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vicuna-v1.5-demo / modules /loaders.py

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	import functools

	import gradio as gr

	from modules import shared

	loaders_and_params = {
	'AutoGPTQ': [
	'triton',
	'no_inject_fused_attention',
	'no_inject_fused_mlp',
	'no_use_cuda_fp16',
	'wbits',
	'groupsize',
	'desc_act',
	'gpu_memory',
	'cpu_memory',
	'cpu',
	'disk',
	'auto_devices',
	'trust_remote_code',
	'autogptq_info',
	],
	'GPTQ-for-LLaMa': [
	'wbits',
	'groupsize',
	'model_type',
	'pre_layer',
	'gptq_for_llama_info',
	],
	'llama.cpp': [
	'n_ctx',
	'n_gqa',
	'rms_norm_eps',
	'n_gpu_layers',
	'n_batch',
	'threads',
	'no_mmap',
	'low_vram',
	'mlock',
	'llama_cpp_seed',
	'alpha_value',
	'compress_pos_emb',
	'cpu',
	],
	'llamacpp_HF': [
	'n_ctx',
	'n_gqa',
	'rms_norm_eps',
	'n_gpu_layers',
	'n_batch',
	'threads',
	'no_mmap',
	'low_vram',
	'mlock',
	'llama_cpp_seed',
	'alpha_value',
	'compress_pos_emb',
	'cpu',
	'llamacpp_HF_info',
	],
	'Transformers': [
	'cpu_memory',
	'gpu_memory',
	'trust_remote_code',
	'load_in_8bit',
	'bf16',
	'cpu',
	'disk',
	'auto_devices',
	'load_in_4bit',
	'use_double_quant',
	'quant_type',
	'compute_dtype',
	'trust_remote_code',
	'alpha_value',
	'compress_pos_emb',
	'transformers_info'
	],
	'ExLlama': [
	'gpu_split',
	'max_seq_len',
	'alpha_value',
	'compress_pos_emb',
	'exllama_info',
	],
	'ExLlama_HF': [
	'gpu_split',
	'max_seq_len',
	'alpha_value',
	'compress_pos_emb',
	'exllama_HF_info',
	]
	}

	loaders_samplers = {
	'Transformers': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'epsilon_cutoff',
	'eta_cutoff',
	'tfs',
	'top_a',
	'repetition_penalty',
	'repetition_penalty_range',
	'encoder_repetition_penalty',
	'no_repeat_ngram_size',
	'min_length',
	'seed',
	'do_sample',
	'penalty_alpha',
	'num_beams',
	'length_penalty',
	'early_stopping',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'guidance_scale',
	'negative_prompt',
	'ban_eos_token',
	'add_bos_token',
	'skip_special_tokens',
	'auto_max_new_tokens',
	},
	'ExLlama_HF': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'epsilon_cutoff',
	'eta_cutoff',
	'tfs',
	'top_a',
	'repetition_penalty',
	'repetition_penalty_range',
	'encoder_repetition_penalty',
	'no_repeat_ngram_size',
	'min_length',
	'seed',
	'do_sample',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'ban_eos_token',
	'add_bos_token',
	'skip_special_tokens',
	'auto_max_new_tokens',
	},
	'ExLlama': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'repetition_penalty',
	'repetition_penalty_range',
	'seed',
	'guidance_scale',
	'negative_prompt',
	'ban_eos_token',
	'auto_max_new_tokens',
	},
	'AutoGPTQ': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'epsilon_cutoff',
	'eta_cutoff',
	'tfs',
	'top_a',
	'repetition_penalty',
	'repetition_penalty_range',
	'encoder_repetition_penalty',
	'no_repeat_ngram_size',
	'min_length',
	'seed',
	'do_sample',
	'penalty_alpha',
	'num_beams',
	'length_penalty',
	'early_stopping',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'guidance_scale',
	'negative_prompt',
	'ban_eos_token',
	'add_bos_token',
	'skip_special_tokens',
	'auto_max_new_tokens',
	},
	'GPTQ-for-LLaMa': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'epsilon_cutoff',
	'eta_cutoff',
	'tfs',
	'top_a',
	'repetition_penalty',
	'repetition_penalty_range',
	'encoder_repetition_penalty',
	'no_repeat_ngram_size',
	'min_length',
	'seed',
	'do_sample',
	'penalty_alpha',
	'num_beams',
	'length_penalty',
	'early_stopping',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'guidance_scale',
	'negative_prompt',
	'ban_eos_token',
	'add_bos_token',
	'skip_special_tokens',
	'auto_max_new_tokens',
	},
	'llama.cpp': {
	'temperature',
	'top_p',
	'top_k',
	'tfs',
	'repetition_penalty',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'ban_eos_token',
	},
	'llamacpp_HF': {
	'temperature',
	'top_p',
	'top_k',
	'typical_p',
	'epsilon_cutoff',
	'eta_cutoff',
	'tfs',
	'top_a',
	'repetition_penalty',
	'repetition_penalty_range',
	'encoder_repetition_penalty',
	'no_repeat_ngram_size',
	'min_length',
	'seed',
	'do_sample',
	'mirostat_mode',
	'mirostat_tau',
	'mirostat_eta',
	'ban_eos_token',
	'add_bos_token',
	'skip_special_tokens',
	'auto_max_new_tokens',
	},
	}


	@functools.cache
	def list_all_samplers():
	all_samplers = set()
	for k in loaders_samplers:
	for sampler in loaders_samplers[k]:
	all_samplers.add(sampler)

	return sorted(all_samplers)


	def blacklist_samplers(loader):
	all_samplers = list_all_samplers()
	if loader == 'All':
	return [gr.update(visible=True) for sampler in all_samplers]
	else:
	return [gr.update(visible=True) if sampler in loaders_samplers[loader] else gr.update(visible=False) for sampler in all_samplers]


	def get_gpu_memory_keys():
	return [k for k in shared.gradio if k.startswith('gpu_memory')]


	@functools.cache
	def get_all_params():
	all_params = set()
	for k in loaders_and_params:
	for el in loaders_and_params[k]:
	all_params.add(el)

	if 'gpu_memory' in all_params:
	all_params.remove('gpu_memory')
	for k in get_gpu_memory_keys():
	all_params.add(k)

	return sorted(all_params)


	def make_loader_params_visible(loader):
	params = []
	all_params = get_all_params()
	if loader in loaders_and_params:
	params = loaders_and_params[loader]

	if 'gpu_memory' in params:
	params.remove('gpu_memory')
	params += get_gpu_memory_keys()

	return [gr.update(visible=True) if k in params else gr.update(visible=False) for k in all_params]