Add "seed" menu in the Parameters tab

api-example-stream.py

@@ -34,6 +34,7 @@ async def run(context):
         'penalty_alpha': 0,
         'length_penalty': 1,
         'early_stopping': False,
+        'seed': -1,
     }
     session = random_hash()
 
@@ -44,14 +45,14 @@ async def run(context):
                 case "send_hash":
                     await websocket.send(json.dumps({
                         "session_hash": session,
-                        "fn_index": 9
+                        "fn_index": 12
                     }))
                 case "estimation":
                     pass
                 case "send_data":
                     await websocket.send(json.dumps({
                         "session_hash": session,
-                        "fn_index": 9,
+                        "fn_index": 12,
                         "data": [
                             context,
                             params['max_new_tokens'],
@@ -68,6 +69,7 @@ async def run(context):
                             params['penalty_alpha'],
                             params['length_penalty'],
                             params['early_stopping'],
+                            params['seed'],
                         ]
                     }))
                 case "process_starts":

api-example.py

@@ -32,6 +32,7 @@ params = {
     'penalty_alpha': 0,
     'length_penalty': 1,
     'early_stopping': False,
+    'seed': -1,
 }
 
 # Input prompt
@@ -54,6 +55,7 @@ response = requests.post(f"http://{server}:7860/run/textgen", json={
         params['penalty_alpha'],
         params['length_penalty'],
         params['early_stopping'],
+        params['seed'],
     ]
 }).json()
 

extensions/api/script.py

@@ -56,6 +56,7 @@ class Handler(BaseHTTPRequestHandler):
                 penalty_alpha=0, 
                 length_penalty=1,
                 early_stopping=False,
+                seed=-1,
             )
 
             answer = ''

modules/chat.py

@@ -91,7 +91,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
 def stop_everything_event():
     shared.stop_everything = True
 
-def chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
+def chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
     shared.stop_everything = False
     just_started = True
     eos_token = '\n' if check else None
@@ -127,7 +127,7 @@ 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, 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_string=f"\n{name1}:"):
 
             # Extracting the reply
             reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
@@ -154,7 +154,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
 
     yield shared.history['visible']
 
-def impersonate_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
+def impersonate_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
     eos_token = '\n' if check else None
 
     if 'pygmalion' in shared.model_name.lower():
@@ -166,18 +166,18 @@ 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, eos_token=eos_token, stopping_string=f"\n{name2}:"):
+        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)
             yield reply
             if next_character_found:
                 break
         yield reply
 
-def cai_chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
-    for _history in chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
+def cai_chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
+    for _history in chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
         yield generate_chat_html(_history, name1, name2, shared.character)
 
-def regenerate_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
+def regenerate_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
     if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
         yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
     else:
@@ -185,7 +185,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
         last_internal = shared.history['internal'].pop()
         # Yield '*Is typing...*'
         yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character)
-        for _history in chatbot_wrapper(last_internal[0], 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
+        for _history in chatbot_wrapper(last_internal[0], 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
             if shared.args.cai_chat:
                 shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
             else:

modules/text_generation.py

@@ -93,8 +93,15 @@ def clear_torch_cache():
     if not shared.args.cpu:
         torch.cuda.empty_cache()
 
-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, eos_token=None, stopping_string=None):
+def set_manual_seed(seed):
+    if seed != -1:
+        torch.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):
     clear_torch_cache()
+    set_manual_seed(seed)
     t0 = time.time()
 
     # These models are not part of Hugging Face, so we handle them

server.py

@@ -130,10 +130,6 @@ def create_model_and_preset_menus():
 def create_settings_menus(default_preset):
     generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
 
-    with gr.Row():
-        shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
-        ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
-
     with gr.Row():
         with gr.Column():
             with gr.Box():
@@ -164,6 +160,12 @@ def create_settings_menus(default_preset):
                         shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
                 shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
 
+            shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)')
+
+    with gr.Row():
+        shared.gradio['preset_menu_mirror'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
+        ui.create_refresh_button(shared.gradio['preset_menu_mirror'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
+
     with gr.Row():
         shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
         ui.create_refresh_button(shared.gradio['lora_menu'], lambda : None, lambda : {'choices': get_available_loras()}, 'refresh-button')
@@ -330,7 +332,7 @@ def create_interface():
                 create_settings_menus(default_preset)
 
             function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
-            shared.input_params = [shared.gradio[k] for k in ['textbox', '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', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', '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', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
 
             gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
             gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
@@ -393,7 +395,7 @@ def create_interface():
             with gr.Tab("Parameters", elem_id="parameters"):
                 create_settings_menus(default_preset)
 
-            shared.input_params = [shared.gradio[k] for k in ['textbox', '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']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', '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']]
             output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
             gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
             gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
@@ -425,7 +427,7 @@ def create_interface():
             with gr.Tab("Parameters", elem_id="parameters"):
                 create_settings_menus(default_preset)
 
-            shared.input_params = [shared.gradio[k] for k in ['textbox', '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']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', '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']]
             output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
             gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
             gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))