Refactor text_generation.py, add support for custom generation functions (#1817)

docs/Extensions.md

@@ -45,7 +45,9 @@ Most of these have been created by the extremely talented contributors that you
 | `def ui()` | Creates custom gradio elements when the UI is launched. | 
 | `def input_modifier(string)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
 | `def output_modifier(string)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
+| `def state_modifier(state)`  | Modifies the dictionary containing the input parameters before it is used by the text generation functions. |
 | `def bot_prefix_modifier(string)`  | Applied in chat mode to the prefix for the bot's reply (more on that below). |
+| `def custom_generate_reply(...)` | Overrides the main text generation function. |
 | `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
 | `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See `llava` extension for an example |
 
@@ -104,6 +106,23 @@ python server.py --extensions enthusiasm translate # First apply enthusiasm, the
 python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm
 ```
 
+## `custom_generate_reply` example
+
+Once defined in a `script.py`, this function is executed in place of the main generation functions. You can use it to connect the web UI to an external API, or to load a custom model that is not supported yet.
+
+```python
+import datetime
+
+def custom_generate_reply(question, original_question, seed, state, eos_token, stopping_strings):
+    cumulative = ''
+    for i in range(10):
+        cumulative += f"Counting: {i}...\n"
+        yield cumulative
+
+    cumulative += f"Done! {str(datetime.datetime.now())}"
+    yield cumulative
+```
+
 ## `custom_generate_chat_prompt` example
 
 Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode.
@@ -114,51 +133,64 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
     _continue = kwargs['_continue'] if '_continue' in kwargs else False
     also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
     is_instruct = state['mode'] == 'instruct'
-    rows = [f"{state['context'].strip()}\n"]
+    rows = [state['context'] if is_instruct else f"{state['context'].strip()}\n"]
+    min_rows = 3
 
     # Finding the maximum prompt size
     chat_prompt_size = state['chat_prompt_size']
     if shared.soft_prompt:
         chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
+
     max_length = min(get_max_prompt_length(state), chat_prompt_size)
 
+    # Building the turn templates
+    if 'turn_template' not in state or state['turn_template'] == '':
         if is_instruct:
-        prefix1 = f"{state['name1']}\n"
+            template = '<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n'
-        prefix2 = f"{state['name2']}\n"
         else:
-        prefix1 = f"{state['name1']}: "
+            template = '<|user|>: <|user-message|>\n<|bot|>: <|bot-message|>\n'
-        prefix2 = f"{state['name2']}: "
+    else:
+        template = state['turn_template'].replace(r'\n', '\n')
 
+    replacements = {
+        '<|user|>': state['name1'].strip(),
+        '<|bot|>': state['name2'].strip(),
+    }
+
+    user_turn = replace_all(template.split('<|bot|>')[0], replacements)
+    bot_turn = replace_all('<|bot|>' + template.split('<|bot|>')[1], replacements)
+    user_turn_stripped = replace_all(user_turn.split('<|user-message|>')[0], replacements)
+    bot_turn_stripped = replace_all(bot_turn.split('<|bot-message|>')[0], replacements)
+
+    # Building the prompt
     i = len(shared.history['internal']) - 1
     while i >= 0 and len(encode(''.join(rows))[0]) < max_length:
         if _continue and i == len(shared.history['internal']) - 1:
-            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}")
+            rows.insert(1, bot_turn_stripped + shared.history['internal'][i][1].strip())
         else:
-            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{state['end_of_turn']}\n")
+            rows.insert(1, bot_turn.replace('<|bot-message|>', shared.history['internal'][i][1].strip()))
+
         string = shared.history['internal'][i][0]
         if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']:
-            rows.insert(1, f"{prefix1}{string.strip()}{state['end_of_turn']}\n")
+            rows.insert(1, replace_all(user_turn, {'<|user-message|>': string.strip(), '<|round|>': str(i)}))
+
         i -= 1
 
     if impersonate:
-        rows.append(f"{prefix1.strip() if not is_instruct else prefix1}")
+        min_rows = 2
-        limit = 2
+        rows.append(user_turn_stripped.rstrip(' '))
-    elif _continue:
+    elif not _continue:
-        limit = 3
-    else:
         # Adding the user message
-        user_input = fix_newlines(user_input)
         if len(user_input) > 0:
-            rows.append(f"{prefix1}{user_input}{state['end_of_turn']}\n")
+            rows.append(replace_all(user_turn, {'<|user-message|>': user_input.strip(), '<|round|>': str(len(shared.history["internal"]))}))
 
         # Adding the Character prefix
-        rows.append(apply_extensions(f"{prefix2.strip() if not is_instruct else prefix2}", "bot_prefix"))
+        rows.append(apply_extensions("bot_prefix", bot_turn_stripped.rstrip(' ')))
-        limit = 3
 
-    while len(rows) > limit and len(encode(''.join(rows))[0]) >= max_length:
+    while len(rows) > min_rows and len(encode(''.join(rows))[0]) >= max_length:
         rows.pop(1)
-    prompt = ''.join(rows)
 
+    prompt = ''.join(rows)
     if also_return_rows:
         return prompt, rows
     else:

extensions/api/blocking_api.py

@@ -33,6 +33,7 @@ class Handler(BaseHTTPRequestHandler):
             prompt = body['prompt']
             generate_params = build_parameters(body)
             stopping_strings = generate_params.pop('stopping_strings')
+            generate_params['stream'] = False
 
             generator = generate_reply(
                 prompt, generate_params, stopping_strings=stopping_strings)

extensions/api/streaming_api.py

@@ -23,6 +23,7 @@ async def _handle_connection(websocket, path):
         prompt = message['prompt']
         generate_params = build_parameters(message)
         stopping_strings = generate_params.pop('stopping_strings')
+        generate_params['stream'] = True
 
         generator = generate_reply(
             prompt, generate_params, stopping_strings=stopping_strings)

extensions/elevenlabs_tts/script.py

@@ -18,15 +18,8 @@ wav_idx = 0
 user = ElevenLabsUser(params['api_key'])
 user_info = None
 
-if not shared.args.no_stream:
-    print("Please add --no-stream. This extension is not meant to be used with streaming.")
-    raise ValueError
-
 # Check if the API is valid and refresh the UI accordingly.
-
-
 def check_valid_api():
-
     global user, user_info, params
 
     user = ElevenLabsUser(params['api_key'])
@@ -41,9 +34,8 @@ def check_valid_api():
         print('Got an API Key!')
         return gr.update(value='Connected')
 
+
 # Once the API is verified, get the available voices and update the dropdown list
-
-
 def refresh_voices():
 
     global user, user_info
@@ -63,6 +55,11 @@ def remove_surrounded_chars(string):
     return re.sub('\*[^\*]*?(\*|$)', '', string)
 
 
+def state_modifier(state):
+    state['stream'] = False
+    return state
+
+
 def input_modifier(string):
     """
     This function is applied to your text inputs before
@@ -109,6 +106,7 @@ def ui():
     with gr.Row():
         activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
         connection_status = gr.Textbox(value='Disconnected', label='Connection Status')
+
     voice = gr.Dropdown(value=params['selected_voice'], choices=initial_voice, label='TTS Voice')
     with gr.Row():
         api_key = gr.Textbox(placeholder="Enter your API key.", label='API Key')

extensions/openai/script.py

@@ -266,8 +266,7 @@ class Handler(BaseHTTPRequestHandler):
                 stopping_strings += standard_stopping_strings
                 req_params['custom_stopping_strings'] = stopping_strings
 
-            shared.args.no_stream = not req_params['stream']
+            if req_params['stream']:
-            if not shared.args.no_stream:
                 shared.args.chat = True
                 # begin streaming
                 chunk = {
@@ -337,7 +336,7 @@ class Handler(BaseHTTPRequestHandler):
                 if buffer_and_continue:
                     continue
 
-                if not shared.args.no_stream:
+                if req_params['stream']:
                     # Streaming
                     new_content = answer[len_seen:]
 
@@ -365,7 +364,7 @@ class Handler(BaseHTTPRequestHandler):
                     self.wfile.write(response.encode('utf-8'))
                     completion_token_count += len(encode(new_content)[0])
 
-            if not shared.args.no_stream:
+            if req_params['stream']:
                 chunk = {
                     "id": cmpl_id,
                     "object": stream_object_type,

extensions/sd_api_pictures/script.py

@@ -75,7 +75,6 @@ if params['manage_VRAM']:
 samplers = ['DDIM', 'DPM++ 2M Karras']  # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers
 SD_models = ['NeverEndingDream']  # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
 
-streaming_state = shared.args.no_stream  # remember if chat streaming was enabled
 picture_response = False  # specifies if the next model response should appear as a picture
 
 def remove_surrounded_chars(string):
@@ -92,6 +91,13 @@ def triggers_are_in(string):
     return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string))
 
 
+def state_modifier(state):
+    if picture_response:
+        state['stream'] = False
+
+    return state
+
+
 def input_modifier(string):
     """
     This function is applied to your text inputs before
@@ -218,14 +224,13 @@ def bot_prefix_modifier(string):
 
 
 def toggle_generation(*args):
-    global picture_response, shared, streaming_state
+    global picture_response, shared
 
     if not args:
         picture_response = not picture_response
     else:
         picture_response = args[0]
 
-    shared.args.no_stream = True if picture_response else streaming_state  # Disable streaming cause otherwise the SD-generated picture would return as a dud
     shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*"
 
 

extensions/send_pictures/script.py

@@ -43,5 +43,5 @@ def ui():
     picture_select.upload(
         lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None).then(
         gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-        chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+        chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
         lambda: None, None, picture_select, show_progress=False)

extensions/silero_tts/script.py

@@ -29,7 +29,6 @@ current_params = params.copy()
 voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
 voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
 voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
-streaming_state = shared.args.no_stream  # remember if chat streaming was enabled
 
 # Used for making text xml compatible, needed for voice pitch and speed control
 table = str.maketrans({
@@ -76,6 +75,11 @@ def toggle_text_in_history(name1, name2, mode):
     return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 
 
+def state_modifier(state):
+    state['stream'] = False
+    return state
+
+
 def input_modifier(string):
     """
     This function is applied to your text inputs before
@@ -87,7 +91,6 @@ def input_modifier(string):
         shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>', 'controls>')]
 
     shared.processing_message = "*Is recording a voice message...*"
-    shared.args.no_stream = True  # Disable streaming cause otherwise the audio output will stutter and begin anew every time the message is being updated
     return string
 
 
@@ -124,7 +127,6 @@ def output_modifier(string):
             string += f'\n\n{original_string}'
 
     shared.processing_message = "*Is typing...*"
-    shared.args.no_stream = streaming_state  # restore the streaming option to the previous value
     return string
 
 

modules/extensions.py

@@ -86,6 +86,15 @@ def _apply_custom_generate_chat_prompt(text, state, **kwargs):
     return None
 
 
+# Extension that modifies the input parameters before they are used
+def _apply_state_modifier_extensions(state):
+    for extension, _ in iterator():
+        if hasattr(extension, "state_modifier"):
+            state = getattr(extension, "state_modifier")(state)
+
+    return state
+ 
+
 # Extension functions that override the default tokenizer output
 def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_embeds):
     for extension, _ in iterator():
@@ -95,13 +104,24 @@ def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_e
     return prompt, input_ids, input_embeds
 
 
+# Custom generate reply handling
+def _apply_custom_generate_reply():
+    for extension, _ in iterator():
+        if hasattr(extension, 'custom_generate_reply'):
+            return getattr(extension, 'custom_generate_reply')
+
+    return None
+
+
 EXTENSION_MAP = {
     "input": partial(_apply_string_extensions, "input_modifier"),
     "output": partial(_apply_string_extensions, "output_modifier"),
+    "state": _apply_state_modifier_extensions,
     "bot_prefix": partial(_apply_string_extensions, "bot_prefix_modifier"),
     "tokenizer": partial(_apply_tokenizer_extensions, "tokenizer_modifier"),
     "input_hijack": _apply_input_hijack,
-    "custom_generate_chat_prompt": _apply_custom_generate_chat_prompt
+    "custom_generate_chat_prompt": _apply_custom_generate_chat_prompt,
+    "custom_generate_reply": _apply_custom_generate_reply
 }
 
 

modules/text_generation.py

@@ -21,6 +21,7 @@ def get_max_prompt_length(state):
     max_length = state['truncation_length'] - state['max_new_tokens']
     if shared.soft_prompt:
         max_length -= shared.soft_prompt_tensor.shape[1]
+
     return max_length
 
 
@@ -62,6 +63,36 @@ def decode(output_ids, skip_special_tokens=True):
     return shared.tokenizer.decode(output_ids, skip_special_tokens)
 
 
+def generate_softprompt_input_tensors(input_ids):
+    inputs_embeds = shared.model.transformer.wte(input_ids)
+    inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
+    filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
+    # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
+    return inputs_embeds, filler_input_ids
+
+
+# Removes empty replies from gpt4chan outputs
+def fix_gpt4chan(s):
+    for i in range(10):
+        s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
+        s = re.sub("--- [0-9]*\n *\n---", "---", s)
+        s = re.sub("--- [0-9]*\n\n\n---", "---", s)
+
+    return s
+
+
+# Fix the LaTeX equations in galactica
+def fix_galactica(s):
+    s = s.replace(r'\[', r'$')
+    s = s.replace(r'\]', r'$')
+    s = s.replace(r'\(', r'$')
+    s = s.replace(r'\)', r'$')
+    s = s.replace(r'$$', r'$')
+    s = re.sub(r'\n', r'\n\n', s)
+    s = re.sub(r"\n{3,}", "\n\n", s)
+    return s
+
+
 def get_reply_from_output_ids(output_ids, input_ids, original_question, state):
     if shared.model_type == 'HF_seq2seq':
         reply = decode(output_ids, state['skip_special_tokens'])
@@ -81,35 +112,6 @@ def get_reply_from_output_ids(output_ids, input_ids, original_question, state):
     return reply
 
 
-def generate_softprompt_input_tensors(input_ids):
-    inputs_embeds = shared.model.transformer.wte(input_ids)
-    inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
-    filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
-    # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
-    return inputs_embeds, filler_input_ids
-
-
-# Removes empty replies from gpt4chan outputs
-def fix_gpt4chan(s):
-    for i in range(10):
-        s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
-        s = re.sub("--- [0-9]*\n *\n---", "---", s)
-        s = re.sub("--- [0-9]*\n\n\n---", "---", s)
-    return s
-
-
-# Fix the LaTeX equations in galactica
-def fix_galactica(s):
-    s = s.replace(r'\[', r'$')
-    s = s.replace(r'\]', r'$')
-    s = s.replace(r'\(', r'$')
-    s = s.replace(r'\)', r'$')
-    s = s.replace(r'$$', r'$')
-    s = re.sub(r'\n', r'\n\n', s)
-    s = re.sub(r"\n{3,}", "\n\n", s)
-    return s
-
-
 def formatted_outputs(reply, model_name):
     if not shared.is_chat():
         if shared.model_type == 'galactica':
@@ -140,25 +142,39 @@ def stop_everything_event():
     shared.stop_everything = True
 
 
-def get_generate_params(state):
+def generate_reply(question, state, eos_token=None, stopping_strings=[]):
-    generate_params = {}
+    state = apply_extensions('state', state)
+    generate_func = apply_extensions('custom_generate_reply')
+    if generate_func is None:
+        if shared.model_name == 'None' or shared.model is None:
+            logging.error("No model is loaded! Select one in the Model tab.")
+            yield formatted_outputs(question, shared.model_name)
+            return
 
-    # Models that are not on transformers
         if shared.model_type in ['rwkv', 'llamacpp']:
-        generate_params['token_count'] = state['max_new_tokens']
+            generate_func = generate_reply_custom
-        for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
+        elif shared.args.flexgen:
-            generate_params[k] = state[k]
+            generate_func = generate_reply_flexgen
         else:
-        # FlexGen
+            generate_func = generate_reply_HF
-        if shared.args.flexgen:
-            for k in ['max_new_tokens', 'do_sample', 'temperature']:
-                generate_params[k] = state[k]
 
-            if not shared.args.no_stream:
+    # Preparing the input
-                generate_params['max_new_tokens'] = 8
+    original_question = question
+    if not shared.is_chat():
+        question = apply_extensions('input', question)
 
-        # transformers
+    if shared.args.verbose:
-        else:
+        print(f'\n\n{question}\n--------------------\n')
+
+    shared.stop_everything = False
+    clear_torch_cache()
+    seed = set_manual_seed(state['seed'])
+    for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings):
+        yield formatted_outputs(reply, shared.model_name)
+
+
+def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
+    generate_params = {}
     for k in ['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']:
         generate_params[k] = state[k]
 
@@ -171,65 +187,10 @@ def get_generate_params(state):
     if shared.args.deepspeed:
         generate_params.update({'synced_gpus': True})
 
-    return generate_params
-
-
-def generate_reply(question, state, eos_token=None, stopping_strings=[]):
-    if shared.model_name == 'None' or shared.model is None:
-        logging.error("No model is loaded! Select one in the Model tab.")
-        yield formatted_outputs(question, shared.model_name)
-        return
-
-    clear_torch_cache()
-    seed = set_manual_seed(state['seed'])
-    shared.stop_everything = False
-    generate_params = get_generate_params(state)
-    t0 = time.time()
-
-    # Preparing the input
-    original_question = question
-    if not shared.is_chat():
-        question = apply_extensions('input', question)
-
-    if shared.args.verbose:
-        print(f'\n\n{question}\n--------------------\n')
-
-    # If the model is not on transformers, handle it separately and end this
-    # function call earlier.
-    if shared.model_type in ['rwkv', 'llamacpp']:
-
-        try:
-            if shared.args.no_stream:
-                reply = shared.model.generate(context=question, **generate_params)
-                output = original_question + reply
-                if not shared.is_chat():
-                    reply = original_question + apply_extensions('output', reply)
-
-                yield formatted_outputs(reply, shared.model_name)
-            else:
-                if not shared.is_chat():
-                    yield formatted_outputs(question, shared.model_name)
-
-                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('output', reply)
-
-                    yield formatted_outputs(reply, shared.model_name)
-
-        except Exception:
-            traceback.print_exc()
-        finally:
-            t1 = time.time()
-            original_tokens = len(encode(original_question)[0])
-            new_tokens = len(encode(output)[0]) - original_tokens
-            print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
-            return
-
     # Encode the input
     input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
     output = input_ids[0]
-    cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
+    cuda = not any((shared.args.cpu, shared.args.deepspeed))
 
     # Find the eos tokens
     eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
@@ -259,15 +220,16 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
             break
 
     # Update generate_params with the eos token and the stopping strings
-    if shared.args.flexgen:
-        generate_params['stop'] = eos_token_ids[-1]
-    else:
     generate_params['eos_token_id'] = eos_token_ids
     generate_params['stopping_criteria'] = stopping_criteria_list
 
+    t0 = time.time()
     try:
+        if not shared.is_chat() and shared.model_type != 'HF_seq2seq':
+            yield original_question
+
         # Generate the entire reply at once.
-        if shared.args.no_stream:
+        if not state['stream']:
             with torch.no_grad():
                 output = shared.model.generate(**generate_params)[0]
                 if cuda:
@@ -276,12 +238,11 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
             if shared.soft_prompt:
                 output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
 
-            reply = get_reply_from_output_ids(output, input_ids, original_question, state)
+            yield get_reply_from_output_ids(output, input_ids, original_question, state)
-            yield formatted_outputs(reply, shared.model_name)
 
         # Stream the reply 1 token at a time.
         # This is based on the trick of using 'stopping_criteria' to create an iterator.
-        elif not shared.args.flexgen:
+        else:
 
             def generate_with_callback(callback=None, **kwargs):
                 kwargs['stopping_criteria'].append(Stream(callback_func=callback))
@@ -292,45 +253,118 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
             def generate_with_streaming(**kwargs):
                 return Iteratorize(generate_with_callback, kwargs, callback=None)
 
-            if not shared.is_chat() and shared.model_type != 'HF_seq2seq':
-                yield formatted_outputs(original_question, shared.model_name)
-
             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]:]))
 
-                    reply = get_reply_from_output_ids(output, input_ids, original_question, state)
+                    yield get_reply_from_output_ids(output, input_ids, original_question, state)
                     if output[-1] in eos_token_ids:
                         break
 
-                    yield formatted_outputs(reply, shared.model_name)
+    except Exception:
-
+        traceback.print_exc()
-        # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
+    finally:
-        else:
+        t1 = time.time()
-            for i in range(state['max_new_tokens'] // 8 + 1):
+        original_tokens = len(original_input_ids[0])
-                clear_torch_cache()
+        new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0)
-                with torch.no_grad():
+        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
-                    output = shared.model.generate(**generate_params)[0]
+        return
-
+
-                if shared.soft_prompt:
+
-                    output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
+def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
-
+    seed = set_manual_seed(state['seed'])
-                reply = get_reply_from_output_ids(output, input_ids, original_question, state)
+    generate_params = {'token_count': state['max_new_tokens']}
-                if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
+    for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
-                    break
+        generate_params[k] = state[k]
-
+
-                yield formatted_outputs(reply, shared.model_name)
+    t0 = time.time()
-                input_ids = np.reshape(output, (1, output.shape[0]))
+    try:
-                if shared.soft_prompt:
+        if not shared.is_chat():
-                    inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
+            yield question
-                    generate_params.update({'inputs_embeds': inputs_embeds})
+
-                    generate_params.update({'inputs': filler_input_ids})
+        if not state['stream']:
-                else:
+            reply = shared.model.generate(context=question, **generate_params)
-                    generate_params.update({'inputs': input_ids})
+            output = original_question + reply
-
+            if not shared.is_chat():
-            yield formatted_outputs(reply, shared.model_name)
+                reply = original_question + apply_extensions('output', reply)
-
+
+            yield reply
+        else:
+
+            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('output', reply)
+
+                yield reply
+
+    except Exception:
+        traceback.print_exc()
+    finally:
+        t1 = time.time()
+        original_tokens = len(encode(original_question)[0])
+        new_tokens = len(encode(output)[0]) - original_tokens
+        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
+        return
+
+
+def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
+    generate_params = {}
+    for k in ['max_new_tokens', 'do_sample', 'temperature']:
+        generate_params[k] = state[k]
+
+    if state['stream']:
+        generate_params['max_new_tokens'] = 8
+
+    # Encode the input
+    input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
+    output = input_ids[0]
+
+    # Find the eos tokens
+    eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
+    if eos_token is not None:
+        eos_token_ids.append(int(encode(eos_token)[0][-1]))
+
+    # Add the encoded tokens to generate_params
+    question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
+    original_input_ids = input_ids
+    generate_params.update({'inputs': input_ids})
+    if inputs_embeds is not None:
+        generate_params.update({'inputs_embeds': inputs_embeds})
+
+    # Update generate_params with the eos token and the stopping strings
+    generate_params['stop'] = eos_token_ids[-1]
+
+    t0 = time.time()
+    try:
+        if not shared.is_chat():
+            yield question
+
+        # Generate the entire reply at once.
+        if not state['stream']:
+            with torch.no_grad():
+                output = shared.model.generate(**generate_params)[0]
+
+            yield get_reply_from_output_ids(output, input_ids, original_question, state)
+
+        # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
+        else:
+            for i in range(state['max_new_tokens'] // 8 + 1):
+                if shared.stop_everything:
+                    break
+
+                clear_torch_cache()
+                with torch.no_grad():
+                    output = shared.model.generate(**generate_params)[0]
+
+                if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
+                    break
+
+                yield get_reply_from_output_ids(output, original_input_ids, original_question, state)
+                input_ids = np.reshape(output, (1, output.shape[0]))
+                generate_params.update({'inputs': input_ids})
+
     except Exception:
         traceback.print_exc()
     finally:

modules/ui.py

@@ -34,7 +34,7 @@ def list_model_elements():
 
 
 def list_interface_input_elements(chat=False):
-    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu']
+    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream']
     if chat:
         elements += ['name1', 'name2', 'greeting', 'context', 'turn_template', 'chat_prompt_size', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu']
 

server.py

@@ -15,6 +15,7 @@ def my_get(url, **kwargs):
     kwargs.setdefault('allow_redirects', True)
     return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
 
+
 original_get = requests.get
 requests.get = my_get
 import gradio as gr
@@ -454,6 +455,7 @@ def create_settings_menus(default_preset):
                         shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.')
 
                         shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
+                        shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
 
     with gr.Accordion('Soft prompt', open=False):
         with gr.Row():
@@ -721,46 +723,46 @@ def create_interface():
             gen_events.append(shared.gradio['Generate'].click(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                 lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
-                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
             )
 
             gen_events.append(shared.gradio['textbox'].submit(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                 lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
-                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
             )
 
             gen_events.append(shared.gradio['Regenerate'].click(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
             )
 
             gen_events.append(shared.gradio['Continue'].click(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
             )
 
             gen_events.append(shared.gradio['Impersonate'].click(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)
+                chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False)
             )
 
             shared.gradio['Replace last reply'].click(
-                chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=False).then(
                 lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
 
             shared.gradio['Send dummy message'].click(
-                chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=False).then(
                 lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
 
             shared.gradio['Send dummy reply'].click(
-                chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=False).then(
                 lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                 chat.save_history, shared.gradio['mode'], None, show_progress=False)
 
@@ -786,7 +788,7 @@ def create_interface():
                 chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then(
                 chat.redraw_html, reload_inputs, shared.gradio['display'])
 
-            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=shared.args.no_stream)
+            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False)
             shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
             shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
             shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
@@ -808,14 +810,14 @@ def create_interface():
             gen_events.append(shared.gradio['Generate'].click(
                 lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)  # .then(
+                generate_reply, shared.input_params, output_params, show_progress=False)  # .then(
                 # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
             )
 
             gen_events.append(shared.gradio['textbox'].submit(
                 lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
                 ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)  # .then(
+                generate_reply, shared.input_params, output_params, show_progress=False)  # .then(
                 # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
             )
 
@@ -824,13 +826,13 @@ def create_interface():
                 gen_events.append(shared.gradio['Regenerate'].click(
                     lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then(
                     ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                    generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)  # .then(
+                    generate_reply, shared.input_params, output_params, show_progress=False)  # .then(
                     # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
                 )
             else:
                 gen_events.append(shared.gradio['Continue'].click(
                     ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-                    generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream)  # .then(
+                    generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False)  # .then(
                     # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
                 )