mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 18:45:09 +02:00
b740c5b284
Not sure if I did this right but it does move with the conversation and seems to match value.
274 lines
12 KiB
Python
274 lines
12 KiB
Python
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 shared.is_RWKV:
|
|
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 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.args.chat or shared.args.cai_chat):
|
|
if model_name.lower().startswith('galactica'):
|
|
reply = fix_galactica(reply)
|
|
return reply, reply, generate_basic_html(reply)
|
|
elif model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '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 generate_reply(question, 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, seed, eos_token=None, stopping_strings=[]):
|
|
clear_torch_cache()
|
|
set_manual_seed(seed)
|
|
t0 = time.time()
|
|
|
|
original_question = question
|
|
if not (shared.args.chat or shared.args.cai_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 shared.is_RWKV:
|
|
try:
|
|
if shared.args.no_stream:
|
|
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
|
|
if not (shared.args.chat or shared.args.cai_chat):
|
|
reply = original_question + apply_extensions(reply, "output")
|
|
yield formatted_outputs(reply, shared.model_name)
|
|
else:
|
|
if not (shared.args.chat or shared.args.cai_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, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
|
|
if not (shared.args.chat or shared.args.cai_chat):
|
|
reply = original_question + apply_extensions(reply, "output")
|
|
yield formatted_outputs(reply, shared.model_name)
|
|
|
|
except Exception:
|
|
traceback.print_exc()
|
|
finally:
|
|
t1 = time.time()
|
|
output = encode(reply)[0]
|
|
input_ids = encode(question)
|
|
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
|
|
return
|
|
|
|
input_ids = encode(question, 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])))
|
|
|
|
generate_params = {}
|
|
if not shared.args.flexgen:
|
|
generate_params.update({
|
|
"max_new_tokens": max_new_tokens,
|
|
"eos_token_id": eos_token_ids,
|
|
"stopping_criteria": stopping_criteria_list,
|
|
"do_sample": do_sample,
|
|
"temperature": temperature,
|
|
"top_p": top_p,
|
|
"typical_p": typical_p,
|
|
"repetition_penalty": repetition_penalty,
|
|
"encoder_repetition_penalty": encoder_repetition_penalty,
|
|
"top_k": top_k,
|
|
"min_length": min_length if shared.args.no_stream else 0,
|
|
"no_repeat_ngram_size": no_repeat_ngram_size,
|
|
"num_beams": num_beams,
|
|
"penalty_alpha": penalty_alpha,
|
|
"length_penalty": length_penalty,
|
|
"early_stopping": early_stopping,
|
|
})
|
|
else:
|
|
generate_params.update({
|
|
"max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
|
|
"do_sample": do_sample,
|
|
"temperature": temperature,
|
|
"stop": eos_token_ids[-1],
|
|
})
|
|
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.args.chat or shared.args.cai_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.args.chat or shared.args.cai_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.args.chat or shared.args.cai_chat):
|
|
reply = original_question + apply_extensions(reply, "output")
|
|
|
|
if output[-1] in eos_token_ids:
|
|
break
|
|
yield formatted_outputs(reply, shared.model_name)
|
|
|
|
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(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.args.chat or shared.args.cai_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()
|
|
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens, context {len(original_input_ids[0])})")
|
|
return
|