Stop the bot from talking for you in chat mode

modules/RWKV.py

@@ -45,11 +45,11 @@ class RWKVModel:
             token_stop = token_stop
         )
 
-        return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
+        return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
 
     def generate_with_streaming(self, **kwargs):
         with Iteratorize(self.generate, kwargs, callback=None) as generator:
-            reply = kwargs['context']
+            reply = ''
             for token in generator:
                 reply += token
                 yield reply

modules/callbacks.py

@@ -11,24 +11,22 @@ import modules.shared as shared
 # Copied from https://github.com/PygmalionAI/gradio-ui/
 class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
 
-    def __init__(self, sentinel_token_ids: torch.LongTensor,
-                 starting_idx: int):
+    def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
         transformers.StoppingCriteria.__init__(self)
         self.sentinel_token_ids = sentinel_token_ids
         self.starting_idx = starting_idx
 
-    def __call__(self, input_ids: torch.LongTensor,
-                 _scores: torch.FloatTensor) -> bool:
+    def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
         for sample in input_ids:
             trimmed_sample = sample[self.starting_idx:]
-            # Can't unfold, output is still too tiny. Skip.
-            if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
-                continue
-
-            for window in trimmed_sample.unfold(
-                    0, self.sentinel_token_ids.shape[-1], 1):
-                if torch.all(torch.eq(self.sentinel_token_ids, window)):
-                    return True
+
+            for i in range(len(self.sentinel_token_ids)):
+                # Can't unfold, output is still too tiny. Skip.
+                if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
+                    continue
+                for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
+                    if torch.all(torch.eq(self.sentinel_token_ids[i], window)):
+                        return True
         return False
 
 class Stream(transformers.StoppingCriteria):

modules/chat.py

@@ -51,41 +51,31 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
     prompt = ''.join(rows)
     return prompt
 
-def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
+def extract_message_from_reply(reply, name1, name2, check):
     next_character_found = False
 
-    asker = name1 if not impersonate else name2
-    replier = name2 if not impersonate else name1
-
-    previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
-    idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
-    idx = idx[max(len(previous_idx)-1, 0)]
-
-    if not impersonate:
-        reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
-    else:
-        reply = reply[idx + 1 + len(f"{replier}:"):]
-
     if check:
         lines = reply.split('\n')
         reply = lines[0].strip()
         if len(lines) > 1:
             next_character_found = True
     else:
-        idx = reply.find(f"\n{asker}:")
-        if idx != -1:
-            reply = reply[:idx]
-            next_character_found = True
-        reply = fix_newlines(reply)
+        for string in [f"\n{name1}:", f"\n{name2}:"]:
+            idx = reply.find(string)
+            if idx != -1:
+                reply = reply[:idx]
+                next_character_found = True
 
         # If something like "\nYo" is generated just before "\nYou:"
         # is completed, trim it
-        next_turn = f"\n{asker}:"
-        for j in range(len(next_turn)-1, 0, -1):
-            if reply[-j:] == next_turn[:j]:
-                reply = reply[:-j]
-                break
-
+        if not next_character_found:
+            for string in [f"\n{name1}:", f"\n{name2}:"]:
+                for j in range(len(string)-1, 0, -1):
+                    if reply[-j:] == string[:j]:
+                        reply = reply[:-j]
+                        break
+
+    reply = fix_newlines(reply)
     return reply, next_character_found
 
 def stop_everything_event():
@@ -127,10 +117,10 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
     # Generate
     reply = ''
     for i in range(chat_generation_attempts):
-        for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name1}:"):
+        for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
 
             # Extracting the reply
-            reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
+            reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
             visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
             visible_reply = apply_extensions(visible_reply, "output")
             if shared.args.chat:
@@ -166,8 +156,8 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
     # Yield *Is typing...*
     yield shared.processing_message
     for i in range(chat_generation_attempts):
-        for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name2}:"):
-            reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
+        for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
+            reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
             yield reply
             if next_character_found:
                 break

modules/text_generation.py

@@ -99,25 +99,37 @@ def set_manual_seed(seed):
         if torch.cuda.is_available():
             torch.cuda.manual_seed_all(seed)
 
-def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_string=None):
+def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_strings=[]):
     clear_torch_cache()
     set_manual_seed(seed)
     t0 = time.time()
 
+    original_question = question
+    if not (shared.args.chat or shared.args.cai_chat):
+        question = apply_extensions(question, "input")
+    if shared.args.verbose:
+        print(f"\n\n{question}\n--------------------\n")
+
     # These models are not part of Hugging Face, so we handle them
     # separately and terminate the function call earlier
     if shared.is_RWKV:
         try:
             if shared.args.no_stream:
                 reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
+                if not (shared.args.chat or shared.args.cai_chat):
+                    reply = original_question + apply_extensions(reply, "output")
                 yield formatted_outputs(reply, shared.model_name)
             else:
                 if not (shared.args.chat or shared.args.cai_chat):
                     yield formatted_outputs(question, shared.model_name)
+
                 # RWKV has proper streaming, which is very nice.
                 # No need to generate 8 tokens at a time.
                 for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
+                    if not (shared.args.chat or shared.args.cai_chat):
+                        reply = original_question + apply_extensions(reply, "output")
                     yield formatted_outputs(reply, shared.model_name)
+
         except Exception:
             traceback.print_exc()
         finally:
@@ -127,12 +139,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
             return
 
-    original_question = question
-    if not (shared.args.chat or shared.args.cai_chat):
-        question = apply_extensions(question, "input")
-    if shared.args.verbose:
-        print(f"\n\n{question}\n--------------------\n")
-
     input_ids = encode(question, max_new_tokens)
     original_input_ids = input_ids
     output = input_ids[0]
@@ -142,9 +148,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
     if eos_token is not None:
         eos_token_ids.append(int(encode(eos_token)[0][-1]))
     stopping_criteria_list = transformers.StoppingCriteriaList()
-    if stopping_string is not None:
-        # Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
-        t = encode(stopping_string, 0, add_special_tokens=False)
+    if type(stopping_strings) is list and len(stopping_strings) > 0:
+        t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
         stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
 
     generate_params = {}
@@ -195,12 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             if shared.soft_prompt:
                 output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
 
+            new_tokens = len(output) - len(input_ids[0])
+            reply = decode(output[-new_tokens:])
             if not (shared.args.chat or shared.args.cai_chat):
-                new_tokens = len(output) - len(input_ids[0])
-                reply = decode(output[-new_tokens:])
                 reply = original_question + apply_extensions(reply, "output")
-            else:
-                reply = decode(output)
 
             yield formatted_outputs(reply, shared.model_name)
 
@@ -223,12 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
                 for output in generator:
                     if shared.soft_prompt:
                         output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
+
+                    new_tokens = len(output) - len(input_ids[0])
+                    reply = decode(output[-new_tokens:])
                     if not (shared.args.chat or shared.args.cai_chat):
-                        new_tokens = len(output) - len(input_ids[0])
-                        reply = decode(output[-new_tokens:])
                         reply = original_question + apply_extensions(reply, "output")
-                    else:
-                        reply = decode(output)
 
                     if output[-1] in eos_token_ids:
                         break
@@ -244,12 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
                     output = shared.model.generate(**generate_params)[0]
                 if shared.soft_prompt:
                     output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
+
+                new_tokens = len(output) - len(original_input_ids[0])
+                reply = decode(output[-new_tokens:])
                 if not (shared.args.chat or shared.args.cai_chat):
-                    new_tokens = len(output) - len(original_input_ids[0])
-                    reply = decode(output[-new_tokens:])
                     reply = original_question + apply_extensions(reply, "output")
-                else:
-                    reply = decode(output)
 
                 if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
                     break