initial multi-lora support (#1103)

---------

Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
This commit is contained in:
Alex "mcmonkey" Goodwin 2023-04-14 10:52:06 -07:00 committed by GitHub
parent ebb81eb176
commit 64e3b44e0f
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4 changed files with 43 additions and 24 deletions

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@ -4,19 +4,31 @@ import torch
from peft import PeftModel from peft import PeftModel
import modules.shared as shared import modules.shared as shared
from modules.models import reload_model
def add_lora_to_model(lora_name): def add_lora_to_model(lora_names):
prior_set = set(shared.lora_names)
added_set = set(lora_names) - prior_set
removed_set = prior_set - set(lora_names)
shared.lora_names = list(lora_names)
# If a LoRA had been previously loaded, or if we want # Nothing to do = skip.
# to unload a LoRA, reload the model if len(added_set) == 0 and len(removed_set) == 0:
if shared.lora_name not in ['None', ''] or lora_name in ['None', '']: return
reload_model()
shared.lora_name = lora_name
if lora_name not in ['None', '']: # Only adding, and already peft? Do it the easy way.
print(f"Adding the LoRA {lora_name} to the model...") if len(removed_set) == 0 and len(prior_set) > 0:
print(f"Adding the LoRA(s) named {added_set} to the model...")
for lora in added_set:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
return
# If removing anything, disable all and re-add.
if len(removed_set) > 0:
shared.model.disable_adapter()
if len(lora_names) > 0:
print("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
params = {} params = {}
if not shared.args.cpu: if not shared.args.cpu:
params['dtype'] = shared.model.dtype params['dtype'] = shared.model.dtype
@ -25,7 +37,11 @@ def add_lora_to_model(lora_name):
elif shared.args.load_in_8bit: elif shared.args.load_in_8bit:
params['device_map'] = {'': 0} params['device_map'] = {'': 0}
shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_name}"), **params) shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params)
for lora in lora_names[1:]:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
if not shared.args.load_in_8bit and not shared.args.cpu: if not shared.args.load_in_8bit and not shared.args.cpu:
shared.model.half() shared.model.half()
if not hasattr(shared.model, "hf_device_map"): if not hasattr(shared.model, "hf_device_map"):

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@ -6,7 +6,7 @@ import yaml
model = None model = None
tokenizer = None tokenizer = None
model_name = "None" model_name = "None"
lora_name = "None" lora_names = []
soft_prompt_tensor = None soft_prompt_tensor = None
soft_prompt = False soft_prompt = False
is_RWKV = False is_RWKV = False
@ -68,7 +68,7 @@ settings = {
}, },
'lora_prompts': { 'lora_prompts': {
'default': 'QA', 'default': 'QA',
'.*(alpaca-lora-7b|alpaca-lora-13b|alpaca-lora-30b)': "Alpaca", '.*alpaca': "Alpaca",
} }
} }

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@ -5,13 +5,13 @@ gradio==3.24.1
markdown markdown
numpy numpy
Pillow>=9.5.0 Pillow>=9.5.0
peft==0.2.0
requests requests
rwkv==0.7.3 rwkv==0.7.3
safetensors==0.3.0 safetensors==0.3.0
sentencepiece sentencepiece
pyyaml pyyaml
tqdm tqdm
git+https://github.com/huggingface/peft
transformers==4.28.0 transformers==4.28.0
bitsandbytes==0.38.1; platform_system != "Windows" bitsandbytes==0.38.1; platform_system != "Windows"
llama-cpp-python==0.1.33; platform_system != "Windows" llama-cpp-python==0.1.33; platform_system != "Windows"

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@ -88,9 +88,10 @@ def load_model_wrapper(selected_model):
yield traceback.format_exc() yield traceback.format_exc()
def load_lora_wrapper(selected_lora): def load_lora_wrapper(selected_loras):
add_lora_to_model(selected_lora) yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
return selected_lora add_lora_to_model(selected_loras)
yield ("Successfuly applied the LoRAs")
def load_preset_values(preset_menu, state, return_dict=False): def load_preset_values(preset_menu, state, return_dict=False):
@ -275,12 +276,14 @@ def create_model_menus():
with gr.Column(): with gr.Column():
with gr.Row(): with gr.Row():
shared.gradio['lora_menu'] = gr.Dropdown(choices=get_available_loras(), value=shared.lora_name, label='LoRA') shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=get_available_loras(), value=shared.lora_names, label='LoRA(s)')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button') ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras(), 'value': shared.lora_names}, 'refresh-button')
with gr.Column(): with gr.Column():
unload = gr.Button("Unload the model") shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs')
reload = gr.Button("Reload the model") with gr.Row():
unload = gr.Button("Unload the model")
reload = gr.Button("Reload the model")
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
@ -338,7 +341,7 @@ def create_model_menus():
update_model_parameters, shared.gradio['interface_state'], None).then( update_model_parameters, shared.gradio['interface_state'], None).then(
load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True) load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)
shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True) shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False) shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
@ -428,8 +431,8 @@ def create_interface():
# Defining some variables # Defining some variables
gen_events = [] gen_events = []
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
if shared.lora_name != "None": if len(shared.lora_names) == 1:
default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]) default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_names[0].lower())), 'default')])
else: else:
default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]) default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
title = 'Text generation web UI' title = 'Text generation web UI'
@ -861,7 +864,7 @@ if __name__ == "__main__":
# Load the model # Load the model
shared.model, shared.tokenizer = load_model(shared.model_name) shared.model, shared.tokenizer = load_model(shared.model_name)
if shared.args.lora: if shared.args.lora:
add_lora_to_model(shared.args.lora) add_lora_to_model([shared.args.lora])
# Launch the web UI # Launch the web UI
create_interface() create_interface()