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1
.gitignore vendored
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@ -14,6 +14,7 @@ torch-dumps
*/*/pycache*
venv/
.venv/
.vscode
repositories
settings.json

34
README.md
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@ -1,6 +1,6 @@
# Text generation web UI
A gradio web UI for running Large Language Models like GPT-J 6B, OPT, GALACTICA, LLaMA, and Pygmalion.
A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.
Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
@ -28,6 +28,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
* [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed).
* Get responses via API, [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-streaming.py) or [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming.
* [LLaMA model, including 4-bit GPTQ support](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model).
* [llama.cpp support](https://github.com/oobabooga/text-generation-webui/wiki/llama.cpp-models). **\*NEW!\***
* [RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model).
* [Supports LoRAs](https://github.com/oobabooga/text-generation-webui/wiki/Using-LoRAs).
* Supports softprompts.
@ -175,24 +176,31 @@ Optionally, you can use the following command-line flags:
| Flag | Description |
|------------------|-------------|
| `-h`, `--help` | show this help message and exit |
| `--model MODEL` | Name of the model to load by default. |
| `--lora LORA` | Name of the LoRA to apply to the model by default. |
| `--notebook` | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
| `--chat` | Launch the web UI in chat mode.|
| `--cai-chat` | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. |
| `--model MODEL` | Name of the model to load by default. |
| `--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 |
| `--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. |
| `--verbose` | Print the prompts to the terminal. |
| `--cpu` | Use the CPU to generate text.|
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
| `--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.|
| `--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/`. |
| `--load-in-8bit` | Load the model with 8-bit precision.|
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
| `--no-cache` | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
| `--threads` | Number of threads to use in llama.cpp. |
| `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
| `--groupsize GROUPSIZE` | GPTQ: Group size. |
| `--pre_layer 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. 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).|
@ -202,12 +210,6 @@ Optionally, you can use the following command-line flags:
| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. |
| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. |
| `--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. |
| `--model-dir MODEL_DIR` | Path to directory with all the models |
| `--lora-dir LORA_DIR` | Path to directory with all the loras |
| `--verbose` | Print the prompts to the terminal. |
| `--listen` | Make the web UI reachable from your local network. |
| `--listen-port LISTEN_PORT` | The listening port that the server will use. |
| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. |

89
download-model.py
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@ -9,6 +9,7 @@ python download-model.py facebook/opt-1.3b
import argparse
import base64
import datetime
import hashlib
import json
import re
import sys
@ -24,11 +25,28 @@ parser.add_argument('--branch', type=str, default='main', help='Name of the Git
parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
args = parser.parse_args()
def get_file(url, output_folder):
r = requests.get(url, stream=True)
with open(output_folder / Path(url.rsplit('/', 1)[1]), 'wb') as f:
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
if output_path.exists() and not args.clean:
# Check if the file has already been downloaded completely
r = requests.get(url, stream=True)
total_size = int(r.headers.get('content-length', 0))
if output_path.stat().st_size >= total_size:
return
# Otherwise, resume the download from where it left off
headers = {'Range': f'bytes={output_path.stat().st_size}-'}
mode = 'ab'
else:
headers = {}
mode = 'wb'
r = requests.get(url, stream=True, headers=headers)
with open(output_path, mode) as f:
total_size = int(r.headers.get('content-length', 0))
block_size = 1024
with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
@ -97,6 +115,7 @@ def get_download_links_from_huggingface(model, branch):
classifications = []
has_pytorch = False
has_pt = False
has_ggml = False
has_safetensors = False
is_lora = False
while True:
@ -114,6 +133,7 @@ def get_download_links_from_huggingface(model, branch):
is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
is_safetensors = re.match(".*\.safetensors", fname)
is_pt = re.match(".*\.pt", fname)
is_ggml = re.match("ggml.*\.bin", fname)
is_tokenizer = re.match("tokenizer.*\.model", fname)
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
@ -135,6 +155,9 @@ def get_download_links_from_huggingface(model, branch):
elif is_pt:
has_pt = True
classifications.append('pt')
elif is_ggml:
has_ggml = True
classifications.append('ggml')
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
cursor = base64.b64encode(cursor)
@ -149,7 +172,7 @@ def get_download_links_from_huggingface(model, branch):
return links, sha256, is_lora
def download_files(file_list, output_folder, num_threads=8):
thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads)
thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads, disable=True)
if __name__ == '__main__':
model = args.MODEL
@ -179,22 +202,48 @@ if __name__ == '__main__':
output_folder = f"{'_'.join(model.split('/')[-2:])}"
if branch != 'main':
output_folder += f'_{branch}'
# Creating the folder and writing the metadata
output_folder = Path(base_folder) / output_folder
if not output_folder.exists():
output_folder.mkdir()
with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
f.write(f'url: https://huggingface.co/{model}\n')
f.write(f'branch: {branch}\n')
f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
sha256_str = ''
for i in range(len(sha256)):
sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
if sha256_str != '':
f.write(f'sha256sum:\n{sha256_str}')
# Downloading the files
print(f"Downloading the model to {output_folder}")
download_files(links, output_folder, args.threads)
print()
if args.check:
# Validate the checksums
validated = True
for i in range(len(sha256)):
fpath = (output_folder / sha256[i][0])
if not fpath.exists():
print(f"The following file is missing: {fpath}")
validated = False
continue
with open(output_folder / sha256[i][0], "rb") as f:
bytes = f.read()
file_hash = hashlib.sha256(bytes).hexdigest()
if file_hash != sha256[i][1]:
print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}')
validated = False
else:
print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
if validated:
print('[+] Validated checksums of all model files!')
else:
print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
else:
# Creating the folder and writing the metadata
if not output_folder.exists():
output_folder.mkdir()
with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
f.write(f'url: https://huggingface.co/{model}\n')
f.write(f'branch: {branch}\n')
f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
sha256_str = ''
for i in range(len(sha256)):
sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
if sha256_str != '':
f.write(f'sha256sum:\n{sha256_str}')
# Downloading the files
print(f"Downloading the model to {output_folder}")
download_files(links, output_folder, args.threads)

40
extensions/gallery/script.py
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@ -2,19 +2,29 @@ from pathlib import Path
import gradio as gr
from modules.chat import load_character
from modules.html_generator import get_image_cache
from modules.shared import gradio, settings
def generate_html():
def generate_css():
css = """
.character-gallery {
.character-gallery > .gallery {
margin: 1rem 0;
display: grid;
display: grid !important;
grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
grid-column-gap: 0.4rem;
grid-row-gap: 1.2rem;
}
.character-gallery > .label {
display: none !important;
}
.character-gallery button.gallery-item {
display: contents;
}
.character-container {
cursor: pointer;
text-align: center;
@ -45,14 +55,16 @@ def generate_html():
overflow-wrap: anywhere;
}
"""
return css
container_html = f'<style>{css}</style><div class="character-gallery">'
def generate_html():
cards = []
# Iterate through files in image folder
for file in sorted(Path("characters").glob("*")):
if file.name.endswith(".json"):
character = file.name.replace(".json", "")
container_html += f'<div class="character-container" onclick=\'document.getElementById("character-menu").children[1].children[1].value = "{character}"; document.getElementById("character-menu").children[1].children[1].dispatchEvent(new Event("change"));\'>'
container_html = f'<div class="character-container">'
image_html = "<div class='placeholder'></div>"
for i in [
@ -71,12 +83,24 @@ def generate_html():
container_html += f'{image_html} <span class="character-name">{character}</span>'
container_html += "</div>"
cards.append([container_html, character])
return cards
def select_character(evt: gr.SelectData):
return (evt.value[1])
container_html += "</div>"
return container_html
def ui():
with gr.Accordion("Character gallery", open=False):
update = gr.Button("Refresh")
gallery = gr.HTML(value=generate_html())
gr.HTML(value="<style>"+generate_css()+"</style>")
gallery = gr.Dataset(components=[gr.HTML(visible=False)],
label="",
samples=generate_html(),
elem_classes=["character-gallery"],
samples_per_page=50
)
update.click(generate_html, [], gallery)
gallery.select(select_character, None, gradio['character_menu'])

0
loras/place-your-loras-here.txt Normal file
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2
modules/RWKV.py
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@ -34,7 +34,7 @@ class RWKVModel:
result.pipeline = pipeline
return result
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
args = PIPELINE_ARGS(
temperature = temperature,
top_p = top_p,

2
modules/callbacks.py
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@ -12,7 +12,7 @@ import modules.shared as shared
# Copied from https://github.com/PygmalionAI/gradio-ui/
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
def __init__(self, sentinel_token_ids: list, starting_idx: int):
transformers.StoppingCriteria.__init__(self)
self.sentinel_token_ids = sentinel_token_ids
self.starting_idx = starting_idx

8
modules/chat.py
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@ -22,7 +22,7 @@ def generate_chat_output(history, name1, name2, character):
else:
return history
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=False):
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=False, also_return_rows=False):
user_input = fix_newlines(user_input)
rows = [f"{context.strip()}\n"]
@ -51,7 +51,11 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
rows.pop(1)
prompt = ''.join(rows)
return prompt
if also_return_rows:
return prompt, rows
else:
return prompt
def extract_message_from_reply(reply, name1, name2, check):
next_character_found = False

82
modules/llamacpp_model.py Normal file
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@ -0,0 +1,82 @@
import multiprocessing
import llamacpp
from modules import shared
from modules.callbacks import Iteratorize
class LlamaCppTokenizer:
"""A thin wrapper over the llamacpp tokenizer"""
def __init__(self, model: llamacpp.LlamaInference):
self._tokenizer = model.get_tokenizer()
self.eos_token_id = 2
self.bos_token_id = 0
@classmethod
def from_model(cls, model: llamacpp.LlamaInference):
return cls(model)
def encode(self, prompt: str):
return self._tokenizer.tokenize(prompt)
def decode(self, ids):
return self._tokenizer.detokenize(ids)
class LlamaCppModel:
def __init__(self):
self.initialized = False
@classmethod
def from_pretrained(self, path):
params = llamacpp.InferenceParams()
params.path_model = str(path)
params.n_threads = shared.args.threads or multiprocessing.cpu_count() // 2
_model = llamacpp.LlamaInference(params)
result = self()
result.model = _model
result.params = params
tokenizer = LlamaCppTokenizer.from_model(_model)
return result, tokenizer
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None):
params = self.params
params.n_predict = token_count
params.top_p = top_p
params.top_k = top_k
params.temp = temperature
params.repeat_penalty = repetition_penalty
#params.repeat_last_n = repeat_last_n
#self.model.params = params
self.model.add_bos()
self.model.update_input(context)
output = ""
is_end_of_text = False
ctr = 0
while ctr < token_count and not is_end_of_text:
if self.model.has_unconsumed_input():
self.model.ingest_all_pending_input()
else:
self.model.eval()
token = self.model.sample()
text = self.model.token_to_str(token)
output += text
is_end_of_text = token == self.model.token_eos()
if callback:
callback(text)
ctr += 1
return output
def generate_with_streaming(self, **kwargs):
with Iteratorize(self.generate, kwargs, callback=None) as generator:
reply = ''
for token in generator:
reply += token
yield reply

13
modules/models.py
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@ -42,9 +42,10 @@ def load_model(model_name):
t0 = time.time()
shared.is_RWKV = 'rwkv-' in model_name.lower()
shared.is_llamacpp = len(list(Path(f'models/{model_name}').glob('ggml*.bin'))) > 0
# Default settings
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV, shared.is_llamacpp]):
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else:
@ -100,6 +101,16 @@ def load_model(model_name):
model = load_quantized(model_name)
# llamacpp model
elif shared.is_llamacpp:
from modules.llamacpp_model import LlamaCppModel
model_file = list(Path(f'models/{model_name}').glob('ggml*.bin'))[0]
print(f"llama.cpp weights detected: {model_file}\n")
model, tokenizer = LlamaCppModel.from_pretrained(model_file)
return model, tokenizer
# Custom
else:
params = {"low_cpu_mem_usage": True}

58
modules/shared.py
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@ -27,6 +27,7 @@ settings = {
'max_new_tokens': 200,
'max_new_tokens_min': 1,
'max_new_tokens_max': 2000,
'seed': -1,
'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.',
@ -68,51 +69,68 @@ def str2bool(v):
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
parser.add_argument('--model', type=str, help='Name of the model to load by default.')
parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
# Basic settings
parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode.')
parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
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('--model', type=str, help='Name of the model to load by default.')
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('--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.')
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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.')
# Accelerate/transformers
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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('--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('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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.')
# llama.cpp
parser.add_argument('--threads', type=int, default=0, help='Number of threads to use in llama.cpp.')
# GPTQ
parser.add_argument('--wbits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
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 preload.')
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.')
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=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.')
# FlexGen
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.")
parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).")
# DeepSpeed
parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
# RWKV
parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
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.')
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('--verbose', action='store_true', help='Print the prompts to the terminal.')
# Gradio
parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None)
args = parser.parse_args()
# Provisional, this will be deleted later
deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
for k in deprecated_dict:

20
modules/text_generation.py
View File

@ -22,7 +22,7 @@ def get_max_prompt_length(tokens):
return max_length
def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
if shared.is_RWKV:
if any((shared.is_RWKV, shared.is_llamacpp)):
input_ids = shared.tokenizer.encode(str(prompt))
input_ids = np.array(input_ids).reshape(1, len(input_ids))
return input_ids
@ -116,10 +116,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if shared.is_RWKV:
if any((shared.is_RWKV, shared.is_llamacpp)):
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)
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty)
output = original_question+reply
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
@ -129,7 +130,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
# 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):
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, repetition_penalty=repetition_penalty):
output = original_question+reply
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
@ -138,9 +140,9 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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)")
original_tokens = len(encode(original_question)[0])
new_tokens = len(encode(output)[0]) - original_tokens
print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
return
input_ids = encode(question, max_new_tokens)
@ -272,5 +274,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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, context {len(original_input_ids[0])})")
original_tokens = len(original_input_ids[0])
new_tokens = len(output)-original_tokens
print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
return

3
requirements.txt
View File

@ -1,7 +1,8 @@
accelerate==0.18.0
bitsandbytes==0.37.2
flexgen==0.1.7
gradio==3.23.0
gradio==3.24.0
llamacpp==0.1.11
markdown
numpy
peft==0.2.0

26
server.py
View File

@ -166,7 +166,7 @@ def create_settings_menus(default_preset):
with gr.Column():
create_model_and_preset_menus()
with gr.Column():
shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
with gr.Row():
with gr.Column():
@ -217,10 +217,11 @@ def create_settings_menus(default_preset):
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']])
def set_interface_arguments(interface_mode, extensions, cmd_active):
def set_interface_arguments(interface_mode, extensions, bool_active):
modes = ["default", "notebook", "chat", "cai_chat"]
cmd_list = vars(shared.args)
cmd_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]
#int_list = [k for k in cmd_list if type(k) is int]
shared.args.extensions = extensions
for k in modes[1:]:
@ -228,9 +229,9 @@ def set_interface_arguments(interface_mode, extensions, cmd_active):
if interface_mode != "default":
exec(f"shared.args.{interface_mode} = True")
for k in cmd_list:
for k in bool_list:
exec(f"shared.args.{k} = False")
for k in cmd_active:
for k in bool_active:
exec(f"shared.args.{k} = True")
shared.need_restart = True
@ -408,7 +409,7 @@ def create_interface():
with gr.Row():
with gr.Column(scale=4):
with gr.Tab('Raw'):
shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=25)
shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=27)
with gr.Tab('Markdown'):
shared.gradio['markdown'] = gr.Markdown()
with gr.Tab('HTML'):
@ -442,7 +443,7 @@ def create_interface():
with gr.Tab("Text generation", elem_id="main"):
with gr.Row():
with gr.Column():
shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input')
shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=21, label='Input')
shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
shared.gradio['Generate'] = gr.Button('Generate')
with gr.Row():
@ -455,7 +456,7 @@ def create_interface():
with gr.Column():
with gr.Tab('Raw'):
shared.gradio['output_textbox'] = gr.Textbox(lines=25, label='Output')
shared.gradio['output_textbox'] = gr.Textbox(lines=27, label='Output')
with gr.Tab('Markdown'):
shared.gradio['markdown'] = gr.Markdown()
with gr.Tab('HTML'):
@ -483,16 +484,17 @@ def create_interface():
current_mode = mode
break
cmd_list = vars(shared.args)
cmd_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
active_cmd_list = [k for k in cmd_list if vars(shared.args)[k]]
bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]
bool_active = [k for k in bool_list if vars(shared.args)[k]]
#int_list = [k for k in cmd_list if type(k) is int]
gr.Markdown("*Experimental*")
shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=get_available_extensions(), value=shared.args.extensions, label="Available extensions")
shared.gradio['cmd_arguments_menu'] = gr.CheckboxGroup(choices=cmd_list, value=active_cmd_list, label="Boolean command-line flags")
shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags")
shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", type="primary")
shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'cmd_arguments_menu']], None)
shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None)
shared.gradio['reset_interface'].click(lambda : None, None, None, _js='() => {document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}')
if shared.args.extensions is not None:

3
settings-template.json
View File

@ -2,6 +2,7 @@
"max_new_tokens": 200,
"max_new_tokens_min": 1,
"max_new_tokens_max": 2000,
"seed": -1,
"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.",
@ -18,7 +19,7 @@
],
"presets": {
"default": "NovelAI-Sphinx Moth",
".*pygmalion": "Pygmalion",
".*pygmalion": "NovelAI-Storywriter",
".*RWKV": "Naive"
},
"prompts": {