Mirrors/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-12-24 13:28:59 +01:00
Code Issues Packages Projects Releases Wiki Activity

[extension/openai] add edits & image endpoints & fix prompt return in non --chat modes (#1935)

Browse Source
This commit is contained in:
matatonic 2023-05-11 10:06:39 -04:00 committed by GitHub
parent 23d3f6909a
commit 309b72e549
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 203 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
characters/instruction-following/ChatGLM.yaml
View File

@ -1,4 +1,4 @@
user: "[Round <|round|>]\n问:"
bot: "答:"
user: "[Round <|round|>]\n问："
bot: "答："
turn_template: "<|user|><|user-message|>\n<|bot|><|bot-message|>\n"
context: ""

27
extensions/openai/README.md
View File

@ -11,6 +11,15 @@ Optional (for flask_cloudflared, embeddings):
pip3 install -r requirements.txt
```
It listens on tcp port 5001 by default. You can use the OPENEDAI_PORT environment variable to change this.
To enable the bare bones image generation (txt2img) set: SD_WEBUI_URL to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
Example:
```
SD_WEBUI_URL=http://127.0.0.1:7861
```
### Embeddings (alpha)
Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
@ -67,17 +76,22 @@ const api = new ChatGPTAPI({
## Compatibility & not so compatibility
What's working:
| API endpoint | tested with | notes |
| --- | --- | --- |
| /v1/models | openai.Model.list() | returns the currently loaded model_name and some mock compatibility options |
| /v1/models/{id} | openai.Model.get() | returns whatever you ask for, model does nothing yet anyways |
| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far |
| /v1/chat/completions | openai.ChatCompletion.create() | depending on the model, this may add leading linefeeds |
| /v1/edits | openai.Edit.create() | Assumes an instruction following model, but may work with others |
| /v1/images/generations | openai.Image.create() | Bare bones, no model configuration, response_format='b64_json' only. |
| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. |
| /v1/moderations | openai.Moderation.create() | does nothing. successfully. |
| /v1/engines/\*/... completions, embeddings, generate | python-openai v0.25 and earlier | Legacy engines endpoints |
| /v1/images/edits | openai.Image.create_edit() | not supported |
| /v1/images/variations | openai.Image.create_variation() | not supported |
| /v1/audio/\* | openai.Audio.\* | not supported |
| /v1/files\* | openai.Files.\* | not supported |
| /v1/fine-tunes\* | openai.FineTune.\* | not supported |
The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048 tokens instead of 4096, 8k, etc). To mitigate some of this, the max_tokens value is halved until it is less than truncation_length for the model (typically 2k).
@ -99,6 +113,10 @@ Some hacky mappings:
defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent.
### Models
This has been successfully tested with Koala, Alpaca, gpt4-x-alpaca, GPT4all-snoozy, wizard-vicuna, stable-vicuna and Vicuna 1.1 - ie. Instruction Following models. If you test with other models please let me know how it goes. Less than satisfying results (so far): RWKV-4-Raven, llama, mpt-7b-instruct/chat
### Applications
Everything needs OPENAI_API_KEY=dummy set.
@ -120,4 +138,7 @@ Everything needs OPENAI_API_KEY=dummy set.
* model changing, esp. something for swapping loras or embedding models
* consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard)
* do something about rate limiting or locking requests for completions, most systems will only be able handle a single request at a time before OOM
* the whole api, images (stable diffusion), audio (whisper), fine-tunes (training), edits, files, etc.
## Bugs? Feedback? Comments? Pull requests?
Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible.

8
extensions/openai/cache_embedding_model.py Executable file
View File

@ -0,0 +1,8 @@
#!/usr/bin/env python3
# preload the embedding model, useful for Docker images to prevent re-download on config change
# Dockerfile:
# ENV OPENEDAI_EMBEDDING_MODEL=all-mpnet-base-v2 # Optional
# RUN python3 cache_embedded_model.py
import os, sentence_transformers
st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
model = sentence_transformers.SentenceTransformer(st_model)

170
extensions/openai/script.py
View File

@ -2,6 +2,8 @@ import base64
import json
import os
import time
import requests
import yaml
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from threading import Thread
@ -48,6 +50,31 @@ def clamp(value, minvalue, maxvalue):
return max(minvalue, min(value, maxvalue))
def deduce_template():
# Alpaca is verbose so a good default prompt
default_template = (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
# Use the special instruction/input/response template for anything trained like Alpaca
if shared.settings['instruction_template'] in ['Alpaca', 'Alpaca-Input']:
return default_template
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
template = template\
.replace('<|user|>', instruct.get('user', ''))\
.replace('<|bot|>', instruct.get('bot', ''))\
.replace('<|user-message|>', '{instruction}\n{input}')
return instruct.get('context', '') + template[:template.find('<|bot-message|>')]
except:
return default_template
def float_list_to_base64(float_list):
# Convert the list to a float32 array that the OpenAPI client expects
float_array = np.array(float_list, dtype="float32")
@ -120,11 +147,20 @@ class Handler(BaseHTTPRequestHandler):
self.send_error(404)
def do_POST(self):
# ... haaack.
is_chat = shared.args.chat
try:
shared.args.chat = True
self.do_POST_wrap()
finally:
shared.args.chat = is_chat
def do_POST_wrap(self):
if debug:
print(self.headers) # did you know... python-openai sends your linux kernel & python version?
content_length = int(self.headers['Content-Length'])
body = json.loads(self.rfile.read(content_length).decode('utf-8'))
if debug:
print(self.headers) # did you know... python-openai sends your linux kernel & python version?
if debug:
print(body)
@ -150,7 +186,7 @@ class Handler(BaseHTTPRequestHandler):
truncation_length = default(shared.settings, 'truncation_length', 2048)
truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it., the default for chat is "inf"
default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it.
max_tokens_str = 'length' if is_legacy else 'max_tokens'
max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
@ -440,6 +476,129 @@ class Handler(BaseHTTPRequestHandler):
else:
resp[resp_list][0]["text"] = answer
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/edits' in self.path:
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
created_time = int(time.time())
# Using Alpaca format, this may work with other models too.
instruction = body['instruction']
input = body.get('input', '')
instruction_template = deduce_template()
edit_task = instruction_template.format(instruction=instruction, input=input)
truncation_length = default(shared.settings, 'truncation_length', 2048)
token_count = len(encode(edit_task)[0])
max_tokens = truncation_length - token_count
req_params = {
'max_new_tokens': max_tokens,
'temperature': clamp(default(body, 'temperature', 1.0), 0.001, 1.999),
'top_p': clamp(default(body, 'top_p', 1.0), 0.001, 1.0),
'top_k': 1,
'repetition_penalty': 1.18,
'encoder_repetition_penalty': 1.0,
'suffix': None,
'stream': False,
'echo': False,
'seed': shared.settings.get('seed', -1),
# 'n' : default(body, 'n', 1), # 'n' doesn't have a direct map
'truncation_length': truncation_length,
'add_bos_token': shared.settings.get('add_bos_token', True),
'do_sample': True,
'typical_p': 1.0,
'min_length': 0,
'no_repeat_ngram_size': 0,
'num_beams': 1,
'penalty_alpha': 0.0,
'length_penalty': 1,
'early_stopping': False,
'ban_eos_token': False,
'skip_special_tokens': True,
'custom_stopping_strings': [],
}
if debug:
print({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count})
generator = generate_reply(edit_task, req_params, stopping_strings=standard_stopping_strings)
answer = ''
for a in generator:
if isinstance(a, str):
answer = a
else:
answer = a[0]
completion_token_count = len(encode(answer)[0])
resp = {
"object": "edit",
"created": created_time,
"choices": [{
"text": answer,
"index": 0,
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
if debug:
print({'answer': answer, 'completion_token_count': completion_token_count})
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/images/generations' in self.path and 'SD_WEBUI_URL' in os.environ:
# Stable Diffusion callout wrapper for txt2img
# Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
# the results will be limited and likely poor. SD has hundreds of models and dozens of settings.
# If you want high quality tailored results you should just use the Stable Diffusion API directly.
# it's too general an API to try and shape the result with specific tags like "masterpiece", etc,
# Will probably work best with the stock SD models.
# SD configuration is beyond the scope of this API.
# At this point I will not add the edits and variations endpoints (ie. img2img) because they
# require changing the form data handling to accept multipart form data, also to properly support
# url return types will require file management and a web serving files... Perhaps later!
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
width, height = [ int(x) for x in default(body, 'size', '1024x1024').split('x') ] # ignore the restrictions on size
response_format = default(body, 'response_format', 'url') # or b64_json
payload = {
'prompt': body['prompt'], # ignore prompt limit of 1000 characters
'width': width,
'height': height,
'batch_size': default(body, 'n', 1) # ignore the batch limits of max 10
}
resp = {
'created': int(time.time()),
'data': []
}
# TODO: support SD_WEBUI_AUTH username:password pair.
sd_url = f"{os.environ['SD_WEBUI_URL']}/sdapi/v1/txt2img"
response = requests.post(url=sd_url, json=payload)
r = response.json()
# r['parameters']...
for b64_json in r['images']:
if response_format == 'b64_json':
resp['data'].extend([{'b64_json': b64_json}])
else:
resp['data'].extend([{'url': f'data:image/png;base64,{b64_json}'}]) # yeah it's lazy. requests.get() will not work with this
response = json.dumps(resp)
self.wfile.write(response.encode('utf-8'))
elif '/embeddings' in self.path and embedding_model is not None:
@ -540,11 +699,12 @@ def run_server():
try:
from flask_cloudflared import _run_cloudflared
public_url = _run_cloudflared(params['port'], params['port'] + 1)
print(f'Starting OpenAI compatible api at {public_url}/')
print(f'Starting OpenAI compatible api at\nOPENAI_API_BASE={public_url}/v1')
except ImportError:
print('You should install flask_cloudflared manually')
else:
print(f'Starting OpenAI compatible api at http://{server_addr[0]}:{server_addr[1]}/')
print(f'Starting OpenAI compatible api:\nOPENAI_API_BASE=http://{server_addr[0]}:{server_addr[1]}/v1')
server.serve_forever()

5
models/config.yaml
View File

@ -54,6 +54,9 @@
.*vicuna.*(1.1|1_1):
mode: 'instruct'
instruction_template: 'Vicuna-v1.1'
.*wizard.*vicuna:
mode: 'instruct'
instruction_template: 'Vicuna-v1.1'
.*stable.*vicuna:
mode: 'instruct'
instruction_template: 'StableVicuna'
@ -135,4 +138,4 @@
instruction_template: 'INCITE-Chat'
.*incite.*instruct:
mode: 'instruct'
instruction_template: 'INCITE-Instruct'
instruction_template: 'INCITE-Instruct'