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Stop the bot from talking for you in chat mode

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oobabooga 2023-03-23 21:38:20 -03:00 committed by GitHub
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commit 4578e88ffd
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4 changed files with 51 additions and 62 deletions
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4
modules/RWKV.py
View File

@ -45,11 +45,11 @@ class RWKVModel:
token_stop = token_stop
)
return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
def generate_with_streaming(self, **kwargs):
with Iteratorize(self.generate, kwargs, callback=None) as generator:
reply = kwargs['context']
reply = ''
for token in generator:
reply += token
yield reply

20
modules/callbacks.py
View File

@ -11,24 +11,22 @@ import modules.shared as shared
# Copied from https://github.com/PygmalionAI/gradio-ui/
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
def __init__(self, sentinel_token_ids: torch.LongTensor,
starting_idx: int):
def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
transformers.StoppingCriteria.__init__(self)
self.sentinel_token_ids = sentinel_token_ids
self.starting_idx = starting_idx
def __call__(self, input_ids: torch.LongTensor,
_scores: torch.FloatTensor) -> bool:
def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
for sample in input_ids:
trimmed_sample = sample[self.starting_idx:]
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
continue
for window in trimmed_sample.unfold(
0, self.sentinel_token_ids.shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids, window)):
return True
for i in range(len(self.sentinel_token_ids)):
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
continue
for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids[i], window)):
return True
return False
class Stream(transformers.StoppingCriteria):

44
modules/chat.py
View File

@ -51,41 +51,31 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
prompt = ''.join(rows)
return prompt
def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
def extract_message_from_reply(reply, name1, name2, check):
next_character_found = False
asker = name1 if not impersonate else name2
replier = name2 if not impersonate else name1
previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
idx = idx[max(len(previous_idx)-1, 0)]
if not impersonate:
reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
else:
reply = reply[idx + 1 + len(f"{replier}:"):]
if check:
lines = reply.split('\n')
reply = lines[0].strip()
if len(lines) > 1:
next_character_found = True
else:
idx = reply.find(f"\n{asker}:")
if idx != -1:
reply = reply[:idx]
next_character_found = True
reply = fix_newlines(reply)
for string in [f"\n{name1}:", f"\n{name2}:"]:
idx = reply.find(string)
if idx != -1:
reply = reply[:idx]
next_character_found = True
# If something like "\nYo" is generated just before "\nYou:"
# is completed, trim it
next_turn = f"\n{asker}:"
for j in range(len(next_turn)-1, 0, -1):
if reply[-j:] == next_turn[:j]:
reply = reply[:-j]
break
if not next_character_found:
for string in [f"\n{name1}:", f"\n{name2}:"]:
for j in range(len(string)-1, 0, -1):
if reply[-j:] == string[:j]:
reply = reply[:-j]
break
reply = fix_newlines(reply)
return reply, next_character_found
def stop_everything_event():
@ -127,10 +117,10 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
# Generate
reply = ''
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, seed, 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_strings=[f"\n{name1}:", f"\n{name2}:"]):
# Extracting the reply
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
visible_reply = apply_extensions(visible_reply, "output")
if shared.args.chat:
@ -166,8 +156,8 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
# Yield *Is typing...*
yield shared.processing_message
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, 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)
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_strings=[f"\n{name1}:", f"\n{name2}:"]):
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
yield reply
if next_character_found:
break

45
modules/text_generation.py
View File

@ -99,25 +99,37 @@ def set_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):
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_strings=[]):
clear_torch_cache()
set_manual_seed(seed)
t0 = time.time()
original_question = question
if not (shared.args.chat or shared.args.cai_chat):
question = apply_extensions(question, "input")
if shared.args.verbose:
print(f"\n\n{question}\n--------------------\n")
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if shared.is_RWKV:
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
else:
if not (shared.args.chat or shared.args.cai_chat):
yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply, "output")
yield formatted_outputs(reply, shared.model_name)
except Exception:
traceback.print_exc()
finally:
@ -127,12 +139,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
return
original_question = question
if not (shared.args.chat or shared.args.cai_chat):
question = apply_extensions(question, "input")
if shared.args.verbose:
print(f"\n\n{question}\n--------------------\n")
input_ids = encode(question, max_new_tokens)
original_input_ids = input_ids
output = input_ids[0]
@ -142,9 +148,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
stopping_criteria_list = transformers.StoppingCriteriaList()
if stopping_string is not None:
# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
t = encode(stopping_string, 0, add_special_tokens=False)
if type(stopping_strings) is list and len(stopping_strings) > 0:
t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
generate_params = {}
@ -195,12 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
reply = original_question + apply_extensions(reply, "output")
else:
reply = decode(output)
yield formatted_outputs(reply, shared.model_name)
@ -223,12 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
reply = original_question + apply_extensions(reply, "output")
else:
reply = decode(output)
if output[-1] in eos_token_ids:
break
@ -244,12 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
output = shared.model.generate(**generate_params)[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
new_tokens = len(output) - len(original_input_ids[0])
reply = decode(output[-new_tokens:])
if not (shared.args.chat or shared.args.cai_chat):
new_tokens = len(output) - len(original_input_ids[0])
reply = decode(output[-new_tokens:])
reply = original_question + apply_extensions(reply, "output")
else:
reply = decode(output)
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
break