mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
551 lines
22 KiB
Python
551 lines
22 KiB
Python
import base64
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import json
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import numpy as np
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import os
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import time
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from threading import Thread
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from modules import shared
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from modules.text_generation import encode, generate_reply
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params = {
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'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
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}
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debug = True if 'OPENEDAI_DEBUG' in os.environ else False
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# Optional, install the module and download the model to enable
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# v1/embeddings
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try:
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from sentence_transformers import SentenceTransformer
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except ImportError:
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pass
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st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
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embedding_model = None
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standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ]
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# little helper to get defaults if arg is present but None and should be the same type as default.
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def default(dic, key, default):
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val = dic.get(key, default)
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if type(val) != type(default):
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# maybe it's just something like 1 instead of 1.0
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try:
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v = type(default)(val)
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if type(val)(v) == val: # if it's the same value passed in, it's ok.
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return v
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except:
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pass
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val = default
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return val
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def clamp(value, minvalue, maxvalue):
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return max(minvalue, min(value, maxvalue))
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def float_list_to_base64(float_list):
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# Convert the list to a float32 array that the OpenAPI client expects
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float_array = np.array(float_list, dtype="float32")
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# Get raw bytes
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bytes_array = float_array.tobytes()
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# Encode bytes into base64
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encoded_bytes = base64.b64encode(bytes_array)
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# Turn raw base64 encoded bytes into ASCII
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ascii_string = encoded_bytes.decode('ascii')
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return ascii_string
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class Handler(BaseHTTPRequestHandler):
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def do_GET(self):
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if self.path.startswith('/v1/models'):
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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# TODO: list all models and allow model changes via API? Lora's?
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# This API should list capabilities, limits and pricing...
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models = [{
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"id": shared.model_name, # The real chat/completions model
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": st_model, # The real sentence transformer embeddings model
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, { # these are expected by so much, so include some here as a dummy
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"id": "gpt-3.5-turbo", # /v1/chat/completions
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": "text-curie-001", # /v1/completions, 2k context
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
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"object": "model",
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"owned_by": "user",
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"permission": []
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}]
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response = ''
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if self.path == '/v1/models':
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response = json.dumps({
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"object": "list",
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"data": models,
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})
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else:
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the_model_name = self.path[len('/v1/models/'):]
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response = json.dumps({
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"id": the_model_name,
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"object": "model",
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"owned_by": "user",
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"permission": []
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})
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self.wfile.write(response.encode('utf-8'))
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else:
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self.send_error(404)
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def do_POST(self):
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content_length = int(self.headers['Content-Length'])
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body = json.loads(self.rfile.read(content_length).decode('utf-8'))
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if debug:
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print(self.headers) # did you know... python-openai sends your linux kernel & python version?
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if debug:
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print(body)
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if '/completions' in self.path or '/generate' in self.path:
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is_legacy = '/generate' in self.path
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is_chat = 'chat' in self.path
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resp_list = 'data' if is_legacy else 'choices'
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# XXX model is ignored for now
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# model = body.get('model', shared.model_name) # ignored, use existing for now
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model = shared.model_name
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created_time = int(time.time())
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cmpl_id = "conv-%d" % (created_time)
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# Try to use openai defaults or map them to something with the same intent
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stopping_strings = default(shared.settings, 'custom_stopping_strings', [])
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if 'stop' in body:
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if isinstance(body['stop'], str):
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stopping_strings = [body['stop']]
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elif isinstance(body['stop'], list):
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stopping_strings = body['stop']
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truncation_length = default(shared.settings, 'truncation_length', 2048)
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truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
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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"
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max_tokens_str = 'length' if is_legacy else 'max_tokens'
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max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
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# hard scale this, assuming the given max is for GPT3/4, perhaps inspect the requested model and lookup the context max
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while truncation_length <= max_tokens:
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max_tokens = max_tokens // 2
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req_params = {
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'max_new_tokens': max_tokens,
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'temperature': default(body, 'temperature', 1.0),
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'top_p': default(body, 'top_p', 1.0),
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'top_k': default(body, 'best_of', 1),
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# XXX not sure about this one, seems to be the right mapping, but the range is different (-2..2.0) vs 0..2
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# 0 is default in openai, but 1.0 is default in other places. Maybe it's scaled? scale it.
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'repetition_penalty': 1.18, # (default(body, 'presence_penalty', 0) + 2.0 ) / 2.0, # 0 the real default, 1.2 is the model default, but 1.18 works better.
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# XXX not sure about this one either, same questions. (-2..2.0), 0 is default not 1.0, scale it.
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'encoder_repetition_penalty': 1.0, # (default(body, 'frequency_penalty', 0) + 2.0) / 2.0,
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'suffix': body.get('suffix', None),
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'stream': default(body, 'stream', False),
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'echo': default(body, 'echo', False),
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#####################################################
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'seed': shared.settings.get('seed', -1),
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# int(body.get('n', 1)) # perhaps this should be num_beams or chat_generation_attempts? 'n' doesn't have a direct map
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# unofficial, but it needs to get set anyways.
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'truncation_length': truncation_length,
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# no more args.
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'add_bos_token': shared.settings.get('add_bos_token', True),
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'do_sample': True,
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'typical_p': 1.0,
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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': 0.0,
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'length_penalty': 1,
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'early_stopping': False,
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'ban_eos_token': False,
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'skip_special_tokens': True,
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}
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# fixup absolute 0.0's
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for par in ['temperature', 'repetition_penalty', 'encoder_repetition_penalty']:
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req_params[par] = clamp(req_params[par], 0.001, 1.999)
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self.send_response(200)
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if req_params['stream']:
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self.send_header('Content-Type', 'text/event-stream')
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self.send_header('Cache-Control', 'no-cache')
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# self.send_header('Connection', 'keep-alive')
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else:
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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token_count = 0
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completion_token_count = 0
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prompt = ''
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stream_object_type = ''
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object_type = ''
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if is_chat:
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stream_object_type = 'chat.completions.chunk'
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object_type = 'chat.completions'
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messages = body['messages']
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system_msg = '' # You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
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if 'prompt' in body: # Maybe they sent both? This is not documented in the API, but some clients seem to do this.
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system_msg = body['prompt']
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chat_msgs = []
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for m in messages:
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role = m['role']
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content = m['content']
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# name = m.get('name', 'user')
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if role == 'system':
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system_msg += content
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else:
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chat_msgs.extend([f"\n{role}: {content.strip()}"]) # Strip content? linefeed?
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system_token_count = len(encode(system_msg)[0])
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remaining_tokens = req_params['truncation_length'] - req_params['max_new_tokens'] - system_token_count
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chat_msg = ''
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while chat_msgs:
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new_msg = chat_msgs.pop()
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new_size = len(encode(new_msg)[0])
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if new_size <= remaining_tokens:
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chat_msg = new_msg + chat_msg
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remaining_tokens -= new_size
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else:
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# TODO: clip a message to fit?
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# ie. user: ...<clipped message>
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break
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if len(chat_msgs) > 0:
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print(f"truncating chat messages, dropping {len(chat_msgs)} messages.")
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if system_msg:
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prompt = 'system: ' + system_msg + '\n' + chat_msg + '\nassistant: '
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else:
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prompt = chat_msg + '\nassistant: '
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token_count = len(encode(prompt)[0])
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# pass with some expected stop strings.
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# some strange cases of "##| Instruction: " sneaking through.
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stopping_strings += standard_stopping_strings
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req_params['custom_stopping_strings'] = stopping_strings
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else:
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stream_object_type = 'text_completion.chunk'
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object_type = 'text_completion'
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# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
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if is_legacy:
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prompt = body['context'] # Older engines.generate API
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else:
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prompt = body['prompt'] # XXX this can be different types
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if isinstance(prompt, list):
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prompt = ''.join(prompt) # XXX this is wrong... need to split out to multiple calls?
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token_count = len(encode(prompt)[0])
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if token_count >= req_params['truncation_length']:
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new_len = int(len(prompt) * (float(shared.settings['truncation_length']) - req_params['max_new_tokens']) / token_count)
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prompt = prompt[-new_len:]
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print(f"truncating prompt to {new_len} characters, was {token_count} tokens. Now: {len(encode(prompt)[0])} tokens.")
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# pass with some expected stop strings.
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# some strange cases of "##| Instruction: " sneaking through.
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stopping_strings += standard_stopping_strings
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req_params['custom_stopping_strings'] = stopping_strings
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if req_params['stream']:
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shared.args.chat = True
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# begin streaming
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chunk = {
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"id": cmpl_id,
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"object": stream_object_type,
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"created": created_time,
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"model": shared.model_name,
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resp_list: [{
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"index": 0,
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"finish_reason": None,
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}],
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}
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if stream_object_type == 'text_completion.chunk':
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chunk[resp_list][0]["text"] = ""
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else:
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# This is coming back as "system" to the openapi cli, not sure why.
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# So yeah... do both methods? delta and messages.
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chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
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chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
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# { "role": "assistant" }
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response = 'data: ' + json.dumps(chunk) + '\n'
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self.wfile.write(response.encode('utf-8'))
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# generate reply #######################################
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if debug:
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print({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings})
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generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings)
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answer = ''
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seen_content = ''
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longest_stop_len = max([len(x) for x in stopping_strings])
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for a in generator:
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if isinstance(a, str):
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answer = a
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else:
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answer = a[0]
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stop_string_found = False
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len_seen = len(seen_content)
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search_start = max(len_seen - longest_stop_len, 0)
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for string in stopping_strings:
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idx = answer.find(string, search_start)
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if idx != -1:
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answer = answer[:idx] # clip it.
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stop_string_found = True
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if stop_string_found:
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break
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# If something like "\nYo" is generated just before "\nYou:"
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# is completed, buffer and generate more, don't send it
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buffer_and_continue = False
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for string in stopping_strings:
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for j in range(len(string) - 1, 0, -1):
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if answer[-j:] == string[:j]:
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buffer_and_continue = True
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break
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else:
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continue
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break
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if buffer_and_continue:
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continue
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if req_params['stream']:
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# Streaming
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new_content = answer[len_seen:]
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if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
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continue
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seen_content = answer
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chunk = {
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"id": cmpl_id,
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"object": stream_object_type,
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"created": created_time,
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"model": shared.model_name,
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resp_list: [{
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"index": 0,
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"finish_reason": None,
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}],
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}
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if stream_object_type == 'text_completion.chunk':
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chunk[resp_list][0]['text'] = new_content
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else:
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# So yeah... do both methods? delta and messages.
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chunk[resp_list][0]['message'] = {'content': new_content}
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chunk[resp_list][0]['delta'] = {'content': new_content}
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response = 'data: ' + json.dumps(chunk) + '\n'
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self.wfile.write(response.encode('utf-8'))
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completion_token_count += len(encode(new_content)[0])
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if req_params['stream']:
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chunk = {
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"id": cmpl_id,
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"object": stream_object_type,
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"created": created_time,
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"model": model, # TODO: add Lora info?
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resp_list: [{
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"index": 0,
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"finish_reason": "stop",
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}],
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"usage": {
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"prompt_tokens": token_count,
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"completion_tokens": completion_token_count,
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"total_tokens": token_count + completion_token_count
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}
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}
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if stream_object_type == 'text_completion.chunk':
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chunk[resp_list][0]['text'] = ''
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else:
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# So yeah... do both methods? delta and messages.
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chunk[resp_list][0]['message'] = {'content': ''}
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chunk[resp_list][0]['delta'] = {}
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response = 'data: ' + json.dumps(chunk) + '\ndata: [DONE]\n'
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self.wfile.write(response.encode('utf-8'))
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# Finished if streaming.
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if debug:
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print({'response': answer})
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return
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if debug:
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print({'response': answer})
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completion_token_count = len(encode(answer)[0])
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stop_reason = "stop"
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if token_count + completion_token_count >= req_params['truncation_length']:
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stop_reason = "length"
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resp = {
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"id": cmpl_id,
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"object": object_type,
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"created": created_time,
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"model": model, # TODO: add Lora info?
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resp_list: [{
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"index": 0,
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"finish_reason": stop_reason,
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}],
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"usage": {
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"prompt_tokens": token_count,
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"completion_tokens": completion_token_count,
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"total_tokens": token_count + completion_token_count
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}
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}
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if is_chat:
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resp[resp_list][0]["message"] = {"role": "assistant", "content": answer}
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else:
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resp[resp_list][0]["text"] = answer
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response = json.dumps(resp)
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self.wfile.write(response.encode('utf-8'))
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elif '/embeddings' in self.path and embedding_model is not None:
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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input = body['input'] if 'input' in body else body['text']
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if type(input) is str:
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input = [input]
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embeddings = embedding_model.encode(input).tolist()
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def enc_emb(emb):
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# If base64 is specified, encode. Otherwise, do nothing.
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if body.get("encoding_format", "") == "base64":
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return float_list_to_base64(emb)
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else:
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return emb
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data = [{"object": "embedding", "embedding": enc_emb(emb), "index": n} for n, emb in enumerate(embeddings)]
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response = json.dumps({
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"object": "list",
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"data": data,
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"model": st_model, # return the real model
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"usage": {
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"prompt_tokens": 0,
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"total_tokens": 0,
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}
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})
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if debug:
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print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
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self.wfile.write(response.encode('utf-8'))
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elif '/moderations' in self.path:
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# for now do nothing, just don't error.
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self.send_response(200)
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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response = json.dumps({
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"id": "modr-5MWoLO",
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"model": "text-moderation-001",
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"results": [{
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"categories": {
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"hate": False,
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"hate/threatening": False,
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"self-harm": False,
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"sexual": False,
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"sexual/minors": False,
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"violence": False,
|
|
"violence/graphic": False
|
|
},
|
|
"category_scores": {
|
|
"hate": 0.0,
|
|
"hate/threatening": 0.0,
|
|
"self-harm": 0.0,
|
|
"sexual": 0.0,
|
|
"sexual/minors": 0.0,
|
|
"violence": 0.0,
|
|
"violence/graphic": 0.0
|
|
},
|
|
"flagged": False
|
|
}]
|
|
})
|
|
self.wfile.write(response.encode('utf-8'))
|
|
|
|
elif self.path == '/api/v1/token-count':
|
|
# NOT STANDARD. lifted from the api extension, but it's still very useful to calculate tokenized length client side.
|
|
self.send_response(200)
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
tokens = encode(body['prompt'])[0]
|
|
response = json.dumps({
|
|
'results': [{
|
|
'tokens': len(tokens)
|
|
}]
|
|
})
|
|
self.wfile.write(response.encode('utf-8'))
|
|
else:
|
|
print(self.path, self.headers)
|
|
self.send_error(404)
|
|
|
|
|
|
def run_server():
|
|
global embedding_model
|
|
try:
|
|
embedding_model = SentenceTransformer(st_model)
|
|
print(f"\nLoaded embedding model: {st_model}, max sequence length: {embedding_model.max_seq_length}")
|
|
except:
|
|
print(f"\nFailed to load embedding model: {st_model}")
|
|
pass
|
|
|
|
server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port'])
|
|
server = ThreadingHTTPServer(server_addr, Handler)
|
|
if shared.args.share:
|
|
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}/')
|
|
except ImportError:
|
|
print('You should install flask_cloudflared manually')
|
|
else:
|
|
print(f'Starting OpenAI compatible api at http://{server_addr[0]}:{server_addr[1]}/')
|
|
server.serve_forever()
|
|
|
|
|
|
def setup():
|
|
Thread(target=run_server, daemon=True).start()
|