release v1.1.0

* Add RWKV-4 World 0.1B

* Update manifest.json

---------

Co-authored-by: josc146 <josStorer@outlook.com>

.gitignore

@@ -11,6 +11,7 @@ __pycache__
 /frontend/stats.html
 /frontend/package.json.md5
 /backend-python/get-pip.py
+/backend-python/.get-pip.py
 /py310
 *.zip
 /cmd-helper.bat

README.md

@@ -15,7 +15,7 @@ compatible with the OpenAI API, which means that every ChatGPT client is an RWKV
 
 English | [简体中文](README_ZH.md)
 
-[Preview](#Preview) | [Download][download-url]
+[FAQs](https://github.com/josStorer/RWKV-Runner/wiki/FAQs) | [Preview](#Preview) | [Download][download-url]
 
 [license-image]: http://img.shields.io/badge/license-MIT-blue.svg
 
@@ -47,6 +47,25 @@ English | [简体中文](README_ZH.md)
 - Theme switching
 - Automatic updates
 
+## API Concurrency Stress Testing
+
+```bash
+ab -p body.json -T application/json -c 20 -n 100 -l http://127.0.0.1:8000/chat/completions
+```
+
+body.json:
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "Hello"
+    }
+  ]
+}
+```
+
 ## Todo
 
 - [ ] Model training functionality

README_ZH.md

@@ -14,7 +14,7 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 
 [English](README.md) | 简体中文
 
-[视频演示](https://www.bilibili.com/video/BV1hM4y1v76R) | [预览](#Preview) | [下载][download-url]
+[视频演示](https://www.bilibili.com/video/BV1hM4y1v76R) | [疑难解答](https://www.bilibili.com/read/cv23921171) | [预览](#Preview) | [下载][download-url]
 
 [license-image]: http://img.shields.io/badge/license-MIT-blue.svg
 
@@ -47,6 +47,25 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 - 主题切换
 - 自动更新
 
+## API并发压力测试
+
+```bash
+ab -p body.json -T application/json -c 20 -n 100 -l http://127.0.0.1:8000/chat/completions
+```
+
+body.json:
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "Hello"
+    }
+  ]
+}
+```
+
 ## Todo
 
 - [ ] 模型训练功能

backend-golang/rwkv.go

@@ -3,6 +3,7 @@ package backend_golang
 import (
 	"errors"
 	"os/exec"
+	"runtime"
 	"strconv"
 )
 
@@ -39,6 +40,9 @@ func (a *App) InstallPyDep(cnMirror bool) (string, error) {
 	if err != nil {
 		return "", err
 	}
+	if runtime.GOOS == "windows" {
+		ChangeFileLine("./py310/python310._pth", 3, "Lib\\site-packages")
+	}
 	if cnMirror {
 		_, err = Cmd(python, "./backend-python/get-pip.py", "-i", "https://pypi.tuna.tsinghua.edu.cn/simple")
 	} else {
@@ -47,7 +51,6 @@ func (a *App) InstallPyDep(cnMirror bool) (string, error) {
 	if err != nil {
 		return "", err
 	}
-	ChangeFileLine("./py310/python310._pth", 3, "Lib\\site-packages")
 	_, err = Cmd(python, "-m", "pip", "install", "torch==1.13.1", "torchvision==0.14.1", "torchaudio==0.13.1", "--index-url", "https://download.pytorch.org/whl/cu117")
 	if err != nil {
 		return "", err

backend-golang/utils.go

@@ -3,8 +3,10 @@ package backend_golang
 import (
 	"archive/zip"
 	"bufio"
+	"embed"
 	"errors"
 	"io"
+	"io/fs"
 	"os"
 	"os/exec"
 	"path/filepath"
@@ -13,22 +15,60 @@ import (
 )
 
 func Cmd(args ...string) (string, error) {
-	_, err := os.Stat("cmd-helper.bat")
-	if err != nil {
-		if err := os.WriteFile("./cmd-helper.bat", []byte("start %*"), 0644); err != nil {
+	if runtime.GOOS == "windows" {
+		_, err := os.Stat("cmd-helper.bat")
+		if err != nil {
+			if err := os.WriteFile("./cmd-helper.bat", []byte("start %*"), 0644); err != nil {
+				return "", err
+			}
+		}
+		cmdHelper, err := filepath.Abs("./cmd-helper")
+		if err != nil {
 			return "", err
 		}
+		cmd := exec.Command(cmdHelper, args...)
+		out, err := cmd.CombinedOutput()
+		if err != nil {
+			return "", err
+		}
+		return string(out), nil
+	} else {
+		cmd := exec.Command(args[0], args[1:]...)
+		err := cmd.Start()
+		if err != nil {
+			return "", err
+		}
+		cmd.Wait()
+		return "", nil
 	}
-	cmdHelper, err := filepath.Abs("./cmd-helper")
-	if err != nil {
-		return "", err
-	}
-	cmd := exec.Command(cmdHelper, args...)
-	out, err := cmd.CombinedOutput()
-	if err != nil {
-		return "", err
-	}
-	return string(out), nil
+}
+
+func CopyEmbed(efs embed.FS) error {
+	err := fs.WalkDir(efs, ".", func(path string, d fs.DirEntry, err error) error {
+		if d.IsDir() {
+			return nil
+		}
+		if err != nil {
+			return err
+		}
+		content, err := efs.ReadFile(path)
+		if err != nil {
+			return err
+		}
+
+		err = os.MkdirAll(path[:strings.LastIndex(path, "/")], 0755)
+		if err != nil {
+			return err
+		}
+
+		err = os.WriteFile(path, content, 0644)
+		if err != nil {
+			return err
+		}
+
+		return nil
+	})
+	return err
 }
 
 func GetPython() (string, error) {

backend-python/dep_check.py

@@ -1,3 +1,4 @@
+import cyac
 import GPUtil
 import torch
 import rwkv

backend-python/main.py

@@ -11,7 +11,7 @@ import uvicorn
 from utils.rwkv import *
 from utils.torch import *
 from utils.ngrok import *
-from routes import completion, config
+from routes import completion, config, state_cache
 import global_var
 
 app = FastAPI()
@@ -26,11 +26,13 @@ app.add_middleware(
 
 app.include_router(completion.router)
 app.include_router(config.router)
+app.include_router(state_cache.router)
 
 
 @app.on_event("startup")
 def init():
     global_var.init()
+    state_cache.init()
 
     set_torch()
 

backend-python/routes/completion.py

@@ -11,10 +11,6 @@ import global_var
 
 router = APIRouter()
 
-interface = ":"
-user = "Bob"
-bot = "Alice"
-
 
 class Message(BaseModel):
     role: str
@@ -44,17 +40,27 @@ async def chat_completions(body: ChatCompletionBody, request: Request):
     else:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "no question found")
 
-    completion_text = f"""
+    interface = model.interface
+    user = model.user
+    bot = model.bot
+
+    completion_text = (
+        f"""
 The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
 {bot} is very intelligent, creative and friendly. \
 {bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
 {bot} likes to tell {user} a lot about herself and her opinions. \
 {bot} usually gives {user} kind, helpful and informative advices.\n
 """
+        if user == "Bob"
+        else ""
+    )
     for message in body.messages:
         if message.role == "system":
             completion_text = (
                 f"The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. "
+                if user == "Bob"
+                else ""
                 + message.content.replace("\\n", "\n")
                 .replace("\r\n", "\n")
                 .replace("\n\n", "\n")
@@ -93,14 +99,15 @@ The following is a coherent verbose detailed conversation between a girl named {
 
     async def eval_rwkv():
         while completion_lock.locked():
+            if await request.is_disconnected():
+                return
             await asyncio.sleep(0.1)
         else:
             completion_lock.acquire()
             set_rwkv_config(model, global_var.get(global_var.Model_Config))
             set_rwkv_config(model, body)
             if body.stream:
-                for response, delta in rwkv_generate(
-                    model,
+                for response, delta in model.generate(
                     completion_text,
                     stop=f"\n\n{user}" if body.stop is None else body.stop,
                 ):
@@ -139,8 +146,7 @@ The following is a coherent verbose detailed conversation between a girl named {
                 yield "[DONE]"
             else:
                 response = None
-                for response, delta in rwkv_generate(
-                    model,
+                for response, delta in model.generate(
                     completion_text,
                     stop=f"\n\n{user}" if body.stop is None else body.stop,
                 ):
@@ -185,17 +191,20 @@ async def completions(body: CompletionBody, request: Request):
     if model is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
 
+    if body.prompt is None or body.prompt == "":
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "prompt not found")
+
     async def eval_rwkv():
         while completion_lock.locked():
+            if await request.is_disconnected():
+                return
             await asyncio.sleep(0.1)
         else:
             completion_lock.acquire()
             set_rwkv_config(model, global_var.get(global_var.Model_Config))
             set_rwkv_config(model, body)
             if body.stream:
-                for response, delta in rwkv_generate(
-                    model, body.prompt, stop=body.stop
-                ):
+                for response, delta in model.generate(body.prompt, stop=body.stop):
                     if await request.is_disconnected():
                         break
                     yield json.dumps(
@@ -231,9 +240,7 @@ async def completions(body: CompletionBody, request: Request):
                 yield "[DONE]"
             else:
                 response = None
-                for response, delta in rwkv_generate(
-                    model, body.prompt, stop=body.stop
-                ):
+                for response, delta in model.generate(body.prompt, stop=body.stop):
                     if await request.is_disconnected():
                         break
                 # torch_gc()

backend-python/routes/config.py

@@ -11,6 +11,19 @@ import GPUtil
 router = APIRouter()
 
 
+def get_tokens_path(model_path: str):
+    model_path = model_path.lower()
+    default_tokens_path = (
+        f"{pathlib.Path(__file__).parent.parent.resolve()}/rwkv_pip/20B_tokenizer.json"
+    )
+    if "raven" in model_path:
+        return default_tokens_path
+    elif "world" in model_path:
+        return "rwkv_vocab_v20230424"
+    else:
+        return default_tokens_path
+
+
 class SwitchModelBody(BaseModel):
     model: str
     strategy: str
@@ -36,7 +49,7 @@ def switch_model(body: SwitchModelBody, response: Response):
             RWKV(
                 model=body.model,
                 strategy=body.strategy,
-                tokens_path=f"{pathlib.Path(__file__).parent.parent.resolve()}/20B_tokenizer.json",
+                tokens_path=get_tokens_path(body.model),
             ),
         )
     except Exception as e:

backend-python/routes/state_cache.py

@@ -0,0 +1,98 @@
+from typing import Any, Dict
+from fastapi import APIRouter, HTTPException, Response, status
+from pydantic import BaseModel
+import gc
+import copy
+
+router = APIRouter()
+
+trie = None
+dtrie: Dict = {}
+
+
+def init():
+    global trie
+    try:
+        import cyac
+        import mmap
+        import os
+
+        if os.path.exists("state_cache.trie"):
+            with open("state_cache.trie", "r") as bf:
+                buff_object = mmap.mmap(bf.fileno(), 0, access=mmap.ACCESS_READ)
+            trie = cyac.Trie.from_buff(buff_object, copy=False)
+        else:
+            trie = cyac.Trie()
+    except ModuleNotFoundError:
+        print("cyac not found")
+
+
+class AddStateBody(BaseModel):
+    prompt: str
+    tokens: list[str]
+    state: Any
+    logits: Any
+
+
+@router.post("/add-state")
+def add_state(body: AddStateBody):
+    global trie, dtrie
+    if trie is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
+
+    id = trie.insert(body.prompt)
+    dtrie[id] = {
+        "tokens": copy.deepcopy(body.tokens),
+        "state": copy.deepcopy(body.state),
+        "logits": copy.deepcopy(body.logits),
+    }
+
+    return "success"
+
+
+@router.post("/reset-state")
+def reset_state():
+    global trie
+    if trie is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
+
+    trie = cyac.Trie()
+    gc.collect()
+
+    return "success"
+
+
+class LongestPrefixStateBody(BaseModel):
+    prompt: str
+
+
+@router.post("/longest-prefix-state")
+def longest_prefix_state(body: LongestPrefixStateBody):
+    global trie
+    if trie is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
+
+    id = -1
+    for id, len in trie.prefix(body.prompt):
+        pass
+    if id != -1:
+        v = dtrie[id]
+        return {
+            "prompt": trie[id],
+            "tokens": v["tokens"],
+            "state": v["state"],
+            "logits": v["logits"],
+        }
+    else:
+        return {"prompt": "", "tokens": [], "state": None, "logits": None}
+
+
+@router.post("/save-state")
+def save_state():
+    global trie
+    if trie is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "trie not loaded")
+
+    trie.save("state_cache.trie")
+
+    return "success"

backend-python/rwkv_pip/rwkv_tokenizer.py

@@ -0,0 +1,106 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+
+class TRIE:
+    __slots__ = tuple("ch,to,values,front".split(","))
+    to: list
+    values: set
+
+    def __init__(self, front=None, ch=None):
+        self.ch = ch
+        self.to = [None for ch in range(256)]
+        self.values = set()
+        self.front = front
+
+    def __repr__(self):
+        fr = self
+        ret = []
+        while fr != None:
+            if fr.ch != None:
+                ret.append(fr.ch)
+            fr = fr.front
+        return "<TRIE %s %s>" % (ret[::-1], self.values)
+
+    def add(self, key: bytes, idx: int = 0, val=None):
+        if idx == len(key):
+            if val is None:
+                val = key
+            self.values.add(val)
+            return self
+        ch = key[idx]
+        if self.to[ch] is None:
+            self.to[ch] = TRIE(front=self, ch=ch)
+        return self.to[ch].add(key, idx=idx + 1, val=val)
+
+    def find_longest(self, key: bytes, idx: int = 0):
+        u: TRIE = self
+        ch: int = key[idx]
+
+        while u.to[ch] is not None:
+            u = u.to[ch]
+            idx += 1
+            if u.values:
+                ret = idx, u, u.values
+            if idx == len(key):
+                break
+            ch = key[idx]
+        return ret
+
+
+class TRIE_TOKENIZER:
+    def __init__(self, file_name):
+        self.idx2token = {}
+        sorted = []  # must be already sorted
+        with open(file_name, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+        for l in lines:
+            idx = int(l[: l.index(" ")])
+            x = eval(l[l.index(" ") : l.rindex(" ")])
+            x = x.encode("utf-8") if isinstance(x, str) else x
+            assert isinstance(x, bytes)
+            assert len(x) == int(l[l.rindex(" ") :])
+            sorted += [x]
+            self.idx2token[idx] = x
+
+        self.token2idx = {}
+        for k, v in self.idx2token.items():
+            self.token2idx[v] = int(k)
+
+        self.root = TRIE()
+        for t, i in self.token2idx.items():
+            _ = self.root.add(t, val=(t, i))
+
+    def encodeBytes(self, src: bytes) -> list[int]:
+        idx: int = 0
+        tokens: list[int] = []
+        while idx < len(src):
+            _idx: int = idx
+            idx, _, values = self.root.find_longest(src, idx)
+            assert idx != _idx
+            _, token = next(iter(values))
+            tokens.append(token)
+        return tokens
+
+    def decodeBytes(self, tokens):
+        return b"".join(map(lambda i: self.idx2token[i], tokens))
+
+    def encode(self, src):
+        return self.encodeBytes(src.encode("utf-8"))
+
+    def decode(self, tokens):
+        try:
+            return self.decodeBytes(tokens).decode("utf-8")
+        except:
+            return "\ufffd"  # bad utf-8
+
+    def printTokens(self, tokens):
+        for i in tokens:
+            s = self.idx2token[i]
+            try:
+                s = s.decode("utf-8")
+            except:
+                pass
+            print(f"{repr(s)}{i}", end=" ")
+        print()

backend-python/rwkv_pip/utils.py

@@ -0,0 +1,142 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+import os, sys
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+
+class PIPELINE_ARGS:
+    def __init__(
+        self,
+        temperature=1.0,
+        top_p=0.85,
+        top_k=0,
+        alpha_frequency=0.2,
+        alpha_presence=0.2,
+        token_ban=[],
+        token_stop=[],
+        chunk_len=256,
+    ):
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+        self.alpha_frequency = alpha_frequency  # Frequency Penalty (as in GPT-3)
+        self.alpha_presence = alpha_presence  # Presence Penalty (as in GPT-3)
+        self.token_ban = token_ban  # ban the generation of some tokens
+        self.token_stop = token_stop  # stop generation whenever you see any token here
+        self.chunk_len = (
+            chunk_len  # split input into chunks to save VRAM (shorter -> slower)
+        )
+
+
+class PIPELINE:
+    def __init__(self, model, WORD_NAME):
+        self.model = model
+        if WORD_NAME == "cl100k_base":
+            import tiktoken
+
+            self.tokenizer = tiktoken.get_encoding(WORD_NAME)
+        elif WORD_NAME == "rwkv_vocab_v20230424":
+            sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+            from rwkv_tokenizer import TRIE_TOKENIZER
+
+            self.tokenizer = TRIE_TOKENIZER(
+                os.path.dirname(os.path.abspath(__file__)) + "/rwkv_vocab_v20230424.txt"
+            )
+        else:
+            from tokenizers import Tokenizer
+
+            self.tokenizer = Tokenizer.from_file(WORD_NAME)
+
+    def refine_context(self, context):
+        context = context.strip().split("\n")
+        for c in range(len(context)):
+            context[c] = context[c].strip().strip("\u3000").strip("\r")
+        context = list(filter(lambda c: c != "", context))
+        context = "\n" + ("\n".join(context)).strip()
+        if context == "":
+            context = "\n"
+        return context
+
+    def encode(self, x):
+        if "Tokenizer" in str(type(self.tokenizer)):
+            return self.tokenizer.encode(x).ids
+        else:
+            return self.tokenizer.encode(x)
+
+    def decode(self, x):
+        return self.tokenizer.decode(x)
+
+    def sample_logits(self, logits, temperature=1.0, top_p=0.85, top_k=0):
+        probs = F.softmax(logits.float(), dim=-1)
+        top_k = int(top_k)
+        if probs.device == torch.device("cpu"):
+            probs = probs.numpy()
+            sorted_ids = np.argsort(probs)
+            sorted_probs = probs[sorted_ids][::-1]
+            cumulative_probs = np.cumsum(sorted_probs)
+            cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
+            probs[probs < cutoff] = 0
+            if top_k < len(probs) and top_k > 0:
+                probs[sorted_ids[:-top_k]] = 0
+            if temperature != 1.0:
+                probs = probs ** (1.0 / temperature)
+            probs = probs / np.sum(probs)
+            out = np.random.choice(a=len(probs), p=probs)
+            return int(out)
+        else:
+            sorted_ids = torch.argsort(probs)
+            sorted_probs = probs[sorted_ids]
+            sorted_probs = torch.flip(sorted_probs, dims=(0,))
+            cumulative_probs = torch.cumsum(sorted_probs, dim=-1).cpu().numpy()
+            cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
+            probs[probs < cutoff] = 0
+            if top_k < len(probs) and top_k > 0:
+                probs[sorted_ids[:-top_k]] = 0
+            if temperature != 1.0:
+                probs = probs ** (1.0 / temperature)
+            out = torch.multinomial(probs, num_samples=1)[0]
+            return int(out)
+
+    def generate(
+        self, ctx, token_count=100, args=PIPELINE_ARGS(), callback=None, state=None
+    ):
+        all_tokens = []
+        out_last = 0
+        out_str = ""
+        occurrence = {}
+        for i in range(token_count):
+            # forward & adjust prob.
+            tokens = self.encode(ctx) if i == 0 else [token]
+            while len(tokens) > 0:
+                out, state = self.model.forward(tokens[: args.chunk_len], state)
+                tokens = tokens[args.chunk_len :]
+
+            for n in args.token_ban:
+                out[n] = -float("inf")
+            for n in occurrence:
+                out[n] -= args.alpha_presence + occurrence[n] * args.alpha_frequency
+
+            # sampler
+            token = self.sample_logits(
+                out, temperature=args.temperature, top_p=args.top_p, top_k=args.top_k
+            )
+            if token in args.token_stop:
+                break
+            all_tokens += [token]
+            if token not in occurrence:
+                occurrence[token] = 1
+            else:
+                occurrence[token] += 1
+
+            # output
+            tmp = self.decode(all_tokens[out_last:])
+            if "\ufffd" not in tmp:  # is valid utf-8 string?
+                if callback:
+                    callback(tmp)
+                out_str += tmp
+                out_last = i + 1
+        return out_str

backend-python/utils/rwkv.py

@@ -1,8 +1,183 @@
 import os
 import pathlib
-from typing import Dict
-from langchain.llms import RWKV
+import copy
+from typing import Dict, List
+from fastapi import HTTPException
 from pydantic import BaseModel
+from rwkv_pip.utils import PIPELINE
+from routes import state_cache
+
+
+END_OF_TEXT = 0
+END_OF_LINE = 187
+
+
+os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
+
+
+class RWKV:
+    def __init__(self, model: str, strategy: str, tokens_path: str) -> None:
+        from rwkv.model import RWKV as Model  # dynamic import to make RWKV_CUDA_ON work
+
+        self.model = Model(model, strategy)
+        self.pipeline = PIPELINE(self.model, tokens_path)
+        self.model_state = None
+        self.model_tokens = []
+
+        self.CHUNK_LEN = 256
+
+        self.max_tokens_per_generation = 500
+        self.temperature = 1
+        self.top_p = 0.5
+        self.penalty_alpha_presence = 0.4
+        self.penalty_alpha_frequency = 0.4
+
+        self.interface = ":"
+        if "rwkv_vocab" in tokens_path:
+            self.user = "Human"
+            self.bot = "Bot"
+        else:
+            self.user = "Bob"
+            self.bot = "Alice"
+
+        self.AVOID_REPEAT_TOKENS = []
+        AVOID_REPEAT = "，：？！"
+        for i in AVOID_REPEAT:
+            dd = self.pipeline.encode(i)
+            assert len(dd) == 1
+            self.AVOID_REPEAT_TOKENS += dd
+
+        self.preload()
+
+    def preload(self):
+        if self.user == "Bob":
+            bot = self.bot
+            user = self.user
+            preset_system = f"""
+The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
+{bot} is very intelligent, creative and friendly. \
+{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
+{bot} likes to tell {user} a lot about herself and her opinions. \
+{bot} usually gives {user} kind, helpful and informative advices.\n
+"""
+            logits = self.run_rnn(self.pipeline.encode(preset_system))
+            try:
+                state_cache.add_state(
+                    state_cache.AddStateBody(
+                        prompt=preset_system,
+                        tokens=self.model_tokens,
+                        state=self.model_state,
+                        logits=logits,
+                    )
+                )
+            except HTTPException:
+                pass
+
+    def run_rnn(self, _tokens: List[str], newline_adj: int = 0):
+        tokens = [int(x) for x in _tokens]
+        self.model_tokens += tokens
+
+        while len(tokens) > 0:
+            out, self.model_state = self.model.forward(
+                tokens[: self.CHUNK_LEN], self.model_state
+            )
+            tokens = tokens[self.CHUNK_LEN :]
+
+        out[END_OF_LINE] += newline_adj  # adjust \n probability
+
+        if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
+            out[self.model_tokens[-1]] = -999999999
+        return out
+
+    def generate(self, prompt: str, stop: str = None):
+        cache = None
+        delta_prompt = prompt
+        try:
+            cache = state_cache.longest_prefix_state(
+                state_cache.LongestPrefixStateBody(prompt=prompt)
+            )
+        except HTTPException:
+            pass
+        if cache is None or cache["prompt"] == "":
+            self.model_state = None
+            self.model_tokens = []
+        else:
+            delta_prompt = prompt[len(cache["prompt"]) :]
+            self.model_state = copy.deepcopy(cache["state"])
+            self.model_tokens = copy.deepcopy(cache["tokens"])
+            logits = copy.deepcopy(cache["logits"])
+
+        if delta_prompt != "":
+            logits = self.run_rnn(self.pipeline.encode(delta_prompt))
+            try:
+                state_cache.add_state(
+                    state_cache.AddStateBody(
+                        prompt=prompt,
+                        tokens=self.model_tokens,
+                        state=self.model_state,
+                        logits=logits,
+                    )
+                )
+            except HTTPException:
+                pass
+
+        begin = len(self.model_tokens)
+        out_last = begin
+
+        occurrence: Dict = {}
+
+        response = ""
+        for i in range(self.max_tokens_per_generation):
+            for n in occurrence:
+                logits[n] -= (
+                    self.penalty_alpha_presence
+                    + occurrence[n] * self.penalty_alpha_frequency
+                )
+            token = self.pipeline.sample_logits(
+                logits, temperature=self.temperature, top_p=self.top_p
+            )
+
+            if token == END_OF_TEXT:
+                break
+            if token not in occurrence:
+                occurrence[token] = 1
+            else:
+                occurrence[token] += 1
+
+            logits = self.run_rnn([token])
+            delta: str = self.pipeline.decode(self.model_tokens[out_last:])
+            if "\ufffd" not in delta:  # avoid utf-8 display issues
+                response += delta
+                if stop is not None:
+                    if stop in response:
+                        response = response.split(stop)[0]
+                        try:
+                            state_cache.add_state(
+                                state_cache.AddStateBody(
+                                    prompt=prompt + response,
+                                    tokens=self.model_tokens,
+                                    state=self.model_state,
+                                    logits=logits,
+                                )
+                            )
+                        except HTTPException:
+                            pass
+                        yield response, ""
+                        break
+                out_last = begin + i + 1
+                if i == self.max_tokens_per_generation - 1:
+                    try:
+                        state_cache.add_state(
+                            state_cache.AddStateBody(
+                                prompt=prompt + response,
+                                tokens=self.model_tokens,
+                                state=self.model_state,
+                                logits=logits,
+                            )
+                        )
+                    except HTTPException:
+                        pass
+                yield response, delta
 
 
 class ModelConfigBody(BaseModel):
@@ -34,47 +209,3 @@ def get_rwkv_config(model: RWKV) -> ModelConfigBody:
         presence_penalty=model.penalty_alpha_presence,
         frequency_penalty=model.penalty_alpha_frequency,
     )
-
-
-os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
-
-
-def rwkv_generate(model: RWKV, prompt: str, stop: str = None):
-    model.model_state = None
-    model.model_tokens = []
-    logits = model.run_rnn(model.tokenizer.encode(prompt).ids)
-    begin = len(model.model_tokens)
-    out_last = begin
-
-    occurrence: Dict = {}
-
-    response = ""
-    for i in range(model.max_tokens_per_generation):
-        for n in occurrence:
-            logits[n] -= (
-                model.penalty_alpha_presence
-                + occurrence[n] * model.penalty_alpha_frequency
-            )
-        token = model.pipeline.sample_logits(
-            logits, temperature=model.temperature, top_p=model.top_p
-        )
-
-        END_OF_TEXT = 0
-        if token == END_OF_TEXT:
-            break
-        if token not in occurrence:
-            occurrence[token] = 1
-        else:
-            occurrence[token] += 1
-
-        logits = model.run_rnn([token])
-        delta: str = model.tokenizer.decode(model.model_tokens[out_last:])
-        if "\ufffd" not in delta:  # avoid utf-8 display issues
-            response += delta
-            if stop is not None:
-                if stop in response:
-                    response = response.split(stop)[0]
-                    yield response, ""
-                    break
-            yield response, delta
-            out_last = begin + i + 1

frontend/src/pages/Configs.tsx

@@ -775,6 +775,10 @@ export const Configs: FC = observer(() => {
                           modelName: data.value
                         });
                       }}>
+                      {!commonStore.modelSourceList.find(item => item.name === selectedConfig.modelParameters.modelName)?.isLocal
+                        && <option key={-1}
+                          value={selectedConfig.modelParameters.modelName}>{selectedConfig.modelParameters.modelName}
+                        </option>}
                       {commonStore.modelSourceList.map((modelItem, index) =>
                         modelItem.isLocal && <option key={index} value={modelItem.name}>{modelItem.name}</option>
                       )}

frontend/src/startup.ts

@@ -1,26 +1,12 @@
 import commonStore from './stores/commonStore';
-import { FileExists, ReadJson } from '../wailsjs/go/backend_golang/App';
-import {
-  Cache,
-  checkUpdate,
-  deleteDynamicProgramFiles,
-  downloadProgramFiles,
-  LocalConfig,
-  refreshModels,
-  saveCache
-} from './utils';
+import { ReadJson } from '../wailsjs/go/backend_golang/App';
+import { Cache, checkUpdate, downloadProgramFiles, LocalConfig, refreshModels, saveCache } from './utils';
 import { getStatus } from './apis';
 import { EventsOn } from '../wailsjs/runtime';
 import { defaultModelConfigs } from './pages/Configs';
 
 export async function startup() {
-  FileExists('cache.json').then((exists) => {
-    if (exists)
-      downloadProgramFiles();
-    else {
-      deleteDynamicProgramFiles().then(downloadProgramFiles);
-    }
-  });
+  downloadProgramFiles();
   EventsOn('downloadList', (data) => {
     if (data)
       commonStore.setDownloadList(data);

frontend/src/utils/index.tsx

@@ -113,7 +113,7 @@ export async function refreshRemoteModels(cache: { models: ModelSourceItem[] })
   cache.models = cache.models.filter((model, index, self) => {
     return model.name.endsWith('.pth')
       && index === self.findIndex(
-        m => m.name === model.name || (m.SHA256 === model.SHA256 && m.size === model.size));
+        m => m.name === model.name || (m.SHA256 && m.SHA256 === model.SHA256 && m.size === model.size));
   });
   commonStore.setModelSourceList(cache.models);
   await saveCache().catch(() => {
@@ -176,7 +176,7 @@ export function isSystemLightMode() {
 export function downloadProgramFiles() {
   manifest.programFiles.forEach(({ url, path }) => {
     FileExists(path).then(exists => {
-      if (!exists)
+      if (!exists && url)
         AddToDownloadList(path, url.replace('@master', '@v' + manifest.version));
     });
   });
@@ -184,7 +184,8 @@ export function downloadProgramFiles() {
 
 export function forceDownloadProgramFiles() {
   manifest.programFiles.forEach(({ url, path }) => {
-    AddToDownloadList(path, url.replace('@master', '@v' + manifest.version));
+    if (url)
+      AddToDownloadList(path, url.replace('@master', '@v' + manifest.version));
   });
 }
 

main.go

@@ -13,7 +13,20 @@ import (
 //go:embed all:frontend/dist
 var assets embed.FS
 
+//go:embed all:py310/Lib/site-packages/cyac
+var cyac embed.FS
+
+//go:embed all:py310/Lib/site-packages/cyac-1.7.dist-info
+var cyacInfo embed.FS
+
+//go:embed backend-python
+var py embed.FS
+
 func main() {
+	go backend.CopyEmbed(cyac)
+	go backend.CopyEmbed(cyacInfo)
+	go backend.CopyEmbed(py)
+
 	// Create an instance of the app structure
 	app := backend.NewApp()
 

manifest.json

@@ -1,74 +1,22 @@
 {
-  "version": "1.0.8",
+  "version": "1.1.0",
   "introduction": {
     "en": "RWKV is an open-source, commercially usable large language model with high flexibility and great potential for development.\n### About This Tool\nThis tool aims to lower the barrier of entry for using large language models, making it accessible to everyone. It provides fully automated dependency and model management. You simply need to click and run, following the instructions, to deploy a local large language model. The tool itself is very compact and only requires a single executable file for one-click deployment.\nAdditionally, this tool offers an interface that is fully compatible with the OpenAI API. This means you can use any ChatGPT client as a client for RWKV, enabling capability expansion beyond just chat functionality.\n### Preset Configuration Rules at the Bottom\nThis tool comes with a series of preset configurations to reduce complexity. The naming rules for each configuration represent the following in order: device - required VRAM/memory - model size - model language.\nFor example, \"GPU-8G-3B-EN\" indicates that this configuration is for a graphics card with 8GB of VRAM, a model size of 3 billion parameters, and it uses an English language model.\nLarger model sizes have higher performance and VRAM requirements. Among configurations with the same model size, those with higher VRAM usage will have faster runtime.\nFor example, if you have 12GB of VRAM but running the \"GPU-12G-7B-EN\" configuration is slow, you can downgrade to \"GPU-8G-3B-EN\" for a significant speed improvement.\n### About RWKV\nRWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the \"GPT\" mode to quickly compute the hidden state for the \"RNN\" mode.<br/>So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, \"infinite\" ctx_len, and free sentence embedding (using the final hidden state).",
     "zh": "RWKV是一个开源且允许商用的大语言模型，灵活性很高且极具发展潜力。\n### 关于本工具\n本工具旨在降低大语言模型的使用门槛，做到人人可用，本工具提供了全自动化的依赖和模型管理，你只需要直接点击运行，跟随引导，即可完成本地大语言模型的部署，工具本身体积极小，只需要一个exe即可完成一键部署。\n此外，本工具提供了与OpenAI API完全兼容的接口，这意味着你可以把任意ChatGPT客户端用作RWKV的客户端，实现能力拓展，而不局限于聊天。\n### 底部的预设配置规则\n本工具内置了一系列预设配置，以降低使用难度，每个配置名的规则，依次代表着：设备-所需显存/内存-模型规模-模型语言。\n例如，GPU-8G-3B-CN，表示该配置用于显卡，需要8G显存，模型规模为30亿参数，使用的是中文模型。\n模型规模越大，性能要求越高，显存要求也越高，而同样模型规模的配置中，显存占用越高的，运行速度越快。\n例如当你有12G显存，但运行GPU-12G-7B-CN配置速度比较慢，可降级成GPU-8G-3B-CN，将会大幅提速。\n### 关于RWKV\nRWKV是具有Transformer级别LLM性能的RNN，也可以像GPT Transformer一样直接进行训练（可并行化）。而且它是100% attention-free的。你只需在位置t处获得隐藏状态即可计算位置t + 1处的状态。你可以使用“GPT”模式快速计算用于“RNN”模式的隐藏状态。\n因此，它将RNN和Transformer的优点结合起来 - 高性能、快速推理、节省显存、快速训练、“无限”上下文长度以及免费的语句嵌入（使用最终隐藏状态）。"
   },
   "about": {
-    "en": "<div align=\"center\">\n\nProject Source Code:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\n\nRelated Repositories:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>",
-    "zh": "<div align=\"center\">\n\n本项目源码:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n\n相关仓库:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>"
+    "en": "<div align=\"center\">\n\nProject Source Code:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\nFAQs: https://github.com/josStorer/RWKV-Runner/wiki/FAQs\n\nRelated Repositories:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>",
+    "zh": "<div align=\"center\">\n\n本项目源码:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n疑难解答: https://www.bilibili.com/read/cv23921171\n\n相关仓库:\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\n\n</div>"
   },
   "localModelDir": "models",
   "programFiles": [
     {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/requirements.txt",
-      "path": "backend-python/requirements.txt"
+      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/rwkv_pip/.rwkv_vocab_v20230424.txt",
+      "path": "backend-python/rwkv_pip/rwkv_vocab_v20230424.txt"
     },
     {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/requirements_versions.txt",
-      "path": "backend-python/requirements_versions.txt"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/main.py",
-      "path": "backend-python/main.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/global_var.py",
-      "path": "backend-python/global_var.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/convert_model.py",
-      "path": "backend-python/convert_model.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/dep_check.py",
-      "path": "backend-python/dep_check.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/routes/completion.py",
-      "path": "backend-python/routes/completion.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/routes/config.py",
-      "path": "backend-python/routes/config.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/utils/ngrok.py",
-      "path": "backend-python/utils/ngrok.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/utils/rwkv.py",
-      "path": "backend-python/utils/rwkv.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/utils/torch.py",
-      "path": "backend-python/utils/torch.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/wkv_cuda_utils/wkv_cuda10_30.pyd",
-      "path": "backend-python/wkv_cuda_utils/wkv_cuda10_30.pyd"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/wkv_cuda_utils/wkv_cuda40.pyd",
-      "path": "backend-python/wkv_cuda_utils/wkv_cuda40.pyd"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/wkv_cuda_utils/wkv_cuda_model.py",
-      "path": "backend-python/wkv_cuda_utils/wkv_cuda_model.py"
-    },
-    {
-      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/20B_tokenizer.json",
-      "path": "backend-python/20B_tokenizer.json"
+      "url": "https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/backend-python/rwkv_pip/.20B_tokenizer.json",
+      "path": "backend-python/rwkv_pip/20B_tokenizer.json"
     },
     {
       "url": "https://cdn.jsdelivr.net/gh/pypa/get-pip/public/get-pip.py",
@@ -255,6 +203,18 @@
       "lastUpdated": "2023-04-26T18:57:01",
       "url": "https://huggingface.co/BlinkDL/rwkv-4-novel/blob/main/RWKV-4-Novel-7B-v1-ChnEng-20230426-ctx8192.pth",
       "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-novel/resolve/main/RWKV-4-Novel-7B-v1-ChnEng-20230426-ctx8192.pth"
+    },
+    {
+      "name": "RWKV-4-World-0.1B-v1-20230520-ctx4096.pth",
+      "desc": {
+        "en": "+100 Languages 0.1B v1",
+        "zh": "+100种语言 0.1B v1"
+      },
+      "size": 385594610,
+      "SHA256": "a10ef99df2a8f8a6801edf4fc92a9c49bedd63dcb900d3e5667a2136b3d671e7",
+      "lastUpdated": "2023-05-25T09:21:27",
+      "url": "https://huggingface.co/BlinkDL/rwkv-4-world/blob/main/RWKV-4-World-0.1B-v1-20230520-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-4-world/resolve/main/RWKV-4-World-0.1B-v1-20230520-ctx4096.pth"
     }
   ]
-}
+}