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Merge remote-tracking branch 'origin/main' into triton

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Light 2023-04-13 19:38:51 +08:00
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3
README.md
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@ -125,7 +125,7 @@ cp .env.example .env
docker compose up --build
```
Make sure to edit `.env.example` and set the appropriate CUDA version for your GPU.
Make sure to edit `.env.example` and set the appropriate CUDA version for your GPU, which can be found on [developer.nvidia.com](https://developer.nvidia.com/cuda-gpus).
You need to have docker compose v2.17 or higher installed in your system. For installation instructions, see [Docker compose installation](https://github.com/oobabooga/text-generation-webui/wiki/Docker-compose-installation).
@ -203,6 +203,7 @@ Optionally, you can use the following command-line flags:
| `--lora LORA` | Name of the LoRA to apply to the model by default. |
| `--model-dir MODEL_DIR` | Path to directory with all the models. |
| `--lora-dir LORA_DIR` | Path to directory with all the loras. |
| `--model-menu` | Show a model menu in the terminal when the web UI is first launched. |
| `--no-stream` | Don't stream the text output in real time. |
| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag. |
| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |

9
modules/shared.py
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@ -32,7 +32,7 @@ settings = {
'name1': 'You',
'name2': 'Assistant',
'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.',
'greeting': 'Hello there!',
'greeting': '',
'end_of_turn': '',
'custom_stopping_strings': '',
'stop_at_newline': False,
@ -41,6 +41,7 @@ settings = {
'truncation_length': 2048,
'truncation_length_min': 0,
'truncation_length_max': 4096,
'mode': 'cai-chat',
'chat_prompt_size': 2048,
'chat_prompt_size_min': 0,
'chat_prompt_size_max': 2048,
@ -89,6 +90,7 @@ parser.add_argument('--model', type=str, help='Name of the model to load by defa
parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models")
parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.')
parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.')
parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
@ -116,9 +118,6 @@ parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quan
parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.')
parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.')
parser.add_argument('--warmup_autotune', action=argparse.BooleanOptionalAction, default=True, help='GPTQ: Enable warmup autotune. Only usable for triton.')
parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.')
parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.')
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.')
# FlexGen
parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
@ -145,7 +144,7 @@ parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authent
args = parser.parse_args()
# Deprecation warnings for parameters that have been renamed
deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
deprecated_dict = {}
for k in deprecated_dict:
if eval(f"args.{k}") != deprecated_dict[k][1]:
print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")

217
server.py
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@ -5,6 +5,7 @@ os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
import importlib
import io
import json
import math
import os
import re
import sys
@ -15,6 +16,8 @@ from datetime import datetime
from pathlib import Path
import gradio as gr
import psutil
import torch
from PIL import Image
import modules.extensions as extensions_module
@ -37,11 +40,18 @@ if settings_file is not None:
shared.settings[item] = new_settings[item]
def special_sort(model_name):
if '_' in model_name:
return ('_'.join(model_name.split('_')[1:])).lower()
else:
return model_name.lower()
def get_available_models():
if shared.args.flexgen:
return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower)
return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=special_sort)
else:
return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=special_sort)
def get_available_presets():
@ -78,18 +88,20 @@ def get_available_softprompts():
def get_available_loras():
return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=special_sort)
def load_model_wrapper(selected_model):
if selected_model != shared.model_name:
try:
yield f"Loading {selected_model}..."
shared.model_name = selected_model
unload_model()
if selected_model != '':
shared.model, shared.tokenizer = load_model(shared.model_name)
return selected_model
yield f"Successfully loaded {selected_model}"
except:
yield traceback.format_exc()
def load_lora_wrapper(selected_lora):
@ -203,31 +215,146 @@ def download_model_wrapper(repo_id):
yield traceback.format_exc()
def create_model_menus():
with gr.Row():
with gr.Column():
with gr.Row():
shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
with gr.Column():
with gr.Row():
shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA",
info="Enter Hugging Face username/model path, e.g: facebook/galactica-125m")
with gr.Column():
shared.gradio['download_button'] = gr.Button("Download")
shared.gradio['download_status'] = gr.Markdown()
with gr.Column():
pass
# Model parameters: list the relevant interface elements
def list_model_parameters():
parameters = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'wbits', 'groupsize', 'model_type', 'pre_layer']
for i in range(torch.cuda.device_count()):
parameters.append(f'gpu_memory_{i}')
return parameters
# Model parameters: update the command-line arguments based on the interface values
def update_model_parameters(*args):
args = list(args) # the values of the parameters
elements = list_model_parameters() # the names of the parameters
gpu_memories = []
for i, element in enumerate(elements):
if element.startswith('gpu_memory'):
gpu_memories.append(args[i])
continue
if element == 'cpu_memory' and args[i] == 0:
args[i] = None
if element == 'wbits' and args[i] == 'None':
args[i] = 0
if element == 'groupsize' and args[i] == 'None':
args[i] = -1
if element == 'model_type' and args[i] == 'None':
args[i] = None
if element in ['wbits', 'groupsize', 'pre_layer']:
args[i] = int(args[i])
elif element == 'cpu_memory' and args[i] is not None:
args[i] = f"{args[i]}MiB"
#print(element, repr(eval(f"shared.args.{element}")), repr(args[i]))
#print(f"shared.args.{element} = args[i]")
exec(f"shared.args.{element} = args[i]")
found_positive = False
for i in gpu_memories:
if i > 0:
found_positive = True
break
if found_positive:
shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
else:
shared.args.gpu_memory = None
def create_model_menus():
# Finding the default values for the GPU and CPU memories
total_mem = []
for i in range(torch.cuda.device_count()):
total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024*1024)))
default_gpu_mem = []
if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
for i in shared.args.gpu_memory:
if 'mib' in i.lower():
default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)))
else:
default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i))*1000)
while len(default_gpu_mem) < len(total_mem):
default_gpu_mem.append(0)
total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024*1024))
if shared.args.cpu_memory is not None:
default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory)
else:
default_cpu_mem = 0
components = {}
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Row():
shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
with gr.Column():
with gr.Row():
shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')
with gr.Column():
unload = gr.Button("Unload the model")
reload = gr.Button("Reload the model")
with gr.Row():
with gr.Column():
with gr.Box():
gr.Markdown('Transformers parameters')
with gr.Row():
with gr.Column():
for i in range(len(total_mem)):
components[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i])
components['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)
with gr.Column():
components['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
components['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
components['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
components['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
components['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
with gr.Column():
with gr.Box():
gr.Markdown('GPTQ parameters')
with gr.Row():
with gr.Column():
components['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
components['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")
with gr.Column():
components['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gpt-j"], value=shared.args.model_type or "None")
components['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer)
with gr.Row():
with gr.Column():
shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter Hugging Face username/model path, e.g: facebook/galactica-125m")
shared.gradio['download_button'] = gr.Button("Download")
with gr.Column():
shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
shared.gradio['model_menu'].change(
update_model_parameters, [components[k] for k in list_model_parameters()], None).then(
load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)
unload.click(
unload_model, None, None).then(
lambda: "Model unloaded", None, shared.gradio['model_status'])
reload.click(
unload_model, None, None).then(
update_model_parameters, [components[k] for k in list_model_parameters()], None).then(
load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)
shared.gradio['model_menu'].change(load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_menu'], show_progress=True)
shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['download_status'], show_progress=False)
shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
def create_settings_menus(default_preset):
@ -333,7 +460,8 @@ else:
# Default model
if shared.args.model is not None:
shared.model_name = shared.args.model
else:
shared.model, shared.tokenizer = load_model(shared.model_name)
elif shared.args.model_menu:
if len(available_models) == 0:
print('No models are available! Please download at least one.')
sys.exit(0)
@ -347,8 +475,9 @@ else:
i = int(input()) - 1
print()
shared.model_name = available_models[i]
shared.model, shared.tokenizer = load_model(shared.model_name)
if shared.args.lora:
shared.model, shared.tokenizer = load_model(shared.model_name)
if shared.args.model is not None and shared.args.lora:
add_lora_to_model(shared.args.lora)
# Default UI settings
@ -372,12 +501,12 @@ def create_interface():
shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
shared.gradio['Chat input'] = gr.State()
with gr.Tab("Text generation", elem_id="main"):
with gr.Tab('Text generation', elem_id='main'):
shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'cai-chat'))
shared.gradio['textbox'] = gr.Textbox(label='Input')
with gr.Row():
shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate')
shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
with gr.Row():
shared.gradio['Regenerate'] = gr.Button('Regenerate')
shared.gradio['Continue'] = gr.Button('Continue')
@ -389,24 +518,24 @@ def create_interface():
shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
with gr.Row():
shared.gradio['Clear history'] = gr.Button('Clear history')
shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False)
shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
shared.gradio['Remove last'] = gr.Button('Remove last')
shared.gradio["mode"] = gr.Radio(choices=["cai-chat", "chat", "instruct"], value="cai-chat", label="Mode")
shared.gradio["Instruction templates"] = gr.Dropdown(choices=get_available_instruction_templates(), label="Instruction template", value="None", visible=False, info="Change this according to the model/LoRA that you are using.")
shared.gradio['mode'] = gr.Radio(choices=['cai-chat', 'chat', 'instruct'], value=shared.settings['mode'], label='Mode')
shared.gradio['Instruction templates'] = gr.Dropdown(choices=get_available_instruction_templates(), label='Instruction template', value='None', visible=False, info='Change this according to the model/LoRA that you are using.')
with gr.Tab("Character", elem_id="chat-settings"):
with gr.Tab('Character', elem_id='chat-settings'):
with gr.Row():
with gr.Column(scale=8):
shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name')
shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name')
shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting')
shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context')
shared.gradio['end_of_turn'] = gr.Textbox(value=shared.settings["end_of_turn"], lines=1, label='End of turn string')
shared.gradio['end_of_turn'] = gr.Textbox(value=shared.settings['end_of_turn'], lines=1, label='End of turn string')
with gr.Column(scale=1):
shared.gradio['character_picture'] = gr.Image(label='Character picture', type="pil")
shared.gradio['your_picture'] = gr.Image(label='Your picture', type="pil", value=Image.open(Path("cache/pfp_me.png")) if Path("cache/pfp_me.png").exists() else None)
shared.gradio['character_picture'] = gr.Image(label='Character picture', type='pil')
shared.gradio['your_picture'] = gr.Image(label='Your picture', type='pil', value=Image.open(Path('cache/pfp_me.png')) if Path('cache/pfp_me.png').exists() else None)
with gr.Row():
shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': get_available_characters()}, 'refresh-button')
@ -422,7 +551,7 @@ def create_interface():
shared.gradio['download'] = gr.File()
shared.gradio['download_button'] = gr.Button(value='Click me')
with gr.Tab('Upload character'):
gr.Markdown("# JSON format")
gr.Markdown('# JSON format')
with gr.Row():
with gr.Column():
gr.Markdown('1. Select the JSON file')
@ -432,7 +561,7 @@ def create_interface():
shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image'])
shared.gradio['Upload character'] = gr.Button(value='Submit')
gr.Markdown("# TavernAI PNG format")
gr.Markdown('# TavernAI PNG format')
shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
with gr.Tab("Parameters", elem_id="parameters"):
@ -648,7 +777,7 @@ def create_interface():
current_mode = mode
break
cmd_list = vars(shared.args)
bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes + list_model_parameters()]
bool_active = [k for k in bool_list if vars(shared.args)[k]]
gr.Markdown("*Experimental*")

5
settings-template.json
View File

@ -6,15 +6,16 @@
"name1": "You",
"name2": "Assistant",
"context": "This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.",
"greeting": "Hello there!",
"greeting": "",
"end_of_turn": "",
"custom_stopping_strings": "",
"stop_at_newline": false,
"add_bos_token": true,
"ban_eos_token": true,
"ban_eos_token": false,
"truncation_length": 2048,
"truncation_length_min": 0,
"truncation_length_max": 4096,
"mode": "cai-chat",
"chat_prompt_size": 2048,
"chat_prompt_size_min": 0,
"chat_prompt_size_max": 2048,