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