release v1.4.4

.gitattributes

@@ -2,6 +2,7 @@ backend-python/rwkv_pip/** linguist-vendored
 backend-python/wkv_cuda_utils/** linguist-vendored
 backend-python/get-pip.py linguist-vendored
 backend-python/convert_model.py linguist-vendored
+backend-python/convert_safetensors.py linguist-vendored
 backend-python/utils/midi.py linguist-vendored
 build/** linguist-vendored
 finetune/lora/** linguist-vendored

.github/workflows/release.yml

@@ -48,6 +48,11 @@ jobs:
         id: cp310
         with:
           python-version: '3.10'
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: stable
+          override: true
+          target: wasm32-unknown-unknown
       - uses: crazy-max/ghaction-chocolatey@v2
         with:
           args: install upx
@@ -60,6 +65,11 @@ jobs:
           Copy-Item -Path "${{ steps.cp310.outputs.python-path }}/../include" -Destination "py310/include" -Recurse
           Copy-Item -Path "${{ steps.cp310.outputs.python-path }}/../libs" -Destination "py310/libs" -Recurse
           ./py310/python -m pip install cyac==1.7
+          git clone https://github.com/josStorer/ai00_rwkv_server --depth=1
+          cd ai00_rwkv_server
+          cargo build --release
+          mv ./target/release/ai00_server.exe ../backend-rust/webgpu_server.exe
+          cd ..
           go install github.com/wailsapp/wails/v2/cmd/wails@latest
           make
           Rename-Item -Path "build/bin/RWKV-Runner.exe" -NewName "RWKV-Runner_windows_x64.exe"
@@ -76,10 +86,23 @@ jobs:
       - uses: actions/setup-go@v4
         with:
           go-version: '1.20.5'
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: stable
+          override: true
+          target: wasm32-unknown-unknown
       - run: |
           sudo apt-get update
           sudo apt-get install upx
           sudo apt-get install build-essential libgtk-3-dev libwebkit2gtk-4.0-dev
+          git clone https://github.com/josStorer/ai00_rwkv_server --depth=1
+          cd ai00_rwkv_server
+          sudo apt-get install libudev-dev
+          sudo apt-get install libasound2-dev
+          rustup target add x86_64-unknown-linux-gnu
+          cargo build --release --target x86_64-unknown-linux-gnu
+          mv ./target/x86_64-unknown-linux-gnu/release/ai00_server ../backend-rust/webgpu_server
+          cd ..
           go install github.com/wailsapp/wails/v2/cmd/wails@latest
           rm -rf ./backend-python/wkv_cuda_utils
           rm ./backend-python/get-pip.py
@@ -101,7 +124,17 @@ jobs:
       - uses: actions/setup-go@v4
         with:
           go-version: '1.20.5'
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: stable
+          override: true
+          target: wasm32-unknown-unknown
       - run: |
+          git clone https://github.com/josStorer/ai00_rwkv_server --depth=1
+          cd ai00_rwkv_server
+          cargo build --release
+          mv ./target/release/ai00_server ../backend-rust/webgpu_server
+          cd ..
           go install github.com/wailsapp/wails/v2/cmd/wails@latest
           rm -rf ./backend-python/wkv_cuda_utils
           rm ./backend-python/get-pip.py

.gitignore

@@ -5,6 +5,8 @@ __pycache__
 .idea
 .vs
 *.pth
+*.st
+*.safetensors
 *.bin
 /config.json
 /cache.json

CURRENT_CHANGE.md

@@ -1,17 +1,10 @@
-## Breaking Changes
-
-Due to performance and bandwidth considerations, the `/chat/completions` and `/completions` API no longer return
-the `response` field. If necessary, you can
-still [uncomment it](https://github.com/josStorer/RWKV-Runner/commit/aecacde81927e26816558f1a629cdcf507b7cb5b) yourself.
-Please note that this is never part of the OpenAI API, it existed previously only for API development
-convenience. If you follow the OpenAI API specification, you will not be affected in any way.
-
 ## Changes
 
-- improve `/chat/completions` and `/completions` API performance (remove `response` field)
-- improve default ChatCompletion `stop`
-- improve python backend startup speed
-- update defaultConfigs
+- webgpu support (AMD, Intel, Nvidia, Apple)
+- add rwkv-cuda-beta support (faster)
+- add misc API (`/models` and `/dashboard/billing/credit_grants`)
+- allow multiple systems
+- allow completions input to be null
 
 ## Install
 

backend-golang/rwkv.go

@@ -10,7 +10,7 @@ import (
 	"strings"
 )
 
-func (a *App) StartServer(python string, port int, host string) (string, error) {
+func (a *App) StartServer(python string, port int, host string, rwkvBeta bool) (string, error) {
 	var err error
 	if python == "" {
 		python, err = GetPython()
@@ -18,7 +18,19 @@ func (a *App) StartServer(python string, port int, host string) (string, error)
 	if err != nil {
 		return "", err
 	}
-	return Cmd(python, "./backend-python/main.py", strconv.Itoa(port), host)
+	args := []string{python, "./backend-python/main.py"}
+	if rwkvBeta {
+		args = append(args, "--rwkv-beta")
+	}
+	args = append(args, "--port", strconv.Itoa(port), "--host", host)
+	return Cmd(args...)
+}
+
+func (a *App) StartWebGPUServer(port int, host string) (string, error) {
+	args := []string{"./backend-rust/webgpu_server"}
+	args = append(args, "-a", "0", "-t", "backend-rust/assets/rwkv_vocab_v20230424.json",
+		"--port", strconv.Itoa(port), "--ip", host)
+	return Cmd(args...)
 }
 
 func (a *App) ConvertModel(python string, modelPath string, strategy string, outPath string) (string, error) {
@@ -32,6 +44,17 @@ func (a *App) ConvertModel(python string, modelPath string, strategy string, out
 	return Cmd(python, "./backend-python/convert_model.py", "--in", modelPath, "--out", outPath, "--strategy", strategy)
 }
 
+func (a *App) ConvertSafetensors(python string, modelPath string, outPath string) (string, error) {
+	var err error
+	if python == "" {
+		python, err = GetPython()
+	}
+	if err != nil {
+		return "", err
+	}
+	return Cmd(python, "./backend-python/convert_safetensors.py", "--input", modelPath, "--output", outPath)
+}
+
 func (a *App) ConvertData(python string, input string, outputPrefix string, vocab string) (string, error) {
 	var err error
 	if python == "" {

backend-python/convert_safetensors.py

@@ -0,0 +1,53 @@
+import json
+import os
+import sys
+import copy
+import torch
+from safetensors.torch import load_file, save_file
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--input", type=str, help="Path to input pth model")
+parser.add_argument(
+    "--output",
+    type=str,
+    default="./converted.st",
+    help="Path to output safetensors model",
+)
+args = parser.parse_args()
+
+
+def convert_file(
+    pt_filename: str,
+    sf_filename: str,
+):
+    loaded = torch.load(pt_filename, map_location="cpu")
+    if "state_dict" in loaded:
+        loaded = loaded["state_dict"]
+
+    loaded = {k: v.clone().half() for k, v in loaded.items()}
+    for k, v in loaded.items():
+        print(f"{k}\t{v.shape}\t{v.dtype}")
+
+    # For tensors to be contiguous
+    loaded = {k: v.contiguous() for k, v in loaded.items()}
+
+    dirname = os.path.dirname(sf_filename)
+    os.makedirs(dirname, exist_ok=True)
+    save_file(loaded, sf_filename, metadata={"format": "pt"})
+    reloaded = load_file(sf_filename)
+    for k in loaded:
+        pt_tensor = loaded[k]
+        sf_tensor = reloaded[k]
+        if not torch.equal(pt_tensor, sf_tensor):
+            raise RuntimeError(f"The output tensors do not match for key {k}")
+
+
+if __name__ == "__main__":
+    try:
+        convert_file(args.input, args.output)
+        print(f"Saved to {args.output}")
+    except Exception as e:
+        with open("error.txt", "w") as f:
+            f.write(str(e))

backend-python/dep_check.py

@@ -1,3 +1,4 @@
+import safetensors
 import midi2audio
 import mido
 import lm_dataformat

backend-python/global_var.py

@@ -1,5 +1,6 @@
 from enum import Enum, auto
 
+Args = "args"
 Model = "model"
 Model_Status = "model_status"
 Model_Config = "model_config"

backend-python/main.py

@@ -1,5 +1,11 @@
+import time
+
+start_time = time.time()
+
 import os
 import sys
+import argparse
+from typing import Sequence
 
 sys.path.append(os.path.dirname(os.path.realpath(__file__)))
 
@@ -12,7 +18,7 @@ from utils.rwkv import *
 from utils.torch import *
 from utils.ngrok import *
 from utils.log import log_middleware
-from routes import completion, config, state_cache, midi
+from routes import completion, config, state_cache, midi, misc
 import global_var
 
 app = FastAPI(dependencies=[Depends(log_middleware)])
@@ -28,12 +34,18 @@ app.add_middleware(
 app.include_router(completion.router)
 app.include_router(config.router)
 app.include_router(midi.router)
+app.include_router(misc.router)
 app.include_router(state_cache.router)
 
 
 @app.on_event("startup")
 def init():
     global_var.init()
+    cmd_params = os.environ["RWKV_RUNNER_PARAMS"]
+    global_var.set(
+        global_var.Args, get_args(cmd_params.split(" ") if cmd_params else None)
+    )
+
     state_cache.init()
 
     set_torch()
@@ -56,9 +68,34 @@ def exit():
     parent.kill()
 
 
-if __name__ == "__main__":
-    uvicorn.run(
-        "main:app",
-        port=8000 if len(sys.argv) < 2 else int(sys.argv[1]),
-        host="127.0.0.1" if len(sys.argv) < 3 else sys.argv[2],
+def get_args(args: Union[Sequence[str], None] = None):
+    parser = argparse.ArgumentParser()
+    group = parser.add_argument_group(title="server arguments")
+    group.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="port to run the server on (default: 8000)",
     )
+    group.add_argument(
+        "--host",
+        type=str,
+        default="127.0.0.1",
+        help="host to run the server on (default: 127.0.0.1)",
+    )
+    group = parser.add_argument_group(title="mode arguments")
+    group.add_argument(
+        "--rwkv-beta",
+        action="store_true",
+        help="whether to use rwkv-beta (default: False)",
+    )
+    args = parser.parse_args(args)
+
+    return args
+
+
+if __name__ == "__main__":
+    args = get_args()
+    os.environ["RWKV_RUNNER_PARAMS"] = " ".join(sys.argv[1:])
+    print("--- %s seconds ---" % (time.time() - start_time))
+    uvicorn.run("main:app", port=args.port, host=args.host, workers=1)

backend-python/routes/completion.py

@@ -2,11 +2,12 @@ import asyncio
 import json
 from threading import Lock
 from typing import List, Union
+from enum import Enum
 import base64
 
 from fastapi import APIRouter, Request, status, HTTPException
 from sse_starlette.sse import EventSourceResponse
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 import numpy as np
 import tiktoken
 from utils.rwkv import *
@@ -16,30 +17,40 @@ import global_var
 router = APIRouter()
 
 
+class Role(Enum):
+    User = "user"
+    Assistant = "assistant"
+    System = "system"
+
+
 class Message(BaseModel):
-    role: str
-    content: str
+    role: Role
+    content: str = Field(min_length=1)
 
 
 class ChatCompletionBody(ModelConfigBody):
-    messages: List[Message]
+    messages: Union[List[Message], None]
     model: str = "rwkv"
     stream: bool = False
-    stop: Union[str, List[str]] = [
+    stop: Union[str, List[str], None] = [
         "\n\nUser",
         "\n\nQuestion",
         "\n\nQ",
         "\n\nHuman",
         "\n\nBob",
     ]
+    user_name: Union[str, None] = None
+    assistant_name: Union[str, None] = None
 
     class Config:
         schema_extra = {
             "example": {
-                "messages": [{"role": "user", "content": "hello"}],
+                "messages": [{"role": Role.User.value, "content": "hello"}],
                 "model": "rwkv",
                 "stream": False,
                 "stop": None,
+                "user_name": None,
+                "assistant_name": None,
                 "max_tokens": 1000,
                 "temperature": 1.2,
                 "top_p": 0.5,
@@ -50,10 +61,10 @@ class ChatCompletionBody(ModelConfigBody):
 
 
 class CompletionBody(ModelConfigBody):
-    prompt: Union[str, List[str]]
+    prompt: Union[str, List[str], None]
     model: str = "rwkv"
     stream: bool = False
-    stop: Union[str, List[str]] = None
+    stop: Union[str, List[str], None] = None
 
     class Config:
         schema_extra = {
@@ -83,7 +94,7 @@ async def eval_rwkv(
     body: ModelConfigBody,
     prompt: str,
     stream: bool,
-    stop: Union[str, List[str]],
+    stop: Union[str, List[str], None],
     chat_mode: bool,
 ):
     global requests_num
@@ -196,7 +207,7 @@ async def eval_rwkv(
                     "choices": [
                         {
                             "message": {
-                                "role": "assistant",
+                                "role": Role.Assistant.value,
                                 "content": response,
                             },
                             "index": 0,
@@ -219,78 +230,82 @@ async def chat_completions(body: ChatCompletionBody, request: Request):
     if model is None:
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
 
-    question = body.messages[-1]
-    if question.role == "user":
-        question = question.content
-    elif question.role == "system":
-        question = body.messages[-2]
-        if question.role == "user":
-            question = question.content
-        else:
-            raise HTTPException(status.HTTP_400_BAD_REQUEST, "no question found")
-    else:
-        raise HTTPException(status.HTTP_400_BAD_REQUEST, "no question found")
+    if body.messages is None or body.messages == []:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "messages not found")
+
+    basic_system: str = ""
+    if body.messages[0].role == Role.System:
+        basic_system = body.messages[0].content
 
     interface = model.interface
-    user = model.user
-    bot = model.bot
+    user = model.user if body.user_name is None else body.user_name
+    bot = model.bot if body.assistant_name is None else body.assistant_name
 
-    completion_text = (
-        f"""
+    is_raven = model.rwkv_type == RWKVType.Raven
+
+    completion_text: str = ""
+    if basic_system == "":
+        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 f"{user}{interface} hi\n\n{bot}{interface} Hi. "
-        + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
-    )
-    for message in body.messages:
-        if message.role == "system":
-            completion_text = (
+            if is_raven
+            else (
+                f"{user}{interface} hi\n\n{bot}{interface} Hi. "
+                + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
+            )
+        )
+    elif basic_system != "":
+        completion_text = (
+            (
                 f"The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. "
-                if user == "Bob"
+                if is_raven
                 else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
-                + message.content.replace("\\n", "\n")
-                .replace("\r\n", "\n")
-                .replace("\n\n", "\n")
-                .replace("\n", " ")
-                .strip()
-                .replace("You are", f"{bot} is" if user == "Bob" else "I am")
-                .replace("you are", f"{bot} is" if user == "Bob" else "I am")
-                .replace("You're", f"{bot} is" if user == "Bob" else "I'm")
-                .replace("you're", f"{bot} is" if user == "Bob" else "I'm")
-                .replace("You", f"{bot}" if user == "Bob" else "I")
-                .replace("you", f"{bot}" if user == "Bob" else "I")
-                .replace("Your", f"{bot}'s" if user == "Bob" else "My")
-                .replace("your", f"{bot}'s" if user == "Bob" else "my")
-                .replace("你", f"{bot}" if user == "Bob" else "我")
-                + "\n\n"
-            )
-            break
-    for message in body.messages:
-        if message.role == "user":
-            completion_text += (
-                f"{user}{interface} "
-                + message.content.replace("\\n", "\n")
-                .replace("\r\n", "\n")
-                .replace("\n\n", "\n")
-                .strip()
-                + "\n\n"
-            )
-        elif message.role == "assistant":
-            completion_text += (
-                f"{bot}{interface} "
-                + message.content.replace("\\n", "\n")
-                .replace("\r\n", "\n")
-                .replace("\n\n", "\n")
-                .strip()
-                + "\n\n"
             )
+            + basic_system.replace("\r\n", "\n")
+            .replace("\r", "\n")
+            .replace("\n\n", "\n")
+            .replace("\n", " ")
+            .strip()
+            .replace("You are", f"{bot} is" if is_raven else "I am")
+            .replace("you are", f"{bot} is" if is_raven else "I am")
+            .replace("You're", f"{bot} is" if is_raven else "I'm")
+            .replace("you're", f"{bot} is" if is_raven else "I'm")
+            .replace("You", f"{bot}" if is_raven else "I")
+            .replace("you", f"{bot}" if is_raven else "I")
+            .replace("Your", f"{bot}'s" if is_raven else "My")
+            .replace("your", f"{bot}'s" if is_raven else "my")
+            .replace("你", f"{bot}" if is_raven else "我")
+            + "\n\n"
+        )
+
+    for message in body.messages[(0 if basic_system == "" else 1) :]:
+        append_message: str = ""
+        if message.role == Role.User:
+            append_message = f"{user}{interface} " + message.content
+        elif message.role == Role.Assistant:
+            append_message = f"{bot}{interface} " + message.content
+        elif message.role == Role.System:
+            append_message = message.content
+        completion_text += (
+            append_message.replace("\r\n", "\n")
+            .replace("\r", "\n")
+            .replace("\n\n", "\n")
+            .strip()
+            + "\n\n"
+        )
     completion_text += f"{bot}{interface}"
 
+    if type(body.stop) == str:
+        body.stop = [body.stop, f"\n\n{user}", f"\n\n{bot}"]
+    else:
+        body.stop.append(f"\n\n{user}")
+        body.stop.append(f"\n\n{bot}")
+
     if body.stream:
         return EventSourceResponse(
             eval_rwkv(
@@ -333,7 +348,7 @@ async def completions(body: CompletionBody, request: Request):
 
 
 class EmbeddingsBody(BaseModel):
-    input: Union[str, List[str], List[List[int]]]
+    input: Union[str, List[str], List[List[int]], None]
     model: str = "rwkv"
     encoding_format: str = None
     fast_mode: bool = False

backend-python/routes/misc.py

@@ -0,0 +1,131 @@
+from fastapi import APIRouter, HTTPException, status
+from utils.rwkv import AbstractRWKV
+import global_var
+
+router = APIRouter()
+
+
+@router.get("/dashboard/billing/credit_grants", tags=["MISC"])
+def credit_grants():
+    return {
+        "object": "credit_summary",
+        "total_granted": 10000,
+        "total_used": 0,
+        "total_available": 10000,
+        "grants": {
+            "object": "list",
+            "data": [
+                {
+                    "object": "credit_grant",
+                    "grant_amount": 10000,
+                    "used_amount": 0,
+                    "effective_at": 1672531200,
+                    "expires_at": 33229440000,
+                }
+            ],
+        },
+    }
+
+
+fake_models = [
+    {
+        "id": "gpt-3.5-turbo",
+        "object": "model",
+        "created": 1677610602,
+        "owned_by": "openai",
+        "permission": [
+            {
+                "id": "modelperm-zy5TOjnE2zVaicIcKO9bQDgX",
+                "object": "model_permission",
+                "created": 1690864883,
+                "allow_create_engine": False,
+                "allow_sampling": True,
+                "allow_logprobs": True,
+                "allow_search_indices": False,
+                "allow_view": True,
+                "allow_fine_tuning": False,
+                "organization": "*",
+                "group": None,
+                "is_blocking": False,
+            }
+        ],
+        "root": "gpt-3.5-turbo",
+        "parent": None,
+    },
+    {
+        "id": "text-davinci-003",
+        "object": "model",
+        "created": 1669599635,
+        "owned_by": "openai-internal",
+        "permission": [
+            {
+                "id": "modelperm-a6niqBmW2JaGmo0fDO7FEt1n",
+                "object": "model_permission",
+                "created": 1690930172,
+                "allow_create_engine": False,
+                "allow_sampling": True,
+                "allow_logprobs": True,
+                "allow_search_indices": False,
+                "allow_view": True,
+                "allow_fine_tuning": False,
+                "organization": "*",
+                "group": None,
+                "is_blocking": False,
+            }
+        ],
+        "root": "text-davinci-003",
+        "parent": None,
+    },
+]
+
+
+@router.get("/v1/models", tags=["MISC"])
+@router.get("/models", tags=["MISC"])
+def models():
+    model: AbstractRWKV = global_var.get(global_var.Model)
+    model_name = model.name if model else "rwkv"
+
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": model_name,
+                "object": "model",
+                "owned_by": "rwkv",
+                "root": model_name,
+                "parent": None,
+            },
+            *fake_models,
+        ],
+    }
+
+
+@router.get("/v1/models/{model_id}", tags=["MISC"])
+@router.get("/models/{model_id}", tags=["MISC"])
+def model(model_id: str):
+    for fake_model in fake_models:
+        if fake_model["id"] == model_id:
+            return fake_model
+
+    if "rwkv" in model_id.lower():
+        model: AbstractRWKV = global_var.get(global_var.Model)
+        model_name = model.name if model else "rwkv"
+        return {
+            "id": model_name,
+            "object": "model",
+            "owned_by": "rwkv",
+            "root": model_name,
+            "parent": None,
+        }
+
+    raise HTTPException(
+        status.HTTP_404_NOT_FOUND,
+        {
+            "error": {
+                "message": f"The model '{model_id}' does not exist",
+                "type": "invalid_request_error",
+                "param": "model",
+                "code": "model_not_found",
+            }
+        },
+    )

backend-python/routes/state_cache.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
 from utils.log import quick_log
 from fastapi import APIRouter, HTTPException, Request, Response, status
 from pydantic import BaseModel
@@ -60,7 +60,7 @@ def enable_state_cache():
 
 class AddStateBody(BaseModel):
     prompt: str
-    tokens: List[str]
+    tokens: List[Union[str, int]]
     state: Any
     logits: Any
 

backend-python/rwkv_pip/beta/cuda/att_one.cu

@@ -0,0 +1,124 @@
+#include "ATen/ATen.h"
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+
+#include "element_wise.h"
+#include "util.h"
+
+// Equivalent Python code:
+// ww = t_first + k
+// p = torch.maximum(pp, ww)
+// e1 = torch.exp(pp - p)
+// e2 = torch.exp(ww - p)
+// wkv = ((e1 * aa + e2 * v) / (e1 * bb + e2)).to(dtype=x.dtype)
+// ww = t_decay + pp
+// p = torch.maximum(ww, k)
+// e1 = torch.exp(ww - p)
+// e2 = torch.exp(k - p)
+// t1 = e1 * aa + e2 * v
+// t2 = e1 * bb + e2
+// r = r * wkv
+// return t1, t2, p, r
+struct WkvForwardOne {
+  const float *t_first;
+  const float *k;
+  const float *pp;
+  const float *aa;
+  const float *bb;
+  const float *t_decay;
+  const float *v;
+  /* out */ float *t1;
+  /* out */ float *t2;
+  /* out */ float *p;
+  /* in & out */ half *r;
+
+  __device__ void operator()(int i) const {
+    float ww = t_first[i] + k[i];
+    float pp_ = pp[i];
+    float p_ = (pp_ > ww) ? pp_ : ww;
+    float e1 = expf(pp_ - p_);
+    float e2 = expf(ww - p_);
+    float aa_ = aa[i];
+    float bb_ = bb[i];
+    float v_ = v[i];
+    r[i] = __hmul(r[i], __float2half(((e1 * aa_ + e2 * v_) / (e1 * bb_ + e2))));
+    ww = t_decay[i] + pp_;
+    float k_ = k[i];
+    p_ = (ww > k_) ? ww : k_;
+    e1 = expf(ww - p_);
+    e2 = expf(k_ - p_);
+    t1[i] = e1 * aa_ + e2 * v_;
+    t2[i] = e1 * bb_ + e2;
+    p[i] = p_;
+  }
+};
+
+/*
+   Equivalent Python code:
+   kx = xx * k_mix + sx * (1 - k_mix)
+   vx = xx * v_mix + sx * (1 - v_mix)
+   rx = xx * r_mix + sx * (1 - r_mix)
+*/
+
+struct Mix {
+  const half *xx;
+  const half *sx;
+  const half *k_mix;
+  const half *v_mix;
+  const half *r_mix;
+  /* out */ half *kx;
+  /* out */ half *vx;
+  /* out */ half *rx;
+
+  __device__ void operator()(int i) const {
+    half xx_ = xx[i];
+    half sx_ = sx[i];
+    half k_mix_ = k_mix[i];
+    half v_mix_ = v_mix[i];
+    half r_mix_ = r_mix[i];
+    kx[i] = __hadd(__hmul(xx_, k_mix_),
+                   __hmul(sx_, __hsub(__float2half(1), k_mix_)));
+    vx[i] = __hadd(__hmul(xx_, v_mix_),
+                   __hmul(sx_, __hsub(__float2half(1), v_mix_)));
+    rx[i] = __hadd(__hmul(xx_, r_mix_),
+                   __hmul(sx_, __hsub(__float2half(1), r_mix_)));
+  }
+};
+
+using torch::Tensor;
+
+void gemm_fp16_cublas(Tensor a, Tensor b, Tensor c);
+
+Tensor att_one(Tensor x, Tensor ln_w, Tensor ln_b, Tensor sx, Tensor k_mix,
+             Tensor v_mix, Tensor r_mix, Tensor kw,
+             /* imm */ Tensor kx, Tensor vw, /* imm */ Tensor vx, Tensor rw,
+             /* imm */ Tensor rx, Tensor ow, Tensor t_first,
+             /* imm */ Tensor k, Tensor pp, Tensor ww, Tensor aa, Tensor bb,
+             Tensor t_decay, /* imm */ Tensor v, /* in & out */ Tensor r,
+             /* out */ Tensor x_plus_out, /* out */ Tensor t1,
+             /* out */ Tensor t2, /* out */ Tensor p) {
+  Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
+  element_wise(Mix{data_ptr<half>(xx), data_ptr<half>(sx),
+                   data_ptr<half>(k_mix), data_ptr<half>(v_mix),
+                   data_ptr<half>(r_mix), data_ptr<half>(kx),
+                   data_ptr<half>(vx), data_ptr<half>(rx)},
+               x.numel());
+
+  gemm_fp16_cublas(kx, kw, k);
+  gemm_fp16_cublas(vx, vw, v);
+  gemm_fp16_cublas(rx, rw, r);
+  at::sigmoid_(r);
+
+  element_wise(WkvForwardOne{data_ptr<float>(t_first), data_ptr<float>(k),
+                             data_ptr<float>(pp), data_ptr<float>(aa),
+                             data_ptr<float>(bb), data_ptr<float>(t_decay),
+                             data_ptr<float>(v), data_ptr<float>(t1),
+                             data_ptr<float>(t2), data_ptr<float>(p),
+                             data_ptr<half>(r)},
+               x.numel());
+
+  gemm_fp16_cublas(r, ow, x_plus_out);
+  x_plus_out += x;
+  return xx;
+}

backend-python/rwkv_pip/beta/cuda/att_seq.cu

@@ -0,0 +1,179 @@
+#include "ATen/ATen.h"
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+
+#include "util.h"
+#include "element_wise.h"
+
+using torch::Tensor;
+
+void gemm_fp16_cublas(Tensor a, Tensor b, Tensor c);
+void gemm_fp16_cublas(const void *a, const void *b, void *c, int m,
+                      int n, int k, bool output_fp32);
+
+// based on `kernel_wkv_forward`, fusing more operations
+__global__ void kernel_wkv_forward_new(
+    const int B, const int T, const int C, const float *__restrict__ const _w,
+    const float *__restrict__ const _u, const float *__restrict__ const _k,
+    const float *__restrict__ const _v, const half *__restrict__ const r,
+    half *__restrict__ const _y, float *__restrict__ const _aa,
+    float *__restrict__ const _bb, float *__restrict__ const _pp) {
+  const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  const int _b = idx / C;
+  const int _c = idx % C;
+  const int _offset = _b * T * C + _c;
+  const int _state_offset = _b * C + _c;
+
+  float u = _u[_c];
+  float w = _w[_c];
+  const float *__restrict__ const k = _k + _offset;
+  const float *__restrict__ const v = _v + _offset;
+  half *__restrict__ const y = _y + _offset;
+
+  float aa = _aa[_state_offset];
+  float bb = _bb[_state_offset];
+  float pp = _pp[_state_offset];
+  for (int i = 0; i < T; i++) {
+    const int ii = i * C;
+    const float kk = k[ii];
+    const float vv = v[ii];
+    float ww = u + kk;
+    float p = max(pp, ww);
+    float e1 = exp(pp - p);
+    float e2 = exp(ww - p);
+    y[ii] = __float2half((e1 * aa + e2 * vv) / (e1 * bb + e2));
+    ww = w + pp;
+    p = max(ww, kk);
+    e1 = exp(ww - p);
+    e2 = exp(kk - p);
+    aa = e1 * aa + e2 * vv;
+    bb = e1 * bb + e2;
+    pp = p;
+  }
+  _aa[_state_offset] = aa;
+  _bb[_state_offset] = bb;
+  _pp[_state_offset] = pp;
+}
+
+void cuda_wkv_forward_new(int B, int T, int C, float *w, float *u, float *k,
+                          float *v, half *r, half *y, float *aa, float *bb,
+                          float *pp) {
+  dim3 threadsPerBlock(min(C, 32));
+  assert(B * C % threadsPerBlock.x == 0);
+  dim3 numBlocks(B * C / threadsPerBlock.x);
+  kernel_wkv_forward_new<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, r,
+                                                         y, aa, bb, pp);
+}
+
+__global__ void _att_mix(const half *xx, const half *sx, const half *k_mix,
+                        const half *v_mix, const half *r_mix,
+                        const int outer_size, const int inner_size, half *kx,
+                        half *vx, half *rx) {
+  for (int idx2 = blockIdx.x * blockDim.x + threadIdx.x; idx2 < inner_size;
+       idx2 += blockDim.x * gridDim.x) {
+    half k_mix_ = k_mix[idx2];
+    half v_mix_ = v_mix[idx2];
+    half r_mix_ = r_mix[idx2];
+    for (int row = 0; row < outer_size; ++row) {
+      int idx1 = row * inner_size + idx2;
+      half xx_ = xx[idx1];
+      half sx_ = sx[idx1];
+      kx[idx1] = __hadd(__hmul(xx_, k_mix_),
+                        __hmul(sx_, __hsub(__float2half(1), k_mix_)));
+      vx[idx1] = __hadd(__hmul(xx_, v_mix_),
+                        __hmul(sx_, __hsub(__float2half(1), v_mix_)));
+      rx[idx1] = __hadd(__hmul(xx_, r_mix_),
+                        __hmul(sx_, __hsub(__float2half(1), r_mix_)));
+    }
+  }
+}
+
+void att_mix(const half *xx, const half *sx, const half *k_mix,
+            const half *v_mix, const half *r_mix, const int outer_size,
+            const int inner_size, half *kx, half *vx, half *rx) {
+  // 256 is good enough on most GPUs
+  const int32_t BLOCK_SIZE = 256;
+  assert(inner_size % BLOCK_SIZE == 0);
+  _att_mix<<<inner_size / BLOCK_SIZE, BLOCK_SIZE>>>(
+      xx, sx, k_mix, v_mix, r_mix, outer_size, inner_size, kx, vx, rx);
+}
+
+struct InplaceSigmoid {
+  __device__ __forceinline__ half operator()(int i) const {
+    ptr[i] = __float2half(1.0 / (1.0 + exp(-__half2float(ptr[i]))));
+  }
+  half *ptr;
+};
+
+struct InplaceMul {
+  __device__ __forceinline__ half operator()(int i) const {
+    y[i] = __hmul(x[i], y[i]);
+  }
+  half *y;
+  half *x;
+};
+
+/*
+   Equivalent Python code:
+
+   xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+   sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+   kx = xx * k_mix + sx * (1 - k_mix)
+   vx = xx * v_mix + sx * (1 - v_mix)
+   rx = xx * r_mix + sx * (1 - r_mix)
+
+   r = torch.sigmoid(gemm(rx, rw))
+   k = gemm(kx, kw, output_dtype=torch.float32)
+   v = gemm(vx, vw, output_dtype=torch.float32)
+
+   T = x.shape[0]
+   for t in range(T):
+       kk = k[t]
+       vv = v[t]
+       ww = t_first + kk
+       p = torch.maximum(pp, ww)
+       e1 = torch.exp(pp - p)
+       e2 = torch.exp(ww - p)
+       sx[t] = ((e1 * aa + e2 * vv) / (e1 * bb + e2)).to(dtype=x.dtype)
+       ww = t_decay + pp
+       p = torch.maximum(ww, kk)
+       e1 = torch.exp(ww - p)
+       e2 = torch.exp(kk - p)
+       aa = e1 * aa + e2 * vv
+       bb = e1 * bb + e2
+       pp = p
+   out = gemm(r * sx, ow)
+   return x + out, xx[-1,:], aa, bb, pp
+*/
+Tensor att_seq(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor v_mix, Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               Tensor ow, Tensor t_first, Tensor pp, Tensor aa, Tensor bb,
+               Tensor t_decay, /* imm */ Tensor buf, /* out */ Tensor x_plus_out) {
+  Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
+  sx = at::cat({sx.unsqueeze(0), xx.slice(0, 0, -1)}, 0);
+  char* buf_ptr = (char*)buf.data_ptr();
+  half* kx = (half*)buf_ptr;
+  half* vx = kx + x.numel();
+  half* rx = vx + x.numel();
+  half* wkv_y = rx + x.numel();
+  att_mix(data_ptr<half>(xx), data_ptr<half>(sx), data_ptr<half>(k_mix),
+         data_ptr<half>(v_mix), data_ptr<half>(r_mix), xx.size(0), xx.size(1),
+         kx, vx, rx);
+  float* k = reinterpret_cast<float*>(wkv_y + x.numel());
+  float* v = k + x.size(0) * kw.size(1);
+  half* r = reinterpret_cast<half*>(v + x.size(0) * vw.size(1));
+
+  gemm_fp16_cublas(kx, kw.data_ptr(), k, x.size(0), kw.size(1), kw.size(0), true);
+  gemm_fp16_cublas(vx, vw.data_ptr(), v, x.size(0), vw.size(1), vw.size(0), true);
+  gemm_fp16_cublas(rx, rw.data_ptr(), r, x.size(0), rw.size(1), rw.size(0), false);
+  element_wise(InplaceSigmoid{r}, x.size(0) * rw.size(1));
+  cuda_wkv_forward_new(1, x.size(0), x.size(1), data_ptr<float>(t_decay),
+                       data_ptr<float>(t_first), k, v, r,
+                       wkv_y, data_ptr<float>(aa),
+                       data_ptr<float>(bb), data_ptr<float>(pp));
+  element_wise(InplaceMul{wkv_y, r}, x.numel());
+  gemm_fp16_cublas(wkv_y, ow.data_ptr(), x_plus_out.data_ptr(), x.size(0), ow.size(1), ow.size(0), false);
+  x_plus_out += x;
+  return xx;
+}

backend-python/rwkv_pip/beta/cuda/element_wise.h

@@ -0,0 +1,21 @@
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+
+template <typename Func> __global__ void _element_wise(Func func, int n) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n;
+       i += blockDim.x * gridDim.x) {
+    func(i);
+  }
+}
+
+// NOTE: packed data type (e.g. float4) is a overkill for current sizes
+// (4096 in 7B model and 768 in 0.1B model),
+// and is not faster than the plain float version.
+template <typename Func>
+void element_wise(Func func, int n) {
+  // 256 is good enough on most GPUs
+  const int32_t BLOCK_SIZE = 256;
+  assert(n % BLOCK_SIZE == 0);
+  _element_wise<<<n / BLOCK_SIZE, BLOCK_SIZE>>>(func, n);
+}

backend-python/rwkv_pip/beta/cuda/ffn.cu

@@ -0,0 +1,165 @@
+#include "ATen/ATen.h"
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+
+#include "element_wise.h"
+#include "util.h"
+
+using torch::Tensor;
+
+void gemm_fp16_cublas(const void *a, const void *b, void *c, int ori_m,
+                      int ori_n, int ori_k, bool output_fp32);
+
+__global__ void _ffn_seq_mix(const half *xx, const half *sx, const half *k_mix,
+                             const half *r_mix, const int outer_size,
+                             const int inner_size, half *kx, half *rx) {
+  for (int idx2 = blockIdx.x * blockDim.x + threadIdx.x; idx2 < inner_size;
+       idx2 += blockDim.x * gridDim.x) {
+    half k_mix_ = k_mix[idx2];
+    half r_mix_ = r_mix[idx2];
+    for (int row = 0; row < outer_size; ++row) {
+      int idx1 = row * inner_size + idx2;
+      half xx_ = xx[idx1];
+      half sx_ = sx[idx1];
+      kx[idx1] = __hadd(__hmul(xx_, k_mix_),
+                        __hmul(sx_, __hsub(__float2half(1), k_mix_)));
+      rx[idx1] = __hadd(__hmul(xx_, r_mix_),
+                        __hmul(sx_, __hsub(__float2half(1), r_mix_)));
+    }
+  }
+}
+
+void ffn_seq_mix(const half *xx, const half *sx, const half *k_mix,
+                 const half *r_mix, const int outer_size, const int inner_size,
+                 half *kx, half *rx) {
+  // 256 is good enough on most GPUs
+  const int32_t BLOCK_SIZE = 256;
+  assert(inner_size % BLOCK_SIZE == 0);
+  _ffn_seq_mix<<<inner_size / BLOCK_SIZE, BLOCK_SIZE>>>(
+      xx, sx, k_mix, r_mix, outer_size, inner_size, kx, rx);
+}
+
+struct InplaceSigmoid {
+  __device__ __forceinline__ void operator()(int i) const {
+    ptr[i] = __float2half(1.0 / (1.0 + exp(-__half2float(ptr[i]))));
+  }
+  half *ptr;
+};
+
+struct InplaceReLUAndSquare {
+  __device__ __forceinline__ void operator()(int i) const {
+    // __hmax is not defined in old cuda
+    if (__hgt(ptr[i], __float2half(0))) {
+      ptr[i] = __hmul(ptr[i], ptr[i]);
+    } else {
+      ptr[i] = __float2half(0);
+    }
+  }
+  half *ptr;
+};
+
+struct InplaceFma {
+  __device__ __forceinline__ void operator()(int i) const {
+    a[i] = __hfma(a[i], b[i], c[i]);
+  }
+  half *a;
+  const half *b;
+  const half *c;
+};
+
+/*
+   Equivalent Python code:
+
+   xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+   sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
+   kx = xx * k_mix + sx * (1 - k_mix)
+   rx = xx * r_mix + sx * (1 - r_mix)
+
+   r = torch.sigmoid(gemm(rx, rw))
+   vx = torch.square(torch.relu(gemm(kx, kw)))
+   out = r * gemm(vx, vw)
+   return x + out, xx[-1,:]
+*/
+Tensor ffn_seq(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               /* imm */ Tensor buf,
+               /* out */ Tensor x_plus_out) {
+  Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
+  sx = at::cat({sx.unsqueeze(0), xx.slice(0, 0, -1)}, 0);
+  char *buf_ptr = (char *)buf.data_ptr();
+  half *kx = (half *)buf_ptr;
+  half *rx = kx + x.numel();
+  half *vx = rx + x.numel();
+  half *r = vx + x.size(0) * kw.size(1);
+  ffn_seq_mix(data_ptr<half>(xx), data_ptr<half>(sx), data_ptr<half>(k_mix),
+              data_ptr<half>(r_mix), xx.size(0), xx.size(1), kx, rx);
+
+  gemm_fp16_cublas(rx, rw.data_ptr(), r, x.size(0), rw.size(1), x.size(1),
+                   false);
+  element_wise(InplaceSigmoid{r}, x.size(0) * rw.size(1));
+  gemm_fp16_cublas(kx, kw.data_ptr(), vx, x.size(0), kw.size(1), x.size(1),
+                   false);
+  element_wise(InplaceReLUAndSquare{vx}, x.size(0) * kw.size(1));
+  gemm_fp16_cublas(vx, vw.data_ptr(), x_plus_out.data_ptr(), x.size(0),
+                   vw.size(1), vw.size(0), false);
+  element_wise(InplaceFma{data_ptr<half>(x_plus_out), r, data_ptr<half>(x)},
+               x_plus_out.numel());
+  return xx;
+}
+
+struct FfnOneMix {
+  __device__ __forceinline__ void operator()(int idx) {
+    half k_mix_ = k_mix[idx];
+    half r_mix_ = r_mix[idx];
+    half xx_ = xx[idx];
+    half sx_ = sx[idx];
+    kx[idx] = __hadd(__hmul(xx_, k_mix_),
+                     __hmul(sx_, __hsub(__float2half(1), k_mix_)));
+    rx[idx] = __hadd(__hmul(xx_, r_mix_),
+                     __hmul(sx_, __hsub(__float2half(1), r_mix_)));
+  }
+  half *k_mix;
+  half *r_mix;
+  half *xx;
+  half *sx;
+  half *kx;
+  half *rx;
+};
+
+/*
+  Equivalent Python code:
+
+  xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
+  kx = xx * k_mix + sx * (1 - k_mix)
+  rx = xx * r_mix + sx * (1 - r_mix)
+
+  r = torch.sigmoid(gemm(rx, rw))
+  vx = torch.square(torch.relu(gemm(kx, kw)))
+  out = r * gemm(vx, vw)
+  return x + out, xx
+*/
+Tensor ffn_one(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               /* imm */ Tensor buf,
+               /* out */ Tensor x_plus_out) {
+  Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
+  char *buf_ptr = (char *)buf.data_ptr();
+  half *kx = (half *)buf_ptr;
+  half *rx = kx + x.numel();
+  half *vx = rx + x.numel();
+  half *r = vx + x.size(0) * kw.size(1);
+  element_wise(FfnOneMix{data_ptr<half>(k_mix), data_ptr<half>(r_mix),
+                         data_ptr<half>(xx), data_ptr<half>(sx), kx, rx},
+               x.numel());
+  // vector * matrix, so m = 1
+  gemm_fp16_cublas(rx, rw.data_ptr(), r, 1, rw.size(1), rw.size(0), false);
+  element_wise(InplaceSigmoid{r}, rw.size(1));
+  gemm_fp16_cublas(kx, kw.data_ptr(), vx, 1, kw.size(1), kw.size(0), false);
+  element_wise(InplaceReLUAndSquare{vx}, kw.size(1));
+  gemm_fp16_cublas(vx, vw.data_ptr(), x_plus_out.data_ptr(), 1, vw.size(1),
+                   vw.size(0), false);
+  element_wise(InplaceFma{data_ptr<half>(x_plus_out), r, data_ptr<half>(x)},
+               x_plus_out.numel());
+  return xx;
+}

backend-python/rwkv_pip/beta/cuda/gemm_fp16_cublas.cpp

@@ -0,0 +1,80 @@
+#include <cublas_v2.h>
+#include <cuda.h>
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+
+#define CUBLAS_CHECK(condition)                                                \
+  for (cublasStatus_t _cublas_check_status = (condition);                      \
+       _cublas_check_status != CUBLAS_STATUS_SUCCESS;)                         \
+    throw std::runtime_error("cuBLAS error " +                                 \
+                             std::to_string(_cublas_check_status) + " at " +   \
+                             std::to_string(__LINE__));
+
+#define CUDA_CHECK(condition)                                                  \
+  for (cudaError_t _cuda_check_status = (condition);                           \
+       _cuda_check_status != cudaSuccess;)                                     \
+    throw std::runtime_error(                                                  \
+        "CUDA error " + std::string(cudaGetErrorString(_cuda_check_status)) +  \
+        " at " + std::to_string(__LINE__));
+
+cublasHandle_t get_cublas_handle() {
+  static cublasHandle_t cublas_handle = []() {
+    cublasHandle_t handle = nullptr;
+    CUBLAS_CHECK(cublasCreate(&handle));
+#if CUDA_VERSION < 11000
+    CUBLAS_CHECK(cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH));
+#else
+    CUBLAS_CHECK(cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH));
+#endif // CUDA_VERSION < 11000
+    return handle;
+  }();
+  return cublas_handle;
+}
+
+/*
+  NOTE: blas gemm is column-major by default, but we need row-major output.
+  The data of row-major, transposed matrix is exactly the same as the
+  column-major, non-transposed matrix, and C = A * B ---> C^T = B^T * A^T
+ */
+void gemm_fp16_cublas(const void *a, const void *b, void *c, int ori_m,
+                      int ori_n, int ori_k, bool output_fp32) {
+  const auto cuda_data_type = CUDA_R_16F;
+  const auto cuda_c_data_type = output_fp32 ? CUDA_R_32F : CUDA_R_16F;
+  const auto compute_type = CUDA_R_32F;
+  const float sp_alpha = 1.f;
+  // use CUBLAS_OP_N. see the notes above
+  const cublasOperation_t cublas_trans_a = CUBLAS_OP_N;
+  const cublasOperation_t cublas_trans_b = CUBLAS_OP_N;
+  // m = (B^T).size(0) = B.size(1) = n;
+  const int cublas_m = ori_n;
+  const int cublas_k = ori_k;
+  // comptiable with rwkv one mode, where 1-D tensor * 2-D tensor
+  // const int n = a.dense_dim() == 1 ? 1 : a.size(0);
+  const int cublas_n = ori_m;
+  const int cublas_lda = cublas_m;
+  const int cublas_ldb = cublas_k;
+  const int cublas_ldc = cublas_m;
+  cublasHandle_t cublas_handle = get_cublas_handle();
+
+#if CUDA_VERSION >= 11000
+  cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT;
+#else
+  cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
+#endif
+  const float sp_beta = 0.f;
+  CUBLAS_CHECK(cublasGemmEx(
+      cublas_handle, cublas_trans_a, cublas_trans_b, cublas_m, cublas_n,
+      cublas_k, &sp_alpha, b, cuda_data_type, cublas_lda,
+      a, cuda_data_type, cublas_ldb, &sp_beta, c,
+      cuda_c_data_type, cublas_ldc, compute_type, algo));
+}
+
+void gemm_fp16_cublas(torch::Tensor a, torch::Tensor b, torch::Tensor c) {
+  // comptiable with rwkv one mode, 1-D tensor * 2-D tensor
+  const int m = a.dense_dim() == 1 ? 1 : a.size(0);
+  const int n = b.size(1);
+  const int k = b.size(0);
+  gemm_fp16_cublas(a.data_ptr(), b.data_ptr(), c.data_ptr(), m, n, k,
+                   c.dtype() == torch::kFloat32);
+}

backend-python/rwkv_pip/beta/cuda/operators.cu

@@ -0,0 +1,246 @@
+#include <stdio.h>
+#include <assert.h>
+#include "ATen/ATen.h"
+#include <cuda_fp16.h>
+#define MIN_VALUE (-1e38)
+typedef at::Half fp16;
+__half *cast(fp16 *ptr) {
+    return reinterpret_cast<__half *>(ptr);
+}
+
+template <typename F>
+__global__ void kernel_wkv_forward(const int B, const int T, const int C,
+                               const float *__restrict__ const _w, const float *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v,
+                               F *__restrict__ const _y, float *__restrict__ const _aa, float *__restrict__ const _bb, float *__restrict__ const _pp) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+    const int _state_offset = _b * C + _c;
+
+    float u = _u[_c];
+    float w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    F *__restrict__ const y = _y + _offset;
+
+    float aa = _aa[_state_offset];
+    float bb = _bb[_state_offset];
+    float pp = _pp[_state_offset];
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        y[ii] = F((e1 * aa + e2 * vv) / (e1 * bb + e2));
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    _aa[_state_offset] = aa;
+    _bb[_state_offset] = bb;
+    _pp[_state_offset] = pp;
+}
+
+template <typename F>
+void cuda_wkv_forward(int B, int T, int C, float *w, float *u, F *k, F *v, F *y, float *aa, float *bb, float *pp) {
+    dim3 threadsPerBlock( min(C, 32) );
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_wkv_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, aa, bb, pp);
+}
+
+template void cuda_wkv_forward<fp16>(
+    int B, int T, int C,
+    float *w, float *u, fp16 *k, fp16 *v, fp16 *y,
+    float *aa, float *bb, float *pp);
+template void cuda_wkv_forward<float>(
+    int B, int T, int C,
+    float *w, float *u, float *k, float *v, float *y,
+    float *aa, float *bb, float *pp);
+
+__global__ void kernel_mm_seq_fp32i8(
+    const int B, const int N, const int M,
+    const float *__restrict__ const x, const int x_stride,
+    const uint8_t *__restrict__ const w, const int w_stride,
+    const float *__restrict__ const mx,
+    const float *__restrict__ const rx,
+    const float *__restrict__ const my,
+    const float *__restrict__ const ry,
+    float *__restrict__ const y, const int y_stride) {
+
+    const int i = blockIdx.x * blockDim.x + threadIdx.x;
+    const int k = blockIdx.y * blockDim.y + threadIdx.y;
+
+    if (i < B && k < M) {
+        float y_local = 0;
+        for (int j = 0; j < N; ++j) {
+            y_local += x[i * x_stride + j] * (
+                (float(w[j * w_stride + k]) + 0.5f)
+                * rx[k] * ry[j] + mx[k] + my[j]
+            );
+        }
+        y[i * y_stride + k] = y_local;
+    }
+}
+
+template <typename F>
+void cuda_mm8_seq(int B, int N, int M,
+                  F *x, int x_stride,
+                  uint8_t *w, int w_stride,
+                  F *mx, F *rx,
+                  F *my, F *ry,
+                  F *y, int y_stride);
+
+template <>
+void cuda_mm8_seq<float>(int B, int N, int M,
+                         float *x, int x_stride,
+                         uint8_t *w, int w_stride,
+                         float *mx, float *rx,
+                         float *my, float *ry,
+                         float *y, int y_stride) {
+    dim3 blockSize(1, 128);
+    dim3 gridSize((B + blockSize.x - 1) / blockSize.x, (M + blockSize.y - 1) / blockSize.y);
+    kernel_mm_seq_fp32i8<<<gridSize, blockSize>>>(
+        B, N, M, x, x_stride, w, w_stride,
+        mx, rx, my, ry, y, y_stride);
+}
+
+__global__ void kernel_mm_seq_fp16i8(
+    const int B, const int N, const int M,
+    const __half *__restrict__ const x, const int x_stride,
+    const uint8_t *__restrict__ const w, const int w_stride,
+    const __half *__restrict__ const mx,
+    const __half *__restrict__ const rx,
+    const __half *__restrict__ const my,
+    const __half *__restrict__ const ry,
+    __half *__restrict__ const y, const int y_stride) {
+
+    const int i = blockIdx.x * blockDim.x + threadIdx.x;
+    const int k = blockIdx.y * blockDim.y + threadIdx.y;
+
+    if (i < B && k < M) {
+        float y_local = 0;
+        for (int j = 0; j < N; ++j) {
+            y_local += __half2float(x[i * x_stride + j]) * (
+                (float(w[j * w_stride + k]) + 0.5f)
+                * __half2float(rx[k]) * __half2float(ry[j])
+                + __half2float(mx[k]) + __half2float(my[j])
+            );
+        }
+        y[i * y_stride + k] = __float2half(y_local);
+    }
+}
+
+template <>
+void cuda_mm8_seq<fp16>(int B, int N, int M,
+                        fp16 *x, int x_stride,
+                        uint8_t *w, int w_stride,
+                        fp16 *mx, fp16 *rx,
+                        fp16 *my, fp16 *ry,
+                        fp16 *y, int y_stride) {
+    dim3 blockSize(1, 128);
+    dim3 gridSize((B + blockSize.x - 1) / blockSize.x, (M + blockSize.y - 1) / blockSize.y);
+    kernel_mm_seq_fp16i8<<<gridSize, blockSize>>>(
+        B, N, M, cast(x), x_stride, w, w_stride,
+        cast(mx), cast(rx), cast(my), cast(ry), cast(y), y_stride);
+}
+
+#define MM8_ONE_JSPLIT 24
+#define MM8_ONE_TILE 1024
+
+__global__ void kernel_mm_one_fp32i8(
+    const int N, const int M,
+    const float *__restrict__ const x,
+    const uint8_t *__restrict__ const w, const int w_stride,
+    const float *__restrict__ const mx,
+    const float *__restrict__ const rx,
+    const float *__restrict__ const my,
+    const float *__restrict__ const ry,
+    float *__restrict__ const y) {
+
+    const int k = blockIdx.y * blockDim.y + threadIdx.y;
+    const int j0 = min(N, blockIdx.x * ((N + MM8_ONE_JSPLIT - 1) / MM8_ONE_JSPLIT));
+    const int j1 = min(N, (blockIdx.x + 1) * ((N + MM8_ONE_JSPLIT - 1) / MM8_ONE_JSPLIT));
+
+    if (k < M) {
+        float y_local = 0;
+        for (int j = j0; j < j1; ++j) {
+            y_local += x[j] * (
+                (float(w[j * w_stride + k]) + 0.5f)
+                * rx[k] * ry[j] + mx[k] + my[j]
+            );
+        }
+        atomicAdd(&y[k], y_local);
+    }
+}
+
+template <typename F>
+void cuda_mm8_one(int N, int M,
+                  F *x,
+                  uint8_t *w, int w_stride,
+                  F *mx, F *rx,
+                  F *my, F *ry,
+                  float *y);
+
+template <>
+void cuda_mm8_one<float>(int N, int M,
+                        float *x,
+                        uint8_t *w, int w_stride,
+                        float *mx, float *rx,
+                        float *my, float *ry,
+                        float *y) {
+    dim3 blockSize(1, MM8_ONE_TILE);
+    dim3 gridSize(MM8_ONE_JSPLIT, (M + blockSize.y - 1) / blockSize.y);
+    kernel_mm_one_fp32i8<<<gridSize, blockSize>>>(
+        N, M, x, w, w_stride,
+        mx, rx, my, ry, y);
+}
+
+__global__ void kernel_mm_one_fp16i8(
+    const int N, const int M,
+    const __half *__restrict__ const x,
+    const uint8_t *__restrict__ const w, const int w_stride,
+    const __half *__restrict__ const mx,
+    const __half *__restrict__ const rx,
+    const __half *__restrict__ const my,
+    const __half *__restrict__ const ry,
+    float *__restrict__ const y) {
+
+    const int k = blockIdx.y * blockDim.y + threadIdx.y;
+    const int j0 = min(N, blockIdx.x * ((N + MM8_ONE_JSPLIT - 1) / MM8_ONE_JSPLIT));
+    const int j1 = min(N, (blockIdx.x + 1) * ((N + MM8_ONE_JSPLIT - 1) / MM8_ONE_JSPLIT));
+
+    if (k < M) {
+        float y_local = 0;
+        for (int j = j0; j < j1; ++j) {
+            y_local += __half2float(x[j]) * (
+                (float(w[j * w_stride + k]) + 0.5f)
+                * __half2float(rx[k]) * __half2float(ry[j])
+                + __half2float(mx[k]) + __half2float(my[j])
+            );
+        }
+        atomicAdd(&y[k], y_local);
+    }
+}
+
+template <>
+void cuda_mm8_one<fp16>(int N, int M,
+                        fp16 *x,
+                        uint8_t *w, int w_stride,
+                        fp16 *mx, fp16 *rx,
+                        fp16 *my, fp16 *ry,
+                        float *y) {
+    dim3 blockSize(1, MM8_ONE_TILE);
+    dim3 gridSize(MM8_ONE_JSPLIT, (M + blockSize.y - 1) / blockSize.y);
+    kernel_mm_one_fp16i8<<<gridSize, blockSize>>>(
+        N, M, cast(x), w, w_stride,
+        cast(mx), cast(rx), cast(my), cast(ry), y);
+}

backend-python/rwkv_pip/beta/cuda/util.h

@@ -0,0 +1,7 @@
+#include "ATen/ATen.h"
+#include <cuda_fp16.h>
+
+template <typename T> T *data_ptr(torch::Tensor x) { return x.data_ptr<T>(); }
+template <> inline half *data_ptr(torch::Tensor x) {
+  return reinterpret_cast<half *>(x.data_ptr<at::Half>());
+}

backend-python/rwkv_pip/beta/cuda/wrapper.cpp

@@ -0,0 +1,167 @@
+#include <torch/extension.h>
+#include "ATen/ATen.h"
+#include <iostream>
+#include <c10/cuda/CUDAGuard.h>
+
+typedef at::Half fp16;
+
+template <typename F>
+void cuda_wkv_forward(int B, int T, int C,
+                      float *w, float *u, F *k, F *v, F *y,
+                      float *aa, float *bb, float *pp);
+template <typename F>
+void cuda_mm8_seq(int B, int N, int M,
+                  F *x, int x_stride,
+                  uint8_t *w, int w_stride,
+                  F *mx, F *rx,
+                  F *my, F *ry,
+                  F *y, int y_stride);
+template <typename F>
+void cuda_mm8_one(int N, int M,
+                  F *x,
+                  uint8_t *w, int w_stride,
+                  F *mx, F *rx,
+                  F *my, F *ry,
+                  float *y);
+
+void wkv_forward(int64_t B, int64_t T, int64_t C,
+                 torch::Tensor &w, torch::Tensor &u,
+                 torch::Tensor &k, torch::Tensor &v, torch::Tensor &y,
+                 torch::Tensor &aa, torch::Tensor &bb, torch::Tensor &pp) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(w));
+    switch (k.scalar_type()) {
+    case c10::ScalarType::Half:
+        cuda_wkv_forward(B, T, C,
+                         w.data_ptr<float>(), u.data_ptr<float>(),
+                         k.data_ptr<fp16>(), v.data_ptr<fp16>(), y.data_ptr<fp16>(),
+                         aa.data_ptr<float>(), bb.data_ptr<float>(), pp.data_ptr<float>());
+        break;
+    case c10::ScalarType::Float:
+        cuda_wkv_forward(B, T, C,
+                         w.data_ptr<float>(), u.data_ptr<float>(),
+                         k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>(),
+                         aa.data_ptr<float>(), bb.data_ptr<float>(), pp.data_ptr<float>());
+        break;
+    default:
+        assert(false && "Only FP16 and FP32 are currently supported");
+    }
+}
+
+void mm8_seq(int64_t B, int64_t N, int64_t M,
+             torch::Tensor &x, torch::Tensor &w,
+             torch::Tensor &mx, torch::Tensor &rx,
+             torch::Tensor &my, torch::Tensor &ry,
+             torch::Tensor &y) {
+    assert(x.stride(1) == 1);
+    assert(w.stride(1) == 1);
+    assert(mx.stride(0) == 1 && rx.stride(0) == 1);
+    assert(my.stride(0) == 1 && ry.stride(0) == 1);
+    assert(y.stride(1) == 1);
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(w));
+    switch (x.scalar_type()) {
+    case c10::ScalarType::Half:
+        cuda_mm8_seq(
+            B, N, M,
+            x.data_ptr<fp16>(), x.stride(0),
+            w.data_ptr<uint8_t>(), w.stride(0),
+            mx.data_ptr<fp16>(), rx.data_ptr<fp16>(),
+            my.data_ptr<fp16>(), ry.data_ptr<fp16>(),
+            y.data_ptr<fp16>(), y.stride(0));
+        break;
+    case c10::ScalarType::Float:
+        cuda_mm8_seq(
+            B, N, M,
+            x.data_ptr<float>(), x.stride(0),
+            w.data_ptr<uint8_t>(), w.stride(0),
+            mx.data_ptr<float>(), rx.data_ptr<float>(),
+            my.data_ptr<float>(), ry.data_ptr<float>(),
+            y.data_ptr<float>(), y.stride(0));
+        break;
+    default:
+        assert(false && "Only FP16 and FP32 are currently supported");
+    }
+}
+void mm8_one(int64_t N, int64_t M,
+             torch::Tensor &x, torch::Tensor &w,
+             torch::Tensor &mx, torch::Tensor &rx,
+             torch::Tensor &my, torch::Tensor &ry,
+             torch::Tensor &y) {
+    assert(x.stride(0) == 1);
+    assert(w.stride(1) == 1);
+    assert(mx.stride(0) == 1 && rx.stride(0) == 1);
+    assert(my.stride(0) == 1 && ry.stride(0) == 1);
+    assert(y.stride(0) == 1);
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(w));
+    switch (x.scalar_type()) {
+    case c10::ScalarType::Half:
+        cuda_mm8_one(
+            N, M,
+            x.data_ptr<fp16>(),
+            w.data_ptr<uint8_t>(), w.stride(0),
+            mx.data_ptr<fp16>(), rx.data_ptr<fp16>(),
+            my.data_ptr<fp16>(), ry.data_ptr<fp16>(),
+            y.data_ptr<float>());
+        break;
+    case c10::ScalarType::Float:
+        cuda_mm8_one(
+            N, M,
+            x.data_ptr<float>(),
+            w.data_ptr<uint8_t>(), w.stride(0),
+            mx.data_ptr<float>(), rx.data_ptr<float>(),
+            my.data_ptr<float>(), ry.data_ptr<float>(),
+            y.data_ptr<float>());
+        break;
+    default:
+        assert(false && "Only FP16 and FP32 are currently supported");
+    }
+}
+
+using torch::Tensor;
+
+void gemm_fp16_cublas(Tensor a, Tensor b, Tensor c);
+
+Tensor att_one(Tensor x, Tensor ln_w, Tensor ln_b, Tensor sx, Tensor k_mix,
+             Tensor v_mix, Tensor r_mix, Tensor kw,
+             /* imm */ Tensor kx, Tensor vw, /* imm */ Tensor vx, Tensor rw,
+             /* imm */ Tensor rx, Tensor ow, Tensor t_first,
+             /* imm */ Tensor k, Tensor pp, Tensor ww, Tensor aa, Tensor bb,
+             Tensor t_decay, /* imm */ Tensor v, /* in & out */ Tensor r,
+             /* out */ Tensor x_plus_out, /* out */ Tensor t1,
+             /* out */ Tensor t2, /* out */ Tensor p);
+
+Tensor att_seq(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor v_mix, Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               Tensor ow, Tensor t_first, Tensor pp, Tensor aa, Tensor bb,
+               Tensor t_decay, /* imm */ Tensor buf, /* out */ Tensor x_plus_out);
+
+Tensor ffn_seq(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               /* imm */ Tensor buf,
+               /* out */ Tensor x_plus_out);
+
+Tensor ffn_one(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
+               Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
+               /* imm */ Tensor buf,
+               /* out */ Tensor x_plus_out);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("wkv_forward", &wkv_forward, "wkv forward");
+    m.def("mm8_seq", &mm8_seq, "mm8 seq");
+    m.def("mm8_one", &mm8_one, "mm8 one");
+    m.def("gemm_fp16_cublas", &gemm_fp16_cublas, "gemv fp16 cublas");
+    m.def("att_one", &att_one, "att one");
+    m.def("att_seq", &att_seq, "att seq");
+    m.def("ffn_seq", &ffn_seq, "ffn seq");
+    m.def("ffn_one", &ffn_one, "ffn one");
+}
+
+TORCH_LIBRARY(rwkv, m) {
+    m.def("wkv_forward", wkv_forward);
+    m.def("mm8_seq", mm8_seq);
+    m.def("mm8_one", mm8_one);
+    m.def("gemm_fp16_cublas", gemm_fp16_cublas);
+    m.def("att_one", att_one);
+    m.def("att_seq", att_seq);
+    m.def("ffn_seq", ffn_seq);
+    m.def("ffn_one", ffn_one);
+}

backend-python/utils/log.py

@@ -2,6 +2,8 @@ import json
 import logging
 from typing import Any
 from fastapi import Request
+from pydantic import BaseModel
+from enum import Enum
 
 
 logger = logging.getLogger()
@@ -14,12 +16,21 @@ fh.setFormatter(formatter)
 logger.addHandler(fh)
 
 
+class ClsEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, BaseModel):
+            return obj.dict()
+        if isinstance(obj, Enum):
+            return obj.value
+        return super().default(obj)
+
+
 def quick_log(request: Request, body: Any, response: str):
     try:
         logger.info(
             f"Client: {request.client if request else ''}\nUrl: {request.url if request else ''}\n"
             + (
-                f"Body: {json.dumps(body.__dict__, default=vars, ensure_ascii=False)}\n"
+                f"Body: {json.dumps(body.__dict__, ensure_ascii=False, cls=ClsEncoder)}\n"
                 if body
                 else ""
             )

backend-python/utils/rwkv.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from enum import Enum, auto
 import os
 import pathlib
 import copy
@@ -9,6 +10,7 @@ from fastapi import HTTPException
 from pydantic import BaseModel, Field
 import numpy as np
 from routes import state_cache
+import global_var
 
 
 END_OF_TEXT = 0
@@ -18,9 +20,25 @@ END_OF_LINE_DOUBLE = 535
 os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
 
 
+class RWKVType(Enum):
+    Raven = auto()
+    World = auto()
+    Music = auto()
+
+
 class AbstractRWKV(ABC):
     def __init__(self, model: str, strategy: str, tokens_path: str):
-        from rwkv.model import RWKV as Model  # dynamic import to make RWKV_CUDA_ON work
+        rwkv_beta = global_var.get(global_var.Args).rwkv_beta
+
+        # dynamic import to make RWKV_CUDA_ON work
+        if rwkv_beta:
+            from rwkv_pip.beta.model import (
+                RWKV as Model,
+            )
+        else:
+            from rwkv.model import (
+                RWKV as Model,
+            )
         from rwkv_pip.utils import PIPELINE
 
         filename, _ = os.path.splitext(os.path.basename(model))
@@ -29,6 +47,7 @@ class AbstractRWKV(ABC):
         self.pipeline = PIPELINE(self.model, tokens_path)
         self.model_state = None
         self.model_tokens = []
+        self.rwkv_type: RWKVType = None
 
         self.max_tokens_per_generation = 500
         self.temperature = 1
@@ -213,7 +232,7 @@ class AbstractRWKV(ABC):
                 return state[0].tolist(), token_len
 
     def generate(
-        self, prompt: str, stop: Union[str, List[str]] = None
+        self, prompt: str, stop: Union[str, List[str], None] = None
     ) -> Iterable[Tuple[str, str, int, int]]:
         quick_log(None, None, "Generation Prompt:\n" + prompt)
         cache = None
@@ -343,10 +362,12 @@ class TextRWKV(AbstractRWKV):
 
         self.interface = ":"
         if "world" in self.name.lower():
+            self.rwkv_type = RWKVType.World
             self.user = "Question"
             self.bot = "Answer"
             self.END_OF_LINE = 11
         else:
+            self.rwkv_type = RWKVType.Raven
             self.user = "Bob"
             self.bot = "Alice"
             self.END_OF_LINE = 187
@@ -375,9 +396,19 @@ class TextRWKV(AbstractRWKV):
                 + occurrence[n] * self.penalty_alpha_frequency
             )
 
+        if i == 0:
+            for token in self.model_tokens:
+                token = int(token)
+                for xxx in occurrence:
+                    occurrence[xxx] *= 0.996
+                if token not in occurrence:
+                    occurrence[token] = 1
+                else:
+                    occurrence[token] += 1
+
     # Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
     def fix_tokens(self, tokens) -> List[int]:
-        if "world" in self.name.lower():
+        if self.rwkv_type == RWKVType.World:
             return tokens
         if len(tokens) > 0 and tokens[-1] == END_OF_LINE_DOUBLE:
             tokens = tokens[:-1] + [self.END_OF_LINE, self.END_OF_LINE]
@@ -417,9 +448,11 @@ The following is a coherent verbose detailed conversation between a girl named {
 {bot} likes to tell {user} a lot about herself and her opinions. \
 {bot} usually gives {user} kind, helpful and informative advices.\n
 """
-            if self.user == "Bob"
-            else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
-            + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
+            if self.rwkv_type == RWKVType.Raven
+            else (
+                f"{user}{interface} hi\n\n{bot}{interface} Hi. "
+                + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
+            )
         )
         logits, _ = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
         try:
@@ -444,6 +477,8 @@ class MusicRWKV(AbstractRWKV):
         self.top_p = 0.8
         self.top_k = 8
 
+        self.rwkv_type = RWKVType.Music
+
     def adjust_occurrence(self, occurrence: Dict, token: int):
         for n in occurrence:
             occurrence[n] *= 0.997  #### decay repetition penalty

frontend/src/_locales/ja/main.json

@@ -0,0 +1,248 @@
+{
+  "Home": "ホーム",
+  "Train": "トレーニング",
+  "About": "約",
+  "Settings": "設定",
+  "Go to chat page": "チャットページに移動する",
+  "Manage your configs": "あなたの設定を管理する",
+  "Manage models": "モデルの管理",
+  "Run": "実行",
+  "Offline": "オフライン",
+  "Starting": "起動中",
+  "Loading": "モデルを読み込み中",
+  "Working": "動作中",
+  "Stop": "停止",
+  "Enable High Precision For Last Layer": "最後の層で高精度を有効にする",
+  "Stored Layers": "メモリ層読み込み",
+  "Precision": "精度",
+  "Device": "デバイス",
+  "Convert model with these configs. Using a converted model will greatly improve the loading speed, but model parameters of the converted model cannot be modified.": "これらの設定でモデルを変換します。変換されたモデルを使用すると、読み込み速度が大幅に向上しますが、変換したモデルのパラメータを変更することはできません。",
+  "Manage Models": "モデルの管理",
+  "Model": "モデル",
+  "Model Parameters": "モデルのパラメータ",
+  "Frequency Penalty": "周波数のペナルティ",
+  "Presence Penalty": "存在のペナルティ",
+  "Top_P": "Top_P",
+  "Temperature": "温度",
+  "Max Response Token": "最大レスポンストークン",
+  "API Port": "API ポート",
+  "Hover your mouse over the text to view a detailed description. Settings marked with * will take effect immediately after being saved.": "マウスをテキストに一定時間置いて詳細な説明を表示します。 * が付いている設定は保存後すぐに有効化されます。",
+  "Default API Parameters": "デフォルトのAPIパラメータ",
+  "Provide JSON file URLs for the models manifest. Separate URLs with semicolons. The \"models\" field in JSON files will be parsed into the following table.": "モデルマニフェストのためのJSONファイルURLを提供します。URLはセミコロンで分割します。JSONファイルの\"models\"フィールドは次の表に解析されます。",
+  "Config Name": "構成名",
+  "Refresh": "リフレッシュ",
+  "Save Config": "構成を保存",
+  "Model Source Manifest List": "モデルソースマニフェストリスト",
+  "Models": "モデル",
+  "Delete Config": "設定を削除",
+  "Help": "ヘルプ",
+  "Version": "バージョン",
+  "New Config": "新たな設定",
+  "Open Url": "URLを開く",
+  "Download": "ダウンロード",
+  "Open Folder": "フォルダを開く",
+  "Configs": "設定",
+  "Automatic Updates Check": "自動更新チェック",
+  "Updates Check Error": "更新チェックエラー",
+  "Introduction": "序文",
+  "Dark Mode": "ダークモード",
+  "Language": "言語",
+  "In Development": "開発中",
+  "Chat": "チャット",
+  "Convert": "変更",
+  "Actions": "行動",
+  "Last updated": "最後に更新",
+  "Desc": "説明",
+  "Size": "サイズ",
+  "File": "ファイル",
+  "Config Saved": "設定が保存されました",
+  "Downloading": "ダウンロード中",
+  "Loading Model": "モデルを読み込んでいます",
+  "Startup Completed": "起動完了",
+  "Failed to switch model": "モデルの切り替えに失敗しました",
+  "Start Converting": "変換を開始",
+  "Convert Success": "変換成功",
+  "Convert Failed": "変換失敗",
+  "Model Not Found": "モデルが見つかりません",
+  "Model Status": "モデルの状態",
+  "Clear": "クリア",
+  "Send": "送信",
+  "Type your message here": "ここにメッセージを入力してください",
+  "Copy": "コピー",
+  "Read Aloud": "読み上げ",
+  "Hello! I'm RWKV, an open-source and commercially usable large language model.": "こんにちは！私はRWKV、オープンソースで商用利用可能な大規模な言語モデルです。",
+  "This tool's API is compatible with OpenAI API. It can be used with any ChatGPT tool you like. Go to the settings of some ChatGPT tool, replace the 'https://api.openai.com' part in the API address with '": "このツールのAPIはOpenAI APIと互換性があります。 お好きなChatGPTツールで使用することができます。いくつかのChatGPTツールの設定に移動し、APIアドレスの 'https://api.openai.com' 部分を '",
+  "New Version Available": "新しいバージョンが存在します",
+  "Update": "更新",
+  "Please click the button in the top right corner to start the model": "右上角のボタンをクリックしてモデルを起動してください",
+  "Update Error": "更新エラー",
+  "Open the following URL with your browser to view the API documentation": "以下のURLをブラウザで開いてAPIドキュメンテーションを確認してください",
+  "By default, the maximum number of tokens that can be answered in a single response, it can be changed by the user by specifying API parameters.": "デフォルトでは、一度に回答できるトークンの最大数は、APIパラメータを指定することでユーザーが変更できます。",
+  "Sampling temperature, it's like giving alcohol to a model, the higher the stronger the randomness and creativity, while the lower, the more focused and deterministic it will be.": "サンプリング温度は、モデルにアルコールを与えるようなもので、高いほどランダム性と創造性が強く、低いほど焦点を絞り、決定論的になります。",
+  "Just like feeding sedatives to the model. Consider the results of the top n% probability mass, 0.1 considers the top 10%, with higher quality but more conservative, 1 considers all results, with lower quality but more diverse.": "モデルに鎮静剤を与えるようなもの。上位n％の確率質量の結果を考えてみてください。0.1は上位10％を考えており、質が高いが保守的で、1は全ての結果を考慮しており、質は低いが多様性があります。",
+  "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.": "ポジティヴ値は、新しいトークンが今までのテキストに出現していたかどうかに基づいてこれらをペナルティとし、新しいトピックについて話す可能性を増加させます。",
+  "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.": "ポジティブ値は、新しいトークンが既存のテキストでどれだけ頻繁に使われているかに基づいてペナルティを与え、モデルが同じ行を完全に繰り返す可能性を減らします。",
+  "int8 uses less VRAM, but has slightly lower quality. fp16 has higher quality, and fp32 has the best quality.": "int8はVRAMの使用量が少ないですが、質が若干低いです。fp16は高品質、fp32は最高品質です。",
+  "Number of the neural network layers loaded into VRAM, the more you load, the faster the speed, but it consumes more VRAM. (If your VRAM is not enough, it will fail to load)": "VRAMにロードされるニューラルネットワークの層の数。ロードする量が多いほど速度は速くなりますが、VRAMを多く消費します。(VRAMが不足している場合、ロードに失敗します)",
+  "Whether to use CPU to calculate the last output layer of the neural network with FP32 precision to obtain better quality.": "ネットワークの最終出力層をFP32精度で計算するためにCPUを使用するかどうか。",
+  "Downloads": "ダウンロード",
+  "Pause": "ポーズ",
+  "Continue": "続行",
+  "Resume": "続行",
+  "Check": "確認",
+  "Model file not found": "モデルファイルが見つかりません",
+  "Can not find download url": "ダウンロードURLが見つかりません",
+  "Python target not found, would you like to download it?": "Pythonターゲットが見つかりません、ダウンロードしますか？",
+  "Python dependencies are incomplete, would you like to install them?": "Pythonの依存関係が不完全です、インストールしますか？",
+  "Install": "インストール",
+  "This is the latest version": "これは最新バージョンです",
+  "Use Tsinghua Pip Mirrors": "清華大学Pipミラーサーバーを使用",
+  "Model Config Exception": "モデル設定例外",
+  "Use Gitee Updates Source": "Gitee更新ソースを使用",
+  "Use Custom CUDA kernel to Accelerate": "カスタムCUDAカーネルを使用して加速",
+  "Enabling this option can greatly improve inference speed and save some VRAM, but there may be compatibility issues. If it fails to start, please turn off this option.": "このオプションを有効にすると、推論速度が大幅に向上し、一部のVRAMを節約できますが、互換性の問題が生じる可能性があります。起動に失敗した場合は、このオプションをオフにしてください。",
+  "Supported custom cuda file not found": "対応しているカスタムCUDAファイルが見つかりません",
+  "Failed to copy custom cuda file": "カスタムCUDAファイルのコピーに失敗しました",
+  "Downloading update, please wait. If it is not completed, please manually download the program from GitHub and replace the original program.": "更新をダウンロード中です、お待ちください。完了しない場合は、GitHubから手動でプログラムをダウンロードし、元のプログラムを置き換えてください。",
+  "Completion": "補完",
+  "Parameters": "パラメータ",
+  "Stop Sequences": "シーケンスを停止",
+  "When this content appears in the response result, the generation will end.": "この内容が応答結果に表示されると、生成が終了します。",
+  "Reset": "リセット",
+  "Generate": "生成",
+  "Writer": "ライター",
+  "Translator": "翻訳者",
+  "Catgirl": "ネコガール",
+  "Code Generation": "コード生成",
+  "Werewolf": "人狼",
+  "Instruction": "指示",
+  "Blank": "空白",
+  "The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n": "以下は、壮大な描写と共に、不滅のエピックサイエンスフィクションの傑作で、星間文明戦争が繊細に描かれています。\n第1章\n",
+  "The following is a conversation between a cat girl and her owner. The cat girl is a humanized creature that behaves like a cat but is humanoid. At the end of each sentence in the dialogue, she will add \"Meow~\". In the following content, User represents the owner and Assistant represents the cat girl.\n\nUser: Hello.\n\nAssistant: I'm here, meow~.\n\nUser: Can you tell jokes?": "以下は、猫少女とその飼い主との会話です。猫少女は、猫のように振る舞いながらもヒトの姿をした生物です。会話の各文の終わりには必ず「にゃ〜」とつけています。以下の文章では、Userが飼い主、Assistantが猫少女を表しています。\n\nUser: こんにちは。\n\nAssistant: ここにいますよ、にゃ〜。\n\nUser: 笑い話を話せますか？",
+  "When response finished, inject this content.": "応答終了時に、この内容を注入します。",
+  "Inject start text": "開始テキストを注入",
+  "Inject end text": "終了テキストを注入",
+  "Before the response starts, inject this content.": "応答が始まる前に、この内容を注入します。",
+  "There is currently a game of Werewolf with six players, including a Seer (who can check identities at night), two Werewolves (who can choose someone to kill at night), a Bodyguard (who can choose someone to protect at night), two Villagers (with no special abilities), and a game host. User will play as Player 1, Assistant will play as Players 2-6 and the game host, and they will begin playing together. Every night, the host will ask User for his action and simulate the actions of the other players. During the day, the host will oversee the voting process and ask User for his vote. \n\nAssistant: Next, I will act as the game host and assign everyone their roles, including randomly assigning yours. Then, I will simulate the actions of Players 2-6 and let you know what happens each day. Based on your assigned role, you can tell me your actions and I will let you know the corresponding results each day.\n\nUser: Okay, I understand. Let's begin. Please assign me a role. Am I the Seer, Werewolf, Villager, or Bodyguard?\n\nAssistant: You are the Seer. Now that night has fallen, please choose a player to check his identity.\n\nUser: Tonight, I want to check Player 2 and find out his role.": "現在、6人のプレイヤーが参加する人狼ゲームが行われています。その中には、夜に任意のプレイヤーの正体を確認できる占い師、夜に誰かを殺すことができる人狼2名、夜に誰かを守ることができるボディガード、特殊な能力を持っていない村人2名、そしてゲームのホストがいます。Userはプレイヤー1として、Assistantはプレーヤー2から6まで及びゲームのホストとして参加し、一緒にゲームを始めます。ホストは毎晩、Userに彼の行動を問い、他のプレーヤーの行動をシミュレートします。昼には、ホストが投票プロセスを監督し、Userに彼の投票を求めます。\n\nAssistant: 次に、私はゲームのホストとして参加者全員に役割を割り当てることになります。それには、あなたの役割もランダムに割り当てます。その後、私はプレーヤー2から6の行動をシミュレートし、毎日何が起こったかを報告します。あなたに割り当てられた役割に基づいて、あなたの行動を教えてください。私は毎日、それに対する結果を報告します。\n\nUser: 了解しました。では、始めましょう。私の役割を割り当ててください。占い師、人狼、村人、ボディーガードのいずれなのでしょうか？\n\nAssistant: あなたの役割は占い師です。今夜が来たので、誰の正体を確認するか選んでください。\n\nUser: 今夜、プレイヤー2の役割を確認したい。",
+  "Writer, Translator, Role-playing": "ライター、翻訳者、ロールプレイング",
+  "Chinese Kongfu": "中国武術",
+  "Allow external access to the API (service must be restarted)": "APIへの外部アクセスを許可する (サービスを再起動する必要があります)",
+  "Custom": "カスタム",
+  "CUDA (Beta, Faster)": "CUDA (ベータ、高速)",
+  "Reset All Configs": "すべての設定をリセット",
+  "Cancel": "キャンセル",
+  "Confirm": "確認",
+  "Are you sure you want to reset all configs? This will obtain the latest preset configs, but will override your custom configs and cannot be undone.": "本当にすべての設定をリセットしますか？これにより最新のプリセット設定が取得されますが、カスタム設定は上書きされ、元に戻すことはできません。",
+  "Advanced": "高度な",
+  "Custom Python Path": "カスタムPythonパス",
+  "Custom Models Path": "カスタムモデルパス",
+  "Microsoft Visual C++ Redistributable is not installed, would you like to download it?": "Microsoft Visual C++ 再頒布可能パッケージがインストールされていません。ダウンロードしますか？",
+  "File Path Cannot Contain Space": "ファイルのパスにスペースを含めることはできません",
+  "Current Strategy": "現在の戦略",
+  "MacOS is not yet supported for performing this operation, please do it manually.": "MacOSはまだこの操作を実行するサポートがありませんので、手動で行ってください。",
+  "Linux is not yet supported for performing this operation, please do it manually.": "Linuxはまだこの操作を実行するサポートがありませんので、手動で行ってください。",
+  "On Linux system, you must manually install python dependencies.": "Linuxシステムでは、pythonの依存関係を手動でインストールする必要があります。",
+  "Update completed, please restart the program.": "更新が完了したら、プログラムを再起動してください。",
+  "Are you sure you want to reset this page? It cannot be undone.": "本当にこのページをリセットしてもよろしいですか？元に戻すことはできません。",
+  "Model file download is not complete": "モデルファイルのダウンロードが完了していません",
+  "Error": "エラー",
+  "Are you sure you want to clear the conversation? It cannot be undone.": "会話をクリアしてもよろしいですか？元に戻すことはできません。",
+  "Save": "保存",
+  "Conversation Saved": "会話が保存されました",
+  "Open": "開く",
+  "DPI Scaling": "DPIスケーリング",
+  "Restart the app to apply DPI Scaling.": "DPIスケーリングを適用するためにアプリを再起動してください。",
+  "Restart": "再起動",
+  "API Chat Model Name": "APIチャットモデル名",
+  "API Completion Model Name": "API完成モデル名",
+  "Localhost": "ローカルホスト",
+  "Retry": "リトライ",
+  "Delete": "削除",
+  "Edit": "編集",
+  "Memory is not enough, try to increase the virtual memory or use a smaller model.": "メモリが不足しています。仮想メモリを増やすか、もしくは小さなモデルを使ってみてください",
+  "Bad PyTorch version, please reinstall PyTorch with cuda.": "不適切なPyTorchのバージョンです。cudaと共にPyTorchを再インストールしてください。",
+  "The model file is corrupted, please download again.": "モデルファイルが破損しています。再度ダウンロードしてください。",
+  "Found no NVIDIA driver, please install the latest driver.": "NVIDIAのドライバが見つかりません。最新版のドライバをインストールしてください。",
+  "VRAM is not enough, please reduce stored layers or use a lower precision in Configs page.": "VRAMが足りません。設定ページで保存されているレイヤーを減らすか、精度を下げてください。",
+  "Failed to enable custom CUDA kernel, ninja is required to load C++ extensions. You may be using the CPU version of PyTorch, please reinstall PyTorch with CUDA. Or if you are using a custom Python interpreter, you must compile the CUDA kernel by yourself or disable Custom CUDA kernel acceleration.": "カスタムCUDAカーネルの有効化に失敗しました。C++拡張を読み込むためにはNinjaが必要です。あなたは恐らくCPU版のPyTorchを使用しており、CUDA版のPyTorchを再インストールする必要があります。または、あなたがカスタムPythonインタプリタを使用している場合は、CUDAカーネルを自分でコンパイルするか、カスタムCUDAカーネルのアクセラレーションを無効にする必要があります。",
+  "Presets": "プリセット",
+  "Online": "オンライン",
+  "english": "英語",
+  "chinese": "中国語",
+  "default": "デフォルト",
+  "japanese": "日本語",
+  "New Preset": "新規プリセット",
+  "Import": "インポート",
+  "Name": "名前",
+  "Imported successfully": "インポート成功",
+  "Failed to import. Please copy a preset to the clipboard.": "インポートに失敗しました。プリセットをクリップボードにコピーしてください。",
+  "Clipboard is empty.": "クリップボードが空です。",
+  "Successfully copied to clipboard.": "クリップボードにコピーしました。",
+  "Edit Messages": "メッセージの編集",
+  "Go Back": "戻る",
+  "Description": "説明",
+  "Avatar Url": "アバターURL",
+  "Welcome Message": "ウェルカムメッセージ",
+  "Display Preset Messages": "プリセットメッセージの表示",
+  "Tag": "タグ",
+  "Activate": "アクティブ化",
+  "New": "新規",
+  "user": "ユーザー",
+  "assistant": "アシスタント",
+  "system": "システム",
+  "Regenerate": "再生成",
+  "LoRA Finetune": "LoRAの微調整",
+  "Command Stopped": "コマンドが停止しました",
+  "Please convert data first.": "先にデータを変換してください。",
+  "Ubuntu is not installed, do you want to install it?": "Ubuntuがインストールされていません、インストールしますか？",
+  "Install Ubuntu": "Ubuntuをインストール",
+  "Please install Ubuntu using Microsoft Store, after installation click the Open button in Microsoft Store and then click the Train button": "UbuntuをMicrosoftストアからインストールすることができます。インストールが完了したら、MicrosoftストアのOpenボタンを押し、Trainボタンを押してください",
+  "WSL is not enabled, do you want to enable it?": "WSLが有効になっていません、有効化しますか？",
+  "Enable WSL": "WSLを有効化",
+  "After installation, please restart your computer to enable WSL": "インストールが完了したら、WSLを有効化するためにコンピュータを再起動してください",
+  "Data Process": "データ処理",
+  "Data Path": "データパス",
+  "Vocab Path": "語彙パス",
+  "Train Parameters": "トレーニングパラメータ",
+  "Base Model": "基本モデル",
+  "LoRA Model": "LoRAモデル",
+  "Merge Model": "モデルの統合",
+  "Devices": "デバイス",
+  "Gradient Checkpoint": "勾配チェックポイント",
+  "Context Length": "コンテキストの長さ",
+  "Epoch Steps": "エポックステップ数",
+  "Epoch Count": "エポックの数",
+  "Epoch Begin": "エポックの起点",
+  "Epoch Save": "エポックの保存",
+  "Learning Rate Init": "初期学習率",
+  "Learning Rate Final": "最終学習率",
+  "Micro Batch Size": "マイクロバッチサイズ",
+  "Accumulate Gradient Batches": "勾配バッチの累計",
+  "Warmup Steps": "ウォームアップステップ",
+  "Pre-FFN": "FFNの前処理",
+  "None": "なし",
+  "Merge model successfully": "モデルのマージが成功しました",
+  "Convert Data successfully": "データ変換に成功しました",
+  "Please select a LoRA model": "LoRAモデルを選択してください",
+  "You are using sample data for training. For formal training, please make sure to create your own jsonl file.": "トレーニングにはサンプルデータを使用しています。正式なトレーニングのためには、自身でjsonlファイルを作成してください。",
+  "WSL is not running, please retry. If it keeps happening, it means you may be using an outdated version of WSL, run \"wsl --update\" to update.": "WSLが実行されていません、もう一度試してください。これが続く場合、古いバージョンのWSLを使用している可能性があります。\"wsl --update\"を実行して更新してください。",
+  "Memory is not enough, try to increase the virtual memory or use a smaller base model.": "メモリが不足しています、仮想メモリを増やすか小さなベースモデルを使用してみてください。",
+  "VRAM is not enough": "ビデオRAMが不足しています",
+  "Training data is not enough, reduce context length or add more data for training": "トレーニングデータが不足しています、コンテキストの長さを減らすか、トレーニング用のデータをさらに追加してください",
+  "You are using WSL 1 for training, please upgrade to WSL 2. e.g. Run \"wsl --set-version Ubuntu-22.04 2\"": "トレーニングにWSL 1を使用しています、WSL 2にアップグレードしてください。例:\"wsl --set-version Ubuntu-22.04 2\"を実行する",
+  "Matched CUDA is not installed": "対応するCUDAがインストールされていません",
+  "Failed to convert data": "データの変換に失敗しました",
+  "Failed to merge model": "モデルのマージに失敗しました",
+  "The data path should be a directory or a file in jsonl format (more formats will be supported in the future).\n\nWhen you provide a directory path, all the txt files within that directory will be automatically converted into training data. This is commonly used for large-scale training in writing, code generation, or knowledge bases.\n\nThe jsonl format file can be referenced at https://github.com/Abel2076/json2binidx_tool/blob/main/sample.jsonl.\nYou can also write it similar to OpenAI's playground format, as shown in https://platform.openai.com/playground/p/default-chat.\nEven for multi-turn conversations, they must be written in a single line using `\\n` to indicate line breaks. If they are different dialogues or topics, they should be written in separate lines.": "データのパスはディレクトリまたはjsonl形式のファイルでなければなりません（将来的にはより多くの形式がサポートされる予定です）。ディレクトリパスを提供した場合、そのディレクトリ内のすべてのtxtファイルが自動的にトレーニングデータに変換されます。これは大規模なライティング、コード生成、または知識ベースのトレーニングで一般的に使用されます。jsonl形式のファイルは、https://github.com/Abel2076/json2binidx_tool/blob/main/sample.jsonl を参照してください。\nhttps://platform.openai.com/playground/p/default-chat のように、OpenAIのプレイグラウンド形式に似た形式で書くこともできます。複数ターンの対話であっても、一行で書く必要があり、行の区切りを示すために`\\n`を使用します。それらが異なる対話やトピックであれば、それらは別々の行に書かれるべきです。",
+  "Size mismatch for blocks. You are attempting to continue training from the LoRA model, but it does not match the base model. Please set LoRA model to None.": "ブロックのサイズが一致しません。LoRAモデルからトレーニングを続けようとしていますが、それはベースモデルと一致しません。LoRAモデルをNoneに設定してください。",
+  "Instruction: Write a story using the following information\n\nInput: A man named Alex chops a tree down\n\nResponse:": "Instruction: Write a story using the following information\n\nInput: アレックスという男が木を切り倒す\n\nResponse:",
+  "Composition": "作曲",
+  "Use Local Sound Font": "ローカルサウンドフォントを使用する",
+  "Auto Play At The End": "最後に自動再生",
+  "No File to save": "保存するファイルがありません",
+  "File Saved": "ファイルが保存されました",
+  "Failed to load local sound font, please check if the files exist - assets/sound-font": "ローカルサウンドフォントの読み込みに失敗しました、ファイルが存在するか確認してください - assets/sound-font",
+  "Please convert model to safe tensors format first": "モデルを安全なテンソル形式に変換してください",
+  "Convert To Safe Tensors Format": "安全なテンソル形式に変換",
+  "Please change Strategy to WebGPU to use safetensors format": "StrategyをWebGPUに変更して、安全なテンソル形式を使用してください"
+}

frontend/src/_locales/resources.ts

@@ -1,9 +1,10 @@
 import zhHans from './zh-hans/main.json';
+import ja from './ja/main.json';
 
 export const resources = {
   zh: {
     translation: zhHans
-  }
+  },
   // de: {
   //   translation: de,
   // },
@@ -19,9 +20,9 @@ export const resources = {
   // it: {
   //   translation: it,
   // },
-  // ja: {
-  //   translation: ja,
-  // },
+  ja: {
+    translation: ja
+  }
   // ko: {
   //   translation: ko,
   // },

frontend/src/_locales/zh-hans/main.json

@@ -128,6 +128,7 @@
   "Chinese Kongfu": "情境冒险",
   "Allow external access to the API (service must be restarted)": "允许外部访问API (必须重启服务)",
   "Custom": "自定义",
+  "CUDA (Beta, Faster)": "CUDA (Beta, 更快)",
   "Reset All Configs": "重置所有配置",
   "Cancel": "取消",
   "Confirm": "确认",
@@ -240,5 +241,8 @@
   "Auto Play At The End": "结束时自动播放",
   "No File to save": "无文件可保存",
   "File Saved": "文件已保存",
-  "Failed to load local sound font, please check if the files exist - assets/sound-font": "加载本地音色资源失败，请检查文件是否存在 - assets/sound-font"
+  "Failed to load local sound font, please check if the files exist - assets/sound-font": "加载本地音色资源失败，请检查文件是否存在 - assets/sound-font",
+  "Please convert model to safe tensors format first": "请先将模型转换为Safetensors格式",
+  "Convert To Safe Tensors Format": "转换为Safetensors格式",
+  "Please change Strategy to WebGPU to use safetensors format": "请将Strategy改为WebGPU以使用safetensors格式"
 }

frontend/src/components/ResetConfigsButton.tsx

@@ -11,7 +11,7 @@ export const ResetConfigsButton: FC<{ afterConfirm?: () => void }> = ({ afterCon
   return <DialogButton icon={<ArrowReset20Regular />} tooltip={t('Reset All Configs')} title={t('Reset All Configs')}
     contentText={t('Are you sure you want to reset all configs? This will obtain the latest preset configs, but will override your custom configs and cannot be undone.')}
     onConfirm={() => {
-      commonStore.setModelConfigs(commonStore.platform != 'darwin' ? defaultModelConfigs : defaultModelConfigsMac, false);
+      commonStore.setModelConfigs(commonStore.platform !== 'darwin' ? defaultModelConfigs : defaultModelConfigsMac, false);
       commonStore.setCurrentConfigIndex(0, true);
       afterConfirm?.();
     }} />;

frontend/src/components/RunButton.tsx

@@ -1,6 +1,12 @@
 import React, { FC, MouseEventHandler, ReactElement } from 'react';
 import commonStore, { ModelStatus } from '../stores/commonStore';
-import { AddToDownloadList, CopyFile, FileExists, StartServer } from '../../wailsjs/go/backend_golang/App';
+import {
+  AddToDownloadList,
+  CopyFile,
+  FileExists,
+  StartServer,
+  StartWebGPUServer
+} from '../../wailsjs/go/backend_golang/App';
 import { Button } from '@fluentui/react-components';
 import { observer } from 'mobx-react-lite';
 import { exit, getStatus, readRoot, switchModel, updateConfig } from '../apis';
@@ -39,6 +45,7 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
       commonStore.setStatus({ status: ModelStatus.Starting });
 
       const modelConfig = commonStore.getCurrentModelConfig();
+      const webgpu = modelConfig.modelParameters.device === 'WebGPU';
       let modelName = '';
       let modelPath = '';
       if (modelConfig && modelConfig.modelParameters) {
@@ -50,9 +57,32 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
         return;
       }
 
-      const ok = await checkDependencies(navigate);
-      if (!ok)
-        return;
+      if (webgpu) {
+        if (!['.st', '.safetensors'].some(ext => modelPath.endsWith(ext))) {
+          const stModelPath = modelPath.replace(/\.pth$/, '.st');
+          if (await FileExists(stModelPath)) {
+            modelPath = stModelPath;
+          } else {
+            toast(t('Please convert model to safe tensors format first'), { type: 'error' });
+            commonStore.setStatus({ status: ModelStatus.Offline });
+            return;
+          }
+        }
+      }
+
+      if (!webgpu) {
+        if (['.st', '.safetensors'].some(ext => modelPath.endsWith(ext))) {
+          toast(t('Please change Strategy to WebGPU to use safetensors format'), { type: 'error' });
+          commonStore.setStatus({ status: ModelStatus.Offline });
+          return;
+        }
+      }
+
+      if (!webgpu) {
+        const ok = await checkDependencies(navigate);
+        if (!ok)
+          return;
+      }
 
       const currentModelSource = commonStore.modelSourceList.find(item => item.name === modelName);
 
@@ -85,7 +115,14 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
 
       await exit(1000).catch(() => {
       });
-      StartServer(commonStore.settings.customPythonPath, port, commonStore.settings.host !== '127.0.0.1' ? '0.0.0.0' : '127.0.0.1').catch((e) => {
+
+      const startServer = webgpu ?
+        (_: string, port: number, host: string) => StartWebGPUServer(port, host)
+        : StartServer;
+
+      startServer(commonStore.settings.customPythonPath, port, commonStore.settings.host !== '127.0.0.1' ? '0.0.0.0' : '127.0.0.1',
+        modelConfig.modelParameters.device === 'CUDA-Beta'
+      ).catch((e) => {
         const errMsg = e.message || e;
         if (errMsg.includes('path contains space'))
           toast(`${t('Error')} - ${t('File Path Cannot Contain Space')}`, { type: 'error' });
@@ -102,23 +139,27 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
           if (r.ok && !loading) {
             loading = true;
             clearInterval(intervalId);
-            await getStatus().then(status => {
-              if (status)
-                commonStore.setStatus(status);
-            });
+            if (!webgpu) {
+              await getStatus().then(status => {
+                if (status)
+                  commonStore.setStatus(status);
+              });
+            }
             commonStore.setStatus({ status: ModelStatus.Loading });
             toast(t('Loading Model'), { type: 'info' });
-            updateConfig({
-              max_tokens: modelConfig.apiParameters.maxResponseToken,
-              temperature: modelConfig.apiParameters.temperature,
-              top_p: modelConfig.apiParameters.topP,
-              presence_penalty: modelConfig.apiParameters.presencePenalty,
-              frequency_penalty: modelConfig.apiParameters.frequencyPenalty
-            });
+            if (!webgpu) {
+              updateConfig({
+                max_tokens: modelConfig.apiParameters.maxResponseToken,
+                temperature: modelConfig.apiParameters.temperature,
+                top_p: modelConfig.apiParameters.topP,
+                presence_penalty: modelConfig.apiParameters.presencePenalty,
+                frequency_penalty: modelConfig.apiParameters.frequencyPenalty
+              });
+            }
 
             const strategy = getStrategy(modelConfig);
             let customCudaFile = '';
-            if ((modelConfig.modelParameters.device === 'CUDA' || modelConfig.modelParameters.device === 'Custom')
+            if ((modelConfig.modelParameters.device.includes('CUDA') || modelConfig.modelParameters.device === 'Custom')
               && modelConfig.modelParameters.useCustomCuda && !strategy.includes('fp32')) {
               if (commonStore.platform === 'windows') {
                 customCudaFile = getSupportedCustomCudaFile();

frontend/src/pages/Configs.tsx

@@ -13,13 +13,14 @@ import { Page } from '../components/Page';
 import { useNavigate } from 'react-router';
 import { RunButton } from '../components/RunButton';
 import { updateConfig } from '../apis';
-import { ConvertModel, FileExists, GetPyError } from '../../wailsjs/go/backend_golang/App';
-import { getStrategy } from '../utils';
+import { ConvertModel, ConvertSafetensors, FileExists, GetPyError } from '../../wailsjs/go/backend_golang/App';
+import { checkDependencies, getStrategy } from '../utils';
 import { useTranslation } from 'react-i18next';
 import { WindowShow } from '../../wailsjs/runtime/runtime';
 import strategyImg from '../assets/images/strategy.jpg';
 import strategyZhImg from '../assets/images/strategy_zh.jpg';
 import { ResetConfigsButton } from '../components/ResetConfigsButton';
+import { useMediaQuery } from 'usehooks-ts';
 
 export type ApiParameters = {
   apiPort: number
@@ -30,7 +31,7 @@ export type ApiParameters = {
   frequencyPenalty: number;
 }
 
-export type Device = 'CPU' | 'CUDA' | 'MPS' | 'Custom';
+export type Device = 'CPU' | 'CUDA' | 'CUDA-Beta' | 'WebGPU' | 'MPS' | 'Custom';
 export type Precision = 'fp16' | 'int8' | 'fp32';
 
 export type ModelParameters = {
@@ -56,6 +57,7 @@ export const Configs: FC = observer(() => {
   const [selectedIndex, setSelectedIndex] = React.useState(commonStore.currentModelConfigIndex);
   const [selectedConfig, setSelectedConfig] = React.useState(commonStore.modelConfigs[selectedIndex]);
   const [displayStrategyImg, setDisplayStrategyImg] = React.useState(false);
+  const mq = useMediaQuery('(min-width: 640px)');
   const navigate = useNavigate();
   const port = selectedConfig.apiParameters.apiPort;
 
@@ -128,7 +130,8 @@ export const Configs: FC = observer(() => {
             setSelectedIndex(0);
             setSelectedConfig(commonStore.modelConfigs[0]);
           }} />
-          <ToolTipButton desc={t('Save Config')} icon={<Save20Regular />} onClick={onClickSave} />
+          <ToolTipButton desc={mq ? '' : t('Save Config')} icon={<Save20Regular />} text={mq ? t('Save Config') : ''}
+            onClick={onClickSave} />
         </div>
         <div className="flex items-center gap-4">
           <Label>{t('Config Name')}</Label>
@@ -237,40 +240,84 @@ export const Configs: FC = observer(() => {
                     }} />
                   </div>
                 } />
-                <ToolTipButton text={t('Convert')}
-                  desc={t('Convert model with these configs. Using a converted model will greatly improve the loading speed, but model parameters of the converted model cannot be modified.')}
-                  onClick={async () => {
-                    if (commonStore.platform == 'darwin') {
-                      toast(t('MacOS is not yet supported for performing this operation, please do it manually.'), { type: 'info' });
-                      return;
-                    } else if (commonStore.platform == 'linux') {
-                      toast(t('Linux is not yet supported for performing this operation, please do it manually.'), { type: 'info' });
-                      return;
-                    }
-
-                    const modelPath = `${commonStore.settings.customModelsPath}/${selectedConfig.modelParameters.modelName}`;
-                    if (await FileExists(modelPath)) {
-                      const strategy = getStrategy(selectedConfig);
-                      const newModelPath = modelPath + '-' + strategy.replace(/[:> *+]/g, '-');
-                      toast(t('Start Converting'), { autoClose: 1000, type: 'info' });
-                      ConvertModel(commonStore.settings.customPythonPath, modelPath, strategy, newModelPath).then(async () => {
-                        if (!await FileExists(newModelPath + '.pth')) {
-                          toast(t('Convert Failed') + ' - ' + await GetPyError(), { type: 'error' });
-                        } else {
-                          toast(`${t('Convert Success')} - ${newModelPath}`, { type: 'success' });
+                {
+                  selectedConfig.modelParameters.device !== 'WebGPU' ?
+                    <ToolTipButton text={t('Convert')}
+                      desc={t('Convert model with these configs. Using a converted model will greatly improve the loading speed, but model parameters of the converted model cannot be modified.')}
+                      onClick={async () => {
+                        if (commonStore.platform === 'darwin') {
+                          toast(t('MacOS is not yet supported for performing this operation, please do it manually.') + ' (backend-python/convert_model.py)', { type: 'info' });
+                          return;
+                        } else if (commonStore.platform === 'linux') {
+                          toast(t('Linux is not yet supported for performing this operation, please do it manually.') + ' (backend-python/convert_model.py)', { type: 'info' });
+                          return;
                         }
-                      }).catch(e => {
-                        const errMsg = e.message || e;
-                        if (errMsg.includes('path contains space'))
-                          toast(`${t('Convert Failed')} - ${t('File Path Cannot Contain Space')}`, { type: 'error' });
-                        else
-                          toast(`${t('Convert Failed')} - ${e.message || e}`, { type: 'error' });
-                      });
-                      setTimeout(WindowShow, 1000);
-                    } else {
-                      toast(`${t('Model Not Found')} - ${modelPath}`, { type: 'error' });
-                    }
-                  }} />
+
+                        const ok = await checkDependencies(navigate);
+                        if (!ok)
+                          return;
+
+                        const modelPath = `${commonStore.settings.customModelsPath}/${selectedConfig.modelParameters.modelName}`;
+                        if (await FileExists(modelPath)) {
+                          const strategy = getStrategy(selectedConfig);
+                          const newModelPath = modelPath + '-' + strategy.replace(/[:> *+]/g, '-');
+                          toast(t('Start Converting'), { autoClose: 1000, type: 'info' });
+                          ConvertModel(commonStore.settings.customPythonPath, modelPath, strategy, newModelPath).then(async () => {
+                            if (!await FileExists(newModelPath + '.pth')) {
+                              toast(t('Convert Failed') + ' - ' + await GetPyError(), { type: 'error' });
+                            } else {
+                              toast(`${t('Convert Success')} - ${newModelPath}`, { type: 'success' });
+                            }
+                          }).catch(e => {
+                            const errMsg = e.message || e;
+                            if (errMsg.includes('path contains space'))
+                              toast(`${t('Convert Failed')} - ${t('File Path Cannot Contain Space')}`, { type: 'error' });
+                            else
+                              toast(`${t('Convert Failed')} - ${e.message || e}`, { type: 'error' });
+                          });
+                          setTimeout(WindowShow, 1000);
+                        } else {
+                          toast(`${t('Model Not Found')} - ${modelPath}`, { type: 'error' });
+                        }
+                      }} /> :
+                    <ToolTipButton text={t('Convert To Safe Tensors Format')}
+                      desc=""
+                      onClick={async () => {
+                        if (commonStore.platform === 'darwin') {
+                          toast(t('MacOS is not yet supported for performing this operation, please do it manually.') + ' (backend-python/convert_safetensors.py)', { type: 'info' });
+                          return;
+                        } else if (commonStore.platform === 'linux') {
+                          toast(t('Linux is not yet supported for performing this operation, please do it manually.') + ' (backend-python/convert_safetensors.py)', { type: 'info' });
+                          return;
+                        }
+
+                        const ok = await checkDependencies(navigate);
+                        if (!ok)
+                          return;
+
+                        const modelPath = `${commonStore.settings.customModelsPath}/${selectedConfig.modelParameters.modelName}`;
+                        if (await FileExists(modelPath)) {
+                          toast(t('Start Converting'), { autoClose: 1000, type: 'info' });
+                          const newModelPath = modelPath.replace(/\.pth$/, '.st');
+                          ConvertSafetensors(commonStore.settings.customPythonPath, modelPath, newModelPath).then(async () => {
+                            if (!await FileExists(newModelPath)) {
+                              toast(t('Convert Failed') + ' - ' + await GetPyError(), { type: 'error' });
+                            } else {
+                              toast(`${t('Convert Success')} - ${newModelPath}`, { type: 'success' });
+                            }
+                          }).catch(e => {
+                            const errMsg = e.message || e;
+                            if (errMsg.includes('path contains space'))
+                              toast(`${t('Convert Failed')} - ${t('File Path Cannot Contain Space')}`, { type: 'error' });
+                            else
+                              toast(`${t('Convert Failed')} - ${e.message || e}`, { type: 'error' });
+                          });
+                          setTimeout(WindowShow, 1000);
+                        } else {
+                          toast(`${t('Model Not Found')} - ${modelPath}`, { type: 'error' });
+                        }
+                      }} />
+                }
                 <Labeled label={t('Strategy')} content={
                   <Dropdown style={{ minWidth: 0 }} className="grow" value={t(selectedConfig.modelParameters.device)!}
                     selectedOptions={[selectedConfig.modelParameters.device]}
@@ -284,11 +331,13 @@ export const Configs: FC = observer(() => {
                     <Option value="CPU">CPU</Option>
                     {commonStore.platform === 'darwin' && <Option value="MPS">MPS</Option>}
                     <Option value="CUDA">CUDA</Option>
+                    <Option value="CUDA-Beta">{t('CUDA (Beta, Faster)')!}</Option>
+                    <Option value="WebGPU">WebGPU</Option>
                     <Option value="Custom">{t('Custom')!}</Option>
                   </Dropdown>
                 } />
                 {
-                  selectedConfig.modelParameters.device != 'Custom' && <Labeled label={t('Precision')}
+                  selectedConfig.modelParameters.device !== 'Custom' && <Labeled label={t('Precision')}
                     desc={t('int8 uses less VRAM, but has slightly lower quality. fp16 has higher quality, and fp32 has the best quality.')}
                     content={
                       <Dropdown style={{ minWidth: 0 }} className="grow"
@@ -303,17 +352,17 @@ export const Configs: FC = observer(() => {
                         }}>
                         <Option>fp16</Option>
                         <Option>int8</Option>
-                        <Option>fp32</Option>
+                        {selectedConfig.modelParameters.device !== 'WebGPU' && <Option>fp32</Option>}
                       </Dropdown>
                     } />
                 }
                 {
-                  selectedConfig.modelParameters.device == 'CUDA' &&
+                  selectedConfig.modelParameters.device.includes('CUDA') &&
                   <Labeled label={t('Current Strategy')}
                     content={<Text> {getStrategy(selectedConfig)} </Text>} />
                 }
                 {
-                  selectedConfig.modelParameters.device == 'CUDA' &&
+                  selectedConfig.modelParameters.device.includes('CUDA') &&
                   <Labeled label={t('Stored Layers')}
                     desc={t('Number of the neural network layers loaded into VRAM, the more you load, the faster the speed, but it consumes more VRAM. (If your VRAM is not enough, it will fail to load)')}
                     content={
@@ -326,9 +375,7 @@ export const Configs: FC = observer(() => {
                         }} />
                     } />
                 }
-                {
-                  selectedConfig.modelParameters.device == 'CUDA' && <div />
-                }
+                {selectedConfig.modelParameters.device.includes('CUDA') && <div />}
                 {
                   displayStrategyImg &&
                   <img style={{ width: '80vh', height: 'auto', zIndex: 100 }}
@@ -336,13 +383,13 @@ export const Configs: FC = observer(() => {
                     src={commonStore.settings.language === 'zh' ? strategyZhImg : strategyImg} />
                 }
                 {
-                  selectedConfig.modelParameters.device == 'Custom' &&
+                  selectedConfig.modelParameters.device === 'Custom' &&
                   <Labeled label="Strategy"
                     onMouseEnter={() => setDisplayStrategyImg(true)}
                     onMouseLeave={() => setDisplayStrategyImg(false)}
                     content={
                       <Input className="grow"
-                        placeholder={commonStore.platform != 'darwin' ? 'cuda:0 fp16 *20 -> cuda:1 fp16' : 'mps fp32'}
+                        placeholder={commonStore.platform !== 'darwin' ? 'cuda:0 fp16 *20 -> cuda:1 fp16' : 'mps fp32'}
                         value={selectedConfig.modelParameters.customStrategy}
                         onChange={(e, data) => {
                           setSelectedConfigModelParams({
@@ -351,9 +398,9 @@ export const Configs: FC = observer(() => {
                         }} />
                     } />
                 }
-                {selectedConfig.modelParameters.device == 'Custom' && <div />}
+                {selectedConfig.modelParameters.device === 'Custom' && <div />}
                 {
-                  selectedConfig.modelParameters.device != 'CPU' && selectedConfig.modelParameters.device != 'MPS' &&
+                  (selectedConfig.modelParameters.device.includes('CUDA') || selectedConfig.modelParameters.device === 'Custom') &&
                   <Labeled label={t('Use Custom CUDA kernel to Accelerate')}
                     desc={t('Enabling this option can greatly improve inference speed and save some VRAM, but there may be compatibility issues. If it fails to start, please turn off this option.')}
                     content={

frontend/src/pages/Models.tsx

@@ -27,7 +27,7 @@ export type ModelSourceItem = {
   name: string;
   size: number;
   lastUpdated: string;
-  desc?: { [lang: string]: string; };
+  desc?: { [lang: string]: string | undefined; };
   SHA256?: string;
   url?: string;
   downloadUrl?: string;
@@ -63,10 +63,10 @@ const columns: TableColumnDefinition<ModelSourceItem>[] = [
       const lang: string = commonStore.settings.language;
 
       if (a.desc && b.desc) {
-        if (lang in a.desc && lang in b.desc)
-          return b.desc[lang].localeCompare(a.desc[lang]);
-        else if ('en' in a.desc && 'en' in b.desc)
-          return b.desc['en'].localeCompare(a.desc['en']);
+        if (lang in a.desc && lang in b.desc && a.desc[lang] && b.desc[lang])
+          return b.desc[lang]!.localeCompare(a.desc[lang]!);
+        else if ('en' in a.desc && 'en' in b.desc && a.desc['en'] && b.desc['en'])
+          return b.desc['en']!.localeCompare(a.desc['en']!);
       }
       return 0;
     },

frontend/src/pages/Settings.tsx

@@ -19,7 +19,8 @@ import { RestartApp } from '../../wailsjs/go/backend_golang/App';
 
 export const Languages = {
   dev: 'English', // i18n default
-  zh: '简体中文'
+  zh: '简体中文',
+  ja: '日本語'
 };
 
 export type Language = keyof typeof Languages;
@@ -125,7 +126,7 @@ export const Settings: FC = observer(() => {
           } />
         }
         {
-          commonStore.settings.language === 'zh' && commonStore.platform != 'linux' &&
+          commonStore.settings.language === 'zh' && commonStore.platform !== 'linux' &&
           <Labeled label={t('Use Tsinghua Pip Mirrors')} flex spaceBetween content={
             <Switch checked={commonStore.settings.cnMirror}
               onChange={(e, data) => {

frontend/src/pages/defaultConfigs.ts

@@ -1131,7 +1131,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       apiPort: 8000,
       maxResponseToken: 4100,
       temperature: 1.0,
-      topP: 0.3,
+      topP: 0.8,
       presencePenalty: 0,
       frequencyPenalty: 1
     },
@@ -1143,6 +1143,24 @@ export const defaultModelConfigs: ModelConfig[] = [
       maxStoredLayers: 41
     }
   },
+  {
+    name: 'CPU-560M-Music',
+    apiParameters: {
+      apiPort: 8000,
+      maxResponseToken: 4100,
+      temperature: 1.0,
+      topP: 0.8,
+      presencePenalty: 0,
+      frequencyPenalty: 1
+    },
+    modelParameters: {
+      modelName: 'RWKV-4-MIDI-560M-v1-20230717-ctx4096.pth',
+      device: 'CPU',
+      precision: 'fp32',
+      storedLayers: 41,
+      maxStoredLayers: 41
+    }
+  },
   {
     name: 'CPU-6G-1B5-World',
     apiParameters: {

frontend/src/startup.ts

@@ -70,7 +70,7 @@ async function initConfig() {
       configData.currentModelConfigIndex >= 0 && configData.currentModelConfigIndex < configData.modelConfigs.length)
       commonStore.setCurrentConfigIndex(configData.currentModelConfigIndex, false);
   }).catch(() => {
-    commonStore.setModelConfigs(commonStore.platform != 'darwin' ? defaultModelConfigs : defaultModelConfigsMac, true);
+    commonStore.setModelConfigs(commonStore.platform !== 'darwin' ? defaultModelConfigs : defaultModelConfigsMac, true);
   });
 }
 

frontend/src/stores/commonStore.ts

@@ -167,7 +167,7 @@ class CommonStore {
   createModelConfig = (config: ModelConfig = defaultModelConfigs[0], saveConfig: boolean = true) => {
     if (config.name === defaultModelConfigs[0].name) {
       // deep copy
-      config = JSON.parse(JSON.stringify(commonStore.platform != 'darwin' ? defaultModelConfigs[0] : defaultModelConfigsMac[0]));
+      config = JSON.parse(JSON.stringify(commonStore.platform !== 'darwin' ? defaultModelConfigs[0] : defaultModelConfigsMac[0]));
       config.name = new Date().toLocaleString();
     }
     this.modelConfigs.push(config);

frontend/src/utils/index.tsx

@@ -57,6 +57,8 @@ export async function refreshBuiltInModels(readCache: boolean = false) {
   return cache;
 }
 
+const modelSuffix = ['.pth', '.st', '.safetensors'];
+
 export async function refreshLocalModels(cache: {
   models: ModelSourceItem[]
 }, filter: boolean = true, initUnfinishedModels: boolean = false) {
@@ -65,7 +67,7 @@ export async function refreshLocalModels(cache: {
 
   await ListDirFiles(commonStore.settings.customModelsPath).then((data) => {
     cache.models.push(...data.flatMap(d => {
-      if (!d.isDir && d.name.endsWith('.pth'))
+      if (!d.isDir && modelSuffix.some((ext => d.name.endsWith(ext))))
         return [{
           name: d.name,
           size: d.size,
@@ -146,7 +148,7 @@ export async function refreshRemoteModels(cache: { models: ModelSourceItem[] })
   .catch(() => {
   });
   cache.models = cache.models.filter((model, index, self) => {
-    return model.name.endsWith('.pth')
+    return modelSuffix.some((ext => model.name.endsWith(ext)))
       && index === self.findIndex(
         m => m.name === model.name || (m.SHA256 && m.SHA256 === model.SHA256 && m.size === model.size));
   });
@@ -176,7 +178,11 @@ export const getStrategy = (modelConfig: ModelConfig | undefined = undefined) =>
       strategy += 'cpu ';
       strategy += params.precision === 'int8' ? 'fp32i8' : 'fp32';
       break;
+    case 'WebGPU':
+      strategy += params.precision === 'int8' ? 'fp16i8' : 'fp16';
+      break;
     case 'CUDA':
+    case 'CUDA-Beta':
       if (avoidOverflow)
         strategy = 'cuda fp32 *1 -> ';
       strategy += 'cuda ';
@@ -239,7 +245,7 @@ export function downloadProgramFiles() {
   manifest.programFiles.forEach(({ url, path }) => {
     if (path)
       ReadFileInfo(path).then(info => {
-        if (info.size == 0 && url)
+        if (info.size === 0 && url)
           AddToDownloadList(path, url.replace('@master', '@v' + manifest.version));
       }).catch(() => {
         if (url)
@@ -372,7 +378,7 @@ export const checkDependencies = async (navigate: NavigateFunction) => {
           });
         } else {
           toast(depErrorMsg, { type: 'info', position: 'bottom-left' });
-          if (commonStore.platform != 'linux')
+          if (commonStore.platform !== 'linux')
             toastWithButton(t('Python dependencies are incomplete, would you like to install them?'), t('Install'), () => {
               InstallPyDep(commonStore.settings.customPythonPath, commonStore.settings.cnMirror).catch((e) => {
                 const errMsg = e.message || e;

frontend/wailsjs/go/backend_golang/App.d.ts

@@ -10,6 +10,8 @@ export function ConvertData(arg1:string,arg2:string,arg3:string,arg4:string):Pro
 
 export function ConvertModel(arg1:string,arg2:string,arg3:string,arg4:string):Promise<string>;
 
+export function ConvertSafetensors(arg1:string,arg2:string,arg3:string):Promise<string>;
+
 export function CopyFile(arg1:string,arg2:string):Promise<void>;
 
 export function DeleteFile(arg1:string):Promise<void>;
@@ -46,7 +48,9 @@ export function RestartApp():Promise<void>;
 
 export function SaveJson(arg1:string,arg2:any):Promise<void>;
 
-export function StartServer(arg1:string,arg2:number,arg3:string):Promise<string>;
+export function StartServer(arg1:string,arg2:number,arg3:string,arg4:boolean):Promise<string>;
+
+export function StartWebGPUServer(arg1:number,arg2:string):Promise<string>;
 
 export function UpdateApp(arg1:string):Promise<boolean>;
 

frontend/wailsjs/go/backend_golang/App.js

@@ -18,6 +18,10 @@ export function ConvertModel(arg1, arg2, arg3, arg4) {
   return window['go']['backend_golang']['App']['ConvertModel'](arg1, arg2, arg3, arg4);
 }
 
+export function ConvertSafetensors(arg1, arg2, arg3) {
+  return window['go']['backend_golang']['App']['ConvertSafetensors'](arg1, arg2, arg3);
+}
+
 export function CopyFile(arg1, arg2) {
   return window['go']['backend_golang']['App']['CopyFile'](arg1, arg2);
 }
@@ -90,8 +94,12 @@ export function SaveJson(arg1, arg2) {
   return window['go']['backend_golang']['App']['SaveJson'](arg1, arg2);
 }
 
-export function StartServer(arg1, arg2, arg3) {
-  return window['go']['backend_golang']['App']['StartServer'](arg1, arg2, arg3);
+export function StartServer(arg1, arg2, arg3, arg4) {
+  return window['go']['backend_golang']['App']['StartServer'](arg1, arg2, arg3, arg4);
+}
+
+export function StartWebGPUServer(arg1, arg2) {
+  return window['go']['backend_golang']['App']['StartWebGPUServer'](arg1, arg2);
 }
 
 export function UpdateApp(arg1) {

main.go

@@ -49,6 +49,9 @@ var cyacInfo embed.FS
 //go:embed backend-python
 var py embed.FS
 
+//go:embed backend-rust
+var webgpu embed.FS
+
 //go:embed finetune
 var finetune embed.FS
 
@@ -63,6 +66,7 @@ func main() {
 		backend.CopyEmbed(cyac)
 		backend.CopyEmbed(cyacInfo)
 		backend.CopyEmbed(py)
+		backend.CopyEmbed(webgpu)
 		backend.CopyEmbed(finetune)
 		backend.CopyEmbed(midi)
 		backend.CopyEmbed(midiAssets)

manifest.json

@@ -1,5 +1,5 @@
 {
-  "version": "1.4.1",
+  "version": "1.4.3",
   "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）。"
@@ -19,7 +19,8 @@
       "name": "RWKV-4-World-CHNtuned-0.1B-v1-20230617-ctx4096.pth",
       "desc": {
         "en": "Global Languages 0.1B v1 Enhanced Chinese",
-        "zh": "全球语言 0.1B v1 中文增强"
+        "zh": "全球语言 0.1B v1 中文增强",
+        "ja": "グローバル言語 0.1B v1 中国語強化"
       },
       "size": 385594610,
       "SHA256": "a3888f9958d378ee6d4976ae1c02edb698f4382e426086febafb4a69417b9080",
@@ -31,7 +32,8 @@
       "name": "RWKV-4-World-0.1B-v1-20230520-ctx4096.pth",
       "desc": {
         "en": "Global Languages 0.1B v1",
-        "zh": "全球语言 0.1B v1"
+        "zh": "全球语言 0.1B v1",
+        "ja": "グローバル言語 0.1B v1"
       },
       "size": 385594610,
       "SHA256": "a10ef99df2a8f8a6801edf4fc92a9c49bedd63dcb900d3e5667a2136b3d671e7",
@@ -43,7 +45,8 @@
       "name": "RWKV-4-World-CHNtuned-0.4B-v1-20230618-ctx4096.pth",
       "desc": {
         "en": "Global Languages 0.4B v1 Enhanced Chinese",
-        "zh": "全球语言 0.4B v1 中文增强"
+        "zh": "全球语言 0.4B v1 中文增强",
+        "ja": "グローバル言語 0.4B v1 中国語強化"
       },
       "size": 923362866,
       "SHA256": "dbd5302cbee596bbc900f97eb10b2af3001a7f2c7e4d8643bf8683b2cdbdd324",
@@ -55,7 +58,8 @@
       "name": "RWKV-4-World-0.4B-v1-20230529-ctx4096.pth",
       "desc": {
         "en": "Global Languages 0.4B v1",
-        "zh": "全球语言 0.4B v1"
+        "zh": "全球语言 0.4B v1",
+        "ja": "グローバル言語 0.4B v1"
       },
       "size": 923362866,
       "SHA256": "4b4a2733cf5e5dc97dd62106f391d99895d16b11c5ccd10c89f28c52067a4919",
@@ -67,7 +71,8 @@
       "name": "RWKV-4-World-CHNtuned-1.5B-v1-20230620-ctx4096.pth",
       "desc": {
         "en": "Global Languages 1.5B v1 Enhanced Chinese",
-        "zh": "全球语言 1.5B v1 中文增强"
+        "zh": "全球语言 1.5B v1 中文增强",
+        "ja": "グローバル言語 1.5B v1 中国語強化"
       },
       "size": 3155281586,
       "SHA256": "9f31f2ed5fe52dcf2d50208eb2efd764b9674dba2adb1baeff61997b4390a26b",
@@ -118,7 +123,8 @@
       "name": "RWKV-4-World-1.5B-v1-fixed-20230612-ctx4096.pth",
       "desc": {
         "en": "Global Languages 1.5B v1 fixed",
-        "zh": "全球语言 1.5B v1 修复"
+        "zh": "全球语言 1.5B v1 修复",
+        "ja": "グローバル言語 1.5B v1"
       },
       "size": 3155281586,
       "SHA256": "71f0c3229f9227cbcb8ae5fee6461197129a57e26366c4d23a49058417b046c9",
@@ -182,7 +188,8 @@
       "name": "RWKV-4-World-3B-v1-20230619-ctx4096.pth",
       "desc": {
         "en": "Global Languages 3B v1",
-        "zh": "全球语言 3B v1"
+        "zh": "全球语言 3B v1",
+        "ja": "グローバル言語 3B v1"
       },
       "size": 6125597618,
       "SHA256": "1b227af317fa25b6939ab3c7cd321226ca48b8fe4bbbd2df3db669f1482c54ba",
@@ -194,7 +201,8 @@
       "name": "RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth",
       "desc": {
         "en": "Global Languages 3B v1 Enhanced Chinese",
-        "zh": "全球语言 3B v1 中文增强"
+        "zh": "全球语言 3B v1 中文增强",
+        "ja": "グローバル言語 3B v1 中国語強化"
       },
       "size": 6125597618,
       "SHA256": "7d3b5a4d0e9780a3e3d9ae7c2defbe8564d240bc9a238db4ba70cfb66dc33888",
@@ -284,7 +292,8 @@
       "name": "RWKV-4-World-7B-v1-20230626-ctx4096.pth",
       "desc": {
         "en": "Global Languages 7B v1",
-        "zh": "全球语言 7B v1"
+        "zh": "全球语言 7B v1",
+        "ja": "グローバル言語 7B v1"
       },
       "size": 15035393586,
       "SHA256": "db7b011247a0fe4389e1d76e3d6a904185f85d509c8a44ad18bf401094efc293",
@@ -296,7 +305,8 @@
       "name": "RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth",
       "desc": {
         "en": "Global Languages 7B v1 Enhanced Chinese",
-        "zh": "全球语言 7B v1 中文增强"
+        "zh": "全球语言 7B v1 中文增强",
+        "ja": "グローバル言語 7B v1 中国語強化"
       },
       "size": 15035393458,
       "SHA256": "52d33e8352a40158d21425fee4f68df1515d6324056f788d2c78a366ef578ffa",
@@ -308,7 +318,8 @@
       "name": "Readflow-RWKV-4-World-CHNtuned-7B-v1-20230709-ctx32k.pth",
       "desc": {
         "en": "Global Languages 7B v1 Enhanced Chinese Ctx32k Summary Ability",
-        "zh": "全球语言 7B v1 中文增强 32k上下文 总结能力"
+        "zh": "全球语言 7B v1 中文增强 32k上下文 总结能力",
+        "ja": "グローバル言語 7B v1 中国語強化 32kコンテキスト まとめる能力"
       },
       "size": 15035391543,
       "SHA256": "1bd1de8cdbd56b67e1374588fe5d202884049c71278ffcb12f5c4efbdb422ee1",
@@ -320,7 +331,8 @@
       "name": "RWKV-4-World-JPNtuned-7B-v1-20230718-ctx4096.pth",
       "desc": {
         "en": "Global Languages 7B v1 Enhanced Japanese",
-        "zh": "全球语言 7B v1 日文增强"
+        "zh": "全球语言 7B v1 日文增强",
+        "ja": "グローバル言語 7B v1 日本語強化"
       },
       "size": 15035393458,
       "SHA256": "3e4c7664ce893ac1f6bb59cd76664fb5c872cb076bb82dbd534db0555b6e9fa5",
@@ -555,7 +567,8 @@
       "name": "RWKV-4-MIDI-120M-v1-20230714-ctx4096.pth",
       "desc": {
         "en": "Music 120M v1",
-        "zh": "作曲 120M v1"
+        "zh": "作曲 120M v1",
+        "ja": "作曲 120M v1"
       },
       "size": 239224753,
       "SHA256": "161d27dcf50d0958d230601ba1e0f8e7dd9c236105e92d2b833496412ace430c",
@@ -567,7 +580,8 @@
       "name": "RWKV-4-MIDI-560M-v1-20230717-ctx4096.pth",
       "desc": {
         "en": "Music 560M v1",
-        "zh": "作曲 560M v1"
+        "zh": "作曲 560M v1",
+        "ja": "作曲 560M v1"
       },
       "size": 1130577457,
       "SHA256": "62b21841b24af38ef176e9e9d895d9fff730cea8aa0623f53a1784d74ce828d6",

vendor.yml

@@ -2,6 +2,7 @@
 - ^backend-python/wkv_cuda_utils/
 - ^backend-python/get-pip\.py
 - ^backend-python/convert_model\.py
+- ^backend-python/convert_safetensors\.py
 - ^backend-python/utils/midi\.py
 - ^build/
 - ^finetune/lora/