mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-29 19:09:32 +01:00
387 lines
14 KiB
Python
387 lines
14 KiB
Python
import ast
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import random
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import re
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import time
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import traceback
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import numpy as np
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import torch
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import transformers
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import modules.shared as shared
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from modules.callbacks import (Iteratorize, Stream,
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_SentinelTokenStoppingCriteria)
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from modules.extensions import apply_extensions
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from modules.html_generator import generate_4chan_html, generate_basic_html
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from modules.logging_colors import logger
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from modules.models import clear_torch_cache, local_rank
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def generate_reply(*args, **kwargs):
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shared.generation_lock.acquire()
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try:
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for result in _generate_reply(*args, **kwargs):
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yield result
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finally:
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shared.generation_lock.release()
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def get_max_prompt_length(state):
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return state['truncation_length'] - state['max_new_tokens']
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def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel']:
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input_ids = shared.tokenizer.encode(str(prompt))
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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return input_ids
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else:
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
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# This is a hack for making replies more creative.
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if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id:
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input_ids = input_ids[:, 1:]
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# Llama adds this extra token when the first character is '\n', and this
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# compromises the stopping criteria, so we just remove it
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if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
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input_ids = input_ids[:, 1:]
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# Handling truncation
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if truncation_length is not None:
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input_ids = input_ids[:, -truncation_length:]
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel'] or shared.args.cpu:
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return input_ids
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elif shared.args.flexgen:
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return input_ids.numpy()
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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elif torch.has_mps:
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device = torch.device('mps')
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return input_ids.to(device)
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else:
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return input_ids.cuda()
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def get_encoded_length(prompt):
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length_after_extensions = apply_extensions('tokenized_length', prompt)
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if length_after_extensions is not None:
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return length_after_extensions
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return len(encode(prompt)[0])
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def decode(output_ids, skip_special_tokens=True):
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return shared.tokenizer.decode(output_ids, skip_special_tokens)
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# Removes empty replies from gpt4chan outputs
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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# Fix the LaTeX equations in galactica
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def fix_galactica(s):
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s = s.replace(r'\[', r'$')
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s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
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s = s.replace(r'\)', r'$')
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s = s.replace(r'$$', r'$')
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s = re.sub(r'\n', r'\n\n', s)
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s = re.sub(r"\n{3,}", "\n\n", s)
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return s
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def get_reply_from_output_ids(output_ids, input_ids, original_question, state, is_chat=False):
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if shared.is_seq2seq:
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reply = decode(output_ids, state['skip_special_tokens'])
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else:
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new_tokens = len(output_ids) - len(input_ids[0])
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reply = decode(output_ids[-new_tokens:], state['skip_special_tokens'])
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# Prevent LlamaTokenizer from skipping a space
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if type(shared.tokenizer) is transformers.LlamaTokenizer and len(output_ids) > 0:
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if shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'):
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reply = ' ' + reply
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if not is_chat:
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reply = apply_extensions('output', reply)
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return reply
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def formatted_outputs(reply, model_name):
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if any(s in model_name for s in ['gpt-4chan', 'gpt4chan']):
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reply = fix_gpt4chan(reply)
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return reply, generate_4chan_html(reply)
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else:
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return reply, generate_basic_html(reply)
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def set_manual_seed(seed):
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seed = int(seed)
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if seed == -1:
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seed = random.randint(1, 2**31)
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torch.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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return seed
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def stop_everything_event():
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shared.stop_everything = True
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def generate_reply_wrapper(question, state, eos_token=None, stopping_strings=None):
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for reply in generate_reply(question, state, eos_token, stopping_strings, is_chat=False):
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if not shared.is_seq2seq:
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reply = question + reply
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yield formatted_outputs(reply, shared.model_name)
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def _generate_reply(question, state, eos_token=None, stopping_strings=None, is_chat=False):
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state = apply_extensions('state', state)
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generate_func = apply_extensions('custom_generate_reply')
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if generate_func is None:
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if shared.model_name == 'None' or shared.model is None:
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logger.error("No model is loaded! Select one in the Model tab.")
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yield ''
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return
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if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel']:
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generate_func = generate_reply_custom
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elif shared.args.flexgen:
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generate_func = generate_reply_flexgen
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else:
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generate_func = generate_reply_HF
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# Preparing the input
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original_question = question
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if not is_chat:
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question = apply_extensions('input', question)
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if shared.args.verbose:
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print(f'\n\n{question}\n--------------------\n')
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shared.stop_everything = False
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clear_torch_cache()
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seed = set_manual_seed(state['seed'])
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is_stream = state['stream']
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last_update = -1
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reply = ''
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for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings, is_chat=is_chat):
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if is_stream:
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cur_time = time.time()
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if cur_time - last_update > 0.041666666666666664: # Limit streaming to 24 fps
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last_update = cur_time
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yield reply
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else:
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yield reply
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if is_stream:
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yield reply
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def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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generate_params = {}
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for k in ['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', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
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generate_params[k] = state[k]
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for k in ['epsilon_cutoff', 'eta_cutoff']:
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if state[k] > 0:
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generate_params[k] = state[k] * 1e-4
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if state['ban_eos_token']:
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generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
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if shared.args.no_cache:
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generate_params.update({'use_cache': False})
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if shared.args.deepspeed:
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generate_params.update({'synced_gpus': True})
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# Encode the input
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input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
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output = input_ids[0]
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cuda = not any((shared.args.cpu, shared.args.deepspeed))
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# Find the eos tokens
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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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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# Add the encoded tokens to generate_params
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question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
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original_input_ids = input_ids
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generate_params.update({'inputs': input_ids})
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if inputs_embeds is not None:
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generate_params.update({'inputs_embeds': inputs_embeds})
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# Create the StoppingCriteriaList with the stopping strings (needs to be done after tokenizer extensions)
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stopping_criteria_list = transformers.StoppingCriteriaList()
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for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
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if type(st) is list and len(st) > 0:
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sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st]
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stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
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break
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# Update generate_params with the eos token and the stopping strings
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generate_params['eos_token_id'] = eos_token_ids
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generate_params['stopping_criteria'] = stopping_criteria_list
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t0 = time.time()
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try:
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if not is_chat and not shared.is_seq2seq:
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yield ''
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# Generate the entire reply at once.
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if not state['stream']:
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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if cuda:
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output = output.cuda()
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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# Stream the reply 1 token at a time.
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# This is based on the trick of using 'stopping_criteria' to create an iterator.
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else:
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def generate_with_callback(callback=None, **kwargs):
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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clear_torch_cache()
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with torch.no_grad():
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shared.model.generate(**kwargs)
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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with generate_with_streaming(**generate_params) as generator:
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for output in generator:
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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if output[-1] in eos_token_ids:
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break
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(original_input_ids[0])
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new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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seed = set_manual_seed(state['seed'])
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generate_params = {'token_count': state['max_new_tokens']}
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for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
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generate_params[k] = state[k]
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if shared.model.__class__.__name__ in ['LlamaCppModel']:
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for k in ['mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
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generate_params[k] = state[k]
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t0 = time.time()
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reply = ''
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try:
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if not is_chat:
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yield ''
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if not state['stream']:
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reply = shared.model.generate(context=question, **generate_params)
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if not is_chat:
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reply = apply_extensions('output', reply)
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yield reply
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else:
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for reply in shared.model.generate_with_streaming(context=question, **generate_params):
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if not is_chat:
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reply = apply_extensions('output', reply)
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yield reply
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(encode(original_question)[0])
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new_tokens = len(encode(original_question + reply)[0]) - original_tokens
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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generate_params = {}
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for k in ['max_new_tokens', 'do_sample', 'temperature']:
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generate_params[k] = state[k]
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if state['stream']:
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generate_params['max_new_tokens'] = 8
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# Encode the input
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input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
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output = input_ids[0]
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# Find the eos tokens
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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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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# Add the encoded tokens to generate_params
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question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
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original_input_ids = input_ids
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generate_params.update({'inputs': input_ids})
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if inputs_embeds is not None:
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generate_params.update({'inputs_embeds': inputs_embeds})
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# Update generate_params with the eos token and the stopping strings
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generate_params['stop'] = eos_token_ids[-1]
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t0 = time.time()
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try:
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if not is_chat:
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yield ''
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# Generate the entire reply at once.
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if not state['stream']:
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(state['max_new_tokens'] // 8 + 1):
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if shared.stop_everything:
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break
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clear_torch_cache()
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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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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yield get_reply_from_output_ids(output, original_input_ids, original_question, state)
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input_ids = np.reshape(output, (1, output.shape[0]))
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generate_params.update({'inputs': input_ids})
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except Exception:
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traceback.print_exc()
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finally:
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t1 = time.time()
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original_tokens = len(original_input_ids[0])
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new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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