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1
.github/FUNDING.yml vendored Normal file
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@ -0,0 +1 @@
ko_fi: oobabooga

23
README.md
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@ -27,7 +27,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
* [FlexGen offload](https://github.com/oobabooga/text-generation-webui/wiki/FlexGen).
* [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed).
* Get responses via API, [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-streaming.py) or [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming.
* [Supports the LLaMA model](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model).
* [Supports the LLaMA model, including 4-bit mode](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model).
* [Supports the RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model).
* Supports softprompts.
* [Supports extensions](https://github.com/oobabooga/text-generation-webui/wiki/Extensions).
@ -60,11 +60,13 @@ pip3 install torch torchvision torchaudio --extra-index-url https://download.pyt
conda install pytorch torchvision torchaudio git -c pytorch
```
See also: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
## Installation option 2: one-click installers
[oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga-windows.zip)
[oobabooga-windows.zip](https://github.com/oobabooga/one-click-installers/archive/refs/heads/oobabooga-windows.zip)
[oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga-linux.zip)
[oobabooga-linux.zip](https://github.com/oobabooga/one-click-installers/archive/refs/heads/oobabooga-linux.zip)
Just download the zip above, extract it, and double click on "install". The web UI and all its dependencies will be installed in the same folder.
@ -139,7 +141,7 @@ Optionally, you can use the following command-line flags:
| `--cpu` | Use the CPU to generate text.|
| `--load-in-8bit` | Load the model with 8-bit precision.|
| `--load-in-4bit` | Load the model with 4-bit precision. Currently only works with LLaMA.|
| `--gptq-bits` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA. |
| `--gptq-bits GPTQ_BITS` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA. |
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
@ -155,12 +157,13 @@ Optionally, you can use the following command-line flags:
| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. |
| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. |
| `--no-stream` | Don't stream the text output in real time. This improves the text generation performance.|
| `--no-stream` | Don't stream the text output in real time. |
| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag.|
| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
| `--listen` | Make the web UI reachable from your local network.|
| `--listen-port LISTEN_PORT` | The listening port that the server will use. |
| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
| `--auto-launch` | Open the web UI in the default browser upon launch. |
| `--verbose` | Print the prompts to the terminal. |
Out of memory errors? [Check this guide](https://github.com/oobabooga/text-generation-webui/wiki/Low-VRAM-guide).
@ -179,14 +182,10 @@ Check the [wiki](https://github.com/oobabooga/text-generation-webui/wiki/System-
Pull requests, suggestions, and issue reports are welcome.
Before reporting a bug, make sure that you have created a conda environment and installed the dependencies exactly as in the *Installation* section above.
Before reporting a bug, make sure that you have:
These issues are known:
* 8-bit doesn't work properly on Windows or older GPUs.
* DeepSpeed doesn't work properly on Windows.
For these two, please try commenting on an existing issue instead of creating a new one.
1. Created a conda environment and installed the dependencies exactly as in the *Installation* section above.
2. [Searched](https://github.com/oobabooga/text-generation-webui/issues) to see if an issue already exists for the issue you encountered.
## Credits

94
extensions/silero_tts/script.py
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@ -1,8 +1,12 @@
import time
from pathlib import Path
import gradio as gr
import torch
import modules.chat as chat
import modules.shared as shared
torch._C._jit_set_profiling_mode(False)
params = {
@ -12,10 +16,28 @@ params = {
'model_id': 'v3_en',
'sample_rate': 48000,
'device': 'cpu',
'show_text': False,
'autoplay': True,
'voice_pitch': 'medium',
'voice_speed': 'medium',
}
current_params = params.copy()
voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
wav_idx = 0
voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
# Used for making text xml compatible, needed for voice pitch and speed control
table = str.maketrans({
"<": "&lt;",
">": "&gt;",
"&": "&amp;",
"'": "&apos;",
'"': "&quot;",
})
def xmlesc(txt):
return txt.translate(table)
def load_model():
model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
@ -33,12 +55,32 @@ def remove_surrounded_chars(string):
new_string += char
return new_string
def remove_tts_from_history(name1, name2):
for i, entry in enumerate(shared.history['internal']):
shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
def toggle_text_in_history(name1, name2):
for i, entry in enumerate(shared.history['visible']):
visible_reply = entry[1]
if visible_reply.startswith('<audio'):
if params['show_text']:
reply = shared.history['internal'][i][1]
shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
else:
shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
def input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
# Remove autoplay from the last reply
if (shared.args.chat or shared.args.cai_chat) and len(shared.history['internal']) > 0:
shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')]
return string
def output_modifier(string):
@ -46,7 +88,7 @@ def output_modifier(string):
This function is applied to the model outputs.
"""
global wav_idx, model, current_params
global model, current_params
for i in params:
if params[i] != current_params[i]:
@ -57,6 +99,7 @@ def output_modifier(string):
if params['activate'] == False:
return string
original_string = string
string = remove_surrounded_chars(string)
string = string.replace('"', '')
string = string.replace('“', '')
@ -64,13 +107,17 @@ def output_modifier(string):
string = string.strip()
if string == '':
string = 'empty reply, try regenerating'
string = '*Empty reply, try regenerating*'
else:
output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav')
prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
output_file = Path(f'extensions/silero_tts/outputs/{wav_idx:06d}.wav')
model.save_wav(text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
string = f'<audio src="file/{output_file.as_posix()}" controls></audio>'
wav_idx += 1
autoplay = 'autoplay' if params['autoplay'] else ''
string = f'<audio src="file/{output_file.as_posix()}" controls {autoplay}></audio>'
if params['show_text']:
string += f'\n\n{original_string}'
return string
@ -85,9 +132,36 @@ def bot_prefix_modifier(string):
def ui():
# Gradio elements
activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
with gr.Accordion("Silero TTS"):
with gr.Row():
activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically')
show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player')
voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
with gr.Row():
v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch')
v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed')
with gr.Row():
convert = gr.Button('Permanently replace audios with the message texts')
convert_cancel = gr.Button('Cancel', visible=False)
convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
# Convert history with confirmation
convert_arr = [convert_confirm, convert, convert_cancel]
convert.click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
convert_confirm.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
convert_confirm.click(remove_tts_from_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
convert_confirm.click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
convert_cancel.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
# Toggle message text in history
show_text.change(lambda x: params.update({"show_text": x}), show_text, None)
show_text.change(toggle_text_in_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
show_text.change(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
# Event functions to update the parameters in the backend
activate.change(lambda x: params.update({"activate": x}), activate, None)
autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None)
voice.change(lambda x: params.update({"speaker": x}), voice, None)
v_pitch.change(lambda x: params.update({"voice_pitch": x}), v_pitch, None)
v_speed.change(lambda x: params.update({"voice_speed": x}), v_speed, None)

8
modules/RWKV.py
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@ -25,10 +25,10 @@ class RWKVModel:
tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
if shared.args.rwkv_strategy is None:
model = RWKV(model=os.path.abspath(path), strategy=f'{device} {dtype}')
model = RWKV(model=str(path), strategy=f'{device} {dtype}')
else:
model = RWKV(model=os.path.abspath(path), strategy=shared.args.rwkv_strategy)
pipeline = PIPELINE(model, os.path.abspath(tokenizer_path))
model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy)
pipeline = PIPELINE(model, str(tokenizer_path))
result = self()
result.pipeline = pipeline
@ -61,7 +61,7 @@ class RWKVTokenizer:
@classmethod
def from_pretrained(self, path):
tokenizer_path = path / "20B_tokenizer.json"
tokenizer = Tokenizer.from_file(os.path.abspath(tokenizer_path))
tokenizer = Tokenizer.from_file(str(tokenizer_path))
result = self()
result.tokenizer = tokenizer

56
modules/chat.py
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@ -22,6 +22,12 @@ def clean_chat_message(text):
text = text.strip()
return text
def generate_chat_output(history, name1, name2, character):
if shared.args.cai_chat:
return generate_chat_html(history, name1, name2, character)
else:
return history
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=False):
user_input = clean_chat_message(user_input)
rows = [f"{context.strip()}\n"]
@ -53,7 +59,6 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
next_character_found = False
substring_found = False
asker = name1 if not impersonate else name2
replier = name2 if not impersonate else name1
@ -79,15 +84,15 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
next_character_found = True
reply = clean_chat_message(reply)
# Detect if something like "\nYo" is generated just before
# "\nYou:" is completed
tmp = f"\n{asker}:"
for j in range(1, len(tmp)):
if reply[-j:] == tmp[:j]:
# If something like "\nYo" is generated just before "\nYou:"
# is completed, trim it
next_turn = f"\n{asker}:"
for j in range(len(next_turn)-1, 0, -1):
if reply[-j:] == next_turn[:j]:
reply = reply[:-j]
substring_found = True
break
return reply, next_character_found, substring_found
return reply, next_character_found
def stop_everything_event():
shared.stop_everything = True
@ -122,7 +127,6 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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
@ -131,7 +135,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{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{name1}:"):
# Extracting the reply
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check)
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
visible_reply = apply_extensions(visible_reply, "output")
if shared.args.chat:
@ -148,7 +152,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
shared.history['internal'][-1] = [text, reply]
shared.history['visible'][-1] = [visible_text, visible_reply]
if not substring_found and not shared.args.no_stream:
if not shared.args.no_stream:
yield shared.history['visible']
if next_character_found:
break
@ -163,15 +167,12 @@ 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 = ''
yield '*Is typing...*'
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}:"):
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
if not substring_found:
yield reply
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
yield reply
if next_character_found:
break
yield reply
@ -182,21 +183,18 @@ def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
def regenerate_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):
if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
if shared.args.cai_chat:
yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
else:
yield shared.history['visible']
yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
else:
last_visible = shared.history['visible'].pop()
last_internal = shared.history['internal'].pop()
yield generate_chat_output(shared.history['visible']+[[last_visible[0], '*Is typing...*']], name1, name2, shared.character)
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)
else:
shared.history['visible'][-1] = (last_visible[0], _history[-1][1])
yield shared.history['visible']
yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
def remove_last_message(name1, name2):
if len(shared.history['visible']) > 0 and not shared.history['internal'][-1][0] == '<|BEGIN-VISIBLE-CHAT|>':
@ -204,6 +202,7 @@ def remove_last_message(name1, name2):
shared.history['internal'].pop()
else:
last = ['', '']
if shared.args.cai_chat:
return generate_chat_html(shared.history['visible'], name1, name2, shared.character), last[0]
else:
@ -223,10 +222,7 @@ def replace_last_reply(text, name1, name2):
shared.history['visible'][-1] = (shared.history['visible'][-1][0], text)
shared.history['internal'][-1][1] = apply_extensions(text, "input")
if shared.args.cai_chat:
return generate_chat_html(shared.history['visible'], name1, name2, shared.character)
else:
return shared.history['visible']
return generate_chat_output(shared.history['visible'], name1, name2, shared.character)
def clear_html():
return generate_chat_html([], "", "", shared.character)
@ -246,10 +242,8 @@ def clear_chat_log(name1, name2):
else:
shared.history['internal'] = []
shared.history['visible'] = []
if shared.args.cai_chat:
return generate_chat_html(shared.history['visible'], name1, name2, shared.character)
else:
return shared.history['visible']
return generate_chat_output(shared.history['visible'], name1, name2, shared.character)
def redraw_html(name1, name2):
return generate_chat_html(shared.history['visible'], name1, name2, shared.character)

7
modules/quantized_LLaMA.py
View File

@ -1,4 +1,3 @@
import os
import sys
from pathlib import Path
@ -7,7 +6,7 @@ import torch
import modules.shared as shared
sys.path.insert(0, os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
from llama import load_quant
@ -41,9 +40,9 @@ def load_quantized_LLaMA(model_name):
print(f"Could not find {pt_model}, exiting...")
exit()
model = load_quant(path_to_model, os.path.abspath(pt_path), bits)
model = load_quant(str(path_to_model), str(pt_path), bits)
# Multi-GPU setup
# Multiple GPUs or GPU+CPU
if shared.args.gpu_memory:
max_memory = {}
for i in range(len(shared.args.gpu_memory)):

4
modules/shared.py
View File

@ -85,12 +85,12 @@ parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory t
parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time. This improves the text generation performance.')
parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.')
parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
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('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
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()

30
modules/text_generation.py
View File

@ -37,9 +37,13 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
return input_ids.cuda()
def decode(output_ids):
reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
reply = reply.replace(r'<|endoftext|>', '')
return reply
# Open Assistant relies on special tokens like <|endoftext|>
if re.match('oasst-*', 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)
@ -119,7 +123,9 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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])
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 stopping_string is not None:
# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
@ -129,7 +135,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if not shared.args.flexgen:
generate_params = [
f"max_new_tokens=max_new_tokens",
f"eos_token_id={n}",
f"eos_token_id={eos_token_ids}",
f"stopping_criteria=stopping_criteria_list",
f"do_sample={do_sample}",
f"temperature={temperature}",
@ -149,7 +155,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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}",
f"stop={eos_token_ids[-1]}",
]
if shared.args.deepspeed:
generate_params.append("synced_gpus=True")
@ -196,10 +202,12 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
if output[-1] in eos_token_ids:
break
yield formatted_outputs(reply, shared.model_name)
if output[-1] == n:
break
yield formatted_outputs(reply, shared.model_name)
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
@ -213,15 +221,17 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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):
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)
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)")

8
requirements.txt
View File

@ -1,12 +1,12 @@
accelerate==0.16.0
accelerate==0.17.0
bitsandbytes==0.37.0
flexgen==0.1.7
gradio==3.18.0
numpy
requests
rwkv==0.1.0
safetensors==0.2.8
rwkv==0.3.1
safetensors==0.3.0
sentencepiece
tqdm
markdown
git+https://github.com/zphang/transformers@llama_push
git+https://github.com/zphang/transformers.git@68d640f7c368bcaaaecfc678f11908ebbd3d6176

8
server.py
View File

@ -269,10 +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=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))
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))
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)