Merge branch 'main' into fix/api-reload

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oobabooga 2023-03-24 16:54:41 -03:00 committed by GitHub
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24 changed files with 429 additions and 196 deletions

1
.gitignore vendored
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@ -2,6 +2,7 @@ cache/*
characters/*
extensions/silero_tts/outputs/*
extensions/elevenlabs_tts/outputs/*
extensions/sd_api_pictures/outputs/*
logs/*
loras/*
models/*

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@ -84,10 +84,6 @@ pip install -r requirements.txt
>
> For bitsandbytes and `--load-in-8bit` to work on Linux/WSL, this dirty fix is currently necessary: https://github.com/oobabooga/text-generation-webui/issues/400#issuecomment-1474876859
### Alternative: native Windows installation
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
### Alternative: one-click installers
[oobabooga-windows.zip](https://github.com/oobabooga/one-click-installers/archive/refs/heads/oobabooga-windows.zip)
@ -101,7 +97,13 @@ Just download the zip above, extract it, and double click on "install". The web
Source codes: https://github.com/oobabooga/one-click-installers
This method lags behind the newest developments and does not support 8-bit mode on Windows without additional set up: https://github.com/oobabooga/text-generation-webui/issues/147#issuecomment-1456040134, https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652
> **Note**
>
> To get 8-bit and 4-bit models working in your 1-click Windows installation, you can use the [one-click-bandaid](https://github.com/ClayShoaf/oobabooga-one-click-bandaid).
### Alternative: native Windows installation
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
### Alternative: Docker
@ -175,14 +177,16 @@ Optionally, you can use the following command-line flags:
| `--cpu` | Use the CPU to generate text.|
| `--load-in-8bit` | Load the model with 8-bit precision.|
| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. |
| `--gptq-bits GPTQ_BITS` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
| `--gptq-model-type MODEL_TYPE` | Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
| `--gptq-bits GPTQ_BITS` | GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. |
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to `cache/`. |
| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. |
| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.|
| `--no-cache` | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
| `--flexgen` | Enable the use of FlexGen offloading. |
| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
| `--compress-weight` | FlexGen: Whether to compress weight (default: False).|
@ -201,7 +205,7 @@ Optionally, you can use the following command-line flags:
| `--auto-launch` | Open the web UI in the default browser upon launch. |
| `--verbose` | Print the prompts to the terminal. |
Out of memory errors? [Check this guide](https://github.com/oobabooga/text-generation-webui/wiki/Low-VRAM-guide).
Out of memory errors? [Check the low VRAM guide](https://github.com/oobabooga/text-generation-webui/wiki/Low-VRAM-guide).
## Presets

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@ -34,6 +34,7 @@ async def run(context):
'penalty_alpha': 0,
'length_penalty': 1,
'early_stopping': False,
'seed': -1,
}
session = random_hash()
@ -44,14 +45,14 @@ async def run(context):
case "send_hash":
await websocket.send(json.dumps({
"session_hash": session,
"fn_index": 7
"fn_index": 12
}))
case "estimation":
pass
case "send_data":
await websocket.send(json.dumps({
"session_hash": session,
"fn_index": 7,
"fn_index": 12,
"data": [
context,
params['max_new_tokens'],
@ -68,6 +69,7 @@ async def run(context):
params['penalty_alpha'],
params['length_penalty'],
params['early_stopping'],
params['seed'],
]
}))
case "process_starts":

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@ -32,6 +32,7 @@ params = {
'penalty_alpha': 0,
'length_penalty': 1,
'early_stopping': False,
'seed': -1,
}
# Input prompt
@ -54,6 +55,7 @@ response = requests.post(f"http://{server}:7860/run/textgen", json={
params['penalty_alpha'],
params['length_penalty'],
params['early_stopping'],
params['seed'],
]
}).json()

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@ -50,3 +50,7 @@ ol li p, ul li p {
#main, #parameters, #chat-settings, #interface-mode, #lora {
border: 0;
}
.gradio-container-3-18-0 .prose * h1, h2, h3, h4 {
color: white;
}

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@ -116,10 +116,11 @@ def get_download_links_from_huggingface(model, branch):
is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
is_safetensors = re.match("model.*\.safetensors", fname)
is_pt = re.match(".*\.pt", fname)
is_tokenizer = re.match("tokenizer.*\.model", fname)
is_text = re.match(".*\.(txt|json)", fname) or is_tokenizer
is_text = re.match(".*\.(txt|json|py)", fname) or is_tokenizer
if any((is_pytorch, is_safetensors, is_text, is_tokenizer)):
if any((is_pytorch, is_safetensors, is_pt, is_tokenizer, is_text)):
if is_text:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
classifications.append('text')
@ -132,7 +133,8 @@ def get_download_links_from_huggingface(model, branch):
elif is_pytorch:
has_pytorch = True
classifications.append('pytorch')
elif is_pt:
classifications.append('pt')
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
cursor = base64.b64encode(cursor)

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@ -57,6 +57,7 @@ class Handler(BaseHTTPRequestHandler):
penalty_alpha=0,
length_penalty=1,
early_stopping=False,
seed=-1,
)
answer = ''

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@ -1,6 +1,8 @@
import re
from pathlib import Path
import gradio as gr
import modules.shared as shared
from elevenlabslib import ElevenLabsUser
from elevenlabslib.helpers import save_bytes_to_path
@ -15,6 +17,9 @@ wav_idx = 0
user = ElevenLabsUser(params['api_key'])
user_info = None
if not shared.args.no_stream:
print("Please add --no-stream. This extension is not meant to be used with streaming.")
raise ValueError
# Check if the API is valid and refresh the UI accordingly.
def check_valid_api():
@ -47,14 +52,9 @@ def refresh_voices():
return
def remove_surrounded_chars(string):
new_string = ""
in_star = False
for char in string:
if char == '*':
in_star = not in_star
elif not in_star:
new_string += char
return new_string
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)','',string)
def input_modifier(string):
"""

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@ -0,0 +1,179 @@
import base64
import io
import re
from pathlib import Path
import gradio as gr
import modules.chat as chat
import modules.shared as shared
import requests
import torch
from PIL import Image
torch._C._jit_set_profiling_mode(False)
# parameters which can be customized in settings.json of webui
params = {
'enable_SD_api': False,
'address': 'http://127.0.0.1:7860',
'save_img': False,
'SD_model': 'NeverEndingDream', # not really used right now
'prompt_prefix': '(Masterpiece:1.1), (solo:1.3), detailed, intricate, colorful',
'negative_prompt': '(worst quality, low quality:1.3)',
'side_length': 512,
'restore_faces': False
}
SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
streaming_state = shared.args.no_stream # remember if chat streaming was enabled
picture_response = False # specifies if the next model response should appear as a picture
pic_id = 0
def remove_surrounded_chars(string):
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)','',string)
# I don't even need input_hijack for this as visible text will be commited to history as the unmodified string
def input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
global params, picture_response
if not params['enable_SD_api']:
return string
commands = ['send', 'mail', 'me']
mediums = ['image', 'pic', 'picture', 'photo']
subjects = ['yourself', 'own']
lowstr = string.lower()
# TODO: refactor out to separate handler and also replace detection with a regexp
if any(command in lowstr for command in commands) and any(case in lowstr for case in mediums): # trigger the generation if a command signature and a medium signature is found
picture_response = True
shared.args.no_stream = True # Disable streaming cause otherwise the SD-generated picture would return as a dud
shared.processing_message = "*Is sending a picture...*"
string = "Please provide a detailed description of your surroundings, how you look and the situation you're in and what you are doing right now"
if any(target in lowstr for target in subjects): # the focus of the image should be on the sending character
string = "Please provide a detailed and vivid description of how you look and what you are wearing"
return string
# Get and save the Stable Diffusion-generated picture
def get_SD_pictures(description):
global params, pic_id
payload = {
"prompt": params['prompt_prefix'] + description,
"seed": -1,
"sampler_name": "DPM++ 2M Karras",
"steps": 32,
"cfg_scale": 7,
"width": params['side_length'],
"height": params['side_length'],
"restore_faces": params['restore_faces'],
"negative_prompt": params['negative_prompt']
}
response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
r = response.json()
visible_result = ""
for img_str in r['images']:
image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",",1)[0])))
if params['save_img']:
output_file = Path(f'extensions/sd_api_pictures/outputs/{pic_id:06d}.png')
image.save(output_file.as_posix())
pic_id += 1
# lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
image.thumbnail((300, 300))
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
buffered.seek(0)
image_bytes = buffered.getvalue()
img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
return visible_result
# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
# and replace it with 'text' for the purposes of logging?
def output_modifier(string):
"""
This function is applied to the model outputs.
"""
global pic_id, picture_response, streaming_state
if not picture_response:
return string
string = remove_surrounded_chars(string)
string = string.replace('"', '')
string = string.replace('', '')
string = string.replace('\n', ' ')
string = string.strip()
if string == '':
string = 'no viable description in reply, try regenerating'
# I can't for the love of all that's holy get the name from shared.gradio['name1'], so for now it will be like this
text = f'*Description: "{string}"*'
image = get_SD_pictures(string)
picture_response = False
shared.processing_message = "*Is typing...*"
shared.args.no_stream = streaming_state
return image + "\n" + text
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
the prefix text for the Bot and can be used to bias its
behavior.
"""
return string
def force_pic():
global picture_response
picture_response = True
def ui():
# Gradio elements
with gr.Accordion("Stable Diffusion api integration", open=True):
with gr.Row():
with gr.Column():
enable = gr.Checkbox(value=params['enable_SD_api'], label='Activate SD Api integration')
save_img = gr.Checkbox(value=params['save_img'], label='Keep original received images in the outputs subdir')
with gr.Column():
address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Stable Diffusion host address')
with gr.Row():
force_btn = gr.Button("Force the next response to be a picture")
generate_now_btn = gr.Button("Generate an image response to the input")
with gr.Accordion("Generation parameters", open=False):
prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
with gr.Row():
negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
dimensions = gr.Slider(256,702,value=params['side_length'],step=64,label='Image dimensions')
# model = gr.Dropdown(value=SD_models[0], choices=SD_models, label='Model')
# Event functions to update the parameters in the backend
enable.change(lambda x: params.update({"enable_SD_api": x}), enable, None)
save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
address.change(lambda x: params.update({"address": x}), address, None)
prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None)
negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None)
dimensions.change(lambda x: params.update({"side_length": x}), dimensions, None)
# model.change(lambda x: params.update({"SD_model": x}), model, None)
force_btn.click(force_pic)
generate_now_btn.click(force_pic)
generate_now_btn.click(eval('chat.cai_chatbot_wrapper'), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)

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@ -2,11 +2,11 @@ import base64
from io import BytesIO
import gradio as gr
import torch
from transformers import BlipForConditionalGeneration, BlipProcessor
import modules.chat as chat
import modules.shared as shared
import torch
from PIL import Image
from transformers import BlipForConditionalGeneration, BlipProcessor
# If 'state' is True, will hijack the next chat generation with
# custom input text given by 'value' in the format [text, visible_text]
@ -25,10 +25,12 @@ def caption_image(raw_image):
def generate_chat_picture(picture, name1, name2):
text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
# lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
picture.thumbnail((300, 300))
buffer = BytesIO()
picture.save(buffer, format="JPEG")
img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
visible_text = f'<img src="data:image/jpeg;base64,{img_str}">'
visible_text = f'<img src="data:image/jpeg;base64,{img_str}" alt="{text}">'
return text, visible_text
def ui():

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@ -1,11 +1,11 @@
import re
import time
from pathlib import Path
import gradio as gr
import torch
import modules.chat as chat
import modules.shared as shared
import torch
torch._C._jit_set_profiling_mode(False)
@ -46,14 +46,9 @@ def load_model():
model = load_model()
def remove_surrounded_chars(string):
new_string = ""
in_star = False
for char in string:
if char == '*':
in_star = not in_star
elif not in_star:
new_string += char
return new_string
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)','',string)
def remove_tts_from_history(name1, name2):
for i, entry in enumerate(shared.history['internal']):

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@ -1,3 +1,4 @@
import re
import sys
from pathlib import Path
@ -8,6 +9,7 @@ import modules.shared as shared
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
import llama
import llama_inference_offload
import opt
@ -23,7 +25,10 @@ def load_quantized(model_name):
model_type = shared.args.gptq_model_type.lower()
if model_type == 'llama':
if not shared.args.gptq_pre_layer:
load_quant = llama.load_quant
else:
load_quant = llama_inference_offload.load_quant
elif model_type == 'opt':
load_quant = opt.load_quant
else:
@ -52,20 +57,28 @@ def load_quantized(model_name):
print(f"Could not find {pt_model}, exiting...")
exit()
# qwopqwop200's offload
if shared.args.gptq_pre_layer:
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer)
else:
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
# Multiple GPUs or GPU+CPU
# accelerate offload (doesn't work properly)
if shared.args.gpu_memory:
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
max_memory = {}
for i in range(len(shared.args.gpu_memory)):
max_memory[i] = f"{shared.args.gpu_memory[i]}GiB"
max_memory['cpu'] = f"{shared.args.cpu_memory or '99'}GiB"
for i in range(len(memory_map)):
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
max_memory['cpu'] = max_cpu_memory
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
model = accelerate.dispatch_model(model, device_map=device_map)
print("Using the following device map for the 4-bit model:", device_map)
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
# Single GPU
else:
# No offload
elif not shared.args.cpu:
model = model.to(torch.device('cuda:0'))
return model

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@ -2,21 +2,36 @@ from pathlib import Path
import modules.shared as shared
from modules.models import load_model
from modules.text_generation import clear_torch_cache
def reload_model():
shared.model = shared.tokenizer = None
clear_torch_cache()
shared.model, shared.tokenizer = load_model(shared.model_name)
def add_lora_to_model(lora_name):
from peft import PeftModel
# Is there a more efficient way of returning to the base model?
if lora_name == "None":
print("Reloading the model to remove the LoRA...")
shared.model, shared.tokenizer = load_model(shared.model_name)
else:
# Why doesn't this work in 16-bit mode?
print(f"Adding the LoRA {lora_name} to the model...")
# If a LoRA had been previously loaded, or if we want
# to unload a LoRA, reload the model
if shared.lora_name != "None" or lora_name == "None":
reload_model()
shared.lora_name = lora_name
if lora_name != "None":
print(f"Adding the LoRA {lora_name} to the model...")
params = {}
#params['device_map'] = {'': 0}
#params['dtype'] = shared.model.dtype
if not shared.args.cpu:
params['dtype'] = shared.model.dtype
if hasattr(shared.model, "hf_device_map"):
params['device_map'] = {"base_model.model."+k: v for k, v in shared.model.hf_device_map.items()}
elif shared.args.load_in_8bit:
params['device_map'] = {'': 0}
shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params)
if not shared.args.load_in_8bit and not shared.args.cpu:
shared.model.half()
if not hasattr(shared.model, "hf_device_map"):
shared.model.cuda()

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@ -45,11 +45,11 @@ class RWKVModel:
token_stop = token_stop
)
return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
def generate_with_streaming(self, **kwargs):
with Iteratorize(self.generate, kwargs, callback=None) as generator:
reply = kwargs['context']
reply = ''
for token in generator:
reply += token
yield reply

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@ -11,23 +11,21 @@ import modules.shared as shared
# Copied from https://github.com/PygmalionAI/gradio-ui/
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
def __init__(self, sentinel_token_ids: torch.LongTensor,
starting_idx: int):
def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
transformers.StoppingCriteria.__init__(self)
self.sentinel_token_ids = sentinel_token_ids
self.starting_idx = starting_idx
def __call__(self, input_ids: torch.LongTensor,
_scores: torch.FloatTensor) -> bool:
def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
for sample in input_ids:
trimmed_sample = sample[self.starting_idx:]
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
continue
for window in trimmed_sample.unfold(
0, self.sentinel_token_ids.shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids, window)):
for i in range(len(self.sentinel_token_ids)):
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
continue
for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids[i], window)):
return True
return False

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@ -51,47 +51,37 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
prompt = ''.join(rows)
return prompt
def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
def extract_message_from_reply(reply, name1, name2, check):
next_character_found = False
asker = name1 if not impersonate else name2
replier = name2 if not impersonate else name1
previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
idx = idx[max(len(previous_idx)-1, 0)]
if not impersonate:
reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
else:
reply = reply[idx + 1 + len(f"{replier}:"):]
if check:
lines = reply.split('\n')
reply = lines[0].strip()
if len(lines) > 1:
next_character_found = True
else:
idx = reply.find(f"\n{asker}:")
for string in [f"\n{name1}:", f"\n{name2}:"]:
idx = reply.find(string)
if idx != -1:
reply = reply[:idx]
next_character_found = True
reply = fix_newlines(reply)
# If something like "\nYo" is generated just before "\nYou:"
# is completed, trim it
next_turn = f"\n{asker}:"
for j in range(len(next_turn)-1, 0, -1):
if reply[-j:] == next_turn[:j]:
if not next_character_found:
for string in [f"\n{name1}:", f"\n{name2}:"]:
for j in range(len(string)-1, 0, -1):
if reply[-j:] == string[:j]:
reply = reply[:-j]
break
reply = fix_newlines(reply)
return reply, next_character_found
def stop_everything_event():
shared.stop_everything = True
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
shared.stop_everything = False
just_started = True
eos_token = '\n' if check else None
@ -125,12 +115,13 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
yield shared.history['visible']+[[visible_text, shared.processing_message]]
# Generate
reply = ''
cumulative_reply = ''
for i in range(chat_generation_attempts):
for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
reply = cumulative_reply + reply
# Extracting the reply
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
visible_reply = apply_extensions(visible_reply, "output")
if shared.args.chat:
@ -152,9 +143,11 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
if next_character_found:
break
cumulative_reply = reply
yield shared.history['visible']
def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
eos_token = '\n' if check else None
if 'pygmalion' in shared.model_name.lower():
@ -162,22 +155,27 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
reply = ''
# Yield *Is typing...*
yield shared.processing_message
cumulative_reply = ''
for i in range(chat_generation_attempts):
for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
reply = cumulative_reply + reply
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
yield reply
if next_character_found:
break
cumulative_reply = reply
yield reply
def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
yield generate_chat_html(_history, name1, name2, shared.character)
def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
else:
@ -185,7 +183,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
last_internal = shared.history['internal'].pop()
# Yield '*Is typing...*'
yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character)
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
if shared.args.cai_chat:
shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
else:

View File

@ -1,5 +1,6 @@
import json
import os
import re
import time
import zipfile
from pathlib import Path
@ -120,11 +121,12 @@ def load_model(model_name):
params["torch_dtype"] = torch.float16
if shared.args.gpu_memory:
memory_map = shared.args.gpu_memory
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
max_memory = {}
for i in range(len(memory_map)):
max_memory[i] = f'{memory_map[i]}GiB'
max_memory['cpu'] = f'{shared.args.cpu_memory or 99}GiB'
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
max_memory['cpu'] = max_cpu_memory
params['max_memory'] = max_memory
elif shared.args.auto_devices:
total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024*1024))

View File

@ -27,9 +27,9 @@ settings = {
'max_new_tokens': 200,
'max_new_tokens_min': 1,
'max_new_tokens_max': 2000,
'name1': 'Person 1',
'name2': 'Person 2',
'context': 'This is a conversation between two people.',
'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.',
'stop_at_newline': False,
'chat_prompt_size': 2048,
'chat_prompt_size_min': 0,
@ -56,7 +56,7 @@ settings = {
},
'lora_prompts': {
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
'alpaca-lora-7b': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n"
'(alpaca-lora-7b|alpaca-lora-13b|alpaca-lora-30b)': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n"
}
}
@ -79,14 +79,16 @@ parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI i
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
parser.add_argument('--gptq-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
parser.add_argument('--gptq-bits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
parser.add_argument('--gpu-memory', type=int, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs.')
parser.add_argument('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs.')
parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.')
parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.')
parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).')
parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.")

View File

@ -1,6 +1,7 @@
import gc
import re
import time
import traceback
import numpy as np
import torch
@ -92,30 +93,16 @@ def clear_torch_cache():
if not shared.args.cpu:
torch.cuda.empty_cache()
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
clear_torch_cache()
t0 = time.time()
def set_manual_seed(seed):
if seed != -1:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if shared.is_RWKV:
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
yield formatted_outputs(reply, shared.model_name)
else:
if not (shared.args.chat or shared.args.cai_chat):
yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
yield formatted_outputs(reply, shared.model_name)
finally:
t1 = time.time()
output = encode(reply)[0]
input_ids = encode(question)
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
return
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_strings=[]):
clear_torch_cache()
set_manual_seed(seed)
t0 = time.time()
original_question = question
if not (shared.args.chat or shared.args.cai_chat):
@ -123,17 +110,46 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if shared.args.verbose:
print(f"\n\n{question}\n--------------------\n")
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if shared.is_RWKV:
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
else:
if not (shared.args.chat or shared.args.cai_chat):
yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
output = encode(reply)[0]
input_ids = encode(question)
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
return
input_ids = encode(question, max_new_tokens)
original_input_ids = input_ids
output = input_ids[0]
cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
stopping_criteria_list = transformers.StoppingCriteriaList()
if stopping_string is not None:
# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
t = encode(stopping_string, 0, add_special_tokens=False)
if type(stopping_strings) is list and len(stopping_strings) > 0:
t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
generate_params = {}
@ -163,6 +179,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
"temperature": temperature,
"stop": eos_token_ids[-1],
})
if shared.args.no_cache:
generate_params.update({"use_cache": False})
if shared.args.deepspeed:
generate_params.update({"synced_gpus": True})
if shared.soft_prompt:
@ -182,9 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
@ -207,10 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
reply = original_question + apply_extensions(reply, "output")
if output[-1] in eos_token_ids:
break
@ -226,10 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
output = shared.model.generate(**generate_params)[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
new_tokens = len(output) - len(original_input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
reply = original_question + apply_extensions(reply, "output")
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
break
@ -238,9 +259,15 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({"inputs_embeds": inputs_embeds})
generate_params.update({"inputs": filler_input_ids})
else:
generate_params.update({"inputs": input_ids})
yield formatted_outputs(reply, shared.model_name)
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)")

View File

@ -1,12 +1,7 @@
do_sample=True
temperature=1
top_p=1
typical_p=1
repetition_penalty=1
top_k=50
num_beams=1
penalty_alpha=0
min_length=0
length_penalty=1
no_repeat_ngram_size=0
top_p=0.5
top_k=40
temperature=0.7
repetition_penalty=1.2
typical_p=1.0
early_stopping=False

View File

@ -1,6 +0,0 @@
do_sample=True
top_p=0.9
top_k=50
temperature=1.39
repetition_penalty=1.08
typical_p=0.2

View File

@ -6,6 +6,7 @@ markdown
numpy
peft==0.2.0
requests
rwkv==0.7.0
safetensors==0.3.0
sentencepiece
tqdm

View File

@ -1,4 +1,3 @@
import gc
import io
import json
import re
@ -8,7 +7,6 @@ import zipfile
from pathlib import Path
import gradio as gr
import torch
import modules.chat as chat
import modules.extensions as extensions_module
@ -17,7 +15,7 @@ import modules.ui as ui
from modules.html_generator import generate_chat_html
from modules.LoRA import add_lora_to_model
from modules.models import load_model, load_soft_prompt
from modules.text_generation import generate_reply
from modules.text_generation import clear_torch_cache, generate_reply
# Loading custom settings
settings_file = None
@ -56,21 +54,14 @@ def load_model_wrapper(selected_model):
if selected_model != shared.model_name:
shared.model_name = selected_model
shared.model = shared.tokenizer = None
if not shared.args.cpu:
gc.collect()
torch.cuda.empty_cache()
clear_torch_cache()
shared.model, shared.tokenizer = load_model(shared.model_name)
return selected_model
def load_lora_wrapper(selected_lora):
shared.lora_name = selected_lora
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
if not shared.args.cpu:
gc.collect()
torch.cuda.empty_cache()
add_lora_to_model(selected_lora)
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
return selected_lora, default_text
@ -102,7 +93,7 @@ def load_preset_values(preset_menu, return_dict=False):
if return_dict:
return generate_params
else:
return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
return preset_menu, generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
def upload_soft_prompt(file):
with zipfile.ZipFile(io.BytesIO(file)) as zf:
@ -160,6 +151,12 @@ def create_settings_menus(default_preset):
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
with gr.Row():
shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
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')
@ -174,7 +171,8 @@ def create_settings_menus(default_preset):
shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
shared.gradio['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True)
shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio['do_sample'], shared.gradio['temperature'], shared.gradio['top_p'], shared.gradio['typical_p'], shared.gradio['repetition_penalty'], shared.gradio['encoder_repetition_penalty'], shared.gradio['top_k'], shared.gradio['min_length'], shared.gradio['no_repeat_ngram_size'], shared.gradio['num_beams'], shared.gradio['penalty_alpha'], shared.gradio['length_penalty'], shared.gradio['early_stopping']])
shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio[k] for k in ['preset_menu_mirror', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
shared.gradio['preset_menu_mirror'].change(load_preset_values, [shared.gradio['preset_menu_mirror']], [shared.gradio[k] for k in ['preset_menu', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
shared.gradio['lora_menu'].change(load_lora_wrapper, [shared.gradio['lora_menu']], [shared.gradio['lora_menu'], shared.gradio['textbox']], show_progress=True)
shared.gradio['softprompts_menu'].change(load_soft_prompt, [shared.gradio['softprompts_menu']], [shared.gradio['softprompts_menu']], show_progress=True)
shared.gradio['upload_softprompt'].upload(upload_soft_prompt, [shared.gradio['upload_softprompt']], [shared.gradio['softprompts_menu']])
@ -235,9 +233,7 @@ else:
shared.model_name = available_models[i]
shared.model, shared.tokenizer = load_model(shared.model_name)
if shared.args.lora:
print(shared.args.lora)
shared.lora_name = shared.args.lora
add_lora_to_model(shared.lora_name)
add_lora_to_model(shared.args.lora)
# Default UI settings
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
@ -325,13 +321,13 @@ def create_interface():
create_settings_menus(default_preset)
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events)
shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events, queue=False)
shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream)
@ -388,7 +384,7 @@ def create_interface():
with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset)
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
@ -420,7 +416,7 @@ def create_interface():
with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset)
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))

View File

@ -2,9 +2,9 @@
"max_new_tokens": 200,
"max_new_tokens_min": 1,
"max_new_tokens_max": 2000,
"name1": "Person 1",
"name2": "Person 2",
"context": "This is a conversation between two people.",
"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.",
"stop_at_newline": false,
"chat_prompt_size": 2048,
"chat_prompt_size_min": 0,