[coati] inference supports profanity check (#3295)

applications/Chat/inference/server.py

@@ -14,7 +14,7 @@ from slowapi.errors import RateLimitExceeded
 from slowapi.util import get_remote_address
 from sse_starlette.sse import EventSourceResponse
 from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM
-from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn
+from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn, load_json
 
 CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.'
 MAX_LEN = 512
@@ -111,6 +111,8 @@ def generate(data: GenerationTaskReq, request: Request):
 @limiter.limit('1/second')
 def generate_no_stream(data: GenerationTaskReq, request: Request):
     prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens)
+    if prompt_processor.has_censored_words(prompt):
+        return prompt_processor.SAFE_RESPONSE
     inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()}
     with running_lock:
         output = model.generate(**inputs, **data.dict(exclude={'history'}))
@@ -118,7 +120,10 @@ def generate_no_stream(data: GenerationTaskReq, request: Request):
     prompt_len = inputs['input_ids'].size(1)
     response = output[0, prompt_len:]
     out_string = tokenizer.decode(response, skip_special_tokens=True)
-    return prompt_processor.postprocess_output(out_string)
+    out_string = prompt_processor.postprocess_output(out_string)
+    if prompt_processor.has_censored_words(out_string):
+        return prompt_processor.SAFE_RESPONSE
+    return out_string
 
 
 if __name__ == '__main__':
@@ -140,13 +145,19 @@ if __name__ == '__main__':
                         help='Group size for GPTQ. This is only useful when quantization mode is 4bit. Default: 128.')
     parser.add_argument('--http_host', default='0.0.0.0')
     parser.add_argument('--http_port', type=int, default=7070)
+    parser.add_argument('--profanity_file', default=None, help='Path to profanity words list. It should be a JSON file containing a list of words.')
     args = parser.parse_args()
 
     if args.quant == '4bit':
         assert args.gptq_checkpoint is not None, 'Please specify a GPTQ checkpoint.'
 
     tokenizer = AutoTokenizer.from_pretrained(args.pretrained)
-    prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN)
+
+    if args.profanity_file is not None:
+        censored_words = load_json(args.profanity_file)
+    else:
+        censored_words = []
+    prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN, censored_words=censored_words)
 
     if args.quant == '4bit':
         model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size)

applications/Chat/inference/utils.py

@@ -1,6 +1,7 @@
 import re
 from threading import Lock
 from typing import Any, Callable, Generator, List, Optional
+import json
 
 import torch
 import torch.distributed as dist
@@ -123,11 +124,16 @@ STOP_PAT = re.compile(r'(###|instruction:).*', flags=(re.I | re.S))
 
 
 class ChatPromptProcessor:
+    SAFE_RESPONSE = 'The input/response contains inappropriate content, please rephrase your prompt.'
 
-    def __init__(self, tokenizer, context: str, max_len: int = 2048):
+    def __init__(self, tokenizer, context: str, max_len: int = 2048, censored_words: List[str]=[]):
         self.tokenizer = tokenizer
         self.context = context
         self.max_len = max_len
+        if len(censored_words) > 0:
+            self.censored_pat = re.compile(f'({"|".join(map(re.escape, censored_words))})', flags=re.I)
+        else:
+            self.censored_pat = None
         # These will be initialized after the first call of preprocess_prompt()
         self.context_len: Optional[int] = None
         self.dialogue_placeholder_len: Optional[int] = None
@@ -172,6 +178,10 @@ class ChatPromptProcessor:
         output = STOP_PAT.sub('', output)
         return output.strip()
 
+    def has_censored_words(self, text: str) -> bool:
+        if self.censored_pat is None:
+            return False
+        return self.censored_pat.search(text) is not None
 
 class LockedIterator:
 
@@ -185,3 +195,7 @@ class LockedIterator:
     def __next__(self):
         with self.lock:
             return next(self.it)
+
+def load_json(path: str):
+    with open(path) as f:
+        return json.load(f)