text-generation-webui/modules/text_generation.py

376 lines
14 KiB
Python

import ast
import logging
import random
import re
import time
import traceback
import numpy as np
import torch
import transformers
import modules.shared as shared
from modules.callbacks import (Iteratorize, Stream,
_SentinelTokenStoppingCriteria)
from modules.extensions import apply_extensions
from modules.html_generator import generate_4chan_html, generate_basic_html
from modules.models import clear_torch_cache, local_rank
def get_max_prompt_length(state):
max_length = state['truncation_length'] - state['max_new_tokens']
if shared.soft_prompt:
max_length -= shared.soft_prompt_tensor.shape[1]
return max_length
def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
if shared.model_type in ['rwkv', 'llamacpp']:
input_ids = shared.tokenizer.encode(str(prompt))
input_ids = np.array(input_ids).reshape(1, len(input_ids))
return input_ids
else:
input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
# This is a hack for making replies more creative.
if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id:
input_ids = input_ids[:, 1:]
# Llama adds this extra token when the first character is '\n', and this
# compromises the stopping criteria, so we just remove it
if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
input_ids = input_ids[:, 1:]
# Handling truncation
if truncation_length is not None:
input_ids = input_ids[:, -truncation_length:]
if shared.model_type in ['rwkv', 'llamacpp'] or shared.args.cpu:
return input_ids
elif shared.args.flexgen:
return input_ids.numpy()
elif shared.args.deepspeed:
return input_ids.to(device=local_rank)
elif torch.has_mps:
device = torch.device('mps')
return input_ids.to(device)
else:
return input_ids.cuda()
def decode(output_ids, skip_special_tokens=True):
return shared.tokenizer.decode(output_ids, skip_special_tokens)
def generate_softprompt_input_tensors(input_ids):
inputs_embeds = shared.model.transformer.wte(input_ids)
inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
# filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
return inputs_embeds, filler_input_ids
# Removes empty replies from gpt4chan outputs
def fix_gpt4chan(s):
for i in range(10):
s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
s = re.sub("--- [0-9]*\n *\n---", "---", s)
s = re.sub("--- [0-9]*\n\n\n---", "---", s)
return s
# Fix the LaTeX equations in galactica
def fix_galactica(s):
s = s.replace(r'\[', r'$')
s = s.replace(r'\]', r'$')
s = s.replace(r'\(', r'$')
s = s.replace(r'\)', r'$')
s = s.replace(r'$$', r'$')
s = re.sub(r'\n', r'\n\n', s)
s = re.sub(r"\n{3,}", "\n\n", s)
return s
def get_reply_from_output_ids(output_ids, input_ids, original_question, state):
if shared.model_type == 'HF_seq2seq':
reply = decode(output_ids, state['skip_special_tokens'])
if not shared.is_chat():
reply = apply_extensions('output', reply)
else:
new_tokens = len(output_ids) - len(input_ids[0])
reply = decode(output_ids[-new_tokens:], state['skip_special_tokens'])
if type(shared.tokenizer) is transformers.LlamaTokenizer:
if len(original_question) > 0 and original_question[-1] not in [' ', '\n']:
reply = ' ' + reply
if not shared.is_chat():
reply = original_question + apply_extensions('output', reply)
return reply
def formatted_outputs(reply, model_name):
if not shared.is_chat():
if shared.model_type == 'galactica':
reply = fix_galactica(reply)
return reply, reply, generate_basic_html(reply)
elif shared.model_type == 'gpt4chan':
reply = fix_gpt4chan(reply)
return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
else:
return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
else:
return reply
def set_manual_seed(seed):
seed = int(seed)
if seed == -1:
seed = random.randint(1, 2**31)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
return seed
def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, state, eos_token=None, stopping_strings=[]):
state = apply_extensions('state', state)
generate_func = apply_extensions('custom_generate_reply')
if generate_func is None:
if shared.model_name == 'None' or shared.model is None:
logging.error("No model is loaded! Select one in the Model tab.")
yield formatted_outputs(question, shared.model_name)
return
if shared.model_type in ['rwkv', 'llamacpp']:
generate_func = generate_reply_custom
elif shared.args.flexgen:
generate_func = generate_reply_flexgen
else:
generate_func = generate_reply_HF
# Preparing the input
original_question = question
if not shared.is_chat():
question = apply_extensions('input', question)
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
shared.stop_everything = False
clear_torch_cache()
seed = set_manual_seed(state['seed'])
for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings):
yield formatted_outputs(reply, shared.model_name)
def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
generate_params = {}
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']:
generate_params[k] = state[k]
if state['ban_eos_token']:
generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
if shared.args.no_cache:
generate_params.update({'use_cache': False})
if shared.args.deepspeed:
generate_params.update({'synced_gpus': True})
# Encode the input
input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
output = input_ids[0]
cuda = not any((shared.args.cpu, shared.args.deepspeed))
# Find the eos tokens
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
# Add the encoded tokens to generate_params
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds)
original_input_ids = input_ids
generate_params.update({'inputs_embeds': inputs_embeds})
generate_params.update({'inputs': filler_input_ids})
else:
question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
original_input_ids = input_ids
generate_params.update({'inputs': input_ids})
if inputs_embeds is not None:
generate_params.update({'inputs_embeds': inputs_embeds})
# Create the StoppingCriteriaList with the stopping strings (needs to be done after tokenizer extensions)
stopping_criteria_list = transformers.StoppingCriteriaList()
for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
if type(st) is list and len(st) > 0:
sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
break
# Update generate_params with the eos token and the stopping strings
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = stopping_criteria_list
t0 = time.time()
try:
if not shared.is_chat() and shared.model_type != 'HF_seq2seq':
yield original_question
# Generate the entire reply at once.
if not state['stream']:
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
if cuda:
output = output.cuda()
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
yield get_reply_from_output_ids(output, input_ids, original_question, state)
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator.
else:
def generate_with_callback(callback=None, **kwargs):
kwargs['stopping_criteria'].append(Stream(callback_func=callback))
clear_torch_cache()
with torch.no_grad():
shared.model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
with generate_with_streaming(**generate_params) as generator:
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
yield get_reply_from_output_ids(output, input_ids, original_question, state)
if output[-1] in eos_token_ids:
break
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
original_tokens = len(original_input_ids[0])
new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0)
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
return
def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
seed = set_manual_seed(state['seed'])
generate_params = {'token_count': state['max_new_tokens']}
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = state[k]
t0 = time.time()
try:
if not shared.is_chat():
yield question
if not state['stream']:
reply = shared.model.generate(context=question, **generate_params)
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions('output', reply)
yield reply
else:
for reply in shared.model.generate_with_streaming(context=question, **generate_params):
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions('output', reply)
yield reply
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
original_tokens = len(encode(original_question)[0])
new_tokens = len(encode(output)[0]) - original_tokens
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
return
def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
generate_params = {}
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = state[k]
if state['stream']:
generate_params['max_new_tokens'] = 8
# Encode the input
input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
output = input_ids[0]
# Find the eos tokens
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
# Add the encoded tokens to generate_params
question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
original_input_ids = input_ids
generate_params.update({'inputs': input_ids})
if inputs_embeds is not None:
generate_params.update({'inputs_embeds': inputs_embeds})
# Update generate_params with the eos token and the stopping strings
generate_params['stop'] = eos_token_ids[-1]
t0 = time.time()
try:
if not shared.is_chat():
yield question
# Generate the entire reply at once.
if not state['stream']:
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
yield get_reply_from_output_ids(output, input_ids, original_question, state)
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(state['max_new_tokens'] // 8 + 1):
if shared.stop_everything:
break
clear_torch_cache()
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
break
yield get_reply_from_output_ids(output, original_input_ids, original_question, state)
input_ids = np.reshape(output, (1, output.shape[0]))
generate_params.update({'inputs': input_ids})
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
original_tokens = len(original_input_ids[0])
new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0)
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
return