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Add "seed" menu in the Parameters tab

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wywywywy 2023-03-22 18:40:20 +00:00 committed by GitHub
parent 45b7e53565
commit 61346b88ea
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6 changed files with 33 additions and 19 deletions
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6
api-example-stream.py
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@ -34,6 +34,7 @@ async def run(context):
'penalty_alpha': 0, 'penalty_alpha': 0,
'length_penalty': 1, 'length_penalty': 1,
'early_stopping': False, 'early_stopping': False,
'seed': -1,
} }
session = random_hash() session = random_hash()
@ -44,14 +45,14 @@ async def run(context):
case "send_hash": case "send_hash":
await websocket.send(json.dumps({ await websocket.send(json.dumps({
"session_hash": session, "session_hash": session,
"fn_index": 9 "fn_index": 12
})) }))
case "estimation": case "estimation":
pass pass
case "send_data": case "send_data":
await websocket.send(json.dumps({ await websocket.send(json.dumps({
"session_hash": session, "session_hash": session,
"fn_index": 9, "fn_index": 12,
"data": [ "data": [
context, context,
params['max_new_tokens'], params['max_new_tokens'],
@ -68,6 +69,7 @@ async def run(context):
params['penalty_alpha'], params['penalty_alpha'],
params['length_penalty'], params['length_penalty'],
params['early_stopping'], params['early_stopping'],
params['seed'],
] ]
})) }))
case "process_starts": case "process_starts":

2
api-example.py
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@ -32,6 +32,7 @@ params = {
'penalty_alpha': 0, 'penalty_alpha': 0,
'length_penalty': 1, 'length_penalty': 1,
'early_stopping': False, 'early_stopping': False,
'seed': -1,
} }
# Input prompt # Input prompt
@ -54,6 +55,7 @@ response = requests.post(f"http://{server}:7860/run/textgen", json={
params['penalty_alpha'], params['penalty_alpha'],
params['length_penalty'], params['length_penalty'],
params['early_stopping'], params['early_stopping'],
params['seed'],
] ]
}).json() }).json()

1
extensions/api/script.py
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@ -56,6 +56,7 @@ class Handler(BaseHTTPRequestHandler):
penalty_alpha=0, penalty_alpha=0,
length_penalty=1, length_penalty=1,
early_stopping=False, early_stopping=False,
seed=-1,
) )
answer = '' answer = ''

16
modules/chat.py
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@ -91,7 +91,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
def stop_everything_event(): def stop_everything_event():
shared.stop_everything = True shared.stop_everything = True
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False): def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
shared.stop_everything = False shared.stop_everything = False
just_started = True just_started = True
eos_token = '\n' if check else None eos_token = '\n' if check else None
@ -127,7 +127,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
# Generate # Generate
reply = '' reply = ''
for i in range(chat_generation_attempts): for i in range(chat_generation_attempts):
for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_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}:"): for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name1}:"):
# Extracting the reply # Extracting the reply
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check) reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
@ -154,7 +154,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
yield shared.history['visible'] yield shared.history['visible']
def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
eos_token = '\n' if check else None eos_token = '\n' if check else None
if 'pygmalion' in shared.model_name.lower(): if 'pygmalion' in shared.model_name.lower():
@ -166,18 +166,18 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
# Yield *Is typing...* # Yield *Is typing...*
yield shared.processing_message yield shared.processing_message
for i in range(chat_generation_attempts): for i in range(chat_generation_attempts):
for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_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}:"): for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name2}:"):
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True) reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
yield reply yield reply
if next_character_found: if next_character_found:
break break
yield reply yield reply
def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts): for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
yield generate_chat_html(_history, name1, name2, shared.character) yield generate_chat_html(_history, name1, name2, shared.character)
def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0: if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
yield generate_chat_output(shared.history['visible'], name1, name2, shared.character) yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
else: else:
@ -185,7 +185,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
last_internal = shared.history['internal'].pop() last_internal = shared.history['internal'].pop()
# Yield '*Is typing...*' # Yield '*Is typing...*'
yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character) yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character)
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True): for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
if shared.args.cai_chat: if shared.args.cai_chat:
shared.history['visible'][-1] = [last_visible[0], _history[-1][1]] shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
else: else:

9
modules/text_generation.py
View File

@ -93,8 +93,15 @@ def clear_torch_cache():
if not shared.args.cpu: if not shared.args.cpu:
torch.cuda.empty_cache() torch.cuda.empty_cache()
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None): def set_manual_seed(seed):
if seed != -1:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_string=None):
clear_torch_cache() clear_torch_cache()
set_manual_seed(seed)
t0 = time.time() t0 = time.time()
# These models are not part of Hugging Face, so we handle them # These models are not part of Hugging Face, so we handle them

16
server.py
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@ -130,10 +130,6 @@ def create_model_and_preset_menus():
def create_settings_menus(default_preset): def create_settings_menus(default_preset):
generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True) generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
with gr.Row():
shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
with gr.Box(): with gr.Box():
@ -164,6 +160,12 @@ def create_settings_menus(default_preset):
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty') shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping') shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
with gr.Row():
shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
with gr.Row(): with gr.Row():
shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA') shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button') ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button')
@ -330,7 +332,7 @@ def create_interface():
create_settings_menus(default_preset) create_settings_menus(default_preset)
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']] shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
@ -393,7 +395,7 @@ def create_interface():
with gr.Tab("Parameters", elem_id="parameters"): with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset) create_settings_menus(default_preset)
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
@ -425,7 +427,7 @@ def create_interface():
with gr.Tab("Parameters", elem_id="parameters"): with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset) create_settings_menus(default_preset)
shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'seed']]
output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))