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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 = '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
def update_model_parameters(state, initial=False):
'''
UI: update the command-line arguments based on the interface values
'''
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
def apply_model_settings_to_state(model, state):
'''
UI: update the state variable with the model settings
'''
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
def save_model_settings(model, state):
'''
Save the settings for this model to models/config-user.yaml
'''
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}`.")
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