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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_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] | |
# 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", "0.4")) | |
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", "200")) | |
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. Your name is SeaLLM and 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. | |
""" | |
# ============ CONSTANT ============ | |
# https://github.com/gradio-app/gradio/issues/884 | |
MODEL_NAME = "SeaLLM-13B" | |
MODEL_NAME = str(os.environ.get("MODEL_NAME", "SeaLLM-13B")) | |
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>SeaLLMs - Large Language Models for Southeast Asia</h1> | |
</div> | |
</div> | |
""" | |
# <a href='https://arxiv.org/pdf/2312.00738.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a> | |
# MODEL_DESC = """ | |
# <div style='display:flex; gap: 0.25rem; '> | |
# <a href='https://github.com/SeaLLMs/SeaLLMs'><img src='https://img.shields.io/badge/Github-Code-success'></a> | |
# <a href='https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat-13b'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> | |
# <a href='https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> | |
# </div> | |
# <span style="font-size: larger"> | |
# This is <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b" target="_blank">SeaLLM-13B-Chat</a> - a chatbot assistant optimized for Southeast Asian Languages. It produces helpful responses in English 🇬🇧, Vietnamese 🇻🇳, Indonesian 🇮🇩 and Thai 🇹🇭. | |
# Explore <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b" target="_blank">our article</a> for more details. | |
# </span> | |
# <br> | |
# <span > | |
# NOTE: The chatbot may produce inaccurate and harmful information about people, places, or facts. | |
# <span style="color: red">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">SeaLLM Terms Of Use</a>, which include:</span><br> | |
# <ul> | |
# <li > | |
# You must not use our service to generate any harmful, unethical or illegal content that violates locally applicable and international laws or regulations, | |
# including but not limited to hate speech, violence, pornography and deception.</li> | |
# <li > | |
# The service collects user dialogue data for testing and performance improvement, and reserves the right to distribute it under | |
# <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution (CC-BY)</a> or similar license. So do not enter any personal information! | |
# </li> | |
# </ul> | |
# </span> | |
# """.strip() | |
# <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b" target="_blank">SeaLLM-13B-Chat</a> - a helpful chatbot assistant for Southeast Asian Languages. It supports English 🇬🇧, Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. | |
MODEL_DESC = f""" | |
<div style='display:flex; gap: 0.25rem; '> | |
<a href='https://github.com/SeaLLMs/SeaLLMs'><img src='https://img.shields.io/badge/Github-Code-success'></a> | |
<a href='https://huggingface.co/spaces/SeaLLMs/SeaLLM-Chat-13b'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> | |
<a href='https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b'><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-Chat-13b" target="_blank">{MODEL_NAME}</a> - a helpful assistant for Southeast Asian Languages. It supports English 🇬🇧, Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. | |
Explore <a href="https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b" 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 that violates local and international laws. | |
The service collects user dialogue data for testing and performance improvement, and reserves the right to distribute it 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*, 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} | |
""" | |
def custom_hf_model_weights_iterator( | |
model_name_or_path: str, | |
cache_dir: Optional[str] = None, | |
use_np_cache: bool = False, | |
) -> Iterator[Tuple[str, torch.Tensor]]: | |
# ! if use vllm==0.1.4, use this to augment hf_model_weights_iterator loader | |
from vllm.model_executor.weight_utils import Disabledtqdm | |
# Prepare file lock directory to prevent multiple processes from | |
# downloading the same model weights at the same time. | |
lock_dir = cache_dir if cache_dir is not None else "/tmp" | |
lock_file_name = model_name_or_path.replace("/", "-") + ".lock" | |
lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name)) | |
# Download model weights from huggingface. | |
is_local = os.path.isdir(model_name_or_path) | |
if not is_local: | |
with lock: | |
hf_folder = snapshot_download(model_name_or_path, | |
allow_patterns="*.bin", | |
cache_dir=cache_dir, | |
local_files_only=True, | |
tqdm_class=Disabledtqdm) | |
else: | |
hf_folder = model_name_or_path | |
hf_bin_files = [ | |
x for x in glob.glob(os.path.join(hf_folder, "*model*.bin")) | |
if not x.endswith("training_args.bin") | |
] | |
hf_safetensors_files = [ | |
x for x in glob.glob(os.path.join(hf_folder, "*model*.safetensors")) | |
if not x.endswith("training_args.bin") | |
] | |
if use_np_cache: | |
# Convert the model weights from torch tensors to numpy arrays for | |
# faster loading. | |
np_folder = os.path.join(hf_folder, "np") | |
os.makedirs(np_folder, exist_ok=True) | |
weight_names_file = os.path.join(np_folder, "weight_names.json") | |
with lock: | |
if not os.path.exists(weight_names_file): | |
weight_names = [] | |
for bin_file in hf_bin_files: | |
state = torch.load(bin_file, map_location="cpu") | |
for name, param in state.items(): | |
param_path = os.path.join(np_folder, name) | |
with open(param_path, "wb") as f: | |
np.save(f, param.cpu().detach().numpy()) | |
weight_names.append(name) | |
with open(weight_names_file, "w") as f: | |
json.dump(weight_names, f) | |
with open(weight_names_file, "r") as f: | |
weight_names = json.load(f) | |
for name in weight_names: | |
param_path = os.path.join(np_folder, name) | |
with open(param_path, "rb") as f: | |
param = np.load(f) | |
yield name, torch.from_numpy(param) | |
else: | |
if len(hf_bin_files) > 0: | |
print(F'Load bin files: {hf_bin_files}') | |
for bin_file in hf_bin_files: | |
state = torch.load(bin_file, map_location="cpu") | |
for name, param in state.items(): | |
yield name, param | |
del state | |
torch.cuda.empty_cache() | |
elif len(hf_safetensors_files) > 0: | |
print(F'Load safetensor files: {hf_safetensors_files}') | |
from safetensors.torch import load_file | |
for safe_file in hf_safetensors_files: | |
# state = torch.load(bin_file, map_location="cpu") | |
state = load_file(safe_file) | |
for name, param in state.items(): | |
yield name, param | |
del state | |
torch.cuda.empty_cache() | |
else: | |
raise ValueError(f'no files available either bin or safe') | |
def convert_pyslice_to_tensor(x: Any) -> torch.Tensor: | |
"""convert PySafeSlice object from safetensors to torch.Tensor | |
PySafeSlice object supports indexing, which is done before loading the | |
actual tensor and can reduce the amount of memory being read into the | |
memory. However, it does not support more advanced functionalities | |
like `.view()` or `.t()`. Therefore, if we need to modify the loaded | |
tensor with these more complicated operators, we need to convert to | |
tensor first. | |
""" | |
if not isinstance(x, torch.Tensor): | |
x = x[:] | |
return x | |
def load_padded_tensor_parallel_vocab( | |
param: torch.Tensor, | |
loaded_weight: Any, # `torch.Tensor` or `PySafeSlice` | |
tensor_model_parallel_rank: int, | |
) -> None: | |
shard_size = param.shape[0] | |
start_idx = tensor_model_parallel_rank * shard_size | |
end_idx = (tensor_model_parallel_rank + 1) * shard_size | |
loaded_weight = loaded_weight[start_idx:end_idx] | |
loaded_weight = convert_pyslice_to_tensor(loaded_weight) | |
param[:loaded_weight.shape[0]].copy_(loaded_weight) | |
def llama_load_weights( | |
self, | |
model_name_or_path: str, | |
cache_dir: Optional[str] = None, | |
use_np_cache: bool = False, | |
load_format: str = "auto", | |
revision: Optional[str] = None | |
): | |
# if use vllm==0.1.4 | |
from vllm.model_executor.weight_utils import ( | |
load_tensor_parallel_weights | |
) | |
from vllm.model_executor.parallel_utils.parallel_state import ( | |
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) | |
tp_size = get_tensor_model_parallel_world_size() | |
tensor_model_parallel_rank = get_tensor_model_parallel_rank() | |
q_proj_shard_size = (self.config.hidden_size // tp_size) | |
kv_proj_shard_size = (self.config.hidden_size // | |
self.config.num_attention_heads * | |
getattr(self.config, "num_key_value_heads", self.config.num_attention_heads) // tp_size) | |
attention_weight_specs = [ | |
# (weight_name, shard_size, offset) | |
("q_proj", q_proj_shard_size, 0), | |
("k_proj", kv_proj_shard_size, q_proj_shard_size), | |
("v_proj", kv_proj_shard_size, | |
q_proj_shard_size + kv_proj_shard_size), | |
] | |
state_dict = self.state_dict() | |
need_to_load = len(state_dict) | |
loaded = 0 | |
iterator = custom_hf_model_weights_iterator(model_name_or_path, cache_dir, use_np_cache) | |
for name, loaded_weight in iterator: | |
if "rotary_emb.inv_freq" in name: | |
continue | |
if "embed_tokens" in name or "lm_head" in name: | |
param = state_dict[name] | |
# Consider padding in the vocab size. | |
padded_vocab_size = (param.shape[0] * tp_size) | |
# num_extra_rows = padded_vocab_size - self.config.vocab_size | |
num_extra_rows = padded_vocab_size - loaded_weight.size(0) | |
load_size = loaded_weight.size() | |
extra_rows = torch.empty(num_extra_rows, | |
loaded_weight.shape[1]) | |
extra_rows = extra_rows.to(loaded_weight) | |
loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0) | |
if num_extra_rows > 0: | |
print(f'Add empty to {num_extra_rows} extra row for {name}') | |
print(f'Load: {name} | {padded_vocab_size=} | {self.config.vocab_size=} | {num_extra_rows=} | {param.size()=} | {loaded_weight.size()=} | {load_size=}') | |
is_attention_weight = False | |
for weight_name, shard_size, offset in attention_weight_specs: | |
if weight_name not in name or "qkv_proj" in name: | |
continue | |
param = state_dict[name.replace(weight_name, "qkv_proj")] | |
loaded_weight = loaded_weight[ | |
shard_size * tensor_model_parallel_rank:shard_size * | |
(tensor_model_parallel_rank + 1)] | |
param_slice = param.data[offset:offset + shard_size] | |
assert param_slice.shape == loaded_weight.shape | |
param_slice.copy_(loaded_weight) | |
loaded += 1.0 / 3 | |
is_attention_weight = True | |
break | |
if is_attention_weight: | |
continue | |
# ! qkv_proj is sharded differently if concatenated into qkv | |
# qkv: qqqq kkkk vvvv | |
# lweight: qq0qq1 kk0kk1 vv0vv1 | |
# q_shard_size: hidden_size // tp_size = qq | |
# qkv_s0: qq0_kk0_vv0 | |
# qkv_s1: qq1_kk1_vv1 | |
if "qkv_proj" in name: | |
param = state_dict[name] | |
# loaded_weight | |
qsize = self.config.hidden_size | |
kvsize = self.config.hidden_size // self.config.num_attention_heads * getattr(self.config, "num_key_value_heads", self.config.num_attention_heads) | |
q_offsets = ( | |
q_proj_shard_size * tensor_model_parallel_rank, | |
q_proj_shard_size * (tensor_model_parallel_rank + 1) | |
) | |
k_offsets = ( | |
qsize + kv_proj_shard_size * tensor_model_parallel_rank, | |
qsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1) | |
) | |
v_offsets = ( | |
qsize + kvsize + kv_proj_shard_size * tensor_model_parallel_rank, | |
qsize + kvsize + kv_proj_shard_size * (tensor_model_parallel_rank + 1) | |
) | |
_loaded_weight = torch.cat( | |
[ | |
loaded_weight[q_offsets[0]:q_offsets[1]], | |
loaded_weight[k_offsets[0]:k_offsets[1]], | |
loaded_weight[v_offsets[0]:v_offsets[1]], | |
], 0 | |
) | |
assert param.shape == _loaded_weight.shape, f'{param.shape=} != {_loaded_weight.shape=}' | |
param.data.copy_(_loaded_weight) | |
loaded += 1.0 | |
is_attention_weight = True | |
if is_attention_weight: | |
continue | |
is_gate_up_weight = False | |
for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]): | |
if weight_name not in name or "gate_up_proj" in name: | |
continue | |
param = state_dict[name.replace(weight_name, "gate_up_proj")] | |
shard_size = param.shape[0] // 2 | |
loaded_weight = loaded_weight[ | |
shard_size * tensor_model_parallel_rank:shard_size * | |
(tensor_model_parallel_rank + 1)] | |
param_slice = param.data[shard_size * stride_id:shard_size * | |
(stride_id + 1)] | |
assert param_slice.shape == loaded_weight.shape | |
param_slice.copy_(loaded_weight) | |
loaded += 1.0 / 2 | |
is_gate_up_weight = True | |
break | |
if is_gate_up_weight: | |
continue | |
if "gate_up_proj" in name: | |
param = state_dict[name] | |
shard_size = param.shape[0] // 2 | |
intermediate_size = self.config.intermediate_size | |
g_offsets = ( | |
shard_size * tensor_model_parallel_rank, | |
shard_size * (tensor_model_parallel_rank + 1) | |
) | |
u_offsets = ( | |
intermediate_size + shard_size * tensor_model_parallel_rank, | |
intermediate_size + shard_size * (tensor_model_parallel_rank + 1) | |
) | |
_loaded_weight = torch.cat( | |
[ | |
loaded_weight[g_offsets[0]:g_offsets[1]], | |
loaded_weight[u_offsets[0]:u_offsets[1]], | |
], 0 | |
) | |
assert param.shape == _loaded_weight.shape | |
param.data.copy_(_loaded_weight) | |
loaded += 1.0 | |
is_gate_up_weight = True | |
if is_gate_up_weight: | |
continue | |
param = state_dict[name] | |
load_tensor_parallel_weights(param, loaded_weight, name, | |
self._column_parallel_weights, | |
self._row_parallel_weights, | |
tensor_model_parallel_rank) | |
loaded += 1 | |
if np.abs(loaded - need_to_load) < 0.01: | |
print(f'WARNING: only {loaded} params loaded out of {need_to_load}') | |
else: | |
print(f'Loaded all {loaded} params loaded out of {need_to_load}') | |
def new_llama_load_weights( | |
self, | |
model_name_or_path: str, | |
cache_dir: Optional[str] = None, | |
load_format: str = "auto", | |
revision: Optional[str] = None | |
): | |
# If use newest vllm, not been thoroughly tested yet. | |
from vllm.model_executor.weight_utils import ( | |
load_tensor_parallel_weights, hf_model_weights_iterator | |
) | |
from vllm.model_executor.parallel_utils.parallel_state import ( | |
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) | |
if self.quant_config is None: | |
weight_suffixes = ["weight"] | |
else: | |
weight_suffixes = self.quant_config.get_tp_tensor_names() | |
column_parallel_weights: List[str] = [] | |
for layer in self._column_parallel_layers: | |
for suffix in weight_suffixes: | |
column_parallel_weights.append(f"{layer}.{suffix}") | |
row_parallel_weights: List[str] = [] | |
for layer in self._row_parallel_layers: | |
for suffix in weight_suffixes: | |
row_parallel_weights.append(f"{layer}.{suffix}") | |
tp_size = get_tensor_model_parallel_world_size() | |
tp_rank = get_tensor_model_parallel_rank() | |
assert tp_size == 1, f'tensorparallel >=2 not allowed. {tp_size}' | |
q_proj_shard_size = (self.config.hidden_size // tp_size) | |
num_kv_heads_replicas = max(1, | |
tp_size // self.config.num_key_value_heads) | |
num_kv_heads_per_gpu = max(1, | |
self.config.num_key_value_heads // tp_size) | |
kv_proj_shard_size = (self.config.hidden_size // | |
self.config.num_attention_heads * | |
num_kv_heads_per_gpu) | |
attention_weight_specs = [ | |
# (weight_name, shard_size, offset) | |
("q_proj", q_proj_shard_size, 0), | |
("k_proj", kv_proj_shard_size, q_proj_shard_size), | |
("v_proj", kv_proj_shard_size, | |
q_proj_shard_size + kv_proj_shard_size), | |
] | |
state_dict = self.state_dict() | |
need_to_load = len(state_dict) | |
loaded = 0 | |
for name, loaded_weight in hf_model_weights_iterator( | |
model_name_or_path, cache_dir, load_format, revision): | |
if "rotary_emb.inv_freq" in name: | |
continue | |
is_packed = False | |
is_transposed = False | |
if self.quant_config is not None: | |
is_packed = self.quant_config.is_packed(name) | |
is_transposed = self.quant_config.is_transposed(name) | |
if is_transposed: | |
loaded_weight = convert_pyslice_to_tensor(loaded_weight) | |
loaded_weight = loaded_weight.T | |
is_attention_weight = False | |
for weight_name, shard_size, offset in attention_weight_specs: | |
if weight_name not in name or "qkv_proj" in name: | |
continue | |
param = state_dict[name.replace(weight_name, "qkv_proj")] | |
if is_transposed: | |
param = param.T | |
if is_packed: | |
shard_size //= self.quant_config.pack_factor | |
offset //= self.quant_config.pack_factor | |
if weight_name in ["k_proj", "v_proj"]: | |
shard_id = tp_rank // num_kv_heads_replicas | |
else: | |
shard_id = tp_rank | |
loaded_weight = loaded_weight[shard_size * | |
shard_id:shard_size * | |
(shard_id + 1)] | |
param_slice = param.data[offset:offset + shard_size] | |
assert param_slice.shape == loaded_weight.shape | |
param_slice.copy_(loaded_weight) | |
loaded += 1.0 / 3 | |
is_attention_weight = True | |
break | |
if is_attention_weight: | |
continue | |
# TODO: need to figure out to do sharding with qkv_proj fused | |
is_gate_up_weight = False | |
for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]): | |
if weight_name not in name or "gate_up_proj" in name: | |
continue | |
param = state_dict[name.replace(weight_name, "gate_up_proj")] | |
if is_transposed: | |
param = param.T | |
shard_size = param.shape[0] // 2 | |
loaded_weight = loaded_weight[shard_size * tp_rank:shard_size * | |
(tp_rank + 1)] | |
param_slice = param.data[shard_size * stride_id:shard_size * | |
(stride_id + 1)] | |
assert param_slice.shape == loaded_weight.shape | |
param_slice.copy_(loaded_weight) | |
loaded += 1.0 / 2 | |
is_gate_up_weight = True | |
break | |
if is_gate_up_weight: | |
continue | |
# TODO: need to figure out to do sharding with gate_up_proj fused | |
param = state_dict[name] | |
if is_transposed: | |
param = param.T | |
if "embed_tokens" in name or "lm_head" in name: | |
load_padded_tensor_parallel_vocab(param, loaded_weight, | |
tp_rank) | |
loaded += 1 | |
continue | |
load_tensor_parallel_weights(param, loaded_weight, name, | |
column_parallel_weights, | |
row_parallel_weights, tp_rank) | |
loaded += 1 | |
if np.abs(loaded - need_to_load) < 0.01: | |
print(f'WARNING: only {loaded} params loaded out of {need_to_load}') | |
else: | |
print(f'Loaded all {loaded} params loaded out of {need_to_load}') | |
# Reassign LlamaForCausalLM.load_weights with llama_load_weights | |
if not DEBUG: | |
try: | |
import vllm | |
from vllm.model_executor.model_loader import _MODEL_REGISTRY | |
from vllm.model_executor.models import LlamaForCausalLM | |
_MODEL_REGISTRY['FasterLlamaForCausalLM'] = LlamaForCausalLM | |
if vllm.__version__ == "0.1.4": | |
LlamaForCausalLM.load_weights = llama_load_weights | |
else: | |
LlamaForCausalLM.load_weights = new_llama_load_weights | |
if DTYPE == "bfloat16": | |
try: | |
compute_capability = torch.cuda.get_device_capability() | |
if compute_capability[0] < 8: | |
gpu_name = torch.cuda.get_device_name() | |
print( | |
"Bfloat16 is only supported on GPUs with compute capability " | |
f"of at least 8.0. Your {gpu_name} GPU has compute capability " | |
f"{compute_capability[0]}.{compute_capability[1]}. --> Move to FLOAT16") | |
DTYPE = "float16" | |
except Exception as e: | |
print(f'Unable to obtain compute_capability: {e}') | |
except Exception as e: | |
print(f'Failing import and reconfigure VLLM: {str(e)}') | |
# ! ================================================================== | |
set_documentation_group("component") | |
RES_PRINTED = False | |
def llama_chat_sys_input_seq_constructor(text, sys_prompt=SYSTEM_PROMPT_1, bos_token=BOS_TOKEN, eos_token=EOS_TOKEN): | |
return f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {text} {E_INST}" | |
def llama_chat_multiturn_sys_input_seq_constructor( | |
message: str, | |
history: List[Tuple[str, str]], | |
sys_prompt=SYSTEM_PROMPT_1, | |
bos_token=BOS_TOKEN, | |
eos_token=EOS_TOKEN, | |
include_end_instruct=True, | |
): | |
""" | |
``` | |
<bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos> | |
<bos>[INST] Prompt [/INST] Answer <eos> | |
<bos>[INST] Prompt [/INST] | |
``` | |
""" | |
text = '' | |
end_instr = f" {E_INST}" if include_end_instruct else "" | |
for i, (prompt, res) in enumerate(history): | |
if i == 0: | |
text += f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {prompt}{end_instr}" | |
else: | |
text += f"{bos_token}{B_INST} {prompt}{end_instr}" | |
if res is not None: | |
text += f" {res} {eos_token} " | |
if len(history) == 0 or text.strip() == '': | |
text = f"{bos_token}{B_INST} {B_SYS} {sys_prompt} {E_SYS} {message}{end_instr}" | |
else: | |
text += f"{bos_token}{B_INST} {message}{end_instr}" | |
return text | |
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.update(visible=False), | |
Button.update(visible=True), | |
), | |
None, | |
[self.submit_btn, self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: (Button.update(visible=True), Button.update(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.update(visible=True), | |
None, | |
[self.stop_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: Button.update(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.update(interactive=False), | |
None, | |
[self.submit_btn], | |
api_name=False, | |
queue=False, | |
) | |
event_to_cancel.then( | |
lambda: Button.update(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.update(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: | |
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 | |
# if self.clear_btn: | |
# self.clear_btn.click( | |
# lambda: ([], [], None), | |
# None, | |
# [self.chatbot, self.chatbot_state, self.saved_input], | |
# queue=False, | |
# api_name=False, | |
# cancels=submit_event, | |
# ) | |
# replace | |
gr.ChatInterface._setup_stop_events = _setup_stop_events | |
gr.ChatInterface._setup_events = _setup_events | |
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: Any): | |
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." | |
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: | |
print(f'Error: {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 | |
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 | |
) -> str: | |
global LOG_FILE, LOG_PATH | |
from vllm import LLM, SamplingParams | |
"""Build multi turn | |
<bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos> | |
<bos>[INST] Prompt [/INST] Answer <eos> | |
<bos>[INST] Prompt [/INST] | |
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' | |
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 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: | |
yield message_safety | |
return | |
# history will be appended with message later on | |
full_prompt = llama_chat_multiturn_sys_input_seq_constructor( | |
message, history, sys_prompt=system_prompt | |
) | |
if len(tokenizer.encode(full_prompt, add_special_tokens=False)) >= 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]'] | |
) | |
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: | |
yield message_safety | |
return | |
yield cur_out | |
assert len(gen) == 1, f'{gen}' | |
item = next(iter(gen.values())) | |
cur_out = item.outputs[0].text | |
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: | |
yield message_safety | |
return | |
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 maybe_upload_to_dataset(): | |
global LOG_FILE, DATA_SET_REPO_PATH, SAVE_LOGS | |
if SAVE_LOGS and os.path.exists(LOG_PATH) and DATA_SET_REPO_PATH != "": | |
with open(LOG_PATH, 'r', encoding='utf-8') as f: | |
convos = {} | |
for l in f: | |
if l: | |
item = json.loads(l) | |
convos[item['key']] = item | |
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 = {} | |
for l in f: | |
if l: | |
item = json.loads(l) | |
convos[item['key']] = item | |
print(f'Printing log from {LOG_PATH}') | |
for k, v in convos.items(): | |
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, add_special_tokens=False)) >= 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 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>", | |
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 = llama_chat_multiturn_sys_input_seq_constructor | |
elif prompt_mode == 'few-shot': | |
from functools import partial | |
prompt_format_fn = partial( | |
llama_chat_multiturn_sys_input_seq_constructor, include_end_instruct=False | |
) | |
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, add_special_tokens=False)) >= 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] | |
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."], | |
["ຂໍແຈ້ງ 5 ສະຖານທີ່ທ່ອງທ່ຽວໃນນະຄອນຫຼວງວຽງຈັນ"], | |
] | |
# performance items | |
def launch(): | |
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| BLOCK_LANGS={BLOCK_LANGS} ' | |
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| KEYWORDS={KEYWORDS} ' | |
f'\n| LOG_PATH={LOG_PATH} | SAVE_LOGS={SAVE_LOGS} ' | |
f'\n| DATA_SET_REPO_PATH={DATA_SET_REPO_PATH} ' | |
f'\n| GET_LOG_CMD={GET_LOG_CMD} ' | |
f'\n| Sys={SYSTEM_PROMPT_1}' | |
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 | |
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") | |
else: | |
llm = LLM(model=model_path, dtype=dtype, tensor_parallel_size=tensor_parallel, gpu_memory_utilization=gpu_memory_utilization) | |
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 = 4096 | |
llm.llm_engine.scheduler_config.max_num_batched_tokens = 4096 | |
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 = 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="[STOP],[END],<s>,</s>", 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, "[STOP],[END],<s>,</s>"], | |
["upload_few_shot.json", "few-shot", 0.2, 128, 0.5, 0, "[STOP],[END],<s>,</s>,\\n"] | |
], | |
cache_examples=False, | |
) | |
demo_chat = gr.ChatInterface( | |
response_fn, | |
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=f"{model_title}", | |
# description=f"{model_desc}", | |
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) | |
], | |
examples=CHAT_EXAMPLES, | |
cache_examples=False | |
) | |
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], | |
tab_names=["Chat Interface", "Batch Inference"], | |
title=f"{model_title}", | |
description=descriptions, | |
) | |
demo.title = MODEL_NAME | |
callback = None | |
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) | |
demo.queue() | |
demo.launch(server_port=PORT) | |
else: | |
descriptions = model_desc | |
if DISPLAY_MODEL_PATH: | |
descriptions += f"<br> {path_markdown.format(model_path=model_path)}" | |
demo = gr.ChatInterface( | |
response_fn, | |
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=f"{model_title}", | |
description=descriptions, | |
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) | |
], | |
examples=CHAT_EXAMPLES, | |
cache_examples=False | |
) | |
demo.title = MODEL_NAME | |
with demo: | |
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) | |
demo.queue() | |
demo.launch(server_port=PORT) | |
def main(): | |
launch() | |
if __name__ == "__main__": | |
main() | |