import json import re from pathlib import Path import yaml from modules import loaders, metadata_gguf, shared, ui def get_fallback_settings(): return { 'wbits': 'None', 'groupsize': 'None', 'desc_act': False, 'model_type': 'None', 'max_seq_len': 2048, 'n_ctx': 2048, 'rope_freq_base': 0, 'compress_pos_emb': 1, 'truncation_length': shared.settings['truncation_length'], 'skip_special_tokens': shared.settings['skip_special_tokens'], 'custom_stopping_strings': shared.settings['custom_stopping_strings'], } def get_model_metadata(model): model_settings = {} # Get settings from models/config.yaml and models/config-user.yaml settings = shared.model_config for pat in settings: if re.match(pat.lower(), model.lower()): for k in settings[pat]: model_settings[k] = settings[pat][k] if 'loader' not in model_settings: loader = infer_loader(model, model_settings) if 'wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0: loader = 'AutoGPTQ' model_settings['loader'] = loader # Read GGUF metadata if model_settings['loader'] in ['llama.cpp', 'llamacpp_HF', 'ctransformers']: path = Path(f'{shared.args.model_dir}/{model}') if path.is_file(): model_file = path else: model_file = list(path.glob('*.gguf'))[0] metadata = metadata_gguf.load_metadata(model_file) if 'llama.context_length' in metadata: model_settings['n_ctx'] = metadata['llama.context_length'] if 'llama.rope.scale_linear' in metadata: model_settings['compress_pos_emb'] = metadata['llama.rope.scale_linear'] if 'llama.rope.freq_base' in metadata: model_settings['rope_freq_base'] = metadata['llama.rope.freq_base'] else: # Read transformers metadata path = Path(f'{shared.args.model_dir}/{model}/config.json') if path.exists(): metadata = json.loads(open(path, 'r').read()) if 'max_position_embeddings' in metadata: model_settings['truncation_length'] = metadata['max_position_embeddings'] model_settings['max_seq_len'] = metadata['max_position_embeddings'] if 'rope_theta' in metadata: model_settings['rope_freq_base'] = metadata['rope_theta'] if 'rope_scaling' in metadata and type(metadata['rope_scaling']) is dict and all(key in metadata['rope_scaling'] for key in ('type', 'factor')): if metadata['rope_scaling']['type'] == 'linear': model_settings['compress_pos_emb'] = metadata['rope_scaling']['factor'] if 'quantization_config' in metadata: if 'bits' in metadata['quantization_config']: model_settings['wbits'] = metadata['quantization_config']['bits'] if 'group_size' in metadata['quantization_config']: model_settings['groupsize'] = metadata['quantization_config']['group_size'] if 'desc_act' in metadata['quantization_config']: model_settings['desc_act'] = metadata['quantization_config']['desc_act'] # Read AutoGPTQ metadata path = Path(f'{shared.args.model_dir}/{model}/quantize_config.json') if path.exists(): metadata = json.loads(open(path, 'r').read()) if 'bits' in metadata: model_settings['wbits'] = metadata['bits'] if 'group_size' in metadata: model_settings['groupsize'] = metadata['group_size'] if 'desc_act' in metadata: model_settings['desc_act'] = metadata['desc_act'] # Ignore rope_freq_base if set to the default value if 'rope_freq_base' in model_settings and model_settings['rope_freq_base'] == 10000: model_settings.pop('rope_freq_base') # Apply user settings from models/config-user.yaml settings = shared.user_config for pat in settings: if re.match(pat.lower(), model.lower()): for k in settings[pat]: model_settings[k] = settings[pat][k] return model_settings def infer_loader(model_name, model_settings): path_to_model = Path(f'{shared.args.model_dir}/{model_name}') if not path_to_model.exists(): loader = None elif (path_to_model / 'quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0): loader = 'ExLlama_HF' elif (path_to_model / 'quant_config.json').exists() or re.match(r'.*-awq', model_name.lower()): loader = 'AutoAWQ' elif len(list(path_to_model.glob('*.gguf'))) > 0: loader = 'llama.cpp' elif re.match(r'.*\.gguf', model_name.lower()): loader = 'llama.cpp' elif re.match(r'.*rwkv.*\.pth', model_name.lower()): loader = 'RWKV' elif re.match(r'.*exl2', model_name.lower()): loader = 'ExLlamav2_HF' else: loader = 'Transformers' return loader # UI: update the command-line arguments based on the interface values def update_model_parameters(state, initial=False): elements = ui.list_model_elements() # the names of the parameters gpu_memories = [] for i, element in enumerate(elements): if element not in state: continue value = state[element] if element.startswith('gpu_memory'): gpu_memories.append(value) continue if initial and element in shared.provided_arguments: continue # Setting null defaults if element in ['wbits', 'groupsize', 'model_type'] and value == 'None': value = vars(shared.args_defaults)[element] elif element in ['cpu_memory'] and value == 0: value = vars(shared.args_defaults)[element] # Making some simple conversions if element in ['wbits', 'groupsize', 'pre_layer']: value = int(value) elif element == 'cpu_memory' and value is not None: value = f"{value}MiB" if element in ['pre_layer']: value = [value] if value > 0 else None setattr(shared.args, element, value) found_positive = False for i in gpu_memories: if i > 0: found_positive = True break if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']): if found_positive: shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories] else: shared.args.gpu_memory = None # UI: update the state variable with the model settings def apply_model_settings_to_state(model, state): model_settings = get_model_metadata(model) if 'loader' in model_settings: loader = model_settings.pop('loader') # If the user is using an alternative loader for the same model type, let them keep using it if not (loader == 'AutoGPTQ' and state['loader'] in ['GPTQ-for-LLaMa', 'ExLlama', 'ExLlama_HF', 'ExLlamav2', 'ExLlamav2_HF']) and not (loader == 'llama.cpp' and state['loader'] in ['llamacpp_HF', 'ctransformers']): state['loader'] = loader for k in model_settings: if k in state: if k in ['wbits', 'groupsize']: state[k] = str(model_settings[k]) else: state[k] = model_settings[k] return state # Save the settings for this model to models/config-user.yaml def save_model_settings(model, state): if model == 'None': yield ("Not saving the settings because no model is loaded.") return with Path(f'{shared.args.model_dir}/config-user.yaml') as p: if p.exists(): user_config = yaml.safe_load(open(p, 'r').read()) else: user_config = {} model_regex = model + '$' # For exact matches if model_regex not in user_config: user_config[model_regex] = {} for k in ui.list_model_elements(): if k == 'loader' or k in loaders.loaders_and_params[state['loader']]: user_config[model_regex][k] = state[k] shared.user_config = user_config output = yaml.dump(user_config, sort_keys=False) with open(p, 'w') as f: f.write(output) yield (f"Settings for `{model}` saved to `{p}`.")