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Add penalty_alpha parameter (contrastive search)

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oobabooga 2023-02-11 14:48:12 -03:00
parent 8aafb55693
commit 0dd1409f24
3 changed files with 31 additions and 22 deletions
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1
README.md
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@ -186,4 +186,5 @@ For these two, please try commenting on an existing issue instead of creating a
- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
- Pygmalion preset, code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/
- Verbose preset: Anonymous 4chan user.
- Instruct-Joi preset: https://huggingface.co/Rallio67/joi\_12B\_instruct\_alpha
- Gradio dropdown menu refresh button: https://github.com/AUTOMATIC1111/stable-diffusion-webui

5
presets/Instruct-Joi.txt Normal file
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@ -0,0 +1,5 @@
top_p=0.95,
temperature=0.5,
penalty_alpha=0.6,
top_k=4,
repetition_penalty=1.03,

47
server.py
View file

@ -174,6 +174,7 @@ def load_preset_values(preset_menu, return_dict=False):
'repetition_penalty': 1,
'top_k': 50,
'num_beams': 1,
'penalty_alpha': 0,
'min_length': 0,
'length_penalty': 1,
'no_repeat_ngram_size': 0,
@ -191,7 +192,7 @@ def load_preset_values(preset_menu, return_dict=False):
if return_dict:
return generate_params
else:
return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['length_penalty'], generate_params['early_stopping']
return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
# Removes empty replies from gpt4chan outputs
def fix_gpt4chan(s):
@ -237,7 +238,7 @@ def formatted_outputs(reply, model_name):
else:
return reply
def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=None, stopping_string=None):
def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
global model_name, model, tokenizer
original_question = question
@ -274,6 +275,7 @@ def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top
f"min_length={min_length if args.no_stream else 0}",
f"no_repeat_ngram_size={no_repeat_ngram_size}",
f"num_beams={num_beams}",
f"penalty_alpha={penalty_alpha}",
f"length_penalty={length_penalty}",
f"early_stopping={early_stopping}",
]
@ -392,6 +394,7 @@ def create_settings_menus():
repetition_penalty = gr.Slider(1.0,4.99,value=generate_params['repetition_penalty'],step=0.01,label="repetition_penalty")
top_k = gr.Slider(0,200,value=generate_params['top_k'],step=1,label="top_k")
no_repeat_ngram_size = gr.Slider(0, 20, step=1, value=generate_params["no_repeat_ngram_size"], label="no_repeat_ngram_size")
penalty_alpha = gr.Slider(0, 5, value=generate_params["penalty_alpha"], label="penalty_alpha")
gr.Markdown("Special parameters (only use them if you really need them):")
with gr.Row():
@ -403,8 +406,8 @@ def create_settings_menus():
early_stopping = gr.Checkbox(value=generate_params["early_stopping"], label="early_stopping")
model_menu.change(load_model_wrapper, [model_menu], [])
preset_menu.change(load_preset_values, [preset_menu], [do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping])
return preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping
preset_menu.change(load_preset_values, [preset_menu], [do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping])
return preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping
# This gets the new line characters right.
def clean_chat_message(text):
@ -475,14 +478,14 @@ def extract_message_from_reply(question, reply, current, other, check, extension
return reply, next_character_found, substring_found
def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
original_text = text
text = apply_extensions(text, "input")
question = generate_chat_prompt(text, tokens, name1, name2, context, history_size)
history['internal'].append(['', ''])
history['visible'].append(['', ''])
eos_token = '\n' if check else None
for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name2, name1, check, extensions=True)
history['internal'][-1] = [text, reply]
history['visible'][-1] = [original_text, apply_extensions(reply, "output")]
@ -492,10 +495,10 @@ def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p,
break
yield history['visible']
def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
question = generate_chat_prompt(text, tokens, name1, name2, context, history_size, impersonate=True)
eos_token = '\n' if check else None
for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name1, name2, check, extensions=False)
if not substring_found:
yield apply_extensions(reply, "output")
@ -503,19 +506,19 @@ def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, to
break
yield apply_extensions(reply, "output")
def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
yield generate_chat_html(_history, name1, name2, character)
def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
last = history['visible'].pop()
history['internal'].pop()
text = last[0]
if args.cai_chat:
for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
yield i
else:
for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
yield i
def remove_last_message(name1, name2):
@ -775,7 +778,7 @@ if args.chat or args.cai_chat:
with gr.Column():
history_size_slider = gr.Slider(minimum=settings['history_size_min'], maximum=settings['history_size_max'], step=1, label='Chat history size in prompt (0 for no limit)', value=settings['history_size'])
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
name1 = gr.Textbox(value=settings[f'name1{suffix}'], lines=1, label='Your name')
name2 = gr.Textbox(value=settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
@ -813,7 +816,7 @@ if args.chat or args.cai_chat:
if args.extensions is not None:
create_extensions_block()
input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
if args.cai_chat:
gen_events.append(buttons["Generate"].click(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream))
@ -860,13 +863,13 @@ elif args.notebook:
max_new_tokens = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
if args.extensions is not None:
create_extensions_block()
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream))
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream))
buttons["Stop"].click(None, None, None, cancels=gen_events)
else:
@ -883,7 +886,7 @@ else:
with gr.Column():
buttons["Stop"] = gr.Button("Stop")
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
if args.extensions is not None:
create_extensions_block()
@ -895,9 +898,9 @@ else:
with gr.Tab('HTML'):
html = gr.HTML()
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
buttons["Stop"].click(None, None, None, cancels=gen_events)
interface.queue()