mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 10:35:10 +02:00
Merge branch 'main' of https://github.com/xanthousm/text-generation-webui
This commit is contained in:
Xan
commit
5648a41a27
16 changed files with 352 additions and 166 deletions
1
.gitignore
vendored
1
.gitignore
vendored
|
|
@ -1,6 +1,7 @@
|
|||
cache/*
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||||
characters/*
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||||
extensions/silero_tts/outputs/*
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||||
extensions/elevenlabs_tts/outputs/*
|
||||
logs/*
|
||||
models/*
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||||
softprompts/*
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||||
|
|
|
|||
14
README.md
14
README.md
|
|
@ -21,12 +21,13 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
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|||
* Advanced chat features (send images, get audio responses with TTS).
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||||
* Stream the text output in real time.
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||||
* Load parameter presets from text files.
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||||
* Load large models in 8-bit mode (see [here](https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652) and [here](https://www.reddit.com/r/PygmalionAI/comments/1115gom/running_pygmalion_6b_with_8gb_of_vram/) if you are on Windows).
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||||
* Load large models in 8-bit mode (see [here](https://github.com/oobabooga/text-generation-webui/issues/147#issuecomment-1456040134), [here](https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652) and [here](https://www.reddit.com/r/PygmalionAI/comments/1115gom/running_pygmalion_6b_with_8gb_of_vram/) if you are on Windows).
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* Split large models across your GPU(s), CPU, and disk.
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* CPU mode.
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* [FlexGen offload](https://github.com/oobabooga/text-generation-webui/wiki/FlexGen).
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* [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed).
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* [Get responses via API](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py).
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* 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.
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* [Supports the RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model).
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* Supports softprompts.
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* [Supports extensions](https://github.com/oobabooga/text-generation-webui/wiki/Extensions).
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* [Works on Google Colab](https://github.com/oobabooga/text-generation-webui/wiki/Running-on-Colab).
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|
|
@ -82,8 +83,8 @@ Models should be placed under `models/model-name`. For instance, `models/gpt-j-6
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|||
* [Pythia](https://huggingface.co/models?search=eleutherai/pythia)
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* [OPT](https://huggingface.co/models?search=facebook/opt)
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* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
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* [\*-Erebus](https://huggingface.co/models?search=erebus)
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||||
* [Pygmalion](https://huggingface.co/models?search=pygmalion)
|
||||
* [\*-Erebus](https://huggingface.co/models?search=erebus) (NSFW)
|
||||
* [Pygmalion](https://huggingface.co/models?search=pygmalion) (NSFW)
|
||||
|
||||
You can automatically download a model from HF using the script `download-model.py`:
|
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|
||||
|
|
@ -149,9 +150,10 @@ Optionally, you can use the following command-line flags:
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|||
| `--deepspeed` | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
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| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
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| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. |
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| `--rwkv-strategy RWKV_STRATEGY` | The strategy to use while loading RWKV models. Examples: `"cpu fp32"`, `"cuda fp16"`, `"cuda fp16 *30 -> cpu fp32"`. |
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| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
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| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. |
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| `--no-stream` | Don't stream the text output in real time. This improves the text generation performance.|
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| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example.|
|
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| `--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.|
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| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
|
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| `--listen` | Make the web UI reachable from your local network.|
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| `--listen-port LISTEN_PORT` | The listening port that the server will use. |
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|
|
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90
api-example-stream.py
Normal file
90
api-example-stream.py
Normal file
|
|
@ -0,0 +1,90 @@
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|||
'''
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|
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Contributed by SagsMug. Thank you SagsMug.
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||||
https://github.com/oobabooga/text-generation-webui/pull/175
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|
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'''
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import asyncio
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import json
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import random
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import string
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import websockets
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def random_hash():
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letters = string.ascii_lowercase + string.digits
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return ''.join(random.choice(letters) for i in range(9))
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async def run(context):
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server = "127.0.0.1"
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params = {
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'max_new_tokens': 200,
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'do_sample': True,
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'temperature': 0.5,
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'top_p': 0.9,
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'typical_p': 1,
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'repetition_penalty': 1.05,
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'top_k': 0,
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'min_length': 0,
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'no_repeat_ngram_size': 0,
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'num_beams': 1,
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'penalty_alpha': 0,
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'length_penalty': 1,
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'early_stopping': False,
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}
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session = random_hash()
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|
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async with websockets.connect(f"ws://{server}:7860/queue/join") as websocket:
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while content := json.loads(await websocket.recv()):
|
||||
#Python3.10 syntax, replace with if elif on older
|
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match content["msg"]:
|
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case "send_hash":
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await websocket.send(json.dumps({
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"session_hash": session,
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"fn_index": 7
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}))
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case "estimation":
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pass
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case "send_data":
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await websocket.send(json.dumps({
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"session_hash": session,
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"fn_index": 7,
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"data": [
|
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context,
|
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params['max_new_tokens'],
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params['do_sample'],
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params['temperature'],
|
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params['top_p'],
|
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params['typical_p'],
|
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params['repetition_penalty'],
|
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params['top_k'],
|
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params['min_length'],
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params['no_repeat_ngram_size'],
|
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params['num_beams'],
|
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params['penalty_alpha'],
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params['length_penalty'],
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params['early_stopping'],
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||||
]
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}))
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case "process_starts":
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||||
pass
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case "process_generating" | "process_completed":
|
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yield content["output"]["data"][0]
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# You can search for your desired end indicator and
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# stop generation by closing the websocket here
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if (content["msg"] == "process_completed"):
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break
|
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prompt = "What I would like to say is the following: "
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|
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async def get_result():
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async for response in run(prompt):
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# Print intermediate steps
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print(response)
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# Print final result
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print(response)
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|
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asyncio.run(get_result())
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3
extensions/elevenlabs_tts/requirements.txt
Normal file
3
extensions/elevenlabs_tts/requirements.txt
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
elevenlabslib
|
||||
soundfile
|
||||
sounddevice
|
||||
113
extensions/elevenlabs_tts/script.py
Normal file
113
extensions/elevenlabs_tts/script.py
Normal file
|
|
@ -0,0 +1,113 @@
|
|||
from pathlib import Path
|
||||
|
||||
import gradio as gr
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||||
from elevenlabslib import *
|
||||
from elevenlabslib.helpers import *
|
||||
|
||||
params = {
|
||||
'activate': True,
|
||||
'api_key': '12345',
|
||||
'selected_voice': 'None',
|
||||
}
|
||||
|
||||
initial_voice = ['None']
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||||
wav_idx = 0
|
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user = ElevenLabsUser(params['api_key'])
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user_info = None
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||||
|
||||
|
||||
# Check if the API is valid and refresh the UI accordingly.
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def check_valid_api():
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|
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global user, user_info, params
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|
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user = ElevenLabsUser(params['api_key'])
|
||||
user_info = user._get_subscription_data()
|
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print('checking api')
|
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if params['activate'] == False:
|
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return gr.update(value='Disconnected')
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elif user_info is None:
|
||||
print('Incorrect API Key')
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return gr.update(value='Disconnected')
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else:
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print('Got an API Key!')
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return gr.update(value='Connected')
|
||||
|
||||
# Once the API is verified, get the available voices and update the dropdown list
|
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def refresh_voices():
|
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|
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global user, user_info
|
||||
|
||||
your_voices = [None]
|
||||
if user_info is not None:
|
||||
for voice in user.get_available_voices():
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your_voices.append(voice.initialName)
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return gr.Dropdown.update(choices=your_voices)
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||||
else:
|
||||
return
|
||||
|
||||
def remove_surrounded_chars(string):
|
||||
new_string = ""
|
||||
in_star = False
|
||||
for char in string:
|
||||
if char == '*':
|
||||
in_star = not in_star
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||||
elif not in_star:
|
||||
new_string += char
|
||||
return new_string
|
||||
|
||||
def input_modifier(string):
|
||||
"""
|
||||
This function is applied to your text inputs before
|
||||
they are fed into the model.
|
||||
"""
|
||||
|
||||
return string
|
||||
|
||||
def output_modifier(string):
|
||||
"""
|
||||
This function is applied to the model outputs.
|
||||
"""
|
||||
|
||||
global params, wav_idx, user, user_info
|
||||
|
||||
if params['activate'] == False:
|
||||
return string
|
||||
elif user_info == None:
|
||||
return string
|
||||
|
||||
string = remove_surrounded_chars(string)
|
||||
string = string.replace('"', '')
|
||||
string = string.replace('“', '')
|
||||
string = string.replace('\n', ' ')
|
||||
string = string.strip()
|
||||
|
||||
if string == '':
|
||||
string = 'empty reply, try regenerating'
|
||||
|
||||
output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'.format(wav_idx))
|
||||
voice = user.get_voices_by_name(params['selected_voice'])[0]
|
||||
audio_data = voice.generate_audio_bytes(string)
|
||||
save_bytes_to_path(Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'), audio_data)
|
||||
|
||||
string = f'<audio src="file/{output_file.as_posix()}" controls></audio>'
|
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wav_idx += 1
|
||||
return string
|
||||
|
||||
def ui():
|
||||
|
||||
# Gradio elements
|
||||
with gr.Row():
|
||||
activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
|
||||
connection_status = gr.Textbox(value='Disconnected', label='Connection Status')
|
||||
voice = gr.Dropdown(value=params['selected_voice'], choices=initial_voice, label='TTS Voice')
|
||||
with gr.Row():
|
||||
api_key = gr.Textbox(placeholder="Enter your API key.", label='API Key')
|
||||
connect = gr.Button(value='Connect')
|
||||
|
||||
# Event functions to update the parameters in the backend
|
||||
activate.change(lambda x: params.update({'activate': x}), activate, None)
|
||||
voice.change(lambda x: params.update({'selected_voice': x}), voice, None)
|
||||
api_key.change(lambda x: params.update({'api_key': x}), api_key, None)
|
||||
connect.click(check_valid_api, [], connection_status)
|
||||
connect.click(refresh_voices, [], voice)
|
||||
|
|
@ -1,4 +1,3 @@
|
|||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
|
|
@ -94,7 +93,7 @@ def output_modifier(string):
|
|||
string ='<speak>'+prosody+xmlesc(string)+'</prosody></speak>'
|
||||
|
||||
output_file = Path(f'extensions/silero_tts/outputs/{wav_idx:06d}.wav')
|
||||
audio = model.save_wav(ssml_text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
|
||||
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>'
|
||||
|
||||
#reset if too many wavs. set max to -1 for unlimited.
|
||||
|
|
|
|||
|
|
@ -1,96 +0,0 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Tuple
|
||||
|
||||
import fire
|
||||
import torch
|
||||
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
|
||||
from llama import LLaMA, ModelArgs, Tokenizer, Transformer
|
||||
|
||||
os.environ['RANK'] = '0'
|
||||
os.environ['WORLD_SIZE'] = '1'
|
||||
os.environ['MP'] = '1'
|
||||
os.environ['MASTER_ADDR'] = '127.0.0.1'
|
||||
os.environ['MASTER_PORT'] = '2223'
|
||||
|
||||
def setup_model_parallel() -> Tuple[int, int]:
|
||||
local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
||||
world_size = int(os.environ.get("WORLD_SIZE", -1))
|
||||
|
||||
torch.distributed.init_process_group("gloo")
|
||||
initialize_model_parallel(world_size)
|
||||
torch.cuda.set_device(local_rank)
|
||||
|
||||
# seed must be the same in all processes
|
||||
torch.manual_seed(1)
|
||||
return local_rank, world_size
|
||||
|
||||
def load(
|
||||
ckpt_dir: str,
|
||||
tokenizer_path: str,
|
||||
local_rank: int,
|
||||
world_size: int,
|
||||
max_seq_len: int,
|
||||
max_batch_size: int,
|
||||
) -> LLaMA:
|
||||
start_time = time.time()
|
||||
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
|
||||
assert world_size == len(
|
||||
checkpoints
|
||||
), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
|
||||
ckpt_path = checkpoints[local_rank]
|
||||
print("Loading")
|
||||
checkpoint = torch.load(ckpt_path, map_location="cpu")
|
||||
with open(Path(ckpt_dir) / "params.json", "r") as f:
|
||||
params = json.loads(f.read())
|
||||
|
||||
model_args: ModelArgs = ModelArgs(
|
||||
max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
|
||||
)
|
||||
tokenizer = Tokenizer(model_path=tokenizer_path)
|
||||
model_args.vocab_size = tokenizer.n_words
|
||||
torch.set_default_tensor_type(torch.cuda.HalfTensor)
|
||||
model = Transformer(model_args)
|
||||
torch.set_default_tensor_type(torch.FloatTensor)
|
||||
model.load_state_dict(checkpoint, strict=False)
|
||||
|
||||
generator = LLaMA(model, tokenizer)
|
||||
print(f"Loaded in {time.time() - start_time:.2f} seconds")
|
||||
return generator
|
||||
|
||||
|
||||
class LLaMAModel:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(self, path, max_seq_len=2048, max_batch_size=1):
|
||||
tokenizer_path = path / "tokenizer.model"
|
||||
path = os.path.abspath(path)
|
||||
tokenizer_path = os.path.abspath(tokenizer_path)
|
||||
|
||||
local_rank, world_size = setup_model_parallel()
|
||||
if local_rank > 0:
|
||||
sys.stdout = open(os.devnull, "w")
|
||||
|
||||
generator = load(
|
||||
path, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
|
||||
)
|
||||
|
||||
result = self()
|
||||
result.pipeline = generator
|
||||
return result
|
||||
|
||||
def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
|
||||
|
||||
results = self.pipeline.generate(
|
||||
[prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
|
||||
)
|
||||
|
||||
return results[0]
|
||||
|
|
@ -1,14 +1,17 @@
|
|||
import os
|
||||
from pathlib import Path
|
||||
from queue import Queue
|
||||
from threading import Thread
|
||||
|
||||
import numpy as np
|
||||
from tokenizers import Tokenizer
|
||||
|
||||
import modules.shared as shared
|
||||
|
||||
np.set_printoptions(precision=4, suppress=True, linewidth=200)
|
||||
|
||||
os.environ['RWKV_JIT_ON'] = '1'
|
||||
os.environ["RWKV_CUDA_ON"] = '0' # '1' : use CUDA kernel for seq mode (much faster)
|
||||
os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
|
||||
|
||||
from rwkv.model import RWKV
|
||||
from rwkv.utils import PIPELINE, PIPELINE_ARGS
|
||||
|
|
@ -32,10 +35,11 @@ class RWKVModel:
|
|||
result.pipeline = pipeline
|
||||
return result
|
||||
|
||||
def generate(self, context, token_count=20, temperature=1, top_p=1, alpha_frequency=0.25, alpha_presence=0.25, token_ban=[0], token_stop=[], callback=None):
|
||||
def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
|
||||
args = PIPELINE_ARGS(
|
||||
temperature = temperature,
|
||||
top_p = top_p,
|
||||
top_k = top_k,
|
||||
alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3)
|
||||
alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3)
|
||||
token_ban = token_ban, # ban the generation of some tokens
|
||||
|
|
@ -43,3 +47,64 @@ 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
|
||||
|
||||
class RWKVTokenizer:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(self, path):
|
||||
tokenizer_path = path / "20B_tokenizer.json"
|
||||
tokenizer = Tokenizer.from_file(os.path.abspath(tokenizer_path))
|
||||
|
||||
result = self()
|
||||
result.tokenizer = tokenizer
|
||||
return result
|
||||
|
||||
def encode(self, prompt):
|
||||
return self.tokenizer.encode(prompt).ids
|
||||
|
||||
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
|
||||
|
|
|
|||
|
|
@ -51,23 +51,29 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
|
|||
prompt = ''.join(rows)
|
||||
return prompt
|
||||
|
||||
def extract_message_from_reply(question, reply, current, other, check, extensions=False):
|
||||
def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
|
||||
next_character_found = False
|
||||
substring_found = False
|
||||
|
||||
previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", question)]
|
||||
idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", reply)]
|
||||
idx = idx[len(previous_idx)-1]
|
||||
asker = name1 if not impersonate else name2
|
||||
replier = name2 if not impersonate else name1
|
||||
|
||||
if extensions:
|
||||
reply = reply[idx + 1 + len(apply_extensions(f"{current}:", "bot_prefix")):]
|
||||
previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
|
||||
idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
|
||||
idx = idx[max(len(previous_idx)-1, 0)]
|
||||
|
||||
if not impersonate:
|
||||
reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
|
||||
else:
|
||||
reply = reply[idx + 1 + len(f"{current}:"):]
|
||||
reply = reply[idx + 1 + len(f"{replier}:"):]
|
||||
|
||||
if check:
|
||||
reply = reply.split('\n')[0].strip()
|
||||
lines = reply.split('\n')
|
||||
reply = lines[0].strip()
|
||||
if len(lines) > 1:
|
||||
next_character_found = True
|
||||
else:
|
||||
idx = reply.find(f"\n{other}:")
|
||||
idx = reply.find(f"\n{asker}:")
|
||||
if idx != -1:
|
||||
reply = reply[:idx]
|
||||
next_character_found = True
|
||||
|
|
@ -75,7 +81,7 @@ def extract_message_from_reply(question, reply, current, other, check, extension
|
|||
|
||||
# Detect if something like "\nYo" is generated just before
|
||||
# "\nYou:" is completed
|
||||
tmp = f"\n{other}:"
|
||||
tmp = f"\n{asker}:"
|
||||
for j in range(1, len(tmp)):
|
||||
if reply[-j:] == tmp[:j]:
|
||||
substring_found = True
|
||||
|
|
@ -89,6 +95,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
|
|||
shared.stop_everything = False
|
||||
just_started = True
|
||||
eos_token = '\n' if check else None
|
||||
name1_original = name1
|
||||
if 'pygmalion' in shared.model_name.lower():
|
||||
name1 = "You"
|
||||
|
||||
|
|
@ -119,8 +126,9 @@ 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, name2, name1, check, extensions=True)
|
||||
visible_reply = apply_extensions(reply, "output")
|
||||
reply, next_character_found, substring_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:
|
||||
visible_reply = visible_reply.replace('\n', '<br>')
|
||||
|
||||
|
|
@ -139,6 +147,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
|
|||
yield shared.history['visible']
|
||||
if next_character_found:
|
||||
break
|
||||
|
||||
yield shared.history['visible']
|
||||
|
||||
def impersonate_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):
|
||||
|
|
@ -152,7 +161,7 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
|
|||
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}:"):
|
||||
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, extensions=False)
|
||||
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
|
||||
if not substring_found:
|
||||
yield reply
|
||||
if next_character_found:
|
||||
|
|
|
|||
|
|
@ -39,10 +39,9 @@ def load_model(model_name):
|
|||
t0 = time.time()
|
||||
|
||||
shared.is_RWKV = model_name.lower().startswith('rwkv-')
|
||||
shared.is_LLaMA = model_name.lower().startswith('llama-')
|
||||
|
||||
# Default settings
|
||||
if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV or shared.is_LLaMA):
|
||||
if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV):
|
||||
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
|
||||
else:
|
||||
|
|
@ -80,20 +79,12 @@ def load_model(model_name):
|
|||
|
||||
# RMKV model (not on HuggingFace)
|
||||
elif shared.is_RWKV:
|
||||
from modules.RWKV import RWKVModel
|
||||
from modules.RWKV import RWKVModel, RWKVTokenizer
|
||||
|
||||
model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
|
||||
tokenizer = RWKVTokenizer.from_pretrained(Path('models'))
|
||||
|
||||
return model, None
|
||||
|
||||
# LLaMA model (not on HuggingFace)
|
||||
elif shared.is_LLaMA:
|
||||
import modules.LLaMA
|
||||
from modules.LLaMA import LLaMAModel
|
||||
|
||||
model = LLaMAModel.from_pretrained(Path(f'models/{model_name}'))
|
||||
|
||||
return model, None
|
||||
return model, tokenizer
|
||||
|
||||
# Custom
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ model_name = ""
|
|||
soft_prompt_tensor = None
|
||||
soft_prompt = False
|
||||
is_RWKV = False
|
||||
is_LLaMA = False
|
||||
|
||||
# Chat variables
|
||||
history = {'internal': [], 'visible': []}
|
||||
|
|
@ -44,7 +43,6 @@ settings = {
|
|||
'default': 'NovelAI-Sphinx Moth',
|
||||
'pygmalion-*': 'Pygmalion',
|
||||
'RWKV-*': 'Naive',
|
||||
'llama-*': 'Naive',
|
||||
'(rosey|chip|joi)_.*_instruct.*': 'Instruct Joi (Contrastive Search)'
|
||||
},
|
||||
'prompts': {
|
||||
|
|
@ -84,9 +82,10 @@ parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, defaul
|
|||
parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
|
||||
parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
|
||||
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='The strategy to use while loading RWKV models. Examples: "cpu fp32", "cuda fp16", "cuda fp16 *30 -> cpu fp32".')
|
||||
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('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example.')
|
||||
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.')
|
||||
|
|
|
|||
|
|
@ -21,21 +21,20 @@ def get_max_prompt_length(tokens):
|
|||
return max_length
|
||||
|
||||
def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
|
||||
|
||||
# These models do not have explicit tokenizers for now, so
|
||||
# we return an estimate for the number of tokens
|
||||
if shared.is_RWKV or shared.is_LLaMA:
|
||||
return np.zeros((1, len(prompt)//4))
|
||||
|
||||
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:
|
||||
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
|
||||
elif shared.args.flexgen:
|
||||
return input_ids.numpy()
|
||||
elif shared.args.deepspeed:
|
||||
return input_ids.to(device=local_rank)
|
||||
else:
|
||||
return input_ids.cuda()
|
||||
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)
|
||||
else:
|
||||
return input_ids.cuda()
|
||||
|
||||
def decode(output_ids):
|
||||
reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
|
||||
|
|
@ -81,26 +80,30 @@ def formatted_outputs(reply, model_name):
|
|||
else:
|
||||
return reply
|
||||
|
||||
def generate_reply(question, 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=None, stopping_string=None):
|
||||
def clear_torch_cache():
|
||||
gc.collect()
|
||||
if not shared.args.cpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def generate_reply(question, 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=None, stopping_string=None):
|
||||
clear_torch_cache()
|
||||
t0 = time.time()
|
||||
|
||||
# These models are not part of Hugging Face, so we handle them
|
||||
# separately and terminate the function call earlier
|
||||
if shared.is_RWKV or shared.is_LLaMA:
|
||||
if shared.is_RWKV:
|
||||
if shared.args.no_stream:
|
||||
reply = shared.model.generate(question, token_count=max_new_tokens, temperature=temperature, top_p=top_p)
|
||||
t1 = time.time()
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds.")
|
||||
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:
|
||||
for i in tqdm(range(max_new_tokens//8+1)):
|
||||
reply = shared.model.generate(question, token_count=8, temperature=temperature, top_p=top_p)
|
||||
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)
|
||||
question = reply
|
||||
|
||||
t1 = time.time()
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds.")
|
||||
return
|
||||
|
||||
original_question = question
|
||||
|
|
@ -111,8 +114,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
|||
|
||||
input_ids = encode(question, max_new_tokens)
|
||||
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 encode(eos_token)[0][-1]
|
||||
|
||||
n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
|
||||
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
|
||||
|
|
@ -149,14 +151,12 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
|||
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")
|
||||
|
|
@ -184,6 +184,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
|||
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()
|
||||
|
||||
with torch.no_grad():
|
||||
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
|
||||
if shared.soft_prompt:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
do_sample=True
|
||||
top_p=0.95
|
||||
temperature=0.8
|
||||
temperature=0.7
|
||||
top_p=0.85
|
||||
top_k=50
|
||||
|
|
|
|||
|
|
@ -3,7 +3,8 @@ bitsandbytes==0.37.0
|
|||
flexgen==0.1.7
|
||||
gradio==3.18.0
|
||||
numpy
|
||||
rwkv==0.0.6
|
||||
rwkv==0.1.0
|
||||
safetensors==0.2.8
|
||||
git+https://github.com/huggingface/transformers
|
||||
tensorboard
|
||||
sentencepiece
|
||||
git+https://github.com/oobabooga/transformers@llama_push
|
||||
|
|
|
|||
|
|
@ -22,8 +22,14 @@ 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():
|
||||
new_settings = json.loads(open(Path(shared.args.settings), 'r').read())
|
||||
settings_file = Path(shared.args.settings)
|
||||
elif Path('settings.json').exists():
|
||||
settings_file = Path('settings.json')
|
||||
if settings_file is not None:
|
||||
print(f"Loading settings from {settings_file}...")
|
||||
new_settings = json.loads(open(settings_file, 'r').read())
|
||||
for item in new_settings:
|
||||
shared.settings[item] = new_settings[item]
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue