Spaces:
Runtime error
Runtime error
Commit
•
32c203b
1
Parent(s):
65121b5
Update with h2oGPT hash f2a7ba06a6f9e200e59d7e1388fa02b52bd20e8d
Browse files- app.py +1 -1
- gradio_runner.py +903 -0
- gradio_themes.py +142 -0
- utils.py +5 -4
app.py
CHANGED
@@ -833,7 +833,7 @@ def evaluate(
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target_func = generate_with_exceptions
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if concurrency_count == 1:
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# otherwise can't do this
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-
KThread.kill_threads(target_func.__name__)
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target = wrapped_partial(generate_with_exceptions, model.generate, prompt, inputs_decoded,
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raise_generate_gpu_exceptions, **gen_kwargs)
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thread = KThread(target=target)
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target_func = generate_with_exceptions
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if concurrency_count == 1:
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# otherwise can't do this
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+
KThread.kill_threads(target_func.__name__, debug=debug)
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target = wrapped_partial(generate_with_exceptions, model.generate, prompt, inputs_decoded,
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raise_generate_gpu_exceptions, **gen_kwargs)
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thread = KThread(target=target)
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gradio_runner.py
ADDED
@@ -0,0 +1,903 @@
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1 |
+
import functools
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2 |
+
import inspect
|
3 |
+
import os
|
4 |
+
import sys
|
5 |
+
|
6 |
+
from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js
|
7 |
+
from utils import get_githash, flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print
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8 |
+
from finetune import prompt_type_to_model_name, prompt_types_strings, generate_prompt, inv_prompt_type_to_model_lower
|
9 |
+
from generate import get_model, languages_covered, evaluate, eval_func_param_names, score_qa
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10 |
+
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11 |
+
import gradio as gr
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12 |
+
from apscheduler.schedulers.background import BackgroundScheduler
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13 |
+
|
14 |
+
|
15 |
+
def go_gradio(**kwargs):
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16 |
+
allow_api = kwargs['allow_api']
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17 |
+
is_public = kwargs['is_public']
|
18 |
+
is_hf = kwargs['is_hf']
|
19 |
+
is_low_mem = kwargs['is_low_mem']
|
20 |
+
n_gpus = kwargs['n_gpus']
|
21 |
+
admin_pass = kwargs['admin_pass']
|
22 |
+
model_state0 = kwargs['model_state0']
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23 |
+
score_model_state0 = kwargs['score_model_state0']
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24 |
+
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25 |
+
# easy update of kwargs needed for evaluate() etc.
|
26 |
+
kwargs.update(locals())
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27 |
+
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28 |
+
if 'mbart-' in kwargs['model_lower']:
|
29 |
+
instruction_label_nochat = "Text to translate"
|
30 |
+
else:
|
31 |
+
instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \
|
32 |
+
" use Enter for multiple input lines)"
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33 |
+
if kwargs['input_lines'] > 1:
|
34 |
+
instruction_label = "You (Shift-Enter or push Submit to send message, use Enter for multiple input lines)"
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35 |
+
else:
|
36 |
+
instruction_label = "You (Enter or push Submit to send message, shift-enter for more lines)"
|
37 |
+
|
38 |
+
title = 'h2oGPT'
|
39 |
+
if 'h2ogpt-research' in kwargs['base_model']:
|
40 |
+
title += " [Research demonstration]"
|
41 |
+
if kwargs['verbose']:
|
42 |
+
description = f"""Model {kwargs['base_model']} Instruct dataset.
|
43 |
+
For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
|
44 |
+
Command: {str(' '.join(sys.argv))}
|
45 |
+
Hash: {get_githash()}
|
46 |
+
"""
|
47 |
+
else:
|
48 |
+
description = "For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).<br>"
|
49 |
+
if is_public:
|
50 |
+
description += "If this host is busy, try [gpt.h2o.ai 20B](https://gpt.h2o.ai) and [HF Spaces1 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot) and [HF Spaces2 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
|
51 |
+
description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.</i></li>"""
|
52 |
+
if kwargs['load_8bit']:
|
53 |
+
description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
|
54 |
+
description += """<i><li>Conversations may be used to improve h2oGPT. Do not share sensitive information.</i></li>"""
|
55 |
+
if 'h2ogpt-research' in kwargs['base_model']:
|
56 |
+
description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
|
57 |
+
description += """<i><li>By using h2oGPT, you accept our [Terms of Service](https://github.com/h2oai/h2ogpt/blob/main/tos.md).</i></li></ul></p>"""
|
58 |
+
|
59 |
+
if kwargs['verbose']:
|
60 |
+
task_info_md = f"""
|
61 |
+
### Task: {kwargs['task_info']}"""
|
62 |
+
else:
|
63 |
+
task_info_md = ''
|
64 |
+
|
65 |
+
if kwargs['h2ocolors']:
|
66 |
+
css_code = """footer {visibility: hidden;}
|
67 |
+
body{background:linear-gradient(#f5f5f5,#e5e5e5);}
|
68 |
+
body.dark{background:linear-gradient(#000000,#0d0d0d);}
|
69 |
+
"""
|
70 |
+
else:
|
71 |
+
css_code = """footer {visibility: hidden}"""
|
72 |
+
|
73 |
+
if kwargs['gradio_avoid_processing_markdown']:
|
74 |
+
from gradio_client import utils as client_utils
|
75 |
+
from gradio.components import Chatbot
|
76 |
+
|
77 |
+
# gradio has issue with taking too long to process input/output for markdown etc.
|
78 |
+
# Avoid for now, allow raw html to render, good enough for chatbot.
|
79 |
+
def _postprocess_chat_messages(self, chat_message: str):
|
80 |
+
if chat_message is None:
|
81 |
+
return None
|
82 |
+
elif isinstance(chat_message, (tuple, list)):
|
83 |
+
filepath = chat_message[0]
|
84 |
+
mime_type = client_utils.get_mimetype(filepath)
|
85 |
+
filepath = self.make_temp_copy_if_needed(filepath)
|
86 |
+
return {
|
87 |
+
"name": filepath,
|
88 |
+
"mime_type": mime_type,
|
89 |
+
"alt_text": chat_message[1] if len(chat_message) > 1 else None,
|
90 |
+
"data": None, # These last two fields are filled in by the frontend
|
91 |
+
"is_file": True,
|
92 |
+
}
|
93 |
+
elif isinstance(chat_message, str):
|
94 |
+
return chat_message
|
95 |
+
else:
|
96 |
+
raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
|
97 |
+
|
98 |
+
Chatbot._postprocess_chat_messages = _postprocess_chat_messages
|
99 |
+
|
100 |
+
theme = H2oTheme() if kwargs['h2ocolors'] else SoftTheme()
|
101 |
+
demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False)
|
102 |
+
callback = gr.CSVLogger()
|
103 |
+
|
104 |
+
model_options = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
|
105 |
+
if kwargs['base_model'].strip() not in model_options:
|
106 |
+
lora_options = [kwargs['base_model'].strip()] + model_options
|
107 |
+
lora_options = kwargs['extra_lora_options']
|
108 |
+
if kwargs['lora_weights'].strip() not in lora_options:
|
109 |
+
lora_options = [kwargs['lora_weights'].strip()] + lora_options
|
110 |
+
# always add in no lora case
|
111 |
+
# add fake space so doesn't go away in gradio dropdown
|
112 |
+
no_lora_str = no_model_str = '[None/Remove]'
|
113 |
+
lora_options = [no_lora_str] + kwargs['extra_lora_options'] # FIXME: why double?
|
114 |
+
# always add in no model case so can free memory
|
115 |
+
# add fake space so doesn't go away in gradio dropdown
|
116 |
+
model_options = [no_model_str] + model_options
|
117 |
+
|
118 |
+
# transcribe, will be detranscribed before use by evaluate()
|
119 |
+
if not kwargs['lora_weights'].strip():
|
120 |
+
kwargs['lora_weights'] = no_lora_str
|
121 |
+
|
122 |
+
if not kwargs['base_model'].strip():
|
123 |
+
kwargs['base_model'] = no_model_str
|
124 |
+
|
125 |
+
# transcribe for gradio
|
126 |
+
kwargs['gpu_id'] = str(kwargs['gpu_id'])
|
127 |
+
|
128 |
+
no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]'
|
129 |
+
output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get(
|
130 |
+
'base_model') else no_model_msg
|
131 |
+
output_label0_model2 = no_model_msg
|
132 |
+
|
133 |
+
with demo:
|
134 |
+
# avoid actual model/tokenizer here or anything that would be bad to deepcopy
|
135 |
+
# https://github.com/gradio-app/gradio/issues/3558
|
136 |
+
model_state = gr.State(['model', 'tokenizer', kwargs['device'], kwargs['base_model']])
|
137 |
+
model_state2 = gr.State([None, None, None, None])
|
138 |
+
model_options_state = gr.State([model_options])
|
139 |
+
lora_options_state = gr.State([lora_options])
|
140 |
+
gr.Markdown(f"""
|
141 |
+
{get_h2o_title(title) if kwargs['h2ocolors'] else get_simple_title(title)}
|
142 |
+
|
143 |
+
{description}
|
144 |
+
{task_info_md}
|
145 |
+
""")
|
146 |
+
if is_hf:
|
147 |
+
gr.HTML(
|
148 |
+
'''<center><a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate this Space to skip the queue and run in a private space</center>''')
|
149 |
+
|
150 |
+
# go button visible if
|
151 |
+
base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0']
|
152 |
+
go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary")
|
153 |
+
normal_block = gr.Row(visible=not base_wanted)
|
154 |
+
with normal_block:
|
155 |
+
with gr.Tabs():
|
156 |
+
with gr.Row():
|
157 |
+
col_nochat = gr.Column(visible=not kwargs['chat'])
|
158 |
+
with col_nochat: # FIXME: for model comparison, and check rest
|
159 |
+
text_output_nochat = gr.Textbox(lines=5, label=output_label0)
|
160 |
+
instruction_nochat = gr.Textbox(
|
161 |
+
lines=kwargs['input_lines'],
|
162 |
+
label=instruction_label_nochat,
|
163 |
+
placeholder=kwargs['placeholder_instruction'],
|
164 |
+
)
|
165 |
+
iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
|
166 |
+
placeholder=kwargs['placeholder_input'])
|
167 |
+
submit_nochat = gr.Button("Submit")
|
168 |
+
flag_btn_nochat = gr.Button("Flag")
|
169 |
+
if not kwargs['auto_score']:
|
170 |
+
with gr.Column(visible=kwargs['score_model']):
|
171 |
+
score_btn_nochat = gr.Button("Score last prompt & response")
|
172 |
+
score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
|
173 |
+
else:
|
174 |
+
with gr.Column(visible=kwargs['score_model']):
|
175 |
+
score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
|
176 |
+
col_chat = gr.Column(visible=kwargs['chat'])
|
177 |
+
with col_chat:
|
178 |
+
with gr.Row():
|
179 |
+
text_output = gr.Chatbot(label=output_label0).style(height=kwargs['height'] or 400)
|
180 |
+
text_output2 = gr.Chatbot(label=output_label0_model2, visible=False).style(
|
181 |
+
height=kwargs['height'] or 400)
|
182 |
+
with gr.Row():
|
183 |
+
with gr.Column(scale=50):
|
184 |
+
instruction = gr.Textbox(
|
185 |
+
lines=kwargs['input_lines'],
|
186 |
+
label=instruction_label,
|
187 |
+
placeholder=kwargs['placeholder_instruction'],
|
188 |
+
)
|
189 |
+
with gr.Row():
|
190 |
+
submit = gr.Button(value='Submit').style(full_width=False, size='sm')
|
191 |
+
stop_btn = gr.Button(value="Stop").style(full_width=False, size='sm')
|
192 |
+
with gr.Row():
|
193 |
+
clear = gr.Button("New Conversation")
|
194 |
+
flag_btn = gr.Button("Flag")
|
195 |
+
if not kwargs['auto_score']: # FIXME: For checkbox model2
|
196 |
+
with gr.Column(visible=kwargs['score_model']):
|
197 |
+
with gr.Row():
|
198 |
+
score_btn = gr.Button("Score last prompt & response").style(
|
199 |
+
full_width=False, size='sm')
|
200 |
+
score_text = gr.Textbox("Response Score: NA", show_label=False)
|
201 |
+
score_res2 = gr.Row(visible=False)
|
202 |
+
with score_res2:
|
203 |
+
score_btn2 = gr.Button("Score last prompt & response 2").style(
|
204 |
+
full_width=False, size='sm')
|
205 |
+
score_text2 = gr.Textbox("Response Score2: NA", show_label=False)
|
206 |
+
else:
|
207 |
+
with gr.Column(visible=kwargs['score_model']):
|
208 |
+
score_text = gr.Textbox("Response Score: NA", show_label=False)
|
209 |
+
score_text2 = gr.Textbox("Response Score2: NA", show_label=False, visible=False)
|
210 |
+
retry = gr.Button("Regenerate")
|
211 |
+
undo = gr.Button("Undo")
|
212 |
+
with gr.TabItem("Input/Output"):
|
213 |
+
with gr.Row():
|
214 |
+
if 'mbart-' in kwargs['model_lower']:
|
215 |
+
src_lang = gr.Dropdown(list(languages_covered().keys()),
|
216 |
+
value=kwargs['src_lang'],
|
217 |
+
label="Input Language")
|
218 |
+
tgt_lang = gr.Dropdown(list(languages_covered().keys()),
|
219 |
+
value=kwargs['tgt_lang'],
|
220 |
+
label="Output Language")
|
221 |
+
with gr.TabItem("Expert"):
|
222 |
+
with gr.Row():
|
223 |
+
with gr.Column():
|
224 |
+
stream_output = gr.components.Checkbox(label="Stream output",
|
225 |
+
value=kwargs['stream_output'])
|
226 |
+
prompt_type = gr.Dropdown(prompt_types_strings,
|
227 |
+
value=kwargs['prompt_type'], label="Prompt Type",
|
228 |
+
visible=not is_public)
|
229 |
+
prompt_type2 = gr.Dropdown(prompt_types_strings,
|
230 |
+
value=kwargs['prompt_type'], label="Prompt Type Model 2",
|
231 |
+
visible=not is_public and False)
|
232 |
+
do_sample = gr.Checkbox(label="Sample",
|
233 |
+
info="Enable sampler, required for use of temperature, top_p, top_k",
|
234 |
+
value=kwargs['do_sample'])
|
235 |
+
temperature = gr.Slider(minimum=0.01, maximum=3,
|
236 |
+
value=kwargs['temperature'],
|
237 |
+
label="Temperature",
|
238 |
+
info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
|
239 |
+
top_p = gr.Slider(minimum=0, maximum=1,
|
240 |
+
value=kwargs['top_p'], label="Top p",
|
241 |
+
info="Cumulative probability of tokens to sample from")
|
242 |
+
top_k = gr.Slider(
|
243 |
+
minimum=0, maximum=100, step=1,
|
244 |
+
value=kwargs['top_k'], label="Top k",
|
245 |
+
info='Num. tokens to sample from'
|
246 |
+
)
|
247 |
+
max_beams = 8 if not is_low_mem else 2
|
248 |
+
num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
|
249 |
+
value=min(max_beams, kwargs['num_beams']), label="Beams",
|
250 |
+
info="Number of searches for optimal overall probability. "
|
251 |
+
"Uses more GPU memory/compute")
|
252 |
+
max_max_new_tokens = 2048 if not is_low_mem else kwargs['max_new_tokens']
|
253 |
+
max_new_tokens = gr.Slider(
|
254 |
+
minimum=1, maximum=max_max_new_tokens, step=1,
|
255 |
+
value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
|
256 |
+
)
|
257 |
+
min_new_tokens = gr.Slider(
|
258 |
+
minimum=0, maximum=max_max_new_tokens, step=1,
|
259 |
+
value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
|
260 |
+
)
|
261 |
+
early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
|
262 |
+
value=kwargs['early_stopping'])
|
263 |
+
max_max_time = 60 * 5 if not is_low_mem else 60
|
264 |
+
max_time = gr.Slider(minimum=0, maximum=max_max_time, step=1,
|
265 |
+
value=min(max_max_time, kwargs['max_time']), label="Max. time",
|
266 |
+
info="Max. time to search optimal output.")
|
267 |
+
repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
|
268 |
+
value=kwargs['repetition_penalty'],
|
269 |
+
label="Repetition Penalty")
|
270 |
+
num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
|
271 |
+
value=kwargs['num_return_sequences'],
|
272 |
+
label="Number Returns", info="Must be <= num_beams",
|
273 |
+
visible=not is_public)
|
274 |
+
iinput = gr.Textbox(lines=4, label="Input",
|
275 |
+
placeholder=kwargs['placeholder_input'],
|
276 |
+
visible=not is_public)
|
277 |
+
context = gr.Textbox(lines=3, label="System Pre-Context",
|
278 |
+
info="Directly pre-appended without prompt processing",
|
279 |
+
visible=not is_public)
|
280 |
+
chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
|
281 |
+
visible=not is_public)
|
282 |
+
|
283 |
+
with gr.TabItem("Models"):
|
284 |
+
load_msg = "Load-Unload Model/LORA" if not is_public \
|
285 |
+
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
|
286 |
+
load_msg2 = "Load-Unload Model/LORA 2" if not is_public \
|
287 |
+
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
|
288 |
+
compare_checkbox = gr.components.Checkbox(label="Compare Mode",
|
289 |
+
value=False, visible=not is_public)
|
290 |
+
with gr.Row():
|
291 |
+
n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
|
292 |
+
with gr.Column():
|
293 |
+
with gr.Row():
|
294 |
+
with gr.Column(scale=50):
|
295 |
+
model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
|
296 |
+
value=kwargs['base_model'])
|
297 |
+
lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
|
298 |
+
value=kwargs['lora_weights'], visible=kwargs['show_lora'])
|
299 |
+
with gr.Column(scale=1):
|
300 |
+
load_model_button = gr.Button(load_msg)
|
301 |
+
model_load8bit_checkbox = gr.components.Checkbox(
|
302 |
+
label="Load 8-bit [requires support]",
|
303 |
+
value=kwargs['load_8bit'])
|
304 |
+
model_infer_devices_checkbox = gr.components.Checkbox(
|
305 |
+
label="Choose Devices [If not Checked, use all GPUs]",
|
306 |
+
value=kwargs['infer_devices'])
|
307 |
+
model_gpu = gr.Dropdown(n_gpus_list,
|
308 |
+
label="GPU ID 2 [-1 = all GPUs, if Choose is enabled]",
|
309 |
+
value=kwargs['gpu_id'])
|
310 |
+
model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'])
|
311 |
+
lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'],
|
312 |
+
visible=kwargs['show_lora'])
|
313 |
+
with gr.Row():
|
314 |
+
with gr.Column(scale=50):
|
315 |
+
new_model = gr.Textbox(label="New Model HF name/path")
|
316 |
+
new_lora = gr.Textbox(label="New LORA HF name/path", visible=kwargs['show_lora'])
|
317 |
+
with gr.Column(scale=1):
|
318 |
+
add_model_button = gr.Button("Add new model name")
|
319 |
+
add_lora_button = gr.Button("Add new LORA name", visible=kwargs['show_lora'])
|
320 |
+
col_model2 = gr.Column(visible=False)
|
321 |
+
with col_model2:
|
322 |
+
with gr.Row():
|
323 |
+
with gr.Column(scale=50):
|
324 |
+
model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
|
325 |
+
value=no_model_str)
|
326 |
+
lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
|
327 |
+
value=no_lora_str,
|
328 |
+
visible=kwargs['show_lora'])
|
329 |
+
with gr.Column(scale=1):
|
330 |
+
load_model_button2 = gr.Button(load_msg2)
|
331 |
+
model_load8bit_checkbox2 = gr.components.Checkbox(
|
332 |
+
label="Load 8-bit 2 [requires support]",
|
333 |
+
value=kwargs['load_8bit'])
|
334 |
+
model_infer_devices_checkbox2 = gr.components.Checkbox(
|
335 |
+
label="Choose Devices 2 [If not Checked, use all GPUs]",
|
336 |
+
value=kwargs[
|
337 |
+
'infer_devices'])
|
338 |
+
model_gpu2 = gr.Dropdown(n_gpus_list,
|
339 |
+
label="GPU ID [-1 = all GPUs, if choose is enabled]",
|
340 |
+
value=kwargs['gpu_id'])
|
341 |
+
# no model/lora loaded ever in model2 by default
|
342 |
+
model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str)
|
343 |
+
lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
|
344 |
+
visible=kwargs['show_lora'])
|
345 |
+
with gr.TabItem("System"):
|
346 |
+
admin_row = gr.Row()
|
347 |
+
with admin_row:
|
348 |
+
admin_pass_textbox = gr.Textbox(label="Admin Password", type='password', visible=is_public)
|
349 |
+
admin_btn = gr.Button(value="Admin Access", visible=is_public)
|
350 |
+
system_row = gr.Row(visible=not is_public)
|
351 |
+
with system_row:
|
352 |
+
with gr.Column():
|
353 |
+
with gr.Row():
|
354 |
+
system_btn = gr.Button(value='Get System Info')
|
355 |
+
system_text = gr.Textbox(label='System Info')
|
356 |
+
|
357 |
+
with gr.Row():
|
358 |
+
zip_btn = gr.Button("Zip")
|
359 |
+
zip_text = gr.Textbox(label="Zip file name")
|
360 |
+
file_output = gr.File()
|
361 |
+
with gr.Row():
|
362 |
+
s3up_btn = gr.Button("S3UP")
|
363 |
+
s3up_text = gr.Textbox(label='S3UP result')
|
364 |
+
|
365 |
+
# Get flagged data
|
366 |
+
zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
|
367 |
+
zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text])
|
368 |
+
s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text)
|
369 |
+
|
370 |
+
def check_admin_pass(x):
|
371 |
+
return gr.update(visible=x == admin_pass)
|
372 |
+
|
373 |
+
def close_admin(x):
|
374 |
+
return gr.update(visible=not (x == admin_pass))
|
375 |
+
|
376 |
+
admin_btn.click(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row) \
|
377 |
+
.then(close_admin, inputs=admin_pass_textbox, outputs=admin_row)
|
378 |
+
|
379 |
+
# Get inputs to evaluate()
|
380 |
+
all_kwargs = kwargs.copy()
|
381 |
+
all_kwargs.update(locals())
|
382 |
+
inputs_list = get_inputs_list(all_kwargs, kwargs['model_lower'])
|
383 |
+
from functools import partial
|
384 |
+
kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
|
385 |
+
# ensure present
|
386 |
+
for k in inputs_kwargs_list:
|
387 |
+
assert k in kwargs_evaluate, "Missing %s" % k
|
388 |
+
fun = partial(evaluate,
|
389 |
+
**kwargs_evaluate)
|
390 |
+
fun2 = partial(evaluate,
|
391 |
+
**kwargs_evaluate)
|
392 |
+
|
393 |
+
dark_mode_btn = gr.Button("Dark Mode", variant="primary").style(
|
394 |
+
size="sm",
|
395 |
+
)
|
396 |
+
dark_mode_btn.click(
|
397 |
+
None,
|
398 |
+
None,
|
399 |
+
None,
|
400 |
+
_js=get_dark_js(),
|
401 |
+
api_name="dark" if allow_api else None,
|
402 |
+
)
|
403 |
+
|
404 |
+
# Control chat and non-chat blocks, which can be independently used by chat checkbox swap
|
405 |
+
def col_nochat_fun(x):
|
406 |
+
return gr.Column.update(visible=not x)
|
407 |
+
|
408 |
+
def col_chat_fun(x):
|
409 |
+
return gr.Column.update(visible=x)
|
410 |
+
|
411 |
+
def context_fun(x):
|
412 |
+
return gr.Textbox.update(visible=not x)
|
413 |
+
|
414 |
+
chat.select(col_nochat_fun, chat, col_nochat, api_name="chat_checkbox" if allow_api else None) \
|
415 |
+
.then(col_chat_fun, chat, col_chat) \
|
416 |
+
.then(context_fun, chat, context)
|
417 |
+
|
418 |
+
# examples after submit or any other buttons for chat or no chat
|
419 |
+
if kwargs['examples'] is not None and kwargs['show_examples']:
|
420 |
+
gr.Examples(examples=kwargs['examples'], inputs=inputs_list)
|
421 |
+
|
422 |
+
# Score
|
423 |
+
def score_last_response(*args, nochat=False, model2=False):
|
424 |
+
""" Similar to user() """
|
425 |
+
args_list = list(args)
|
426 |
+
|
427 |
+
max_length_tokenize = 512 if is_low_mem else 2048
|
428 |
+
cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM
|
429 |
+
smodel = score_model_state0[0]
|
430 |
+
stokenizer = score_model_state0[1]
|
431 |
+
sdevice = score_model_state0[2]
|
432 |
+
if not nochat:
|
433 |
+
history = args_list[-1]
|
434 |
+
if history is None:
|
435 |
+
if not model2:
|
436 |
+
# maybe only doing first model, no need to complain
|
437 |
+
print("Bad history in scoring last response, fix for now", flush=True)
|
438 |
+
history = []
|
439 |
+
if smodel is not None and \
|
440 |
+
stokenizer is not None and \
|
441 |
+
sdevice is not None and \
|
442 |
+
history is not None and len(history) > 0 and \
|
443 |
+
history[-1] is not None and \
|
444 |
+
len(history[-1]) >= 2:
|
445 |
+
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
|
446 |
+
|
447 |
+
question = history[-1][0]
|
448 |
+
|
449 |
+
answer = history[-1][1]
|
450 |
+
else:
|
451 |
+
return 'Response Score: NA'
|
452 |
+
else:
|
453 |
+
answer = args_list[-1]
|
454 |
+
instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat')
|
455 |
+
question = args_list[instruction_nochat_arg_id]
|
456 |
+
|
457 |
+
if question is None:
|
458 |
+
return 'Response Score: Bad Question'
|
459 |
+
if answer is None:
|
460 |
+
return 'Response Score: Bad Answer'
|
461 |
+
score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len)
|
462 |
+
if isinstance(score, str):
|
463 |
+
return 'Response Score: NA'
|
464 |
+
return 'Response Score: {:.1%}'.format(score)
|
465 |
+
|
466 |
+
def noop_score_last_response(*args, **kwargs):
|
467 |
+
return "Response Score: Disabled"
|
468 |
+
|
469 |
+
if kwargs['score_model']:
|
470 |
+
score_fun = score_last_response
|
471 |
+
else:
|
472 |
+
score_fun = noop_score_last_response
|
473 |
+
|
474 |
+
score_args = dict(fn=score_fun,
|
475 |
+
inputs=inputs_list + [text_output],
|
476 |
+
outputs=[score_text],
|
477 |
+
)
|
478 |
+
score_args2 = dict(fn=partial(score_fun, model2=True),
|
479 |
+
inputs=inputs_list + [text_output2],
|
480 |
+
outputs=[score_text2],
|
481 |
+
)
|
482 |
+
|
483 |
+
score_args_nochat = dict(fn=partial(score_fun, nochat=True),
|
484 |
+
inputs=inputs_list + [text_output_nochat],
|
485 |
+
outputs=[score_text_nochat],
|
486 |
+
)
|
487 |
+
if not kwargs['auto_score']:
|
488 |
+
score_event = score_btn.click(**score_args, queue=stream_output, api_name='score' if allow_api else None) \
|
489 |
+
.then(**score_args2, queue=stream_output, api_name='score2' if allow_api else None)
|
490 |
+
score_event_nochat = score_btn_nochat.click(**score_args_nochat, queue=stream_output,
|
491 |
+
api_name='score_nochat' if allow_api else None)
|
492 |
+
|
493 |
+
def user(*args, undo=False, sanitize_user_prompt=True, model2=False):
|
494 |
+
"""
|
495 |
+
User that fills history for bot
|
496 |
+
:param args:
|
497 |
+
:param undo:
|
498 |
+
:param sanitize_user_prompt:
|
499 |
+
:param model2:
|
500 |
+
:return:
|
501 |
+
"""
|
502 |
+
args_list = list(args)
|
503 |
+
user_message = args_list[0]
|
504 |
+
input1 = args_list[1]
|
505 |
+
context1 = args_list[2]
|
506 |
+
if input1 and not user_message.endswith(':'):
|
507 |
+
user_message1 = user_message + ":" + input1
|
508 |
+
elif input1:
|
509 |
+
user_message1 = user_message + input1
|
510 |
+
else:
|
511 |
+
user_message1 = user_message
|
512 |
+
if sanitize_user_prompt:
|
513 |
+
from better_profanity import profanity
|
514 |
+
user_message1 = profanity.censor(user_message1)
|
515 |
+
|
516 |
+
history = args_list[-1]
|
517 |
+
if undo and history:
|
518 |
+
history.pop()
|
519 |
+
args_list = args_list[:-1] # FYI, even if unused currently
|
520 |
+
if history is None:
|
521 |
+
if not model2:
|
522 |
+
# no need to complain so often unless model1
|
523 |
+
print("Bad history, fix for now", flush=True)
|
524 |
+
history = []
|
525 |
+
# ensure elements not mixed across models as output,
|
526 |
+
# even if input is currently same source
|
527 |
+
history = history.copy()
|
528 |
+
if undo:
|
529 |
+
return history
|
530 |
+
else:
|
531 |
+
# FIXME: compare, same history for now
|
532 |
+
return history + [[user_message1, None]]
|
533 |
+
|
534 |
+
def bot(*args, retry=False):
|
535 |
+
"""
|
536 |
+
bot that consumes history for user input
|
537 |
+
instruction (from input_list) itself is not consumed by bot
|
538 |
+
:param args:
|
539 |
+
:param retry:
|
540 |
+
:return:
|
541 |
+
"""
|
542 |
+
args_list = list(args).copy()
|
543 |
+
history = args_list[-1] # model_state is -2
|
544 |
+
if retry and history:
|
545 |
+
history.pop()
|
546 |
+
if not history:
|
547 |
+
print("No history", flush=True)
|
548 |
+
return
|
549 |
+
# ensure output will be unique to models
|
550 |
+
history = history.copy()
|
551 |
+
instruction1 = history[-1][0]
|
552 |
+
context1 = ''
|
553 |
+
if kwargs['chat_history'] > 0:
|
554 |
+
prompt_type_arg_id = eval_func_param_names.index('prompt_type')
|
555 |
+
prompt_type1 = args_list[prompt_type_arg_id]
|
556 |
+
chat_arg_id = eval_func_param_names.index('chat')
|
557 |
+
chat1 = args_list[chat_arg_id]
|
558 |
+
context1 = ''
|
559 |
+
for histi in range(len(history) - 1):
|
560 |
+
data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
|
561 |
+
context1 += generate_prompt(data_point, prompt_type1, chat1, reduced=True)[0].replace(
|
562 |
+
'<br>', '\n')
|
563 |
+
if not context1.endswith('\n'):
|
564 |
+
context1 += '\n'
|
565 |
+
if context1 and not context1.endswith('\n'):
|
566 |
+
context1 += '\n' # ensure if terminates abruptly, then human continues on next line
|
567 |
+
args_list[0] = instruction1 # override original instruction with history from user
|
568 |
+
# only include desired chat history
|
569 |
+
args_list[2] = context1[-kwargs['chat_history']:]
|
570 |
+
model_state1 = args_list[-2]
|
571 |
+
if model_state1[0] is None or model_state1[0] == no_model_str:
|
572 |
+
return
|
573 |
+
args_list = args_list[:-2]
|
574 |
+
fun1 = partial(evaluate,
|
575 |
+
model_state1,
|
576 |
+
**kwargs_evaluate)
|
577 |
+
try:
|
578 |
+
for output in fun1(*tuple(args_list)):
|
579 |
+
bot_message = output
|
580 |
+
history[-1][1] = bot_message
|
581 |
+
yield history
|
582 |
+
except StopIteration:
|
583 |
+
yield history
|
584 |
+
except RuntimeError as e:
|
585 |
+
if "generator raised StopIteration" in str(e):
|
586 |
+
# assume last entry was bad, undo
|
587 |
+
history.pop()
|
588 |
+
yield history
|
589 |
+
raise
|
590 |
+
except Exception as e:
|
591 |
+
# put error into user input
|
592 |
+
history[-1][0] = "Exception: %s" % str(e)
|
593 |
+
yield history
|
594 |
+
raise
|
595 |
+
return
|
596 |
+
|
597 |
+
# NORMAL MODEL
|
598 |
+
user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
|
599 |
+
inputs=inputs_list + [text_output],
|
600 |
+
outputs=text_output,
|
601 |
+
)
|
602 |
+
bot_args = dict(fn=bot,
|
603 |
+
inputs=inputs_list + [model_state] + [text_output],
|
604 |
+
outputs=text_output,
|
605 |
+
)
|
606 |
+
retry_bot_args = dict(fn=functools.partial(bot, retry=True),
|
607 |
+
inputs=inputs_list + [model_state] + [text_output],
|
608 |
+
outputs=text_output,
|
609 |
+
)
|
610 |
+
undo_user_args = dict(fn=functools.partial(user, undo=True),
|
611 |
+
inputs=inputs_list + [text_output],
|
612 |
+
outputs=text_output,
|
613 |
+
)
|
614 |
+
|
615 |
+
# MODEL2
|
616 |
+
user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt'], model2=True),
|
617 |
+
inputs=inputs_list + [text_output2],
|
618 |
+
outputs=text_output2,
|
619 |
+
)
|
620 |
+
bot_args2 = dict(fn=bot,
|
621 |
+
inputs=inputs_list + [model_state2] + [text_output2],
|
622 |
+
outputs=text_output2,
|
623 |
+
)
|
624 |
+
retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
|
625 |
+
inputs=inputs_list + [model_state2] + [text_output2],
|
626 |
+
outputs=text_output2,
|
627 |
+
)
|
628 |
+
undo_user_args2 = dict(fn=functools.partial(user, undo=True),
|
629 |
+
inputs=inputs_list + [text_output2],
|
630 |
+
outputs=text_output2,
|
631 |
+
)
|
632 |
+
|
633 |
+
def clear_instruct():
|
634 |
+
return gr.Textbox.update(value='')
|
635 |
+
|
636 |
+
if kwargs['auto_score']:
|
637 |
+
# in case 2nd model, consume instruction first, so can clear quickly
|
638 |
+
# bot doesn't consume instruction itself, just history from user, so why works
|
639 |
+
submit_event = instruction.submit(**user_args, queue=stream_output,
|
640 |
+
api_name='instruction' if allow_api else None) \
|
641 |
+
.then(**user_args2, queue=stream_output, api_name='instruction2' if allow_api else None) \
|
642 |
+
.then(clear_instruct, None, instruction) \
|
643 |
+
.then(clear_instruct, None, iinput) \
|
644 |
+
.then(**bot_args, api_name='instruction_bot' if allow_api else None) \
|
645 |
+
.then(**score_args, api_name='instruction_bot_score' if allow_api else None) \
|
646 |
+
.then(**bot_args2, api_name='instruction_bot2' if allow_api else None) \
|
647 |
+
.then(**score_args2, api_name='instruction_bot_score2' if allow_api else None) \
|
648 |
+
.then(clear_torch_cache)
|
649 |
+
submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit' if allow_api else None) \
|
650 |
+
.then(**user_args2, queue=stream_output, api_name='submit2' if allow_api else None) \
|
651 |
+
.then(clear_instruct, None, instruction) \
|
652 |
+
.then(clear_instruct, None, iinput) \
|
653 |
+
.then(**bot_args, api_name='submit_bot' if allow_api else None) \
|
654 |
+
.then(**score_args, api_name='submit_bot_score' if allow_api else None) \
|
655 |
+
.then(**bot_args2, api_name='submit_bot2' if allow_api else None) \
|
656 |
+
.then(**score_args2, api_name='submit_bot_score2' if allow_api else None) \
|
657 |
+
.then(clear_torch_cache)
|
658 |
+
submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry' if allow_api else None) \
|
659 |
+
.then(**user_args2, queue=stream_output, api_name='retry2' if allow_api else None) \
|
660 |
+
.then(clear_instruct, None, instruction) \
|
661 |
+
.then(clear_instruct, None, iinput) \
|
662 |
+
.then(**retry_bot_args, api_name='retry_bot' if allow_api else None) \
|
663 |
+
.then(**score_args, api_name='retry_bot_score' if allow_api else None) \
|
664 |
+
.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None) \
|
665 |
+
.then(**score_args2, api_name='retry_bot_score2' if allow_api else None) \
|
666 |
+
.then(clear_torch_cache)
|
667 |
+
submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo' if allow_api else None) \
|
668 |
+
.then(**undo_user_args2, queue=stream_output, api_name='undo2' if allow_api else None) \
|
669 |
+
.then(clear_instruct, None, instruction) \
|
670 |
+
.then(clear_instruct, None, iinput) \
|
671 |
+
.then(**score_args, api_name='undo_score' if allow_api else None) \
|
672 |
+
.then(**score_args2, api_name='undo_score2' if allow_api else None)
|
673 |
+
else:
|
674 |
+
submit_event = instruction.submit(**user_args, queue=stream_output,
|
675 |
+
api_name='instruction' if allow_api else None) \
|
676 |
+
.then(**user_args2, queue=stream_output, api_name='instruction2' if allow_api else None) \
|
677 |
+
.then(clear_instruct, None, instruction) \
|
678 |
+
.then(clear_instruct, None, iinput) \
|
679 |
+
.then(**bot_args, api_name='instruction_bot' if allow_api else None) \
|
680 |
+
.then(**bot_args2, api_name='instruction_bot2' if allow_api else None) \
|
681 |
+
.then(clear_torch_cache)
|
682 |
+
submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit' if allow_api else None) \
|
683 |
+
.then(**user_args2, queue=stream_output, api_name='submit2' if allow_api else None) \
|
684 |
+
.then(clear_instruct, None, instruction) \
|
685 |
+
.then(clear_instruct, None, iinput) \
|
686 |
+
.then(**bot_args, api_name='submit_bot' if allow_api else None) \
|
687 |
+
.then(**bot_args2, api_name='submit_bot2' if allow_api else None) \
|
688 |
+
.then(clear_torch_cache)
|
689 |
+
submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry' if allow_api else None) \
|
690 |
+
.then(**user_args2, queue=stream_output, api_name='retry2' if allow_api else None) \
|
691 |
+
.then(clear_instruct, None, instruction) \
|
692 |
+
.then(clear_instruct, None, iinput) \
|
693 |
+
.then(**retry_bot_args, api_name='retry_bot' if allow_api else None) \
|
694 |
+
.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None) \
|
695 |
+
.then(clear_torch_cache)
|
696 |
+
submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo' if allow_api else None) \
|
697 |
+
.then(**undo_user_args2, queue=stream_output, api_name='undo2' if allow_api else None)
|
698 |
+
|
699 |
+
# does both models
|
700 |
+
clear.click(lambda: None, None, text_output, queue=False, api_name='clear' if allow_api else None) \
|
701 |
+
.then(lambda: None, None, text_output2, queue=False, api_name='clear2' if allow_api else None)
|
702 |
+
# NOTE: clear of instruction/iinput for nochat has to come after score,
|
703 |
+
# because score for nochat consumes actual textbox, while chat consumes chat history filled by user()
|
704 |
+
submit_event_nochat = submit_nochat.click(fun, inputs=[model_state] + inputs_list,
|
705 |
+
outputs=text_output_nochat,
|
706 |
+
api_name='submit_nochat' if allow_api else None) \
|
707 |
+
.then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None) \
|
708 |
+
.then(clear_instruct, None, instruction_nochat) \
|
709 |
+
.then(clear_instruct, None, iinput_nochat) \
|
710 |
+
.then(clear_torch_cache)
|
711 |
+
|
712 |
+
def load_model(model_name, lora_weights, model_state_old, prompt_type_old, load_8bit, infer_devices, gpu_id):
|
713 |
+
# ensure old model removed from GPU memory
|
714 |
+
if kwargs['debug']:
|
715 |
+
print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True)
|
716 |
+
|
717 |
+
model0 = model_state0[0]
|
718 |
+
if isinstance(model_state_old[0], str) and model0 is not None:
|
719 |
+
# best can do, move model loaded at first to CPU
|
720 |
+
model0.cpu()
|
721 |
+
|
722 |
+
if model_state_old[0] is not None and not isinstance(model_state_old[0], str):
|
723 |
+
try:
|
724 |
+
model_state_old[0].cpu()
|
725 |
+
except Exception as e:
|
726 |
+
# sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
|
727 |
+
print("Unable to put model on CPU: %s" % str(e), flush=True)
|
728 |
+
del model_state_old[0]
|
729 |
+
model_state_old[0] = None
|
730 |
+
|
731 |
+
if model_state_old[1] is not None and not isinstance(model_state_old[1], str):
|
732 |
+
del model_state_old[1]
|
733 |
+
model_state_old[1] = None
|
734 |
+
|
735 |
+
clear_torch_cache()
|
736 |
+
if kwargs['debug']:
|
737 |
+
print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)
|
738 |
+
|
739 |
+
if model_name is None or model_name == no_model_str:
|
740 |
+
# no-op if no model, just free memory
|
741 |
+
# no detranscribe needed for model, never go into evaluate
|
742 |
+
lora_weights = no_lora_str
|
743 |
+
return [None, None, None, model_name], model_name, lora_weights, prompt_type_old
|
744 |
+
|
745 |
+
all_kwargs1 = all_kwargs.copy()
|
746 |
+
all_kwargs1['base_model'] = model_name.strip()
|
747 |
+
all_kwargs1['load_8bit'] = load_8bit
|
748 |
+
all_kwargs1['infer_devices'] = infer_devices
|
749 |
+
all_kwargs1['gpu_id'] = int(gpu_id) # detranscribe
|
750 |
+
model_lower = model_name.strip().lower()
|
751 |
+
if model_lower in inv_prompt_type_to_model_lower:
|
752 |
+
prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
|
753 |
+
else:
|
754 |
+
prompt_type1 = prompt_type_old
|
755 |
+
|
756 |
+
# detranscribe
|
757 |
+
if lora_weights == no_lora_str:
|
758 |
+
lora_weights = ''
|
759 |
+
|
760 |
+
all_kwargs1['lora_weights'] = lora_weights.strip()
|
761 |
+
model1, tokenizer1, device1 = get_model(**all_kwargs1)
|
762 |
+
clear_torch_cache()
|
763 |
+
|
764 |
+
if kwargs['debug']:
|
765 |
+
print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True)
|
766 |
+
return [model1, tokenizer1, device1, model_name], model_name, lora_weights, prompt_type1
|
767 |
+
|
768 |
+
def dropdown_prompt_type_list(x):
|
769 |
+
return gr.Dropdown.update(value=x)
|
770 |
+
|
771 |
+
def chatbot_list(x, model_used_in):
|
772 |
+
return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')
|
773 |
+
|
774 |
+
load_model_args = dict(fn=load_model,
|
775 |
+
inputs=[model_choice, lora_choice, model_state, prompt_type,
|
776 |
+
model_load8bit_checkbox, model_infer_devices_checkbox, model_gpu],
|
777 |
+
outputs=[model_state, model_used, lora_used, prompt_type])
|
778 |
+
prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
|
779 |
+
chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
|
780 |
+
nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
|
781 |
+
if not is_public:
|
782 |
+
load_model_event = load_model_button.click(**load_model_args) \
|
783 |
+
.then(**prompt_update_args) \
|
784 |
+
.then(**chatbot_update_args) \
|
785 |
+
.then(**nochat_update_args) \
|
786 |
+
.then(clear_torch_cache)
|
787 |
+
|
788 |
+
load_model_args2 = dict(fn=load_model,
|
789 |
+
inputs=[model_choice2, lora_choice2, model_state2, prompt_type2,
|
790 |
+
model_load8bit_checkbox2, model_infer_devices_checkbox2, model_gpu2],
|
791 |
+
outputs=[model_state2, model_used2, lora_used2, prompt_type2])
|
792 |
+
prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
|
793 |
+
chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
|
794 |
+
if not is_public:
|
795 |
+
load_model_event2 = load_model_button2.click(**load_model_args2) \
|
796 |
+
.then(**prompt_update_args2) \
|
797 |
+
.then(**chatbot_update_args2) \
|
798 |
+
.then(clear_torch_cache)
|
799 |
+
|
800 |
+
def dropdown_model_list(list0, x):
|
801 |
+
new_state = [list0[0] + [x]]
|
802 |
+
new_options = [*new_state[0]]
|
803 |
+
return gr.Dropdown.update(value=x, choices=new_options), \
|
804 |
+
gr.Dropdown.update(value=x, choices=new_options), \
|
805 |
+
'', new_state
|
806 |
+
|
807 |
+
add_model_event = add_model_button.click(fn=dropdown_model_list,
|
808 |
+
inputs=[model_options_state, new_model],
|
809 |
+
outputs=[model_choice, model_choice2, new_model, model_options_state])
|
810 |
+
|
811 |
+
def dropdown_lora_list(list0, x, model_used1, lora_used1, model_used2, lora_used2):
|
812 |
+
new_state = [list0[0] + [x]]
|
813 |
+
new_options = [*new_state[0]]
|
814 |
+
# don't switch drop-down to added lora if already have model loaded
|
815 |
+
x1 = x if model_used1 == no_model_str else lora_used1
|
816 |
+
x2 = x if model_used2 == no_model_str else lora_used2
|
817 |
+
return gr.Dropdown.update(value=x1, choices=new_options), \
|
818 |
+
gr.Dropdown.update(value=x2, choices=new_options), \
|
819 |
+
'', new_state
|
820 |
+
|
821 |
+
add_lora_event = add_lora_button.click(fn=dropdown_lora_list,
|
822 |
+
inputs=[lora_options_state, new_lora, model_used, lora_used, model_used2,
|
823 |
+
lora_used2],
|
824 |
+
outputs=[lora_choice, lora_choice2, new_lora, lora_options_state])
|
825 |
+
|
826 |
+
go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None) \
|
827 |
+
.then(lambda: gr.update(visible=True), None, normal_block) \
|
828 |
+
.then(**load_model_args).then(**prompt_update_args)
|
829 |
+
|
830 |
+
def compare_textbox_fun(x):
|
831 |
+
return gr.Textbox.update(visible=x)
|
832 |
+
|
833 |
+
def compare_column_fun(x):
|
834 |
+
return gr.Column.update(visible=x)
|
835 |
+
|
836 |
+
def compare_prompt_fun(x):
|
837 |
+
return gr.Dropdown.update(visible=x)
|
838 |
+
|
839 |
+
compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2,
|
840 |
+
api_name="compare_checkbox" if allow_api else None) \
|
841 |
+
.then(compare_column_fun, compare_checkbox, col_model2) \
|
842 |
+
.then(compare_prompt_fun, compare_checkbox, prompt_type2) \
|
843 |
+
.then(compare_textbox_fun, compare_checkbox, score_text2)
|
844 |
+
# FIXME: add score_res2 in condition, but do better
|
845 |
+
|
846 |
+
# callback for logging flagged input/output
|
847 |
+
callback.setup(inputs_list + [text_output, text_output2], "flagged_data_points")
|
848 |
+
flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2], None, preprocess=False,
|
849 |
+
api_name='flag' if allow_api else None)
|
850 |
+
flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None, preprocess=False,
|
851 |
+
api_name='flag_nochat' if allow_api else None)
|
852 |
+
|
853 |
+
def get_system_info():
|
854 |
+
return gr.Textbox.update(value=system_info_print())
|
855 |
+
|
856 |
+
system_event = system_btn.click(get_system_info, outputs=system_text,
|
857 |
+
api_name='system_info' if allow_api else None)
|
858 |
+
|
859 |
+
# don't pass text_output, don't want to clear output, just stop it
|
860 |
+
# FIXME: have to click once to stop output and second time to stop GPUs going
|
861 |
+
stop_btn.click(lambda: None, None, None,
|
862 |
+
cancels=[submit_event_nochat, submit_event, submit_event2, submit_event3],
|
863 |
+
queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache)
|
864 |
+
demo.load(None, None, None, _js=get_dark_js() if kwargs['h2ocolors'] else None)
|
865 |
+
|
866 |
+
demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
|
867 |
+
favicon_path = "h2o-logo.svg"
|
868 |
+
|
869 |
+
scheduler = BackgroundScheduler()
|
870 |
+
scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20)
|
871 |
+
scheduler.start()
|
872 |
+
|
873 |
+
demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
|
874 |
+
favicon_path=favicon_path, prevent_thread_lock=True) # , enable_queue=True)
|
875 |
+
print("Started GUI", flush=True)
|
876 |
+
if kwargs['block_gradio_exit']:
|
877 |
+
demo.block_thread()
|
878 |
+
|
879 |
+
|
880 |
+
input_args_list = ['model_state']
|
881 |
+
inputs_kwargs_list = ['debug', 'save_dir', 'hard_stop_list', 'sanitize_bot_response', 'model_state0', 'is_low_mem',
|
882 |
+
'raise_generate_gpu_exceptions', 'chat_context', 'concurrency_count']
|
883 |
+
|
884 |
+
|
885 |
+
def get_inputs_list(inputs_dict, model_lower):
|
886 |
+
"""
|
887 |
+
map gradio objects in locals() to inputs for evaluate().
|
888 |
+
:param inputs_dict:
|
889 |
+
:param model_lower:
|
890 |
+
:return:
|
891 |
+
"""
|
892 |
+
inputs_list_names = list(inspect.signature(evaluate).parameters)
|
893 |
+
inputs_list = []
|
894 |
+
for k in inputs_list_names:
|
895 |
+
if k == 'kwargs':
|
896 |
+
continue
|
897 |
+
if k in input_args_list + inputs_kwargs_list:
|
898 |
+
# these are added via partial, not taken as input
|
899 |
+
continue
|
900 |
+
if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
|
901 |
+
continue
|
902 |
+
inputs_list.append(inputs_dict[k])
|
903 |
+
return inputs_list
|
gradio_themes.py
ADDED
@@ -0,0 +1,142 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
from gradio.themes.soft import Soft
|
3 |
+
from gradio.themes.utils import Color, colors, sizes
|
4 |
+
|
5 |
+
h2o_yellow = Color(
|
6 |
+
name="yellow",
|
7 |
+
c50="#fffef2",
|
8 |
+
c100="#fff9e6",
|
9 |
+
c200="#ffecb3",
|
10 |
+
c300="#ffe28c",
|
11 |
+
c400="#ffd659",
|
12 |
+
c500="#fec925",
|
13 |
+
c600="#e6ac00",
|
14 |
+
c700="#bf8f00",
|
15 |
+
c800="#a67c00",
|
16 |
+
c900="#664d00",
|
17 |
+
c950="#403000",
|
18 |
+
)
|
19 |
+
h2o_gray = Color(
|
20 |
+
name="gray",
|
21 |
+
c50="#f8f8f8",
|
22 |
+
c100="#e5e5e5",
|
23 |
+
c200="#cccccc",
|
24 |
+
c300="#b2b2b2",
|
25 |
+
c400="#999999",
|
26 |
+
c500="#7f7f7f",
|
27 |
+
c600="#666666",
|
28 |
+
c700="#4c4c4c",
|
29 |
+
c800="#333333",
|
30 |
+
c900="#191919",
|
31 |
+
c950="#0d0d0d",
|
32 |
+
)
|
33 |
+
|
34 |
+
|
35 |
+
class H2oTheme(Soft):
|
36 |
+
def __init__(
|
37 |
+
self,
|
38 |
+
*,
|
39 |
+
primary_hue: colors.Color | str = h2o_yellow,
|
40 |
+
secondary_hue: colors.Color | str = h2o_yellow,
|
41 |
+
neutral_hue: colors.Color | str = h2o_gray,
|
42 |
+
spacing_size: sizes.Size | str = sizes.spacing_md,
|
43 |
+
radius_size: sizes.Size | str = sizes.radius_md,
|
44 |
+
text_size: sizes.Size | str = sizes.text_lg,
|
45 |
+
):
|
46 |
+
super().__init__(
|
47 |
+
primary_hue=primary_hue,
|
48 |
+
secondary_hue=secondary_hue,
|
49 |
+
neutral_hue=neutral_hue,
|
50 |
+
spacing_size=spacing_size,
|
51 |
+
radius_size=radius_size,
|
52 |
+
text_size=text_size,
|
53 |
+
)
|
54 |
+
super().set(
|
55 |
+
link_text_color="#3344DD",
|
56 |
+
link_text_color_hover="#3344DD",
|
57 |
+
link_text_color_visited="#3344DD",
|
58 |
+
link_text_color_dark="#74abff",
|
59 |
+
link_text_color_hover_dark="#a3c8ff",
|
60 |
+
link_text_color_active_dark="#a3c8ff",
|
61 |
+
link_text_color_visited_dark="#74abff",
|
62 |
+
button_primary_text_color="*neutral_950",
|
63 |
+
button_primary_text_color_dark="*neutral_950",
|
64 |
+
button_primary_background_fill="*primary_500",
|
65 |
+
button_primary_background_fill_dark="*primary_500",
|
66 |
+
block_label_background_fill="*primary_500",
|
67 |
+
block_label_background_fill_dark="*primary_500",
|
68 |
+
block_label_text_color="*neutral_950",
|
69 |
+
block_label_text_color_dark="*neutral_950",
|
70 |
+
block_title_text_color="*neutral_950",
|
71 |
+
block_title_text_color_dark="*neutral_950",
|
72 |
+
block_background_fill_dark="*neutral_950",
|
73 |
+
body_background_fill="*neutral_50",
|
74 |
+
body_background_fill_dark="*neutral_900",
|
75 |
+
background_fill_primary_dark="*block_background_fill",
|
76 |
+
block_radius="0 0 8px 8px",
|
77 |
+
)
|
78 |
+
|
79 |
+
|
80 |
+
class SoftTheme(Soft):
|
81 |
+
def __init__(
|
82 |
+
self,
|
83 |
+
*,
|
84 |
+
primary_hue: colors.Color | str = colors.indigo,
|
85 |
+
secondary_hue: colors.Color | str = colors.indigo,
|
86 |
+
neutral_hue: colors.Color | str = colors.gray,
|
87 |
+
spacing_size: sizes.Size | str = sizes.spacing_md,
|
88 |
+
radius_size: sizes.Size | str = sizes.radius_md,
|
89 |
+
text_size: sizes.Size | str = sizes.text_md,
|
90 |
+
):
|
91 |
+
super().__init__(
|
92 |
+
primary_hue=primary_hue,
|
93 |
+
secondary_hue=secondary_hue,
|
94 |
+
neutral_hue=neutral_hue,
|
95 |
+
spacing_size=spacing_size,
|
96 |
+
radius_size=radius_size,
|
97 |
+
text_size=text_size,
|
98 |
+
)
|
99 |
+
|
100 |
+
|
101 |
+
h2o_logo = '<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="100%" height="100%"' \
|
102 |
+
' viewBox="0 0 600.28 600.28"><defs><style>.cls-1{fill:#fec925;}.cls-2{fill:#161616;}.cls-3{fill:' \
|
103 |
+
'#54585a;}</style></defs><g id="Fill-1"><rect class="cls-1" width="600.28" height="600.28" ' \
|
104 |
+
'rx="23.24"/></g><path class="cls-2" d="M174.33,246.06v92.78H152.86v-38H110.71v38H89.24V246.06h21.' \
|
105 |
+
'47v36.58h42.15V246.06Z"/><path class="cls-2" d="M259.81,321.34v17.5H189.7V324.92l35.78-33.8c8.22-7.' \
|
106 |
+
'82,9.68-12.59,9.68-17.09,0-7.29-5-11.53-14.85-11.53-7.95,0-14.71,3-19.21,9.27L185.46,261.7c7.15-10' \
|
107 |
+
'.47,20.14-17.23,36.84-17.23,20.68,0,34.46,10.6,34.46,27.44,0,9-2.52,17.22-15.51,29.29l-21.33,20.14Z"' \
|
108 |
+
'/><path class="cls-2" d="M268.69,292.45c0-27.57,21.47-48,50.76-48s50.76,20.28,50.76,48-21.6,48-50.' \
|
109 |
+
'76,48S268.69,320,268.69,292.45Zm79.78,0c0-17.63-12.46-29.69-29-29.69s-29,12.06-29,29.69,12.46,29.69' \
|
110 |
+
',29,29.69S348.47,310.08,348.47,292.45Z"/><path class="cls-3" d="M377.23,326.91c0-7.69,5.7-12.73,12.' \
|
111 |
+
'85-12.73s12.86,5,12.86,12.73a12.86,12.86,0,1,1-25.71,0Z"/><path class="cls-3" d="M481.4,298.15v40.' \
|
112 |
+
'69H462.05V330c-3.84,6.49-11.27,9.94-21.74,9.94-16.7,0-26.64-9.28-26.64-21.61,0-12.59,8.88-21.34,30.' \
|
113 |
+
'62-21.34h16.43c0-8.87-5.3-14-16.43-14-7.55,0-15.37,2.51-20.54,6.62l-7.43-14.44c7.82-5.57,19.35-8.' \
|
114 |
+
'62,30.75-8.62C468.81,266.47,481.4,276.54,481.4,298.15Zm-20.68,18.16V309H446.54c-9.67,0-12.72,3.57-' \
|
115 |
+
'12.72,8.35,0,5.16,4.37,8.61,11.66,8.61C452.37,326,458.34,322.8,460.72,316.31Z"/><path class="cls-3"' \
|
116 |
+
' d="M497.56,246.06c0-6.49,5.17-11.53,12.86-11.53s12.86,4.77,12.86,11.13c0,6.89-5.17,11.93-12.86,' \
|
117 |
+
'11.93S497.56,252.55,497.56,246.06Zm2.52,21.47h20.68v71.31H500.08Z"/></svg>'
|
118 |
+
|
119 |
+
|
120 |
+
def get_h2o_title(title):
|
121 |
+
return f"""<div style="display:flex; justify-content:center; margin-bottom:30px;">
|
122 |
+
<div style="height: 60px; width: 60px; margin-right:20px;">{h2o_logo}</div>
|
123 |
+
<h1 style="line-height:60px">{title}</h1>
|
124 |
+
</div>
|
125 |
+
<div style="float:right; height: 80px; width: 80px; margin-top:-100px">
|
126 |
+
<img src=https://raw.githubusercontent.com/h2oai/h2ogpt/main/h2o-qr.png></img>
|
127 |
+
</div>
|
128 |
+
"""
|
129 |
+
|
130 |
+
|
131 |
+
def get_simple_title(title):
|
132 |
+
return f"""<h1 align="center"> {title}</h1>"""
|
133 |
+
|
134 |
+
|
135 |
+
def get_dark_js():
|
136 |
+
return """() => {
|
137 |
+
if (document.querySelectorAll('.dark').length) {
|
138 |
+
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
139 |
+
} else {
|
140 |
+
document.querySelector('body').classList.add('dark');
|
141 |
+
}
|
142 |
+
}"""
|
utils.py
CHANGED
@@ -284,13 +284,14 @@ class KThread(threading.Thread):
|
|
284 |
print(thread.name, flush=True)
|
285 |
|
286 |
@staticmethod
|
287 |
-
def kill_threads(name):
|
288 |
for thread in threading.enumerate():
|
289 |
if name in thread.name:
|
290 |
-
|
291 |
-
|
292 |
thread.kill()
|
293 |
-
|
|
|
294 |
|
295 |
|
296 |
def wrapped_partial(func, *args, **kwargs):
|
|
|
284 |
print(thread.name, flush=True)
|
285 |
|
286 |
@staticmethod
|
287 |
+
def kill_threads(name, debug=False):
|
288 |
for thread in threading.enumerate():
|
289 |
if name in thread.name:
|
290 |
+
if debug:
|
291 |
+
print("Trying to kill %s %s" % (thread.ident, thread), flush=True)
|
292 |
thread.kill()
|
293 |
+
if debug:
|
294 |
+
print(thread, flush=True)
|
295 |
|
296 |
|
297 |
def wrapped_partial(func, *args, **kwargs):
|