Rename variables

modules/chat.py

@@ -96,9 +96,9 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
     reply = fix_newlines(reply)
     return reply, next_character_found
 
-def chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_turn, regenerate=False):
+def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
     just_started = True
-    eos_token = '\n' if generate_params['stop_at_newline'] else None
+    eos_token = '\n' if generate_state['stop_at_newline'] else None
     name1_original = name1
     if 'pygmalion' in shared.model_name.lower():
         name1 = "You"
@@ -119,9 +119,9 @@ def chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_t
 
     kwargs = {'end_of_turn': end_of_turn, 'is_instruct': mode == 'instruct'}
     if custom_generate_chat_prompt is None:
-        prompt = generate_chat_prompt(text, generate_params['max_new_tokens'], name1, name2, context, generate_params['chat_prompt_size'], **kwargs)
+        prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
     else:
-        prompt = custom_generate_chat_prompt(text, generate_params['max_new_tokens'], name1, name2, context, generate_params['chat_prompt_size'], **kwargs)
+        prompt = custom_generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
 
     # Yield *Is typing...*
     if not regenerate:
@@ -129,13 +129,13 @@ def chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_t
 
     # Generate
     cumulative_reply = ''
-    for i in range(generate_params['chat_generation_attempts']):
+    for i in range(generate_state['chat_generation_attempts']):
         reply = None
-        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_params, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
+        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
             reply = cumulative_reply + reply
 
             # Extracting the reply
-            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_params['stop_at_newline'])
+            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
             visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
             visible_reply = apply_extensions(visible_reply, "output")
 
@@ -160,23 +160,23 @@ def chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_t
 
     yield shared.history['visible']
 
-def impersonate_wrapper(text, generate_params, name1, name2, context, mode, end_of_turn):
-    eos_token = '\n' if generate_params['stop_at_newline'] else None
+def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+    eos_token = '\n' if generate_state['stop_at_newline'] else None
 
     if 'pygmalion' in shared.model_name.lower():
         name1 = "You"
 
-    prompt = generate_chat_prompt(text, generate_params['max_new_tokens'], name1, name2, context, generate_params['chat_prompt_size'], impersonate=True, end_of_turn=end_of_turn)
+    prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], impersonate=True, end_of_turn=end_of_turn)
 
     # Yield *Is typing...*
     yield shared.processing_message
 
     cumulative_reply = ''
-    for i in range(generate_params['chat_generation_attempts']):
+    for i in range(generate_state['chat_generation_attempts']):
         reply = None
-        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_params, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
+        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
             reply = cumulative_reply + reply
-            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_params['stop_at_newline'])
+            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
             yield reply
             if next_character_found:
                 break
@@ -186,11 +186,11 @@ def impersonate_wrapper(text, generate_params, name1, name2, context, mode, end_
 
     yield reply
 
-def cai_chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_turn):
-    for history in chatbot_wrapper(text, generate_params, name1, name2, context, mode, end_of_turn, regenerate=False):
+def cai_chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+    for history in chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
         yield chat_html_wrapper(history, name1, name2, mode)
 
-def regenerate_wrapper(text, generate_params, name1, name2, context, mode, end_of_turn):
+def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
     if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
         yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
     else:
@@ -198,7 +198,7 @@ def regenerate_wrapper(text, generate_params, name1, name2, context, mode, end_o
         last_internal = shared.history['internal'].pop()
         # Yield '*Is typing...*'
         yield chat_html_wrapper(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, mode)
-        for history in chatbot_wrapper(last_internal[0], generate_params, name1, name2, context, mode, end_of_turn, regenerate=True):
+        for history in chatbot_wrapper(last_internal[0], generate_state, name1, name2, context, mode, end_of_turn, regenerate=True):
             shared.history['visible'][-1] = [last_visible[0], history[-1][1]]
             yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 

modules/text_generation.py

@@ -102,11 +102,11 @@ def set_manual_seed(seed):
 def stop_everything_event():
     shared.stop_everything = True
 
-def generate_reply(question, generate_params, eos_token=None, stopping_strings=[]):
+def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
     clear_torch_cache()
-    set_manual_seed(generate_params['seed'])
+    set_manual_seed(generate_state['seed'])
     shared.stop_everything = False
-    updated_params = {}
+    generate_params = {}
     t0 = time.time()
 
     original_question = question
@@ -119,11 +119,11 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
     # separately and terminate the function call earlier
     if any((shared.is_RWKV, shared.is_llamacpp)):
         for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
-            updated_params[k] = generate_params[k]
-        updated_params["token_count"] = generate_params["max_new_tokens"]
+            generate_params[k] = generate_state[k]
+        generate_params["token_count"] = generate_state["max_new_tokens"]
         try:
             if shared.args.no_stream:
-                reply = shared.model.generate(context=question, **updated_params)
+                reply = shared.model.generate(context=question, **generate_params)
                 output = original_question+reply
                 if not shared.is_chat():
                     reply = original_question + apply_extensions(reply, "output")
@@ -134,7 +134,7 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
 
                 # 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, **updated_params):
+                for reply in shared.model.generate_with_streaming(context=question, **generate_params):
                     output = original_question+reply
                     if not shared.is_chat():
                         reply = original_question + apply_extensions(reply, "output")
@@ -149,7 +149,7 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
             print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
             return
 
-    input_ids = encode(question, generate_params['max_new_tokens'])
+    input_ids = encode(question, generate_state['max_new_tokens'])
     original_input_ids = input_ids
     output = input_ids[0]
 
@@ -162,37 +162,37 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
         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])))
 
-    updated_params["max_new_tokens"] = generate_params['max_new_tokens']
+    generate_params["max_new_tokens"] = generate_state['max_new_tokens']
     if not shared.args.flexgen:
         for k in ["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"]:
-            updated_params[k] = generate_params[k]
-        updated_params["eos_token_id"] = eos_token_ids
-        updated_params["stopping_criteria"] = stopping_criteria_list
+            generate_params[k] = generate_state[k]
+        generate_params["eos_token_id"] = eos_token_ids
+        generate_params["stopping_criteria"] = stopping_criteria_list
         if shared.args.no_stream:
-            updated_params["min_length"] = 0
+            generate_params["min_length"] = 0
     else:
         for k in ["do_sample", "temperature"]:
-            updated_params[k] = generate_params[k]
-        updated_params["stop"] = generate_params["eos_token_ids"][-1]
+            generate_params[k] = generate_state[k]
+        generate_params["stop"] = generate_state["eos_token_ids"][-1]
         if not shared.args.no_stream:
-            updated_params["max_new_tokens"] = 8
+            generate_params["max_new_tokens"] = 8
 
     if shared.args.no_cache:
-        updated_params.update({"use_cache": False})
+        generate_params.update({"use_cache": False})
     if shared.args.deepspeed:
-        updated_params.update({"synced_gpus": True})
+        generate_params.update({"synced_gpus": True})
     if shared.soft_prompt:
         inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-        updated_params.update({"inputs_embeds": inputs_embeds})
-        updated_params.update({"inputs": filler_input_ids})
+        generate_params.update({"inputs_embeds": inputs_embeds})
+        generate_params.update({"inputs": filler_input_ids})
     else:
-        updated_params.update({"inputs": input_ids})
+        generate_params.update({"inputs": input_ids})
 
     try:
         # Generate the entire reply at once.
         if shared.args.no_stream:
             with torch.no_grad():
-                output = shared.model.generate(**updated_params)[0]
+                output = shared.model.generate(**generate_params)[0]
                 if cuda:
                     output = output.cuda()
             if shared.soft_prompt:
@@ -220,7 +220,7 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
 
             if not shared.is_chat():
                 yield formatted_outputs(original_question, shared.model_name)
-            with generate_with_streaming(**updated_params) as generator:
+            with generate_with_streaming(**generate_params) as generator:
                 for output in generator:
                     if shared.soft_prompt:
                         output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -236,10 +236,10 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
 
         # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
         else:
-            for i in range(generate_params['max_new_tokens']//8+1):
+            for i in range(generate_state['max_new_tokens']//8+1):
                 clear_torch_cache()
                 with torch.no_grad():
-                    output = shared.model.generate(**updated_params)[0]
+                    output = shared.model.generate(**generate_params)[0]
                 if shared.soft_prompt:
                     output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
 
@@ -255,10 +255,10 @@ def generate_reply(question, generate_params, eos_token=None, stopping_strings=[
                 input_ids = np.reshape(output, (1, output.shape[0]))
                 if shared.soft_prompt:
                     inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-                    updated_params.update({"inputs_embeds": inputs_embeds})
-                    updated_params.update({"inputs": filler_input_ids})
+                    generate_params.update({"inputs_embeds": inputs_embeds})
+                    generate_params.update({"inputs": filler_input_ids})
                 else:
-                    updated_params.update({"inputs": input_ids})
+                    generate_params.update({"inputs": input_ids})
 
             yield formatted_outputs(reply, shared.model_name)
 

server.py

@@ -173,7 +173,7 @@ def create_settings_menus(default_preset):
     generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True)
     for k in ['max_new_tokens', 'seed', 'stop_at_newline', 'chat_prompt_size', 'chat_generation_attempts']:
         generate_params[k] = shared.settings[k]
-    shared.gradio['generation_state'] = gr.State(generate_params)
+    shared.gradio['generate_state'] = gr.State(generate_params)
 
     with gr.Row():
         with gr.Column():
@@ -225,7 +225,7 @@ def create_settings_menus(default_preset):
             shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
 
     shared.gradio['model_menu'].change(load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_menu'], show_progress=True)
-    shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'generation_state']], [shared.gradio[k] for k in ['generation_state', '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.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'generate_state']], [shared.gradio[k] for k in ['generate_state', '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.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
     shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True)
     shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu'])
@@ -378,7 +378,7 @@ def create_interface():
 
                 create_settings_menus(default_preset)
 
-            shared.input_params = [shared.gradio[k] for k in ['Chat input', 'generation_state', 'name1', 'name2', 'context', 'Chat mode', 'end_of_turn']]
+            shared.input_params = [shared.gradio[k] for k in ['Chat input', 'generate_state', 'name1', 'name2', 'context', 'Chat mode', 'end_of_turn']]
 
             def set_chat_input(textbox):
                 return textbox, ""
@@ -458,7 +458,7 @@ 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', 'generation_state']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', 'generate_state']]
             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))
             gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
@@ -491,7 +491,7 @@ 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', 'generation_state']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', 'generate_state']]
             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))
             gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
@@ -530,9 +530,9 @@ def create_interface():
             if k not in shared.gradio:
                 continue
             if type(shared.gradio[k]) in [gr.Checkbox, gr.Number]:
-                shared.gradio[k].change(lambda state, value, copy=k: state.update({copy: value}), inputs=[shared.gradio['generation_state'], shared.gradio[k]], outputs=shared.gradio['generation_state'])
+                shared.gradio[k].change(lambda state, value, copy=k: state.update({copy: value}), inputs=[shared.gradio['generate_state'], shared.gradio[k]], outputs=shared.gradio['generate_state'])
             else:
-                shared.gradio[k].release(lambda state, value, copy=k: state.update({copy: value}), inputs=[shared.gradio['generation_state'], shared.gradio[k]], outputs=shared.gradio['generation_state'])
+                shared.gradio[k].release(lambda state, value, copy=k: state.update({copy: value}), inputs=[shared.gradio['generate_state'], shared.gradio[k]], outputs=shared.gradio['generate_state'])
 
     # Authentication
     auth = None