-
Notifications
You must be signed in to change notification settings - Fork 4
/
batch_inference.py
246 lines (200 loc) · 8.62 KB
/
batch_inference.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
import os
from gradio.themes import ThemeClass as Theme
import numpy as np
import argparse
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, Generator
from gradio_client.documentation import document, set_documentation_group
from gradio.components import Button, Component
from gradio.events import Dependency, EventListenerMethod
from typing import List, Optional, Union, Dict, Tuple
from tqdm.auto import tqdm
from huggingface_hub import snapshot_download
import inspect
from typing import AsyncGenerator, Callable, Literal, Union, cast
import anyio
from gradio_client import utils as client_utils
from gradio_client.documentation import document
from gradio.blocks import Blocks
from gradio.components import (
Button,
Chatbot,
Component,
Markdown,
State,
Textbox,
get_component_instance,
)
from gradio.events import Dependency, on
from gradio.helpers import create_examples as Examples # noqa: N812
from gradio.helpers import special_args
from gradio.layouts import Accordion, Group, Row
from gradio.routes import Request
from gradio.themes import ThemeClass as Theme
from gradio.utils import SyncToAsyncIterator, async_iteration
from .base_demo import register_demo, get_demo_class, BaseDemo
from ..configs import (
SYSTEM_PROMPT,
MODEL_NAME,
MAX_TOKENS,
TEMPERATURE,
USE_PANEL,
CHATBOT_HEIGHT,
)
from ..globals import MODEL_ENGINE
from .chat_interface import gradio_history_to_conversation_prompt
# 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")
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?"}}]
```
"""
def validate_file_item(filename, index, item: Dict[str, str]):
"""
check safety for items in files
"""
global MODEL_ENGINE
message = item['prompt'].strip()
if len(message) == 0:
raise gr.Error(f'Prompt {index} empty')
num_tokens = len(MODEL_ENGINE.tokenizer.encode(message))
if num_tokens >= MODEL_ENGINE.max_position_embeddings - 128:
raise gr.Error(f"Conversation or prompt is too long ({num_tokens} toks), please clear the chatbox or try shorter input.")
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))
def free_form_prompt(prompt, history=None, system_prompt=None):
return prompt
def batch_inference_engine(
files: Union[str, List[str]],
prompt_mode: str,
temperature: float,
max_tokens: int,
stop_strings: str = "<s>,</s>,<|im_start|>",
system_prompt: Optional[str] = SYSTEM_PROMPT,
):
global MODEL_ENGINE
temperature = float(temperature)
max_tokens = int(max_tokens)
stop_strings = [x.strip() for x in stop_strings.strip().split(",")]
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 = gradio_history_to_conversation_prompt
elif prompt_mode == 'few-shot':
from functools import partial
prompt_format_fn = free_form_prompt
else:
raise gr.Error(f'Wrong mode {prompt_mode}')
full_prompts = [
prompt_format_fn(
x['prompt'], [], system_prompt=system_prompt
)
for i, x in enumerate(all_items)
]
print(f'{full_prompts[0]}\n')
full_num_tokens = [
len(MODEL_ENGINE.tokenizer.encode(p))
for p in full_prompts
]
if any(x >= MODEL_ENGINE.max_position_embeddings - 128 for x in full_num_tokens):
raise gr.Error(f"Some prompt is too long!")
# ! batch inference
responses = MODEL_ENGINE.batch_generate(
full_prompts,
temperature=temperature, max_tokens=max_tokens,
stop_strings=stop_strings,
)
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)
print_items = all_items[:2]
print(json.dumps(print_items, indent=4, ensure_ascii=False))
return save_path, print_items
class BatchInferenceDemo(BaseDemo):
def tab_name(self):
return "Batch Inference"
def create_demo(
self,
title: str | None = None,
description: str | None = None,
**kwargs
) -> gr.Blocks:
system_prompt = kwargs.get("system_prompt", SYSTEM_PROMPT)
max_tokens = kwargs.get("max_tokens", MAX_TOKENS)
temperature = kwargs.get("temperature", TEMPERATURE)
model_name = kwargs.get("model_name", MODEL_NAME)
demo_file_upload = gr.Interface(
batch_inference_engine,
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),
gr.Textbox(value=system_prompt, label='System prompt', lines=4)
],
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"],
["upload_few_shot.json", "few-shot"],
],
cache_examples=False,
)
return demo_file_upload