Merge branch 'main' of https://github.com/xanthousm/text-generation-webui

.gitignore

@@ -1,6 +1,7 @@
 cache/*
 characters/*
 extensions/silero_tts/outputs/*
+extensions/elevenlabs_tts/outputs/*
 logs/*
 models/*
 softprompts/*

README.md

@@ -21,12 +21,13 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
 * Advanced chat features (send images, get audio responses with TTS).
 * Stream the text output in real time.
 * Load parameter presets from text files.
-* Load large models in 8-bit mode (see [here](https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652) and [here](https://www.reddit.com/r/PygmalionAI/comments/1115gom/running_pygmalion_6b_with_8gb_of_vram/) if you are on Windows).
+* Load large models in 8-bit mode (see [here](https://github.com/oobabooga/text-generation-webui/issues/147#issuecomment-1456040134), [here](https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652) and [here](https://www.reddit.com/r/PygmalionAI/comments/1115gom/running_pygmalion_6b_with_8gb_of_vram/) if you are on Windows).
 * Split large models across your GPU(s), CPU, and disk.
 * CPU mode.
 * [FlexGen offload](https://github.com/oobabooga/text-generation-webui/wiki/FlexGen).
 * [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed).
-* [Get responses via API](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py).
+* Get responses via API, [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-streaming.py) or [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming.
+* [Supports the RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model).
 * Supports softprompts.
 * [Supports extensions](https://github.com/oobabooga/text-generation-webui/wiki/Extensions).
 * [Works on Google Colab](https://github.com/oobabooga/text-generation-webui/wiki/Running-on-Colab).
@@ -82,8 +83,8 @@ Models should be placed under `models/model-name`. For instance, `models/gpt-j-6
 * [Pythia](https://huggingface.co/models?search=eleutherai/pythia)
 * [OPT](https://huggingface.co/models?search=facebook/opt)
 * [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
-* [\*-Erebus](https://huggingface.co/models?search=erebus)
-* [Pygmalion](https://huggingface.co/models?search=pygmalion)
+* [\*-Erebus](https://huggingface.co/models?search=erebus) (NSFW)
+* [Pygmalion](https://huggingface.co/models?search=pygmalion) (NSFW)
 
 You can automatically download a model from HF using the script `download-model.py`:
 
@@ -149,9 +150,10 @@ Optionally, you can use the following command-line flags:
 | `--deepspeed`    | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
 | `--nvme-offload-dir NVME_OFFLOAD_DIR`    | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
 | `--local_rank LOCAL_RANK`    | DeepSpeed: Optional argument for distributed setups. |
-| `--rwkv-strategy RWKV_STRATEGY`         |    The strategy to use while loading RWKV models. Examples: `"cpu fp32"`, `"cuda fp16"`, `"cuda fp16 *30 -> cpu fp32"`. |
+|  `--rwkv-strategy RWKV_STRATEGY`         |    RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
+|  `--rwkv-cuda-on`                        |   RWKV: Compile the CUDA kernel for better performance. |
 | `--no-stream`   | Don't stream the text output in real time. This improves the text generation performance.|
-| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example.|
+| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag.|
 |  `--extensions EXTENSIONS [EXTENSIONS ...]` |  The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
 | `--listen`   | Make the web UI reachable from your local network.|
 |  `--listen-port LISTEN_PORT` | The listening port that the server will use. |

api-example-stream.py

@@ -0,0 +1,90 @@
+'''
+
+Contributed by SagsMug. Thank you SagsMug.
+https://github.com/oobabooga/text-generation-webui/pull/175
+
+'''
+
+import asyncio
+import json
+import random
+import string
+
+import websockets
+
+
+def random_hash():
+    letters = string.ascii_lowercase + string.digits
+    return ''.join(random.choice(letters) for i in range(9))
+
+async def run(context):
+    server = "127.0.0.1"
+    params = {
+        'max_new_tokens': 200,
+        'do_sample': True,
+        'temperature': 0.5,
+        'top_p': 0.9,
+        'typical_p': 1,
+        'repetition_penalty': 1.05,
+        'top_k': 0,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+    }
+    session = random_hash()
+
+    async with websockets.connect(f"ws://{server}:7860/queue/join") as websocket:
+        while content := json.loads(await websocket.recv()):
+            #Python3.10 syntax, replace with if elif on older
+            match content["msg"]:
+                case "send_hash":
+                    await websocket.send(json.dumps({
+                        "session_hash": session,
+                        "fn_index": 7
+                    }))
+                case "estimation":
+                    pass
+                case "send_data":
+                    await websocket.send(json.dumps({
+                        "session_hash": session,
+                        "fn_index": 7,
+                        "data": [
+                            context,
+                            params['max_new_tokens'],
+                            params['do_sample'],
+                            params['temperature'],
+                            params['top_p'],
+                            params['typical_p'],
+                            params['repetition_penalty'],
+                            params['top_k'],
+                            params['min_length'],
+                            params['no_repeat_ngram_size'],
+                            params['num_beams'],
+                            params['penalty_alpha'],
+                            params['length_penalty'],
+                            params['early_stopping'],
+                        ]
+                    }))
+                case "process_starts":
+                    pass
+                case "process_generating" | "process_completed":
+                    yield content["output"]["data"][0]
+                    # You can search for your desired end indicator and 
+                    #  stop generation by closing the websocket here
+                    if (content["msg"] == "process_completed"):
+                        break
+
+prompt = "What I would like to say is the following: "
+
+async def get_result():
+    async for response in run(prompt):
+        # Print intermediate steps
+        print(response)
+
+    # Print final result
+    print(response)
+
+asyncio.run(get_result())

extensions/elevenlabs_tts/requirements.txt

@@ -0,0 +1,3 @@
+elevenlabslib
+soundfile
+sounddevice

extensions/elevenlabs_tts/script.py

@@ -0,0 +1,113 @@
+from pathlib import Path
+
+import gradio as gr
+from elevenlabslib import *
+from elevenlabslib.helpers import *
+
+params = {
+    'activate': True,
+    'api_key': '12345',
+    'selected_voice': 'None',
+}
+
+initial_voice = ['None']
+wav_idx = 0
+user = ElevenLabsUser(params['api_key'])
+user_info = None
+
+
+# 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'])
+    user_info = user._get_subscription_data()
+    print('checking api')
+    if params['activate'] == False:
+        return gr.update(value='Disconnected')
+    elif user_info is None:
+        print('Incorrect API Key')
+        return gr.update(value='Disconnected')
+    else:
+        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
+    
+    your_voices = [None]
+    if user_info is not None:
+        for voice in user.get_available_voices():
+            your_voices.append(voice.initialName)
+        return  gr.Dropdown.update(choices=your_voices)
+    else:
+        return
+
+def remove_surrounded_chars(string):
+    new_string = ""
+    in_star = False
+    for char in string:
+        if char == '*':
+            in_star = not in_star
+        elif not in_star:
+            new_string += char
+    return new_string
+
+def input_modifier(string):
+    """
+    This function is applied to your text inputs before
+    they are fed into the model.
+    """
+
+    return string
+
+def output_modifier(string):
+    """
+    This function is applied to the model outputs.
+    """
+
+    global params, wav_idx, user, user_info
+    
+    if params['activate'] == False:
+        return string
+    elif user_info == None:
+        return string
+
+    string = remove_surrounded_chars(string)
+    string = string.replace('"', '')
+    string = string.replace('“', '')
+    string = string.replace('\n', ' ')
+    string = string.strip()
+
+    if string == '':
+        string = 'empty reply, try regenerating'
+        
+    output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'.format(wav_idx))
+    voice = user.get_voices_by_name(params['selected_voice'])[0]
+    audio_data = voice.generate_audio_bytes(string)
+    save_bytes_to_path(Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'), audio_data)
+
+    string = f'<audio src="file/{output_file.as_posix()}" controls></audio>'
+    wav_idx += 1
+    return string
+
+def ui():
+
+    # Gradio elements
+    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')
+        connect = gr.Button(value='Connect')
+
+    # Event functions to update the parameters in the backend
+    activate.change(lambda x: params.update({'activate': x}), activate, None)
+    voice.change(lambda x: params.update({'selected_voice': x}), voice, None)
+    api_key.change(lambda x: params.update({'api_key': x}), api_key, None)
+    connect.click(check_valid_api, [], connection_status)
+    connect.click(refresh_voices, [], voice)

extensions/silero_tts/script.py

@@ -1,4 +1,3 @@
-import asyncio
 from pathlib import Path
 
 import gradio as gr
@@ -94,7 +93,7 @@ def output_modifier(string):
     string ='<speak>'+prosody+xmlesc(string)+'</prosody></speak>'
         
     output_file = Path(f'extensions/silero_tts/outputs/{wav_idx:06d}.wav')
-    audio = model.save_wav(ssml_text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
+    model.save_wav(text=string, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
     string = f'<audio src="file/{output_file.as_posix()}" controls></audio>'
     
     #reset if too many wavs. set max to -1 for unlimited.

modules/LLaMA.py

@@ -1,96 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# This software may be used and distributed according to the terms of the GNU General Public License version 3.
-
-import json
-import os
-import sys
-import time
-from pathlib import Path
-from typing import Tuple
-
-import fire
-import torch
-from fairscale.nn.model_parallel.initialize import initialize_model_parallel
-from llama import LLaMA, ModelArgs, Tokenizer, Transformer
-
-os.environ['RANK'] = '0'
-os.environ['WORLD_SIZE'] = '1'
-os.environ['MP'] = '1'
-os.environ['MASTER_ADDR'] = '127.0.0.1'
-os.environ['MASTER_PORT'] = '2223'
-
-def setup_model_parallel() -> Tuple[int, int]:
-    local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    world_size = int(os.environ.get("WORLD_SIZE", -1))
-
-    torch.distributed.init_process_group("gloo")
-    initialize_model_parallel(world_size)
-    torch.cuda.set_device(local_rank)
-
-    # seed must be the same in all processes
-    torch.manual_seed(1)
-    return local_rank, world_size
-
-def load(
-    ckpt_dir: str,
-    tokenizer_path: str,
-    local_rank: int,
-    world_size: int,
-    max_seq_len: int,
-    max_batch_size: int,
-) -> LLaMA:
-    start_time = time.time()
-    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
-    assert world_size == len(
-        checkpoints
-    ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
-    ckpt_path = checkpoints[local_rank]
-    print("Loading")
-    checkpoint = torch.load(ckpt_path, map_location="cpu")
-    with open(Path(ckpt_dir) / "params.json", "r") as f:
-        params = json.loads(f.read())
-
-    model_args: ModelArgs = ModelArgs(
-        max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
-    )
-    tokenizer = Tokenizer(model_path=tokenizer_path)
-    model_args.vocab_size = tokenizer.n_words
-    torch.set_default_tensor_type(torch.cuda.HalfTensor)
-    model = Transformer(model_args)
-    torch.set_default_tensor_type(torch.FloatTensor)
-    model.load_state_dict(checkpoint, strict=False)
-
-    generator = LLaMA(model, tokenizer)
-    print(f"Loaded in {time.time() - start_time:.2f} seconds")
-    return generator
-
-
-class LLaMAModel:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path, max_seq_len=2048, max_batch_size=1):
-        tokenizer_path = path / "tokenizer.model"
-        path = os.path.abspath(path)
-        tokenizer_path = os.path.abspath(tokenizer_path)
-        
-        local_rank, world_size = setup_model_parallel()
-        if local_rank > 0:
-            sys.stdout = open(os.devnull, "w")
-
-        generator = load(
-            path, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
-        )
-
-        result = self()
-        result.pipeline = generator
-        return result
-
-    def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
-
-        results = self.pipeline.generate(
-            [prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
-        )
-
-        return results[0]

modules/RWKV.py

@@ -1,14 +1,17 @@
 import os
 from pathlib import Path
+from queue import Queue
+from threading import Thread
 
 import numpy as np
+from tokenizers import Tokenizer
 
 import modules.shared as shared
 
 np.set_printoptions(precision=4, suppress=True, linewidth=200)
 
 os.environ['RWKV_JIT_ON'] = '1'
-os.environ["RWKV_CUDA_ON"] = '0' #  '1' : use CUDA kernel for seq mode (much faster)
+os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
 
 from rwkv.model import RWKV
 from rwkv.utils import PIPELINE, PIPELINE_ARGS
@@ -32,10 +35,11 @@ class RWKVModel:
         result.pipeline = pipeline
         return result
 
-    def generate(self, context, token_count=20, temperature=1, top_p=1, alpha_frequency=0.25, alpha_presence=0.25, token_ban=[0], token_stop=[], callback=None):
+    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
         args = PIPELINE_ARGS(
             temperature = temperature,
             top_p = top_p,
+            top_k = top_k,
             alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3)
             alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3)
             token_ban = token_ban, # ban the generation of some tokens
@@ -43,3 +47,64 @@ 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
+
+class RWKVTokenizer:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def from_pretrained(self, path):
+        tokenizer_path = path / "20B_tokenizer.json"
+        tokenizer = Tokenizer.from_file(os.path.abspath(tokenizer_path))
+
+        result = self()
+        result.tokenizer = tokenizer
+        return result
+
+    def encode(self, prompt):
+        return self.tokenizer.encode(prompt).ids
+
+    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/chat.py

@@ -51,23 +51,29 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
     prompt = ''.join(rows)
     return prompt
 
-def extract_message_from_reply(question, reply, current, other, check, extensions=False):
+def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
     next_character_found = False
     substring_found = False
 
-    previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", question)]
-    idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(current)}:", reply)]
-    idx = idx[len(previous_idx)-1]
+    asker = name1 if not impersonate else name2
+    replier = name2 if not impersonate else name1
 
-    if extensions:
-        reply = reply[idx + 1 + len(apply_extensions(f"{current}:", "bot_prefix")):]
+    previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
+    idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
+    idx = idx[max(len(previous_idx)-1, 0)]
+
+    if not impersonate:
+        reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
     else:
-        reply = reply[idx + 1 + len(f"{current}:"):]
+        reply = reply[idx + 1 + len(f"{replier}:"):]
 
     if check:
-        reply = reply.split('\n')[0].strip()
+        lines = reply.split('\n')
+        reply = lines[0].strip()
+        if len(lines) > 1:
+            next_character_found = True
     else:
-        idx = reply.find(f"\n{other}:")
+        idx = reply.find(f"\n{asker}:")
         if idx != -1:
             reply = reply[:idx]
             next_character_found = True
@@ -75,7 +81,7 @@ def extract_message_from_reply(question, reply, current, other, check, extension
 
         # Detect if something like "\nYo" is generated just before
         # "\nYou:" is completed
-        tmp = f"\n{other}:"
+        tmp = f"\n{asker}:"
         for j in range(1, len(tmp)):
             if reply[-j:] == tmp[:j]:
                 substring_found = True
@@ -89,6 +95,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
     shared.stop_everything = False
     just_started = True
     eos_token = '\n' if check else None
+    name1_original = name1
     if 'pygmalion' in shared.model_name.lower():
         name1 = "You"
 
@@ -119,8 +126,9 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
         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, 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}:"):
 
             # Extracting the reply
-            reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name2, name1, check, extensions=True)
-            visible_reply = apply_extensions(reply, "output")
+            reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check)
+            visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
+            visible_reply = apply_extensions(visible_reply, "output")
             if shared.args.chat:
                 visible_reply = visible_reply.replace('\n', '<br>')
 
@@ -139,6 +147,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
                 yield shared.history['visible']
             if next_character_found:
                 break
+
     yield shared.history['visible']
 
 def impersonate_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):
@@ -152,7 +161,7 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
     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}:"):
-            reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, extensions=False)
+            reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
             if not substring_found:
                 yield reply
             if next_character_found:

modules/models.py

@@ -39,10 +39,9 @@ def load_model(model_name):
     t0 = time.time()
 
     shared.is_RWKV = model_name.lower().startswith('rwkv-')
-    shared.is_LLaMA = model_name.lower().startswith('llama-')
 
     # Default settings
-    if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV or shared.is_LLaMA):
+    if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV):
         if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
         else:
@@ -80,20 +79,12 @@ def load_model(model_name):
 
     # RMKV model (not on HuggingFace)
     elif shared.is_RWKV:
-        from modules.RWKV import RWKVModel
+        from modules.RWKV import RWKVModel, RWKVTokenizer
 
         model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
+        tokenizer = RWKVTokenizer.from_pretrained(Path('models'))
 
-        return model, None
-
-    # LLaMA model (not on HuggingFace)
-    elif shared.is_LLaMA:
-        import modules.LLaMA
-        from modules.LLaMA import LLaMAModel
-
-        model = LLaMAModel.from_pretrained(Path(f'models/{model_name}'))
-
-        return model, None
+        return model, tokenizer
 
     # Custom
     else:

modules/shared.py

@@ -6,7 +6,6 @@ model_name = ""
 soft_prompt_tensor = None
 soft_prompt = False
 is_RWKV = False
-is_LLaMA = False
 
 # Chat variables
 history = {'internal': [], 'visible': []}
@@ -44,7 +43,6 @@ settings = {
         'default': 'NovelAI-Sphinx Moth',
         'pygmalion-*': 'Pygmalion',
         'RWKV-*': 'Naive',
-        'llama-*': 'Naive',
         '(rosey|chip|joi)_.*_instruct.*': 'Instruct Joi (Contrastive Search)'
     },
     'prompts': {
@@ -84,9 +82,10 @@ parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, defaul
 parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
 parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
 parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
-parser.add_argument('--rwkv-strategy', type=str, default=None, help='The strategy to use while loading RWKV models. Examples: "cpu fp32", "cuda fp16", "cuda fp16 *30 -> cpu fp32".')
+parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
+parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
 parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time. This improves the text generation performance.')
-parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example.')
+parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.')
 parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
 parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
 parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')

modules/text_generation.py

@@ -21,21 +21,20 @@ def get_max_prompt_length(tokens):
     return max_length
 
 def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
-
-    # These models do not have explicit tokenizers for now, so
-    # we return an estimate for the number of tokens
-    if shared.is_RWKV or shared.is_LLaMA:
-        return np.zeros((1, len(prompt)//4))
-
-    input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
-    if shared.args.cpu:
+    if shared.is_RWKV:
+        input_ids = shared.tokenizer.encode(str(prompt))
+        input_ids = np.array(input_ids).reshape(1, len(input_ids))
         return input_ids
-    elif shared.args.flexgen:
-        return input_ids.numpy()
-    elif shared.args.deepspeed:
-        return input_ids.to(device=local_rank)
     else:
-        return input_ids.cuda()
+        input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
+        if shared.args.cpu:
+            return input_ids
+        elif shared.args.flexgen:
+            return input_ids.numpy()
+        elif shared.args.deepspeed:
+            return input_ids.to(device=local_rank)
+        else:
+            return input_ids.cuda()
 
 def decode(output_ids):
     reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
@@ -81,26 +80,30 @@ def formatted_outputs(reply, model_name):
     else:
         return reply
 
-def generate_reply(question, 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=None, stopping_string=None):
+def clear_torch_cache():
     gc.collect()
     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, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
+    clear_torch_cache()
     t0 = time.time()
 
     # These models are not part of Hugging Face, so we handle them
     # separately and terminate the function call earlier
-    if shared.is_RWKV or shared.is_LLaMA:
+    if shared.is_RWKV:
         if shared.args.no_stream:
-            reply = shared.model.generate(question, token_count=max_new_tokens, temperature=temperature, top_p=top_p)
-            t1 = time.time()
-            print(f"Output generated in {(t1-t0):.2f} seconds.")
+            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:
-            for i in tqdm(range(max_new_tokens//8+1)):
-                reply = shared.model.generate(question, token_count=8, temperature=temperature, top_p=top_p)
+            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)
-                question = reply
+
+        t1 = time.time()
+        print(f"Output generated in {(t1-t0):.2f} seconds.")
         return
 
     original_question = question
@@ -111,8 +114,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
 
     input_ids = encode(question, max_new_tokens)
     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 encode(eos_token)[0][-1]
-
+    n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
     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
@@ -149,14 +151,12 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             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")
@@ -184,6 +184,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
         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()
+
             with torch.no_grad():
                 output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
             if shared.soft_prompt:

presets/Naive.txt

@@ -1,3 +1,4 @@
 do_sample=True
-top_p=0.95
-temperature=0.8
+temperature=0.7
+top_p=0.85
+top_k=50

requirements.txt

@@ -3,7 +3,8 @@ bitsandbytes==0.37.0
 flexgen==0.1.7
 gradio==3.18.0
 numpy
-rwkv==0.0.6
+rwkv==0.1.0
 safetensors==0.2.8
-git+https://github.com/huggingface/transformers
 tensorboard
+sentencepiece
+git+https://github.com/oobabooga/transformers@llama_push

server.py

@@ -22,8 +22,14 @@ 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():
-    new_settings = json.loads(open(Path(shared.args.settings), 'r').read())
+    settings_file = Path(shared.args.settings)
+elif Path('settings.json').exists():
+    settings_file = Path('settings.json')
+if settings_file is not None:
+    print(f"Loading settings from {settings_file}...")
+    new_settings = json.loads(open(settings_file, 'r').read())
     for item in new_settings:
         shared.settings[item] = new_settings[item]