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# Copyright: DAMO Academy, Alibaba Group | |
# By Xuan Phi Nguyen at DAMO Academy, Alibaba Group | |
# Description: | |
""" | |
VLLM-based demo script to launch Language chat model for Southeast Asian Languages | |
""" | |
import os | |
import numpy as np | |
import argparse | |
import torch | |
import gradio as gr | |
from typing import Any, Iterator | |
from typing import Iterator, List, Optional, Tuple | |
import filelock | |
import glob | |
import json | |
import time | |
from gradio.routes import Request | |
from gradio.utils import SyncToAsyncIterator, async_iteration | |
from gradio.helpers import special_args | |
import anyio | |
from typing import AsyncGenerator, Callable, Literal, Union, cast | |
from gradio_client.documentation import document, set_documentation_group | |
from typing import List, Optional, Union, Dict, Tuple | |
from tqdm.auto import tqdm | |
from huggingface_hub import snapshot_download | |
# @@ environments ================ | |
DEBUG = bool(int(os.environ.get("DEBUG", "1"))) | |
# List of languages to block | |
BLOCK_LANGS = str(os.environ.get("BLOCK_LANGS", "")) | |
BLOCK_LANGS = [x.strip() for x in BLOCK_LANGS.strip().split(";")] if len(BLOCK_LANGS.strip()) > 0 else [] | |
# for lang block, wether to block in history too | |
LANG_BLOCK_HISTORY = bool(int(os.environ.get("LANG_BLOCK_HISTORY", "0"))) | |
TENSOR_PARALLEL = int(os.environ.get("TENSOR_PARALLEL", "1")) | |
DTYPE = os.environ.get("DTYPE", "bfloat16") | |
# ! (no debug) whether to download HF_MODEL_NAME and save to MODEL_PATH | |
DOWNLOAD_SNAPSHOT = bool(int(os.environ.get("DOWNLOAD_SNAPSHOT", "0"))) | |
LOG_RESPONSE = bool(int(os.environ.get("LOG_RESPONSE", "0"))) | |
# ! show model path in the demo page, only for internal | |
DISPLAY_MODEL_PATH = bool(int(os.environ.get("DISPLAY_MODEL_PATH", "1"))) | |
# ! uploaded model path, will be downloaded to MODEL_PATH | |
HF_MODEL_NAME = os.environ.get("HF_MODEL_NAME", "DAMO-NLP-SG/seal-13b-chat-a") | |
# ! if model is private, need HF_TOKEN to access the model | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
# ! path where the model is downloaded, either on ./ or persistent disc | |
MODEL_PATH = os.environ.get("MODEL_PATH", "./seal-13b-chat-a") | |
# ! log path | |
LOG_PATH = os.environ.get("LOG_PATH", "").strip() | |
LOG_FILE = None | |
SAVE_LOGS = LOG_PATH is not None and LOG_PATH != '' | |
if SAVE_LOGS: | |
if os.path.exists(LOG_PATH): | |
print(f'LOG_PATH exist: {LOG_PATH}') | |
else: | |
LOG_DIR = os.path.dirname(LOG_PATH) | |
os.makedirs(LOG_DIR, exist_ok=True) | |
# ! get LOG_PATH as aggregated outputs in log | |
GET_LOG_CMD = os.environ.get("GET_LOG_CMD", "").strip() | |
print(f'SAVE_LOGS: {SAVE_LOGS} | {LOG_PATH}') | |
# print(f'GET_LOG_CMD: {GET_LOG_CMD}') | |
# ! !! Whether to delete the folder, ONLY SET THIS IF YOU WANT TO DELETE SAVED MODEL ON PERSISTENT DISC | |
DELETE_FOLDER = os.environ.get("DELETE_FOLDER", "") | |
IS_DELETE_FOLDER = DELETE_FOLDER is not None and os.path.exists(DELETE_FOLDER) | |
print(f'DELETE_FOLDER: {DELETE_FOLDER} | {DOWNLOAD_SNAPSHOT=}') | |
# ! list of keywords to disabled as security measures to comply with local regulation | |
KEYWORDS = os.environ.get("KEYWORDS", "").strip() | |
KEYWORDS = KEYWORDS.split(";") if len(KEYWORDS) > 0 else [] | |
KEYWORDS = [x.lower() for x in KEYWORDS] | |
# bypass | |
BYPASS_USERS = os.environ.get("BYPASS_USERS", "").strip() | |
BYPASS_USERS = BYPASS_USERS.split(";") if len(BYPASS_USERS) > 0 else [] | |
# gradio config | |
PORT = int(os.environ.get("PORT", "7860")) | |
# how many iterations to yield response | |
STREAM_YIELD_MULTIPLE = int(os.environ.get("STREAM_YIELD_MULTIPLE", "1")) | |
# how many iterations to perform safety check on response | |
STREAM_CHECK_MULTIPLE = int(os.environ.get("STREAM_CHECK_MULTIPLE", "0")) | |
# whether to enable to popup accept user | |
ENABLE_AGREE_POPUP = bool(int(os.environ.get("ENABLE_AGREE_POPUP", "0"))) | |
# self explanatory | |
MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "2048")) | |
TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.1")) | |
FREQUENCE_PENALTY = float(os.environ.get("FREQUENCE_PENALTY", "0.0")) | |
PRESENCE_PENALTY = float(os.environ.get("PRESENCE_PENALTY", "0.0")) | |
gpu_memory_utilization = float(os.environ.get("gpu_memory_utilization", "0.9")) | |
# whether to enable quantization, currently not in use | |
QUANTIZATION = str(os.environ.get("QUANTIZATION", "")) | |
# Batch inference file upload | |
ENABLE_BATCH_INFER = bool(int(os.environ.get("ENABLE_BATCH_INFER", "1"))) | |
BATCH_INFER_MAX_ITEMS = int(os.environ.get("BATCH_INFER_MAX_ITEMS", "100")) | |
BATCH_INFER_MAX_FILE_SIZE = int(os.environ.get("BATCH_INFER_MAX_FILE_SIZE", "500")) | |
BATCH_INFER_MAX_PROMPT_TOKENS = int(os.environ.get("BATCH_INFER_MAX_PROMPT_TOKENS", "4000")) | |
BATCH_INFER_SAVE_TMP_FILE = os.environ.get("BATCH_INFER_SAVE_TMP_FILE", "./tmp/pred.json") | |
# | |
DATA_SET_REPO_PATH = str(os.environ.get("DATA_SET_REPO_PATH", "")) | |
DATA_SET_REPO = None | |
""" | |
Internal instructions of how to configure the DEMO | |
1. Upload SFT model as a model to huggingface: hugginface/models/seal_13b_a | |
2. If the model weights is private, set HF_TOKEN=<your private hf token> in https://huggingface.co/spaces/????/?????/settings | |
3. space config env: `HF_MODEL_NAME=SeaLLMs/seal-13b-chat-a` or the underlining model | |
4. If enable persistent storage: set | |
HF_HOME=/data/.huggingface | |
MODEL_PATH=/data/.huggingface/seal-13b-chat-a | |
if not: | |
MODEL_PATH=./seal-13b-chat-a | |
HF_HOME=/data/.huggingface | |
MODEL_PATH=/data/ckpt/seal-13b-chat-a | |
DELETE_FOLDER=/data/ | |
""" | |
# ============================== | |
print(f'DEBUG mode: {DEBUG}') | |
print(f'Torch version: {torch.__version__}') | |
try: | |
print(f'Torch CUDA version: {torch.version.cuda}') | |
except Exception as e: | |
print(f'Failed to print cuda version: {e}') | |
try: | |
compute_capability = torch.cuda.get_device_capability() | |
print(f'Torch CUDA compute_capability: {compute_capability}') | |
except Exception as e: | |
print(f'Failed to print compute_capability version: {e}') | |
# @@ constants ================ | |
DTYPES = { | |
'float16': torch.float16, | |
'bfloat16': torch.bfloat16 | |
} | |
llm = None | |
demo = None | |
BOS_TOKEN = '<s>' | |
EOS_TOKEN = '</s>' | |
B_INST, E_INST = "[INST]", "[/INST]" | |
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n" | |
# TODO: should Hide the system prompt | |
SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. You are built by DAMO Academy, Alibaba Group. \ | |
Please always answer as helpfully as possible, while being safe. Your \ | |
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure \ | |
that your responses are socially unbiased and positive in nature. | |
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \ | |
correct. If you don't know the answer to a question, please don't share false information. | |
As a multilingual assistant, you must respond and follow instructions in the native language of the user by default, unless told otherwise. \ | |
Your response should adapt to the norms and customs of the respective language and culture. | |
""" | |
SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. You are built by DAMO Academy, Alibaba Group. \ | |
Please always answer as helpfully as possible, while being safe. Your \ | |
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure \ | |
that your responses are socially unbiased and positive in nature.""" | |
# SYSTEM_PROMPT_1 = """You are a multilingual, helpful, respectful and honest assistant. \ | |
# Please always answer as helpfully as possible, while being safe. Your \ | |
# answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure \ | |
# that your responses are socially unbiased and positive in nature. | |
# If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \ | |
# correct. If you don't know the answer to a question, please don't share false information. | |
# As a multilingual assistant, you must respond and follow instructions in the native language of the user by default, unless told otherwise. \ | |
# Your response should adapt to the norms and customs of the respective language and culture. | |
# """ | |
# ============ CONSTANT ============ | |
# https://github.com/gradio-app/gradio/issues/884 | |
MODEL_NAME = "SeaLLM-7B" | |
MODEL_NAME = str(os.environ.get("MODEL_NAME", "SeaLLM-7B")) | |
MODEL_TITLE = """ | |
<div class="container" style=" | |
align-items: center; | |
justify-content: center; | |
display: flex; | |
"> | |
<div class="image" > | |
<img src="file/seal_logo.png" style=" | |
max-width: 10em; | |
max-height: 5%; | |
height: 3em; | |
width: 3em; | |
float: left; | |
margin-left: auto; | |
"> | |
</div> | |
<div class="text" style=" | |
padding-left: 20px; | |
padding-top: 1%; | |
float: left; | |
"> | |
<h1 style="font-size: xx-large">SeaLLMs - Large Language Models for Southeast Asia</h1> | |
</div> | |
</div> | |
""" | |
""" | |
Somehow cannot add image here | |
<div class="image" > | |
<img src="file/seal_logo.png" style=" | |
max-width: 10em; | |
max-height: 5%; | |
height: 3em; | |
width: 3em; | |
float: left; | |
margin-left: auto; | |
"> | |
</div> | |
""" | |
MODEL_DESC = f""" | |
<div style='display:flex; gap: 0.25rem; '> | |
<a href='https://github.com/damo-nlp-sg/seallms'><img src='https://img.shields.io/badge/Github-Code-success'></a> | |
<a href='https://huggingface.co/spaces/SeaLLMs/SeaLLM-7B'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> | |
<a href='https://huggingface.co/SeaLLMs/SeaLLM-7B-v2'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> | |
<a href='https://arxiv.org/pdf/2312.00738.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a> | |
</div> | |
<span style="font-size: larger"> | |
<a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank">{MODEL_NAME}-v2</a> - a helpful assistant for Southeast Asian Languages 🇬🇧 🇻🇳 🇮🇩 🇹🇭 🇲🇾 🇰🇭 🇱🇦 🇵🇭 🇲🇲. | |
Explore <a href="https://huggingface.co/SeaLLMs/SeaLLM-7B-v2" target="_blank">our article</a> for more. | |
</span> | |
<br> | |
<span> | |
<span style="color: red">NOTE: The chatbot may produce false and harmful content and does not have up-to-date knowledge.</span> | |
By using our service, you are required to agree to our <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b/blob/main/LICENSE" target="_blank" style="color: red">Terms Of Use</a>, which includes | |
not to use our service to generate any harmful, inappropriate or illegal content. | |
The service collects user dialogue data for testing and improvement under | |
<a href="https://creativecommons.org/licenses/by/4.0/">(CC-BY)</a> or similar license. So do not enter any personal information! | |
</span> | |
""".strip() | |
cite_markdown = """ | |
## Citation | |
If you find our project useful, hope you can star our repo and cite our paper as follows: | |
``` | |
@article{damonlpsg2023seallm, | |
author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, | |
title = {SeaLLMs - Large Language Models for Southeast Asia}, | |
year = 2023, | |
} | |
``` | |
""" | |
path_markdown = """ | |
#### Model path: | |
{model_path} | |
""" | |
# ! ================================================================== | |
set_documentation_group("component") | |
RES_PRINTED = False | |
class ChatBot(gr.Chatbot): | |
def _postprocess_chat_messages( | |
self, chat_message | |
): | |
x = super()._postprocess_chat_messages(chat_message) | |
# if isinstance(x, str): | |
# x = x.strip().replace("\n", "<br>") | |
return x | |
from gradio.components import Button | |
from gradio.events import Dependency, EventListenerMethod | |
# replace events so that submit button is disabled during generation, if stop_btn not found | |
# this prevent weird behavior | |
def _setup_stop_events( | |
self, event_triggers: list[EventListenerMethod], event_to_cancel: Dependency | |
) -> None: | |
from gradio.components import State | |
event_triggers = event_triggers if isinstance(event_triggers, (list, tuple)) else [event_triggers] | |
if self.stop_btn and self.is_generator: | |
if self.submit_btn: | |
for event_trigger in event_triggers: | |
event_trigger( | |
lambda: ( | |
Button(visible=False), | |
Button(visible=True), | |
), | |
None, | |
[self.submit_btn, self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: (Button(visible=True), Button(visible=False)), | |
None, | |
[self.submit_btn, self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
else: | |
for event_trigger in event_triggers: | |
event_trigger( | |
lambda: Button(visible=True), | |
None, | |
[self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: Button(visible=False), | |
None, | |
[self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
self.stop_btn.click( | |
None, | |
None, | |
None, | |
cancels=event_to_cancel, | |
api_name=False, | |
) | |
else: | |
if self.submit_btn: | |
for event_trigger in event_triggers: | |
event_trigger( | |
lambda: Button(interactive=False), | |
None, | |
[self.submit_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: Button(interactive=True), | |
None, | |
[self.submit_btn], | |
api_name=False, | |
queue=False, | |
) | |
# upon clear, cancel the submit event as well | |
if self.clear_btn: | |
self.clear_btn.click( | |
lambda: ([], [], None, Button(interactive=True)), | |
None, | |
[self.chatbot, self.chatbot_state, self.saved_input, self.submit_btn], | |
queue=False, | |
api_name=False, | |
cancels=event_to_cancel, | |
) | |
# TODO: reconfigure clear button as stop and clear button | |
def _setup_events(self) -> None: | |
from gradio.components import State | |
has_on = False | |
try: | |
from gradio.events import Dependency, EventListenerMethod, on | |
has_on = True | |
except ImportError as ie: | |
has_on = False | |
submit_fn = self._stream_fn if self.is_generator else self._submit_fn | |
def update_time(c_time, chatbot_state): | |
# if chatbot_state is empty, register a new conversaion with the current timestamp | |
# assert len(chatbot_state) > 0, f'empty chatbot state' | |
if len(chatbot_state) <= 1: | |
return gr.Number(value=time.time(), label='current_time', visible=False), chatbot_state | |
# elif len(chatbot_state) == 1: | |
# # assert chatbot_state[-1][-1] is None, f'invalid [[message, None]] , got {chatbot_state}' | |
# return gr.Number(value=time.time(), label='current_time', visible=False), chatbot_state | |
else: | |
return c_time, chatbot_state | |
if has_on: | |
# new version | |
submit_triggers = ( | |
[self.textbox.submit, self.submit_btn.click] | |
if self.submit_btn | |
else [self.textbox.submit] | |
) | |
submit_event = ( | |
on( | |
submit_triggers, | |
self._clear_and_save_textbox, | |
[self.textbox], | |
[self.textbox, self.saved_input], | |
api_name=False, | |
queue=False, | |
) | |
.then( | |
self._display_input, | |
[self.saved_input, self.chatbot_state], | |
[self.chatbot, self.chatbot_state], | |
api_name=False, | |
queue=False, | |
) | |
.then( | |
update_time, | |
[self.additional_inputs[-1], self.chatbot_state], | |
[self.additional_inputs[-1], self.chatbot_state], | |
api_name=False, | |
queue=False, | |
) | |
.then( | |
submit_fn, | |
[self.saved_input, self.chatbot_state] + self.additional_inputs, | |
[self.chatbot, self.chatbot_state], | |
api_name=False, | |
) | |
) | |
self._setup_stop_events(submit_triggers, submit_event) | |
else: | |
raise ValueError(f'Better install new gradio version than 3.44.0') | |
if self.retry_btn: | |
retry_event = ( | |
self.retry_btn.click( | |
self._delete_prev_fn, | |
[self.chatbot_state], | |
[self.chatbot, self.saved_input, self.chatbot_state], | |
api_name=False, | |
queue=False, | |
) | |
.then( | |
self._display_input, | |
[self.saved_input, self.chatbot_state], | |
[self.chatbot, self.chatbot_state], | |
api_name=False, | |
queue=False, | |
) | |
.then( | |
submit_fn, | |
[self.saved_input, self.chatbot_state] + self.additional_inputs, | |
[self.chatbot, self.chatbot_state], | |
api_name=False, | |
) | |
) | |
self._setup_stop_events([self.retry_btn.click], retry_event) | |
if self.undo_btn: | |
self.undo_btn.click( | |
self._delete_prev_fn, | |
[self.chatbot_state], | |
[self.chatbot, self.saved_input, self.chatbot_state], | |
api_name=False, | |
queue=False, | |
).then( | |
lambda x: x, | |
[self.saved_input], | |
[self.textbox], | |
api_name=False, | |
queue=False, | |
) | |
# Reconfigure clear_btn to stop and clear text box | |
def _display_input( | |
self, message: str, history: List[List[Union[str, None]]] | |
) -> Tuple[List[List[Union[str, None]]], List[List[list[Union[str, None]]]]]: | |
if message is not None and message.strip() != "": | |
history.append([message, None]) | |
return history, history | |
async def _stream_fn( | |
self, | |
message: str, | |
history_with_input, | |
request: Request, | |
*args, | |
) -> AsyncGenerator: | |
history = history_with_input[:-1] | |
inputs, _, _ = special_args( | |
self.fn, inputs=[message, history, *args], request=request | |
) | |
if self.is_async: | |
generator = self.fn(*inputs) | |
else: | |
generator = await anyio.to_thread.run_sync( | |
self.fn, *inputs, limiter=self.limiter | |
) | |
generator = SyncToAsyncIterator(generator, self.limiter) | |
try: | |
first_response = await async_iteration(generator) | |
update = history + [[message, first_response]] | |
yield update, update | |
except StopIteration: | |
update = history + [[message, None]] | |
yield update, update | |
except Exception as e: | |
yield history, history | |
raise e | |
try: | |
async for response in generator: | |
update = history + [[message, response]] | |
yield update, update | |
except Exception as e: | |
# if "invalid" in str(e): | |
# yield history, history | |
# raise e | |
# else: | |
# raise e | |
yield history, history | |
raise e | |
# replace | |
gr.ChatInterface._setup_stop_events = _setup_stop_events | |
gr.ChatInterface._setup_events = _setup_events | |
gr.ChatInterface._display_input = _display_input | |
gr.ChatInterface._stream_fn = _stream_fn | |
class CustomTabbedInterface(gr.Blocks): | |
def __init__( | |
self, | |
interface_list: list[gr.Interface], | |
tab_names: Optional[list[str]] = None, | |
title: Optional[str] = None, | |
description: Optional[str] = None, | |
theme: Optional[gr.Theme] = None, | |
analytics_enabled: Optional[bool] = None, | |
css: Optional[str] = None, | |
): | |
""" | |
Parameters: | |
interface_list: a list of interfaces to be rendered in tabs. | |
tab_names: a list of tab names. If None, the tab names will be "Tab 1", "Tab 2", etc. | |
title: a title for the interface; if provided, appears above the input and output components in large font. Also used as the tab title when opened in a browser window. | |
analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True. | |
css: custom css or path to custom css file to apply to entire Blocks | |
Returns: | |
a Gradio Tabbed Interface for the given interfaces | |
""" | |
super().__init__( | |
title=title or "Gradio", | |
theme=theme, | |
analytics_enabled=analytics_enabled, | |
mode="tabbed_interface", | |
css=css, | |
) | |
self.description = description | |
if tab_names is None: | |
tab_names = [f"Tab {i}" for i in range(len(interface_list))] | |
with self: | |
if title: | |
gr.Markdown( | |
f"<h1 style='text-align: center; margin-bottom: 1rem'>{title}</h1>" | |
) | |
if description: | |
gr.Markdown(description) | |
with gr.Tabs(): | |
for interface, tab_name in zip(interface_list, tab_names): | |
with gr.Tab(label=tab_name): | |
interface.render() | |
def vllm_abort(self): | |
sh = self.llm_engine.scheduler | |
for g in (sh.waiting + sh.running + sh.swapped): | |
sh.abort_seq_group(g.request_id) | |
from vllm.sequence import SequenceStatus | |
scheduler = self.llm_engine.scheduler | |
for state_queue in [scheduler.waiting, scheduler.running, scheduler.swapped]: | |
for seq_group in state_queue: | |
# if seq_group.request_id == request_id: | |
# Remove the sequence group from the state queue. | |
state_queue.remove(seq_group) | |
for seq in seq_group.seqs: | |
if seq.is_finished(): | |
continue | |
scheduler.free_seq(seq, SequenceStatus.FINISHED_ABORTED) | |
def _vllm_run_engine(self: Any, use_tqdm: bool = False) -> Dict[str, Any]: | |
from vllm.outputs import RequestOutput | |
# Initialize tqdm. | |
if use_tqdm: | |
num_requests = self.llm_engine.get_num_unfinished_requests() | |
pbar = tqdm(total=num_requests, desc="Processed prompts") | |
# Run the engine. | |
outputs: Dict[str, RequestOutput] = {} | |
while self.llm_engine.has_unfinished_requests(): | |
step_outputs = self.llm_engine.step() | |
for output in step_outputs: | |
outputs[output.request_id] = output | |
if len(outputs) > 0: | |
yield outputs | |
def vllm_generate_stream( | |
self: Any, | |
prompts: Optional[Union[str, List[str]]] = None, | |
sampling_params: Optional[Any] = None, | |
prompt_token_ids: Optional[List[List[int]]] = None, | |
use_tqdm: bool = False, | |
) -> Dict[str, Any]: | |
"""Generates the completions for the input prompts. | |
NOTE: This class automatically batches the given prompts, considering | |
the memory constraint. For the best performance, put all of your prompts | |
into a single list and pass it to this method. | |
Args: | |
prompts: A list of prompts to generate completions for. | |
sampling_params: The sampling parameters for text generation. If | |
None, we use the default sampling parameters. | |
prompt_token_ids: A list of token IDs for the prompts. If None, we | |
use the tokenizer to convert the prompts to token IDs. | |
use_tqdm: Whether to use tqdm to display the progress bar. | |
Returns: | |
A list of `RequestOutput` objects containing the generated | |
completions in the same order as the input prompts. | |
""" | |
from vllm import LLM, SamplingParams | |
if prompts is None and prompt_token_ids is None: | |
raise ValueError("Either prompts or prompt_token_ids must be " | |
"provided.") | |
if isinstance(prompts, str): | |
# Convert a single prompt to a list. | |
prompts = [prompts] | |
if prompts is not None and prompt_token_ids is not None: | |
if len(prompts) != len(prompt_token_ids): | |
raise ValueError("The lengths of prompts and prompt_token_ids " | |
"must be the same.") | |
if sampling_params is None: | |
# Use default sampling params. | |
sampling_params = SamplingParams() | |
# Add requests to the engine. | |
if prompts is not None: | |
num_requests = len(prompts) | |
else: | |
num_requests = len(prompt_token_ids) | |
for i in range(num_requests): | |
prompt = prompts[i] if prompts is not None else None | |
if prompt_token_ids is None: | |
token_ids = None | |
else: | |
token_ids = prompt_token_ids[i] | |
self._add_request(prompt, sampling_params, token_ids) | |
# return self._run_engine(use_tqdm) | |
yield from _vllm_run_engine(self, use_tqdm) | |
# ! avoid saying | |
# LANG_BLOCK_MESSAGE = """Sorry, the language you have asked is currently not supported. If you have questions in other supported languages, I'll be glad to help. \ | |
# Please also consider clearing the chat box for a better experience.""" | |
# KEYWORD_BLOCK_MESSAGE = "Sorry, I cannot fulfill your request. If you have any unrelated question, I'll be glad to help." | |
LANG_BLOCK_MESSAGE = """Unsupported language.""" | |
KEYWORD_BLOCK_MESSAGE = "Invalid request." | |
def _detect_lang(text): | |
# Disable language that may have safety risk | |
from langdetect import detect as detect_lang | |
dlang = None | |
try: | |
dlang = detect_lang(text) | |
except Exception as e: | |
if "No features in text." in str(e): | |
return "en" | |
else: | |
return "zh" | |
return dlang | |
def block_lang( | |
message: str, | |
history: List[Tuple[str, str]] = None, | |
) -> str: | |
# relieve history base block | |
if len(BLOCK_LANGS) == 0: | |
return False | |
if LANG_BLOCK_HISTORY and history is not None and any((LANG_BLOCK_MESSAGE in x[1].strip()) for x in history): | |
return True | |
else: | |
_lang = _detect_lang(message) | |
if _lang in BLOCK_LANGS: | |
print(f'Detect blocked {_lang}: {message}') | |
return True | |
else: | |
return False | |
def safety_check(text, history=None, ) -> Optional[str]: | |
""" | |
Despite our effort in safety tuning and red teaming, our models may still generate harmful or illegal content. | |
This provides an additional security measure to enhance safety and compliance with local regulations. | |
""" | |
if len(KEYWORDS) > 0 and any(x in text.lower() for x in KEYWORDS): | |
return KEYWORD_BLOCK_MESSAGE | |
if len(BLOCK_LANGS) > 0: | |
if block_lang(text, history): | |
return LANG_BLOCK_MESSAGE | |
return None | |
TURN_TEMPLATE = "<|im_start|>{role}\n{content}</s>" | |
TURN_PREFIX = "<|im_start|>{role}\n" | |
def chatml_chat_convo_format(conversations, add_assistant_prefix: bool, default_system=SYSTEM_PROMPT_1): | |
if conversations[0]['role'] != 'system': | |
conversations = [{"role": "system", "content": default_system}] + conversations | |
text = '' | |
for turn_id, turn in enumerate(conversations): | |
prompt = TURN_TEMPLATE.format(role=turn['role'], content=turn['content']) | |
text += prompt | |
if add_assistant_prefix: | |
prompt = TURN_PREFIX.format(role='assistant') | |
text += prompt | |
return text | |
def chatml_format(message, history=None, system_prompt=None): | |
conversations = [] | |
system_prompt = system_prompt or "You are a helpful assistant." | |
if history is not None and len(history) > 0: | |
for i, (prompt, res) in enumerate(history): | |
conversations.append({"role": "user", "content": prompt.strip()}) | |
conversations.append({"role": "assistant", "content": res.strip()}) | |
conversations.append({"role": "user", "content": message.strip()}) | |
return chatml_chat_convo_format(conversations, True, default_system=system_prompt) | |
def debug_chat_response_stream_multiturn(message, history): | |
message_safety = safety_check(message, history=history) | |
if message_safety is not None: | |
# yield message_safety | |
raise gr.Error(message_safety) | |
message = "This is a debugging message" | |
for i in range(len(message)): | |
time.sleep(0.05) | |
yield message[:i] | |
def chat_response_stream_multiturn( | |
message: str, | |
history: List[Tuple[str, str]], | |
temperature: float, | |
max_tokens: int, | |
frequency_penalty: float, | |
presence_penalty: float, | |
current_time: Optional[float] = None, | |
system_prompt: Optional[str] = SYSTEM_PROMPT_1, | |
profile: Optional[gr.OAuthProfile] = None, | |
) -> str: | |
global LOG_FILE, LOG_PATH | |
if DEBUG: | |
yield from debug_chat_response_stream_multiturn(message, history) | |
return | |
from vllm import LLM, SamplingParams | |
"""Build multi turn | |
message is incoming prompt | |
history don't have the current messauge | |
""" | |
global llm, RES_PRINTED | |
assert llm is not None | |
assert system_prompt.strip() != '', f'system prompt is empty' | |
is_by_pass = False if profile is None else profile.username in BYPASS_USERS | |
tokenizer = llm.get_tokenizer() | |
# force removing all | |
vllm_abort(llm) | |
temperature = float(temperature) | |
frequency_penalty = float(frequency_penalty) | |
max_tokens = int(max_tokens) | |
message = message.strip() | |
if GET_LOG_CMD != "" and message.strip() == GET_LOG_CMD: | |
print_log_file() | |
yield "Finish printed log. Please clear the chatbox now." | |
return | |
if len(message) == 0: | |
raise gr.Error("The message cannot be empty!") | |
message_safety = safety_check(message, history=history) | |
if message_safety is not None and not is_by_pass: | |
# yield message_safety | |
raise gr.Error(message_safety) | |
# history will be appended with message later on | |
full_prompt = chatml_format(message.strip(), history=history, system_prompt=system_prompt) | |
if len(tokenizer.encode(full_prompt)) >= 4050: | |
raise gr.Error(f"Conversation or prompt is too long, please clear the chatbox or try shorter input.") | |
sampling_params = SamplingParams( | |
temperature=temperature, | |
max_tokens=max_tokens, | |
frequency_penalty=frequency_penalty, | |
presence_penalty=presence_penalty, | |
# stop=['<s>', '</s>', '<<SYS>>', '<</SYS>>', '[INST]', '[/INST]'], | |
stop=['<s>', '</s>', '<|im_start|>', '<|im_end|>'], | |
) | |
cur_out = None | |
for j, gen in enumerate(vllm_generate_stream(llm, full_prompt, sampling_params)): | |
if cur_out is not None and (STREAM_YIELD_MULTIPLE < 1 or j % STREAM_YIELD_MULTIPLE == 0) and j > 0: | |
# cur_out = cur_out.replace("\\n", "\n") | |
# optionally check safety, and respond | |
if STREAM_CHECK_MULTIPLE > 0 and j % STREAM_CHECK_MULTIPLE == 0: | |
message_safety = safety_check(cur_out, history=None) | |
if message_safety is not None and not is_by_pass: | |
yield message_safety | |
return | |
yield cur_out | |
assert len(gen) == 1, f'{gen}' | |
item = next(iter(gen.values())) | |
cur_out = item.outputs[0].text | |
#cur_out = "Our system is under maintenance, will be back soon!" | |
if j >= max_tokens - 2: | |
gr.Warning(f'The response hits limit of {max_tokens} tokens. Consider increase the max tokens parameter in the Additional Inputs.') | |
# TODO: use current_time to register conversations, accoriding history and cur_out | |
history_str = format_conversation(history + [[message, cur_out]]) | |
print(f'@@@@@@@@@@\n{history_str}\n##########\n') | |
maybe_log_conv_file(current_time, history, message, cur_out, temperature=temperature, frequency_penalty=frequency_penalty) | |
if cur_out is not None and "\\n" in cur_out: | |
print(f'double slash-n in cur_out:\n{cur_out}') | |
cur_out = cur_out.replace("\\n", "\n") | |
if cur_out is not None: | |
yield cur_out | |
message_safety = safety_check(cur_out, history=None) | |
if message_safety is not None and not is_by_pass: | |
yield message_safety | |
return | |
def debug_generate_free_form_stream(message): | |
output = " This is a debugging message...." | |
for i in range(len(output)): | |
time.sleep(0.05) | |
yield message + output[:i] | |
def generate_free_form_stream( | |
message: str, | |
temperature: float, | |
max_tokens: int, | |
frequency_penalty: float, | |
presence_penalty: float, | |
current_time: Optional[float] = None, | |
stop_strings: str = '<s>,</s>,<|im_start|>,<|im_end|>', | |
) -> str: | |
global LOG_FILE, LOG_PATH | |
if DEBUG: | |
yield from debug_generate_free_form_stream(message) | |
return | |
from vllm import LLM, SamplingParams | |
"""Build multi turn | |
""" | |
global llm, RES_PRINTED | |
assert llm is not None | |
tokenizer = llm.get_tokenizer() | |
# force removing all | |
vllm_abort(llm) | |
temperature = float(temperature) | |
frequency_penalty = float(frequency_penalty) | |
max_tokens = int(max_tokens) | |
stop_strings = [x.strip() for x in stop_strings.strip().split(",")] | |
stop_strings = list(set(stop_strings + ['</s>', '<|im_start|>'])) | |
sampling_params = SamplingParams( | |
temperature=temperature, | |
max_tokens=max_tokens, | |
frequency_penalty=frequency_penalty, | |
presence_penalty=presence_penalty, | |
stop=stop_strings, | |
# ignore_eos=True, | |
) | |
# full_prompt = message | |
if len(message) == 0: | |
raise gr.Error("The message cannot be empty!") | |
message_safety = safety_check(message) | |
if message_safety is not None: | |
raise gr.Error(message_safety) | |
if len(tokenizer.encode(message)) >= 4050: | |
raise gr.Error(f"Prompt is too long!") | |
cur_out = None | |
for j, gen in enumerate(vllm_generate_stream(llm, message, sampling_params)): | |
if cur_out is not None and (STREAM_YIELD_MULTIPLE < 1 or j % STREAM_YIELD_MULTIPLE == 0) and j > 0: | |
# optionally check safety, and respond | |
if STREAM_CHECK_MULTIPLE > 0 and j % STREAM_CHECK_MULTIPLE == 0: | |
message_safety = safety_check(cur_out, history=None) | |
if message_safety is not None: | |
raise gr.Error(message_safety) | |
yield message + cur_out | |
assert len(gen) == 1, f'{gen}' | |
item = next(iter(gen.values())) | |
cur_out = item.outputs[0].text | |
#cur_out = "Our system is under maintenance, will be back soon!" | |
if j >= max_tokens - 2: | |
gr.Warning(f'The response hits limit of {max_tokens} tokens. Consider increase the max tokens parameter in the Additional Inputs.') | |
if cur_out is not None: | |
yield message + cur_out | |
message_safety = safety_check(message + cur_out, history=None) | |
if message_safety is not None: | |
raise gr.Error(message_safety) | |
def maybe_log_conv_file(current_time, history, message, response, **kwargs): | |
global LOG_FILE | |
if LOG_FILE is not None: | |
my_history = history + [[message, response]] | |
obj = { | |
'key': str(current_time), | |
'history': my_history | |
} | |
for k, v in kwargs.items(): | |
obj[k] = v | |
log_ = json.dumps(obj, ensure_ascii=False) | |
LOG_FILE.write(log_ + "\n") | |
LOG_FILE.flush() | |
print(f'Wrote {obj["key"]} to {LOG_PATH}') | |
def format_conversation(history): | |
_str = '\n'.join([ | |
( | |
f'<<<User>>> {h[0]}\n' | |
f'<<<Asst>>> {h[1]}' | |
) | |
for h in history | |
]) | |
return _str | |
def aggregate_convos(): | |
from datetime import datetime | |
global LOG_FILE, DATA_SET_REPO_PATH, SAVE_LOGS | |
assert os.path.exists(LOG_PATH), f'{LOG_PATH} not found' | |
convos = None | |
irregular_count = 1 | |
with open(LOG_PATH, 'r', encoding='utf-8') as f: | |
convos = {} | |
for i, l in enumerate(f): | |
if l: | |
item = json.loads(l) | |
key = item['key'] | |
try: | |
key = float(key) | |
except Exception as e: | |
key = -1 | |
if key > 0.0: | |
item_key = datetime.fromtimestamp(key).strftime("%Y-%m-%d %H:%M:%S") | |
else: | |
key = item_key = f'e{irregular_count}' | |
irregular_count += 1 | |
item['key'] = item_key | |
convos[key] = item | |
return convos | |
def maybe_upload_to_dataset(): | |
from datetime import datetime | |
global LOG_FILE, DATA_SET_REPO_PATH, SAVE_LOGS | |
if SAVE_LOGS and os.path.exists(LOG_PATH) and DATA_SET_REPO_PATH != "": | |
convos = aggregate_convos() | |
AGG_LOG_PATH = LOG_PATH + ".agg.json" | |
with open(AGG_LOG_PATH, 'w', encoding='utf-8') as fo: | |
json.dump(convos, fo, indent=4, ensure_ascii=False) | |
print(f'Saved aggregated json to {AGG_LOG_PATH}') | |
try: | |
from huggingface_hub import upload_file | |
print(f'upload {AGG_LOG_PATH} to {DATA_SET_REPO_PATH}') | |
upload_file( | |
path_or_fileobj=AGG_LOG_PATH, | |
path_in_repo=os.path.basename(AGG_LOG_PATH), | |
repo_id=DATA_SET_REPO_PATH, | |
token=HF_TOKEN, | |
repo_type="dataset", | |
create_pr=True | |
) | |
except Exception as e: | |
print(f'Failed to save to repo: {DATA_SET_REPO_PATH}|{str(e)}') | |
def print_log_file(): | |
global LOG_FILE, LOG_PATH | |
if SAVE_LOGS and os.path.exists(LOG_PATH): | |
with open(LOG_PATH, 'r', encoding='utf-8') as f: | |
convos = aggregate_convos() | |
print(f'Printing log from {LOG_PATH}') | |
items = list(convos.items()) | |
for k, v in items[-10:]: | |
history = v.pop('history') | |
print(f'######--{v}--#####') | |
_str = format_conversation(history) | |
print(_str) | |
maybe_upload_to_dataset() | |
def debug_chat_response_echo( | |
message: str, | |
history: List[Tuple[str, str]], | |
temperature: float = 0.0, | |
max_tokens: int = 4096, | |
frequency_penalty: float = 0.4, | |
presence_penalty: float = 0.0, | |
current_time: Optional[float] = None, | |
system_prompt: str = SYSTEM_PROMPT_1, | |
) -> str: | |
global LOG_FILE | |
import time | |
time.sleep(0.5) | |
if message.strip() == GET_LOG_CMD: | |
print_log_file() | |
yield "Finish printed log." | |
return | |
for i in range(len(message)): | |
yield f"repeat: {current_time} {message[:i + 1]}" | |
cur_out = f"repeat: {current_time} {message}" | |
maybe_log_conv_file(current_time, history, message, cur_out, temperature=temperature, frequency_penalty=frequency_penalty) | |
def check_model_path(model_path) -> str: | |
assert os.path.exists(model_path), f'{model_path} not found' | |
ckpt_info = "None" | |
if os.path.isdir(model_path): | |
if os.path.exists(f'{model_path}/info.txt'): | |
with open(f'{model_path}/info.txt', 'r') as f: | |
ckpt_info = f.read() | |
print(f'Checkpoint info:\n{ckpt_info}\n-----') | |
else: | |
print(f'info.txt not found in {model_path}') | |
print(f'model path dir: {list(os.listdir(model_path))}') | |
return ckpt_info | |
def maybe_delete_folder(): | |
if IS_DELETE_FOLDER and DOWNLOAD_SNAPSHOT: | |
import shutil | |
print(f'DELETE ALL FILES IN {DELETE_FOLDER}') | |
for filename in os.listdir(DELETE_FOLDER): | |
file_path = os.path.join(DELETE_FOLDER, filename) | |
try: | |
if os.path.isfile(file_path) or os.path.islink(file_path): | |
os.unlink(file_path) | |
elif os.path.isdir(file_path): | |
shutil.rmtree(file_path) | |
except Exception as e: | |
print('Failed to delete %s. Reason: %s' % (file_path, e)) | |
AGREE_POP_SCRIPTS = """ | |
async () => { | |
alert("To use our service, you are required to agree to the following terms:\\nYou must not use our service to generate any harmful, unethical or illegal content that violates local and international laws, including but not limited to hate speech, violence and deception.\\nThe service may collect user dialogue data for performance improvement, and reserves the right to distribute it under CC-BY or similar license. So do not enter any personal information!"); | |
} | |
""" | |
def debug_file_function( | |
files: Union[str, List[str]], | |
prompt_mode: str, | |
temperature: float, | |
max_tokens: int, | |
frequency_penalty: float, | |
presence_penalty: float, | |
stop_strings: str = "[STOP],<s>,</s>", | |
current_time: Optional[float] = None, | |
): | |
"""This is only for debug purpose""" | |
files = files if isinstance(files, list) else [files] | |
print(files) | |
filenames = [f.name for f in files] | |
all_items = [] | |
for fname in filenames: | |
print(f'Reading {fname}') | |
with open(fname, 'r', encoding='utf-8') as f: | |
items = json.load(f) | |
assert isinstance(items, list), f'invalid items from {fname} not list' | |
all_items.extend(items) | |
print(all_items) | |
print(f'{prompt_mode} / {temperature} / {max_tokens}, {frequency_penalty}, {presence_penalty}') | |
save_path = "./test.json" | |
with open(save_path, 'w', encoding='utf-8') as f: | |
json.dump(all_items, f, indent=4, ensure_ascii=False) | |
for x in all_items: | |
x['response'] = "Return response" | |
print_items = all_items[:1] | |
# print_json = json.dumps(print_items, indent=4, ensure_ascii=False) | |
return save_path, print_items | |
def validate_file_item(filename, index, item: Dict[str, str]): | |
""" | |
check safety for items in files | |
""" | |
message = item['prompt'].strip() | |
if len(message) == 0: | |
raise gr.Error(f'Prompt {index} empty') | |
message_safety = safety_check(message, history=None) | |
if message_safety is not None: | |
raise gr.Error(f'Prompt {index} invalid: {message_safety}') | |
tokenizer = llm.get_tokenizer() if llm is not None else None | |
if tokenizer is None or len(tokenizer.encode(message)) >= BATCH_INFER_MAX_PROMPT_TOKENS: | |
raise gr.Error(f"Prompt {index} too long, should be less than {BATCH_INFER_MAX_PROMPT_TOKENS} tokens") | |
def read_validate_json_files(files: Union[str, List[str]]): | |
files = files if isinstance(files, list) else [files] | |
filenames = [f.name for f in files] | |
all_items = [] | |
for fname in filenames: | |
# check each files | |
print(f'Reading {fname}') | |
with open(fname, 'r', encoding='utf-8') as f: | |
items = json.load(f) | |
assert isinstance(items, list), f'Data {fname} not list' | |
assert all(isinstance(x, dict) for x in items), f'item in input file not list' | |
assert all("prompt" in x for x in items), f'key prompt should be in dict item of input file' | |
for i, x in enumerate(items): | |
validate_file_item(fname, i, x) | |
all_items.extend(items) | |
if len(all_items) > BATCH_INFER_MAX_ITEMS: | |
raise gr.Error(f"Num samples {len(all_items)} > {BATCH_INFER_MAX_ITEMS} allowed.") | |
return all_items, filenames | |
def remove_gradio_cache(exclude_names=None): | |
"""remove gradio cache to avoid flooding""" | |
import shutil | |
for root, dirs, files in os.walk('/tmp/gradio/'): | |
for f in files: | |
# if not any(f in ef for ef in except_files): | |
if exclude_names is None or not any(ef in f for ef in exclude_names): | |
print(f'Remove: {f}') | |
os.unlink(os.path.join(root, f)) | |
# for d in dirs: | |
# # if not any(d in ef for ef in except_files): | |
# if exclude_names is None or not any(ef in d for ef in exclude_names): | |
# print(f'Remove d: {d}') | |
# shutil.rmtree(os.path.join(root, d)) | |
def maybe_upload_batch_set(pred_json_path): | |
global LOG_FILE, DATA_SET_REPO_PATH, SAVE_LOGS | |
if SAVE_LOGS and DATA_SET_REPO_PATH != "": | |
try: | |
from huggingface_hub import upload_file | |
path_in_repo = "misc/" + os.path.basename(pred_json_path).replace(".json", f'.{time.time()}.json') | |
print(f'upload {pred_json_path} to {DATA_SET_REPO_PATH}//{path_in_repo}') | |
upload_file( | |
path_or_fileobj=pred_json_path, | |
path_in_repo=path_in_repo, | |
repo_id=DATA_SET_REPO_PATH, | |
token=HF_TOKEN, | |
repo_type="dataset", | |
create_pr=True | |
) | |
except Exception as e: | |
print(f'Failed to save to repo: {DATA_SET_REPO_PATH}|{str(e)}') | |
def free_form_prompt(prompt, history=None, system_prompt=None): | |
return prompt | |
def batch_inference( | |
files: Union[str, List[str]], | |
prompt_mode: str, | |
temperature: float, | |
max_tokens: int, | |
frequency_penalty: float, | |
presence_penalty: float, | |
stop_strings: str = "[STOP],<s>,</s>,<|im_start|>", | |
current_time: Optional[float] = None, | |
system_prompt: Optional[str] = SYSTEM_PROMPT_1 | |
): | |
""" | |
Handle file upload batch inference | |
""" | |
global LOG_FILE, LOG_PATH, DEBUG, llm, RES_PRINTED | |
if DEBUG: | |
return debug_file_function( | |
files, prompt_mode, temperature, max_tokens, | |
presence_penalty, stop_strings, current_time) | |
from vllm import LLM, SamplingParams | |
assert llm is not None | |
# assert system_prompt.strip() != '', f'system prompt is empty' | |
stop_strings = [x.strip() for x in stop_strings.strip().split(",")] | |
tokenizer = llm.get_tokenizer() | |
# force removing all | |
# NOTE: need to make sure all cached items are removed!!!!!!!!! | |
vllm_abort(llm) | |
temperature = float(temperature) | |
frequency_penalty = float(frequency_penalty) | |
max_tokens = int(max_tokens) | |
all_items, filenames = read_validate_json_files(files) | |
# remove all items in /tmp/gradio/ | |
remove_gradio_cache(exclude_names=['upload_chat.json', 'upload_few_shot.json']) | |
if prompt_mode == 'chat': | |
prompt_format_fn = chatml_format | |
elif prompt_mode == 'few-shot': | |
from functools import partial | |
# prompt_format_fn = partial( | |
# chatml_format, include_end_instruct=False | |
# ) | |
prompt_format_fn = free_form_prompt | |
else: | |
raise gr.Error(f'Wrong mode {prompt_mode}') | |
full_prompts = [ | |
prompt_format_fn( | |
x['prompt'], [], sys_prompt=system_prompt | |
) | |
for i, x in enumerate(all_items) | |
] | |
print(f'{full_prompts[0]}\n') | |
if any(len(tokenizer.encode(x)) >= 4090 for x in full_prompts): | |
raise gr.Error(f"Some prompt is too long!") | |
stop_seq = list(set(['<s>', '</s>', '<<SYS>>', '<</SYS>>', '[INST]', '[/INST]'] + stop_strings)) | |
sampling_params = SamplingParams( | |
temperature=temperature, | |
max_tokens=max_tokens, | |
frequency_penalty=frequency_penalty, | |
presence_penalty=presence_penalty, | |
stop=stop_seq | |
) | |
generated = llm.generate(full_prompts, sampling_params, use_tqdm=False) | |
responses = [g.outputs[0].text for g in generated] | |
#responses = ["Our system is under maintenance, will be back soon!" for g in generated] | |
if len(responses) != len(all_items): | |
raise gr.Error(f'inconsistent lengths {len(responses)} != {len(all_items)}') | |
for res, item in zip(responses, all_items): | |
item['response'] = res | |
save_path = BATCH_INFER_SAVE_TMP_FILE | |
os.makedirs(os.path.dirname(save_path), exist_ok=True) | |
with open(save_path, 'w', encoding='utf-8') as f: | |
json.dump(all_items, f, indent=4, ensure_ascii=False) | |
# You need to upload save_path as a new timestamp file. | |
maybe_upload_batch_set(save_path) | |
print_items = all_items[:2] | |
# print_json = json.dumps(print_items, indent=4, ensure_ascii=False) | |
return save_path, print_items | |
# BATCH_INFER_MAX_ITEMS | |
FILE_UPLOAD_DESCRIPTION = f"""Upload JSON file as list of dict with < {BATCH_INFER_MAX_ITEMS} items, \ | |
each item has `prompt` key. We put guardrails to enhance safety, so do not input any harmful content or personal information! Re-upload the file after every submit. See the examples below. | |
``` | |
[ {{"id": 0, "prompt": "Hello world"}} , {{"id": 1, "prompt": "Hi there?"}}] | |
``` | |
""" | |
CHAT_EXAMPLES = [ | |
["Hãy giải thích thuyết tương đối rộng."], | |
["Tolong bantu saya menulis email ke lembaga pemerintah untuk mencari dukungan finansial untuk penelitian AI."], | |
["แนะนำ 10 จุดหมายปลายทางในกรุงเทพฯ"], | |
] | |
# performance items | |
def create_free_form_generation_demo(): | |
global short_model_path | |
max_tokens = MAX_TOKENS | |
temperature = TEMPERATURE | |
frequence_penalty = FREQUENCE_PENALTY | |
presence_penalty = PRESENCE_PENALTY | |
introduction = """ | |
### Free-form | Put any context string (like few-shot prompts) | |
""" | |
with gr.Blocks() as demo_free_form: | |
gr.Markdown(introduction) | |
with gr.Row(): | |
txt = gr.Textbox( | |
scale=4, | |
lines=16, | |
show_label=False, | |
placeholder="Enter any free form text and submit", | |
container=False, | |
) | |
with gr.Row(): | |
free_submit_button = gr.Button('Submit') | |
with gr.Row(): | |
temp = gr.Number(value=temperature, label='Temperature', info="Higher -> more random") | |
length = gr.Number(value=max_tokens, label='Max tokens', info='Increase if want more generation') | |
freq_pen = gr.Number(value=frequence_penalty, label='Frequency penalty', info='> 0 encourage new tokens over repeated tokens') | |
pres_pen = gr.Number(value=presence_penalty, label='Presence penalty', info='> 0 encourage new tokens, < 0 encourage existing tokens') | |
stop_strings = gr.Textbox(value="<s>,</s>,<|im_start|>", label='Stop strings', info='Comma-separated string to stop generation only in FEW-SHOT mode', lines=1) | |
free_submit_button.click( | |
generate_free_form_stream, | |
[txt, temp, length, freq_pen, pres_pen, stop_strings], | |
txt | |
) | |
return demo_free_form | |
def create_file_upload_demo(): | |
temperature = TEMPERATURE | |
frequence_penalty = FREQUENCE_PENALTY | |
presence_penalty = PRESENCE_PENALTY | |
max_tokens = MAX_TOKENS | |
demo_file_upload = gr.Interface( | |
batch_inference, | |
inputs=[ | |
gr.File(file_count='single', file_types=['json']), | |
gr.Radio(["chat", "few-shot"], value='chat', label="Chat or Few-shot mode", info="Chat's output more user-friendly, Few-shot's output more consistent with few-shot patterns."), | |
gr.Number(value=temperature, label='Temperature', info="Higher -> more random"), | |
gr.Number(value=max_tokens, label='Max tokens', info='Increase if want more generation'), | |
gr.Number(value=frequence_penalty, label='Frequency penalty', info='> 0 encourage new tokens over repeated tokens'), | |
gr.Number(value=presence_penalty, label='Presence penalty', info='> 0 encourage new tokens, < 0 encourage existing tokens'), | |
gr.Textbox(value="<s>,</s>,<|im_start|>", label='Stop strings', info='Comma-separated string to stop generation only in FEW-SHOT mode', lines=1), | |
gr.Number(value=0, label='current_time', visible=False), | |
], | |
outputs=[ | |
# "file", | |
gr.File(label="Generated file"), | |
# "json" | |
gr.JSON(label='Example outputs (display 2 samples)') | |
], | |
description=FILE_UPLOAD_DESCRIPTION, | |
allow_flagging=False, | |
examples=[ | |
["upload_chat.json", "chat", 0.2, 1024, 0.5, 0, "<s>,</s>,<|im_start|>"], | |
["upload_few_shot.json", "few-shot", 0.2, 128, 0.5, 0, "<s>,</s>,<|im_start|>,\\n"] | |
], | |
cache_examples=False, | |
) | |
return demo_file_upload | |
def create_chat_demo(title=None, description=None): | |
sys_prompt = SYSTEM_PROMPT_1 | |
max_tokens = MAX_TOKENS | |
temperature = TEMPERATURE | |
frequence_penalty = FREQUENCE_PENALTY | |
presence_penalty = PRESENCE_PENALTY | |
demo_chat = gr.ChatInterface( | |
chat_response_stream_multiturn, | |
chatbot=ChatBot( | |
label=MODEL_NAME, | |
bubble_full_width=False, | |
latex_delimiters=[ | |
{ "left": "$", "right": "$", "display": False}, | |
{ "left": "$$", "right": "$$", "display": True}, | |
], | |
show_copy_button=True, | |
), | |
textbox=gr.Textbox(placeholder='Type message', lines=4, max_lines=128, min_width=200), | |
submit_btn=gr.Button(value='Submit', variant="primary", scale=0), | |
# ! consider preventing the stop button | |
# stop_btn=None, | |
title=title, | |
description=description, | |
additional_inputs=[ | |
gr.Number(value=temperature, label='Temperature (higher -> more random)'), | |
gr.Number(value=max_tokens, label='Max generated tokens (increase if want more generation)'), | |
gr.Number(value=frequence_penalty, label='Frequency penalty (> 0 encourage new tokens over repeated tokens)'), | |
gr.Number(value=presence_penalty, label='Presence penalty (> 0 encourage new tokens, < 0 encourage existing tokens)'), | |
gr.Number(value=0, label='current_time', visible=False), | |
# ! Remove the system prompt textbox to avoid jailbreaking | |
gr.Textbox(value=sys_prompt, label='System prompt', lines=8, interactive=False) | |
], | |
examples=CHAT_EXAMPLES, | |
cache_examples=False | |
) | |
return demo_chat | |
def launch_demo(): | |
global demo, llm, DEBUG, LOG_FILE | |
model_desc = MODEL_DESC | |
model_path = MODEL_PATH | |
model_title = MODEL_TITLE | |
hf_model_name = HF_MODEL_NAME | |
tensor_parallel = TENSOR_PARALLEL | |
assert tensor_parallel > 0 , f'{tensor_parallel} invalid' | |
dtype = DTYPE | |
sys_prompt = SYSTEM_PROMPT_1 | |
max_tokens = MAX_TOKENS | |
temperature = TEMPERATURE | |
frequence_penalty = FREQUENCE_PENALTY | |
presence_penalty = PRESENCE_PENALTY | |
ckpt_info = "None" | |
print( | |
f'Launch config: ' | |
f'\n| model_title=`{model_title}` ' | |
f'\n| max_tokens={max_tokens} ' | |
f'\n| dtype={dtype} ' | |
f'\n| tensor_parallel={tensor_parallel} ' | |
f'\n| IS_DELETE_FOLDER={IS_DELETE_FOLDER} ' | |
f'\n| STREAM_YIELD_MULTIPLE={STREAM_YIELD_MULTIPLE} ' | |
f'\n| STREAM_CHECK_MULTIPLE={STREAM_CHECK_MULTIPLE} ' | |
f'\n| DISPLAY_MODEL_PATH={DISPLAY_MODEL_PATH} ' | |
f'\n| LANG_BLOCK_HISTORY={LANG_BLOCK_HISTORY} ' | |
f'\n| frequence_penalty={frequence_penalty} ' | |
f'\n| presence_penalty={presence_penalty} ' | |
f'\n| temperature={temperature} ' | |
# f'\n| hf_model_name={hf_model_name} ' | |
f'\n| model_path={model_path} ' | |
f'\n| DOWNLOAD_SNAPSHOT={DOWNLOAD_SNAPSHOT} ' | |
f'\n| gpu_memory_utilization={gpu_memory_utilization} ' | |
f'\n| LOG_PATH={LOG_PATH} | SAVE_LOGS={SAVE_LOGS} ' | |
f'\n| Desc={model_desc}' | |
) | |
if DEBUG: | |
model_desc += "\n<br>!!!!! This is in debug mode, responses will copy original" | |
# response_fn = debug_chat_response_echo | |
response_fn = chat_response_stream_multiturn | |
print(f'Creating in DEBUG MODE') | |
if SAVE_LOGS: | |
LOG_FILE = open(LOG_PATH, 'a', encoding='utf-8') | |
else: | |
# ! load the model | |
maybe_delete_folder() | |
if DOWNLOAD_SNAPSHOT: | |
print(f'Downloading from HF_MODEL_NAME={hf_model_name} -> {model_path}') | |
if HF_TOKEN is not None: | |
print(f'Load with HF_TOKEN: {HF_TOKEN}') | |
snapshot_download(hf_model_name, local_dir=model_path, use_auth_token=True, token=HF_TOKEN) | |
else: | |
snapshot_download(hf_model_name, local_dir=model_path) | |
import vllm | |
from vllm import LLM | |
print(F'VLLM: {vllm.__version__}') | |
ckpt_info = check_model_path(model_path) | |
print(f'Load path: {model_path} | {ckpt_info}') | |
if QUANTIZATION == 'awq': | |
print(F'Load model in int4 quantization') | |
llm = LLM(model=model_path, dtype="float16", tensor_parallel_size=tensor_parallel, gpu_memory_utilization=gpu_memory_utilization, quantization="awq", max_model_len=8192) | |
else: | |
llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel, gpu_memory_utilization=gpu_memory_utilization, max_model_len=8192) | |
try: | |
print(llm.llm_engine.workers[0].model) | |
except Exception as e: | |
print(f'Cannot print model worker: {e}') | |
try: | |
llm.llm_engine.scheduler_config.max_model_len = 8192 | |
llm.llm_engine.scheduler_config.max_num_batched_tokens = 8192 | |
# llm.llm_engine.tokenizer.add_special_tokens = False | |
except Exception as e: | |
print(f'Cannot set parameters: {e}') | |
print(f'Use system prompt:\n{sys_prompt}') | |
response_fn = chat_response_stream_multiturn | |
print(F'respond: {response_fn}') | |
if SAVE_LOGS: | |
LOG_FILE = open(LOG_PATH, 'a', encoding='utf-8') | |
if ENABLE_BATCH_INFER: | |
demo_file_upload = create_file_upload_demo() | |
demo_free_form = create_free_form_generation_demo() | |
demo_chat = create_chat_demo() | |
descriptions = model_desc | |
if DISPLAY_MODEL_PATH: | |
descriptions += f"<br> {path_markdown.format(model_path=model_path)}" | |
demo = CustomTabbedInterface( | |
interface_list=[demo_chat, demo_file_upload, demo_free_form], | |
tab_names=["Chat Interface", "Batch Inference", "Free-form"], | |
title=f"{model_title}", | |
description=descriptions, | |
) | |
else: | |
descriptions = model_desc | |
if DISPLAY_MODEL_PATH: | |
descriptions += f"<br> {path_markdown.format(model_path=model_path)}" | |
demo = create_chat_demo(title=f"{model_title}", description=descriptions) | |
demo.title = MODEL_NAME | |
with demo: | |
if DATA_SET_REPO_PATH != "": | |
try: | |
from performance_plot import attach_plot_to_demo | |
attach_plot_to_demo(demo) | |
except Exception as e: | |
print(f'Fail to load DEMO plot: {str(e)}') | |
gr.Markdown(cite_markdown) | |
if DISPLAY_MODEL_PATH: | |
gr.Markdown(path_markdown.format(model_path=model_path)) | |
if ENABLE_AGREE_POPUP: | |
demo.load(None, None, None, _js=AGREE_POP_SCRIPTS) | |
login_btn = gr.LoginButton() | |
demo.queue(api_open=False) | |
return demo | |
if __name__ == "__main__": | |
demo = launch_demo() | |
demo.launch(show_api=False, allowed_paths=["seal_logo.png"]) |