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Merge pull request #189 from oobabooga/new-streaming

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New streaming method (much faster)
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oobabooga 2023-03-12 03:01:26 -03:00 committed by GitHub
commit 3437de686c
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GPG key ID: 4AEE18F83AFDEB23
5 changed files with 183 additions and 134 deletions
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46
modules/RWKV.py
View file

@ -7,6 +7,7 @@ import numpy as np
from tokenizers import Tokenizer
import modules.shared as shared
from modules.callbacks import Iteratorize
np.set_printoptions(precision=4, suppress=True, linewidth=200)
@ -49,11 +50,11 @@ class RWKVModel:
return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
def generate_with_streaming(self, **kwargs):
iterable = Iteratorize(self.generate, kwargs, callback=None)
reply = kwargs['context']
for token in iterable:
reply += token
yield reply
with Iteratorize(self.generate, kwargs, callback=None) as generator:
reply = kwargs['context']
for token in generator:
reply += token
yield reply
class RWKVTokenizer:
def __init__(self):
@ -73,38 +74,3 @@ class RWKVTokenizer:
def decode(self, ids):
return self.tokenizer.decode(ids)
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
"""
def __init__(self, func, kwargs={}, callback=None):
self.mfunc=func
self.c_callback=callback
self.q = Queue(maxsize=1)
self.sentinel = object()
self.kwargs = kwargs
def _callback(val):
self.q.put(val)
def gentask():
ret = self.mfunc(callback=_callback, **self.kwargs)
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
Thread(target=gentask).start()
def __iter__(self):
return self
def __next__(self):
obj = self.q.get(True,None)
if obj is self.sentinel:
raise StopIteration
else:
return obj

98
modules/callbacks.py Normal file
View file

@ -0,0 +1,98 @@
import gc
from queue import Queue
from threading import Thread
import torch
import transformers
import modules.shared as shared
# Copied from https://github.com/PygmalionAI/gradio-ui/
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
def __init__(self, sentinel_token_ids: torch.LongTensor,
starting_idx: int):
transformers.StoppingCriteria.__init__(self)
self.sentinel_token_ids = sentinel_token_ids
self.starting_idx = starting_idx
def __call__(self, input_ids: torch.LongTensor,
_scores: torch.FloatTensor) -> bool:
for sample in input_ids:
trimmed_sample = sample[self.starting_idx:]
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
continue
for window in trimmed_sample.unfold(
0, self.sentinel_token_ids.shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids, window)):
return True
return False
class Stream(transformers.StoppingCriteria):
def __init__(self, callback_func=None):
self.callback_func = callback_func
def __call__(self, input_ids, scores) -> bool:
if self.callback_func is not None:
self.callback_func(input_ids[0])
return False
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
"""
def __init__(self, func, kwargs={}, callback=None):
self.mfunc=func
self.c_callback=callback
self.q = Queue()
self.sentinel = object()
self.kwargs = kwargs
self.stop_now = False
def _callback(val):
if self.stop_now:
raise ValueError
self.q.put(val)
def gentask():
try:
ret = self.mfunc(callback=_callback, **self.kwargs)
except ValueError:
pass
clear_torch_cache()
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
self.thread = Thread(target=gentask)
self.thread.start()
def __iter__(self):
return self
def __next__(self):
obj = self.q.get(True,None)
if obj is self.sentinel:
raise StopIteration
else:
return obj
def __del__(self):
clear_torch_cache()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop_now = True
clear_torch_cache()
def clear_torch_cache():
gc.collect()
if not shared.args.cpu:
torch.cuda.empty_cache()

32
modules/stopping_criteria.py
View file

@ -1,32 +0,0 @@
'''
This code was copied from
https://github.com/PygmalionAI/gradio-ui/
'''
import torch
import transformers
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
def __init__(self, sentinel_token_ids: torch.LongTensor,
starting_idx: int):
transformers.StoppingCriteria.__init__(self)
self.sentinel_token_ids = sentinel_token_ids
self.starting_idx = starting_idx
def __call__(self, input_ids: torch.LongTensor,
_scores: torch.FloatTensor) -> bool:
for sample in input_ids:
trimmed_sample = sample[self.starting_idx:]
# Can't unfold, output is still too tiny. Skip.
if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
continue
for window in trimmed_sample.unfold(
0, self.sentinel_token_ids.shape[-1], 1):
if torch.all(torch.eq(self.sentinel_token_ids, window)):
return True
return False

138
modules/text_generation.py
View file

@ -5,13 +5,13 @@ import time
import numpy as np
import torch
import transformers
from tqdm import tqdm
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
from modules.stopping_criteria import _SentinelTokenStoppingCriteria
def get_max_prompt_length(tokens):
@ -92,19 +92,22 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if shared.is_RWKV:
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)
yield formatted_outputs(reply, shared.model_name)
else:
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):
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)
yield formatted_outputs(reply, shared.model_name)
t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds.")
return
else:
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):
yield formatted_outputs(reply, shared.model_name)
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
original_question = question
if not (shared.args.chat or shared.args.cai_chat):
@ -113,23 +116,19 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
print(f"\n\n{question}\n--------------------\n")
input_ids = encode(question, max_new_tokens)
original_input_ids = input_ids
output = input_ids[0]
cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
stopping_criteria_list = transformers.StoppingCriteriaList()
if stopping_string is not None:
# The stopping_criteria code below was copied from
# https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
t = encode(stopping_string, 0, add_special_tokens=False)
stopping_criteria_list = transformers.StoppingCriteriaList([
_SentinelTokenStoppingCriteria(
sentinel_token_ids=t,
starting_idx=len(input_ids[0])
)
])
else:
stopping_criteria_list = None
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
if not shared.args.flexgen:
generate_params = [
f"max_new_tokens=max_new_tokens",
f"eos_token_id={n}",
f"stopping_criteria=stopping_criteria_list",
f"do_sample={do_sample}",
@ -147,44 +146,23 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
]
else:
generate_params = [
f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
f"do_sample={do_sample}",
f"temperature={temperature}",
f"stop={n}",
]
if shared.args.deepspeed:
generate_params.append("synced_gpus=True")
if shared.args.no_stream:
generate_params.append("max_new_tokens=max_new_tokens")
else:
generate_params.append("max_new_tokens=8")
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.insert(0, "inputs_embeds=inputs_embeds")
generate_params.insert(0, "filler_input_ids")
generate_params.insert(0, "inputs=filler_input_ids")
else:
generate_params.insert(0, "input_ids")
# Generate the entire reply at once
if shared.args.no_stream:
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0)/8:.2f} it/s, {len(output)-len(input_ids[0])} tokens)")
yield formatted_outputs(reply, shared.model_name)
# Generate the reply 8 tokens at a time
else:
yield formatted_outputs(original_question, shared.model_name)
for i in tqdm(range(max_new_tokens//8+1)):
clear_torch_cache()
generate_params.insert(0, "inputs=input_ids")
try:
# Generate the entire reply at once.
if shared.args.no_stream:
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
if shared.soft_prompt:
@ -193,16 +171,58 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
reply = decode(output)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if not shared.args.flexgen:
if output[-1] == n:
break
input_ids = torch.reshape(output, (1, output.shape[0]))
else:
# 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)
yield formatted_outputs(original_question, shared.model_name)
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if output[-1] == n:
break
# 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 = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
break
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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)")
return

3
server.py
View file

@ -18,9 +18,6 @@ from modules.html_generator import generate_chat_html
from modules.models import load_model, load_soft_prompt
from modules.text_generation import generate_reply
if (shared.args.chat or shared.args.cai_chat) and not shared.args.no_stream:
print('Warning: chat mode currently becomes somewhat slower with text streaming on.\nConsider starting the web UI with the --no-stream option.\n')
# Loading custom settings
settings_file = None
if shared.args.settings is not None and Path(shared.args.settings).exists():