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Fix merge conflict in text_generation

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- Need to update `shared.still_streaming = False` before the final `yield formatted_outputs`, shifted the position of some yields.
This commit is contained in:
Xan 2023-03-12 18:56:35 +11:00
commit b3e10e47c0
10 changed files with 298 additions and 155 deletions
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64
.idea/workspace.xml Normal file
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@ -0,0 +1,64 @@
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<change beforePath="$PROJECT_DIR$/modules/RWKV.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/RWKV.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/modules/chat.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/chat.py" afterDir="false" />
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18
extensions/llama_prompts/script.py Normal file
View file

@ -0,0 +1,18 @@
import gradio as gr
import modules.shared as shared
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
def get_prompt_by_name(name):
if name == 'None':
return ''
else:
return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n')
def ui():
if not shared.args.chat or share.args.cai_chat:
choices = ['None'] + list(df['Prompt name'])
prompts_menu = gr.Dropdown(value=choices[0], choices=choices, label='Prompt')
prompts_menu.change(get_prompt_by_name, prompts_menu, shared.gradio['textbox'])

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()

18
modules/chat.py
View file

@ -84,6 +84,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
tmp = f"\n{asker}:"
for j in range(1, len(tmp)):
if reply[-j:] == tmp[:j]:
reply = reply[:-j]
substring_found = True
return reply, next_character_found, substring_found
@ -91,7 +92,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
def stop_everything_event():
shared.stop_everything = True
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
shared.stop_everything = False
just_started = True
eos_token = '\n' if check else None
@ -120,6 +121,10 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
else:
prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
if not regenerate:
# Display user input and "*is typing...*" imediately
yield shared.history['visible']+[[visible_text, '*Is typing...*']]
# Generate
reply = ''
for i in range(chat_generation_attempts):
@ -158,6 +163,9 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
# Display "*is typing...*" imediately
yield '*Is typing...*'
reply = ''
for i in range(chat_generation_attempts):
for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
@ -182,7 +190,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
last_visible = shared.history['visible'].pop()
last_internal = shared.history['internal'].pop()
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
if shared.args.cai_chat:
shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
@ -291,7 +299,7 @@ def save_history(timestamp=True):
fname = f"{prefix}persistent.json"
if not Path('logs').exists():
Path('logs').mkdir()
with open(Path(f'logs/{fname}'), 'w') as f:
with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f:
f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
return Path(f'logs/{fname}')
@ -332,7 +340,7 @@ def load_character(_character, name1, name2):
shared.history['visible'] = []
if _character != 'None':
shared.character = _character
data = json.loads(open(Path(f'characters/{_character}.json'), 'r').read())
data = json.loads(open(Path(f'characters/{_character}.json'), 'r', encoding='utf-8').read())
name2 = data['char_name']
if 'char_persona' in data and data['char_persona'] != '':
context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
@ -372,7 +380,7 @@ def upload_character(json_file, img, tavern=False):
i += 1
if tavern:
outfile_name = f'TavernAI-{outfile_name}'
with open(Path(f'characters/{outfile_name}.json'), 'w') as f:
with open(Path(f'characters/{outfile_name}.json'), 'w', encoding='utf-8') as f:
f.write(json_file)
if img is not None:
img = Image.open(io.BytesIO(img))

1
modules/shared.py
View file

@ -91,4 +91,5 @@ parser.add_argument('--listen', action='store_true', help='Make the web UI reach
parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch')
args = parser.parse_args()

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

159
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}",
@ -147,45 +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)
shared.still_streaming = True
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:
@ -194,22 +171,66 @@ 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")
if not shared.args.flexgen:
if output[-1] == n:
break
input_ids = torch.reshape(output, (1, output.shape[0]))
else:
if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
break
input_ids = np.reshape(output, (1, output.shape[0]))
#Mid-stream yield, ran if no breaks
yield formatted_outputs(reply, shared.model_name)
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
#Stream finished from max tokens or break. Do final yield.
shared.still_streaming = False
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)
shared.still_streaming = True
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")
if output[-1] == n:
break
yield formatted_outputs(reply, shared.model_name)
shared.still_streaming = False
yield formatted_outputs(reply, shared.model_name)
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
shared.still_streaming = True
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")
if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
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)
shared.still_streaming = False
yield formatted_outputs(reply, shared.model_name)
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

1
requirements.txt
View file

@ -3,6 +3,7 @@ bitsandbytes==0.37.0
flexgen==0.1.7
gradio==3.18.0
numpy
requests
rwkv==0.1.0
safetensors==0.2.8
sentencepiece

16
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():
@ -272,10 +269,10 @@ if shared.args.chat or shared.args.cai_chat:
function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=False, api_name='textgen'))
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=False))
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=False))
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False))
shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events)
shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
@ -309,6 +306,7 @@ if shared.args.chat or shared.args.cai_chat:
reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else []
shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']])
shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']])
shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']])
shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None)
shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True)
@ -372,9 +370,9 @@ else:
shared.gradio['interface'].queue()
if shared.args.listen:
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port)
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
else:
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port)
shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
# I think that I will need this later
while True: