Spaces:
Configuration error
Configuration error
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): | |
if skip_special_tokens: | |
reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True) | |
reply = reply.replace(r'<|endoftext|>', '') | |
return reply | |
else: | |
return shared.tokenizer.decode(output_ids, skip_special_tokens=False) | |
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 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 = {} | |
t0 = time.time() | |
original_question = question | |
if not shared.is_chat(): | |
question = apply_extensions('input', question) | |
# These models are not part of Hugging Face, so we handle them | |
# separately and terminate the function call earlier | |
if shared.model_type in ['rwkv', 'llamacpp']: | |
if shared.args.verbose: | |
print(f'\n\n{question}\n--------------------\n') | |
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']: | |
generate_params[k] = state[k] | |
generate_params['token_count'] = 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('output', reply) | |
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('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 | |
input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) | |
output = input_ids[0] | |
if shared.args.verbose: | |
print(f'\n\n{decode(input_ids[0], state["skip_special_tokens"])}\n--------------------\n') | |
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])) | |
# Handling 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 | |
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] = state[k] | |
generate_params['eos_token_id'] = eos_token_ids | |
generate_params['stopping_criteria'] = stopping_criteria_list | |
if state['ban_eos_token']: | |
generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id] | |
else: | |
for k in ['max_new_tokens', 'do_sample', 'temperature']: | |
generate_params[k] = state[k] | |
generate_params['stop'] = 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) | |
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]:])) | |
new_tokens = len(output) - len(input_ids[0]) | |
reply = decode(output[-new_tokens:], state['skip_special_tokens']) | |
if not shared.is_chat(): | |
reply = original_question + apply_extensions('output', reply) | |
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:], state['skip_special_tokens']) | |
if not shared.is_chat(): | |
reply = original_question + apply_extensions('output', reply) | |
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]:])) | |
new_tokens = len(output) - len(original_input_ids[0]) | |
reply = decode(output[-new_tokens:], state['skip_special_tokens']) | |
if not shared.is_chat(): | |
reply = original_question + apply_extensions('output', reply) | |
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}, seed {seed})') | |
return | |