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Remove unused code

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oobabooga 2023-05-10 11:59:59 -03:00
parent ec14d9b725
commit 3316e33d14
3 changed files with 7 additions and 15 deletions
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12
modules/GPTQ_loader.py
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@ -10,7 +10,6 @@ import transformers
from transformers import AutoConfig, AutoModelForCausalLM from transformers import AutoConfig, AutoModelForCausalLM
import modules.shared as shared import modules.shared as shared
from server import get_model_specific_settings
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
@ -115,6 +114,7 @@ def find_quantized_model_file(model_name):
for ext in ['.safetensors', '.pt'] for ext in ['.safetensors', '.pt']
for hyphen in ['-', f'/{model_name}-', '/'] for hyphen in ['-', f'/{model_name}-', '/']
] ]
for path in priority_name_list: for path in priority_name_list:
if path.exists(): if path.exists():
pt_path = path pt_path = path
@ -143,19 +143,13 @@ def find_quantized_model_file(model_name):
# The function that loads the model in modules/models.py # The function that loads the model in modules/models.py
def load_quantized(model_name): def load_quantized(model_name):
# Find the model type if shared.args.model_type is None:
if not shared.args.model_type:
settings = get_model_specific_settings(model_name)
if 'model_type' in settings and settings['model_type'] != 'None':
model_type = settings['model_type']
else:
logging.error("The model could not be loaded because its type could not be inferred from its name.") logging.error("The model could not be loaded because its type could not be inferred from its name.")
logging.error("Please specify the type manually using the --model_type argument.") logging.error("Please specify the type manually using the --model_type argument.")
return return
else:
model_type = shared.args.model_type.lower()
# Select the appropriate load_quant function # Select the appropriate load_quant function
model_type = shared.args.model_type.lower()
if shared.args.pre_layer and model_type == 'llama': if shared.args.pre_layer and model_type == 'llama':
load_quant = llama_inference_offload.load_quant load_quant = llama_inference_offload.load_quant
elif model_type in ('llama', 'opt', 'gptj'): elif model_type in ('llama', 'opt', 'gptj'):

2
modules/models.py
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@ -252,7 +252,7 @@ def load_model(model_name):
else: else:
tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), trust_remote_code=trust_remote_code) tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), trust_remote_code=trust_remote_code)
logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.") logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
return model, tokenizer return model, tokenizer

2
server.py
View File

@ -372,8 +372,6 @@ def create_model_menus():
load.click( load.click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
ui.apply_interface_values, shared.gradio['interface_state'],
[shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then(
update_model_parameters, shared.gradio['interface_state'], None).then( update_model_parameters, shared.gradio['interface_state'], None).then(
partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False)