import ast
import copy
import html
import random
import re
import time
import traceback
import numpy as np
import torch
import transformers
from transformers import LogitsProcessorList, is_torch_xpu_available
import modules.shared as shared
from modules.callbacks import (
Iteratorize,
Stream,
_StopEverythingStoppingCriteria
)
from modules.extensions import apply_extensions
from modules.grammar import GrammarLogitsProcessor
from modules.html_generator import generate_4chan_html, generate_basic_html
from modules.logging_colors import logger
from modules.models import clear_torch_cache, local_rank
def generate_reply(*args, **kwargs):
shared.generation_lock.acquire()
try:
for result in _generate_reply(*args, **kwargs):
yield result
finally:
shared.generation_lock.release()
def _generate_reply(question, state, stopping_strings=None, is_chat=False, escape_html=False):
# Find the appropriate generation function
generate_func = apply_extensions('custom_generate_reply')
if generate_func is None:
if shared.model_name == 'None' or shared.model is None:
logger.error("No model is loaded! Select one in the Model tab.")
yield ''
return
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel', 'Exllamav2Model', 'CtransformersModel']:
generate_func = generate_reply_custom
else:
generate_func = generate_reply_HF
# Prepare the input
original_question = question
if not is_chat:
state = apply_extensions('state', state)
question = apply_extensions('input', question, state)
# Find the stopping strings
all_stop_strings = []
for st in (stopping_strings, state['custom_stopping_strings']):
if type(st) is str:
st = ast.literal_eval(f"[{st}]")
if type(st) is list and len(st) > 0:
all_stop_strings += st
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
shared.stop_everything = False
clear_torch_cache()
seed = set_manual_seed(state['seed'])
last_update = -1
reply = ''
is_stream = state['stream']
if len(all_stop_strings) > 0 and not state['stream']:
state = copy.deepcopy(state)
state['stream'] = True
# Generate
for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat):
if escape_html:
reply = html.escape(reply)
reply, stop_found = apply_stopping_strings(reply, all_stop_strings)
if is_stream:
cur_time = time.time()
# Maximum number of tokens/second
if state['max_tokens_second'] > 0:
diff = 1 / state['max_tokens_second'] - (cur_time - last_update)
if diff > 0:
time.sleep(diff)
last_update = time.time()
yield reply
# Limit updates to 24 per second to not stress low latency networks
else:
if cur_time - last_update > 0.041666666666666664:
last_update = cur_time
yield reply
if stop_found or (state['max_tokens_second'] > 0 and shared.stop_everything):
break
if not is_chat:
reply = apply_extensions('output', reply, state)
yield reply
def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
if shared.tokenizer is None:
raise ValueError('No tokenizer is loaded')
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'CtransformersModel', 'Exllamav2Model']:
input_ids = shared.tokenizer.encode(str(prompt))
if shared.model.__class__.__name__ not in ['Exllamav2Model']:
input_ids = np.array(input_ids).reshape(1, len(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:]
# Handling truncation
if truncation_length is not None:
input_ids = input_ids[:, -truncation_length:]
if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel', 'Exllamav2Model', 'CtransformersModel'] or shared.args.cpu:
return input_ids
elif shared.args.deepspeed:
return input_ids.to(device=local_rank)
elif torch.backends.mps.is_available():
device = torch.device('mps')
return input_ids.to(device)
elif is_torch_xpu_available():
return input_ids.to("xpu:0")
else:
return input_ids.cuda()
def decode(output_ids, skip_special_tokens=True):
if shared.tokenizer is None:
raise ValueError('No tokenizer is loaded')
return shared.tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens)
def get_encoded_length(prompt):
length_after_extensions = apply_extensions('tokenized_length', prompt)
if length_after_extensions is not None:
return length_after_extensions
return len(encode(prompt)[0])
def get_token_ids(prompt):
tokens = encode(prompt)[0]
decoded_tokens = [shared.tokenizer.decode([i]) for i in tokens]
output = ''
for row in list(zip(tokens, decoded_tokens)):
output += f"{str(int(row[0])).ljust(5)} - {repr(row[1])}\n"
return output
def get_max_prompt_length(state):
return state['truncation_length'] - state['max_new_tokens']
def generate_reply_wrapper(question, state, stopping_strings=None):
"""
Returns formatted outputs for the UI
"""
reply = question if not shared.is_seq2seq else ''
yield formatted_outputs(reply, shared.model_name)
for reply in generate_reply(question, state, stopping_strings, is_chat=False, escape_html=True):
if not shared.is_seq2seq:
reply = question + reply
yield formatted_outputs(reply, shared.model_name)
def formatted_outputs(reply, model_name):
if any(s in model_name for s in ['gpt-4chan', 'gpt4chan']):
reply = fix_gpt4chan(reply)
return html.unescape(reply), generate_4chan_html(reply)
else:
return html.unescape(reply), generate_basic_html(reply)
def fix_gpt4chan(s):
"""
Removes empty replies from gpt4chan outputs
"""
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
def fix_galactica(s):
"""
Fix the LaTeX equations in GALACTICA
"""
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, is_chat=False):
if shared.is_seq2seq:
reply = decode(output_ids, state['skip_special_tokens'])
else:
new_tokens = len(output_ids) - len(input_ids[0])
reply = decode(output_ids[-new_tokens:], state['skip_special_tokens'])
# Prevent LlamaTokenizer from skipping a space
if type(shared.tokenizer) in [transformers.LlamaTokenizer, transformers.LlamaTokenizerFast] and len(output_ids) > 0:
if shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'):
reply = ' ' + reply
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)
elif is_torch_xpu_available():
torch.xpu.manual_seed_all(seed)
return seed
def stop_everything_event():
shared.stop_everything = True
def apply_stopping_strings(reply, all_stop_strings):
stop_found = False
for string in all_stop_strings:
idx = reply.find(string)
if idx != -1:
reply = reply[:idx]
stop_found = True
break
if not stop_found:
# If something like "\nYo" is generated just before "\nYou:"
# is completed, trim it
for string in all_stop_strings:
for j in range(len(string) - 1, 0, -1):
if reply[-j:] == string[:j]:
reply = reply[:-j]
break
else:
continue
break
return reply, stop_found
def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
generate_params = {}
for k in ['max_new_tokens', 'do_sample', 'temperature', 'temperature_last', 'top_p', 'min_p', 'typical_p', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', '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', 'guidance_scale']:
generate_params[k] = state[k]
if state['negative_prompt'] != '':
generate_params['negative_prompt_ids'] = encode(state['negative_prompt'])
for k in ['epsilon_cutoff', 'eta_cutoff']:
if state[k] > 0:
generate_params[k] = state[k] * 1e-4
if state['ban_eos_token']:
generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
if state['custom_token_bans']:
to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
if len(to_ban) > 0:
if generate_params.get('suppress_tokens', None):
generate_params['suppress_tokens'] += to_ban
else:
generate_params['suppress_tokens'] = to_ban
generate_params.update({'use_cache': not shared.args.no_cache})
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))
if state['auto_max_new_tokens']:
generate_params['max_new_tokens'] = state['truncation_length'] - input_ids.shape[-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})
# Stopping criteria / eos token
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
processor = state.get('logits_processor', LogitsProcessorList([]))
# In case a processor is passed by itself.
if not isinstance(processor, LogitsProcessorList):
processor = LogitsProcessorList([processor])
processor.append(GrammarLogitsProcessor(state['grammar_string']))
apply_extensions('logits_processor', processor, input_ids)
generate_params['logits_processor'] = processor
t0 = time.time()
try:
if not is_chat and not shared.is_seq2seq:
yield ''
# 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()
yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
# 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, *args, **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 output[-1] in eos_token_ids:
break
yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
original_tokens = len(original_input_ids[0])
new_tokens = len(output) - (original_tokens if not shared.is_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, stopping_strings=None, is_chat=False):
"""
For models that do not use the transformers library for sampling
"""
seed = set_manual_seed(state['seed'])
t0 = time.time()
reply = ''
try:
if not is_chat:
yield ''
if not state['stream']:
reply = shared.model.generate(question, state)
yield reply
else:
for reply in shared.model.generate_with_streaming(question, state):
yield reply
except Exception:
traceback.print_exc()
finally:
t1 = time.time()
original_tokens = len(encode(original_question)[0])
new_tokens = len(encode(original_question + reply)[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