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
import modules.shared as shared
from modules.callbacks import (
Iteratorize,
Stream,
_StopEverythingStoppingCriteria
)
from modules.extensions import apply_extensions
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', '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, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
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:
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.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'CtransformersModel']:
input_ids = shared.tokenizer.encode(str(prompt))
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', '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)
else:
return input_ids.cuda()
def decode(output_ids, skip_special_tokens=True):
return shared.tokenizer.decode(output_ids, 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_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)
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', 'top_p', 'typical_p', 'repetition_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]
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 folks just pass in a processor by itself.
if type(processor) != LogitsProcessorList:
processor = LogitsProcessorList([processor])
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:
yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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 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