text-generation-webui/modules/text_generation.py
2023-04-25 22:47:23 -03:00

342 lines
14 KiB
Python

import ast
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 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 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 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 get_generate_params(state):
generate_params = {}
# Models that are not on transformers
if shared.model_type in ['rwkv', 'llamacpp']:
generate_params['token_count'] = state['max_new_tokens']
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = state[k]
else:
# FlexGen
if shared.args.flexgen:
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = state[k]
if not shared.args.no_stream:
generate_params['max_new_tokens'] = 8
# transformers
else:
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})
return generate_params
def generate_reply(question, state, eos_token=None, stopping_strings=[]):
if shared.model_name == 'None' or shared.model is None:
print("No model is loaded! Select one in the Model tab.")
yield formatted_outputs(question, shared.model_name)
return
clear_torch_cache()
seed = set_manual_seed(state['seed'])
shared.stop_everything = False
generate_params = get_generate_params(state)
t0 = time.time()
# Preparing the input
original_question = question
if not shared.is_chat():
question = apply_extensions('input', question)
# If the model is not on transformers, handle it separately and end this
# function call earlier.
if shared.model_type in ['rwkv', 'llamacpp']:
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
try:
if shared.args.no_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 formatted_outputs(reply, shared.model_name)
else:
if not shared.is_chat():
yield formatted_outputs(question, shared.model_name)
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 formatted_outputs(reply, shared.model_name)
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
# 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, shared.args.flexgen))
if shared.args.verbose:
print(f'\n\n{decode(input_ids[0], state["skip_special_tokens"])}\n--------------------\n')
# 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]))
# Create the StoppingCriteriaList with the stopping strings
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
if shared.args.flexgen:
generate_params['stop'] = eos_token_ids[-1]
else:
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = stopping_criteria_list
# 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})
try:
# Generate the entire reply at once.
if shared.args.no_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]:]))
reply = get_reply_from_output_ids(output, input_ids, original_question, state)
yield formatted_outputs(reply, shared.model_name)
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator.
elif not shared.args.flexgen:
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)
if not shared.is_chat() and shared.model_type != 'HF_seq2seq':
yield formatted_outputs(original_question, shared.model_name)
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]:]))
reply = get_reply_from_output_ids(output, input_ids, original_question, state)
if output[-1] in eos_token_ids:
break
yield formatted_outputs(reply, shared.model_name)
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(state['max_new_tokens'] // 8 + 1):
clear_torch_cache()
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = get_reply_from_output_ids(output, input_ids, original_question, state)
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
break
yield formatted_outputs(reply, shared.model_name)
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({'inputs_embeds': inputs_embeds})
generate_params.update({'inputs': filler_input_ids})
else:
generate_params.update({'inputs': input_ids})
yield formatted_outputs(reply, shared.model_name)
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