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import gc
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 local_rank
def get_max_prompt_length(tokens):
max_length = 2048 - tokens
if shared.soft_prompt:
max_length -= shared.soft_prompt_tensor.shape[1]
return max_length
def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
if any((shared.is_RWKV, shared.is_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', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
input_ids = input_ids[:, 1:]
if 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):
# Open Assistant relies on special tokens like <|endoftext|>
if re.match('.*(oasst|galactica)-*', shared.model_name.lower()):
return shared.tokenizer.decode(output_ids, skip_special_tokens=False)
else:
reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
reply = reply.replace(r'<|endoftext|>', '')
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 'galactica' in model_name.lower():
reply = fix_galactica(reply)
return reply, reply, generate_basic_html(reply)
elif any((k in shared.model_name.lower() for k in ['gpt4chan', 'gpt-4chan'])):
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 clear_torch_cache():
gc.collect()
if not shared.args.cpu:
torch.cuda.empty_cache()
def set_manual_seed(seed):
if seed != -1:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
clear_torch_cache()
set_manual_seed(generate_state['seed'])
shared.stop_everything = False
generate_params = {}
t0 = time.time()
original_question = question
if not shared.is_chat():
question = apply_extensions(question, 'input')
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if any((shared.is_RWKV, shared.is_llamacpp)):
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = generate_state[k]
generate_params['token_count'] = generate_state['max_new_tokens']
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(reply, 'output')
yield formatted_outputs(reply, shared.model_name)
else:
if not shared.is_chat():
yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
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(reply, 'output')
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})')
return
input_ids = encode(question, generate_state['max_new_tokens'])
original_input_ids = input_ids
output = input_ids[0]
cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
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]))
stopping_criteria_list = transformers.StoppingCriteriaList()
if type(stopping_strings) is list and len(stopping_strings) > 0:
t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
if not shared.args.flexgen:
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] = generate_state[k]
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = stopping_criteria_list
if shared.args.no_stream:
generate_params['min_length'] = 0
else:
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = generate_state[k]
generate_params['stop'] = generate_state['eos_token_ids'][-1]
if not shared.args.no_stream:
generate_params['max_new_tokens'] = 8
if shared.args.no_cache:
generate_params.update({'use_cache': False})
if shared.args.deepspeed:
generate_params.update({'synced_gpus': True})
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})
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]:]))
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not shared.is_chat():
reply = original_question + apply_extensions(reply, 'output')
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():
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]:]))
new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:])
if not shared.is_chat():
reply = original_question + apply_extensions(reply, 'output')
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(generate_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]:]))
new_tokens = len(output) - len(original_input_ids[0])
reply = decode(output[-new_tokens:])
if not shared.is_chat():
reply = original_question + apply_extensions(reply, 'output')
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
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
return
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