Add penalty_alpha parameter (contrastive search)

README.md

@@ -186,4 +186,5 @@ For these two, please try commenting on an existing issue instead of creating a
 - NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
 - Pygmalion preset, code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/
 - Verbose preset: Anonymous 4chan user.
+- Instruct-Joi preset: https://huggingface.co/Rallio67/joi\_12B\_instruct\_alpha
 - Gradio dropdown menu refresh button: https://github.com/AUTOMATIC1111/stable-diffusion-webui

presets/Instruct-Joi.txt

@@ -0,0 +1,5 @@
+top_p=0.95,
+temperature=0.5,
+penalty_alpha=0.6,
+top_k=4,
+repetition_penalty=1.03,

server.py

@@ -174,6 +174,7 @@ def load_preset_values(preset_menu, return_dict=False):
         'repetition_penalty': 1,
         'top_k': 50,
         'num_beams': 1,
+        'penalty_alpha': 0,
         'min_length': 0,
         'length_penalty': 1,
         'no_repeat_ngram_size': 0,
@@ -191,7 +192,7 @@ def load_preset_values(preset_menu, return_dict=False):
     if return_dict:
         return generate_params
     else:
-        return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['length_penalty'], generate_params['early_stopping']
+        return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
 
 # Removes empty replies from gpt4chan outputs
 def fix_gpt4chan(s):
@@ -237,7 +238,7 @@ def formatted_outputs(reply, model_name):
     else:
         return reply
 
-def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=None, stopping_string=None):
+def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
     global model_name, model, tokenizer
 
     original_question = question
@@ -274,6 +275,7 @@ def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top
         f"min_length={min_length if args.no_stream else 0}",
         f"no_repeat_ngram_size={no_repeat_ngram_size}",
         f"num_beams={num_beams}",
+        f"penalty_alpha={penalty_alpha}",
         f"length_penalty={length_penalty}",
         f"early_stopping={early_stopping}",
     ]
@@ -392,6 +394,7 @@ def create_settings_menus():
                 repetition_penalty = gr.Slider(1.0,4.99,value=generate_params['repetition_penalty'],step=0.01,label="repetition_penalty")
                 top_k = gr.Slider(0,200,value=generate_params['top_k'],step=1,label="top_k")
                 no_repeat_ngram_size = gr.Slider(0, 20, step=1, value=generate_params["no_repeat_ngram_size"], label="no_repeat_ngram_size")
+                penalty_alpha = gr.Slider(0, 5, value=generate_params["penalty_alpha"], label="penalty_alpha")
 
         gr.Markdown("Special parameters (only use them if you really need them):")
         with gr.Row():
@@ -403,8 +406,8 @@ def create_settings_menus():
                 early_stopping = gr.Checkbox(value=generate_params["early_stopping"], label="early_stopping")
 
     model_menu.change(load_model_wrapper, [model_menu], [])
-    preset_menu.change(load_preset_values, [preset_menu], [do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping])
+    preset_menu.change(load_preset_values, [preset_menu], [do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping])
-    return preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping
+    return preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping
 
 # This gets the new line characters right.
 def clean_chat_message(text):
@@ -475,14 +478,14 @@ def extract_message_from_reply(question, reply, current, other, check, extension
 
     return reply, next_character_found, substring_found
 
-def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
     original_text = text
     text = apply_extensions(text, "input")
     question = generate_chat_prompt(text, tokens, name1, name2, context, history_size)
     history['internal'].append(['', ''])
     history['visible'].append(['', ''])
     eos_token = '\n' if check else None
-    for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
+    for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
         reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name2, name1, check, extensions=True)
         history['internal'][-1] = [text, reply]
         history['visible'][-1] = [original_text, apply_extensions(reply, "output")]
@@ -492,10 +495,10 @@ def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p,
             break
     yield history['visible']
 
-def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
     question = generate_chat_prompt(text, tokens, name1, name2, context, history_size, impersonate=True)
     eos_token = '\n' if check else None
-    for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
+    for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
         reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name1, name2, check, extensions=False)
         if not substring_found:
             yield apply_extensions(reply, "output")
@@ -503,19 +506,19 @@ def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, to
             break
     yield apply_extensions(reply, "output")
 
-def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
-    for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+    for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
         yield generate_chat_html(_history, name1, name2, character)
 
-def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
     last = history['visible'].pop()
     history['internal'].pop()
     text = last[0]
     if args.cai_chat:
-        for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+        for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
             yield i
     else:
-        for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size):
+        for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
             yield i
 
 def remove_last_message(name1, name2):
@@ -775,7 +778,7 @@ if args.chat or args.cai_chat:
             with gr.Column():
                 history_size_slider = gr.Slider(minimum=settings['history_size_min'], maximum=settings['history_size_max'], step=1, label='Chat history size in prompt (0 for no limit)', value=settings['history_size'])
 
-        preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
+        preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
 
         name1 = gr.Textbox(value=settings[f'name1{suffix}'], lines=1, label='Your name')
         name2 = gr.Textbox(value=settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
@@ -813,7 +816,7 @@ if args.chat or args.cai_chat:
         if args.extensions is not None:
             create_extensions_block()
 
-        input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
+        input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
         if args.cai_chat:
             gen_events.append(buttons["Generate"].click(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
             gen_events.append(textbox.submit(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream))
@@ -860,13 +863,13 @@ elif args.notebook:
 
         max_new_tokens = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
 
-        preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
+        preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
 
         if args.extensions is not None:
             create_extensions_block()
 
-        gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
+        gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
-        gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream))
+        gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=args.no_stream))
         buttons["Stop"].click(None, None, None, cancels=gen_events)
 
 else:
@@ -883,7 +886,7 @@ else:
                     with gr.Column():
                         buttons["Stop"] = gr.Button("Stop")
 
-                preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping = create_settings_menus()
+                preset_menu, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping = create_settings_menus()
                 if args.extensions is not None:
                     create_extensions_block()
 
@@ -895,9 +898,9 @@ else:
                 with gr.Tab('HTML'):
                     html = gr.HTML()
 
-        gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
+        gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen"))
-        gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
+        gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
-        gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
+        gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=args.no_stream))
         buttons["Stop"].click(None, None, None, cancels=gen_events)
 
 interface.queue()