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https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 09:19:23 +01:00
Stop the bot from talking for you in chat mode
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@ -45,11 +45,11 @@ class RWKVModel:
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token_stop = token_stop
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)
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return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
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return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
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def generate_with_streaming(self, **kwargs):
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with Iteratorize(self.generate, kwargs, callback=None) as generator:
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reply = kwargs['context']
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reply = ''
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for token in generator:
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reply += token
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yield reply
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@ -11,24 +11,22 @@ import modules.shared as shared
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# Copied from https://github.com/PygmalionAI/gradio-ui/
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class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
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def __init__(self, sentinel_token_ids: torch.LongTensor,
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starting_idx: int):
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def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
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transformers.StoppingCriteria.__init__(self)
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self.sentinel_token_ids = sentinel_token_ids
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self.starting_idx = starting_idx
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def __call__(self, input_ids: torch.LongTensor,
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_scores: torch.FloatTensor) -> bool:
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def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
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for sample in input_ids:
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trimmed_sample = sample[self.starting_idx:]
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# Can't unfold, output is still too tiny. Skip.
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if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
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continue
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for window in trimmed_sample.unfold(
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0, self.sentinel_token_ids.shape[-1], 1):
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if torch.all(torch.eq(self.sentinel_token_ids, window)):
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return True
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for i in range(len(self.sentinel_token_ids)):
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# Can't unfold, output is still too tiny. Skip.
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if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
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continue
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for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
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if torch.all(torch.eq(self.sentinel_token_ids[i], window)):
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return True
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return False
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class Stream(transformers.StoppingCriteria):
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@ -51,41 +51,31 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
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prompt = ''.join(rows)
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return prompt
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def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
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def extract_message_from_reply(reply, name1, name2, check):
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next_character_found = False
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asker = name1 if not impersonate else name2
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replier = name2 if not impersonate else name1
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previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
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idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
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idx = idx[max(len(previous_idx)-1, 0)]
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if not impersonate:
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reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
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else:
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reply = reply[idx + 1 + len(f"{replier}:"):]
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if check:
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lines = reply.split('\n')
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reply = lines[0].strip()
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if len(lines) > 1:
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next_character_found = True
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else:
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idx = reply.find(f"\n{asker}:")
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if idx != -1:
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reply = reply[:idx]
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next_character_found = True
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reply = fix_newlines(reply)
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for string in [f"\n{name1}:", f"\n{name2}:"]:
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idx = reply.find(string)
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if idx != -1:
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reply = reply[:idx]
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next_character_found = True
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# If something like "\nYo" is generated just before "\nYou:"
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# is completed, trim it
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next_turn = f"\n{asker}:"
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for j in range(len(next_turn)-1, 0, -1):
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if reply[-j:] == next_turn[:j]:
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reply = reply[:-j]
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break
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if not next_character_found:
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for string in [f"\n{name1}:", f"\n{name2}:"]:
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for j in range(len(string)-1, 0, -1):
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if reply[-j:] == string[:j]:
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reply = reply[:-j]
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break
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reply = fix_newlines(reply)
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return reply, next_character_found
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def stop_everything_event():
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@ -127,10 +117,10 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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# Generate
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reply = ''
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for i in range(chat_generation_attempts):
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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}:"):
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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}:"]):
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# Extracting the reply
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reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
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reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
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visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
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visible_reply = apply_extensions(visible_reply, "output")
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if shared.args.chat:
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@ -166,8 +156,8 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
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# Yield *Is typing...*
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yield shared.processing_message
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for i in range(chat_generation_attempts):
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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}:"):
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reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
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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}:"]):
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reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
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yield reply
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if next_character_found:
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break
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@ -99,25 +99,37 @@ def set_manual_seed(seed):
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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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):
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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=[]):
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clear_torch_cache()
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set_manual_seed(seed)
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t0 = time.time()
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original_question = question
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if not (shared.args.chat or shared.args.cai_chat):
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question = apply_extensions(question, "input")
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if shared.args.verbose:
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print(f"\n\n{question}\n--------------------\n")
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# These models are not part of Hugging Face, so we handle them
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# separately and terminate the function call earlier
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if shared.is_RWKV:
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try:
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if shared.args.no_stream:
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reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply, "output")
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yield formatted_outputs(reply, shared.model_name)
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else:
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if not (shared.args.chat or shared.args.cai_chat):
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yield formatted_outputs(question, shared.model_name)
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# RWKV has proper streaming, which is very nice.
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# No need to generate 8 tokens at a time.
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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):
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply, "output")
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yield formatted_outputs(reply, shared.model_name)
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except Exception:
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traceback.print_exc()
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finally:
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@ -127,12 +139,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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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)")
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return
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original_question = question
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if not (shared.args.chat or shared.args.cai_chat):
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question = apply_extensions(question, "input")
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if shared.args.verbose:
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print(f"\n\n{question}\n--------------------\n")
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input_ids = encode(question, max_new_tokens)
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original_input_ids = input_ids
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output = input_ids[0]
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@ -142,9 +148,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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if eos_token is not None:
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eos_token_ids.append(int(encode(eos_token)[0][-1]))
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stopping_criteria_list = transformers.StoppingCriteriaList()
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if stopping_string is not None:
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# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
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t = encode(stopping_string, 0, add_special_tokens=False)
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if type(stopping_strings) is list and len(stopping_strings) > 0:
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t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
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stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
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generate_params = {}
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@ -195,12 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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if not (shared.args.chat or shared.args.cai_chat):
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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reply = original_question + apply_extensions(reply, "output")
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else:
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reply = decode(output)
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yield formatted_outputs(reply, shared.model_name)
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@ -223,12 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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for output in generator:
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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if not (shared.args.chat or shared.args.cai_chat):
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new_tokens = len(output) - len(input_ids[0])
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reply = decode(output[-new_tokens:])
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reply = original_question + apply_extensions(reply, "output")
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else:
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reply = decode(output)
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if output[-1] in eos_token_ids:
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break
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@ -244,12 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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output = shared.model.generate(**generate_params)[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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new_tokens = len(output) - len(original_input_ids[0])
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reply = decode(output[-new_tokens:])
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if not (shared.args.chat or shared.args.cai_chat):
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new_tokens = len(output) - len(original_input_ids[0])
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reply = decode(output[-new_tokens:])
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reply = original_question + apply_extensions(reply, "output")
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else:
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reply = decode(output)
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if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
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break
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