README.md

@@ -43,6 +43,8 @@ To set up token auth, add rows to the `token_auth` table in the SQLite database.
 
 ### Use
 
+If you see unexpected errors in the console, make sure `daemon.py` is running or else the required data will be missing from Redis.
+
 Flask may give unusual errors when running `python server.py`. I think this is coming from Flask-Socket. Running with Gunicorn seems to fix the issue: `gunicorn -b :5000 --worker-class gevent server:app`
 
 

llm_server/cluster/model_choices.py

@@ -60,7 +60,8 @@ def get_model_choices(regen: bool = False):
         if len(context_size):
             model_choices[model]['context_size'] = min(context_size)
 
-    model_choices = dict(sorted(model_choices.items()))
+    # Python wants to sort lowercase vs. uppercase letters differently.
+    model_choices = dict(sorted(model_choices.items(), key=lambda item: item[0].upper()))
 
     default_backend = get_a_cluster_backend()
     default_backend_dict = {}

llm_server/llm/llm_backend.py

@@ -2,7 +2,6 @@ from typing import Tuple, Union
 
 import flask
 
-from llm_server import opts
 from llm_server.cluster.cluster_config import cluster_config
 from llm_server.custom_redis import redis
 from llm_server.llm import get_token_count
@@ -36,6 +35,8 @@ class LLMBackend:
         """
         If a backend needs to do other checks not related to the prompt or parameters.
         Default is no extra checks preformed.
+        :param request:
+        :param prompt:
         :param parameters:
         :return:
         """

llm_server/llm/openai/oai_to_vllm.py

@@ -0,0 +1,63 @@
+from flask import jsonify
+
+from llm_server import opts
+
+
+def oai_to_vllm(request_json_body, hashes: bool, mode):
+    if not request_json_body.get('stop'):
+        request_json_body['stop'] = []
+
+    if hashes:
+        request_json_body['stop'].extend(['\n### INSTRUCTION', '\n### USER', '\n### ASSISTANT', '\n### RESPONSE'])
+        if opts.openai_force_no_hashes:
+            request_json_body['stop'].append('### ')
+    else:
+        request_json_body['stop'].extend(['\nuser:', '\nassistant:'])
+
+    if request_json_body.get('frequency_penalty', 0) < -2:
+        request_json_body['frequency_penalty'] = -2
+    elif request_json_body.get('frequency_penalty', 0) > 2:
+        request_json_body['frequency_penalty'] = 2
+
+    if mode == 'vllm' and request_json_body.get('top_p') == 0:
+        request_json_body['top_p'] = 0.01
+
+    return request_json_body
+
+
+def format_oai_err(err_msg):
+    return jsonify({
+        "error": {
+            "message": err_msg,
+            "type": "invalid_request_error",
+            "param": None,
+            "code": None
+        }
+    }), 400
+
+
+def validate_oai(parameters):
+    if parameters['temperature'] > 2:
+        return format_oai_err(f"{parameters['temperature']} is greater than the maximum of 2 - 'temperature'")
+    if parameters['temperature'] < 0:
+        return format_oai_err(f"{parameters['temperature']} less than the minimum of 0 - 'temperature'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")
+
+    if parameters.get('presence_penalty', 1) > 2:
+        return format_oai_err(f"{parameters['presence_penalty']} is greater than the maximum of 2 - 'presence_penalty'")
+    if parameters.get('presence_penalty', 1) < -2:
+        return format_oai_err(f"{parameters['presence_penalty']} less than the minimum of -2 - 'presence_penalty'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")

llm_server/llm/openai/transform.py

@@ -2,73 +2,24 @@ import concurrent.futures
 import re
 import secrets
 import string
-import time
 import traceback
 from typing import Dict, List
 
 import tiktoken
-from flask import jsonify, make_response
 
-import llm_server
 from llm_server import opts
 from llm_server.llm import get_token_count
-from llm_server.custom_redis import redis
 
 ANTI_RESPONSE_RE = re.compile(r'^### (.*?)(?:\:)?\s')  # Match a "### XXX" line.
 ANTI_CONTINUATION_RE = re.compile(r'(.*?### .*?(?:\:)?(.|\n)*)')  # Match everything after a "### XXX" line.
 
 
-def build_openai_response(prompt, response, model=None):
-    # Seperate the user's prompt from the context
-    x = prompt.split('### USER:')
-    if len(x) > 1:
-        prompt = re.sub(r'\n$', '', x[-1].strip(' '))
-
-    # Make sure the bot doesn't put any other instructions in its response
-    # y = response.split('\n### ')
-    # if len(y) > 1:
-    #     response = re.sub(r'\n$', '', y[0].strip(' '))
-    response = re.sub(ANTI_RESPONSE_RE, '', response)
-    response = re.sub(ANTI_CONTINUATION_RE, '', response)
-
-    # TODO: async/await
-    prompt_tokens = llm_server.llm.get_token_count(prompt)
-    response_tokens = llm_server.llm.get_token_count(response)
-    running_model = redis.get('running_model', 'ERROR', dtype=str)
-
-    response = make_response(jsonify({
-        "id": f"chatcmpl-{generate_oai_string(30)}",
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": running_model if opts.openai_expose_our_model else model,
-        "choices": [{
-            "index": 0,
-            "message": {
-                "role": "assistant",
-                "content": response,
-            },
-            "logprobs": None,
-            "finish_reason": "stop"
-        }],
-        "usage": {
-            "prompt_tokens": prompt_tokens,
-            "completion_tokens": response_tokens,
-            "total_tokens": prompt_tokens + response_tokens
-        }
-    }), 200)
-
-    stats = redis.get('proxy_stats', dtype=dict)
-    if stats:
-        response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
-    return response
-
-
 def generate_oai_string(length=24):
     alphabet = string.ascii_letters + string.digits
     return ''.join(secrets.choice(alphabet) for i in range(length))
 
 
-def trim_prompt_to_fit(prompt: List[Dict[str, str]], context_token_limit: int) -> List[Dict[str, str]]:
+def trim_messages_to_fit(prompt: List[Dict[str, str]], context_token_limit: int, backend_url: str) -> List[Dict[str, str]]:
     tokenizer = tiktoken.get_encoding("cl100k_base")
 
     def get_token_count_tiktoken_thread(msg):
@@ -95,13 +46,13 @@ def trim_prompt_to_fit(prompt: List[Dict[str, str]], context_token_limit: int) -
                         break
 
             def get_token_count_thread(msg):
-                return get_token_count(msg["content"])
+                return get_token_count(msg["content"], backend_url)
 
             with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
                 token_counts = list(executor.map(get_token_count_thread, prompt))
 
             total_tokens = sum(token_counts)
-            formatting_tokens = get_token_count(transform_messages_to_prompt(prompt)) - total_tokens
+            formatting_tokens = get_token_count(transform_messages_to_prompt(prompt), backend_url) - total_tokens
 
             if total_tokens + formatting_tokens > context_token_limit:
                 # Start over, but this time calculate the token count using the backend
@@ -109,6 +60,40 @@ def trim_prompt_to_fit(prompt: List[Dict[str, str]], context_token_limit: int) -
                     token_counts = list(executor.map(get_token_count_thread, prompt))
             else:
                 break
+    return prompt
+
+
+def trim_string_to_fit(prompt: str, context_token_limit: int, backend_url: str) -> str:
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+
+    def get_token_count_tiktoken_thread(msg):
+        return len(tokenizer.encode(msg))
+
+    token_count = get_token_count_tiktoken_thread(prompt)
+
+    # If total tokens exceed the limit, start trimming
+    if token_count > context_token_limit:
+        while True:
+            while token_count > context_token_limit:
+                # Calculate the index to start removing characters from
+                remove_index = len(prompt) // 3
+
+                while remove_index < len(prompt):
+                    prompt = prompt[:remove_index] + prompt[remove_index + 100:]
+                    token_count = get_token_count_tiktoken_thread(prompt)
+                    if token_count <= context_token_limit or remove_index == len(prompt):
+                        break
+
+            def get_token_count_thread(msg):
+                return get_token_count(msg, backend_url)
+
+            token_count = get_token_count_thread(prompt)
+
+            if token_count > context_token_limit:
+                # Start over, but this time calculate the token count using the backend
+                token_count = get_token_count_thread(prompt)
+            else:
+                break
 
     return prompt
 

llm_server/llm/vllm/vllm_backend.py

@@ -1,4 +1,3 @@
-import threading
 from typing import Tuple
 
 from flask import jsonify
@@ -35,9 +34,11 @@ class VLLMBackend(LLMBackend):
                 top_p=parameters.get('top_p', self._default_params['top_p']),
                 top_k=top_k,
                 use_beam_search=True if parameters.get('num_beams', 0) > 1 else False,
-                stop=parameters.get('stopping_strings', self._default_params['stop']),
+                stop=list(set(parameters.get('stopping_strings', self._default_params['stop']) or parameters.get('stop', self._default_params['stop']))),
                 ignore_eos=parameters.get('ban_eos_token', False),
-                max_tokens=parameters.get('max_new_tokens', self._default_params['max_tokens'])
+                max_tokens=parameters.get('max_new_tokens', self._default_params['max_tokens']) or parameters.get('max_tokens', self._default_params['max_tokens']),
+                presence_penalty=parameters.get('presence_penalty', self._default_params['presence_penalty']),
+                frequency_penalty=parameters.get('frequency_penalty', self._default_params['frequency_penalty'])
             )
         except ValueError as e:
             return None, str(e).strip('.')

llm_server/routes/openai/chat_completions.py

@@ -1,5 +1,4 @@
 import json
-import threading
 import time
 import traceback
 
@@ -10,11 +9,10 @@ from . import openai_bp
 from ..helpers.http import validate_json
 from ..openai_request_handler import OpenAIRequestHandler
 from ... import opts
-from ...cluster.backend import get_a_cluster_backend
 from ...database.database import log_prompt
 from ...llm.generator import generator
-from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt
-from ...llm.vllm import tokenize
+from ...llm.openai.oai_to_vllm import oai_to_vllm
+from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
 
 
 # TODO: add rate-limit headers?
@@ -25,32 +23,46 @@ def openai_chat_completions():
     if not request_valid_json or not request_json_body.get('messages') or not request_json_body.get('model'):
         return jsonify({'code': 400, 'msg': 'invalid JSON'}), 400
     else:
-        handler = OpenAIRequestHandler(request, request_json_body)
-        if request_json_body.get('stream'):
+        handler = OpenAIRequestHandler(incoming_request=request, incoming_json=request_json_body)
+
+        if handler.cluster_backend_info['mode'] != 'vllm':
+            # TODO: implement other backends
+            raise NotImplementedError
+
+        if not request_json_body.get('stream'):
+            try:
+                return handler.handle_request()
+            except Exception:
+                traceback.print_exc()
+                return 'Internal server error', 500
+        else:
             if not opts.enable_streaming:
                 # TODO: return a proper OAI error message
                 return 'disabled', 401
 
-            if opts.mode != 'vllm':
-                # TODO: implement other backends
-                raise NotImplementedError
+            if opts.openai_silent_trim:
+                handler.request_json_body['messages'] = trim_messages_to_fit(request_json_body['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
 
             response_status_code = 0
             start_time = time.time()
             request_valid, invalid_response = handler.validate_request()
             if not request_valid:
-                # TODO: simulate OAI here
-                raise Exception('TODO: simulate OAI here')
+                return invalid_response
             else:
-                handler.prompt = transform_messages_to_prompt(request_json_body['messages'])
+                if opts.openai_silent_trim:
+                    oai_messages = trim_messages_to_fit(handler.request.json['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
+                else:
+                    oai_messages = handler.request.json['messages']
+
+                handler.prompt = transform_messages_to_prompt(oai_messages)
+                handler.parameters = oai_to_vllm(handler.parameters, hashes=True, mode=handler.cluster_backend_info['mode'])
                 msg_to_backend = {
                     **handler.parameters,
                     'prompt': handler.prompt,
                     'stream': True,
                 }
                 try:
-                    cluster_backend = get_a_cluster_backend()
-                    response = generator(msg_to_backend, cluster_backend)
+                    response = generator(msg_to_backend, handler.backend_url)
                     r_headers = dict(request.headers)
                     r_url = request.url
                     model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
@@ -94,22 +106,20 @@ def openai_chat_completions():
                         end_time = time.time()
                         elapsed_time = end_time - start_time
 
-                        def background_task():
-                            generated_tokens = tokenize(generated_text)
-                            log_prompt(handler.client_ip, handler.token, handler.prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, cluster_backend, response_tokens=generated_tokens)
-
-                        # TODO: use async/await instead of threads
-                        thread = threading.Thread(target=background_task)
-                        thread.start()
-                        thread.join()
+                        log_prompt(
+                            handler.client_ip,
+                            handler.token,
+                            handler.prompt,
+                            generated_text,
+                            elapsed_time,
+                            handler.parameters,
+                            r_headers,
+                            response_status_code,
+                            r_url,
+                            handler.backend_url,
+                        )
 
                     return Response(generate(), mimetype='text/event-stream')
                 except:
                     # TODO: simulate OAI here
                     raise Exception
-        else:
-            try:
-                return handler.handle_request()
-            except Exception:
-                traceback.print_exc()
-                return 'Internal server error', 500

llm_server/routes/openai/completions.py

@@ -1,15 +1,19 @@
 import time
 import traceback
 
-from flask import jsonify, make_response, request
+import simplejson as json
+from flask import Response, jsonify, request
 
-from . import openai_bp
 from llm_server.custom_redis import redis
+from . import openai_bp
 from ..helpers.http import validate_json
 from ..ooba_request_handler import OobaRequestHandler
 from ... import opts
+from ...database.database import log_prompt
 from ...llm import get_token_count
-from ...llm.openai.transform import generate_oai_string
+from ...llm.generator import generator
+from ...llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
+from ...llm.openai.transform import generate_oai_string, trim_string_to_fit
 
 
 # TODO: add rate-limit headers?
@@ -21,40 +25,137 @@ def openai_completions():
         return jsonify({'code': 400, 'msg': 'Invalid JSON'}), 400
     else:
         try:
-            response, status_code = OobaRequestHandler(request).handle_request()
-            if status_code != 200:
-                return status_code
-            output = response.json['results'][0]['text']
+            handler = OobaRequestHandler(incoming_request=request)
 
-            # TODO: async/await
-            prompt_tokens = get_token_count(request_json_body['prompt'])
-            response_tokens = get_token_count(output)
-            running_model = redis.get('running_model', 'ERROR', dtype=str)
+            if handler.cluster_backend_info['mode'] != 'vllm':
+                # TODO: implement other backends
+                raise NotImplementedError
 
-            response = make_response(jsonify({
-                "id": f"cmpl-{generate_oai_string(30)}",
-                "object": "text_completion",
-                "created": int(time.time()),
-                "model": running_model if opts.openai_expose_our_model else request_json_body.get('model'),
-                "choices": [
-                    {
-                        "text": output,
-                        "index": 0,
-                        "logprobs": None,
-                        "finish_reason": None
+            invalid_oai_err_msg = validate_oai(handler.request_json_body)
+            if invalid_oai_err_msg:
+                return invalid_oai_err_msg
+            handler.request_json_body = oai_to_vllm(handler.request_json_body, hashes=False, mode=handler.cluster_backend_info['mode'])
+
+            # Convert parameters to the selected backend type
+            if opts.openai_silent_trim:
+                handler.request_json_body['prompt'] = trim_string_to_fit(request_json_body['prompt'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
+            else:
+                # The handle_request() call below will load the prompt so we don't have
+                # to do anything else here.
+                pass
+
+            if not request_json_body.get('stream'):
+                response, status_code = handler.handle_request()
+                if status_code != 200:
+                    return status_code
+                output = response.json['results'][0]['text']
+
+                # TODO: async/await
+                prompt_tokens = get_token_count(request_json_body['prompt'], handler.backend_url)
+                response_tokens = get_token_count(output, handler.backend_url)
+                running_model = redis.get('running_model', 'ERROR', dtype=str)
+
+                response = jsonify({
+                    "id": f"cmpl-{generate_oai_string(30)}",
+                    "object": "text_completion",
+                    "created": int(time.time()),
+                    "model": running_model if opts.openai_expose_our_model else request_json_body.get('model'),
+                    "choices": [
+                        {
+                            "text": output,
+                            "index": 0,
+                            "logprobs": None,
+                            "finish_reason": "stop"
+                        }
+                    ],
+                    "usage": {
+                        "prompt_tokens": prompt_tokens,
+                        "completion_tokens": response_tokens,
+                        "total_tokens": prompt_tokens + response_tokens
                     }
-                ],
-                "usage": {
-                    "prompt_tokens": prompt_tokens,
-                    "completion_tokens": response_tokens,
-                    "total_tokens": prompt_tokens + response_tokens
-                }
-            }), 200)
+                })
 
-            stats = redis.get('proxy_stats', dtype=dict)
-            if stats:
-                response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
-            return response
+                stats = redis.get('proxy_stats', dtype=dict)
+                if stats:
+                    response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
+                return response, 200
+            else:
+                if not opts.enable_streaming:
+                    # TODO: return a proper OAI error message
+                    return 'disabled', 401
+
+                response_status_code = 0
+                start_time = time.time()
+
+                request_valid, invalid_response = handler.validate_request()
+                if not request_valid:
+                    # TODO: simulate OAI here
+                    raise Exception('TODO: simulate OAI here')
+                else:
+                    handler.prompt = handler.request_json_body['prompt']
+                    msg_to_backend = {
+                        **handler.parameters,
+                        'prompt': handler.prompt,
+                        'stream': True,
+                    }
+                    response = generator(msg_to_backend, handler.backend_url)
+                    r_headers = dict(request.headers)
+                    r_url = request.url
+                    model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
+                    oai_string = generate_oai_string(30)
+
+                    def generate():
+                        generated_text = ''
+                        partial_response = b''
+                        for chunk in response.iter_content(chunk_size=1):
+                            partial_response += chunk
+                            if partial_response.endswith(b'\x00'):
+                                json_strs = partial_response.split(b'\x00')
+                                for json_str in json_strs:
+                                    if json_str:
+                                        try:
+                                            json_obj = json.loads(json_str.decode())
+                                            new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
+                                            generated_text = generated_text + new
+                                        except IndexError:
+                                            # ????
+                                            continue
+
+                                        data = {
+                                            "id": f"chatcmpl-{oai_string}",
+                                            "object": "text_completion",
+                                            "created": int(time.time()),
+                                            "model": model,
+                                            "choices": [
+                                                {
+                                                    "index": 0,
+                                                    "delta": {
+                                                        "content": new
+                                                    },
+                                                    "finish_reason": None
+                                                }
+                                            ]
+                                        }
+                                        yield f'data: {json.dumps(data)}\n\n'
+
+                        yield 'data: [DONE]\n\n'
+                        end_time = time.time()
+                        elapsed_time = end_time - start_time
+
+                        log_prompt(
+                            handler.client_ip,
+                            handler.token,
+                            handler.prompt,
+                            generated_text,
+                            elapsed_time,
+                            handler.parameters,
+                            r_headers,
+                            response_status_code,
+                            r_url,
+                            handler.backend_url,
+                        )
+
+                    return Response(generate(), mimetype='text/event-stream')
         except Exception:
             traceback.print_exc()
             return 'Internal Server Error', 500

llm_server/routes/openai/models.py

@@ -3,24 +3,24 @@ import traceback
 import requests
 from flask import jsonify
 
-from . import openai_bp
 from llm_server.custom_redis import ONE_MONTH_SECONDS, flask_cache, redis
+from . import openai_bp
 from ..stats import server_start_time
 from ... import opts
 from ...cluster.backend import get_a_cluster_backend
+from ...cluster.cluster_config import cluster_config
 from ...helpers import jsonify_pretty
-from ...llm.info import get_running_model
+from ...llm.openai.transform import generate_oai_string
 
 
 @openai_bp.route('/models', methods=['GET'])
 @flask_cache.cached(timeout=60, query_string=True)
 def openai_list_models():
-    model, error = get_running_model()
-    if not model:
+    model_name = cluster_config.get_backend(get_a_cluster_backend()).get('model')
+    if not model_name:
         response = jsonify({
             'code': 502,
             'msg': 'failed to reach backend',
-            'type': error.__class__.__name__
         }), 500  # return 500 so Cloudflare doesn't intercept us
     else:
         running_model = redis.get('running_model', 'ERROR', dtype=str)
@@ -65,7 +65,14 @@ def fetch_openai_models():
     if opts.openai_api_key:
         try:
             response = requests.get('https://api.openai.com/v1/models', headers={'Authorization': f"Bearer {opts.openai_api_key}"}, timeout=10)
-            return response.json()['data']
+            j = response.json()['data']
+
+            # The "modelperm" string appears to be user-specific, so we'll
+            # randomize it just to be safe.
+            for model in range(len(j)):
+                for p in range(len(j[model]['permission'])):
+                    j[model]['permission'][p]['id'] = f'modelperm-{generate_oai_string(24)}'
+            return j
         except:
             traceback.print_exc()
             return []

llm_server/routes/openai/simulated.py

@@ -17,7 +17,7 @@ def openai_organizations():
                 "id": f"org-{generate_oai_string(24)}",
                 "created": int(server_start_time.timestamp()),
                 "title": "Personal",
-                "name": "user-abcdefghijklmnopqrstuvwx",
+                "name": f"user-{generate_oai_string(24)}",
                 "description": "Personal org for bobjoe@0.0.0.0",
                 "personal": True,
                 "is_default": True,

llm_server/routes/openai_request_handler.py

@@ -1,14 +1,19 @@
 import json
+import re
+import time
 import traceback
 from typing import Tuple
 from uuid import uuid4
 
 import flask
-from flask import jsonify
+from flask import Response, jsonify, make_response
 
+import llm_server
 from llm_server import opts
+from llm_server.custom_redis import redis
 from llm_server.database.database import is_api_key_moderated
-from llm_server.llm.openai.transform import build_openai_response, transform_messages_to_prompt, trim_prompt_to_fit
+from llm_server.llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
+from llm_server.llm.openai.transform import ANTI_CONTINUATION_RE, ANTI_RESPONSE_RE, generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
 from llm_server.routes.request_handler import RequestHandler
 from llm_server.workers.moderator import add_moderation_task, get_results
 
@@ -22,7 +27,7 @@ class OpenAIRequestHandler(RequestHandler):
         assert not self.used
 
         if opts.openai_silent_trim:
-            oai_messages = trim_prompt_to_fit(self.request.json['messages'], opts.context_size)
+            oai_messages = trim_messages_to_fit(self.request.json['messages'], self.cluster_backend_info['model_config']['max_position_embeddings'], self.backend_url)
         else:
             oai_messages = self.request.json['messages']
 
@@ -51,13 +56,8 @@ class OpenAIRequestHandler(RequestHandler):
                 print(f'OpenAI moderation endpoint failed:', f'{e.__class__.__name__}: {e}')
                 print(traceback.format_exc())
 
-        # Reconstruct the request JSON with the validated parameters and prompt.
-        self.parameters['stop'].extend(['\n### INSTRUCTION', '\n### USER', '\n### ASSISTANT', '\n### RESPONSE'])
-        if opts.openai_force_no_hashes:
-            self.parameters['stop'].append('### ')
-
-        if opts.mode == 'vllm' and self.request_json_body.get('top_p') == 0:
-            self.request_json_body['top_p'] = 0.01
+        # TODO: support Ooba
+        self.parameters = oai_to_vllm(self.parameters, hashes=True, mode=self.cluster_backend_info['mode'])
 
         llm_request = {**self.parameters, 'prompt': self.prompt}
         (success, _, _, _), (backend_response, backend_response_status_code) = self.generate_response(llm_request)
@@ -65,7 +65,7 @@ class OpenAIRequestHandler(RequestHandler):
         model = self.request_json_body.get('model')
 
         if success:
-            return build_openai_response(self.prompt, backend_response.json['results'][0]['text'], model=model), backend_response_status_code
+            return self.build_openai_response(self.prompt, backend_response.json['results'][0]['text'], model=model), backend_response_status_code
         else:
             return backend_response, backend_response_status_code
 
@@ -75,7 +75,6 @@ class OpenAIRequestHandler(RequestHandler):
         return 'Ratelimited', 429
 
     def handle_error(self, error_msg: str, error_type: str = 'error') -> Tuple[flask.Response, int]:
-        # TODO: return a simulated OpenAI error message
         return jsonify({
             "error": {
                 "message": "Invalid request, check your parameters and try again.",
@@ -84,3 +83,52 @@ class OpenAIRequestHandler(RequestHandler):
                 "code": None
             }
         }), 400
+
+    def build_openai_response(self, prompt, response, model=None):
+        # Seperate the user's prompt from the context
+        x = prompt.split('### USER:')
+        if len(x) > 1:
+            prompt = re.sub(r'\n$', '', x[-1].strip(' '))
+
+        # Make sure the bot doesn't put any other instructions in its response
+        response = re.sub(ANTI_RESPONSE_RE, '', response)
+        response = re.sub(ANTI_CONTINUATION_RE, '', response)
+
+        # TODO: async/await
+        prompt_tokens = llm_server.llm.get_token_count(prompt, self.backend_url)
+        response_tokens = llm_server.llm.get_token_count(response, self.backend_url)
+        running_model = redis.get('running_model', 'ERROR', dtype=str)
+
+        response = make_response(jsonify({
+            "id": f"chatcmpl-{generate_oai_string(30)}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": running_model if opts.openai_expose_our_model else model,
+            "choices": [{
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": response,
+                },
+                "logprobs": None,
+                "finish_reason": "stop"
+            }],
+            "usage": {
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": response_tokens,
+                "total_tokens": prompt_tokens + response_tokens
+            }
+        }), 200)
+
+        stats = redis.get('proxy_stats', dtype=dict)
+        if stats:
+            response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
+        return response
+
+    def validate_request(self, prompt: str = None, do_log: bool = False) -> Tuple[bool, Tuple[Response | None, int]]:
+        invalid_oai_err_msg = validate_oai(self.request_json_body)
+        if invalid_oai_err_msg:
+            return False, invalid_oai_err_msg
+        self.request_json_body = oai_to_vllm(self.request_json_body, hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
+        # If the parameters were invalid, let the superclass deal with it.
+        return super().validate_request(prompt, do_log)

llm_server/routes/request_handler.py

@@ -15,13 +15,13 @@ from llm_server.llm.oobabooga.ooba_backend import OobaboogaBackend
 from llm_server.llm.vllm.vllm_backend import VLLMBackend
 from llm_server.routes.auth import parse_token
 from llm_server.routes.helpers.http import require_api_key, validate_json
-from llm_server.routes.queue import RedisPriorityQueue, priority_queue
+from llm_server.routes.queue import priority_queue
 
 DEFAULT_PRIORITY = 9999
 
 
 class RequestHandler:
-    def __init__(self, incoming_request: flask.Request, selected_model: str, incoming_json: Union[dict, str] = None):
+    def __init__(self, incoming_request: flask.Request, selected_model: str = None, incoming_json: Union[dict, str] = None):
         self.request = incoming_request
         self.enable_backend_blind_rrd = request.headers.get('LLM-Blind-RRD', False) == 'true'
 
@@ -41,7 +41,7 @@ class RequestHandler:
         self.cluster_backend_info = cluster_config.get_backend(self.backend_url)
 
         if not self.cluster_backend_info.get('mode'):
-            print(self.backend_url, self.cluster_backend_info)
+            print(selected_model, self.backend_url, self.cluster_backend_info)
 
         self.backend = get_backend_handler(self.cluster_backend_info['mode'], self.backend_url)
         self.parameters = None

llm_server/routes/v1/generate.py

@@ -5,8 +5,6 @@ from flask import jsonify, request
 from . import bp
 from ..helpers.http import validate_json
 from ..ooba_request_handler import OobaRequestHandler
-from ...cluster.backend import get_a_cluster_backend
-from ...cluster.cluster_config import cluster_config
 
 
 @bp.route('/v1/generate', methods=['POST'])

llm_server/routes/v1/generate_stats.py

@@ -71,6 +71,7 @@ def generate_stats(regen: bool = False):
                 'model': backend_info['model'],
                 'mode': backend_info['mode'],
                 'nvidia': backend_info['nvidia'],
+                'priority': backend_info['priority'],
             }
     else:
         output['backend_info'] = {}

llm_server/routes/v1/generate_stream.py

@@ -84,7 +84,7 @@ def do_stream(ws, model_name):
                 ws.close()
                 return auth_failure
 
-            handler = OobaRequestHandler(request, model_name, request_json_body)
+            handler = OobaRequestHandler(incoming_request=request, selected_model=model_name, incoming_json=request_json_body)
             generated_text = ''
             input_prompt = request_json_body['prompt']
             response_status_code = 0

llm_server/routes/v1/info.py

@@ -4,10 +4,8 @@ from flask import jsonify, request
 
 from llm_server.custom_redis import flask_cache
 from . import bp
-from ..auth import requires_auth
 from ... import opts
-from ...cluster.backend import get_a_cluster_backend, get_backends, get_backends_from_model, is_valid_model
-from ...cluster.cluster_config import cluster_config
+from ...cluster.backend import get_a_cluster_backend, get_backends_from_model, is_valid_model
 
 
 @bp.route('/v1/model', methods=['GET'])
@@ -39,14 +37,3 @@ def get_model(model_name=None):
         flask_cache.set(cache_key, response, timeout=60)
 
     return response
-
-
-@bp.route('/backends', methods=['GET'])
-@requires_auth
-def get_backend():
-    online, offline = get_backends()
-    result = {}
-    for i in online + offline:
-        info = cluster_config.get_backend(i)
-        result[info['hash']] = info
-    return jsonify(result), 200

llm_server/routes/v1/proxy.py

@@ -1,6 +1,11 @@
+from flask import jsonify
+
+from llm_server.custom_redis import flask_cache
 from . import bp
 from .generate_stats import generate_stats
-from llm_server.custom_redis import flask_cache
+from ..auth import requires_auth
+from ...cluster.backend import get_backends
+from ...cluster.cluster_config import cluster_config
 from ...helpers import jsonify_pretty
 
 
@@ -8,3 +13,14 @@ from ...helpers import jsonify_pretty
 @flask_cache.cached(timeout=5, query_string=True)
 def get_stats():
     return jsonify_pretty(generate_stats())
+
+
+@bp.route('/backends', methods=['GET'])
+@requires_auth
+def get_backend():
+    online, offline = get_backends()
+    result = {}
+    for i in online + offline:
+        info = cluster_config.get_backend(i)
+        result[info['hash']] = info
+    return jsonify(result), 200

server.py

@@ -24,11 +24,13 @@ from llm_server.routes.server_error import handle_server_error
 from llm_server.routes.v1 import bp
 from llm_server.sock import init_socketio
 
-# TODO: add a way to cancel VLLM gens. Maybe use websockets?
-# TODO: need to update opts. for workers
-# TODO: add a healthcheck to VLLM
+# TODO: make sure openai_moderation_enabled works on websockets, completions, and chat completions
 
 # Lower priority
+# TODO: support logit_bias on OpenAI and Ooba endpoints.
+# TODO: add a way to cancel VLLM gens. Maybe use websockets?
+# TODO: validate openai_silent_trim works as expected and only when enabled
+# TODO: rewrite config storage. Store in redis so we can reload it.
 # TODO: set VLLM to stream ALL data using socket.io. If the socket disconnects, cancel generation.
 # TODO: estiamted wait time needs to account for full concurrent_gens but the queue is less than concurrent_gens
 # TODO: the estiamted wait time lags behind the stats