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| 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 = 'AutoGPTQ' | |
| 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}") | |