Fix merge conflict in text_generation

- Need to update `shared.still_streaming = False` before the final `yield formatted_outputs`, shifted the position of some yields.

.idea/workspace.xml

@@ -0,0 +1,64 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="edbf3935-4476-45aa-aea0-f1e7cbcf4b9a" name="Changes" comment="">
+      <change afterPath="$PROJECT_DIR$/extensions/llama_prompts/script.py" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/modules/callbacks.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/modules/RWKV.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/RWKV.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/modules/chat.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/chat.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/modules/shared.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/shared.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/modules/stopping_criteria.py" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/modules/text_generation.py" beforeDir="false" afterPath="$PROJECT_DIR$/modules/text_generation.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/requirements.txt" beforeDir="false" afterPath="$PROJECT_DIR$/requirements.txt" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/server.py" beforeDir="false" afterPath="$PROJECT_DIR$/server.py" afterDir="false" />
+    </list>
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectId" id="2MtdH03e5QdbSP16WYYfDkhyFUC" />
+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
+  <component name="ProjectViewState">
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent"><![CDATA[{
+  "keyToString": {
+    "ASKED_SHARE_PROJECT_CONFIGURATION_FILES": "true",
+    "RunOnceActivity.OpenProjectViewOnStart": "true",
+    "RunOnceActivity.ShowReadmeOnStart": "true"
+  }
+}]]></component>
+  <component name="RunManager">
+    <configuration default="true" type="JetRunConfigurationType">
+      <module name="text-generation-webui" />
+      <method v="2">
+        <option name="Make" enabled="true" />
+      </method>
+    </configuration>
+    <configuration default="true" type="KotlinStandaloneScriptRunConfigurationType">
+      <module name="text-generation-webui" />
+      <option name="filePath" />
+      <method v="2">
+        <option name="Make" enabled="true" />
+      </method>
+    </configuration>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="edbf3935-4476-45aa-aea0-f1e7cbcf4b9a" name="Changes" comment="" />
+      <created>1678590722207</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1678590722207</updated>
+    </task>
+    <servers />
+  </component>
+</project>

extensions/llama_prompts/script.py

@@ -0,0 +1,18 @@
+import gradio as gr
+import modules.shared as shared
+import pandas as pd
+
+df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
+
+def get_prompt_by_name(name):
+    if name == 'None':
+        return ''
+    else:
+        return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n')
+
+def ui():
+    if not shared.args.chat or share.args.cai_chat:
+        choices = ['None'] + list(df['Prompt name'])
+
+        prompts_menu = gr.Dropdown(value=choices[0], choices=choices, label='Prompt')
+        prompts_menu.change(get_prompt_by_name, prompts_menu, shared.gradio['textbox'])

modules/RWKV.py

@@ -7,6 +7,7 @@ import numpy as np
 from tokenizers import Tokenizer
 
 import modules.shared as shared
+from modules.callbacks import Iteratorize
 
 np.set_printoptions(precision=4, suppress=True, linewidth=200)
 
@@ -49,11 +50,11 @@ class RWKVModel:
         return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
 
     def generate_with_streaming(self, **kwargs):
-        iterable = Iteratorize(self.generate, kwargs, callback=None)
-        reply = kwargs['context']
-        for token in iterable:
-            reply += token
-            yield reply
+        with Iteratorize(self.generate, kwargs, callback=None) as generator:
+            reply = kwargs['context']
+            for token in generator:
+                reply += token
+                yield reply
 
 class RWKVTokenizer:
     def __init__(self):
@@ -73,38 +74,3 @@ class RWKVTokenizer:
 
     def decode(self, ids):
         return self.tokenizer.decode(ids)
-
-class Iteratorize:
-
-    """
-    Transforms a function that takes a callback
-    into a lazy iterator (generator).
-    """
-
-    def __init__(self, func, kwargs={}, callback=None):
-        self.mfunc=func
-        self.c_callback=callback
-        self.q = Queue(maxsize=1)
-        self.sentinel = object()
-        self.kwargs = kwargs
-
-        def _callback(val):
-            self.q.put(val)
-
-        def gentask():
-            ret = self.mfunc(callback=_callback, **self.kwargs)
-            self.q.put(self.sentinel)
-            if self.c_callback:
-                self.c_callback(ret)
-
-        Thread(target=gentask).start()
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        obj = self.q.get(True,None)
-        if obj is self.sentinel:
-            raise StopIteration
-        else:
-            return obj

modules/callbacks.py

@@ -0,0 +1,98 @@
+import gc
+from queue import Queue
+from threading import Thread
+
+import torch
+import transformers
+
+import modules.shared as shared
+
+# Copied from https://github.com/PygmalionAI/gradio-ui/
+class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
+
+    def __init__(self, sentinel_token_ids: torch.LongTensor,
+                 starting_idx: int):
+        transformers.StoppingCriteria.__init__(self)
+        self.sentinel_token_ids = sentinel_token_ids
+        self.starting_idx = starting_idx
+
+    def __call__(self, input_ids: torch.LongTensor,
+                 _scores: torch.FloatTensor) -> bool:
+        for sample in input_ids:
+            trimmed_sample = sample[self.starting_idx:]
+            # Can't unfold, output is still too tiny. Skip.
+            if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
+                continue
+
+            for window in trimmed_sample.unfold(
+                    0, self.sentinel_token_ids.shape[-1], 1):
+                if torch.all(torch.eq(self.sentinel_token_ids, window)):
+                    return True
+        return False
+
+class Stream(transformers.StoppingCriteria):
+    def __init__(self, callback_func=None):
+        self.callback_func = callback_func
+
+    def __call__(self, input_ids, scores) -> bool:
+        if self.callback_func is not None:
+            self.callback_func(input_ids[0])
+        return False
+
+class Iteratorize:
+
+    """
+    Transforms a function that takes a callback
+    into a lazy iterator (generator).
+    """
+
+    def __init__(self, func, kwargs={}, callback=None):
+        self.mfunc=func
+        self.c_callback=callback
+        self.q = Queue()
+        self.sentinel = object()
+        self.kwargs = kwargs
+        self.stop_now = False
+
+        def _callback(val):
+            if self.stop_now:
+                raise ValueError
+            self.q.put(val)
+
+        def gentask():
+            try:
+                ret = self.mfunc(callback=_callback, **self.kwargs)
+            except ValueError:
+                pass
+            clear_torch_cache()
+            self.q.put(self.sentinel)
+            if self.c_callback:
+                self.c_callback(ret)
+
+        self.thread = Thread(target=gentask)
+        self.thread.start()
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        obj = self.q.get(True,None)
+        if obj is self.sentinel:
+            raise StopIteration
+        else:
+            return obj
+
+    def __del__(self):
+        clear_torch_cache()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop_now = True
+        clear_torch_cache()
+
+def clear_torch_cache():
+    gc.collect()
+    if not shared.args.cpu:
+        torch.cuda.empty_cache()

modules/chat.py

@@ -84,6 +84,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
         tmp = f"\n{asker}:"
         for j in range(1, len(tmp)):
             if reply[-j:] == tmp[:j]:
+                reply = reply[:-j]
                 substring_found = True
 
     return reply, next_character_found, substring_found
@@ -91,7 +92,7 @@ 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, 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 chatbot_wrapper(text, max_new_tokens, 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, 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
@@ -120,6 +121,10 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
     else:
         prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
 
+    if not regenerate:
+        # Display user input and "*is typing...*" imediately
+        yield shared.history['visible']+[[visible_text, '*Is typing...*']]
+
     # Generate
     reply = ''
     for i in range(chat_generation_attempts):
@@ -158,6 +163,9 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
 
     prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
 
+    # Display "*is typing...*" imediately
+    yield '*Is typing...*'
+
     reply = ''
     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, 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}:"):
@@ -182,7 +190,7 @@ def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typi
         last_visible = shared.history['visible'].pop()
         last_internal = shared.history['internal'].pop()
 
-        for _history in chatbot_wrapper(last_internal[0], max_new_tokens, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
+        for _history in chatbot_wrapper(last_internal[0], max_new_tokens, 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, 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]]
                 yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
@@ -291,7 +299,7 @@ def save_history(timestamp=True):
         fname = f"{prefix}persistent.json"
     if not Path('logs').exists():
         Path('logs').mkdir()
-    with open(Path(f'logs/{fname}'), 'w') as f:
+    with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f:
         f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
     return Path(f'logs/{fname}')
 
@@ -332,7 +340,7 @@ def load_character(_character, name1, name2):
     shared.history['visible'] = []
     if _character != 'None':
         shared.character = _character
-        data = json.loads(open(Path(f'characters/{_character}.json'), 'r').read())
+        data = json.loads(open(Path(f'characters/{_character}.json'), 'r', encoding='utf-8').read())
         name2 = data['char_name']
         if 'char_persona' in data and data['char_persona'] != '':
             context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
@@ -372,7 +380,7 @@ def upload_character(json_file, img, tavern=False):
         i += 1
     if tavern:
         outfile_name = f'TavernAI-{outfile_name}'
-    with open(Path(f'characters/{outfile_name}.json'), 'w') as f:
+    with open(Path(f'characters/{outfile_name}.json'), 'w', encoding='utf-8') as f:
         f.write(json_file)
     if img is not None:
         img = Image.open(io.BytesIO(img))

modules/shared.py

@@ -91,4 +91,5 @@ parser.add_argument('--listen', action='store_true', help='Make the web UI reach
 parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
 parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
 parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
+parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch')
 args = parser.parse_args()

modules/stopping_criteria.py

@@ -1,32 +0,0 @@
-'''
-This code was copied from
-
-https://github.com/PygmalionAI/gradio-ui/
-
-'''
-
-import torch
-import transformers
-
-
-class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
-
-    def __init__(self, sentinel_token_ids: torch.LongTensor,
-                 starting_idx: int):
-        transformers.StoppingCriteria.__init__(self)
-        self.sentinel_token_ids = sentinel_token_ids
-        self.starting_idx = starting_idx
-
-    def __call__(self, input_ids: torch.LongTensor,
-                 _scores: torch.FloatTensor) -> bool:
-        for sample in input_ids:
-            trimmed_sample = sample[self.starting_idx:]
-            # Can't unfold, output is still too tiny. Skip.
-            if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
-                continue
-
-            for window in trimmed_sample.unfold(
-                    0, self.sentinel_token_ids.shape[-1], 1):
-                if torch.all(torch.eq(self.sentinel_token_ids, window)):
-                    return True
-        return False

modules/text_generation.py

@@ -5,13 +5,13 @@ import time
 import numpy as np
 import torch
 import transformers
-from tqdm import tqdm
 
 import modules.shared as shared
+from modules.callbacks import (Iteratorize, Stream,
+                               _SentinelTokenStoppingCriteria)
 from modules.extensions import apply_extensions
 from modules.html_generator import generate_4chan_html, generate_basic_html
 from modules.models import local_rank
-from modules.stopping_criteria import _SentinelTokenStoppingCriteria
 
 
 def get_max_prompt_length(tokens):
@@ -92,19 +92,22 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
     # These models are not part of Hugging Face, so we handle them
     # separately and terminate the function call earlier
     if shared.is_RWKV:
-        if shared.args.no_stream:
-            reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
-            yield formatted_outputs(reply, shared.model_name)
-        else:
-            yield formatted_outputs(question, shared.model_name)
-            # RWKV has proper streaming, which is very nice.
-            # No need to generate 8 tokens at a time.
-            for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
+        try:
+            if shared.args.no_stream:
+                reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
                 yield formatted_outputs(reply, shared.model_name)
-
-        t1 = time.time()
-        print(f"Output generated in {(t1-t0):.2f} seconds.")
-        return
+            else:
+                yield formatted_outputs(question, shared.model_name)
+                # RWKV has proper streaming, which is very nice.
+                # No need to generate 8 tokens at a time.
+                for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
+                    yield formatted_outputs(reply, shared.model_name)
+        finally:
+            t1 = time.time()
+            output = encode(reply)[0]
+            input_ids = encode(question)
+            print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
+            return
 
     original_question = question
     if not (shared.args.chat or shared.args.cai_chat):
@@ -113,23 +116,19 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
         print(f"\n\n{question}\n--------------------\n")
 
     input_ids = encode(question, max_new_tokens)
+    original_input_ids = input_ids
+    output = input_ids[0]
     cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
     n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
+    stopping_criteria_list = transformers.StoppingCriteriaList()
     if stopping_string is not None:
-        # The stopping_criteria code below was copied from
-        # https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
+        # Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
         t = encode(stopping_string, 0, add_special_tokens=False)
-        stopping_criteria_list = transformers.StoppingCriteriaList([
-            _SentinelTokenStoppingCriteria(
-                sentinel_token_ids=t,
-                starting_idx=len(input_ids[0])
-            )
-        ])
-    else:
-        stopping_criteria_list = None
+        stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
 
     if not shared.args.flexgen:
         generate_params = [
+            f"max_new_tokens=max_new_tokens",
             f"eos_token_id={n}",
             f"stopping_criteria=stopping_criteria_list",
             f"do_sample={do_sample}",
@@ -147,45 +146,23 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
         ]
     else:
         generate_params = [
+            f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
             f"do_sample={do_sample}",
             f"temperature={temperature}",
             f"stop={n}",
         ]
     if shared.args.deepspeed:
         generate_params.append("synced_gpus=True")
-    if shared.args.no_stream:
-        generate_params.append("max_new_tokens=max_new_tokens")
-    else:
-        generate_params.append("max_new_tokens=8")
     if shared.soft_prompt:
         inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
         generate_params.insert(0, "inputs_embeds=inputs_embeds")
-        generate_params.insert(0, "filler_input_ids")
+        generate_params.insert(0, "inputs=filler_input_ids")
     else:
-        generate_params.insert(0, "input_ids")
-
-    # Generate the entire reply at once
-    if shared.args.no_stream:
-        with torch.no_grad():
-            output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
-        if shared.soft_prompt:
-            output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
-
-        reply = decode(output)
-        if not (shared.args.chat or shared.args.cai_chat):
-            reply = original_question + apply_extensions(reply[len(question):], "output")
-
-        t1 = time.time()
-        print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0)/8:.2f} it/s, {len(output)-len(input_ids[0])} tokens)")
-        yield formatted_outputs(reply, shared.model_name)
-
-    # Generate the reply 8 tokens at a time
-    else:
-        yield formatted_outputs(original_question, shared.model_name)
-        shared.still_streaming = True
-        for i in tqdm(range(max_new_tokens//8+1)):
-            clear_torch_cache()
+        generate_params.insert(0, "inputs=input_ids")
 
+    try:
+        # Generate the entire reply at once.
+        if shared.args.no_stream:
             with torch.no_grad():
                 output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
             if shared.soft_prompt:
@@ -194,22 +171,66 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             reply = decode(output)
             if not (shared.args.chat or shared.args.cai_chat):
                 reply = original_question + apply_extensions(reply[len(question):], "output")
-            
-            if not shared.args.flexgen:
-                if output[-1] == n:
-                    break
-                input_ids = torch.reshape(output, (1, output.shape[0]))
-            else:
-                if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
-                    break
-                input_ids = np.reshape(output, (1, output.shape[0]))
-                
-            #Mid-stream yield, ran if no breaks
+
             yield formatted_outputs(reply, shared.model_name)
 
-            if shared.soft_prompt:
-                inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-                
-        #Stream finished from max tokens or break. Do final yield.
-        shared.still_streaming = False
-        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:
+
+            def generate_with_callback(callback=None, **kwargs):
+                kwargs['stopping_criteria'].append(Stream(callback_func=callback))
+                clear_torch_cache()
+                with torch.no_grad():
+                    shared.model.generate(**kwargs)
+
+            def generate_with_streaming(**kwargs):
+                return Iteratorize(generate_with_callback, kwargs, callback=None)
+
+            shared.still_streaming = True
+            yield formatted_outputs(original_question, shared.model_name)
+            with eval(f"generate_with_streaming({', '.join(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 = decode(output)
+
+                    if not (shared.args.chat or shared.args.cai_chat):
+                        reply = original_question + apply_extensions(reply[len(question):], "output")
+
+                    if output[-1] == n:
+                        break
+                    yield formatted_outputs(reply, shared.model_name)
+
+                shared.still_streaming = False
+                yield formatted_outputs(reply, shared.model_name)
+
+        # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
+        else:
+            shared.still_streaming = True
+            for i in range(max_new_tokens//8+1):
+                clear_torch_cache()
+                with torch.no_grad():
+                    output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
+                if shared.soft_prompt:
+                    output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
+                reply = decode(output)
+
+                if not (shared.args.chat or shared.args.cai_chat):
+                    reply = original_question + apply_extensions(reply[len(question):], "output")
+
+                if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
+                    break
+                yield formatted_outputs(reply, shared.model_name)
+
+                input_ids = np.reshape(output, (1, output.shape[0]))
+                if shared.soft_prompt:
+                    inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
+
+            shared.still_streaming = False
+            yield formatted_outputs(reply, shared.model_name)
+
+    finally:
+        t1 = time.time()
+        print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)")
+        return

requirements.txt

@@ -3,6 +3,7 @@ bitsandbytes==0.37.0
 flexgen==0.1.7
 gradio==3.18.0
 numpy
+requests
 rwkv==0.1.0
 safetensors==0.2.8
 sentencepiece

server.py

@@ -18,9 +18,6 @@ from modules.html_generator import generate_chat_html
 from modules.models import load_model, load_soft_prompt
 from modules.text_generation import generate_reply
 
-if (shared.args.chat or shared.args.cai_chat) and not shared.args.no_stream:
-    print('Warning: chat mode currently becomes somewhat slower with text streaming on.\nConsider starting the web UI with the --no-stream option.\n')
-    
 # Loading custom settings
 settings_file = None
 if shared.args.settings is not None and Path(shared.args.settings).exists():
@@ -272,10 +269,10 @@ if shared.args.chat or shared.args.cai_chat:
 
         function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
 
-        gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen'))
-        gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
-        gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
-        gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
+        gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=False, api_name='textgen'))
+        gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=False))
+        gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=False))
+        gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False))
         shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events)
 
         shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
@@ -309,6 +306,7 @@ if shared.args.chat or shared.args.cai_chat:
         reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else []
         shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']])
         shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']])
+        shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']])
 
         shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None)
         shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True)
@@ -372,9 +370,9 @@ else:
 
 shared.gradio['interface'].queue()
 if shared.args.listen:
-    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port)
+    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
 else:
-    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port)
+    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
 
 # I think that I will need this later
 while True: