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Refactor several function calls and the API

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oobabooga 2023-04-06 01:22:15 -03:00 • committed by GitHub
parent 378d21e80c
commit 3f3e42e26c
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8 changed files with 147 additions and 118 deletions
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modules/chat.py
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@ -18,7 +18,12 @@ from modules.text_generation import (encode, generate_reply,
get_max_prompt_length)
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, is_instruct, end_of_turn="", impersonate=False, also_return_rows=False):
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, **kwargs):
is_instruct = kwargs['is_instruct'] if 'is_instruct' in kwargs else False
end_of_turn = kwargs['end_of_turn'] if 'end_of_turn' in kwargs else ''
impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
user_input = fix_newlines(user_input)
rows = [f"{context.strip()}\n"]
@ -91,9 +96,9 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
reply = fix_newlines(reply)
return reply, next_character_found
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, stop_at_newline, chat_prompt_size, chat_generation_attempts=1, regenerate=False, mode="cai-chat", end_of_turn=""):
def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
just_started = True
eos_token = '\n' if stop_at_newline else None
eos_token = '\n' if generate_state['stop_at_newline'] else None
name1_original = name1
if 'pygmalion' in shared.model_name.lower():
name1 = "You"
@ -112,11 +117,11 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
visible_text = text
text = apply_extensions(text, "input")
is_instruct = mode == 'instruct'
kwargs = {'end_of_turn': end_of_turn, 'is_instruct': mode == 'instruct'}
if custom_generate_chat_prompt is None:
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, is_instruct, end_of_turn=end_of_turn)
prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
else:
prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, is_instruct, end_of_turn=end_of_turn)
prompt = custom_generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
# Yield *Is typing...*
if not regenerate:
@ -124,13 +129,13 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
# Generate
cumulative_reply = ''
for i in range(chat_generation_attempts):
for i in range(generate_state['chat_generation_attempts']):
reply = None
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_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_strings=[f"\n{name1}:", f"\n{name2}:"]):
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
reply = cumulative_reply + reply
# Extracting the reply
reply, next_character_found = extract_message_from_reply(reply, name1, name2, stop_at_newline)
reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
visible_reply = apply_extensions(visible_reply, "output")
@ -155,23 +160,23 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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, seed, name1, name2, context, stop_at_newline, chat_prompt_size, chat_generation_attempts=1, mode="cai-chat", end_of_turn=""):
eos_token = '\n' if stop_at_newline else None
def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
eos_token = '\n' if generate_state['stop_at_newline'] else None
if 'pygmalion' in shared.model_name.lower():
name1 = "You"
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True, end_of_turn=end_of_turn)
prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], impersonate=True, end_of_turn=end_of_turn)
# Yield *Is typing...*
yield shared.processing_message
cumulative_reply = ''
for i in range(chat_generation_attempts):
for i in range(generate_state['chat_generation_attempts']):
reply = None
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_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_strings=[f"\n{name1}:", f"\n{name2}:"]):
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
reply = cumulative_reply + reply
reply, next_character_found = extract_message_from_reply(reply, name1, name2, stop_at_newline)
reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
yield reply
if next_character_found:
break
@ -181,11 +186,11 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
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, seed, name1, name2, context, stop_at_newline, chat_prompt_size, chat_generation_attempts=1, mode="cai-chat", end_of_turn=""):
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, stop_at_newline, chat_prompt_size, chat_generation_attempts, regenerate=False, mode=mode, end_of_turn=end_of_turn):
def cai_chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
for history in chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
yield chat_html_wrapper(history, name1, name2, mode)
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, stop_at_newline, chat_prompt_size, chat_generation_attempts=1, mode="cai-chat", end_of_turn=""):
def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
else:
@ -193,7 +198,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
last_internal = shared.history['internal'].pop()
# Yield '*Is typing...*'
yield chat_html_wrapper(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, mode)
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, stop_at_newline, chat_prompt_size, chat_generation_attempts, regenerate=True, mode=mode, end_of_turn=end_of_turn):
for history in chatbot_wrapper(last_internal[0], generate_state, name1, name2, context, mode, end_of_turn, regenerate=True):
shared.history['visible'][-1] = [last_visible[0], history[-1][1]]
yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)