
	'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
	'tensor_split': tensor_split_list,
	'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
	}
	shared.model.model = llama_cpp.Llama(**params)
	# shared.model.model.lora_path = lora_adapter_path
	# if llama_cpp.llama_model_apply_lora_from_file(
	# shared.model.model.model,
	# shared.model.model.lora_path.encode("utf-8"),
	# scale=1,
	# path_base_model=shared.model.model.lora_base.encode("utf-8") if shared.model.model.lora_base is not None else llama_cpp.c_char_p(0),
	# n_threads=shared.model.model.n_threads,
	# ):
	# raise RuntimeError(
	# f"Failed to apply LoRA from lora path: {shared.model.lora_path} to base path: {shared.model.lora_base}"
	# )
	shared.lora_names = [lora_names[0]]
	logger.info(f"Succesfully Applied Lora {lora_adapter_path} to Model.")
	return
	if not lora_names:
	shared.lora_names = []
	return
	if len(lora_names) > 1:
	logger.warning('Llama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
	lora_name = lora_names[0]
	lora_path = get_lora_path(lora_name)
	lora_adapter_path = str(lora_path / "ggml-adapter-model.bin")
	logger.info(f"Applying the following LoRAs to {shared.model_name}: {lora_name}")
	if not (shared.args.tensor_split and shared.args.tensor_split.strip()):
	tensor_split_list = None
	else:
	tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
	params = {
	'model_path': str(shared.model.model.model_path),
	'lora_path': str(lora_adapter_path),
	'n_ctx': shared.args.n_ctx,
	'seed': int(shared.args.llama_cpp_seed),
	'n_threads': shared.args.threads or None,
	'n_threads_batch': shared.args.threads_batch or None,
	'n_batch': shared.args.n_batch,
	'use_mmap': not shared.args.no_mmap,
	'use_mlock': shared.args.mlock,
	'mul_mat_q': shared.args.mul_mat_q,
	'numa': shared.args.numa,
	'n_gpu_layers': shared.args.n_gpu_layers,
	'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
	'tensor_split': tensor_split_list,
	'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
	}
	shared.model.model = llama_cpp.Llama(**params)
	# shared.model.model.lora_path = lora_adapter_path
	# if llama_cpp.llama_model_apply_lora_from_file(
	# shared.model.model.model,
	# shared.model.model.lora_path.encode("utf-8"),
	# scale=1,
	# path_base_model=shared.model.model.lora_base.encode("utf-8") if shared.model.model.lora_base is not None else llama_cpp.c_char_p(0),
	# n_threads=shared.model.model.n_threads,
	# ):
	# raise RuntimeError(
	# f"Failed to apply LoRA from lora path: {shared.model.lora_path} to base path: {shared.model.lora_base}"
	# )
	shared.lora_names = [lora_name]
	logger.info(f"Succesfully Applied Lora {lora_adapter_path} to Model.")

[Gee1111]

anything new here?