chore(deps): bump docker/build-push-action from 3 to 5

Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 3 to 5.
- [Release notes](https://github.com/docker/build-push-action/releases)
- [Commits](https://github.com/docker/build-push-action/compare/v3...v5)

---
updated-dependencies:
- dependency-name: docker/build-push-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/docker.yml

@@ -0,0 +1,171 @@
+name: Publish Docker Image
+on: [push]
+
+concurrency:
+  group: ${{ github.ref }}-${{ github.workflow }}
+  cancel-in-progress: true
+
+jobs:
+  docker_build:
+    name: Build ${{ matrix.arch }} Image
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        include:
+          - arch: amd64
+            name: amd64
+        #   - arch: arm64
+        #     name: arm64
+
+    steps:
+      - name: Free up disk spaces
+        run: |
+          sudo rm -rf /usr/share/dotnet || true
+          sudo rm -rf /opt/ghc || true
+          sudo rm -rf "/usr/local/share/boost" || true
+          sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
+
+      - name: Get lowercase string for the repository name
+        id: lowercase-repo-name
+        uses: ASzc/change-string-case-action@v2
+        with:
+          string: ${{ github.event.repository.name }}
+
+      - name: Checkout base
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Cache Docker layers
+        uses: actions/cache@v2
+        with:
+          path: /tmp/.buildx-cache
+          key: ${{ github.ref }}-${{ matrix.arch }}
+          restore-keys: |
+            ${{ github.ref }}-${{ matrix.arch }}
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+        with:
+          platforms: linux/${{ matrix.arch }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Docker login
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Get commit SHA
+        id: vars
+        run: echo "::set-output name=sha_short::$(git rev-parse --short HEAD)"
+
+      - name: Build and export
+        id: build
+        if: github.ref == 'refs/heads/master'
+        uses: docker/build-push-action@v5
+        with:
+          push: true
+          platforms: linux/${{ matrix.arch }}
+          tags: ${{ secrets.DOCKER_USERNAME }}/${{ steps.lowercase-repo-name.outputs.lowercase }}:${{ matrix.name }}-latest
+          build-args: |
+            SHA=${{ steps.vars.outputs.sha_short }}
+          outputs: type=image,push=true
+          cache-from: type=local,src=/tmp/.buildx-cache
+          cache-to: type=local,dest=/tmp/.buildx-cache
+
+      - name: Replace tag without `v`
+        if: startsWith(github.ref, 'refs/tags/')
+        uses: actions/github-script@v1
+        id: version
+        with:
+          script: |
+            return context.payload.ref.replace(/\/?refs\/tags\/v/, '')
+          result-encoding: string
+
+      - name: Build release and export
+        id: build_rel
+        if: startsWith(github.ref, 'refs/tags/')
+        uses: docker/build-push-action@v5
+        with:
+          push: true
+          platforms: linux/${{ matrix.arch }}
+          tags: ${{ secrets.DOCKER_USERNAME }}/${{ steps.lowercase-repo-name.outputs.lowercase }}:${{ matrix.name }}-${{steps.version.outputs.result}}
+          build-args: |
+            SHA=${{ steps.version.outputs.result }}
+          outputs: type=image,push=true
+          cache-from: type=local,src=/tmp/.buildx-cache
+          cache-to: type=local,dest=/tmp/.buildx-cache
+
+      - name: Save digest
+        if: github.ref == 'refs/heads/master'
+        run: echo ${{ steps.build.outputs.digest }} > /tmp/digest.txt
+
+      - name: Save release digest
+        if: startsWith(github.ref, 'refs/tags/')
+        run: echo ${{ steps.build_rel.outputs.digest }} > /tmp/digest.txt
+
+      - name: Upload artifact
+        uses: actions/upload-artifact@v3
+        with:
+          name: digest_${{ matrix.name }}
+          path: /tmp/digest.txt
+
+  manifests:
+    name: Build manifests
+    needs: [docker_build]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Get lowercase string for the repository name
+        id: lowercase-repo-name
+        uses: ASzc/change-string-case-action@v2
+        with:
+          string: ${{ github.event.repository.name }}
+
+      - name: Checkout base
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      # https://github.com/docker/setup-qemu-action
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
+      # https://github.com/docker/setup-buildx-action
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+        with:
+          config-inline: |
+            [worker.oci]
+              max-parallelism = 1
+
+      - name: Download artifact
+        uses: actions/download-artifact@v3
+        with:
+          path: /tmp/images/
+
+      - name: Docker login
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Replace tag without `v`
+        if: startsWith(github.ref, 'refs/tags/')
+        uses: actions/github-script@v1
+        id: version
+        with:
+          script: |
+            return context.payload.ref.replace(/\/?refs\/tags\/v/, '')
+          result-encoding: string
+
+      - name: Merge and push manifest on master branch
+        if: github.ref == 'refs/heads/master'
+        run: python scripts/merge_manifest.py "${{ secrets.DOCKER_USERNAME }}/${{ steps.lowercase-repo-name.outputs.lowercase }}"
+
+      - name: Merge and push manifest on release
+        if: startsWith(github.ref, 'refs/tags/')
+        run: python scripts/merge_manifest.py "${{ secrets.DOCKER_USERNAME }}/${{ steps.lowercase-repo-name.outputs.lowercase }}" ${{steps.version.outputs.result}}

.github/workflows/pre-release.yml

@@ -0,0 +1,117 @@
+name: pre-release
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - master
+    paths:
+      - "backend-python/**"
+    tags-ignore:
+      - "v*"
+
+jobs:
+  windows:
+    runs-on: windows-2022
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: master
+      - uses: actions/setup-go@v5
+        with:
+          go-version: '1.20.5'
+      - uses: actions/setup-python@v5
+        id: cp310
+        with:
+          python-version: '3.10'
+      - uses: crazy-max/ghaction-chocolatey@v3
+        with:
+          args: install upx
+      - run: |
+          Start-BitsTransfer https://github.com/josStorer/ai00_rwkv_server/releases/latest/download/webgpu_server_windows_x86_64.exe ./backend-rust/webgpu_server.exe
+          Start-BitsTransfer https://github.com/josStorer/web-rwkv-converter/releases/latest/download/web-rwkv-converter_windows_x86_64.exe ./backend-rust/web-rwkv-converter.exe
+          Start-BitsTransfer https://github.com/josStorer/LibreHardwareMonitor.Console/releases/latest/download/LibreHardwareMonitor.Console.zip ./LibreHardwareMonitor.Console.zip
+          Expand-Archive ./LibreHardwareMonitor.Console.zip -DestinationPath ./components/LibreHardwareMonitor.Console
+          Start-BitsTransfer https://www.python.org/ftp/python/3.10.11/python-3.10.11-embed-amd64.zip ./python-3.10.11-embed-amd64.zip
+          Expand-Archive ./python-3.10.11-embed-amd64.zip -DestinationPath ./py310
+          $content=Get-Content "./py310/python310._pth"; $content | ForEach-Object {if ($_.ReadCount -eq 3) {"Lib\\site-packages"} else {$_}} | Set-Content ./py310/python310._pth
+          ./py310/python ./backend-python/get-pip.py
+          ./py310/python -m pip install Cython==3.0.4
+          Copy-Item -Path "${{ 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==1.9
+          go install github.com/wailsapp/wails/v2/cmd/wails@latest
+          del ./backend-python/rwkv_pip/cpp/librwkv.dylib
+          del ./backend-python/rwkv_pip/cpp/librwkv.so
+          (Get-Content -Path ./backend-golang/app.go) -replace "//go:custom_build windows ", "" | Set-Content -Path ./backend-golang/app.go
+          (Get-Content -Path ./backend-golang/utils.go) -replace "//go:custom_build windows ", "" | Set-Content -Path ./backend-golang/utils.go
+          make
+          Rename-Item -Path "build/bin/RWKV-Runner.exe" -NewName "RWKV-Runner_windows_x64.exe"
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: RWKV-Runner_windows_x64.exe
+          path: build/bin/RWKV-Runner_windows_x64.exe
+
+  linux:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: master
+      - uses: actions/setup-go@v5
+        with:
+          go-version: '1.20.5'
+      - run: |
+          wget https://github.com/josStorer/ai00_rwkv_server/releases/latest/download/webgpu_server_linux_x86_64 -O ./backend-rust/webgpu_server
+          wget https://github.com/josStorer/web-rwkv-converter/releases/latest/download/web-rwkv-converter_linux_x86_64 -O ./backend-rust/web-rwkv-converter
+          sudo apt-get update
+          sudo apt-get install upx
+          sudo apt-get install build-essential libgtk-3-dev libwebkit2gtk-4.0-dev libasound2-dev
+          go install github.com/wailsapp/wails/v2/cmd/wails@latest
+          rm ./backend-python/rwkv_pip/wkv_cuda.pyd
+          rm ./backend-python/rwkv_pip/rwkv5.pyd
+          rm ./backend-python/rwkv_pip/rwkv6.pyd
+          rm ./backend-python/rwkv_pip/beta/wkv_cuda.pyd
+          rm ./backend-python/get-pip.py
+          rm ./backend-python/rwkv_pip/cpp/librwkv.dylib
+          rm ./backend-python/rwkv_pip/cpp/rwkv.dll
+          rm ./backend-python/rwkv_pip/webgpu/web_rwkv_py.cp310-win_amd64.pyd
+          make
+          mv build/bin/RWKV-Runner build/bin/RWKV-Runner_linux_x64
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: RWKV-Runner_linux_x64
+          path: build/bin/RWKV-Runner_linux_x64
+
+  macos:
+    runs-on: macos-13
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: master
+      - uses: actions/setup-go@v5
+        with:
+          go-version: '1.20.5'
+      - run: |
+          wget https://github.com/josStorer/ai00_rwkv_server/releases/latest/download/webgpu_server_darwin_aarch64 -O ./backend-rust/webgpu_server
+          wget https://github.com/josStorer/web-rwkv-converter/releases/latest/download/web-rwkv-converter_darwin_aarch64 -O ./backend-rust/web-rwkv-converter
+          go install github.com/wailsapp/wails/v2/cmd/wails@latest
+          rm ./backend-python/rwkv_pip/wkv_cuda.pyd
+          rm ./backend-python/rwkv_pip/rwkv5.pyd
+          rm ./backend-python/rwkv_pip/rwkv6.pyd
+          rm ./backend-python/rwkv_pip/beta/wkv_cuda.pyd
+          rm ./backend-python/get-pip.py
+          rm ./backend-python/rwkv_pip/cpp/rwkv.dll
+          rm ./backend-python/rwkv_pip/cpp/librwkv.so
+          rm ./backend-python/rwkv_pip/webgpu/web_rwkv_py.cp310-win_amd64.pyd
+          make
+          cp build/darwin/Readme_Install.txt build/bin/Readme_Install.txt
+          cp build/bin/RWKV-Runner.app/Contents/MacOS/RWKV-Runner build/bin/RWKV-Runner_darwin_universal
+          cd build/bin && zip -r RWKV-Runner_macos_universal.zip RWKV-Runner.app Readme_Install.txt
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: RWKV-Runner_macos_universal.zip
+          path: build/bin/RWKV-Runner_macos_universal.zip
+

.github/workflows/release.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - run: echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           ref: master
 
@@ -38,17 +38,17 @@ jobs:
     runs-on: windows-2022
     needs: create-draft
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           ref: master
-      - uses: actions/setup-go@v4
+      - uses: actions/setup-go@v5
         with:
           go-version: '1.20.5'
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         id: cp310
         with:
           python-version: '3.10'
-      - uses: crazy-max/ghaction-chocolatey@v2
+      - uses: crazy-max/ghaction-chocolatey@v3
         with:
           args: install upx
       - run: |
@@ -78,10 +78,10 @@ jobs:
     runs-on: ubuntu-20.04
     needs: create-draft
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           ref: master
-      - uses: actions/setup-go@v4
+      - uses: actions/setup-go@v5
         with:
           go-version: '1.20.5'
       - run: |
@@ -108,10 +108,10 @@ jobs:
     runs-on: macos-13
     needs: create-draft
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           ref: master
-      - uses: actions/setup-go@v4
+      - uses: actions/setup-go@v5
         with:
           go-version: '1.20.5'
       - run: |
@@ -137,5 +137,5 @@ jobs:
     runs-on: ubuntu-22.04
     needs: [ windows, linux, macos ]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - run: gh release edit ${{github.ref_name}} --draft=false

.gitignore

@@ -19,7 +19,6 @@ __pycache__
 /cmd-helper.bat
 /install-py-dep.bat
 /backend-python/wkv_cuda
-/backend-python/rwkv*
 *.exe
 *.old
 .DS_Store

CURRENT_CHANGE.md

@@ -1,12 +1,25 @@
 ## Changes
 
-- improve refreshRemoteModels
-- reduce precompiled web_rwkv_py size
-- webgpu(Python) max_buffer_size (12B support) and turbo
-- improve role-playing effect
-- update manifest.json (a lot of new models)
-- bump webgpu(ai00_server) mode to v0.3.8
-- improve details
+### Features
+
+- add Docker support (#291) @LonghronShen
+
+### Fixes
+
+- fix a generation exception caused by potentially dangerous regex being passed into the stop array
+- fix max_tokens parameter of Chat page not being passed to backend
+- fix the issue where penalty_decay and global_penalty are not being passed to the backend default config when running
+  the model through client
+
+### Improvements
+
+- prevent 'torch' has no attribute 'cuda' error in torch_gc, so user can use CPU or WebGPU (#302)
+
+### Chores
+
+- bump dependencies
+- add pre-release workflow
+- dep_check.py now ignores GPUtil
 
 ## Install
 

Dockerfile

@@ -0,0 +1,55 @@
+FROM node:21-slim AS frontend
+
+RUN echo "registry=https://registry.npmmirror.com/" > ~/.npmrc
+
+WORKDIR /app
+
+COPY manifest.json manifest.json
+COPY frontend frontend
+
+WORKDIR /app/frontend
+
+RUN npm ci
+RUN npm run build
+
+FROM nvidia/cuda:11.6.1-devel-ubuntu20.04 AS runtime
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -yq git curl wget build-essential ninja-build aria2 jq software-properties-common
+
+RUN add-apt-repository -y ppa:deadsnakes/ppa && \
+    add-apt-repository -y ppa:ubuntu-toolchain-r/test && \
+    apt install -y g++-11 python3.10 python3.10-distutils python3.10-dev && \
+    curl -sS http://mirrors.aliyun.com/pypi/get-pip.py | python3.10
+
+RUN python3.10 -m pip install cmake
+
+FROM runtime AS librwkv
+
+WORKDIR /app
+
+RUN git clone https://github.com/RWKV/rwkv.cpp.git && \
+    cd rwkv.cpp && \
+    git submodule update --init --recursive && \
+    mkdir -p build && \
+    cd build && \
+    cmake -G Ninja .. && \
+    cmake --build .
+
+FROM runtime AS final
+
+WORKDIR /app
+
+COPY ./backend-python/requirements.txt ./backend-python/requirements.txt
+
+RUN python3.10 -m pip install --quiet -r ./backend-python/requirements.txt
+
+COPY . .
+COPY --from=frontend /app/frontend/dist /app/frontend/dist
+COPY --from=librwkv /app/rwkv.cpp/build/librwkv.so /app/backend-python/rwkv_pip/cpp/librwkv.so
+
+EXPOSE 27777
+
+CMD ["python3.10", "./backend-python/main.py", "--port", "27777", "--host", "0.0.0.0", "--webui"]

Makefile

@@ -8,7 +8,8 @@ endif
 
 build-windows:
 	@echo ---- build for windows
-	wails build -upx -ldflags '-s -w -extldflags "-static"' -platform windows/amd64
+	wails build -ldflags '-s -w -extldflags "-static"' -platform windows/amd64
+	upx -9 --lzma ./build/bin/RWKV-Runner.exe
 
 build-macos:
 	@echo ---- build for macos
@@ -16,7 +17,8 @@ build-macos:
 
 build-linux:
 	@echo ---- build for linux
-	wails build -upx -ldflags '-s -w' -platform linux/amd64
+	wails build -ldflags '-s -w' -platform linux/amd64
+	upx -9 --lzma ./build/bin/RWKV-Runner
 
 build-web:
 	@echo ---- build for web

README.md

@@ -12,6 +12,7 @@ compatible with the OpenAI API, which means that every ChatGPT client is an RWKV
 
 [![license][license-image]][license-url]
 [![release][release-image]][release-url]
+[![py-version][py-version-image]][py-version-url]
 
 English | [简体中文](README_ZH.md) | [日本語](README_JA.md)
 
@@ -31,6 +32,10 @@ English | [简体中文](README_ZH.md) | [日本語](README_JA.md)
 
 [release-url]: https://github.com/josStorer/RWKV-Runner/releases/latest
 
+[py-version-image]: https://img.shields.io/pypi/pyversions/fastapi.svg
+
+[py-version-url]: https://github.com/josStorer/RWKV-Runner/tree/master/backend-python
+
 [download-url]: https://github.com/josStorer/RWKV-Runner/releases
 
 [Windows-image]: https://img.shields.io/badge/-Windows-blue?logo=windows
@@ -231,10 +236,12 @@ computer keyboard as MIDI input.
 - ChatRWKV: https://github.com/BlinkDL/ChatRWKV
 - RWKV-LM: https://github.com/BlinkDL/RWKV-LM
 - RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- RWKV-v5-lora: https://github.com/JL-er/RWKV-v5-lora
 - MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
 - ai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server
 - rwkv.cpp: https://github.com/saharNooby/rwkv.cpp
 - web-rwkv-py: https://github.com/cryscan/web-rwkv-py
+- web-rwkv: https://github.com/cryscan/web-rwkv
 
 ## Preview
 

README_JA.md

@@ -12,6 +12,7 @@
 
 [![license][license-image]][license-url]
 [![release][release-image]][release-url]
+[![py-version][py-version-image]][py-version-url]
 
 [English](README.md) | [简体中文](README_ZH.md) | 日本語
 
@@ -31,6 +32,10 @@
 
 [release-url]: https://github.com/josStorer/RWKV-Runner/releases/latest
 
+[py-version-image]: https://img.shields.io/pypi/pyversions/fastapi.svg
+
+[py-version-url]: https://github.com/josStorer/RWKV-Runner/tree/master/backend-python
+
 [download-url]: https://github.com/josStorer/RWKV-Runner/releases
 
 [Windows-image]: https://img.shields.io/badge/-Windows-blue?logo=windows
@@ -228,10 +233,12 @@ MIDIキーボードをお持ちでない場合、`Virtual Midi Controller 3 LE`
 - ChatRWKV: https://github.com/BlinkDL/ChatRWKV
 - RWKV-LM: https://github.com/BlinkDL/RWKV-LM
 - RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- RWKV-v5-lora: https://github.com/JL-er/RWKV-v5-lora
 - MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
 - ai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server
 - rwkv.cpp: https://github.com/saharNooby/rwkv.cpp
 - web-rwkv-py: https://github.com/cryscan/web-rwkv-py
+- web-rwkv: https://github.com/cryscan/web-rwkv
 
 ## Preview
 

README_ZH.md

@@ -11,6 +11,7 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 
 [![license][license-image]][license-url]
 [![release][release-image]][release-url]
+[![py-version][py-version-image]][py-version-url]
 
 [English](README.md) | 简体中文 | [日本語](README_JA.md)
 
@@ -30,6 +31,10 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 
 [release-url]: https://github.com/josStorer/RWKV-Runner/releases/latest
 
+[py-version-image]: https://img.shields.io/pypi/pyversions/fastapi.svg
+
+[py-version-url]: https://github.com/josStorer/RWKV-Runner/tree/master/backend-python
+
 [download-url]: https://github.com/josStorer/RWKV-Runner/releases
 
 [Windows-image]: https://img.shields.io/badge/-Windows-blue?logo=windows
@@ -210,10 +215,12 @@ for i in np.argsort(embeddings_cos_sim)[::-1]:
 - ChatRWKV: https://github.com/BlinkDL/ChatRWKV
 - RWKV-LM: https://github.com/BlinkDL/RWKV-LM
 - RWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA
+- RWKV-v5-lora: https://github.com/JL-er/RWKV-v5-lora
 - MIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer
 - ai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server
 - rwkv.cpp: https://github.com/saharNooby/rwkv.cpp
 - web-rwkv-py: https://github.com/cryscan/web-rwkv-py
+- web-rwkv: https://github.com/cryscan/web-rwkv
 
 ## Preview
 

backend-golang/app.go

@@ -1,7 +1,9 @@
 package backend_golang
 
 import (
+	"archive/zip"
 	"bufio"
+	"bytes"
 	"context"
 	"errors"
 	"io"
@@ -10,6 +12,7 @@ import (
 	"os/exec"
 	"path/filepath"
 	"runtime"
+	"strings"
 	"syscall"
 	"time"
 
@@ -23,6 +26,7 @@ type App struct {
 	ctx           context.Context
 	HasConfigData bool
 	ConfigData    map[string]any
+	Dev           bool
 	exDir         string
 	cmdPrefix     string
 }
@@ -39,10 +43,20 @@ func (a *App) OnStartup(ctx context.Context) {
 	a.exDir = ""
 	a.cmdPrefix = ""
 
-	if runtime.GOOS == "darwin" {
-		ex, _ := os.Executable()
-		a.exDir = filepath.Dir(ex) + "/../../../"
-		a.cmdPrefix = "cd " + a.exDir + " && "
+	ex, err := os.Executable()
+	if err == nil {
+		if runtime.GOOS == "darwin" {
+			a.exDir = filepath.Dir(ex) + "/../../../"
+			a.cmdPrefix = "cd " + a.exDir + " && "
+		} else {
+			a.exDir = filepath.Dir(ex) + "/"
+			a.cmdPrefix = "cd " + a.exDir + " && "
+		}
+		if a.Dev {
+			a.exDir = ""
+		} else {
+			os.Chdir(a.exDir)
+		}
 	}
 
 	os.Chmod(a.exDir+"backend-rust/webgpu_server", 0777)
@@ -50,9 +64,9 @@ func (a *App) OnStartup(ctx context.Context) {
 	os.Mkdir(a.exDir+"models", os.ModePerm)
 	os.Mkdir(a.exDir+"lora-models", os.ModePerm)
 	os.Mkdir(a.exDir+"finetune/json2binidx_tool/data", os.ModePerm)
-	trainLogPath := a.exDir + "lora-models/train_log.txt"
+	trainLogPath := "lora-models/train_log.txt"
 	if !a.FileExists(trainLogPath) {
-		f, err := os.Create(trainLogPath)
+		f, err := os.Create(a.exDir + trainLogPath)
 		if err == nil {
 			f.Close()
 		}
@@ -149,6 +163,7 @@ func (a *App) UpdateApp(url string) (broken bool, err error) {
 	ticker := time.NewTicker(250 * time.Millisecond)
 	defer ticker.Stop()
 
+	// update progress
 	go func() {
 		for {
 			<-ticker.C
@@ -168,13 +183,35 @@ func (a *App) UpdateApp(url string) (broken bool, err error) {
 			}
 		}
 	}()
-	err = selfupdate.Apply(pr, selfupdate.Options{})
+
+	var updateFile io.Reader = pr
+	// extract macos binary from zip
+	if strings.HasSuffix(url, ".zip") && runtime.GOOS == "darwin" {
+		zipBytes, err := io.ReadAll(pr)
+		if err != nil {
+			return false, err
+		}
+		archive, err := zip.NewReader(bytes.NewReader(zipBytes), int64(len(zipBytes)))
+		if err != nil {
+			return false, err
+		}
+		file, err := archive.Open("RWKV-Runner.app/Contents/MacOS/RWKV-Runner")
+		if err != nil {
+			return false, err
+		}
+		defer file.Close()
+		updateFile = file
+	}
+
+	// apply update
+	err = selfupdate.Apply(updateFile, selfupdate.Options{})
 	if err != nil {
 		if rerr := selfupdate.RollbackError(err); rerr != nil {
 			return true, rerr
 		}
 		return false, err
 	}
+	// restart app
 	if runtime.GOOS == "windows" {
 		name, err := os.Executable()
 		if err != nil {

backend-golang/download.go

@@ -10,7 +10,11 @@ import (
 )
 
 func (a *App) DownloadFile(path string, url string) error {
-	_, err := grab.Get(a.exDir+path, url)
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return err
+	}
+	_, err = grab.Get(absPath, url)
 	if err != nil {
 		return err
 	}
@@ -88,11 +92,15 @@ func (a *App) ContinueDownload(url string) {
 }
 
 func (a *App) AddToDownloadList(path string, url string) {
-	if !existsInDownloadList(a.exDir+path, url) {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return
+	}
+	if !existsInDownloadList(absPath, url) {
 		downloadList = append(downloadList, &DownloadStatus{
 			resp:        nil,
 			Name:        filepath.Base(path),
-			Path:        a.exDir + path,
+			Path:        absPath,
 			Url:         url,
 			Downloading: false,
 		})

backend-golang/file.go

@@ -14,27 +14,55 @@ import (
 	wruntime "github.com/wailsapp/wails/v2/pkg/runtime"
 )
 
+func (a *App) GetAbsPath(path string) (string, error) {
+	var absPath string
+	var err error
+	if filepath.IsAbs(path) {
+		absPath = filepath.Clean(path)
+	} else {
+		absPath, err = filepath.Abs(filepath.Join(a.exDir, path))
+		if err != nil {
+			return "", err
+		}
+	}
+	absPath = strings.ReplaceAll(absPath, "/", string(os.PathSeparator))
+	println("GetAbsPath:", absPath)
+	return absPath, nil
+}
+
 func (a *App) SaveFile(path string, savedContent []byte) error {
-	if err := os.WriteFile(a.exDir+path, savedContent, 0644); err != nil {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return err
+	}
+	if err := os.WriteFile(absPath, savedContent, 0644); err != nil {
 		return err
 	}
 	return nil
 }
 
-func (a *App) SaveJson(fileName string, jsonData any) error {
+func (a *App) SaveJson(path string, jsonData any) error {
 	text, err := json.MarshalIndent(jsonData, "", "  ")
 	if err != nil {
 		return err
 	}
 
-	if err := os.WriteFile(a.exDir+fileName, text, 0644); err != nil {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return err
+	}
+	if err := os.WriteFile(absPath, text, 0644); err != nil {
 		return err
 	}
 	return nil
 }
 
-func (a *App) ReadJson(fileName string) (any, error) {
-	file, err := os.ReadFile(a.exDir + fileName)
+func (a *App) ReadJson(path string) (any, error) {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return nil, err
+	}
+	file, err := os.ReadFile(absPath)
 	if err != nil {
 		return nil, err
 	}
@@ -48,8 +76,12 @@ func (a *App) ReadJson(fileName string) (any, error) {
 	return data, nil
 }
 
-func (a *App) FileExists(fileName string) bool {
-	_, err := os.Stat(a.exDir + fileName)
+func (a *App) FileExists(path string) bool {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return false
+	}
+	_, err = os.Stat(absPath)
 	return err == nil
 }
 
@@ -60,8 +92,12 @@ type FileInfo struct {
 	ModTime string `json:"modTime"`
 }
 
-func (a *App) ReadFileInfo(fileName string) (*FileInfo, error) {
-	info, err := os.Stat(a.exDir + fileName)
+func (a *App) ReadFileInfo(path string) (*FileInfo, error) {
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return nil, err
+	}
+	info, err := os.Stat(absPath)
 	if err != nil {
 		return nil, err
 	}
@@ -74,7 +110,11 @@ func (a *App) ReadFileInfo(fileName string) (*FileInfo, error) {
 }
 
 func (a *App) ListDirFiles(dirPath string) ([]FileInfo, error) {
-	files, err := os.ReadDir(a.exDir + dirPath)
+	absDirPath, err := a.GetAbsPath(dirPath)
+	if err != nil {
+		return nil, err
+	}
+	files, err := os.ReadDir(absDirPath)
 	if err != nil {
 		return nil, err
 	}
@@ -96,7 +136,11 @@ func (a *App) ListDirFiles(dirPath string) ([]FileInfo, error) {
 }
 
 func (a *App) DeleteFile(path string) error {
-	err := os.Remove(a.exDir + path)
+	absPath, err := a.GetAbsPath(path)
+	if err != nil {
+		return err
+	}
+	err = os.Remove(absPath)
 	if err != nil {
 		return err
 	}
@@ -104,18 +148,27 @@ func (a *App) DeleteFile(path string) error {
 }
 
 func (a *App) CopyFile(src string, dst string) error {
-	sourceFile, err := os.Open(a.exDir + src)
+	absSrc, err := a.GetAbsPath(src)
+	if err != nil {
+		return err
+	}
+	absDst, err := a.GetAbsPath(dst)
+	if err != nil {
+		return err
+	}
+
+	sourceFile, err := os.Open(absSrc)
 	if err != nil {
 		return err
 	}
 	defer sourceFile.Close()
 
-	err = os.MkdirAll(a.exDir+dst[:strings.LastIndex(dst, "/")], 0755)
+	err = os.MkdirAll(filepath.Dir(absDst), 0755)
 	if err != nil {
 		return err
 	}
 
-	destFile, err := os.Create(a.exDir + dst)
+	destFile, err := os.Create(absDst)
 	if err != nil {
 		return err
 	}
@@ -166,14 +219,8 @@ func (a *App) OpenOpenFileDialog(filterPattern string) (string, error) {
 	return path, nil
 }
 
-func (a *App) OpenFileFolder(path string, relative bool) error {
-	var absPath string
-	var err error
-	if relative {
-		absPath, err = filepath.Abs(a.exDir + path)
-	} else {
-		absPath, err = filepath.Abs(path)
-	}
+func (a *App) OpenFileFolder(path string) error {
+	absPath, err := a.GetAbsPath(path)
 	if err != nil {
 		return err
 	}

backend-golang/rwkv.go

@@ -1,3 +1,4 @@
+// Considering some whitespace and multilingual support, the functions in rwkv.go should always be executed with cwd as RWKV-Runner, and never use a.GetAbsPath() here.
 package backend_golang
 
 import (
@@ -11,14 +12,18 @@ import (
 )
 
 func (a *App) StartServer(python string, port int, host string, webui bool, rwkvBeta bool, rwkvcpp bool, webgpu bool) (string, error) {
-	var err error
+	execFile := "./backend-python/main.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
 	if err != nil {
 		return "", err
 	}
-	args := []string{python, "./backend-python/main.py"}
+	args := []string{python, execFile}
 	if webui {
 		args = append(args, "--webui")
 	}
@@ -36,41 +41,77 @@ func (a *App) StartServer(python string, port int, host string, webui bool, rwkv
 }
 
 func (a *App) StartWebGPUServer(port int, host string) (string, error) {
-	args := []string{"./backend-rust/webgpu_server"}
+	var execFile string
+	execFiles := []string{"./backend-rust/webgpu_server", "./backend-rust/webgpu_server.exe"}
+	for _, file := range execFiles {
+		_, err := os.Stat(file)
+		if err == nil {
+			execFile = file
+			break
+		}
+	}
+	if execFile == "" {
+		return "", errors.New(execFiles[0] + " not found")
+	}
+	args := []string{execFile}
 	args = append(args, "--port", strconv.Itoa(port), "--ip", host)
 	return Cmd(args...)
 }
 
 func (a *App) ConvertModel(python string, modelPath string, strategy string, outPath string) (string, error) {
-	var err error
+	execFile := "./backend-python/convert_model.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
 	if err != nil {
 		return "", err
 	}
-	return Cmd(python, "./backend-python/convert_model.py", "--in", modelPath, "--out", outPath, "--strategy", strategy)
+	return Cmd(python, execFile, "--in", modelPath, "--out", outPath, "--strategy", strategy)
 }
 
 func (a *App) ConvertSafetensors(modelPath string, outPath string) (string, error) {
-	args := []string{"./backend-rust/web-rwkv-converter"}
+	var execFile string
+	execFiles := []string{"./backend-rust/web-rwkv-converter", "./backend-rust/web-rwkv-converter.exe"}
+	for _, file := range execFiles {
+		_, err := os.Stat(file)
+		if err == nil {
+			execFile = file
+			break
+		}
+	}
+	if execFile == "" {
+		return "", errors.New(execFiles[0] + " not found")
+	}
+	args := []string{execFile}
 	args = append(args, "--input", modelPath, "--output", outPath)
 	return Cmd(args...)
 }
 
 func (a *App) ConvertSafetensorsWithPython(python string, modelPath string, outPath string) (string, error) {
-	var err error
+	execFile := "./backend-python/convert_safetensors.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
 	if err != nil {
 		return "", err
 	}
-	return Cmd(python, "./backend-python/convert_safetensors.py", "--input", modelPath, "--output", outPath)
+	return Cmd(python, execFile, "--input", modelPath, "--output", outPath)
 }
 
 func (a *App) ConvertGGML(python string, modelPath string, outPath string, Q51 bool) (string, error) {
-	var err error
+	execFile := "./backend-python/convert_pytorch_to_ggml.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
@@ -81,11 +122,15 @@ func (a *App) ConvertGGML(python string, modelPath string, outPath string, Q51 b
 	if Q51 {
 		dataType = "Q5_1"
 	}
-	return Cmd(python, "./backend-python/convert_pytorch_to_ggml.py", modelPath, outPath, dataType)
+	return Cmd(python, execFile, modelPath, outPath, dataType)
 }
 
 func (a *App) ConvertData(python string, input string, outputPrefix string, vocab string) (string, error) {
-	var err error
+	execFile := "./finetune/json2binidx_tool/tools/preprocess_data.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
@@ -129,19 +174,23 @@ func (a *App) ConvertData(python string, input string, outputPrefix string, voca
 		return "", err
 	}
 
-	return Cmd(python, "./finetune/json2binidx_tool/tools/preprocess_data.py", "--input", input, "--output-prefix", outputPrefix, "--vocab", vocab,
+	return Cmd(python, execFile, "--input", input, "--output-prefix", outputPrefix, "--vocab", vocab,
 		"--tokenizer-type", tokenizerType, "--dataset-impl", "mmap", "--append-eod")
 }
 
 func (a *App) MergeLora(python string, useGpu bool, loraAlpha int, baseModel string, loraPath string, outputPath string) (string, error) {
-	var err error
+	execFile := "./finetune/lora/merge_lora.py"
+	_, err := os.Stat(execFile)
+	if err != nil {
+		return "", err
+	}
 	if python == "" {
 		python, err = GetPython()
 	}
 	if err != nil {
 		return "", err
 	}
-	args := []string{python, "./finetune/lora/merge_lora.py"}
+	args := []string{python, execFile}
 	if useGpu {
 		args = append(args, "--use-gpu")
 	}
@@ -157,9 +206,9 @@ func (a *App) DepCheck(python string) error {
 	if err != nil {
 		return err
 	}
-	out, err := exec.Command(python, a.exDir+"./backend-python/dep_check.py").CombinedOutput()
+	out, err := exec.Command(python, a.exDir+"backend-python/dep_check.py").CombinedOutput()
 	if err != nil {
-		return errors.New("DepCheck Error: " + string(out))
+		return errors.New("DepCheck Error: " + string(out) + " GError: " + err.Error())
 	}
 	return nil
 }
@@ -185,7 +234,7 @@ func (a *App) InstallPyDep(python string, cnMirror bool) (string, error) {
 		if !cnMirror {
 			installScript = strings.Replace(installScript, " -i https://pypi.tuna.tsinghua.edu.cn/simple", "", -1)
 		}
-		err = os.WriteFile("./install-py-dep.bat", []byte(installScript), 0644)
+		err = os.WriteFile(a.exDir+"install-py-dep.bat", []byte(installScript), 0644)
 		if err != nil {
 			return "", err
 		}

backend-golang/utils.go

@@ -23,14 +23,19 @@ func CmdHelper(hideWindow bool, args ...string) (*exec.Cmd, error) {
 	if runtime.GOOS != "windows" {
 		return nil, errors.New("unsupported OS")
 	}
-	filename := "./cmd-helper.bat"
-	_, err := os.Stat(filename)
+	ex, err := os.Executable()
 	if err != nil {
-		if err := os.WriteFile(filename, []byte("start %*"), 0644); err != nil {
+		return nil, err
+	}
+	exDir := filepath.Dir(ex) + "/"
+	path := exDir + "cmd-helper.bat"
+	_, err = os.Stat(path)
+	if err != nil {
+		if err := os.WriteFile(path, []byte("start %*"), 0644); err != nil {
 			return nil, err
 		}
 	}
-	cmdHelper, err := filepath.Abs(filename)
+	cmdHelper, err := filepath.Abs(path)
 	if err != nil {
 		return nil, err
 	}
@@ -86,16 +91,18 @@ func Cmd(args ...string) (string, error) {
 }
 
 func CopyEmbed(efs embed.FS) error {
-	prefix := ""
+	ex, err := os.Executable()
+	if err != nil {
+		return err
+	}
+	var prefix string
 	if runtime.GOOS == "darwin" {
-		ex, err := os.Executable()
-		if err != nil {
-			return err
-		}
 		prefix = filepath.Dir(ex) + "/../../../"
+	} else {
+		prefix = filepath.Dir(ex) + "/"
 	}
 
-	err := fs.WalkDir(efs, ".", func(path string, d fs.DirEntry, err error) error {
+	err = fs.WalkDir(efs, ".", func(path string, d fs.DirEntry, err error) error {
 		if d.IsDir() {
 			return nil
 		}
@@ -136,13 +143,19 @@ func CopyEmbed(efs embed.FS) error {
 func GetPython() (string, error) {
 	switch platform := runtime.GOOS; platform {
 	case "windows":
-		_, err := os.Stat("py310/python.exe")
+		ex, err := os.Executable()
 		if err != nil {
-			_, err := os.Stat("python-3.10.11-embed-amd64.zip")
+			return "", err
+		}
+		exDir := filepath.Dir(ex) + "/"
+		pyexe := exDir + "py310/python.exe"
+		_, err = os.Stat(pyexe)
+		if err != nil {
+			_, err := os.Stat(exDir + "python-3.10.11-embed-amd64.zip")
 			if err != nil {
 				return "", errors.New("python zip not found")
 			} else {
-				err := Unzip("python-3.10.11-embed-amd64.zip", "py310")
+				err := Unzip(exDir+"python-3.10.11-embed-amd64.zip", exDir+"py310")
 				if err != nil {
 					return "", errors.New("failed to unzip python")
 				} else {

backend-golang/wsl_windows.go

@@ -9,7 +9,6 @@ import (
 	"io"
 	"os"
 	"os/exec"
-	"path/filepath"
 	"strings"
 	"time"
 
@@ -133,26 +132,20 @@ func (a *App) WslStop() error {
 }
 
 func (a *App) WslIsEnabled() error {
-	ex, err := os.Executable()
-	if err != nil {
-		return err
-	}
-	exDir := filepath.Dir(ex)
-
-	data, err := os.ReadFile(exDir + "/wsl.state")
+	data, err := os.ReadFile(a.exDir + "wsl.state")
 	if err == nil {
 		if strings.Contains(string(data), "Enabled") {
 			return nil
 		}
 	}
 
-	cmd := `-Command (Get-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux).State | Out-File -Encoding utf8 -FilePath ` + exDir + "/wsl.state"
-	_, err = su.ShellExecute(su.RUNAS, "powershell", cmd, exDir)
+	cmd := `-Command (Get-WindowsOptionalFeature -Online -FeatureName VirtualMachinePlatform).State | Out-File -Encoding utf8 -FilePath ` + a.exDir + "wsl.state"
+	_, err = su.ShellExecute(su.RUNAS, "powershell", cmd, a.exDir)
 	if err != nil {
 		return err
 	}
 	time.Sleep(2 * time.Second)
-	data, err = os.ReadFile(exDir + "/wsl.state")
+	data, err = os.ReadFile(a.exDir + "wsl.state")
 	if err != nil {
 		return err
 	}
@@ -164,13 +157,13 @@ func (a *App) WslIsEnabled() error {
 }
 
 func (a *App) WslEnable(forceMode bool) error {
-	cmd := `/online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux`
+	cmd := `/online /enable-feature /featurename:VirtualMachinePlatform`
 	_, err := su.ShellExecute(su.RUNAS, "dism", cmd, `C:\`)
 	if err != nil {
 		return err
 	}
 	if forceMode {
-		os.WriteFile("./wsl.state", []byte("Enabled"), 0644)
+		os.WriteFile(a.exDir+"wsl.state", []byte("Enabled"), 0644)
 	}
 	return nil
 }

backend-python/convert_safetensors.py

@@ -1,9 +1,8 @@
-import json
+import collections
+import numpy
 import os
-import sys
-import copy
 import torch
-from safetensors.torch import load_file, save_file
+from safetensors.torch import serialize_file, load_file
 
 import argparse
 
@@ -26,7 +25,7 @@ def rename_key(rename, name):
 
 
 def convert_file(pt_filename: str, sf_filename: str, rename={}, transpose_names=[]):
-    loaded = torch.load(pt_filename, map_location="cpu")
+    loaded: collections.OrderedDict = torch.load(pt_filename, map_location="cpu")
     if "state_dict" in loaded:
         loaded = loaded["state_dict"]
 
@@ -44,11 +43,9 @@ def convert_file(pt_filename: str, sf_filename: str, rename={}, transpose_names=
         if "time_maa" in x:
             version = max(6, version)
 
-    if version == 5.1 and "midi" in pt_filename.lower():
-        import numpy as np
+    print(f"Model detected: v{version:.1f}")
 
-        np.set_printoptions(precision=4, suppress=True, linewidth=200)
-        kk = list(loaded.keys())
+    if version == 5.1:
         _, n_emb = loaded["emb.weight"].shape
         for k in kk:
             if "time_decay" in k or "time_faaaa" in k:
@@ -57,31 +54,36 @@ def convert_file(pt_filename: str, sf_filename: str, rename={}, transpose_names=
                     loaded[k].unsqueeze(1).repeat(1, n_emb // loaded[k].shape[0])
                 )
 
-    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}')
-
-    loaded = {rename_key(rename, k).lower(): v.contiguous() for k, v in loaded.items()}
-    # For tensors to be contiguous
-    for k, v in loaded.items():
-        for transpose_name in transpose_names:
-            if transpose_name in k:
-                loaded[k] = v.transpose(0, 1)
-
-    loaded = {k: v.clone().half().contiguous() for k, v in loaded.items()}
-
-    for k, v in loaded.items():
-        print(f"{k}\t{v.shape}\t{v.dtype}")
+    with torch.no_grad():
+        for k in kk:
+            new_k = rename_key(rename, k).lower()
+            v = loaded[k].half()
+            del loaded[k]
+            for transpose_name in transpose_names:
+                if transpose_name in new_k:
+                    dims = len(v.shape)
+                    v = v.transpose(dims - 2, dims - 1)
+            print(f"{new_k}\t{v.shape}\t{v.dtype}")
+            loaded[new_k] = {
+                "dtype": str(v.dtype).split(".")[-1],
+                "shape": v.shape,
+                "data": v.numpy().tobytes(),
+            }
 
     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}")
+    serialize_file(loaded, sf_filename, metadata={"format": "pt"})
+    # reloaded = load_file(sf_filename)
+    # for k in loaded:
+    #     pt_tensor = torch.Tensor(
+    #         numpy.frombuffer(
+    #             bytearray(loaded[k]["data"]),
+    #             dtype=getattr(numpy, loaded[k]["dtype"]),
+    #         ).reshape(loaded[k]["shape"])
+    #     )
+    #     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__":

backend-python/dep_check.py

@@ -7,7 +7,6 @@ import lm_dataformat
 import ftfy
 import tqdm
 import tiktoken
-import GPUtil
 
 import torch
 import rwkv

backend-python/global_var.py

@@ -5,6 +5,7 @@ Model = "model"
 Model_Status = "model_status"
 Model_Config = "model_config"
 Deploy_Mode = "deploy_mode"
+Midi_Vocab_Config_Type = "midi_vocab_config_type"
 
 
 class ModelStatus(Enum):
@@ -13,11 +14,17 @@ class ModelStatus(Enum):
     Working = 3
 
 
+class MidiVocabConfig(Enum):
+    Default = auto()
+    Piano = auto()
+
+
 def init():
     global GLOBALS
     GLOBALS = {}
     set(Model_Status, ModelStatus.Offline)
     set(Deploy_Mode, False)
+    set(Midi_Vocab_Config_Type, MidiVocabConfig.Default)
 
 
 def set(key, value):

backend-python/requirements.txt

@@ -1,9 +1,9 @@
 torch
 torchvision
 torchaudio
-rwkv==0.8.22
+rwkv==0.8.25
 langchain==0.0.322
-fastapi==0.104.0
+fastapi==0.109.1
 uvicorn==0.23.2
 sse-starlette==1.6.5
 pydantic==2.4.2
@@ -19,7 +19,7 @@ midi2audio==0.1.1
 mido==1.3.0
 safetensors==0.4.0
 PyMuPDF==1.23.5
-python-multipart==0.0.6
+python-multipart==0.0.7
 Cython==3.0.4
 cyac==1.9
-torch_directml==0.1.13.1.dev230413
+torch-directml==0.1.13.1.dev230413

backend-python/requirements_without_cyac.txt

@@ -1,9 +1,9 @@
 torch
 torchvision
 torchaudio
-rwkv==0.8.22
+rwkv==0.8.25
 langchain==0.0.322
-fastapi==0.104.0
+fastapi==0.109.1
 uvicorn==0.23.2
 sse-starlette==1.6.5
 pydantic==2.4.2
@@ -19,5 +19,5 @@ midi2audio==0.1.1
 mido==1.3.0
 safetensors==0.4.0
 PyMuPDF==1.23.5
-python-multipart==0.0.6
+python-multipart==0.0.7
 Cython==3.0.4

backend-python/routes/completion.py

@@ -70,10 +70,10 @@ class ChatCompletionBody(ModelConfigBody):
                 "assistant_name": None,
                 "presystem": True,
                 "max_tokens": 1000,
-                "temperature": 1.2,
-                "top_p": 0.5,
-                "presence_penalty": 0.4,
-                "frequency_penalty": 0.4,
+                "temperature": 1,
+                "top_p": 0.3,
+                "presence_penalty": 0,
+                "frequency_penalty": 1,
             }
         }
     }
@@ -94,10 +94,10 @@ class CompletionBody(ModelConfigBody):
                 "stream": False,
                 "stop": None,
                 "max_tokens": 100,
-                "temperature": 1.2,
-                "top_p": 0.5,
-                "presence_penalty": 0.4,
-                "frequency_penalty": 0.4,
+                "temperature": 1,
+                "top_p": 0.3,
+                "presence_penalty": 0,
+                "frequency_penalty": 1,
             }
         }
     }
@@ -144,6 +144,7 @@ async def eval_rwkv(
                 return
             set_rwkv_config(model, global_var.get(global_var.Model_Config))
             set_rwkv_config(model, body)
+            print(get_rwkv_config(model))
 
             response, prompt_tokens, completion_tokens = "", 0, 0
             for response, delta, prompt_tokens, completion_tokens in model.generate(
@@ -155,23 +156,27 @@ async def eval_rwkv(
                 if stream:
                     yield json.dumps(
                         {
-                            "object": "chat.completion.chunk"
-                            if chat_mode
-                            else "text_completion",
+                            "object": (
+                                "chat.completion.chunk"
+                                if chat_mode
+                                else "text_completion"
+                            ),
                             # "response": response,
                             "model": model.name,
                             "choices": [
-                                {
-                                    "delta": {"content": delta},
-                                    "index": 0,
-                                    "finish_reason": None,
-                                }
-                                if chat_mode
-                                else {
-                                    "text": delta,
-                                    "index": 0,
-                                    "finish_reason": None,
-                                }
+                                (
+                                    {
+                                        "delta": {"content": delta},
+                                        "index": 0,
+                                        "finish_reason": None,
+                                    }
+                                    if chat_mode
+                                    else {
+                                        "text": delta,
+                                        "index": 0,
+                                        "finish_reason": None,
+                                    }
+                                )
                             ],
                         }
                     )
@@ -193,23 +198,25 @@ async def eval_rwkv(
             if stream:
                 yield json.dumps(
                     {
-                        "object": "chat.completion.chunk"
-                        if chat_mode
-                        else "text_completion",
+                        "object": (
+                            "chat.completion.chunk" if chat_mode else "text_completion"
+                        ),
                         # "response": response,
                         "model": model.name,
                         "choices": [
-                            {
-                                "delta": {},
-                                "index": 0,
-                                "finish_reason": "stop",
-                            }
-                            if chat_mode
-                            else {
-                                "text": "",
-                                "index": 0,
-                                "finish_reason": "stop",
-                            }
+                            (
+                                {
+                                    "delta": {},
+                                    "index": 0,
+                                    "finish_reason": "stop",
+                                }
+                                if chat_mode
+                                else {
+                                    "text": "",
+                                    "index": 0,
+                                    "finish_reason": "stop",
+                                }
+                            )
                         ],
                     }
                 )
@@ -225,20 +232,22 @@ async def eval_rwkv(
                         "total_tokens": prompt_tokens + completion_tokens,
                     },
                     "choices": [
-                        {
-                            "message": {
-                                "role": Role.Assistant.value,
-                                "content": response,
-                            },
-                            "index": 0,
-                            "finish_reason": "stop",
-                        }
-                        if chat_mode
-                        else {
-                            "text": response,
-                            "index": 0,
-                            "finish_reason": "stop",
-                        }
+                        (
+                            {
+                                "message": {
+                                    "role": Role.Assistant.value,
+                                    "content": response,
+                                },
+                                "index": 0,
+                                "finish_reason": "stop",
+                            }
+                            if chat_mode
+                            else {
+                                "text": response,
+                                "index": 0,
+                                "finish_reason": "stop",
+                            }
+                        )
                     ],
                 }
 

backend-python/routes/config.py

@@ -86,32 +86,53 @@ def switch_model(body: SwitchModelBody, response: Response, request: Request):
 
     if body.deploy:
         global_var.set(global_var.Deploy_Mode, True)
-    if global_var.get(global_var.Model_Config) is None:
-        global_var.set(
-            global_var.Model_Config, get_rwkv_config(global_var.get(global_var.Model))
-        )
+
+    saved_model_config = global_var.get(global_var.Model_Config)
+    init_model_config = get_rwkv_config(global_var.get(global_var.Model))
+    if saved_model_config is not None:
+        merge_model(init_model_config, saved_model_config)
+    global_var.set(global_var.Model_Config, init_model_config)
     global_var.set(global_var.Model_Status, global_var.ModelStatus.Working)
 
     return "success"
 
 
+def merge_model(to_model: BaseModel, from_model: BaseModel):
+    from_model_fields = [x for x in from_model.dict().keys()]
+    to_model_fields = [x for x in to_model.dict().keys()]
+
+    for field_name in from_model_fields:
+        if field_name in to_model_fields:
+            from_value = getattr(from_model, field_name)
+
+            if from_value is not None:
+                setattr(to_model, field_name, from_value)
+
+
 @router.post("/update-config", tags=["Configs"])
 def update_config(body: ModelConfigBody):
     """
     Will not update the model config immediately, but set it when completion called to avoid modifications during generation
     """
 
-    print(body)
-    global_var.set(global_var.Model_Config, body)
+    model_config = global_var.get(global_var.Model_Config)
+    if model_config is None:
+        model_config = ModelConfigBody()
+        global_var.set(global_var.Model_Config, model_config)
+    merge_model(model_config, body)
+    print("Updated Model Config:", model_config)
 
     return "success"
 
 
 @router.get("/status", tags=["Configs"])
 def status():
-    import GPUtil
+    try:
+        import GPUtil
 
-    gpus = GPUtil.getGPUs()
+        gpus = GPUtil.getGPUs()
+    except:
+        gpus = []
     if len(gpus) == 0:
         device_name = "CPU"
     else:

backend-python/routes/midi.py

@@ -23,7 +23,11 @@ class TextToMidiBody(BaseModel):
 
 @router.post("/text-to-midi", tags=["MIDI"])
 def text_to_midi(body: TextToMidiBody):
-    vocab_config = "backend-python/utils/midi_vocab_config.json"
+    vocab_config_type = global_var.get(global_var.Midi_Vocab_Config_Type)
+    if vocab_config_type == global_var.MidiVocabConfig.Piano:
+        vocab_config = "backend-python/utils/vocab_config_piano.json"
+    else:
+        vocab_config = "backend-python/utils/midi_vocab_config.json"
     cfg = VocabConfig.from_json(vocab_config)
     mid = convert_str_to_midi(cfg, body.text.strip())
     mid_data = io.BytesIO()
@@ -35,7 +39,11 @@ def text_to_midi(body: TextToMidiBody):
 
 @router.post("/midi-to-text", tags=["MIDI"])
 async def midi_to_text(file_data: UploadFile):
-    vocab_config = "backend-python/utils/midi_vocab_config.json"
+    vocab_config_type = global_var.get(global_var.Midi_Vocab_Config_Type)
+    if vocab_config_type == global_var.MidiVocabConfig.Piano:
+        vocab_config = "backend-python/utils/vocab_config_piano.json"
+    else:
+        vocab_config = "backend-python/utils/midi_vocab_config.json"
     cfg = VocabConfig.from_json(vocab_config)
     filter_config = "backend-python/utils/midi_filter_config.json"
     filter_cfg = FilterConfig.from_json(filter_config)
@@ -69,7 +77,11 @@ def txt_to_midi(body: TxtToMidiBody):
     if not body.midi_path.startswith("midi/"):
         raise HTTPException(status.HTTP_400_BAD_REQUEST, "bad output path")
 
-    vocab_config = "backend-python/utils/midi_vocab_config.json"
+    vocab_config_type = global_var.get(global_var.Midi_Vocab_Config_Type)
+    if vocab_config_type == global_var.MidiVocabConfig.Piano:
+        vocab_config = "backend-python/utils/vocab_config_piano.json"
+    else:
+        vocab_config = "backend-python/utils/midi_vocab_config.json"
     cfg = VocabConfig.from_json(vocab_config)
     with open(body.txt_path, "r") as f:
         text = f.read()

backend-python/routes/state_cache.py

@@ -76,6 +76,31 @@ class AddStateBody(BaseModel):
     logits: Any
 
 
+def copy_tensor_to_cpu(tensors):
+    import torch
+    import numpy as np
+
+    devices: List[torch.device] = []
+    copied: Union[Any, None] = None
+
+    tensors_type = type(tensors)
+    if tensors_type == list:
+        if hasattr(tensors[0], "device"):  # torch state
+            devices = [tensor.device for tensor in tensors]
+            copied = [tensor.cpu() for tensor in tensors]
+        else:  # WebGPU logits
+            copied = tensors
+    elif tensors_type == torch.Tensor:  # torch logits
+        devices = [tensors.device]
+        copied = tensors.cpu()
+    elif tensors_type == np.ndarray:  # rwkv.cpp
+        copied = tensors
+    else:  # WebGPU state
+        copied = tensors.back()
+
+    return copied, devices
+
+
 # @router.post("/add-state", tags=["State Cache"])
 def add_state(body: AddStateBody):
     global trie, dtrie, loop_del_trie_id
@@ -91,32 +116,24 @@ def add_state(body: AddStateBody):
 
     try:
         devices: List[torch.device] = []
+        logits_device: Union[torch.device, None] = None
         state: Union[Any, None] = None
+        logits: Union[Any, None] = None
 
         if body.state is not None:
-            if type(body.state) == list or type(body.state) == np.ndarray:
-                devices = [
-                    (
-                        tensor.device
-                        if hasattr(tensor, "device")
-                        else torch.device("cpu")
-                    )
-                    for tensor in body.state
-                ]
-                state = (
-                    [tensor.cpu() for tensor in body.state]
-                    if hasattr(body.state[0], "device")
-                    else copy.deepcopy(body.state)
-                )
-            else:
-                pass  # WebGPU
+            state, devices = copy_tensor_to_cpu(body.state)
+        if body.logits is not None:
+            logits, logits_devices = copy_tensor_to_cpu(body.logits)
+            if len(logits_devices) > 0:
+                logits_device = logits_devices[0]
 
         id: int = trie.insert(body.prompt)
         dtrie[id] = {
-            "tokens": copy.deepcopy(body.tokens),
+            "tokens": body.tokens,
             "state": state,
-            "logits": copy.deepcopy(body.logits),
+            "logits": logits,
             "devices": devices,
+            "logits_device": logits_device,
         }
 
         if len(trie) >= max_trie_len:
@@ -134,6 +151,7 @@ def add_state(body: AddStateBody):
         )
         return "success"
     except Exception as e:
+        print(e)  # should not happen
         raise HTTPException(
             status.HTTP_400_BAD_REQUEST, f"insert failed, bad prompt.\n{e}"
         )
@@ -199,20 +217,35 @@ def longest_prefix_state(body: LongestPrefixStateBody, request: Request):
     except:
         pass
     if id != -1:
-        v = dtrie[id]
-        devices: List[torch.device] = v["devices"]
         prompt: str = trie[id]
+        v = dtrie[id]
+        tokens: List[Union[str, int]] = copy.deepcopy(v["tokens"])
+        devices: List[torch.device] = v["devices"]
+        logits_device: Union[torch.device, None] = v["logits_device"]
         state: Union[Any, None] = v["state"]
+        logits: Union[Any, None] = v["logits"]
 
-        if state is not None and type(state) == list and hasattr(state[0], "device"):
-            state = [tensor.to(devices[i]) for i, tensor in enumerate(state)]
+        if type(state) == list and hasattr(state[0], "device"):  # torch
+            state = [
+                tensor.to(devices[i])
+                if devices[i] != torch.device("cpu")
+                else tensor.clone()
+                for i, tensor in enumerate(state)
+            ]
+            logits = (
+                logits.to(logits_device)
+                if logits_device != torch.device("cpu")
+                else logits.clone()
+            )
+        else:  # rwkv.cpp, WebGPU
+            logits = np.copy(logits)
 
         quick_log(request, body, "Hit:\n" + prompt)
         return {
             "prompt": prompt,
-            "tokens": v["tokens"],
+            "tokens": tokens,
             "state": state,
-            "logits": v["logits"],
+            "logits": logits,
         }
     else:
         return {"prompt": "", "tokens": [], "state": None, "logits": None}

backend-python/rwkv_pip/beta/model.py

@@ -251,7 +251,7 @@ class RWKV(MyModule):
                 )
                 assert (
                     w["_strategy"] == args.strategy_string
-                )  # if you are using a new strategy, re-convert the model
+                ), "model has been converted and does not match current strategy; if you are using a new strategy, re-convert the model"
                 assert (
                     float(w["_version"]) >= 0.7
                 )  # sometimes you should re-convert using latest convert_model.py

backend-python/rwkv_pip/model.py

@@ -342,7 +342,7 @@ class RWKV(MyModule):
                 )
                 assert (
                     w["_strategy"] == args.strategy_string
-                )  # if you are using a new strategy, re-convert the model
+                ), "model has been converted and does not match current strategy; if you are using a new strategy, re-convert the model"
                 assert (
                     float(w["_version"]) >= 0.7
                 )  # sometimes you should re-convert using latest convert_model.py
@@ -552,7 +552,12 @@ class RWKV(MyModule):
                     elif ".ln_x" in x:  # need fp32 for group_norm
                         w[x] = w[x].float()
                     else:
-                        if (len(w[x].shape) == 2) and ("emb" not in x):
+                        if (
+                            (len(w[x].shape) == 2)
+                            and ("emb" not in x)
+                            and ("_w1" not in x)
+                            and ("_w2" not in x)
+                        ):
                             if WTYPE != torch.uint8:
                                 w[x] = w[x].to(dtype=WTYPE)
                             else:

backend-python/rwkv_pip/utils.py

@@ -34,6 +34,25 @@ class PIPELINE_ARGS:
         )
 
 
+class ABC_TOKENIZER:
+    def __init__(self):
+        self.pad_token_id = 0
+        self.bos_token_id = 2
+        self.eos_token_id = 3
+
+    def encode(self, text):
+        ids = [ord(c) for c in text]
+        return ids
+
+    def decode(self, ids):
+        txt = "".join(
+            chr(idx) if idx > self.eos_token_id else ""
+            for idx in ids
+            if idx != self.eos_token_id
+        )
+        return txt
+
+
 class PIPELINE:
     def __init__(self, model, WORD_NAME: str):
         self.model = model
@@ -48,6 +67,8 @@ class PIPELINE:
             self.tokenizer = TRIE_TOKENIZER(
                 os.path.dirname(os.path.abspath(__file__)) + "/rwkv_vocab_v20230424.txt"
             )
+        elif WORD_NAME == "abc_tokenizer":
+            self.tokenizer = ABC_TOKENIZER()
         else:
             if WORD_NAME.endswith(".txt"):
                 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
@@ -150,10 +171,17 @@ class PIPELINE:
             all_tokens += [token]
             for xxx in occurrence:
                 occurrence[xxx] *= args.alpha_decay
+
+            ttt = self.decode([token])
+            www = 1
+            if ttt in " \t0123456789":
+                www = 0
+            # elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
+            #     www = 0.5
             if token not in occurrence:
-                occurrence[token] = 1
+                occurrence[token] = www
             else:
-                occurrence[token] += 1
+                occurrence[token] += www
             # print(occurrence) # debug
 
             # output

backend-python/rwkv_pip/webgpu/model.py

@@ -13,14 +13,40 @@ except ModuleNotFoundError:
 
 class RWKV:
     def __init__(self, model_path: str, strategy: str = None):
-        self.model = wrp.v5.Model(
-            model_path,
-            turbo=True,
-            quant=32 if "i8" in strategy else None,
-            quant_nf4=26 if "i4" in strategy else None,
-        )
+        self.info = wrp.peek_info(model_path)
         self.w = {}  # fake weight
-        self.w["emb.weight"] = [0] * wrp.peek_info(model_path).num_vocab
+        self.w["emb.weight"] = [0] * self.info.num_vocab
+        self.version = str(self.info.version).lower()
+        self.wrp = getattr(wrp, self.version)
+
+        layer = (
+            int(s.lstrip("layer"))
+            for s in strategy.split()
+            for s in s.split(",")
+            if s.startswith("layer")
+        )
+
+        chunk_size = (
+            int(s.lstrip("chunk"))
+            for s in strategy.split()
+            for s in s.split(",")
+            if s.startswith("chunk")
+        )
+
+        args = {
+            "file": model_path,
+            "turbo": True,
+            "quant": next(layer, 31) if "i8" in strategy else 0,
+            "quant_nf4": next(layer, 26) if "i4" in strategy else 0,
+            "token_chunk_size": next(chunk_size, 32),
+            "lora": None,
+        }
+        self.model = self.wrp.Model(**args)
 
     def forward(self, tokens: List[int], state: Union[Any, None] = None):
-        return wrp.v5.run_one(self.model, tokens, state)
+        if type(state).__name__ == "BackedState":  # memory state
+            gpu_state = self.wrp.ModelState(self.model, 1)
+            gpu_state.load(state)
+        else:
+            gpu_state = state
+        return self.wrp.run_one(self.model, tokens, gpu_state)

backend-python/utils/rwkv.py

@@ -4,18 +4,13 @@ import os
 import pathlib
 import copy
 import re
-from typing import Dict, Iterable, List, Tuple, Union, Type
+from typing import Dict, Iterable, List, Tuple, Union, Type, Callable
 from utils.log import quick_log
 from fastapi import HTTPException
 from pydantic import BaseModel, Field
 from routes import state_cache
 import global_var
 
-
-END_OF_TEXT = 0
-END_OF_LINE_DOUBLE = 535
-
-
 os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
 
 
@@ -28,6 +23,8 @@ class RWKVType(Enum):
 
 class AbstractRWKV(ABC):
     def __init__(self, model, pipeline):
+        self.EOS_ID = 0
+
         self.name = "rwkv"
         self.model = model
         self.pipeline = pipeline
@@ -42,6 +39,8 @@ class AbstractRWKV(ABC):
         self.top_k = 0
         self.penalty_alpha_presence = 0
         self.penalty_alpha_frequency = 1
+        self.penalty_decay = 0.996
+        self.global_penalty = False
 
     @abstractmethod
     def adjust_occurrence(self, occurrence: Dict, token: int):
@@ -239,9 +238,9 @@ class AbstractRWKV(ABC):
             self.model_tokens = []
         else:
             delta_prompt = prompt[len(cache["prompt"]) :]
-            self.model_state = copy.deepcopy(cache["state"])
-            self.model_tokens = copy.deepcopy(cache["tokens"])
-            logits = copy.deepcopy(cache["logits"])
+            self.model_state = cache["state"]
+            self.model_tokens = cache["tokens"]
+            logits = cache["logits"]
 
         prompt_token_len = 0
         if delta_prompt != "":
@@ -274,7 +273,18 @@ class AbstractRWKV(ABC):
                 logits, temperature=self.temperature, top_p=self.top_p, top_k=self.top_k
             )
 
-            if token == END_OF_TEXT:
+            if token == self.EOS_ID:
+                try:
+                    state_cache.add_state(
+                        state_cache.AddStateBody(
+                            prompt=prompt + response,
+                            tokens=self.model_tokens,
+                            state=self.model_state,
+                            logits=logits,
+                        )
+                    )
+                except HTTPException:
+                    pass
                 yield response, "", prompt_token_len, completion_token_len
                 break
 
@@ -305,22 +315,25 @@ class AbstractRWKV(ABC):
                             yield response, "", prompt_token_len, completion_token_len
                             break
                     elif type(stop) == list:
-                        stop_exist_regex = "|".join(stop)
-                        matched = re.search(stop_exist_regex, response)
-                        if matched:
-                            try:
-                                state_cache.add_state(
-                                    state_cache.AddStateBody(
-                                        prompt=prompt + response,
-                                        tokens=self.model_tokens,
-                                        state=self.model_state,
-                                        logits=logits,
+                        exit_flag = False
+                        for s in stop:
+                            if s in response:
+                                try:
+                                    state_cache.add_state(
+                                        state_cache.AddStateBody(
+                                            prompt=prompt + response,
+                                            tokens=self.model_tokens,
+                                            state=self.model_state,
+                                            logits=logits,
+                                        )
                                     )
-                                )
-                            except HTTPException:
-                                pass
-                            response = response.split(matched.group())[0]
-                            yield response, "", prompt_token_len, completion_token_len
+                                except HTTPException:
+                                    pass
+                                exit_flag = True
+                                response = response.split(s)[0]
+                                yield response, "", prompt_token_len, completion_token_len
+                                break
+                        if exit_flag:
                             break
                 out_last = begin + i + 1
                 if i == self.max_tokens_per_generation - 1:
@@ -363,18 +376,24 @@ class TextRWKV(AbstractRWKV):
             self.bot = "Assistant"
             self.END_OF_LINE = 11
 
-        self.AVOID_REPEAT_TOKENS = []
+        self.AVOID_REPEAT_TOKENS = set()
         AVOID_REPEAT = "，：？！"
         for i in AVOID_REPEAT:
             dd = self.pipeline.encode(i)
             assert len(dd) == 1
-            self.AVOID_REPEAT_TOKENS += dd
+            self.AVOID_REPEAT_TOKENS.add(dd[0])
+        self.AVOID_PENALTY_TOKENS = set()
+        AVOID_PENALTY = '\n,.:?!，。：？！"“”<>[]{}/\\|;；~`@#$%^&*()_+-=0123456789 '
+        for i in AVOID_PENALTY:
+            dd = self.pipeline.encode(i)
+            if len(dd) == 1:
+                self.AVOID_PENALTY_TOKENS.add(dd[0])
 
         self.__preload()
 
     def adjust_occurrence(self, occurrence: Dict, token: int):
         for xxx in occurrence:
-            occurrence[xxx] *= 0.996
+            occurrence[xxx] *= self.penalty_decay
         if token not in occurrence:
             occurrence[token] = 1
         else:
@@ -382,26 +401,24 @@ class TextRWKV(AbstractRWKV):
 
     def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
         for n in occurrence:
+            # if n not in self.AVOID_PENALTY_TOKENS:
             logits[n] -= (
                 self.penalty_alpha_presence
                 + occurrence[n] * self.penalty_alpha_frequency
             )
 
-        if i == 0:
+        # set global_penalty to False to get the same generated results as the official RWKV Gradio
+        if self.global_penalty and 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
+                if token not in self.AVOID_PENALTY_TOKENS:
+                    self.adjust_occurrence(occurrence, token)
 
     # 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 self.rwkv_type == RWKVType.World:
             return tokens
-        if len(tokens) > 0 and tokens[-1] == END_OF_LINE_DOUBLE:
+        if len(tokens) > 0 and tokens[-1] == 535:
             tokens = tokens[:-1] + [self.END_OF_LINE, self.END_OF_LINE]
         return tokens
 
@@ -459,7 +476,7 @@ The following is a coherent verbose detailed conversation between a girl named {
             pass
 
 
-class MusicRWKV(AbstractRWKV):
+class MusicMidiRWKV(AbstractRWKV):
     def __init__(self, model, pipeline):
         super().__init__(model, pipeline)
 
@@ -501,8 +518,47 @@ class MusicRWKV(AbstractRWKV):
         return " " + delta
 
 
+class MusicAbcRWKV(AbstractRWKV):
+    def __init__(self, model, pipeline):
+        super().__init__(model, pipeline)
+
+        self.EOS_ID = 3
+
+        self.max_tokens_per_generation = 500
+        self.temperature = 1
+        self.top_p = 0.8
+        self.top_k = 8
+
+        self.rwkv_type = RWKVType.Music
+
+    def adjust_occurrence(self, occurrence: Dict, token: int):
+        pass
+
+    def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
+        pass
+
+    def fix_tokens(self, tokens) -> List[int]:
+        return tokens
+
+    def run_rnn(
+        self, _tokens: List[str], newline_adj: int = 0
+    ) -> Tuple[List[float], int]:
+        tokens = [int(x) for x in _tokens]
+        token_len = len(tokens)
+        self.model_tokens += tokens
+        out, self.model_state = self.model.forward(tokens, self.model_state)
+        return out, token_len
+
+    def delta_postprocess(self, delta: str) -> str:
+        return delta
+
+
 def get_tokenizer(tokenizer_len: int):
     tokenizer_dir = f"{pathlib.Path(__file__).parent.parent.resolve()}/rwkv_pip/"
+    if tokenizer_len < 2176:
+        return "abc_tokenizer"
+    if tokenizer_len < 20096:
+        return tokenizer_dir + "tokenizer-midipiano.json"
     if tokenizer_len < 50277:
         return tokenizer_dir + "tokenizer-midi.json"
     elif tokenizer_len < 65536:
@@ -511,7 +567,41 @@ def get_tokenizer(tokenizer_len: int):
         return "rwkv_vocab_v20230424"
 
 
+def get_model_path(model_path: str) -> str:
+    if os.path.isabs(model_path):
+        return model_path
+
+    working_dir: pathlib.Path = pathlib.Path(os.path.abspath(os.getcwd()))
+
+    parent_paths: List[pathlib.Path] = [
+        working_dir,  # [cwd](RWKV-Runner)/models/xxx
+        working_dir.parent,  # [cwd](backend-python)/../models/xxx
+        pathlib.Path(
+            os.path.abspath(__file__)
+        ).parent.parent,  # backend-python/models/xxx
+        pathlib.Path(
+            os.path.abspath(__file__)
+        ).parent.parent.parent,  # RWKV-Runner/models/xxx
+    ]
+
+    child_paths: List[Callable[[pathlib.Path], pathlib.Path]] = [
+        lambda p: p / model_path,
+        lambda p: p / "build" / "bin" / model_path,  # for dev
+    ]
+
+    for parent_path in parent_paths:
+        for child_path in child_paths:
+            full_path: pathlib.Path = child_path(parent_path)
+
+            if os.path.isfile(full_path):
+                return str(full_path)
+
+    return model_path
+
+
 def RWKV(model: str, strategy: str, tokenizer: Union[str, None]) -> AbstractRWKV:
+    model = get_model_path(model)
+
     rwkv_beta = global_var.get(global_var.Args).rwkv_beta
     rwkv_cpp = getattr(global_var.get(global_var.Args), "rwkv.cpp")
     webgpu = global_var.get(global_var.Args).webgpu
@@ -550,14 +640,30 @@ def RWKV(model: str, strategy: str, tokenizer: Union[str, None]) -> AbstractRWKV
     rwkv_map: dict[str, Type[AbstractRWKV]] = {
         "20B_tokenizer": TextRWKV,
         "rwkv_vocab_v20230424": TextRWKV,
-        "tokenizer-midi": MusicRWKV,
+        "tokenizer-midi": MusicMidiRWKV,
+        "tokenizer-midipiano": MusicMidiRWKV,
+        "abc_tokenizer": MusicAbcRWKV,
     }
     tokenizer_name = os.path.splitext(os.path.basename(tokenizer))[0]
+    global_var.set(
+        global_var.Midi_Vocab_Config_Type,
+        (
+            global_var.MidiVocabConfig.Piano
+            if tokenizer_name == "tokenizer-midipiano"
+            else global_var.MidiVocabConfig.Default
+        ),
+    )
     rwkv: AbstractRWKV
     if tokenizer_name in rwkv_map:
         rwkv = rwkv_map[tokenizer_name](model, pipeline)
     else:
-        rwkv = TextRWKV(model, pipeline)
+        tokenizer_name = tokenizer_name.lower()
+        if "music" in tokenizer_name or "midi" in tokenizer_name:
+            rwkv = MusicMidiRWKV(model, pipeline)
+        elif "abc" in tokenizer_name:
+            rwkv = MusicAbcRWKV(model, pipeline)
+        else:
+            rwkv = TextRWKV(model, pipeline)
     rwkv.name = filename
 
     return rwkv
@@ -565,19 +671,24 @@ def RWKV(model: str, strategy: str, tokenizer: Union[str, None]) -> AbstractRWKV
 
 class ModelConfigBody(BaseModel):
     max_tokens: int = Field(default=None, gt=0, le=102400)
-    temperature: float = Field(default=None, ge=0, le=2)
+    temperature: float = Field(default=None, ge=0, le=3)
     top_p: float = Field(default=None, ge=0, le=1)
     presence_penalty: float = Field(default=None, ge=-2, le=2)
     frequency_penalty: float = Field(default=None, ge=-2, le=2)
+    penalty_decay: float = Field(default=None, ge=0.99, le=0.999)
+    top_k: int = Field(default=None, ge=0, le=25)
+    global_penalty: bool = Field(default=None)
 
     model_config = {
         "json_schema_extra": {
             "example": {
                 "max_tokens": 1000,
-                "temperature": 1.2,
-                "top_p": 0.5,
-                "presence_penalty": 0.4,
-                "frequency_penalty": 0.4,
+                "temperature": 1,
+                "top_p": 0.3,
+                "presence_penalty": 0,
+                "frequency_penalty": 1,
+                "penalty_decay": 0.996,
+                "global_penalty": False,
             }
         }
     }
@@ -597,6 +708,12 @@ def set_rwkv_config(model: AbstractRWKV, body: ModelConfigBody):
         model.penalty_alpha_presence = body.presence_penalty
     if body.frequency_penalty is not None:
         model.penalty_alpha_frequency = body.frequency_penalty
+    if body.penalty_decay is not None:
+        model.penalty_decay = body.penalty_decay
+    if body.top_k is not None:
+        model.top_k = body.top_k
+    if body.global_penalty is not None:
+        model.global_penalty = body.global_penalty
 
 
 def get_rwkv_config(model: AbstractRWKV) -> ModelConfigBody:
@@ -606,4 +723,7 @@ def get_rwkv_config(model: AbstractRWKV) -> ModelConfigBody:
         top_p=model.top_p,
         presence_penalty=model.penalty_alpha_presence,
         frequency_penalty=model.penalty_alpha_frequency,
+        penalty_decay=model.penalty_decay,
+        top_k=model.top_k,
+        global_penalty=model.global_penalty,
     )

backend-python/utils/torch.py

@@ -19,9 +19,12 @@ def set_torch():
 
 
 def torch_gc():
-    import torch
+    try:
+        import torch
 
-    if torch.cuda.is_available():
-        with torch.cuda.device(0):
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
+        if torch.cuda.is_available():
+            with torch.cuda.device(0):
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+    except:
+        pass  # prevent 'torch' has no attribute 'cuda' error, so user can use CPU or WebGPU

backend-python/utils/vocab_config_piano.json

@@ -0,0 +1,279 @@
+{
+    "note_events": 128,
+    "wait_events": 125,
+    "max_wait_time": 1000,
+    "velocity_events": 128,
+    "velocity_bins": 16,
+    "velocity_exp": 0.33,
+    "do_token_sorting": true,
+    "unrolled_tokens": false,
+    "decode_end_held_note_delay": 5.0,
+    "decode_fix_repeated_notes": true,
+    "bin_instrument_names": [
+        "piano"
+    ],
+    "ch10_instrument_bin_name": "",
+    "program_name_to_bin_name": {
+        "Acoustic Grand Piano": "piano",
+        "Bright Acoustic Piano": "piano",
+        "Electric Grand Piano": "piano",
+        "Honky-tonk Piano": "piano",
+        "Electric Piano 1 (Rhodes Piano)": "piano",
+        "Electric Piano 2 (Chorused Piano)": "piano",
+        "Harpsichord": "piano",
+        "Clavinet": "piano",
+        "Celesta": "",
+        "Glockenspiel": "",
+        "Music Box": "",
+        "Vibraphone": "",
+        "Marimba": "",
+        "Xylophone": "",
+        "Tubular Bells": "",
+        "Dulcimer (Santur)": "",
+        "Drawbar Organ (Hammond)": "",
+        "Percussive Organ": "piano",
+        "Rock Organ": "piano",
+        "Church Organ": "piano",
+        "Reed Organ": "piano",
+        "Accordion (French)": "piano",
+        "Harmonica": "piano",
+        "Tango Accordion (Band neon)": "piano",
+        "Acoustic Guitar (nylon)": "",
+        "Acoustic Guitar (steel)": "",
+        "Electric Guitar (jazz)": "",
+        "Electric Guitar (clean)": "",
+        "Electric Guitar (muted)": "",
+        "Overdriven Guitar": "",
+        "Distortion Guitar": "",
+        "Guitar harmonics": "",
+        "Acoustic Bass": "",
+        "Electric Bass (fingered)": "",
+        "Electric Bass (picked)": "",
+        "Fretless Bass": "",
+        "Slap Bass 1": "",
+        "Slap Bass 2": "",
+        "Synth Bass 1": "",
+        "Synth Bass 2": "",
+        "Violin": "",
+        "Viola": "",
+        "Cello": "",
+        "Contrabass": "",
+        "Tremolo Strings": "",
+        "Pizzicato Strings": "",
+        "Orchestral Harp": "",
+        "Timpani": "",
+        "String Ensemble 1 (strings)": "",
+        "String Ensemble 2 (slow strings)": "",
+        "SynthStrings 1": "",
+        "SynthStrings 2": "",
+        "Choir Aahs": "",
+        "Voice Oohs": "",
+        "Synth Voice": "",
+        "Orchestra Hit": "",
+        "Trumpet": "",
+        "Trombone": "",
+        "Tuba": "",
+        "Muted Trumpet": "",
+        "French Horn": "",
+        "Brass Section": "",
+        "SynthBrass 1": "",
+        "SynthBrass 2": "",
+        "Soprano Sax": "",
+        "Alto Sax": "",
+        "Tenor Sax": "",
+        "Baritone Sax": "",
+        "Oboe": "",
+        "English Horn": "",
+        "Bassoon": "",
+        "Clarinet": "",
+        "Piccolo": "",
+        "Flute": "",
+        "Recorder": "",
+        "Pan Flute": "",
+        "Blown Bottle": "",
+        "Shakuhachi": "",
+        "Whistle": "",
+        "Ocarina": "",
+        "Lead 1 (square wave)": "",
+        "Lead 2 (sawtooth wave)": "",
+        "Lead 3 (calliope)": "",
+        "Lead 4 (chiffer)": "",
+        "Lead 5 (charang)": "",
+        "Lead 6 (voice solo)": "",
+        "Lead 7 (fifths)": "",
+        "Lead 8 (bass + lead)": "",
+        "Pad 1 (new age Fantasia)": "",
+        "Pad 2 (warm)": "",
+        "Pad 3 (polysynth)": "",
+        "Pad 4 (choir space voice)": "",
+        "Pad 5 (bowed glass)": "",
+        "Pad 6 (metallic pro)": "",
+        "Pad 7 (halo)": "",
+        "Pad 8 (sweep)": "",
+        "FX 1 (rain)": "",
+        "FX 2 (soundtrack)": "",
+        "FX 3 (crystal)": "",
+        "FX 4 (atmosphere)": "",
+        "FX 5 (brightness)": "",
+        "FX 6 (goblins)": "",
+        "FX 7 (echoes, drops)": "",
+        "FX 8 (sci-fi, star theme)": "",
+        "Sitar": "",
+        "Banjo": "",
+        "Shamisen": "",
+        "Koto": "",
+        "Kalimba": "",
+        "Bag pipe": "",
+        "Fiddle": "",
+        "Shanai": "",
+        "Tinkle Bell": "",
+        "Agogo": "",
+        "Steel Drums": "",
+        "Woodblock": "",
+        "Taiko Drum": "",
+        "Melodic Tom": "",
+        "Synth Drum": "",
+        "Reverse Cymbal": "",
+        "Guitar Fret Noise": "",
+        "Breath Noise": "",
+        "Seashore": "",
+        "Bird Tweet": "",
+        "Telephone Ring": "",
+        "Helicopter": "",
+        "Applause": "",
+        "Gunshot": ""
+    },
+    "bin_name_to_program_name": {
+        "piano": "Acoustic Grand Piano"
+    },
+    "instrument_names": {
+        "0": "Acoustic Grand Piano",
+        "1": "Bright Acoustic Piano",
+        "2": "Electric Grand Piano",
+        "3": "Honky-tonk Piano",
+        "4": "Electric Piano 1 (Rhodes Piano)",
+        "5": "Electric Piano 2 (Chorused Piano)",
+        "6": "Harpsichord",
+        "7": "Clavinet",
+        "8": "Celesta",
+        "9": "Glockenspiel",
+        "10": "Music Box",
+        "11": "Vibraphone",
+        "12": "Marimba",
+        "13": "Xylophone",
+        "14": "Tubular Bells",
+        "15": "Dulcimer (Santur)",
+        "16": "Drawbar Organ (Hammond)",
+        "17": "Percussive Organ",
+        "18": "Rock Organ",
+        "19": "Church Organ",
+        "20": "Reed Organ",
+        "21": "Accordion (French)",
+        "22": "Harmonica",
+        "23": "Tango Accordion (Band neon)",
+        "24": "Acoustic Guitar (nylon)",
+        "25": "Acoustic Guitar (steel)",
+        "26": "Electric Guitar (jazz)",
+        "27": "Electric Guitar (clean)",
+        "28": "Electric Guitar (muted)",
+        "29": "Overdriven Guitar",
+        "30": "Distortion Guitar",
+        "31": "Guitar harmonics",
+        "32": "Acoustic Bass",
+        "33": "Electric Bass (fingered)",
+        "34": "Electric Bass (picked)",
+        "35": "Fretless Bass",
+        "36": "Slap Bass 1",
+        "37": "Slap Bass 2",
+        "38": "Synth Bass 1",
+        "39": "Synth Bass 2",
+        "40": "Violin",
+        "41": "Viola",
+        "42": "Cello",
+        "43": "Contrabass",
+        "44": "Tremolo Strings",
+        "45": "Pizzicato Strings",
+        "46": "Orchestral Harp",
+        "47": "Timpani",
+        "48": "String Ensemble 1 (strings)",
+        "49": "String Ensemble 2 (slow strings)",
+        "50": "SynthStrings 1",
+        "51": "SynthStrings 2",
+        "52": "Choir Aahs",
+        "53": "Voice Oohs",
+        "54": "Synth Voice",
+        "55": "Orchestra Hit",
+        "56": "Trumpet",
+        "57": "Trombone",
+        "58": "Tuba",
+        "59": "Muted Trumpet",
+        "60": "French Horn",
+        "61": "Brass Section",
+        "62": "SynthBrass 1",
+        "63": "SynthBrass 2",
+        "64": "Soprano Sax",
+        "65": "Alto Sax",
+        "66": "Tenor Sax",
+        "67": "Baritone Sax",
+        "68": "Oboe",
+        "69": "English Horn",
+        "70": "Bassoon",
+        "71": "Clarinet",
+        "72": "Piccolo",
+        "73": "Flute",
+        "74": "Recorder",
+        "75": "Pan Flute",
+        "76": "Blown Bottle",
+        "77": "Shakuhachi",
+        "78": "Whistle",
+        "79": "Ocarina",
+        "80": "Lead 1 (square wave)",
+        "81": "Lead 2 (sawtooth wave)",
+        "82": "Lead 3 (calliope)",
+        "83": "Lead 4 (chiffer)",
+        "84": "Lead 5 (charang)",
+        "85": "Lead 6 (voice solo)",
+        "86": "Lead 7 (fifths)",
+        "87": "Lead 8 (bass + lead)",
+        "88": "Pad 1 (new age Fantasia)",
+        "89": "Pad 2 (warm)",
+        "90": "Pad 3 (polysynth)",
+        "91": "Pad 4 (choir space voice)",
+        "92": "Pad 5 (bowed glass)",
+        "93": "Pad 6 (metallic pro)",
+        "94": "Pad 7 (halo)",
+        "95": "Pad 8 (sweep)",
+        "96": "FX 1 (rain)",
+        "97": "FX 2 (soundtrack)",
+        "98": "FX 3 (crystal)",
+        "99": "FX 4 (atmosphere)",
+        "100": "FX 5 (brightness)",
+        "101": "FX 6 (goblins)",
+        "102": "FX 7 (echoes, drops)",
+        "103": "FX 8 (sci-fi, star theme)",
+        "104": "Sitar",
+        "105": "Banjo",
+        "106": "Shamisen",
+        "107": "Koto",
+        "108": "Kalimba",
+        "109": "Bag pipe",
+        "110": "Fiddle",
+        "111": "Shanai",
+        "112": "Tinkle Bell",
+        "113": "Agogo",
+        "114": "Steel Drums",
+        "115": "Woodblock",
+        "116": "Taiko Drum",
+        "117": "Melodic Tom",
+        "118": "Synth Drum",
+        "119": "Reverse Cymbal",
+        "120": "Guitar Fret Noise",
+        "121": "Breath Noise",
+        "122": "Seashore",
+        "123": "Bird Tweet",
+        "124": "Telephone Ring",
+        "125": "Helicopter",
+        "126": "Applause",
+        "127": "Gunshot"
+    }
+}

docker-compose.yml

@@ -0,0 +1,18 @@
+services:
+  rmkv_runner:
+    image: rwkv-runner:latest
+    build: .
+    # Append "--rwkv.cpp" parameter to use rwkv.cpp
+    # command: python3.10 ./backend-python/main.py  --port 27777 --host 0.0.0.0 --webui --rwkv.cpp
+    volumes:
+      - /mnt:/mnt
+    ports:
+      - "27777:27777"
+    # Comment the following lines if use rwkv.cpp
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]

finetune/get_layer_and_embd.py

@@ -32,6 +32,7 @@ cleaner_thread.start()
 w = torch.load(model_file, map_location="cpu")
 gc.collect()
 
+vocab_size = w["emb.weight"].shape[0]
 n_embd = w["emb.weight"].shape[1]
 n_layer = 0
 keys = list(w.keys())
@@ -52,6 +53,9 @@ for x in keys:
         version = max(6, version)
 
 if version <= expected_max_version:
-    print(f"--n_layer {n_layer} --n_embd {n_embd}", end="")
+    print(
+        f"v{int(version)}/train.py --vocab_size {vocab_size} --n_layer {n_layer} --n_embd {n_embd}",
+        end="",
+    )
 else:
     raise Exception(f"RWKV{version} is not supported")

finetune/install-wsl-dep-and-train.sh

@@ -22,6 +22,12 @@ else
   sudo apt -y install python3-pip
 fi
 
+if dpkg -s "python3-dev" >/dev/null 2>&1; then
+  echo "python3-dev installed"
+else
+  sudo apt -y install python3-dev
+fi
+
 if dpkg -s "ninja-build" >/dev/null 2>&1; then
   echo "ninja installed"
 else
@@ -47,11 +53,12 @@ else
 fi
 
 echo "loading $loadModel"
-modelInfo=$(python3 ./finetune/get_layer_and_embd.py $loadModel 4)
+modelInfo=$(python3 ./finetune/get_layer_and_embd.py $loadModel 5.2)
 echo $modelInfo
 if [[ $modelInfo =~ "--n_layer" ]]; then
-  python3 ./finetune/lora/train.py $modelInfo $@ --proj_dir lora-models --data_type binidx --lora \
-    --lora_parts=att,ffn,time,ln --strategy deepspeed_stage_2 --accelerator gpu
+  sudo rm -rf /root/.cache/torch_extensions
+  python3 ./finetune/lora/$modelInfo $@ --proj_dir lora-models --data_type binidx --lora \
+    --lora_parts=att,ffn,time,ln --strategy deepspeed_stage_2 --accelerator gpu --ds_bucket_mb 2
 else
   echo "modelInfo is invalid"
   exit 1

finetune/lora/v4/src/binidx.py

@@ -7,6 +7,7 @@ import struct
 from functools import lru_cache
 from itertools import accumulate
 
+
 def print_rank_0(*message):
     pass
     # """If distributed is initialized print only on rank 0."""
@@ -16,12 +17,14 @@ def print_rank_0(*message):
     # else:
     #     print(*message, flush=True)
 
+
 def _warmup_mmap_file(path):
     pass
     # with open(path, "rb") as stream:
     #     while stream.read(100 * 1024 * 1024):
     #         pass
 
+
 dtypes = {
     1: np.uint8,
     2: np.int8,
@@ -33,18 +36,22 @@ dtypes = {
     8: np.uint16,
 }
 
+
 def code(dtype):
     for k in dtypes.keys():
         if dtypes[k] == dtype:
             return k
     raise ValueError(dtype)
 
+
 def index_file_path(prefix_path):
     return prefix_path + ".idx"
 
+
 def data_file_path(prefix_path):
     return prefix_path + ".bin"
 
+
 class MMapIndexedDataset(torch.utils.data.Dataset):
     class Index(object):
         _HDR_MAGIC = b"MMIDIDX\x00\x00"
@@ -100,7 +107,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
                     self._file.close()
 
             return _Writer()
-        
+
         def __init__(self, path, skip_warmup=False):
             with open(path, "rb") as stream:
                 magic_test = stream.read(9)
@@ -217,8 +224,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
         elif isinstance(idx, slice):
             start, stop, step = idx.indices(len(self))
             if step != 1:
-                raise ValueError(
-                    "Slices into indexed_dataset must be contiguous")
+                raise ValueError("Slices into indexed_dataset must be contiguous")
             ptr = self._index._pointers[start]
             sizes = self._index._sizes[idx]
             offsets = list(accumulate(sizes))

finetune/lora/v4/src/dataset.py

@@ -17,9 +17,11 @@ class MyDataset(Dataset):
 
         if args.data_type == "binidx":
             self.vocab_size = args.vocab_size
-            rank_zero_info(f"Current vocab size = {self.vocab_size} (make sure it's correct)")
+            rank_zero_info(
+                f"Current vocab size = {self.vocab_size} (make sure it's correct)"
+            )
 
-            if args.data_file.endswith('/'):
+            if args.data_file.endswith("/"):
                 d_all = []
                 for p in os.listdir(args.data_file):
                     if p.endswith(".idx"):
@@ -29,33 +31,52 @@ class MyDataset(Dataset):
                 exit(0)
             else:
                 self.data = MMapIndexedDataset(args.data_file)
-                self.data_size = len(self.data._bin_buffer) // self.data._index._dtype_size
+                self.data_size = (
+                    len(self.data._bin_buffer) // self.data._index._dtype_size
+                )
                 rank_zero_info(f"Data has {self.data_size} tokens.")
 
             if args.my_qa_mask > 0:
-                self.data_pile = MMapIndexedDataset('/fsx/BlinkDL/pile/pile_20B_tokenizer_text_document')
-                self.data_pile_size = len(self.data_pile._bin_buffer) // self.data._index._dtype_size
+                self.data_pile = MMapIndexedDataset(
+                    "/fsx/BlinkDL/pile/pile_20B_tokenizer_text_document"
+                )
+                self.data_pile_size = (
+                    len(self.data_pile._bin_buffer) // self.data._index._dtype_size
+                )
 
             if args.my_pile_stage > 0:
                 # assert self.data_size == 332115325534 and self.vocab_size == 50277
                 self.samples_per_epoch = args.epoch_steps * args.real_bsz
                 assert self.samples_per_epoch == 40320
-                rank_zero_info(f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########")
+                rank_zero_info(
+                    f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########"
+                )
                 dataset_slot = self.data_size // args.ctx_len
                 if args.my_pile_stage != 4:
                     assert MaybeIsPrime(args.magic_prime)
                     assert args.magic_prime % 3 == 2
-                    assert args.magic_prime / dataset_slot > 0.99 and args.magic_prime / dataset_slot <= 1
+                    assert (
+                        args.magic_prime / dataset_slot > 0.99
+                        and args.magic_prime / dataset_slot <= 1
+                    )
         elif args.data_type == "numpy":
             self.data = np.load(args.data_file).astype("int")
             self.vocab_size = args.vocab_size
-            rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)")
+            rank_zero_info(
+                "Current vocab size =", self.vocab_size, "(make sure it's correct)"
+            )
             self.data_size = len(self.data)
             rank_zero_info(f"Data has {self.data_size} tokens.")
         elif args.data_type == "uint16":
-            self.data = np.fromfile(args.data_file, dtype=np.uint16).astype("int32").reshape(-1, args.my_sample_len)
+            self.data = (
+                np.fromfile(args.data_file, dtype=np.uint16)
+                .astype("int32")
+                .reshape(-1, args.my_sample_len)
+            )
             self.vocab_size = args.vocab_size
-            rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)")
+            rank_zero_info(
+                "Current vocab size =", self.vocab_size, "(make sure it's correct)"
+            )
             self.data_size = self.data.shape[0]
             rank_zero_info(f"Data has {self.data_size} samples.")
         elif args.data_type == "wds_img":
@@ -86,10 +107,14 @@ class MyDataset(Dataset):
             for u in unique:
                 xxObj[xx] = u
                 xx += 1
-            with open(f"{args.proj_dir}/vocab.json", "w", encoding="utf-16le") as vocab_file:
+            with open(
+                f"{args.proj_dir}/vocab.json", "w", encoding="utf-16le"
+            ) as vocab_file:
                 vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
             self.data_size = len(self.data)
-            rank_zero_info(f"Data has {self.data_size} tokens, {self.vocab_size} vocab size.")
+            rank_zero_info(
+                f"Data has {self.data_size} tokens, {self.vocab_size} vocab size."
+            )
             self.stoi = {ch: i for i, ch in enumerate(unique)}
             self.itos = {i: ch for i, ch in enumerate(unique)}
 
@@ -104,36 +129,53 @@ class MyDataset(Dataset):
         # print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}")
 
         if args.data_type == "wds_img":
+
             def init_wds(self, bias=0):
                 def identity(x):
-                    return x            
+                    return x
+
                 import webdataset as wds
                 import torchvision.transforms as transforms
+
                 # img_transform = transforms.Compose(
                 #     [transforms.CenterCrop(256)]
                 # )
-                img_transform = transforms.Compose([
-                    transforms.CenterCrop(512),
-                    transforms.Resize((args.my_img_size))
-                ])
-                self.data_raw = wds.WebDataset(args.data_file, resampled=True).shuffle(10000, initial=1000, rng=random.Random(epoch*100000+rank+bias*1e9)).decode("torchrgb").to_tuple("jpg", "json", "txt").map_tuple(img_transform, identity, identity)
+                img_transform = transforms.Compose(
+                    [transforms.CenterCrop(512), transforms.Resize((args.my_img_size))]
+                )
+                self.data_raw = (
+                    wds.WebDataset(args.data_file, resampled=True)
+                    .shuffle(
+                        10000,
+                        initial=1000,
+                        rng=random.Random(epoch * 100000 + rank + bias * 1e9),
+                    )
+                    .decode("torchrgb")
+                    .to_tuple("jpg", "json", "txt")
+                    .map_tuple(img_transform, identity, identity)
+                )
                 for pp in self.data_raw.pipeline:
-                    if 'Resampled' in str(pp):
+                    if "Resampled" in str(pp):
                         pp.deterministic = True
+
                         def worker_seed():
-                            return rank*100000+epoch+bias*1e9
+                            return rank * 100000 + epoch + bias * 1e9
+
                         pp.worker_seed = worker_seed
                 self.data = iter(self.data_raw)
                 # print(f"WebDataset loaded for rank {rank} epoch {epoch}")
+
             if self.data == None:
                 init_wds(self)
             trial = 0
             while trial < 10:
                 try:
-                    dd = next(self.data) # jpg, json, txt
+                    dd = next(self.data)  # jpg, json, txt
                     break
                 except:
-                    print(f'[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]')
+                    print(
+                        f"[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]"
+                    )
                     self.error_count += 1
                     init_wds(self, self.error_count)
                     trial += 1
@@ -144,7 +186,7 @@ class MyDataset(Dataset):
             return dd[0], dd[2]
         else:
             if args.data_type == "uint16":
-                i = np.random.randint(0, self.data_size-1)
+                i = np.random.randint(0, self.data_size - 1)
                 dix = self.data[i]
                 x = torch.tensor(dix[:-1], dtype=torch.long)
                 y = torch.tensor(dix[1:], dtype=torch.long)
@@ -196,7 +238,12 @@ class MyDataset(Dataset):
                         z_sum = 0
                         isGood = False
                         for i in range(3, ctx_len):
-                            if dix[i] == 27 and dix[i-1] == 34 and dix[i-2] == 187 and dix[i-3] == 187:
+                            if (
+                                dix[i] == 27
+                                and dix[i - 1] == 34
+                                and dix[i - 2] == 187
+                                and dix[i - 3] == 187
+                            ):
                                 isGood = True
                             if dix[i] == 0:
                                 isGood = False
@@ -206,7 +253,9 @@ class MyDataset(Dataset):
                         if z_sum == 0:
                             z = [1] * ctx_len
                             i = np.random.randint(0, self.data_pile_size - req_len)
-                            dix = self.data_pile.get(idx=0, offset=i, length=req_len).astype(int)
+                            dix = self.data_pile.get(
+                                idx=0, offset=i, length=req_len
+                            ).astype(int)
                     z = torch.tensor(z, dtype=torch.bfloat16)
 
                 x = torch.tensor(dix[:-1], dtype=torch.long)

finetune/lora/v4/src/model.py

@@ -5,6 +5,7 @@
 import functools
 import os, math, gc, importlib
 import torch
+
 # torch._C._jit_set_profiling_executor(True)
 # torch._C._jit_set_profiling_mode(True)
 import torch.nn as nn
@@ -13,7 +14,8 @@ from torch.nn import functional as F
 import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
 from pytorch_lightning.strategies import DeepSpeedStrategy
-if importlib.util.find_spec('deepspeed'):
+
+if importlib.util.find_spec("deepspeed"):
     import deepspeed
     from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
 
@@ -28,9 +30,10 @@ LORA_CONFIG = {
 
 
 try:
-    print('RWKV_MY_TESTING', os.environ["RWKV_MY_TESTING"])
+    print("RWKV_MY_TESTING", os.environ["RWKV_MY_TESTING"])
 except:
-    os.environ["RWKV_MY_TESTING"] = ''
+    os.environ["RWKV_MY_TESTING"] = ""
+
 
 def __nop(ob):
     return ob
@@ -53,7 +56,26 @@ T_MAX = int(os.environ["RWKV_T_MAX"])  # TAKES LOTS OF VRAM!
 from torch.utils.cpp_extension import load
 
 if os.environ["RWKV_FLOAT_MODE"] == "bf16":
-    wkv_cuda = load(name=f"wkv_{T_MAX}_bf16", sources=["finetune/lora/cuda/wkv_op_bf16.cpp", "finetune/lora/cuda/wkv_cuda_bf16.cu"], verbose=True, extra_cuda_cflags=["-t 4", "-std=c++17", "-res-usage", "--maxrregcount 60", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-DTmax={T_MAX}"])
+    wkv_cuda = load(
+        name=f"wkv_{T_MAX}_bf16",
+        sources=[
+            "finetune/lora/v4/cuda/wkv_op_bf16.cpp",
+            "finetune/lora/v4/cuda/wkv_cuda_bf16.cu",
+        ],
+        verbose=True,
+        extra_cuda_cflags=[
+            "-t 4",
+            "-std=c++17",
+            "-res-usage",
+            "--maxrregcount 60",
+            "--use_fast_math",
+            "-O3",
+            "-Xptxas -O3",
+            "--extra-device-vectorization",
+            f"-DTmax={T_MAX}",
+        ],
+    )
+
     class WKV(torch.autograd.Function):
         @staticmethod
         def forward(ctx, B, T, C, w, u, k, v):
@@ -66,10 +88,16 @@ if os.environ["RWKV_FLOAT_MODE"] == "bf16":
             u = u.contiguous()
             k = k.contiguous()
             v = v.contiguous()
-            y = torch.empty((B, T, C), device=w.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16)
+            y = torch.empty(
+                (B, T, C),
+                device=w.device,
+                memory_format=torch.contiguous_format,
+                dtype=torch.bfloat16,
+            )
             wkv_cuda.forward(B, T, C, w, u, k, v, y)
             ctx.save_for_backward(w, u, k, v, y)
             return y
+
         @staticmethod
         def backward(ctx, gy):
             B = ctx.B
@@ -78,16 +106,54 @@ if os.environ["RWKV_FLOAT_MODE"] == "bf16":
             assert T <= T_MAX
             assert B * C % min(C, 32) == 0
             w, u, k, v, y = ctx.saved_tensors
-            gw = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16)
-            gu = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16)
-            gk = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16)
-            gv = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16)
+            gw = torch.empty(
+                (B, C),
+                device=gy.device,
+                memory_format=torch.contiguous_format,
+                dtype=torch.bfloat16,
+            )
+            gu = torch.empty(
+                (B, C),
+                device=gy.device,
+                memory_format=torch.contiguous_format,
+                dtype=torch.bfloat16,
+            )
+            gk = torch.empty(
+                (B, T, C),
+                device=gy.device,
+                memory_format=torch.contiguous_format,
+                dtype=torch.bfloat16,
+            )
+            gv = torch.empty(
+                (B, T, C),
+                device=gy.device,
+                memory_format=torch.contiguous_format,
+                dtype=torch.bfloat16,
+            )
             wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv)
             gw = torch.sum(gw, dim=0)
             gu = torch.sum(gu, dim=0)
             return (None, None, None, gw, gu, gk, gv)
+
 else:
-    wkv_cuda = load(name=f"wkv_{T_MAX}", sources=["finetune/lora/cuda/wkv_op.cpp", "finetune/lora/cuda/wkv_cuda.cu"], verbose=True, extra_cuda_cflags=["-res-usage", "--maxrregcount 60", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-DTmax={T_MAX}"])
+    wkv_cuda = load(
+        name=f"wkv_{T_MAX}",
+        sources=[
+            "finetune/lora/v4/cuda/wkv_op.cpp",
+            "finetune/lora/v4/cuda/wkv_cuda.cu",
+        ],
+        verbose=True,
+        extra_cuda_cflags=[
+            "-res-usage",
+            "--maxrregcount 60",
+            "--use_fast_math",
+            "-O3",
+            "-Xptxas -O3",
+            "--extra-device-vectorization",
+            f"-DTmax={T_MAX}",
+        ],
+    )
+
     class WKV(torch.autograd.Function):
         @staticmethod
         def forward(ctx, B, T, C, w, u, k, v):
@@ -106,7 +172,9 @@ else:
                 u = u.float().contiguous()
                 k = k.float().contiguous()
                 v = v.float().contiguous()
-            y = torch.empty((B, T, C), device=w.device, memory_format=torch.contiguous_format)
+            y = torch.empty(
+                (B, T, C), device=w.device, memory_format=torch.contiguous_format
+            )
             wkv_cuda.forward(B, T, C, w, u, k, v, y)
             ctx.save_for_backward(w, u, k, v, y)
             if "32" in os.environ["RWKV_FLOAT_MODE"]:
@@ -115,6 +183,7 @@ else:
                 return y.half()
             elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
                 return y.bfloat16()
+
         @staticmethod
         def backward(ctx, gy):
             B = ctx.B
@@ -123,14 +192,26 @@ else:
             assert T <= T_MAX
             assert B * C % min(C, 32) == 0
             w, u, k, v, y = ctx.saved_tensors
-            gw = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format)
-            gu = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format)
-            gk = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format)
-            gv = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format)
+            gw = torch.empty(
+                (B, C), device=gy.device, memory_format=torch.contiguous_format
+            )
+            gu = torch.empty(
+                (B, C), device=gy.device, memory_format=torch.contiguous_format
+            )
+            gk = torch.empty(
+                (B, T, C), device=gy.device, memory_format=torch.contiguous_format
+            )
+            gv = torch.empty(
+                (B, T, C), device=gy.device, memory_format=torch.contiguous_format
+            )
             if "32" in os.environ["RWKV_FLOAT_MODE"]:
-                wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv)
+                wkv_cuda.backward(
+                    B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv
+                )
             else:
-                wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.float().contiguous(), gw, gu, gk, gv)
+                wkv_cuda.backward(
+                    B, T, C, w, u, k, v, y, gy.float().contiguous(), gw, gu, gk, gv
+                )
             gw = torch.sum(gw, dim=0)
             gu = torch.sum(gu, dim=0)
             if "32" in os.environ["RWKV_FLOAT_MODE"]:
@@ -138,7 +219,15 @@ else:
             elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
                 return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half())
             elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
-                return (None, None, None, gw.bfloat16(), gu.bfloat16(), gk.bfloat16(), gv.bfloat16())
+                return (
+                    None,
+                    None,
+                    None,
+                    gw.bfloat16(),
+                    gu.bfloat16(),
+                    gk.bfloat16(),
+                    gv.bfloat16(),
+                )
 
 
 def RUN_CUDA(B, T, C, w, u, k, v):
@@ -151,15 +240,17 @@ def RUN_CUDA(B, T, C, w, u, k, v):
 
 
 class LoraLinear(nn.Module):
-
     def __init__(self, in_features: int, out_features: int, bias: bool):
         super().__init__()
 
         self.weight = nn.Parameter(torch.empty((out_features, in_features)))
         assert bias == False, "Biased LoraLinear not supported"
 
-        r, alpha, dropout = LORA_CONFIG["r"], LORA_CONFIG[
-            "alpha"], LORA_CONFIG["dropout"]
+        r, alpha, dropout = (
+            LORA_CONFIG["r"],
+            LORA_CONFIG["alpha"],
+            LORA_CONFIG["dropout"],
+        )
         self.lora_A = nn.Parameter(torch.empty(r, in_features))
         self.lora_B = nn.Parameter(torch.empty(out_features, r))
         self.lora_dropout = nn.Dropout(dropout)
@@ -170,9 +261,9 @@ class LoraLinear(nn.Module):
         nn.init.zeros_(self.lora_B)
 
     def forward(self, x):
-        return (
-            F.linear(x, self.weight) + self.scaling *
-            F.linear(F.linear(self.lora_dropout(x), self.lora_A), self.lora_B))
+        return F.linear(x, self.weight) + self.scaling * F.linear(
+            F.linear(self.lora_dropout(x), self.lora_A), self.lora_B
+        )
 
 
 @functools.wraps(LoraLinear)
@@ -214,17 +305,23 @@ class RWKV_TimeMix(MyModule):
             # fancy time_decay
             decay_speed = torch.ones(args.dim_att)
             for h in range(args.dim_att):
-                decay_speed[h] = -5 + 8 * (h / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
+                decay_speed[h] = -5 + 8 * (h / (args.dim_att - 1)) ** (
+                    0.7 + 1.3 * ratio_0_to_1
+                )
             self.time_decay = nn.Parameter(decay_speed)
             # print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())
 
             # fancy time_first
             zigzag = torch.tensor([(i + 1) % 3 - 1 for i in range(args.dim_att)]) * 0.5
-            self.time_first = nn.Parameter(torch.ones(args.dim_att) * math.log(0.3) + zigzag)
+            self.time_first = nn.Parameter(
+                torch.ones(args.dim_att) * math.log(0.3) + zigzag
+            )
 
             # fancy time_mix
             self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
-            self.time_mix_v = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1)
+            self.time_mix_v = nn.Parameter(
+                torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
+            )
             self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
 
         self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
@@ -235,8 +332,10 @@ class RWKV_TimeMix(MyModule):
 
         self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
 
-        if 'a' in os.environ["RWKV_MY_TESTING"]:
-            self.register_buffer("att_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len)))
+        if "a" in os.environ["RWKV_MY_TESTING"]:
+            self.register_buffer(
+                "att_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
+            )
             d_qkv = args.n_embd // 16
             self.qq = nn.Linear(args.n_embd, d_qkv, bias=False)
             self.kk = nn.Linear(args.n_embd, d_qkv, bias=False)
@@ -245,12 +344,17 @@ class RWKV_TimeMix(MyModule):
             with torch.no_grad():
                 self.time_mix_qq = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
                 self.time_mix_kk = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
-                self.time_mix_vv = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1)
+                self.time_mix_vv = nn.Parameter(
+                    torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
+                )
+
+    if "a" not in os.environ["RWKV_MY_TESTING"]:
 
-    if 'a' not in os.environ["RWKV_MY_TESTING"]:
         @MyFunction
         def jit_func(self, x):
-            xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr
+            xx = self.time_shift(
+                x
+            )  # Mix x with the previous timestep to produce xk, xv, xr
             xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
             xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
             xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
@@ -263,21 +367,26 @@ class RWKV_TimeMix(MyModule):
         def forward(self, x):
             B, T, C = x.size()  # x = (Batch,Time,Channel)
             sr, k, v = self.jit_func(x)
-            rwkv = sr * RUN_CUDA(B, T, self.args.dim_att, self.time_decay, self.time_first, k, v)
+            rwkv = sr * RUN_CUDA(
+                B, T, self.args.dim_att, self.time_decay, self.time_first, k, v
+            )
             return self.output(rwkv)
 
-    if 'a' in os.environ["RWKV_MY_TESTING"]:
+    if "a" in os.environ["RWKV_MY_TESTING"]:
+
         @MyFunction
         def QKV(self, q, k, v):
             att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-            att = att.masked_fill(self.att_mask == 0, float('-inf'))
-            att = F.softmax(att, dim = -1)
+            att = att.masked_fill(self.att_mask == 0, float("-inf"))
+            att = F.softmax(att, dim=-1)
             x = att @ v
             return x
 
         @MyFunction
         def jit_funcQKV(self, x):
-            xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr
+            xx = self.time_shift(
+                x
+            )  # Mix x with the previous timestep to produce xk, xv, xr
             xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
             xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
             xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
@@ -296,12 +405,16 @@ class RWKV_TimeMix(MyModule):
         def forward(self, x):
             B, T, C = x.size()  # x = (Batch,Time,Channel)
             sr, k, v, qq, kk, vv = self.jit_funcQKV(x)
-            rwkv = sr * RUN_CUDA(B, T, self.args.dim_att, self.time_decay, self.time_first, k, v)
+            rwkv = sr * RUN_CUDA(
+                B, T, self.args.dim_att, self.time_decay, self.time_first, k, v
+            )
             rwkv = self.output(rwkv) + self.oo(self.QKV(qq, kk, vv))
             return rwkv
 
+
 ########################################################################################################
 
+
 class RWKV_ChannelMix(MyModule):
     def __init__(self, args, layer_id):
         super().__init__()
@@ -331,6 +444,7 @@ class RWKV_ChannelMix(MyModule):
         kv = self.value(k)
         return torch.sigmoid(self.receptance(xr)) * kv
 
+
 class MishGLU(MyModule):
     def __init__(self, args, layer_id):
         super().__init__()
@@ -360,6 +474,7 @@ class MishGLU(MyModule):
         b = self.bb(xb)
         return self.value(a * F.mish(b))
 
+
 ########################################################################################################
 # The RWKV Model with our blocks
 ########################################################################################################
@@ -377,15 +492,19 @@ class Block(nn.Module):
         if self.layer_id == 0:
             self.ln0 = nn.LayerNorm(args.n_embd)
             if args.my_pos_emb > 0:
-                self.pos_emb_x = nn.Parameter(torch.zeros((1,args.my_pos_emb,args.n_embd)))
-                self.pos_emb_y = nn.Parameter(torch.zeros((args.my_pos_emb,1,args.n_embd)))
+                self.pos_emb_x = nn.Parameter(
+                    torch.zeros((1, args.my_pos_emb, args.n_embd))
+                )
+                self.pos_emb_y = nn.Parameter(
+                    torch.zeros((args.my_pos_emb, 1, args.n_embd))
+                )
 
         if self.layer_id == 0 and self.args.pre_ffn > 0:
             self.ffnPre = RWKV_ChannelMix(args, 0)
         else:
             self.att = RWKV_TimeMix(args, layer_id)
 
-        if 'g' in os.environ["RWKV_MY_TESTING"]:
+        if "g" in os.environ["RWKV_MY_TESTING"]:
             self.ffn = MishGLU(args, layer_id)
         else:
             self.ffn = RWKV_ChannelMix(args, layer_id)
@@ -395,7 +514,9 @@ class Block(nn.Module):
             self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
             self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
             self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False)
-            self.register_buffer("tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len)))
+            self.register_buffer(
+                "tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
+            )
 
     def forward(self, x, x_emb=None):
         args = self.args
@@ -403,7 +524,7 @@ class Block(nn.Module):
         if self.layer_id == 0:
             x = self.ln0(x)
             if args.my_pos_emb > 0:
-                pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T+1, -1)[:-1,:]
+                pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T + 1, -1)[:-1, :]
                 x = x + pos_emb
 
         if self.layer_id == 0 and args.pre_ffn > 0:
@@ -443,13 +564,13 @@ class RWKV(pl.LightningModule):
     def __init__(self, args):
         super().__init__()
         self.args = args
-        if not hasattr(args, 'dim_att'):
+        if not hasattr(args, "dim_att"):
             args.dim_att = args.n_embd
-        if not hasattr(args, 'dim_ffn'):
+        if not hasattr(args, "dim_ffn"):
             args.dim_ffn = args.n_embd * 4
-        if not hasattr(args, 'tiny_att_layer'):
+        if not hasattr(args, "tiny_att_layer"):
             args.tiny_att_layer = -1
-        if not hasattr(args, 'tiny_att_dim'):
+        if not hasattr(args, "tiny_att_dim"):
             args.tiny_att_dim = -1
 
         self.emb = nn.Embedding(args.vocab_size, args.n_embd)
@@ -462,7 +583,9 @@ class RWKV(pl.LightningModule):
         if args.head_qk > 0:
             self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False)
             self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False)
-            self.register_buffer("copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len)))
+            self.register_buffer(
+                "copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
+            )
 
     def configure_optimizers(self):
         args = self.args
@@ -494,19 +617,46 @@ class RWKV(pl.LightningModule):
             param_dict = {n: p for n, p in self.named_parameters()}
             if args.my_pile_stage == 2:
                 optim_groups = [
-                    {"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0},
-                    {"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 2e-3 / args.lr_init},
-                    {"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 3e-3 / args.lr_init},
+                    {
+                        "params": [param_dict[n] for n in lr_1x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 1.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_2x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 5.0,
+                    },  # test: 2e-3 / args.lr_init},
+                    {
+                        "params": [param_dict[n] for n in lr_3x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 5.0,
+                    },  # test: 3e-3 / args.lr_init},
                 ]
             else:
                 optim_groups = [
-                    {"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0},
-                    {"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 2.0},
-                    {"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 3.0},
+                    {
+                        "params": [param_dict[n] for n in lr_1x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 1.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_2x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 2.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_3x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 3.0,
+                    },
                 ]
         else:
             optim_groups = [
-                {"params": [p for n, p in self.named_parameters()], "weight_decay": 0.0},
+                {
+                    "params": [p for n, p in self.named_parameters()],
+                    "weight_decay": 0.0,
+                },
             ]
 
         for g in optim_groups:
@@ -514,8 +664,26 @@ class RWKV(pl.LightningModule):
         optim_groups = [g for g in optim_groups if len(g["params"]) > 0]
 
         if self.deepspeed_offload:
-            return DeepSpeedCPUAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adamw_mode=False, weight_decay=0, amsgrad=False)
-        return FusedAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adam_w_mode=False, weight_decay=0, amsgrad=False)
+            return DeepSpeedCPUAdam(
+                optim_groups,
+                lr=self.args.lr_init,
+                betas=self.args.betas,
+                eps=self.args.adam_eps,
+                bias_correction=True,
+                adamw_mode=False,
+                weight_decay=0,
+                amsgrad=False,
+            )
+        return FusedAdam(
+            optim_groups,
+            lr=self.args.lr_init,
+            betas=self.args.betas,
+            eps=self.args.adam_eps,
+            bias_correction=True,
+            adam_w_mode=False,
+            weight_decay=0,
+            amsgrad=False,
+        )
         # return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False)
 
     @property
@@ -589,10 +757,14 @@ class RWKV(pl.LightningModule):
 
             logits = self(idx)
             if sum_mask == mask.shape[0]:
-                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+                loss = F.cross_entropy(
+                    logits.view(-1, logits.size(-1)), targets.view(-1)
+                )
                 # print('rank', self.global_rank, 'loss', loss.item())
             else:
-                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), reduction='none')
+                loss = F.cross_entropy(
+                    logits.view(-1, logits.size(-1)), targets.view(-1), reduction="none"
+                )
                 # loss_raw = loss
                 loss = torch.sum(loss * mask) / sum_mask
 
@@ -632,7 +804,14 @@ class RWKV(pl.LightningModule):
 
             gain = 1.0
             scale = 1.0
-            if "ln_" in n or ".ln" in n or "time_" in n or "_mask" in n or "pos_emb" in n or '.mask.' in n:
+            if (
+                "ln_" in n
+                or ".ln" in n
+                or "time_" in n
+                or "_mask" in n
+                or "pos_emb" in n
+                or ".mask." in n
+            ):
                 m[n] = p
             else:
                 if n == "emb.weight":
@@ -640,7 +819,19 @@ class RWKV(pl.LightningModule):
                 else:
                     if shape[0] > shape[1]:
                         gain = math.sqrt(shape[0] / shape[1])
-                    for kk in [".att.key.", ".att.receptance.", ".att.output.", ".att.key.", ".ffn.value.", ".ffn.receptance.", ".ffnPre.value.", ".ffnPre.receptance.", "head_q.", '.oo.', '.rr.']:
+                    for kk in [
+                        ".att.key.",
+                        ".att.receptance.",
+                        ".att.output.",
+                        ".att.key.",
+                        ".ffn.value.",
+                        ".ffn.receptance.",
+                        ".ffnPre.value.",
+                        ".ffnPre.receptance.",
+                        "head_q.",
+                        ".oo.",
+                        ".rr.",
+                    ]:
                         if kk in n:
                             scale = 0
                     if n == "head.weight":
@@ -650,7 +841,9 @@ class RWKV(pl.LightningModule):
                     if "head_q." in n:
                         scale = 0
 
-                print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}")
+                print(
+                    f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}"
+                )
 
                 if self.args.accelerator.upper() == "GPU":
                     m[n] = torch.empty((shape[0], shape[1]), device="cuda")

finetune/lora/v4/src/trainer.py

@@ -5,15 +5,17 @@ import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
 from .model import LORA_CONFIG
 
+
 def my_save(dd, ff):
-    if '14b-run1' not in ff:
+    if "14b-run1" not in ff:
         torch.save(dd, ff)
     else:
-        fn = ff.split('/')[-1]
-        fff = '/dev/shm/' + fn
+        fn = ff.split("/")[-1]
+        fff = "/dev/shm/" + fn
         torch.save(dd, fff)
         subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
 
+
 class train_callback(pl.Callback):
     def __init__(self, args):
         super().__init__()
@@ -38,7 +40,9 @@ class train_callback(pl.Callback):
             if args.lr_final == 0 or args.lr_init == 0:  # linear decay
                 lr = args.lr_init + (args.lr_final - args.lr_init) * progress
             else:  # exp decay
-                lr = args.lr_init * math.exp(math.log(args.lr_final / args.lr_init) * pow(progress, 1))
+                lr = args.lr_init * math.exp(
+                    math.log(args.lr_final / args.lr_init) * pow(progress, 1)
+                )
 
             if trainer.global_step < w_step:
                 lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
@@ -60,7 +64,9 @@ class train_callback(pl.Callback):
                 trainer.my_loss_sum = 0
                 trainer.my_loss_count = 0
                 trainer.my_log = open(args.proj_dir + "/train_log.txt", "a")
-                trainer.my_log.write(f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n")
+                trainer.my_log.write(
+                    f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n"
+                )
                 try:
                     print(f"\n{trainer.strategy.config}\n")
                     trainer.my_log.write(f"{trainer.strategy.config}\n")
@@ -70,6 +76,7 @@ class train_callback(pl.Callback):
                 if len(args.wandb) > 0:
                     print("Login to wandb...")
                     import wandb
+
                     wandb.init(
                         project=args.wandb,
                         name=args.run_name + " " + args.my_timestamp,
@@ -102,20 +109,26 @@ class train_callback(pl.Callback):
             # self.log("s", real_step, prog_bar=True, on_step=True)
 
             if len(args.wandb) > 0:
-                lll = {"loss": trainer.my_loss, "lr": trainer.my_lr, "Gtokens": real_step * token_per_step / 1e9}
+                lll = {
+                    "loss": trainer.my_loss,
+                    "lr": trainer.my_lr,
+                    "Gtokens": real_step * token_per_step / 1e9,
+                }
                 if kt_s > 0:
                     lll["kt/s"] = kt_s
                 trainer.my_wandb.log(lll, step=int(real_step))
             if args.magic_prime > 0:
                 expand_factor = 2 if args.my_qa_mask > 0 else 1
-                if int(real_step) == int(args.magic_prime * expand_factor // args.real_bsz) - 1:
+                if (
+                    int(real_step)
+                    == int(args.magic_prime * expand_factor // args.real_bsz) - 1
+                ):
                     to_save_dict = pl_module.state_dict()
                     my_save(
                         to_save_dict,
                         f"{args.proj_dir}/rwkv-final.pth",
                     )
 
-
     def on_train_epoch_start(self, trainer, pl_module):
         args = self.args
         dataset = trainer.train_dataloader.dataset.datasets
@@ -128,24 +141,28 @@ class train_callback(pl.Callback):
     def on_train_epoch_end(self, trainer, pl_module):
         args = self.args
         if trainer.is_global_zero:  # logging & save state_dict
-            if (args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0) or trainer.current_epoch == args.epoch_count - 1:
-                if args.data_type == 'wds_img':
+            if (
+                args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0
+            ) or trainer.current_epoch == args.epoch_count - 1:
+                if args.data_type == "wds_img":
                     raw_dict = pl_module.state_dict()
                     to_save_dict = {}
                     for k in raw_dict:
-                        if k.startswith('encoder.') or k.startswith('decoder.'):
+                        if k.startswith("encoder.") or k.startswith("decoder."):
                             to_save_dict[k] = raw_dict[k]
                 else:
                     to_save_dict = pl_module.state_dict()
 
                 if args.lora:
-                    enable_time_finetune = 'time' in LORA_CONFIG["parts"]
-                    enable_ln_finetune = 'ln' in LORA_CONFIG["parts"]
+                    enable_time_finetune = "time" in LORA_CONFIG["parts"]
+                    enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
                     lora_dict = {}
                     for name, state in to_save_dict.items():
-                        if ('.lora_' in name
-                                or (enable_time_finetune and '.time_' in name)
-                                or (enable_ln_finetune and '.ln' in name)):
+                        if (
+                            ".lora_" in name
+                            or (enable_time_finetune and ".time_" in name)
+                            or (enable_ln_finetune and ".ln" in name)
+                        ):
                             lora_dict[name] = state
                     to_save_dict = lora_dict
 
@@ -155,8 +172,10 @@ class train_callback(pl.Callback):
                         f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth",
                     )
                 except Exception as e:
-                    print('Error\n\n', e, '\n\n')
-            trainer.my_log.write(f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n")
+                    print("Error\n\n", e, "\n\n")
+            trainer.my_log.write(
+                f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n"
+            )
             trainer.my_log.flush()
 
             trainer.my_loss_sum = 0
@@ -178,22 +197,22 @@ def generate_init_weight(model, init_weight_name):
                     mm[k] = src.reshape(mm[k].shape)
                 except:
                     tmp = mm[k].squeeze().clone()
-                    print(k, src.shape, '-->', mm[k].shape)
+                    print(k, src.shape, "-->", mm[k].shape)
                     ss = src.shape[0]
                     dd = tmp.shape[0]
                     for i in range(dd):
                         pos = i / dd * ss
                         if pos >= ss - 1:
-                            tmp[i] = src[ss-1]
+                            tmp[i] = src[ss - 1]
                         else:
                             p0 = int(math.floor(pos))
                             ii = pos - p0
-                            tmp[i] = src[p0] * (1-ii) + src[p0+1] * (ii)
+                            tmp[i] = src[p0] * (1 - ii) + src[p0 + 1] * (ii)
                     mm[k] = tmp.reshape(mm[k].shape)
                     sss = src.squeeze().float().cpu().numpy()
-                    print(sss[:10], '...', sss[-10:])
+                    print(sss[:10], "...", sss[-10:])
                     mmm = mm[k].squeeze().float().cpu().numpy()
-                    print(mmm[:10], '...', mmm[-10:])
+                    print(mmm[:10], "...", mmm[-10:])
 
     print(f"Save to {init_weight_name}...")
     torch.save(mm, init_weight_name)

finetune/lora/v4/src/utils.py

@@ -6,6 +6,7 @@ from torch.nn import functional as F
 time_slot = {}
 time_ref = time.time_ns()
 
+
 def record_time(name):
     if name not in time_slot:
         time_slot[name] = 1e20
@@ -13,20 +14,23 @@ def record_time(name):
     if tt < time_slot[name]:
         time_slot[name] = tt
 
-class TOKENIZER():
-    def __init__(self, WORD_NAME, UNKNOWN_CHAR='\ue083'):
-        if 'list' in str(type(WORD_NAME)):
+
+class TOKENIZER:
+    def __init__(self, WORD_NAME, UNKNOWN_CHAR="\ue083"):
+        if "list" in str(type(WORD_NAME)):
             self.charMode = False
             if WORD_NAME[0] == WORD_NAME[1]:
                 from transformers import PreTrainedTokenizerFast
+
                 self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=WORD_NAME[0])
             else:
                 from transformers import GPT2TokenizerFast
+
                 self.tokenizer = GPT2TokenizerFast(WORD_NAME[0], WORD_NAME[1])
             self.vocab_size = len(self.tokenizer)
         else:
             self.charMode = True
-            with open(WORD_NAME + '.json', "r", encoding="utf-16") as result_file:
+            with open(WORD_NAME + ".json", "r", encoding="utf-16") as result_file:
                 self.word_table = json.load(result_file)
 
             self.vocab_size = len(self.word_table)
@@ -37,23 +41,25 @@ class TOKENIZER():
             self.UNKNOWN_CHAR = self.stoi[UNKNOWN_CHAR]
 
     def refine_context(self, context):
-        context = context.strip().split('\n')
+        context = context.strip().split("\n")
         for c in range(len(context)):
-            context[c] = context[c].strip().strip('\u3000').strip('\r')
-        context = list(filter(lambda c: c != '', context))
-        context = '\n' + ('\n'.join(context)).strip()
-        if context == '':
-            context = '\n'
+            context[c] = context[c].strip().strip("\u3000").strip("\r")
+        context = list(filter(lambda c: c != "", context))
+        context = "\n" + ("\n".join(context)).strip()
+        if context == "":
+            context = "\n"
         return context
 
-    def sample_logits(self, out, x, ctx_len, temperature=1.0, top_p_usual=None, top_p_newline=None):
+    def sample_logits(
+        self, out, x, ctx_len, temperature=1.0, top_p_usual=None, top_p_newline=None
+    ):
         # out[self.UNKNOWN_CHAR] = -float('Inf')
         lastChar = int(x[-1])
 
         probs = F.softmax(out, dim=-1)
 
         if self.charMode:
-            if self.itos[lastChar] == '\n':
+            if self.itos[lastChar] == "\n":
                 top_p = top_p_newline
             else:
                 top_p = top_p_usual
@@ -81,6 +87,7 @@ class TOKENIZER():
             out = torch.multinomial(probs, num_samples=1)[0]
             return out
 
+
 def MaybeIsPrime(number):
     if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number):
         return True
@@ -121,7 +128,9 @@ def MillerRabinPrimalityTest(number):
         if (randomNumberWithPower != 1) and (randomNumberWithPower != number - 1):
             iterationNumber = 1
 
-            while (iterationNumber <= timesTwoDividNumber - 1) and (randomNumberWithPower != number - 1):
+            while (iterationNumber <= timesTwoDividNumber - 1) and (
+                randomNumberWithPower != number - 1
+            ):
                 randomNumberWithPower = pow(randomNumberWithPower, 2, number)
                 iterationNumber = iterationNumber + 1
             if randomNumberWithPower != (number - 1):

finetune/lora/v4/train.py

@@ -184,7 +184,7 @@ if __name__ == "__main__":
     args.num_sanity_val_steps = 0
     args.check_val_every_n_epoch = int(1e20)
     args.log_every_n_steps = int(1e20)
-    args.max_epochs = args.epoch_count  # continue forever
+    args.max_epochs = args.epoch_count  # -1 continue forever
     args.betas = (args.beta1, args.beta2)
     args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
     os.environ["RWKV_T_MAX"] = str(args.ctx_len)
@@ -373,7 +373,7 @@ if __name__ == "__main__":
                     for param in module.parameters():
                         param.requires_grad = True
                 elif enable_time_finetune and any(
-                        n.startswith("time") for n, _ in module.named_parameters()
+                    n.startswith("time") for n, _ in module.named_parameters()
                 ):
                     for pname, param in module.named_parameters():
                         if pname.startswith("time"):
@@ -381,7 +381,7 @@ if __name__ == "__main__":
                             param.requires_grad = True
 
     if (
-            len(args.load_model) == 0 or args.my_pile_stage == 1
+        len(args.load_model) == 0 or args.my_pile_stage == 1
     ):  # shall we build the initial weights?
         init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
         generate_init_weight(model, init_weight_name)  # save initial weights
@@ -423,8 +423,8 @@ if __name__ == "__main__":
     )
 
     if (
-            args.lr_init > 1e-4
-            or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8
+        args.lr_init > 1e-4
+        or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8
     ):
         if "I_KNOW_WHAT_IM_DOING" in os.environ:
             if trainer.global_rank == 0:
@@ -459,10 +459,10 @@ if __name__ == "__main__":
 
     if "deepspeed" in args.strategy:
         trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
-                args.ds_bucket_mb * 1000 * 1000
+            args.ds_bucket_mb * 1000 * 1000
         )
         trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
-                args.ds_bucket_mb * 1000 * 1000
+            args.ds_bucket_mb * 1000 * 1000
         )
 
     # must set shuffle=False, persistent_workers=False (because worker is in another thread)

finetune/lora/v5/cuda/wkv5_cuda.cu

@@ -0,0 +1,202 @@
+#include <stdio.h>
+#include <assert.h>
+#include "ATen/ATen.h"
+typedef at::BFloat16 bf16;
+
+template <typename F>
+__global__ void kernel_forward(const int B, const int T, const int C, const int H,
+                               const F *__restrict__ const _r, const F *__restrict__ const _k, const F *__restrict__ const _v, const float *__restrict__ _w, const F *__restrict__ _u,
+                               F *__restrict__ const _y)
+{
+    const int b = blockIdx.x / H;
+    const int h = blockIdx.x % H;
+    const int i = threadIdx.x;
+    _w += h*_N_;
+    _u += h*_N_;
+
+    __shared__ float r[_N_], k[_N_], u[_N_], w[_N_];
+    float state[_N_] = {0};
+
+    __syncthreads();
+    w[i] = _w[i];
+    u[i] = float(_u[i]);
+    __syncthreads();
+
+    for (int t = b*T*C + h*_N_ + i; t < (b+1)*T*C + h*_N_ + i; t += C)
+    {
+        __syncthreads();
+        r[i] = float(_r[t]);
+        k[i] = float(_k[t]);
+        __syncthreads();
+
+        const float v = float(_v[t]);
+        float y = 0;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j+=4)
+        {
+            const float4& r_ = (float4&)(r[j]);
+            const float4& k_ = (float4&)(k[j]);
+            const float4& w_ = (float4&)(w[j]);
+            const float4& u_ = (float4&)(u[j]);
+            float4& s = (float4&)(state[j]);
+            float4 x;
+
+            x.x = k_.x * v;
+            x.y = k_.y * v;
+            x.z = k_.z * v;
+            x.w = k_.w * v;
+
+            y += r_.x * (u_.x * x.x + s.x);
+            y += r_.y * (u_.y * x.y + s.y);
+            y += r_.z * (u_.z * x.z + s.z);
+            y += r_.w * (u_.w * x.w + s.w);
+
+            s.x = s.x * w_.x + x.x;
+            s.y = s.y * w_.y + x.y;
+            s.z = s.z * w_.z + x.z;
+            s.w = s.w * w_.w + x.w;
+        }
+        _y[t] = F(y);
+    }
+}
+
+template <typename F>
+__global__ void kernel_backward(const int B, const int T, const int C, const int H,
+    const F *__restrict__ const _r, const F *__restrict__ const _k, const F *__restrict__ const _v, const float *__restrict__ _w, const float *__restrict__ __w, const F *__restrict__ _u, const F *__restrict__ const _gy,
+    F *__restrict__ const _gr, F *__restrict__ const _gk, F *__restrict__ const _gv, F *__restrict__ const _gw, F *__restrict__ const _gu)
+{
+    const int b = blockIdx.x / H;
+    const int h = blockIdx.x % H;
+    const int i = threadIdx.x;
+    _w += h*_N_;
+    _u += h*_N_;
+    __w += h*_N_;
+
+    __shared__ float w_[_N_], u_[_N_];
+    __shared__ float r[_N_], k[_N_], v[_N_], gy[_N_];
+    __syncthreads();
+    w_[i] = _w[i];
+    u_[i] = float(_u[i]);
+    __syncthreads();
+
+    const float w = w_[i];
+    const float ww = __w[i];
+    const float u = u_[i];
+
+    float state[_N_] = {0}, saaaa[_N_] = {0}, sbbbb[_N_] = {0}, scccc[_N_] = {0}, sdddd[_N_] = {0};
+
+    float gw = 0, gu = 0;
+    const int t000 = b*T*C + h*_N_ + i;
+    const int t111 = (b+1)*T*C + h*_N_ + i;
+    const int t222 = t111 - 2*C;
+
+    for (int t = t000; t < t111; t += C)
+    {
+        __syncthreads();
+        v[i] = float(_v[t]);
+        gy[i] = float(_gy[t]);
+        __syncthreads();
+
+        const float k = float(_k[t]);
+        float gr = 0, gu_ = 0;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = state[j];
+            float x = k * v[j];
+
+            gr += (u * x + s) * gy[j];
+            gu_ += x * gy[j];
+            s = s * w + x;
+        }
+        _gr[t] = F(gr);
+        gu += float(_r[t]) * gu_;
+    }
+    _gu[b*C + h*_N_ + i] = F(gu);
+    
+    for (int t = t000; t < t222; t += C)
+    {
+        __syncthreads();
+        v[i] = float(_v[t]);
+        gy[i] = float(_gy[t + 2*C]);
+        __syncthreads();
+
+        const float k = float(_k[t]);
+        float gw_ = 0;
+        
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = saaaa[j];
+            float& s2 = sbbbb[j];
+            float x = k * v[j];
+            
+            float tmp = w * (x + s);
+            s = tmp;
+            s2 = tmp + w * s2;
+            gw_ += s2 * gy[j];
+        }
+        gw += float(_r[t + 2*C]) * gw_;
+    }    
+    _gw[b*C + h*_N_ + i] = F(ww * gw);
+
+    for (int t = t111 - C; t >= t000; t -= C)
+    {
+        __syncthreads();
+        v[i] = float(_v[t]);
+        gy[i] = float(_gy[t]);
+        __syncthreads();
+
+        const float rr = float(_r[t]);
+        float gk = 0;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = scccc[j];
+            float x = rr * gy[j];
+            
+            gk += (u * x + s) * v[j];
+            s = x + s * w;
+        }
+        _gk[t] = F(gk);
+    }
+
+    for (int t = t111 - C; t >= t000; t -= C)
+    {
+        __syncthreads();
+        r[i] = float(_r[t]);
+        k[i] = float(_k[t]);
+        __syncthreads();
+
+        const float gyy = float(_gy[t]);
+        float gv = 0;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = sdddd[j];
+            float x = gyy * r[j];
+            
+            gv += (u_[j] * x + s) * k[j];
+            s = x + s * w_[j];
+        }
+        _gv[t] = F(gv);
+    }
+}
+
+void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y)
+{
+    assert(H*_N_ == C);
+    assert(_N_%4 == 0);
+    kernel_forward<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, u, y);
+}
+
+void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, float *ww, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu)
+{
+    assert(H*_N_ == C);
+    assert(_N_%4 == 0);
+    kernel_backward<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, ww, u, gy, gr, gk, gv, gw, gu);
+}

finetune/lora/v5/cuda/wkv5_op.cpp

@@ -0,0 +1,22 @@
+#include <torch/extension.h>
+#include "ATen/ATen.h"
+typedef at::BFloat16 bf16;
+
+void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y);
+void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, float *ww, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu);
+
+void forward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &u, torch::Tensor &y) {
+    cuda_forward(B, T, C, H, r.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), w.data_ptr<float>(), u.data_ptr<bf16>(), y.data_ptr<bf16>());
+}
+void backward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &ww, torch::Tensor &u, torch::Tensor &gy, torch::Tensor &gr, torch::Tensor &gk, torch::Tensor &gv, torch::Tensor &gw, torch::Tensor &gu) {
+    cuda_backward(B, T, C, H, r.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), w.data_ptr<float>(), ww.data_ptr<float>(), u.data_ptr<bf16>(), gy.data_ptr<bf16>(), gr.data_ptr<bf16>(), gk.data_ptr<bf16>(), gv.data_ptr<bf16>(), gw.data_ptr<bf16>(), gu.data_ptr<bf16>());
+}
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &forward, "wkv5 forward");
+    m.def("backward", &backward, "wkv5 backward");
+}
+
+TORCH_LIBRARY(wkv5, m) {
+    m.def("forward", forward);
+    m.def("backward", backward);
+}

finetune/lora/v5/src/binidx.py

@@ -0,0 +1,303 @@
+from lib2to3.pgen2 import token
+import os
+import torch
+import numpy as np
+import shutil
+import struct
+from functools import lru_cache
+from itertools import accumulate
+
+
+def print_rank_0(*message):
+    pass
+    # """If distributed is initialized print only on rank 0."""
+    # if torch.distributed.is_initialized():
+    #     if torch.distributed.get_rank() == 0:
+    #         print(*message, flush=True)
+    # else:
+    #     print(*message, flush=True)
+
+
+def _warmup_mmap_file(path):
+    pass
+    # with open(path, "rb") as stream:
+    #     while stream.read(100 * 1024 * 1024):
+    #         pass
+
+
+dtypes = {
+    1: np.uint8,
+    2: np.int8,
+    3: np.int16,
+    4: np.int32,
+    5: np.int64,
+    6: float,
+    7: np.double,
+    8: np.uint16,
+}
+
+
+def code(dtype):
+    for k in dtypes.keys():
+        if dtypes[k] == dtype:
+            return k
+    raise ValueError(dtype)
+
+
+def index_file_path(prefix_path):
+    return prefix_path + ".idx"
+
+
+def data_file_path(prefix_path):
+    return prefix_path + ".bin"
+
+
+class MMapIndexedDataset(torch.utils.data.Dataset):
+    class Index(object):
+        _HDR_MAGIC = b"MMIDIDX\x00\x00"
+
+        @classmethod
+        def writer(cls, path, dtype):
+            class _Writer(object):
+                def __enter__(self):
+                    self._file = open(path, "wb")
+
+                    # Write Magic string so we can check the file format then opening it again.
+                    self._file.write(cls._HDR_MAGIC)
+                    # Write version number
+                    # Little endian unsigned 64 Bit integer
+                    self._file.write(struct.pack("<Q", 1))
+                    # Little endian unsigned 8 Bit integer
+                    self._file.write(struct.pack("<B", code(dtype)))
+
+                    return self
+
+                @staticmethod
+                def _get_pointers(sizes):
+                    dtype_size = dtype().itemsize
+                    address = 0
+                    pointers = []
+
+                    for size in sizes:
+                        pointers.append(address)
+                        address += size * dtype_size
+
+                    return pointers
+
+                def write(self, sizes, doc_idx):
+                    pointers = self._get_pointers(sizes)
+
+                    # Little endian unsigned 64 Bit integer
+                    self._file.write(struct.pack("<Q", len(sizes)))
+                    # Little endian unsigned 64 Bit integer
+                    self._file.write(struct.pack("<Q", len(doc_idx)))
+
+                    sizes = np.array(sizes, dtype=np.int32)
+                    self._file.write(sizes.tobytes(order="C"))
+                    del sizes
+
+                    pointers = np.array(pointers, dtype=np.int64)
+                    self._file.write(pointers.tobytes(order="C"))
+                    del pointers
+
+                    doc_idx = np.array(doc_idx, dtype=np.int64)
+                    self._file.write(doc_idx.tobytes(order="C"))
+
+                def __exit__(self, exc_type, exc_val, exc_tb):
+                    self._file.close()
+
+            return _Writer()
+
+        def __init__(self, path, skip_warmup=False):
+            with open(path, "rb") as stream:
+                magic_test = stream.read(9)
+                assert self._HDR_MAGIC == magic_test, (
+                    "Index file doesn't match expected format. "
+                    "Make sure that --dataset-impl is configured properly."
+                )
+                # Little endian unsigned 64 Bit integer
+                version = struct.unpack("<Q", stream.read(8))
+                assert (1,) == version
+
+                # Little endian unsigned 8 Bit integer
+                (dtype_code,) = struct.unpack("<B", stream.read(1))
+                self._dtype = dtypes[dtype_code]
+                self._dtype_size = self._dtype().itemsize
+
+                self._len = struct.unpack("<Q", stream.read(8))[0]
+                self._doc_count = struct.unpack("<Q", stream.read(8))[0]
+                offset = stream.tell()
+
+            if not skip_warmup:
+                print_rank_0("    warming up index mmap file...")
+                _warmup_mmap_file(path)
+
+            self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
+            self._bin_buffer = memoryview(self._bin_buffer_mmap)
+            print_rank_0("    reading sizes...")
+            self._sizes = np.frombuffer(
+                self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
+            )
+            print_rank_0("    reading pointers...")
+            self._pointers = np.frombuffer(
+                self._bin_buffer,
+                dtype=np.int64,
+                count=self._len,
+                offset=offset + self._sizes.nbytes,
+            )
+            print_rank_0("    reading document index...")
+            self._doc_idx = np.frombuffer(
+                self._bin_buffer,
+                dtype=np.int64,
+                count=self._doc_count,
+                offset=offset + self._sizes.nbytes + self._pointers.nbytes,
+            )
+
+        def __del__(self):
+            self._bin_buffer_mmap._mmap.close()
+            del self._bin_buffer_mmap
+
+        @property
+        def dtype(self):
+            return self._dtype
+
+        @property
+        def sizes(self):
+            return self._sizes
+
+        @property
+        def doc_idx(self):
+            return self._doc_idx
+
+        @lru_cache(maxsize=8)
+        def __getitem__(self, i):
+            return self._pointers[i], self._sizes[i]
+
+        def __len__(self):
+            return self._len
+
+    def __init__(self, path, skip_warmup=False):
+        super().__init__()
+
+        self._path = None
+        self._index = None
+        self._bin_buffer = None
+
+        self._do_init(path, skip_warmup)
+
+    def __getstate__(self):
+        return self._path
+
+    def __setstate__(self, state):
+        self._do_init(state)
+
+    def _do_init(self, path, skip_warmup):
+        self._path = path
+        self._index = self.Index(index_file_path(self._path), skip_warmup)
+
+        if not skip_warmup:
+            print_rank_0("    warming up data mmap file...")
+            _warmup_mmap_file(data_file_path(self._path))
+        print_rank_0("    creating numpy buffer of mmap...")
+        self._bin_buffer_mmap = np.memmap(
+            data_file_path(self._path), mode="r", order="C"
+        )
+        print_rank_0("    creating memory view of numpy buffer...")
+        self._bin_buffer = memoryview(self._bin_buffer_mmap)
+
+    def __del__(self):
+        self._bin_buffer_mmap._mmap.close()
+        del self._bin_buffer_mmap
+        del self._index
+
+    def __len__(self):
+        return len(self._index)
+
+    # @lru_cache(maxsize=8)
+    def __getitem__(self, idx):
+        if isinstance(idx, int):
+            ptr, size = self._index[idx]
+            np_array = np.frombuffer(
+                self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
+            )
+            return np_array
+        elif isinstance(idx, slice):
+            start, stop, step = idx.indices(len(self))
+            if step != 1:
+                raise ValueError("Slices into indexed_dataset must be contiguous")
+            ptr = self._index._pointers[start]
+            sizes = self._index._sizes[idx]
+            offsets = list(accumulate(sizes))
+            total_size = sum(sizes)
+            np_array = np.frombuffer(
+                self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr
+            )
+            sents = np.split(np_array, offsets[:-1])
+            return sents
+
+    def get(self, idx, offset=0, length=None):
+        """Retrieves a single item from the dataset with the option to only
+        return a portion of the item.
+
+        get(idx) is the same as [idx] but get() does not support slicing.
+        """
+        ptr, size = self._index[idx]
+        if length is None:
+            length = size - offset
+        ptr += offset * np.dtype(self._index.dtype).itemsize
+        np_array = np.frombuffer(
+            self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
+        )
+        return np_array
+
+    def pad(self, idx, length=None):
+        ptr, size = self._index[idx]
+        try:
+            np_array = np.frombuffer(
+                self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
+            )
+        except:
+            np_array = np.frombuffer(
+                self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
+            )
+            ptr0, _ = self._index[0]
+            np_array0 = np.frombuffer(
+                self._bin_buffer,
+                dtype=self._index.dtype,
+                count=length - size,
+                offset=ptr0,
+            )
+            np_array = np.append(np_array, np_array0)
+        return np_array
+
+    def only(self, idx):
+        ptr, size = self._index[idx]
+        np_array = np.frombuffer(
+            self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
+        )
+
+        return np_array
+
+    @property
+    def sizes(self):
+        return self._index.sizes
+
+    @property
+    def doc_idx(self):
+        return self._index.doc_idx
+
+    def get_doc_idx(self):
+        return self._index._doc_idx
+
+    def set_doc_idx(self, doc_idx_):
+        self._index._doc_idx = doc_idx_
+
+    @property
+    def supports_prefetch(self):
+        return False
+
+    @staticmethod
+    def exists(path):
+        return os.path.exists(index_file_path(path)) and os.path.exists(
+            data_file_path(path)
+        )

finetune/lora/v5/src/dataset.py

@@ -0,0 +1,241 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+import json, math, random, os, sys
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from pytorch_lightning.utilities import rank_zero_info
+from .binidx import MMapIndexedDataset
+from .utils import MaybeIsPrime
+
+
+class MyDataset(Dataset):
+    def __init__(self, args):
+        self.args = args
+
+        if args.data_type == "binidx":
+            self.vocab_size = args.vocab_size
+            rank_zero_info(
+                f"Current vocab size = {self.vocab_size} (make sure it's correct)"
+            )
+
+            if args.my_pile_version == 1:
+                self.data = MMapIndexedDataset(args.data_file)
+                self.data_size = (
+                    len(self.data._bin_buffer) // self.data._index._dtype_size
+                )
+                rank_zero_info(f"Data has {self.data_size} tokens.")
+            elif args.my_pile_version == 2:
+                data_list = (
+                    open(args.data_file, "r", encoding="utf-8")
+                    .read()
+                    .strip()
+                    .split("\n")
+                )
+                data_list = [i.strip().split(" ") for i in data_list]
+                self.data = []
+                self.data_size = int(data_list[-1][-1])
+                rank_zero_info(f"Data has {self.data_size} chunks.")
+                for d in data_list:
+                    data = MMapIndexedDataset(d[0])
+                    data_size = len(data._bin_buffer) // data._index._dtype_size
+                    assert (data_size - args.ctx_len) == int(d[1])
+                    self.data += [[int(d[-1]), int(d[1]), data]]
+                # rank_zero_info(self.data)
+
+            if args.my_qa_mask > 0:
+                # self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
+                self.data_pile = MMapIndexedDataset(
+                    "/fsx/pile_deduped/pile_0.87_deduped_text_document"
+                )
+                self.data_pile_size = (
+                    len(self.data_pile._bin_buffer) // self.data._index._dtype_size
+                )
+            else:
+                self.data_pile = None
+                self.data_pile_size = 0
+
+            if args.my_pile_stage > 0:
+                # assert self.data_size == 332115325534 and self.vocab_size == 50277
+                self.samples_per_epoch = args.epoch_steps * args.real_bsz
+                assert self.samples_per_epoch == 40320
+                rank_zero_info(
+                    f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########"
+                )
+                dataset_slot = self.data_size // args.ctx_len
+                if args.my_pile_stage != 4:
+                    assert MaybeIsPrime(args.magic_prime)
+                    assert args.magic_prime % 3 == 2
+                    assert (
+                        args.magic_prime / dataset_slot > 0.99
+                        and args.magic_prime / dataset_slot <= 1
+                    )
+        elif args.data_type == "numpy":
+            self.data = np.load(args.data_file).astype("int")
+            self.vocab_size = args.vocab_size
+            rank_zero_info(
+                f"Current vocab size = {self.vocab_size} (make sure it's correct)"
+            )
+            self.data_size = len(self.data)
+            rank_zero_info(f"Data has {self.data_size} tokens.")
+        elif args.data_type == "uint16":
+            self.data = (
+                np.fromfile(args.data_file, dtype=np.uint16)
+                .astype("int32")
+                .reshape(-1, args.my_sample_len)
+            )
+            self.vocab_size = args.vocab_size
+            rank_zero_info(
+                f"Current vocab size = {self.vocab_size} (make sure it's correct)"
+            )
+            self.data_size = self.data.shape[0]
+            rank_zero_info(f"Data has {self.data_size} samples.")
+        else:
+            if args.data_type == "dummy":
+                rank_zero_info("Building dummy data...")
+                self.data = ""
+                for i in range(100000):
+                    aa = (i) % 10000
+                    bb = (i * i) % 10000
+                    cc = aa + bb
+                    self.data += f".{aa}+{bb}={cc}."
+            else:
+                self.data = open(args.data_file, "r", encoding=args.data_type).read()
+            rank_zero_info("Building token list...")
+            unique = sorted(list(set(self.data)))
+            self.vocab_size = len(unique)
+            # rank_zero_info()
+            # for u in unique:
+            #     print(u, end=' ')
+            # rank_zero_info('\n\n')
+            xx = 0
+            xxObj = {}
+            for u in unique:
+                xxObj[xx] = u
+                xx += 1
+            with open(
+                f"{args.proj_dir}/vocab.json", "w", encoding="utf-8"
+            ) as vocab_file:
+                vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
+            self.data_size = len(self.data)
+            rank_zero_info(
+                f"Data has {self.data_size} tokens, {self.vocab_size} vocab size."
+            )
+            self.stoi = {ch: i for i, ch in enumerate(unique)}
+            self.itos = {i: ch for i, ch in enumerate(unique)}
+
+    def __len__(self):
+        return self.args.epoch_steps * self.args.micro_bsz
+
+    def __getitem__(self, idx):
+        args = self.args
+        rank = self.global_rank
+        epoch = self.real_epoch
+        world_size = self.world_size
+        # print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}")
+
+        if args.data_type == "uint16":
+            i = np.random.randint(0, self.data_size - 1)
+            dix = self.data[i]
+            x = torch.tensor(dix[:-1], dtype=torch.long)
+            y = torch.tensor(dix[1:], dtype=torch.long)
+        else:
+            ctx_len = args.ctx_len
+            req_len = ctx_len + 1
+            magic_prime = args.magic_prime
+            data = self.data
+
+            if args.my_pile_stage > 0:
+                ii = 1 + epoch * self.samples_per_epoch + (idx * world_size) + rank
+
+                if args.my_qa_mask > 0:
+                    ii_orig = ii
+                    if ii % 2 == 0:
+                        ii = -1
+                        data = self.data_pile
+                    else:
+                        ii = ii // 2
+                if data == self.data_pile:
+                    i = np.random.randint(0, self.data_pile_size - req_len)
+                else:
+                    if args.my_pile_stage == 4 or ii < args.my_random_steps:
+                        # cheat: pick a random spot in dataset
+                        if args.my_pile_version == 1:
+                            i = np.random.randint(0, self.data_size - req_len)
+                        else:
+                            i = np.random.randint(0, self.data_size)
+                    else:
+                        ii = ii - args.my_random_steps
+                        factor = (math.sqrt(5) - 1) / 2
+                        factor = int(magic_prime * factor)
+                        i = ((factor * ii * ii * ii) % magic_prime) * ctx_len
+                        i = i + args.my_pile_shift
+                # print(f"epoch {epoch} idx {idx} rank {rank}/{world_size} ii {ii} pos {round(i / self.data_size, 3)}")
+            else:
+                # cheat: pick a random spot in dataset
+                i = np.random.randint(0, self.data_size - req_len)
+
+            if args.data_type == "binidx":
+                if args.my_pile_version == 1:
+                    dix = data.get(idx=0, offset=i, length=req_len).astype(int)
+                else:
+                    # self.data : cutoff, chunk_count, data
+                    for j in range(len(data)):
+                        if i < data[j][0]:
+                            ii = i
+                            i = (i - (data[j - 1][0] if j > 0 else 0)) % data[j][1]
+                            dix = (
+                                data[j][2]
+                                .get(idx=0, offset=i, length=req_len)
+                                .astype(int)
+                            )
+                            # print(ii, j, i)
+                            break
+            elif args.data_type == "numpy":
+                dix = data[i : i + req_len]
+            else:
+                dix = [self.stoi[s] for s in data[i : i + req_len]]
+
+            if args.my_qa_mask == 1:
+                if data == self.data_pile:
+                    z = [1] * ctx_len
+                else:
+                    z = [0] * ctx_len
+                    z_sum = 0
+                    isGood = False
+                    for i in range(3, ctx_len):
+                        if (
+                            dix[i] == 27
+                            and dix[i - 1] == 34
+                            and dix[i - 2] == 187
+                            and dix[i - 3] == 187
+                        ):
+                            isGood = True
+                        if dix[i] == 0:
+                            isGood = False
+                        if isGood:
+                            z[i] = 1
+                            z_sum += 1
+                    if z_sum == 0:
+                        z = [1] * ctx_len
+                        i = np.random.randint(0, self.data_pile_size - req_len)
+                        dix = self.data_pile.get(
+                            idx=0, offset=i, length=req_len
+                        ).astype(int)
+                z = torch.tensor(z, dtype=torch.bfloat16)
+
+            x = torch.tensor(dix[:-1], dtype=torch.long)
+            y = torch.tensor(dix[1:], dtype=torch.long)
+
+            # if ii_orig < 50:
+            #     # if rank == 1:
+            #     print('rank', rank, 'i', ii_orig, ii, i, 'x', x[:5], '...', x[-5:])
+            # else:
+            #     exit(0)
+
+            if args.my_qa_mask == 1:
+                return x, y, z
+
+            return x, y

finetune/lora/v5/src/model.py

@@ -0,0 +1,819 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+import functools
+import os, math, gc, importlib
+import torch
+
+# torch._C._jit_set_profiling_executor(True)
+# torch._C._jit_set_profiling_mode(True)
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint as torch_checkpoint
+from torch.nn import functional as F
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
+from pytorch_lightning.strategies import DeepSpeedStrategy
+
+if importlib.util.find_spec("deepspeed"):
+    import deepspeed
+    from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
+
+
+# from deepspeed.runtime.fp16.onebit.zoadam import ZeroOneAdam
+
+# lora-config
+LORA_CONFIG = {
+    "r": 0,
+    "alpha": 0,
+    "dropout": 0,
+    "parts": {"att", "ln", "time"},
+}
+
+try:
+    print("RWKV_MY_TESTING", os.environ["RWKV_MY_TESTING"])
+except:
+    os.environ["RWKV_MY_TESTING"] = ""
+
+
+def __nop(ob):
+    return ob
+
+
+MyModule = nn.Module
+MyFunction = __nop
+if os.environ["RWKV_JIT_ON"] == "1":
+    MyModule = torch.jit.ScriptModule
+    MyFunction = torch.jit.script_method
+
+
+########################################################################################################
+# CUDA Kernel
+########################################################################################################
+
+from torch.utils.cpp_extension import load
+
+HEAD_SIZE = int(os.environ["RWKV_HEAD_SIZE_A"])
+wkv5_cuda = load(
+    name="wkv5",
+    sources=[
+        "finetune/lora/v5/cuda/wkv5_op.cpp",
+        f"finetune/lora/v5/cuda/wkv5_cuda.cu",
+    ],
+    verbose=True,
+    extra_cuda_cflags=[
+        "-res-usage",
+        "--use_fast_math",
+        "-O3",
+        "-Xptxas -O3",
+        "--extra-device-vectorization",
+        f"-D_N_={HEAD_SIZE}",
+    ],
+)
+
+
+class WKV_5(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, B, T, C, H, r, k, v, w, u):
+        with torch.no_grad():
+            assert r.dtype == torch.bfloat16
+            assert k.dtype == torch.bfloat16
+            assert v.dtype == torch.bfloat16
+            assert w.dtype == torch.bfloat16
+            assert u.dtype == torch.bfloat16
+            assert HEAD_SIZE == C // H
+            ctx.B = B
+            ctx.T = T
+            ctx.C = C
+            ctx.H = H
+            assert r.is_contiguous()
+            assert k.is_contiguous()
+            assert v.is_contiguous()
+            assert w.is_contiguous()
+            assert u.is_contiguous()
+            ew = (-torch.exp(w.float())).contiguous()
+            eew = (torch.exp(ew)).contiguous()
+            ctx.save_for_backward(r, k, v, eew, ew, u)
+            y = torch.empty(
+                (B, T, C),
+                device=r.device,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            wkv5_cuda.forward(B, T, C, H, r, k, v, eew, u, y)
+            return y
+
+    @staticmethod
+    def backward(ctx, gy):
+        with torch.no_grad():
+            assert gy.dtype == torch.bfloat16
+            B = ctx.B
+            T = ctx.T
+            C = ctx.C
+            H = ctx.H
+            assert gy.is_contiguous()
+            r, k, v, eew, ew, u = ctx.saved_tensors
+            gr = torch.empty(
+                (B, T, C),
+                device=gy.device,
+                requires_grad=False,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            gk = torch.empty(
+                (B, T, C),
+                device=gy.device,
+                requires_grad=False,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            gv = torch.empty(
+                (B, T, C),
+                device=gy.device,
+                requires_grad=False,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            gw = torch.empty(
+                (B, C),
+                device=gy.device,
+                requires_grad=False,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            gu = torch.empty(
+                (B, C),
+                device=gy.device,
+                requires_grad=False,
+                dtype=torch.bfloat16,
+                memory_format=torch.contiguous_format,
+            )  # .uniform_(-1, 1)
+            wkv5_cuda.backward(B, T, C, H, r, k, v, eew, ew, u, gy, gr, gk, gv, gw, gu)
+            gw = torch.sum(gw, 0).view(H, C // H)
+            gu = torch.sum(gu, 0).view(H, C // H)
+            return (None, None, None, None, gr, gk, gv, gw, gu)
+
+
+def RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w, u):
+    return WKV_5.apply(B, T, C, H, r, k, v, w, u)
+
+
+#################################################################
+class LoraLinear(nn.Module):
+    def __init__(self, in_features: int, out_features: int, bias: bool):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.empty((out_features, in_features)))
+        assert bias == False, "Biased LoraLinear not supported"
+
+        r, alpha, dropout = (
+            LORA_CONFIG["r"],
+            LORA_CONFIG["alpha"],
+            LORA_CONFIG["dropout"],
+        )
+        self.lora_A = nn.Parameter(torch.empty(r, in_features))
+        self.lora_B = nn.Parameter(torch.empty(out_features, r))
+        self.lora_dropout = nn.Dropout(dropout)
+        self.scaling = alpha / r
+
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
+        nn.init.zeros_(self.lora_B)
+
+    def forward(self, x):
+        return F.linear(x, self.weight) + self.scaling * F.linear(
+            F.linear(self.lora_dropout(x), self.lora_A), self.lora_B
+        )
+
+
+@functools.wraps(LoraLinear)
+def make_linear_att(*args, **kwargs):
+    if "att" in LORA_CONFIG["parts"] and LORA_CONFIG["r"] > 0:
+        return LoraLinear(*args, **kwargs)
+    else:
+        return nn.Linear(*args, **kwargs)
+
+
+@functools.wraps(LoraLinear)
+def make_linear_ffn(*args, **kwargs):
+    if "ffn" in LORA_CONFIG["parts"] and LORA_CONFIG["r"] > 0:
+        return LoraLinear(*args, **kwargs)
+    else:
+        return nn.Linear(*args, **kwargs)
+
+
+########################################################################################################
+
+
+class RWKV_TimeMix_RWKV5(MyModule):
+    def __init__(self, args, layer_id):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+
+        self.head_size = args.head_size_a
+        assert HEAD_SIZE == self.head_size  # change HEAD_SIZE to match args.head_size_a
+        self.n_head = args.dim_att // self.head_size
+        assert args.dim_att % self.n_head == 0
+        self.head_size_divisor = args.head_size_divisor
+
+        with torch.no_grad():
+            ratio_0_to_1 = layer_id / (args.n_layer - 1)  # 0 to 1
+            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
+            ddd = torch.ones(1, 1, args.n_embd)
+            for i in range(args.n_embd):
+                ddd[0, 0, i] = i / args.n_embd
+
+            # fancy time_mix
+            self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
+            self.time_mix_v = nn.Parameter(
+                torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
+            )
+            self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
+            self.time_mix_g = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
+
+            # fancy time_decay
+            decay_speed = torch.ones(args.dim_att)
+            for n in range(args.dim_att):
+                decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (
+                    0.7 + 1.3 * ratio_0_to_1
+                )
+            self.time_decay = nn.Parameter(
+                decay_speed.reshape(self.n_head, self.head_size)
+            )
+            # print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())
+
+            tmp = torch.zeros(args.dim_att)
+            for n in range(args.dim_att):
+                zigzag = ((n + 1) % 3 - 1) * 0.1
+                tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag
+
+            self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))
+
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        self.receptance = make_linear_att(args.n_embd, args.dim_att, bias=False)
+        self.key = make_linear_att(args.n_embd, args.dim_att, bias=False)
+        self.value = make_linear_att(args.n_embd, args.dim_att, bias=False)
+
+        self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
+        self.gate = make_linear_att(args.n_embd, args.dim_att, bias=False)
+        self.ln_x = nn.GroupNorm(self.n_head, args.dim_att)
+
+    @MyFunction
+    def jit_func(self, x):
+        B, T, C = x.size()
+
+        xx = self.time_shift(
+            x
+        )  # Mix x with the previous timestep to produce xk, xv, xr
+        xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
+        xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
+        xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
+        xg = x * self.time_mix_g + xx * (1 - self.time_mix_g)
+
+        r = self.receptance(xr)
+        k = self.key(xk)
+        v = self.value(xv)
+        g = F.silu(self.gate(xg))
+
+        return r, k, v, g
+
+    @MyFunction
+    def jit_func_2(self, x, g):
+        B, T, C = x.size()
+        x = x.view(B * T, C)
+        x = self.ln_x(x / self.head_size_divisor).view(B, T, C)
+        x = self.output(x * g)
+        return x
+
+    def forward(self, x):
+        B, T, C = x.size()
+        H = self.n_head
+        r, k, v, g = self.jit_func(x)
+        x = RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w=self.time_decay, u=self.time_faaaa)
+
+        return self.jit_func_2(x, g)
+
+
+########################################################################################################
+
+
+class RWKV_ChannelMix(MyModule):
+    def __init__(self, args, layer_id):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        with torch.no_grad():  # fancy init of time_mix
+            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)  # 1 to ~0
+            ddd = torch.ones(1, 1, args.n_embd)
+            for i in range(args.n_embd):
+                ddd[0, 0, i] = i / args.n_embd
+            self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
+            self.time_mix_r = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
+
+        self.key = make_linear_ffn(args.n_embd, args.dim_ffn, bias=False)
+        self.receptance = make_linear_ffn(args.n_embd, args.n_embd, bias=False)
+        self.value = make_linear_ffn(args.dim_ffn, args.n_embd, bias=False)
+
+    @MyFunction
+    def forward(self, x):
+        xx = self.time_shift(x)
+        xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
+        xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
+        k = self.key(xk)
+        k = torch.relu(k) ** 2
+        kv = self.value(k)
+        return torch.sigmoid(self.receptance(xr)) * kv
+
+
+class MishGLU(MyModule):
+    def __init__(self, args, layer_id):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
+
+        with torch.no_grad():
+            ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)
+
+            x = torch.ones(1, 1, args.n_embd)
+            for i in range(args.n_embd):
+                x[0, 0, i] = i / args.n_embd
+
+            self.time_mix_k = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
+            self.time_mix_r = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
+            self.aa = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
+            self.bb = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
+            self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
+
+    @MyFunction
+    def forward(self, x):
+        xx = self.time_shift(x)
+        xa = x * self.time_mix_k + xx * (1 - self.time_mix_k)
+        xb = x * self.time_mix_r + xx * (1 - self.time_mix_r)
+        a = self.aa(xa)
+        b = self.bb(xb)
+        return self.value(a * F.mish(b))
+
+
+########################################################################################################
+# The RWKV Model with our blocks
+########################################################################################################
+
+
+class Block(nn.Module):
+    def __init__(self, args, layer_id):
+        super().__init__()
+        self.args = args
+        self.layer_id = layer_id
+
+        self.ln1 = nn.LayerNorm(args.n_embd)
+        self.ln2 = nn.LayerNorm(args.n_embd)
+
+        if self.layer_id == 0:
+            self.ln0 = nn.LayerNorm(args.n_embd)
+            if args.my_pos_emb > 0:
+                self.pos_emb_x = nn.Parameter(
+                    torch.zeros((1, args.my_pos_emb, args.n_embd))
+                )
+                self.pos_emb_y = nn.Parameter(
+                    torch.zeros((args.my_pos_emb, 1, args.n_embd))
+                )
+
+        if self.layer_id == 0 and self.args.pre_ffn > 0:
+            self.ffnPre = RWKV_ChannelMix(args, 0)
+        else:
+            self.att = RWKV_TimeMix_RWKV5(args, layer_id)
+
+        if "g" in os.environ["RWKV_MY_TESTING"]:
+            self.ffn = MishGLU(args, layer_id)
+        else:
+            self.ffn = RWKV_ChannelMix(args, layer_id)
+
+        if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
+            self.tiny_ln = nn.LayerNorm(args.n_embd)
+            self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
+            self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
+            self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False)
+            self.register_buffer(
+                "tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
+            )
+
+        if args.dropout > 0:
+            self.drop0 = nn.Dropout(p=args.dropout)
+            self.drop1 = nn.Dropout(p=args.dropout)
+
+    def forward(self, x, x_emb=None):
+        args = self.args
+        B, T, C = x.size()
+        if self.layer_id == 0:
+            x = self.ln0(x)
+            if args.my_pos_emb > 0:
+                pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T + 1, -1)[:-1, :]
+                x = x + pos_emb
+
+        if self.args.dropout == 0:
+            if self.layer_id == 0 and args.pre_ffn > 0:
+                x = x + self.ffnPre(self.ln1(x))
+            else:
+                x = x + self.att(self.ln1(x))
+            x = x + self.ffn(self.ln2(x))
+        else:
+            if self.layer_id == 0 and args.pre_ffn > 0:
+                x = self.drop0(x + self.ffnPre(self.ln1(x)))
+            else:
+                x = self.drop0(x + self.att(self.ln1(x)))
+            x = self.drop1(x + self.ffn(self.ln2(x)))
+
+        if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
+            xx = self.tiny_ln(x)
+            q = self.tiny_q(xx)[:, :T, :]
+            k = self.tiny_k(xx)[:, :T, :]
+            c = (q @ k.transpose(-2, -1)) * (args.tiny_att_dim ** (-0.5))
+            c = c.masked_fill(self.tiny_mask[:T, :T] == 0, 0)
+            x = x + c @ self.tiny_v(x_emb)
+        return x
+
+
+class L2Wrap(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, loss, y):
+        ctx.save_for_backward(y)
+        return loss
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        y = ctx.saved_tensors[0]
+        # to encourage the logits to be close to 0
+        factor = 1e-4 / (y.shape[0] * y.shape[1])
+        maxx, ids = torch.max(y, -1, keepdim=True)
+        gy = torch.zeros_like(y)
+        gy.scatter_(-1, ids, maxx * factor)
+        return (grad_output, gy)
+
+
+class RWKV(pl.LightningModule):
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+        if not hasattr(args, "dim_att"):
+            args.dim_att = args.n_embd
+        if not hasattr(args, "dim_ffn"):
+            args.dim_ffn = args.n_embd * 4
+        if not hasattr(args, "tiny_att_layer"):
+            args.tiny_att_layer = -1
+        if not hasattr(args, "tiny_att_dim"):
+            args.tiny_att_dim = -1
+        assert args.n_embd % 32 == 0
+        assert args.dim_att % 32 == 0
+        assert args.dim_ffn % 32 == 0
+
+        self.emb = nn.Embedding(args.vocab_size, args.n_embd)
+
+        self.blocks = nn.ModuleList([Block(args, i) for i in range(args.n_layer)])
+
+        self.ln_out = nn.LayerNorm(args.n_embd)
+        self.head = nn.Linear(args.n_embd, args.vocab_size, bias=False)
+
+        if args.head_qk > 0:
+            self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False)
+            self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False)
+            self.register_buffer(
+                "copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
+            )
+        if args.dropout > 0:
+            self.drop0 = nn.Dropout(p=args.dropout)
+
+    def configure_optimizers(self):
+        args = self.args
+
+        lr_decay = set()
+        lr_1x = set()
+        lr_2x = set()
+        lr_3x = set()
+        for n, p in self.named_parameters():
+            if ("time_mix" in n) and (args.layerwise_lr > 0):
+                if args.my_pile_stage == 2:
+                    lr_2x.add(n)
+                else:
+                    lr_1x.add(n)
+            elif ("time_decay" in n) and (args.layerwise_lr > 0):
+                if args.my_pile_stage == 2:
+                    lr_3x.add(n)
+                else:
+                    lr_2x.add(n)
+            elif ("time_faaaa" in n) and (args.layerwise_lr > 0):
+                if args.my_pile_stage == 2:
+                    lr_2x.add(n)
+                else:
+                    lr_1x.add(n)
+            elif ("time_first" in n) and (args.layerwise_lr > 0):
+                lr_3x.add(n)
+            elif (len(p.squeeze().shape) >= 2) and (args.weight_decay > 0):
+                lr_decay.add(n)
+            else:
+                lr_1x.add(n)
+
+        lr_decay = sorted(list(lr_decay))
+        lr_1x = sorted(list(lr_1x))
+        lr_2x = sorted(list(lr_2x))
+        lr_3x = sorted(list(lr_3x))
+        # print('decay', lr_decay)
+        # print('1x', lr_1x)
+        # print('2x', lr_2x)
+        # print('3x', lr_3x)
+        param_dict = {n: p for n, p in self.named_parameters()}
+
+        if args.layerwise_lr > 0:
+            if args.my_pile_stage == 2:
+                optim_groups = [
+                    {
+                        "params": [param_dict[n] for n in lr_1x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 1.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_2x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 5.0,
+                    },  # test: 2e-3 / args.lr_init},
+                    {
+                        "params": [param_dict[n] for n in lr_3x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 5.0,
+                    },  # test: 3e-3 / args.lr_init},
+                ]
+            else:
+                optim_groups = [
+                    {
+                        "params": [param_dict[n] for n in lr_1x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 1.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_2x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 2.0,
+                    },
+                    {
+                        "params": [param_dict[n] for n in lr_3x],
+                        "weight_decay": 0.0,
+                        "my_lr_scale": 3.0,
+                    },
+                ]
+        else:
+            optim_groups = [
+                {
+                    "params": [param_dict[n] for n in lr_1x],
+                    "weight_decay": 0.0,
+                    "my_lr_scale": 1.0,
+                }
+            ]
+
+        if args.weight_decay > 0:
+            optim_groups += [
+                {
+                    "params": [param_dict[n] for n in lr_decay],
+                    "weight_decay": args.weight_decay,
+                    "my_lr_scale": 1.0,
+                }
+            ]
+            if self.deepspeed_offload:
+                return DeepSpeedCPUAdam(
+                    optim_groups,
+                    lr=self.args.lr_init,
+                    betas=self.args.betas,
+                    eps=self.args.adam_eps,
+                    bias_correction=True,
+                    adamw_mode=True,
+                    amsgrad=False,
+                )
+            return FusedAdam(
+                optim_groups,
+                lr=self.args.lr_init,
+                betas=self.args.betas,
+                eps=self.args.adam_eps,
+                bias_correction=True,
+                adam_w_mode=True,
+                amsgrad=False,
+            )
+        else:
+            if self.deepspeed_offload:
+                return DeepSpeedCPUAdam(
+                    optim_groups,
+                    lr=self.args.lr_init,
+                    betas=self.args.betas,
+                    eps=self.args.adam_eps,
+                    bias_correction=True,
+                    adamw_mode=False,
+                    weight_decay=0,
+                    amsgrad=False,
+                )
+            return FusedAdam(
+                optim_groups,
+                lr=self.args.lr_init,
+                betas=self.args.betas,
+                eps=self.args.adam_eps,
+                bias_correction=True,
+                adam_w_mode=False,
+                weight_decay=0,
+                amsgrad=False,
+            )
+        # return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False)
+
+    @property
+    def deepspeed_offload(self) -> bool:
+        strategy = self.trainer.strategy
+        if isinstance(strategy, DeepSpeedStrategy):
+            cfg = strategy.config["zero_optimization"]
+            return cfg.get("offload_optimizer") or cfg.get("offload_param")
+        return False
+
+    def forward(self, idx):
+        args = self.args
+        B, T = idx.size()
+        assert T <= args.ctx_len, "Cannot forward, model ctx_len is exhausted."
+
+        x = self.emb(idx)
+        x_emb = x
+
+        if args.dropout > 0:
+            x = self.drop0(x)
+        if args.tiny_att_dim > 0:
+            for block in self.blocks:
+                if args.grad_cp == 1:
+                    if args.lora:
+                        x = torch_checkpoint(block, x, x_emb, use_reentrant=False)
+                    else:
+                        x = deepspeed.checkpointing.checkpoint(block, x, x_emb)
+                else:
+                    x = block(x, x_emb)
+        else:
+            for block in self.blocks:
+                if args.grad_cp == 1:
+                    if args.lora:
+                        x = torch_checkpoint(block, x, x_emb, use_reentrant=False)
+                    else:
+                        x = deepspeed.checkpointing.checkpoint(block, x)
+                else:
+                    x = block(x)
+
+        x = self.ln_out(x)
+
+        if args.head_qk > 0:
+            q = self.head_q(x)[:, :T, :]
+            k = self.head_k(x)[:, :T, :]
+            c = (q @ k.transpose(-2, -1)) * (1.0 / args.head_qk)
+            c = c.masked_fill(self.copy_mask[:T, :T] == 0, 0)
+
+            if "32" in os.environ["RWKV_FLOAT_MODE"]:
+                c = c @ F.one_hot(idx, num_classes=args.vocab_size)
+            elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
+                c = c @ F.one_hot(idx, num_classes=args.vocab_size).half()
+            elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
+                c = c @ F.one_hot(idx, num_classes=args.vocab_size).bfloat16()
+
+            x = self.head(x) + c
+        else:
+            x = self.head(x)
+
+        return x
+
+    def training_step(self, batch, batch_idx):
+        args = self.args
+        if args.my_qa_mask != 1:
+            idx, targets = batch
+            logits = self(idx)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+            # if '0' in os.environ["RWKV_MY_TESTING"]:
+            #     print('logits', logits)
+            #     torch.set_printoptions(threshold=10000)
+            #     print('idx', idx)
+            #     exit(0)
+        else:
+            idx, targets, mask = batch
+            mask = mask.view(-1)
+            sum_mask = torch.sum(mask).item()
+            # if sum_mask == 0:
+            #     return torch.tensor([0.0], requires_grad=True)
+
+            logits = self(idx)
+            if sum_mask == mask.shape[0]:
+                loss = F.cross_entropy(
+                    logits.view(-1, logits.size(-1)), targets.view(-1)
+                )
+                # print('rank', self.global_rank, 'loss', loss.item())
+            else:
+                loss = F.cross_entropy(
+                    logits.view(-1, logits.size(-1)), targets.view(-1), reduction="none"
+                )
+                # loss_raw = loss
+                loss = torch.sum(loss * mask) / sum_mask
+
+                # torch.set_printoptions(threshold=10000)
+                # if True: #self.global_rank == 1:
+                #     tmp = ''
+                #     sss = 0
+                #     ccc = 0
+                #     for i in range(mask.shape[0]):
+                #         if mask[i] > 0:
+                #             tmp += str(idx.view(-1)[i].item()) + ','
+                #             sss += loss_raw.view(-1)[i].float().item()
+                #             ccc += 1
+                #     print('rank', self.global_rank, 'loss', loss.item(), 'lavg', sss / ccc)#, 'tmp', tmp, 'input', idx)
+        return L2Wrap.apply(loss, logits)
+
+    def training_step_end(self, batch_parts):
+        if pl.__version__[0] != "2":
+            all = self.all_gather(batch_parts)
+            if self.trainer.is_global_zero:
+                self.trainer.my_loss_all = all
+
+    def generate_init_weight(self):
+        print(
+            f"""
+############################################################################
+#
+# Init model weight (slow for large models)...
+#
+############################################################################
+"""
+        )
+        m = {}
+        for n in self.state_dict():
+            p = self.state_dict()[n]
+            shape = p.shape
+
+            gain = 1.0
+            scale = 1.0
+            if (
+                "ln_" in n
+                or ".ln" in n
+                or "time_" in n
+                or "_mask" in n
+                or "pos_emb" in n
+                or ".mask." in n
+            ):
+                if "ln_x.weight" in n:
+                    layer_scale = (1 + int(n.split(".")[1])) / self.args.n_layer
+                    m[n] = (p * 0.0) + (layer_scale**0.7)
+                else:
+                    m[n] = p
+            else:
+                if n == "emb.weight":
+                    scale = -1 * self.args.lr_init
+                else:
+                    if shape[0] > shape[1]:
+                        gain = math.sqrt(shape[0] / shape[1])
+
+                    zero = [
+                        ".att.output.",
+                        ".ffn.value.",
+                        ".ffn.receptance.",
+                        ".ffnPre.value.",
+                        ".ffnPre.receptance.",
+                        "head_q.",
+                        ".oo.",
+                        ".rr.",
+                    ]
+
+                    for kk in zero:
+                        if kk in n:
+                            scale = 0
+                    if n == "head.weight":
+                        scale = 0.5
+                    if "head_k." in n:
+                        scale = 0.1
+                    if "head_q." in n:
+                        scale = 0
+
+                print(
+                    f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}"
+                )
+
+                if self.args.accelerator.upper() == "GPU":
+                    m[n] = torch.empty((shape[0], shape[1]), device="cuda")
+                else:
+                    m[n] = torch.empty((shape[0], shape[1]))
+
+                if scale == 0:
+                    nn.init.zeros_(m[n])
+                elif scale < 0:
+                    nn.init.uniform_(m[n], a=scale, b=-scale)
+                else:
+                    nn.init.orthogonal_(m[n], gain=gain * scale)
+
+            m[n] = m[n].cpu()
+            if os.environ["RWKV_FLOAT_MODE"] == "fp16":
+                m[n] = m[n].half()
+            elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
+                m[n] = m[n].bfloat16()
+
+            # if n == "emb.weight":
+            #     print(m[n])
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        return m

finetune/lora/v5/src/trainer.py

@@ -0,0 +1,310 @@
+import os, math, time, datetime, subprocess
+import torch
+from torch.utils.data import DataLoader
+import pytorch_lightning as pl
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
+from .model import LORA_CONFIG
+
+
+def my_save(args, trainer, dd, ff):
+    if "14b-run1" in ff:
+        fn = ff.split("/")[-1]
+        fff = "/dev/shm/" + fn
+        torch.save(dd, fff)
+        subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
+    elif ("world/14b" in ff) or ("world/7b" in ff):
+        aa = ff.split("/")[1]
+        fn = ff.split("/")[-1]
+        fff = f"/dev/shm/{aa}-{fn}"
+        torch.save(dd, fff)
+        subprocess.Popen(
+            f" aws s3 mv {fff} s3://rwkv-world/{aa}-{fn} --quiet", shell=True
+        )
+    else:
+        if "deepspeed_stage_3" in args.strategy:
+            trainer.save_checkpoint(ff, weights_only=True)
+        else:
+            torch.save(dd, ff)
+
+
+class train_callback(pl.Callback):
+    def __init__(self, args):
+        super().__init__()
+        self.args = args
+
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
+        args = self.args
+        # if args.cuda_cleanup > 0:
+        #     torch.cuda.empty_cache()
+        real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
+
+        # LR schedule
+        w_step = args.warmup_steps
+        if args.lr_final == args.lr_init or args.epoch_count == 0:
+            lr = args.lr_init
+        else:
+            decay_step = real_step - args.my_pile_edecay * args.epoch_steps
+            decay_total = (args.epoch_count - args.my_pile_edecay) * args.epoch_steps
+            progress = (decay_step - w_step + 1) / (decay_total - w_step)
+            progress = min(1, max(0, progress))
+
+            if args.lr_final == 0 or args.lr_init == 0:  # linear decay
+                lr = args.lr_init + (args.lr_final - args.lr_init) * progress
+            else:  # exp decay
+                lr = args.lr_init * math.exp(
+                    math.log(args.lr_final / args.lr_init) * pow(progress, 1)
+                )
+            # if trainer.is_global_zero:
+            #     print(trainer.global_step, decay_step, decay_total, w_step, progress, lr)
+
+        if args.my_exit_tokens != 0:  # cosine decay
+            real_tokens = real_step * args.ctx_len * args.real_bsz
+            warmup_tokens = w_step * args.ctx_len * args.real_bsz
+            progress = (real_tokens - warmup_tokens) / (
+                abs(args.my_exit_tokens) - warmup_tokens
+            )
+            progress = max(0, min(1, progress))
+            lr_final_factor = args.lr_final / args.lr_init
+            lr_mult = (0.5 + lr_final_factor / 2) + (
+                0.5 - lr_final_factor / 2
+            ) * math.cos(math.pi * progress)
+            if args.my_exit_tokens > 0:
+                lr = args.lr_init * lr_mult
+            else:
+                lr = (lr + args.lr_init * lr_mult) / 2
+            if progress >= 1:
+                if (trainer.is_global_zero) or ("deepspeed_stage_3" in args.strategy):
+                    my_save(
+                        args,
+                        trainer,
+                        pl_module.state_dict(),
+                        f"{args.proj_dir}/rwkv-final.pth",
+                    )
+                    exit(0)
+        if trainer.global_step < w_step:
+            lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
+
+        if args.weight_decay_final > 0:
+            wd_now = args.weight_decay * math.exp(
+                math.log(args.weight_decay_final / args.weight_decay) * progress
+            )
+        else:
+            wd_now = args.weight_decay
+
+        for param_group in trainer.optimizers[0].param_groups:
+            if param_group["weight_decay"] > 0:
+                param_group["weight_decay"] = wd_now
+            if args.layerwise_lr > 0:
+                param_group["lr"] = lr * param_group["my_lr_scale"]
+                # print(param_group["lr"], param_group["my_lr_scale"])
+            else:
+                param_group["lr"] = lr
+
+        trainer.my_lr = lr
+        trainer.my_wd = wd_now
+        # rank_zero_info(f"{real_step} {lr}")
+
+        if trainer.global_step == 0:
+            if trainer.is_global_zero:  # logging
+                trainer.my_loss_sum = 0
+                trainer.my_loss_count = 0
+                trainer.my_log = open(args.proj_dir + "/train_log.txt", "a")
+                trainer.my_log.write(
+                    f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n"
+                )
+                try:
+                    print(f"\n{trainer.strategy.config}\n")
+                    trainer.my_log.write(f"{trainer.strategy.config}\n")
+                except:
+                    pass
+                trainer.my_log.flush()
+                if len(args.wandb) > 0:
+                    print("Login to wandb...")
+                    import wandb
+
+                    wandb.init(
+                        project=args.wandb,
+                        name=args.run_name + " " + args.my_timestamp,
+                        config=args,
+                        save_code=False,
+                    )
+                    trainer.my_wandb = wandb
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        args = self.args
+        token_per_step = args.ctx_len * args.real_bsz
+        real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
+        if trainer.is_global_zero:  # logging
+            t_now = time.time_ns()
+            kt_s = 0
+            try:
+                t_cost = (t_now - trainer.my_time_ns) / 1e9
+                kt_s = token_per_step / t_cost / 1000
+                self.log("REAL it/s", 1.0 / t_cost, prog_bar=True, on_step=True)
+                self.log("Kt/s", kt_s, prog_bar=True, on_step=True)
+            except:
+                pass
+            trainer.my_time_ns = t_now
+            if pl.__version__[0] == "2":
+                trainer.my_loss = outputs["loss"]
+            else:
+                trainer.my_loss = trainer.my_loss_all.float().mean().item()
+            trainer.my_loss_sum += trainer.my_loss
+            trainer.my_loss_count += 1
+            trainer.my_epoch_loss = trainer.my_loss_sum / trainer.my_loss_count
+            self.log("lr", trainer.my_lr, prog_bar=True, on_step=True)
+            self.log("loss", trainer.my_epoch_loss, prog_bar=True, on_step=True)
+            # self.log("s", real_step, prog_bar=True, on_step=True)
+
+            if len(args.wandb) > 0:
+                lll = {
+                    "loss": trainer.my_loss,
+                    "lr": trainer.my_lr,
+                    "wd": trainer.my_wd,
+                    "Gtokens": real_step * token_per_step / 1e9,
+                }
+                if kt_s > 0:
+                    lll["kt/s"] = kt_s
+                trainer.my_wandb.log(lll, step=int(real_step))
+        if (trainer.is_global_zero) or (
+            "deepspeed_stage_3" in args.strategy
+        ):  # save pth
+            if args.magic_prime > 0:
+                expand_factor = 2 if args.my_qa_mask > 0 else 1
+                if int(real_step) == int(
+                    args.magic_prime * expand_factor // args.real_bsz
+                ) - 1 + int(args.my_random_steps):
+                    to_save_dict = pl_module.state_dict()
+                    my_save(
+                        args,
+                        trainer,
+                        to_save_dict,
+                        f"{args.proj_dir}/rwkv-final.pth",
+                    )
+        # if args.batch_save==batch_idx :
+        #     to_save_dict = pl_module.state_dict()
+        #     for name, state in to_save_dict.items():
+        #         if 'img' in name:
+        #             to_save_dict[name] = state
+        #     try:
+        #             my_save(
+        #                 args, trainer,
+        #                 to_save_dict,
+        #                 f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}-{batch_idx}.pth",
+        #             )
+        #     except Exception as e:
+        #         print('Error\n\n', e, '\n\n')
+
+    def on_train_epoch_start(self, trainer, pl_module):
+        args = self.args
+        if pl.__version__[0] == "2":
+            dataset = trainer.train_dataloader.dataset
+        else:
+            dataset = trainer.train_dataloader.dataset.datasets
+        assert "MyDataset" in str(dataset)
+        dataset.global_rank = trainer.global_rank
+        dataset.real_epoch = int(args.epoch_begin + trainer.current_epoch)
+        dataset.world_size = trainer.world_size
+        # print(f'########## world_size {dataset.world_size} global_rank {dataset.global_rank} real_epoch {dataset.real_epoch} ##########')
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        args = self.args
+        to_save_dict = {}
+        if (trainer.is_global_zero) or (
+            "deepspeed_stage_3" in args.strategy
+        ):  # save pth
+            if (
+                args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0
+            ) or (trainer.current_epoch == args.epoch_count - 1):
+                if args.data_type == "wds_img":
+                    raw_dict = pl_module.state_dict()
+                    for k in raw_dict:
+                        if k.startswith("encoder.") or k.startswith("decoder."):
+                            to_save_dict[k] = raw_dict[k]
+                else:
+                    to_save_dict = pl_module.state_dict()
+
+                if args.data_type == "img" and not args.lora:
+                    for name, state in to_save_dict.items():
+                        if "img" in name:
+                            to_save_dict[name] = state
+
+                if args.lora:
+                    enable_time_finetune = "time" in LORA_CONFIG["parts"]
+                    enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
+                    lora_dict = {}
+                    for name, state in to_save_dict.items():
+                        if "img" in name:
+                            lora_dict[name] = state
+                        if (
+                            ".lora_" in name
+                            or (enable_time_finetune and ".time_" in name)
+                            or (enable_ln_finetune and ".ln" in name)
+                        ):
+                            lora_dict[name] = state
+                    to_save_dict = lora_dict
+
+                try:
+                    my_save(
+                        args,
+                        trainer,
+                        to_save_dict,
+                        f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth",
+                    )
+                except Exception as e:
+                    print("Error\n\n", e, "\n\n")
+
+        if trainer.is_global_zero:  # logging
+            trainer.my_log.write(
+                f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n"
+            )
+            trainer.my_log.flush()
+
+            trainer.my_loss_sum = 0
+            trainer.my_loss_count = 0
+            if (args.epoch_begin + trainer.current_epoch) >= args.my_exit:
+                exit(0)
+
+
+@rank_zero_only
+def generate_init_weight(model, init_weight_name):
+    mm = model.generate_init_weight()
+
+    if model.args.my_pile_stage == 1:
+        if len(model.args.load_model) > 0:
+            print(f"Combine weights from {model.args.load_model}...")
+            load_dict = torch.load(model.args.load_model, map_location="cpu")
+            for k in load_dict:
+                try:
+                    assert k in mm
+                except:
+                    print("missing", k)
+                    exit(0)
+                src = load_dict[k]
+                try:
+                    mm[k] = src.reshape(mm[k].shape)
+                except:
+                    tmp = mm[k].squeeze().clone()
+                    print(k, src.shape, "-->", mm[k].shape)
+                    ss = src.shape[0]
+                    dd = tmp.shape[0]
+                    for i in range(dd):
+                        pos = i / dd * ss
+                        if pos >= ss - 1:
+                            tmp[i] = src[ss - 1]
+                        else:
+                            p0 = int(math.floor(pos))
+                            ii = pos - p0
+                            tmp[i] = src[p0] * (1 - ii) + src[p0 + 1] * (ii)
+                    mm[k] = tmp.reshape(mm[k].shape)
+                    sss = src.squeeze().float().cpu().numpy()
+                    print(sss[:10], "...", sss[-10:])
+                    mmm = mm[k].squeeze().float().cpu().numpy()
+                    print(mmm[:10], "...", mmm[-10:])
+
+    print(f"Save to {init_weight_name}...")
+    torch.save(mm, init_weight_name)
+
+    if model.args.my_pile_stage == 1:
+        print("Done. Now go for stage 2.")
+        exit(0)

finetune/lora/v5/src/utils.py

@@ -0,0 +1,139 @@
+import json, time, random, os
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+time_slot = {}
+time_ref = time.time_ns()
+
+
+def record_time(name):
+    if name not in time_slot:
+        time_slot[name] = 1e20
+    tt = (time.time_ns() - time_ref) / 1e9
+    if tt < time_slot[name]:
+        time_slot[name] = tt
+
+
+class TOKENIZER:
+    def __init__(self, WORD_NAME, UNKNOWN_CHAR="\ue083"):
+        if "list" in str(type(WORD_NAME)):
+            self.charMode = False
+            if WORD_NAME[0] == WORD_NAME[1]:
+                from transformers import PreTrainedTokenizerFast
+
+                self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=WORD_NAME[0])
+            else:
+                from transformers import GPT2TokenizerFast
+
+                self.tokenizer = GPT2TokenizerFast(WORD_NAME[0], WORD_NAME[1])
+            self.vocab_size = len(self.tokenizer)
+        else:
+            self.charMode = True
+            with open(WORD_NAME + ".json", "r", encoding="utf-16") as result_file:
+                self.word_table = json.load(result_file)
+
+            self.vocab_size = len(self.word_table)
+
+            self.stoi = {v: int(k) for k, v in self.word_table.items()}
+            self.itos = {int(k): v for k, v in self.word_table.items()}
+
+            self.UNKNOWN_CHAR = self.stoi[UNKNOWN_CHAR]
+
+    def refine_context(self, context):
+        context = context.strip().split("\n")
+        for c in range(len(context)):
+            context[c] = context[c].strip().strip("\u3000").strip("\r")
+        context = list(filter(lambda c: c != "", context))
+        context = "\n" + ("\n".join(context)).strip()
+        if context == "":
+            context = "\n"
+        return context
+
+    def sample_logits(
+        self, out, x, ctx_len, temperature=1.0, top_p_usual=None, top_p_newline=None
+    ):
+        # out[self.UNKNOWN_CHAR] = -float('Inf')
+        lastChar = int(x[-1])
+
+        probs = F.softmax(out, dim=-1)
+
+        if self.charMode:
+            if self.itos[lastChar] == "\n":
+                top_p = top_p_newline
+            else:
+                top_p = top_p_usual
+        else:
+            top_p = top_p_usual
+
+        if os.environ["RWKV_RUN_DEVICE"] == "cpu":
+            probs = probs.numpy()
+            sorted_probs = np.sort(probs)[::-1]
+            cumulative_probs = np.cumsum(sorted_probs)
+            cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
+            probs[probs < cutoff] = 0
+            if temperature != 1.0:
+                probs = probs.pow(1.0 / temperature)
+            probs = probs / np.sum(probs)
+            out = np.random.choice(a=len(probs), p=probs)
+            return out
+        else:
+            sorted_probs = torch.sort(probs, descending=True)[0]
+            cumulative_probs = torch.cumsum(sorted_probs, dim=-1).cpu().numpy()
+            cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
+            probs[probs < cutoff] = 0
+            if temperature != 1.0:
+                probs = probs.pow(1.0 / temperature)
+            out = torch.multinomial(probs, num_samples=1)[0]
+            return out
+
+
+def MaybeIsPrime(number):
+    if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number):
+        return True
+    else:
+        return False
+
+
+def FermatPrimalityTest(number):
+    if number > 1:
+        for time in range(3):
+            randomNumber = random.randint(2, number) - 1
+            if pow(randomNumber, number - 1, number) != 1:
+                return False
+        return True
+    else:
+        return False
+
+
+def MillerRabinPrimalityTest(number):
+    if number == 2:
+        return True
+    elif number == 1 or number % 2 == 0:
+        return False
+    oddPartOfNumber = number - 1
+    timesTwoDividNumber = 0
+    while oddPartOfNumber % 2 == 0:
+        oddPartOfNumber = oddPartOfNumber // 2
+        timesTwoDividNumber = timesTwoDividNumber + 1
+
+    for time in range(3):
+        while True:
+            randomNumber = random.randint(2, number) - 1
+            if randomNumber != 0 and randomNumber != 1:
+                break
+
+        randomNumberWithPower = pow(randomNumber, oddPartOfNumber, number)
+
+        if (randomNumberWithPower != 1) and (randomNumberWithPower != number - 1):
+            iterationNumber = 1
+
+            while (iterationNumber <= timesTwoDividNumber - 1) and (
+                randomNumberWithPower != number - 1
+            ):
+                randomNumberWithPower = pow(randomNumberWithPower, 2, number)
+                iterationNumber = iterationNumber + 1
+            if randomNumberWithPower != (number - 1):
+                return False
+
+    return True

finetune/lora/v5/train.py

@@ -0,0 +1,436 @@
+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+if __name__ == "__main__":
+    from argparse import ArgumentParser
+    from pytorch_lightning import Trainer
+    from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
+    import pytorch_lightning as pl
+
+    rank_zero_info("########## work in progress ##########")
+
+    parser = ArgumentParser()
+
+    parser.add_argument("--load_model", default="", type=str)  # full path, with .pth
+    parser.add_argument(
+        "--wandb", default="", type=str
+    )  # wandb project name. if "" then don't use wandb
+    parser.add_argument("--proj_dir", default="out", type=str)
+    parser.add_argument("--random_seed", default="-1", type=int)
+
+    parser.add_argument("--data_file", default="", type=str)
+    parser.add_argument("--data_type", default="utf-8", type=str)
+    parser.add_argument(
+        "--vocab_size", default=0, type=int
+    )  # vocab_size = 0 means auto (for char-level LM and .txt data)
+
+    parser.add_argument("--ctx_len", default=1024, type=int)
+    parser.add_argument(
+        "--epoch_steps", default=1000, type=int
+    )  # a mini "epoch" has [epoch_steps] steps
+    parser.add_argument(
+        "--epoch_count", default=500, type=int
+    )  # train for this many "epochs". will continue afterwards with lr = lr_final
+    parser.add_argument(
+        "--epoch_begin", default=0, type=int
+    )  # if you load a model trained for x "epochs", set epoch_begin = x
+    parser.add_argument(
+        "--epoch_save", default=5, type=int
+    )  # save the model every [epoch_save] "epochs"
+
+    parser.add_argument(
+        "--micro_bsz", default=12, type=int
+    )  # micro batch size (batch size per GPU)
+    parser.add_argument("--n_layer", default=6, type=int)
+    parser.add_argument("--n_embd", default=512, type=int)
+    parser.add_argument("--dim_att", default=0, type=int)
+    parser.add_argument("--dim_ffn", default=0, type=int)
+    parser.add_argument(
+        "--pre_ffn", default=0, type=int
+    )  # replace first att layer by ffn (sometimes better)
+    parser.add_argument("--head_qk", default=0, type=int)  # my headQK trick
+    parser.add_argument("--tiny_att_dim", default=0, type=int)  # tiny attention dim
+    parser.add_argument(
+        "--tiny_att_layer", default=-999, type=int
+    )  # tiny attention @ which layer
+
+    parser.add_argument(
+        "--lr_init", default=6e-4, type=float
+    )  # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048
+    parser.add_argument("--lr_final", default=1e-5, type=float)
+    parser.add_argument(
+        "--warmup_steps", default=-1, type=int
+    )  # try 50 if you load a model
+    parser.add_argument("--beta1", default=0.9, type=float)
+    parser.add_argument(
+        "--beta2", default=0.99, type=float
+    )  # use 0.999 when your model is close to convergence
+    parser.add_argument("--adam_eps", default=1e-8, type=float)
+    parser.add_argument(
+        "--grad_cp", default=0, type=int
+    )  # gradient checkpt: saves VRAM, but slower
+    parser.add_argument(
+        "--dropout", default=0, type=float
+    )  # try 0.01 / 0.02 / 0.05 / 0.1
+    parser.add_argument(
+        "--weight_decay", default=0, type=float
+    )  # try 0.1 / 0.01 / 0.001
+    parser.add_argument("--weight_decay_final", default=-1, type=float)
+
+    parser.add_argument(
+        "--my_pile_version", default=1, type=int
+    )  # my special pile version
+    parser.add_argument("--my_pile_stage", default=0, type=int)  # my special pile mode
+    parser.add_argument(
+        "--my_pile_shift", default=-1, type=int
+    )  # my special pile mode - text shift
+    parser.add_argument("--my_pile_edecay", default=0, type=int)
+    parser.add_argument(
+        "--layerwise_lr", default=1, type=int
+    )  # layerwise lr for faster convergence (but slower it/s)
+    parser.add_argument(
+        "--ds_bucket_mb", default=200, type=int
+    )  # deepspeed bucket size in MB. 200 seems enough
+    # parser.add_argument("--cuda_cleanup", default=0, type=int)  # extra cuda cleanup (sometimes helpful)
+
+    parser.add_argument("--my_sample_len", default=0, type=int)
+    parser.add_argument("--my_ffn_shift", default=1, type=int)
+    parser.add_argument("--my_att_shift", default=1, type=int)
+    parser.add_argument(
+        "--head_size_a", default=64, type=int
+    )  # can try larger values for larger models
+    parser.add_argument("--head_size_divisor", default=8, type=int)
+    parser.add_argument("--my_pos_emb", default=0, type=int)
+    parser.add_argument("--load_partial", default=0, type=int)
+    parser.add_argument("--magic_prime", default=0, type=int)
+    parser.add_argument("--my_qa_mask", default=0, type=int)
+    parser.add_argument("--my_random_steps", default=0, type=int)
+    parser.add_argument("--my_testing", default="", type=str)
+    parser.add_argument("--my_exit", default=99999999, type=int)
+    parser.add_argument("--my_exit_tokens", default=0, type=int)
+
+    # LORA
+    parser.add_argument("--emb", action="store_true")
+    parser.add_argument("--lora", action="store_true")
+    parser.add_argument("--lora_load", default="", type=str)
+    parser.add_argument("--lora_r", default=8, type=int)
+    parser.add_argument("--lora_alpha", default=32, type=float)
+    parser.add_argument("--lora_dropout", default=0.01, type=float)
+    parser.add_argument("--lora_parts", default="att,ln,time", type=str)
+
+    if pl.__version__[0] == "2":
+        parser.add_argument("--accelerator", default="gpu", type=str)
+        parser.add_argument("--strategy", default="auto", type=str)
+        parser.add_argument("--devices", default=1, type=int)
+        parser.add_argument("--num_nodes", default=1, type=int)
+        parser.add_argument("--precision", default="fp16", type=str)
+        parser.add_argument("--accumulate_grad_batches", default=1, type=int)
+    else:
+        parser = Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    ########################################################################################################
+
+    import os, warnings, math, datetime, sys, time
+    import numpy as np
+    import torch
+    from torch.utils.data import DataLoader
+
+    if "deepspeed" in args.strategy:
+        import deepspeed
+    from pytorch_lightning import seed_everything
+
+    if args.random_seed >= 0:
+        print(
+            f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n"
+            * 3
+        )
+        seed_everything(args.random_seed)
+
+    np.set_printoptions(precision=4, suppress=True, linewidth=200)
+    warnings.filterwarnings(
+        "ignore", ".*Consider increasing the value of the `num_workers` argument*"
+    )
+    warnings.filterwarnings(
+        "ignore", ".*The progress bar already tracks a metric with the*"
+    )
+    # os.environ["WDS_SHOW_SEED"] = "1"
+
+    args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
+    args.enable_checkpointing = False
+    args.replace_sampler_ddp = False
+    args.logger = False
+    args.gradient_clip_val = 1.0
+    args.num_sanity_val_steps = 0
+    args.check_val_every_n_epoch = int(1e20)
+    args.log_every_n_steps = int(1e20)
+    args.max_epochs = args.epoch_count  # -1 continue forever
+    args.betas = (args.beta1, args.beta2)
+    args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
+    os.environ["RWKV_MY_TESTING"] = args.my_testing
+    os.environ["RWKV_HEAD_SIZE_A"] = str(args.head_size_a)
+    if args.dim_att <= 0:
+        args.dim_att = args.n_embd
+    if args.dim_ffn <= 0:
+        args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32)  # default = 3.5x emb size
+
+    if args.data_type == "wds_img":
+        args.run_name = f"v{args.my_img_version}-{args.my_img_size}-{args.my_img_bit}bit-{args.my_img_clip}x{args.my_img_clip_scale}"
+        args.proj_dir = f"{args.proj_dir}-{args.run_name}"
+    else:
+        args.run_name = (
+            f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}"
+        )
+    if not os.path.exists(args.proj_dir):
+        os.makedirs(args.proj_dir)
+
+    if args.my_pile_stage > 0:
+        magic_prime_bak = args.magic_prime
+
+        if args.my_pile_shift < 0:
+            args.my_pile_shift = 0
+
+        if magic_prime_bak > 0:
+            args.magic_prime = magic_prime_bak
+        if args.my_qa_mask == 2:
+            args.epoch_count = 2 * args.magic_prime // 40320
+        else:
+            args.epoch_count = args.magic_prime // 40320
+
+        args.epoch_steps = 40320 // args.real_bsz
+        assert args.epoch_steps * args.real_bsz == 40320
+        # if args.my_pile_stage == 2:
+        #     assert args.lr_final == args.lr_init
+        if args.my_pile_stage >= 2:  # find latest saved model
+            list_p = []
+            for p in os.listdir(args.proj_dir):
+                if p.startswith("rwkv") and p.endswith(".pth"):
+                    p = ((p.split("-"))[1].split("."))[0]
+                    if p != "final":
+                        if p == "init":
+                            p = -1
+                        else:
+                            p = int(p)
+                        list_p += [p]
+            list_p.sort()
+            max_p = list_p[-1]
+            if len(list_p) > 1:
+                args.my_pile_prev_p = list_p[-2]  # in case max_p is corrupted
+            if max_p == -1:
+                args.load_model = f"{args.proj_dir}/rwkv-init.pth"
+            else:
+                args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
+                if args.warmup_steps < 0:
+                    if args.my_pile_stage == 2:
+                        args.warmup_steps = 10
+                    else:
+                        args.warmup_steps = 30
+            args.epoch_begin = max_p + 1
+
+    samples_per_epoch = args.epoch_steps * args.real_bsz
+    tokens_per_epoch = samples_per_epoch * args.ctx_len
+    try:
+        deepspeed_version = deepspeed.__version__
+    except:
+        deepspeed_version = None
+        pass
+    rank_zero_info(
+        f"""
+############################################################################
+#
+# RWKV-5 {args.precision.upper()} on {args.num_nodes}x{args.devices} {args.accelerator.upper()}, bsz {args.num_nodes}x{args.devices}x{args.micro_bsz}={args.real_bsz}, {args.strategy} {'with grad_cp' if args.grad_cp > 0 else ''}
+#
+# Data = {args.data_file} ({args.data_type}), ProjDir = {args.proj_dir}
+#
+# Epoch = {args.epoch_begin} to {args.epoch_begin + args.epoch_count - 1}, save every {args.epoch_save} epoch
+#
+# Each "epoch" = {args.epoch_steps} steps, {samples_per_epoch} samples, {tokens_per_epoch} tokens
+#
+# Model = {args.n_layer} n_layer, {args.n_embd} n_embd, {args.ctx_len} ctx_len
+#
+# Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps}
+#
+# Found torch {torch.__version__}, recommend 1.13.1+cu117 or newer
+# Found deepspeed {deepspeed_version}, recommend 0.7.0 (faster than newer versions)
+# Found pytorch_lightning {pl.__version__}, recommend 1.9.5
+#
+############################################################################
+"""
+    )
+    rank_zero_info(str(vars(args)) + "\n")
+
+    assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "uint16"]
+
+    if args.lr_final == 0 or args.lr_init == 0:
+        rank_zero_info(
+            "\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n"
+        )
+
+    assert args.precision in ["fp32", "tf32", "fp16", "bf16"]
+    os.environ["RWKV_FLOAT_MODE"] = args.precision
+    if args.precision == "fp32":
+        for i in range(10):
+            rank_zero_info(
+                "\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n"
+            )
+    if args.precision == "fp16":
+        rank_zero_info(
+            "\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n"
+        )
+
+    os.environ["RWKV_JIT_ON"] = "0"
+    if "deepspeed_stage_3" in args.strategy:
+        os.environ["RWKV_JIT_ON"] = "0"
+
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cudnn.enabled = True
+    if args.precision == "fp32":
+        torch.backends.cudnn.allow_tf32 = False
+        torch.backends.cuda.matmul.allow_tf32 = False
+    else:
+        torch.backends.cudnn.allow_tf32 = True
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if "32" in args.precision:
+        args.precision = 32
+    elif args.precision == "fp16":
+        args.precision = 16
+    else:
+        args.precision = "bf16"
+
+    ########################################################################################################
+
+    from src.trainer import train_callback, generate_init_weight
+    from src.dataset import MyDataset
+
+    train_data = MyDataset(args)
+    args.vocab_size = train_data.vocab_size
+
+    from src.model import RWKV, LORA_CONFIG, LoraLinear
+
+    if args.lora:
+        assert args.lora_r > 0, "LoRA should have its `r` > 0"
+        LORA_CONFIG["r"] = args.lora_r
+        LORA_CONFIG["alpha"] = args.lora_alpha
+        LORA_CONFIG["dropout"] = args.lora_dropout
+        LORA_CONFIG["parts"] = set(str(args.lora_parts).split(","))
+        enable_time_finetune = "time" in LORA_CONFIG["parts"]
+        enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
+    model = RWKV(args)
+    # only train lora parameters
+    if args.lora:
+        model.requires_grad_(False)
+        for name, module in model.named_modules():
+            if any(n.startswith("lora_") for n, _ in module.named_parameters()):
+                print(f"  LoRA additionally training module {name}")
+                for pname, param in module.named_parameters():
+                    param.requires_grad = "lora_" in pname
+            elif enable_ln_finetune and ".ln" in name:
+                print(f"  LoRA additionally training module {name}")
+                for param in module.parameters():
+                    param.requires_grad = True
+            elif enable_time_finetune and any(
+                n.startswith("time") for n, _ in module.named_parameters()
+            ):
+                for pname, param in module.named_parameters():
+                    if pname.startswith("time"):
+                        print(f"  LoRA additionally training parameter {pname}")
+                        param.requires_grad = True
+
+    if (
+        len(args.load_model) == 0 or args.my_pile_stage == 1
+    ):  # shall we build the initial weights?
+        init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
+        generate_init_weight(model, init_weight_name)  # save initial weights
+        args.load_model = init_weight_name
+
+    rank_zero_info(f"########## Loading {args.load_model}... ##########")
+    try:
+        load_dict = torch.load(args.load_model, map_location="cpu")
+        load_keys = list(load_dict.keys())
+        for k in load_keys:
+            if k.startswith("_forward_module."):
+                load_dict[k.replace("_forward_module.", "")] = load_dict[k]
+                del load_dict[k]
+    except:
+        rank_zero_info(f"Bad checkpoint {args.load_model}")
+        if args.my_pile_stage >= 2:  # try again using another checkpoint
+            max_p = args.my_pile_prev_p
+            if max_p == -1:
+                args.load_model = f"{args.proj_dir}/rwkv-init.pth"
+            else:
+                args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
+            args.epoch_begin = max_p + 1
+            rank_zero_info(f"Trying {args.load_model}")
+            load_dict = torch.load(args.load_model, map_location="cpu")
+
+    if args.load_partial == 1:
+        load_keys = load_dict.keys()
+        for k in model.state_dict():
+            if k not in load_keys:
+                load_dict[k] = model.state_dict()[k]
+    # model.load_state_dict(load_dict)
+
+    model.load_state_dict(load_dict, strict=(not args.lora))
+    if os.path.isfile(args.lora_load):
+        model.load_state_dict(
+            torch.load(args.lora_load, map_location="cpu"), strict=False
+        )
+
+    if pl.__version__[0] == "2":
+        trainer = Trainer(
+            accelerator=args.accelerator,
+            strategy=args.strategy,
+            devices=args.devices,
+            num_nodes=args.num_nodes,
+            precision=args.precision,
+            logger=args.logger,
+            callbacks=[train_callback(args)],
+            max_epochs=args.max_epochs,
+            check_val_every_n_epoch=args.check_val_every_n_epoch,
+            num_sanity_val_steps=args.num_sanity_val_steps,
+            log_every_n_steps=args.log_every_n_steps,
+            enable_checkpointing=args.enable_checkpointing,
+            accumulate_grad_batches=args.accumulate_grad_batches,
+            gradient_clip_val=args.gradient_clip_val,
+        )
+    else:
+        trainer = Trainer.from_argparse_args(
+            args,
+            callbacks=[train_callback(args)],
+        )
+
+    if trainer.global_rank == 0:
+        for n in model.state_dict():
+            shape = model.state_dict()[n].shape
+            shape = [i for i in shape if i != 1]
+            if len(shape) > 1:
+                print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}")
+            else:
+                print(f"{str(shape[0]).ljust(5)}       {n}")
+
+    if "deepspeed" in args.strategy:
+        trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
+            args.ds_bucket_mb * 1000 * 1000
+        )
+        trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
+            args.ds_bucket_mb * 1000 * 1000
+        )
+
+    # must set shuffle=False, persistent_workers=False (because worker is in another thread)
+    data_loader = DataLoader(
+        train_data,
+        shuffle=False,
+        pin_memory=True,
+        batch_size=args.micro_bsz,
+        num_workers=1,
+        persistent_workers=False,
+        drop_last=True,
+    )
+
+    trainer.fit(model, data_loader)

finetune/requirements.txt

@@ -1,3 +1,3 @@
-torch==1.13.1
+torch==2.1.2
 pytorch_lightning==1.9.5
-deepspeed==0.11.2
+deepspeed==0.12.6

frontend/package-lock.json

@@ -13,6 +13,7 @@
         "@magenta/music": "^1.23.1",
         "@microsoft/fetch-event-source": "^2.0.1",
         "@primer/octicons-react": "^19.1.0",
+        "abcjs": "^6.2.3",
         "chart.js": "^4.3.0",
         "classnames": "^2.3.2",
         "file-saver": "^2.0.5",
@@ -2690,6 +2691,15 @@
       "integrity": "sha512-nne9/IiQ/hzIhY6pdDnbBtz7DjPTKrY00P/zvPSm5pOFkl6xuGrGnXn/VtTNNfNtAfZ9/1RtehkszU9qcTii0Q==",
       "optional": true
     },
+    "node_modules/abcjs": {
+      "version": "6.2.3",
+      "resolved": "https://registry.npmjs.org/abcjs/-/abcjs-6.2.3.tgz",
+      "integrity": "sha512-epu8C1yRkxV7Ss9hS0Bu72rairl1p2sR3hviVowjtdDJvb5GRE0SrB4TtN4HBbaoYhvxGnSZQxGULfQlW3o3RQ==",
+      "funding": {
+        "type": "github",
+        "url": "https://github.com/sponsors/paulrosen"
+      }
+    },
     "node_modules/acorn": {
       "version": "7.4.1",
       "resolved": "https://registry.npmjs.org/acorn/-/acorn-7.4.1.tgz",
@@ -6420,7 +6430,7 @@
     },
     "node_modules/typescript": {
       "version": "5.0.4",
-      "resolved": "https://registry.npmmirror.com/typescript/-/typescript-5.0.4.tgz",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.0.4.tgz",
       "integrity": "sha512-cW9T5W9xY37cc+jfEnaUvX91foxtHkza3Nw3wkoF4sSlKn0MONdkdEndig/qPBWXNkmplh3NzayQzCiHM4/hqw==",
       "dev": true,
       "bin": {

frontend/package.json

@@ -14,6 +14,7 @@
     "@magenta/music": "^1.23.1",
     "@microsoft/fetch-event-source": "^2.0.1",
     "@primer/octicons-react": "^19.1.0",
+    "abcjs": "^6.2.3",
     "chart.js": "^4.3.0",
     "classnames": "^2.3.2",
     "file-saver": "^2.0.5",

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

@@ -171,6 +171,10 @@
   "chinese": "中文",
   "default": "默认",
   "japanese": "日文",
+  "English": "英文",
+  "Chinese": "中文",
+  "Default": "默认",
+  "Japanese": "日文",
   "New Preset": "新建预设",
   "Import": "导入",
   "Name": "名称",
@@ -305,6 +309,7 @@
   "Loss is too high, please check the training data, and ensure your gpu driver is up to date.": "Loss过高，请检查训练数据，并确保你的显卡驱动是最新的",
   "This version of RWKV is not supported yet.": "暂不支持此版本的RWKV",
   "Main": "主干",
+  "Official": "官方",
   "Finetuned": "微调",
   "Global": "全球",
   "Local": "本地",
@@ -323,5 +328,24 @@
   "Core API URL": "核心 API URL",
   "Override core API URL(/chat/completions and /completions). If you don't know what this is, leave it blank.": "覆盖核心的 API URL (/chat/completions 和 /completions)。如果你不知道这是什么，请留空",
   "Please change Strategy to CPU (rwkv.cpp) to use ggml format": "请将Strategy改为CPU (rwkv.cpp)以使用ggml格式",
-  "Only Auto Play Generated Content": "仅自动播放新生成的内容"
+  "Only Auto Play Generated Content": "仅自动播放新生成的内容",
+  "Model has been converted and does not match current strategy. If you are using a new strategy, re-convert the model.": "所选模型已被转换过，并且不匹配当前的Strategy。如果你正在使用新的Strategy，请重新转换模型",
+  "Instruction 1": "指令1",
+  "Instruction 2": "指令2",
+  "Instruction 3": "指令3",
+  "Instruction: You are an expert assistant for summarizing and extracting information from given content\nGenerate a valid JSON in the following format:\n{\n    \"summary\": \"Summary of content\",\n    \"keywords\": [\"content keyword 1\", \"content keyword 2\"]\n}\n\nInput: The open-source community has introduced Eagle 7B, a new RNN model, built on the RWKV-v5 architecture. This new model has been trained on 1.1 trillion tokens and supports over 100 languages. The RWKV architecture, short for ‘Rotary Weighted Key-Value,’ is a type of architecture used in the field of artificial intelligence, particularly in natural language processing (NLP) and is a variation of the Recurrent Neural Network (RNN) architecture.\nEagle 7B promises lower inference cost and stands out as a leading 7B model in terms of environmental efficiency and language versatility.\nThe model, with its 7.52 billion parameters, shows excellent performance in multi-lingual benchmarks, setting a new standard in its category. It competes closely with larger models in English language evaluations and is distinctive as an “Attention-Free Transformer,” though it requires additional tuning for specific uses. This model is accessible under the Apache 2.0 license and can be downloaded from HuggingFace for both personal and commercial purposes.\nIn terms of multilingual performance, Eagle 7B has claimed to have achieved notable results in benchmarks covering 23 languages. Its English performance has also seen significant advancements, outperforming its predecessor, RWKV v4, and competing with top-tier models.\nWorking towards a more scalable architecture and use of data efficiently, Eagle 7B is a more inclusive AI technology, supporting a broader range of languages. This model challenges the prevailing dominance of transformer models by demonstrating the capabilities of RNNs like RWKV in achieving superior performance when trained on comparable data volumes.\nIn the RWKV model, the rotary mechanism transforms the input data in a way that helps the model better understand the position or or order of elements in a sequence. The weighted key value also makes the model efficient by retrieving the stored information from previous elements in a sequence. \nHowever, questions remain about the scalability of RWKV compared to transformers, although there is optimism regarding its potential. The team plans to include additional training, an in-depth paper on Eagle 7B, and the development of a 2T model.\n\nResponse: {": "Instruction: 你是一个专业的内容分析总结助手\n根据提供的内容生成以下格式的有效JSON信息:\n{\n    \"summary\": \"内容的简短摘要\",\n    \"keywords\": [\"内容关键词 1\", \"内容关键词 2\"]\n}\n\nInput: 开源社区推出了基于RWKV-v5架构的Eagle 7B新的RNN模型。这个新模型以1.1万亿个token进行了训练，并支持100多种语言。RWKV架构是人工智能领域中特别是自然语言处理（NLP）中使用的一种架构，它是循环神经网络（RNN）架构的一种变种。\nEagle 7B承诺低推理成本，并以其环境效益和语言灵活性在领先的7B模型中脱颖而出。\n该模型拥有75.2亿个参数，在多语言基准测试中表现出色，树立了新的行业标准。它在英语语言评估中与更大的模型竞争激烈，并作为“无注意力Transformer”独具特色，尽管它需要针对特定用途进行额外调整。该模型可在Apache 2.0许可下访问，并可从HuggingFace下载，用于个人和商业目的。\n关于多语言性能，Eagle 7B声称在涵盖23种语言的基准测试中取得了显著成绩。它的英语性能也取得了重大进步，超越了它的前身RWKV v4，并与顶级模型竞争。\n为了实现更可扩展的架构和有效利用数据，Eagle 7B是一种更包容的人工智能技术，支持更广泛的语言范围。通过展示RWKV等RNNs在训练相当数据量时实现卓越性能的能力，该模型挑战了Transformer模型的主导地位。\n在RWKV模型中，旋转机制以一种有助于模型更好地理解序列中元素的位置或顺序的方式转换输入数据。加权关键值还通过从序列中先前元素中检索存储的信息，使模型更高效。\n然而，与Transformer相比，人们对RWKV的可扩展性仍然存在疑问，尽管对其潜力持乐观态度。团队计划包括额外的训练、对Eagle 7B进行深入论文研究以及开发一个2T模型。\n\nResponse: {",
+  "Penalty Decay": "惩罚衰减",
+  "If you don't know what it is, keep it default.": "如果你不知道这是什么，保持默认",
+  "Failed to find the base model, please try to change your base model.": "未找到基底模型，请尝试更换基底模型",
+  "Markdown Renderer": "Markdown渲染",
+  "Load Conversation": "读取对话",
+  "The latest X messages will be sent to the server. If you are using the RWKV-Runner server, please use the default value because RWKV-Runner has built-in state cache management which only calculates increments. Sending all messages will have lower cost. If you are using ChatGPT, adjust this value according to your needs to reduce ChatGPT expenses.": "最近的X条消息会发送至服务器. 如果你正在使用RWKV-Runner服务器, 请使用默认值, 因为RWKV-Runner内置了state缓存管理, 只计算增量, 发送所有消息将具有更低的成本. 如果你正在使用ChatGPT, 则根据你的需要调整此值, 这可以降低ChatGPT的费用",
+  "History Message Number": "历史消息数量",
+  "Send All Message": "发送所有消息",
+  "Quantized Layers": "量化层数",
+  "Number of the neural network layers quantized with current precision, the more you quantize, the lower the VRAM usage, but the quality correspondingly decreases.": "神经网络以当前精度量化的层数, 量化越多, 占用显存越低, 但质量相应下降",
+  "Parallel Token Chunk Size": "并行Token块大小",
+  "Maximum tokens to be processed in parallel at once. For high end GPUs, this could be 64 or 128 (faster).": "一次最多可以并行处理的token数量. 对于高端显卡, 这可以是64或128 (更快)",
+  "Global Penalty": "全局惩罚",
+  "When generating a response, whether to include the submitted prompt as a penalty factor. By turning this off, you will get the same generated results as official RWKV Gradio. If you find duplicate results in the generated results, turning this on can help avoid generating duplicates.": "生成响应时, 是否将提交的prompt也纳入到惩罚项. 关闭此项将得到与RWKV官方Gradio完全一致的生成结果. 如果你发现生成结果出现重复, 那么开启此项有助于避免生成重复"
 }

frontend/src/components/MarkdownRender.tsx

@@ -21,27 +21,93 @@ const Hyperlink: FC<any> = ({ href, children }) => {
   );
 };
 
-const MarkdownRender: FC<ReactMarkdownOptions> = (props) => {
+const MarkdownRender: FC<ReactMarkdownOptions & { disabled?: boolean }> = (props) => {
   return (
     <div dir="auto" className="markdown-body">
-      <ReactMarkdown
-        remarkPlugins={[remarkGfm, remarkBreaks]}
-        rehypePlugins={[
-          rehypeRaw,
-          [
-            rehypeHighlight,
-            {
-              detect: true,
-              ignoreMissing: true
-            }
-          ]
-        ]}
-        components={{
-          a: Hyperlink
-        }}
-      >
-        {props.children}
-      </ReactMarkdown>
+      {props.disabled ?
+        <div style={{ whiteSpace: 'pre-wrap' }}>
+          {props.children}
+        </div> :
+        <ReactMarkdown
+          allowedElements={[
+            'div',
+            'p',
+            'span',
+
+            'video',
+            'img',
+
+            'abbr',
+            'acronym',
+            'b',
+            'blockquote',
+            'code',
+            'em',
+            'i',
+            'li',
+            'ol',
+            'ul',
+            'strong',
+            'table',
+            'tr',
+            'td',
+            'th',
+
+            'details',
+            'summary',
+            'kbd',
+            'samp',
+            'sub',
+            'sup',
+            'ins',
+            'del',
+            'var',
+            'q',
+            'dl',
+            'dt',
+            'dd',
+            'ruby',
+            'rt',
+            'rp',
+
+            'br',
+            'hr',
+
+            'h1',
+            'h2',
+            'h3',
+            'h4',
+            'h5',
+            'h6',
+
+            'thead',
+            'tbody',
+            'tfoot',
+            'u',
+            's',
+            'a',
+            'pre',
+            'cite'
+          ]}
+          unwrapDisallowed={true}
+          remarkPlugins={[remarkGfm, remarkBreaks]}
+          rehypePlugins={[
+            rehypeRaw,
+            [
+              rehypeHighlight,
+              {
+                detect: true,
+                ignoreMissing: true
+              }
+            ]
+          ]}
+          components={{
+            a: Hyperlink
+          }}
+        >
+          {props.children}
+        </ReactMarkdown>
+      }
     </div>
   );
 };

frontend/src/components/NumberInput.tsx

@@ -8,10 +8,12 @@ export const NumberInput: FC<{
   max: number,
   step?: number,
   onChange?: (ev: React.ChangeEvent<HTMLInputElement>, data: SliderOnChangeData) => void
-  style?: CSSProperties
-}> = ({ value, min, max, step, onChange, style }) => {
+  style?: CSSProperties,
+  toFixed?: number
+  disabled?: boolean
+}> = ({ value, min, max, step, onChange, style, toFixed = 2, disabled }) => {
   return (
-    <Input type="number" style={style} value={value.toString()} min={min} max={max} step={step}
+    <Input type="number" style={style} value={value.toString()} min={min} max={max} step={step} disabled={disabled}
       onChange={(e, data) => {
         onChange?.(e, { value: Number(data.value) });
       }}
@@ -22,7 +24,7 @@ export const NumberInput: FC<{
             value = Number(((
                 Math.round((value - offset) / step) * step)
               + offset)
-            .toFixed(2)); // avoid precision issues
+            .toFixed(toFixed)); // avoid precision issues
           }
           onChange(e, { value: Math.max(Math.min(value, max), min) });
         }

frontend/src/components/RunButton.tsx

@@ -19,6 +19,7 @@ import { useNavigate } from 'react-router';
 import { WindowShow } from '../../wailsjs/runtime';
 import { convertToGGML, convertToSt } from '../utils/convert-model';
 import { Precision } from '../types/configs';
+import { defaultCompositionABCPrompt, defaultCompositionPrompt } from '../pages/defaultConfigs';
 
 const mainButtonText = {
   [ModelStatus.Offline]: 'Run',
@@ -211,7 +212,9 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
                 temperature: modelConfig.apiParameters.temperature,
                 top_p: modelConfig.apiParameters.topP,
                 presence_penalty: modelConfig.apiParameters.presencePenalty,
-                frequency_penalty: modelConfig.apiParameters.frequencyPenalty
+                frequency_penalty: modelConfig.apiParameters.frequencyPenalty,
+                penalty_decay: modelConfig.apiParameters.penaltyDecay,
+                global_penalty: modelConfig.apiParameters.globalPenalty
               });
             }
 
@@ -257,6 +260,7 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
                 commonStore.setStatus({ status: ModelStatus.Working });
                 let buttonNameMap = {
                   'novel': 'Completion',
+                  'abc': 'Composition',
                   'midi': 'Composition'
                 };
                 let buttonName = 'Chat';
@@ -264,6 +268,13 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
                 const buttonFn = () => {
                   navigate({ pathname: '/' + buttonName.toLowerCase() });
                 };
+                if (modelName.toLowerCase().includes('abc') && commonStore.compositionParams.prompt === defaultCompositionPrompt) {
+                  commonStore.setCompositionParams({
+                    ...commonStore.compositionParams,
+                    prompt: defaultCompositionABCPrompt
+                  });
+                  commonStore.setCompositionSubmittedPrompt(defaultCompositionABCPrompt);
+                }
 
                 if (modelConfig.modelParameters.device.startsWith('CUDA') &&
                   modelConfig.modelParameters.storedLayers < modelConfig.modelParameters.maxStoredLayers &&
@@ -282,7 +293,8 @@ export const RunButton: FC<{ onClickRun?: MouseEventHandler, iconMode?: boolean
                   'invalid header or archive is corrupted': 'The model file is corrupted, please download again.',
                   'no NVIDIA driver': 'Found no NVIDIA driver, please install the latest driver. If you are not using an Nvidia GPU, please switch the \'Strategy\' to WebGPU or CPU in the Configs page.',
                   'CUDA out of memory': 'VRAM is not enough, please reduce stored layers or use a lower precision in Configs page.',
-                  'Ninja is required to load C++ extensions': '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.'
+                  'Ninja is required to load C++ extensions': '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.',
+                  're-convert the model': 'Model has been converted and does not match current strategy. If you are using a new strategy, re-convert the model.'
                 };
                 const matchedError = Object.entries(errorsMap).find(([key, _]) => error.includes(key));
                 const message = matchedError ? t(matchedError[1]) : error;

frontend/src/components/ValuedSlider.tsx

@@ -9,8 +9,10 @@ export const ValuedSlider: FC<{
   max: number,
   step?: number,
   input?: boolean
-  onChange?: (ev: React.ChangeEvent<HTMLInputElement>, data: SliderOnChangeData) => void
-}> = ({ value, min, max, step, input, onChange }) => {
+  onChange?: (ev: React.ChangeEvent<HTMLInputElement>, data: SliderOnChangeData) => void,
+  toFixed?: number
+  disabled?: boolean
+}> = ({ value, min, max, step, input, onChange, toFixed, disabled }) => {
   const sliderRef = useRef<HTMLInputElement>(null);
   useEffect(() => {
     if (step && sliderRef.current && sliderRef.current.parentElement) {
@@ -23,9 +25,10 @@ export const ValuedSlider: FC<{
     <div className="flex items-center">
       <Slider ref={sliderRef} className="grow" style={{ minWidth: '50%' }} value={value} min={min}
         max={max} step={step}
-        onChange={onChange} />
+        onChange={onChange} disabled={disabled} />
       {input
-        ? <NumberInput style={{ minWidth: 0 }} value={value} min={min} max={max} step={step} onChange={onChange} />
+        ? <NumberInput style={{ minWidth: 0 }} value={value} min={min} max={max} step={step} onChange={onChange}
+          toFixed={toFixed} disabled={disabled} />
         : <Text>{value}</Text>}
     </div>
   );

frontend/src/pages/AudiotrackManager/AudiotrackEditor.tsx

@@ -29,14 +29,14 @@ import {
 } from '../../types/composition';
 import { toast } from 'react-toastify';
 import {
-  absPathAsset,
   flushMidiRecordingContent,
   getMidiRawContentMainInstrument,
   getMidiRawContentTime,
   getServerRoot,
+  OpenFileDialog,
   refreshTracksTotalTime
 } from '../../utils';
-import { OpenOpenFileDialog, PlayNote } from '../../../wailsjs/go/backend_golang/App';
+import { PlayNote } from '../../../wailsjs/go/backend_golang/App';
 
 const snapValue = 25;
 const minimalMoveTime = 8; // 1000/125=8ms wait_events=125
@@ -471,15 +471,7 @@ const AudiotrackEditor: FC<{ setPrompt: (prompt: string) => void }> = observer((
                   return;
                 }
 
-                OpenOpenFileDialog('*.mid').then(async filePath => {
-                  if (!filePath)
-                    return;
-
-                  let blob: Blob;
-                  if (commonStore.platform === 'web')
-                    blob = (filePath as unknown as { blob: Blob }).blob;
-                  else
-                    blob = await fetch(absPathAsset(filePath)).then(r => r.blob());
+                OpenFileDialog('*.mid').then(async blob => {
                   const bodyForm = new FormData();
                   bodyForm.append('file_data', blob);
                   fetch(getServerRoot(commonStore.getCurrentModelConfig().apiParameters.apiPort) + '/midi-to-text', {
@@ -510,8 +502,6 @@ const AudiotrackEditor: FC<{ setPrompt: (prompt: string) => void }> = observer((
                   ).catch(e => {
                     toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
                   });
-                }).catch(e => {
-                  toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
                 });
               }}>
               {t('Import MIDI')}

frontend/src/pages/Chat.tsx

@@ -1,6 +1,15 @@
 import React, { FC, useCallback, useEffect, useRef, useState } from 'react';
 import { useTranslation } from 'react-i18next';
-import { Avatar, Button, Menu, MenuPopover, MenuTrigger, PresenceBadge, Textarea } from '@fluentui/react-components';
+import {
+  Avatar,
+  Button,
+  Menu,
+  MenuPopover,
+  MenuTrigger,
+  PresenceBadge,
+  Switch,
+  Textarea
+} from '@fluentui/react-components';
 import commonStore, { ModelStatus } from '../stores/commonStore';
 import { observer } from 'mobx-react-lite';
 import { v4 as uuid } from 'uuid';
@@ -17,6 +26,7 @@ import {
   Delete28Regular,
   Dismiss16Regular,
   Dismiss24Regular,
+  FolderOpenVerticalRegular,
   RecordStop28Regular,
   SaveRegular,
   TextAlignJustify24Regular,
@@ -28,13 +38,22 @@ import { toast } from 'react-toastify';
 import { WorkHeader } from '../components/WorkHeader';
 import { DialogButton } from '../components/DialogButton';
 import { OpenFileFolder, OpenOpenFileDialog, OpenSaveFileDialog } from '../../wailsjs/go/backend_golang/App';
-import { absPathAsset, bytesToReadable, getServerRoot, setActivePreset, toastWithButton } from '../utils';
+import {
+  absPathAsset,
+  bytesToReadable,
+  getServerRoot,
+  newChatConversation,
+  OpenFileDialog,
+  setActivePreset,
+  toastWithButton
+} from '../utils';
 import { useMediaQuery } from 'usehooks-ts';
-import { botName, ConversationMessage, MessageType, userName, welcomeUuid } from '../types/chat';
+import { botName, ConversationMessage, MessageType, Role, userName, welcomeUuid } from '../types/chat';
 import { Labeled } from '../components/Labeled';
 import { ValuedSlider } from '../components/ValuedSlider';
 import { PresetsButton } from './PresetsManager/PresetsButton';
 import { webOpenOpenFileDialog } from '../utils/web-file-operations';
+import { defaultPenaltyDecay } from './defaultConfigs';
 
 let chatSseControllers: {
   [id: string]: AbortController
@@ -136,7 +155,7 @@ const ChatMessageItem: FC<{
     >
       {!editing ?
         <div className="flex flex-col">
-          <MarkdownRender>{messageItem.content}</MarkdownRender>
+          <MarkdownRender disabled={!commonStore.chatParams.markdown}>{messageItem.content}</MarkdownRender>
           {uuid in commonStore.attachments &&
             <div className="flex grow">
               <div className="grow" />
@@ -212,7 +231,7 @@ const SidePanel: FC = observer(() => {
         onClick={() => commonStore.setSidePanelCollapsed(true)}
       />
     </div>
-    <div className="flex flex-col gap-1 overflow-x-hidden overflow-y-auto p-1">
+    <div className="flex flex-col gap-1 overflow-x-hidden overflow-y-auto p-0.5">
       <Labeled flex breakline label={t('Max Response Token')}
         desc={t('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.')}
         content={
@@ -228,7 +247,7 @@ const SidePanel: FC = observer(() => {
       <Labeled flex breakline label={t('Temperature')}
         desc={t('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.')}
         content={
-          <ValuedSlider value={params.temperature} min={0} max={2} step={0.1}
+          <ValuedSlider value={params.temperature} min={0} max={3} step={0.1}
             input
             onChange={(e, data) => {
               commonStore.setChatParams({
@@ -239,7 +258,7 @@ const SidePanel: FC = observer(() => {
       <Labeled flex breakline label={t('Top_P')}
         desc={t('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.')}
         content={
-          <ValuedSlider value={params.topP} min={0} max={1} step={0.1} input
+          <ValuedSlider value={params.topP} min={0} max={1} step={0.05} input
             onChange={(e, data) => {
               commonStore.setChatParams({
                 topP: data.value
@@ -268,14 +287,82 @@ const SidePanel: FC = observer(() => {
               });
             }} />
         } />
+      <Labeled flex breakline
+        label={t('Penalty Decay') + (params.penaltyDecay === defaultPenaltyDecay ? ` (${t('Default')})` : '')}
+        desc={t('If you don\'t know what it is, keep it default.')}
+        content={
+          <ValuedSlider value={params.penaltyDecay!} min={0.99} max={0.999}
+            step={0.001} toFixed={3} input
+            onChange={(e, data) => {
+              commonStore.setChatParams({
+                penaltyDecay: data.value
+              });
+            }} />
+        } />
+      <Labeled flex breakline
+        label={t('History Message Number') + (params.historyN === 0 ? ` (${t('Default')})` : '')}
+        desc={params.historyN === 0 ? t('Send All Message') : t('The latest X messages will be sent to the server. If you are using the RWKV-Runner server, please use the default value because RWKV-Runner has built-in state cache management which only calculates increments. Sending all messages will have lower cost. If you are using ChatGPT, adjust this value according to your needs to reduce ChatGPT expenses.')
+        .replace('X', String(params.historyN))}
+        content={
+          <ValuedSlider value={params.historyN} min={0} max={20}
+            step={1} input
+            onChange={(e, data) => {
+              commonStore.setChatParams({
+                historyN: data.value
+              });
+            }} />
+        } />
     </div>
     <div className="grow" />
-    {/*<Button*/}
-    {/*  icon={<FolderOpenVerticalRegular />}*/}
-    {/*  onClick={() => {*/}
-    {/*  }}>*/}
-    {/*  {t('Load Conversation')}*/}
-    {/*</Button>*/}
+    <Labeled flex spaceBetween
+      label={t('Markdown Renderer')}
+      content={
+        <Switch checked={params.markdown}
+          onChange={(e, data) => {
+            commonStore.setChatParams({
+              markdown: data.checked
+            });
+          }} />
+      } />
+    <Button
+      icon={<FolderOpenVerticalRegular />}
+      onClick={() => {
+        OpenFileDialog('*.txt;*.md').then(async blob => {
+          const userNames = ['User:', 'Question:', 'Q:', 'Human:', 'Bob:'];
+          const assistantNames = ['Assistant:', 'Answer:', 'A:', 'Bot:', 'Alice:'];
+          const names = userNames.concat(assistantNames);
+          const content = await blob.text();
+          const lines = content.split('\n');
+
+          const { pushMessage, saveConversation } = newChatConversation();
+          let messageRole: Role = 'user';
+          let messageContent = '';
+          for (const [i, line] of lines.entries()) {
+            let lineName = '';
+            if (names.some(name => {
+              lineName = name;
+              return line.startsWith(name);
+            })) {
+              if (messageContent.trim())
+                pushMessage(messageRole, messageContent.trim());
+
+              if (userNames.includes(lineName))
+                messageRole = 'user';
+              else
+                messageRole = 'assistant';
+
+              messageContent = line.replace(lineName, '');
+            } else {
+              messageContent += '\n' + line;
+            }
+          }
+          if (messageContent.trim())
+            pushMessage(messageRole, messageContent.trim());
+          saveConversation();
+        });
+      }}>
+      {t('Load Conversation')}
+    </Button>
     <Button
       icon={<SaveRegular />}
       onClick={() => {
@@ -295,7 +382,7 @@ const SidePanel: FC = observer(() => {
         OpenSaveFileDialog('*.txt', 'conversation.txt', savedContent).then((path) => {
           if (path)
             toastWithButton(t('Conversation Saved'), t('Open'), () => {
-              OpenFileFolder(path, false);
+              OpenFileFolder(path);
             });
         }).catch(e => {
           toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
@@ -444,13 +531,15 @@ const ChatPanel: FC = observer(() => {
           Authorization: `Bearer ${commonStore.settings.apiKey}`
         },
         body: JSON.stringify({
-          messages,
+          messages: messages.slice(-commonStore.chatParams.historyN),
           stream: true,
           model: commonStore.settings.apiChatModelName, // 'gpt-3.5-turbo'
+          max_tokens: commonStore.chatParams.maxResponseToken,
           temperature: commonStore.chatParams.temperature,
           top_p: commonStore.chatParams.topP,
           presence_penalty: commonStore.chatParams.presencePenalty,
           frequency_penalty: commonStore.chatParams.frequencyPenalty,
+          penalty_decay: commonStore.chatParams.penaltyDecay === defaultPenaltyDecay ? undefined : commonStore.chatParams.penaltyDecay,
           user_name: commonStore.activePreset?.userName || undefined,
           assistant_name: commonStore.activePreset?.assistantName || undefined,
           presystem: commonStore.activePreset?.presystem && undefined
@@ -519,7 +608,7 @@ const ChatPanel: FC = observer(() => {
           style={{ zIndex: 1 }}
           icon={commonStore.sidePanelCollapsed ? <TextAlignJustify24Regular /> : <TextAlignJustifyRotate9024Regular />}
           onClick={() => commonStore.setSidePanelCollapsed(!commonStore.sidePanelCollapsed)} />
-        <div ref={bodyRef} className="grow overflow-y-scroll overflow-x-hidden pr-2">
+        <div ref={bodyRef} className="grow overflow-y-auto overflow-x-hidden pr-2">
           {commonStore.conversationOrder.map(uuid =>
             <ChatMessageItem key={uuid} uuid={uuid} onSubmit={onSubmit} />
           )}

frontend/src/pages/Completion.tsx

@@ -188,7 +188,7 @@ const CompletionPanel: FC = observer(() => {
           <Labeled flex breakline label={t('Temperature')}
             desc={t('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.')}
             content={
-              <ValuedSlider value={params.temperature} min={0} max={2} step={0.1}
+              <ValuedSlider value={params.temperature} min={0} max={3} step={0.1}
                 input
                 onChange={(e, data) => {
                   setParams({
@@ -199,7 +199,7 @@ const CompletionPanel: FC = observer(() => {
           <Labeled flex breakline label={t('Top_P')}
             desc={t('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.')}
             content={
-              <ValuedSlider value={params.topP} min={0} max={1} step={0.1} input
+              <ValuedSlider value={params.topP} min={0} max={1} step={0.05} input
                 onChange={(e, data) => {
                   setParams({
                     topP: data.value

frontend/src/pages/Composition.tsx

@@ -15,12 +15,13 @@ import { ArrowSync20Regular, Save28Regular } from '@fluentui/react-icons';
 import { PlayerElement, VisualizerElement } from 'html-midi-player';
 import * as mm from '@magenta/music/esm/core.js';
 import { NoteSequence } from '@magenta/music/esm/protobuf.js';
-import { defaultCompositionPrompt } from './defaultConfigs';
+import { defaultCompositionABCPrompt, defaultCompositionPrompt } from './defaultConfigs';
 import {
   CloseMidiPort,
   FileExists,
   OpenFileFolder,
   OpenMidiPort,
+  OpenSaveFileDialog,
   OpenSaveFileDialogBytes,
   SaveFile,
   StartFile
@@ -36,7 +37,9 @@ const CompositionPanel: FC = observer(() => {
   const { t } = useTranslation();
   const mq = useMediaQuery('(min-width: 640px)');
   const inputRef = useRef<HTMLTextAreaElement>(null);
-  const port = commonStore.getCurrentModelConfig().apiParameters.apiPort;
+  const modelConfig = commonStore.getCurrentModelConfig();
+  const port = modelConfig.apiParameters.apiPort;
+  const isABC = modelConfig.modelParameters.modelName.toLowerCase().includes('abc');
   const visualizerRef = useRef<VisualizerElement>(null);
   const playerRef = useRef<PlayerElement>(null);
 
@@ -133,6 +136,13 @@ const CompositionPanel: FC = observer(() => {
   }, [commonStore.midiPorts]);
 
   const generateNs = (autoPlay: boolean) => {
+    if (commonStore.getCurrentModelConfig().modelParameters.modelName.toLowerCase().includes('abc')) {
+      import('abcjs').then(ABCJS => {
+        ABCJS.renderAbc('abc-paper', commonStore.compositionParams.prompt, { responsive: 'resize' });
+      });
+      return;
+    }
+
     fetch(getServerRoot(port) + '/text-to-midi', {
       method: 'POST',
       headers: {
@@ -265,7 +275,7 @@ const CompositionPanel: FC = observer(() => {
             <Labeled flex breakline label={t('Temperature')}
               desc={t('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.')}
               content={
-                <ValuedSlider value={params.temperature} min={0} max={2} step={0.1}
+                <ValuedSlider value={params.temperature} min={0} max={3} step={0.1}
                   input
                   onChange={(e, data) => {
                     setParams({
@@ -276,7 +286,7 @@ const CompositionPanel: FC = observer(() => {
             <Labeled flex breakline label={t('Top_P')}
               desc={t('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.')}
               content={
-                <ValuedSlider value={params.topP} min={0} max={1} step={0.1} input
+                <ValuedSlider value={params.topP} min={0} max={1} step={0.05} input
                   onChange={(e, data) => {
                     setParams({
                       topP: data.value
@@ -370,11 +380,13 @@ const CompositionPanel: FC = observer(() => {
             <DialogButton className="grow" text={t('Reset')} title={t('Reset')}
               contentText={t('Are you sure you want to reset this page? It cannot be undone.')}
               onConfirm={() => {
-                commonStore.setCompositionSubmittedPrompt(defaultCompositionPrompt);
+                const isABC = commonStore.getCurrentModelConfig().modelParameters.modelName.toLowerCase().includes('abc');
+                const defaultPrompt = isABC ? defaultCompositionABCPrompt : defaultCompositionPrompt;
+                commonStore.setCompositionSubmittedPrompt(defaultPrompt);
                 setParams({
                   generationStartTime: 0
                 });
-                setPrompt(defaultCompositionPrompt);
+                setPrompt(defaultPrompt);
               }} />
             <Button className="grow" appearance="primary" onClick={() => {
               if (commonStore.compositionGenerating) {
@@ -394,23 +406,38 @@ const CompositionPanel: FC = observer(() => {
       </div>
       <div className="flex flex-col">
         <div className="ml-auto mr-auto">
-          <midi-visualizer
-            ref={visualizerRef}
-            type="waterfall"
-          />
+          {isABC ? <div /> :
+            <midi-visualizer
+              ref={visualizerRef}
+              type="waterfall"
+            />}
         </div>
         <div className="flex">
-          <midi-player
-            ref={playerRef}
-            style={{ width: '100%' }}
-          />
+          {isABC ? <div className="flex flex-col overflow-y-auto grow m-1" style={{ maxHeight: '260px' }}>
+              <div id="abc-paper" />
+            </div> :
+            <midi-player
+              ref={playerRef}
+              style={{ width: '100%' }}
+            />}
           <Button icon={<Save28Regular />} size={mq ? 'large' : 'medium'} appearance={mq ? 'secondary' : 'subtle'}
             onClick={() => {
+              if (isABC) {
+                OpenSaveFileDialog('*.txt', 'abc-music.txt', commonStore.compositionParams.prompt).then((path) => {
+                  if (path)
+                    toastWithButton(t('File Saved'), t('Open'), () => {
+                      OpenFileFolder(path);
+                    });
+                }).catch((e) => {
+                  toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
+                });
+                return;
+              }
               if (params.midi) {
                 OpenSaveFileDialogBytes('*.mid', 'music.mid', Array.from(new Uint8Array(params.midi))).then((path) => {
                   if (path)
                     toastWithButton(t('File Saved'), t('Open'), () => {
-                      OpenFileFolder(path, false);
+                      OpenFileFolder(path);
                     });
                 }).catch((e) => {
                   toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });

frontend/src/pages/Configs.tsx

@@ -35,6 +35,7 @@ import { ResetConfigsButton } from '../components/ResetConfigsButton';
 import { useMediaQuery } from 'usehooks-ts';
 import { ApiParameters, Device, ModelParameters, Precision } from '../types/configs';
 import { convertModel, convertToGGML, convertToSt } from '../utils/convert-model';
+import { defaultPenaltyDecay } from './defaultConfigs';
 
 const ConfigSelector: FC<{
   selectedIndex: number,
@@ -66,14 +67,17 @@ 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 advancedHeaderRef = useRef<HTMLDivElement>(null);
+  const advancedHeaderRef1 = useRef<HTMLDivElement>(null);
+  const advancedHeaderRef2 = useRef<HTMLDivElement>(null);
   const mq = useMediaQuery('(min-width: 640px)');
   const navigate = useNavigate();
   const port = selectedConfig.apiParameters.apiPort;
 
   useEffect(() => {
-    if (advancedHeaderRef.current)
-      (advancedHeaderRef.current.firstElementChild as HTMLElement).style.padding = '0';
+    if (advancedHeaderRef1.current)
+      (advancedHeaderRef1.current.firstElementChild as HTMLElement).style.padding = '0';
+    if (advancedHeaderRef2.current)
+      (advancedHeaderRef2.current.firstElementChild as HTMLElement).style.padding = '0';
   }, []);
 
   const updateSelectedIndex = useCallback((newIndex: number) => {
@@ -113,7 +117,9 @@ const Configs: FC = observer(() => {
       temperature: selectedConfig.apiParameters.temperature,
       top_p: selectedConfig.apiParameters.topP,
       presence_penalty: selectedConfig.apiParameters.presencePenalty,
-      frequency_penalty: selectedConfig.apiParameters.frequencyPenalty
+      frequency_penalty: selectedConfig.apiParameters.frequencyPenalty,
+      penalty_decay: selectedConfig.apiParameters.penaltyDecay,
+      global_penalty: selectedConfig.apiParameters.globalPenalty
     });
     toast(t('Config Saved'), { autoClose: 300, type: 'success' });
   };
@@ -176,7 +182,7 @@ const Configs: FC = observer(() => {
                 <Labeled label={t('Temperature') + ' *'}
                   desc={t('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.')}
                   content={
-                    <ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={2} step={0.1}
+                    <ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={3} step={0.1}
                       input
                       onChange={(e, data) => {
                         setSelectedConfigApiParams({
@@ -187,35 +193,74 @@ const Configs: FC = observer(() => {
                 <Labeled label={t('Top_P') + ' *'}
                   desc={t('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.')}
                   content={
-                    <ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.1} input
+                    <ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.05} input
                       onChange={(e, data) => {
                         setSelectedConfigApiParams({
                           topP: data.value
                         });
                       }} />
                   } />
-                <Labeled label={t('Presence Penalty') + ' *'}
-                  desc={t('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.')}
-                  content={
-                    <ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2}
-                      step={0.1} input
-                      onChange={(e, data) => {
-                        setSelectedConfigApiParams({
-                          presencePenalty: data.value
-                        });
-                      }} />
-                  } />
-                <Labeled label={t('Frequency Penalty') + ' *'}
-                  desc={t('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.')}
-                  content={
-                    <ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2}
-                      step={0.1} input
-                      onChange={(e, data) => {
-                        setSelectedConfigApiParams({
-                          frequencyPenalty: data.value
-                        });
-                      }} />
-                  } />
+                <Accordion className="sm:col-span-2" collapsible
+                  openItems={!commonStore.apiParamsCollapsed && 'advanced'}
+                  onToggle={(e, data) => {
+                    if (data.value === 'advanced')
+                      commonStore.setApiParamsCollapsed(!commonStore.apiParamsCollapsed);
+                  }}>
+                  <AccordionItem value="advanced">
+                    <AccordionHeader ref={advancedHeaderRef1} size="small">{t('Advanced')}</AccordionHeader>
+                    <AccordionPanel>
+                      <div className="grid grid-cols-1 sm:grid-cols-2 gap-2">
+                        <Labeled label={t('Presence Penalty') + ' *'}
+                          desc={t('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.')}
+                          content={
+                            <ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2}
+                              step={0.1} input
+                              onChange={(e, data) => {
+                                setSelectedConfigApiParams({
+                                  presencePenalty: data.value
+                                });
+                              }} />
+                          } />
+                        <Labeled label={t('Frequency Penalty') + ' *'}
+                          desc={t('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.')}
+                          content={
+                            <ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2}
+                              step={0.1} input
+                              onChange={(e, data) => {
+                                setSelectedConfigApiParams({
+                                  frequencyPenalty: data.value
+                                });
+                              }} />
+                          } />
+                        <Labeled
+                          label={t('Penalty Decay')
+                            + ((!selectedConfig.apiParameters.penaltyDecay || selectedConfig.apiParameters.penaltyDecay === defaultPenaltyDecay)
+                              ? ` (${t('Default')})` : '')
+                            + ' *'}
+                          desc={t('If you don\'t know what it is, keep it default.')}
+                          content={
+                            <ValuedSlider value={selectedConfig.apiParameters.penaltyDecay || defaultPenaltyDecay}
+                              min={0.99} max={0.999} step={0.001} toFixed={3} input
+                              onChange={(e, data) => {
+                                setSelectedConfigApiParams({
+                                  penaltyDecay: data.value
+                                });
+                              }} />
+                          } />
+                        <Labeled label={t('Global Penalty') + ' *'}
+                          desc={t('When generating a response, whether to include the submitted prompt as a penalty factor. By turning this off, you will get the same generated results as official RWKV Gradio. If you find duplicate results in the generated results, turning this on can help avoid generating duplicates.')}
+                          content={
+                            <Switch checked={selectedConfig.apiParameters.globalPenalty}
+                              onChange={(e, data) => {
+                                setSelectedConfigApiParams({
+                                  globalPenalty: data.checked
+                                });
+                              }} />
+                          } />
+                      </div>
+                    </AccordionPanel>
+                  </AccordionItem>
+                </Accordion>
               </div>
             }
           />
@@ -279,9 +324,9 @@ const Configs: FC = observer(() => {
                     }}>
                     <Option value="CPU">CPU</Option>
                     <Option value="CPU (rwkv.cpp)">{t('CPU (rwkv.cpp, Faster)')!}</Option>
-                    {commonStore.platform === 'darwin' && <Option value="MPS">MPS</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="CUDA-Beta">{t('CUDA (Beta, Faster)')!}</Option>*/}
                     <Option value="WebGPU">WebGPU</Option>
                     <Option value="WebGPU (Python)">WebGPU (Python)</Option>
                     <Option value="Custom">{t('Custom')!}</Option>
@@ -331,6 +376,40 @@ const Configs: FC = observer(() => {
                         }} />
                     } />
                 }
+                {
+                  selectedConfig.modelParameters.device.startsWith('WebGPU') &&
+                  <Labeled label={t('Parallel Token Chunk Size')}
+                    desc={t('Maximum tokens to be processed in parallel at once. For high end GPUs, this could be 64 or 128 (faster).')}
+                    content={
+                      <ValuedSlider
+                        value={selectedConfig.modelParameters.tokenChunkSize || 32}
+                        min={16} max={256} step={16} input
+                        onChange={(e, data) => {
+                          setSelectedConfigModelParams({
+                            tokenChunkSize: data.value
+                          });
+                        }} />
+                    } />
+                }
+                {
+                  selectedConfig.modelParameters.device.startsWith('WebGPU') &&
+                  <Labeled label={t('Quantized Layers')}
+                    desc={t('Number of the neural network layers quantized with current precision, the more you quantize, the lower the VRAM usage, but the quality correspondingly decreases.')}
+                    content={
+                      <ValuedSlider
+                        disabled={selectedConfig.modelParameters.precision !== 'int8' && selectedConfig.modelParameters.precision !== 'nf4'}
+                        value={selectedConfig.modelParameters.precision === 'int8' ? (selectedConfig.modelParameters.quantizedLayers || 31) :
+                          selectedConfig.modelParameters.precision === 'nf4' ? (selectedConfig.modelParameters.quantizedLayers || 26) :
+                            selectedConfig.modelParameters.maxStoredLayers
+                        } min={0}
+                        max={selectedConfig.modelParameters.maxStoredLayers} step={1} input
+                        onChange={(e, data) => {
+                          setSelectedConfigModelParams({
+                            quantizedLayers: data.value
+                          });
+                        }} />
+                    } />
+                }
                 {selectedConfig.modelParameters.device.startsWith('CUDA') && <div />}
                 {
                   displayStrategyImg &&
@@ -376,7 +455,7 @@ const Configs: FC = observer(() => {
                         commonStore.setModelParamsCollapsed(!commonStore.modelParamsCollapsed);
                     }}>
                     <AccordionItem value="advanced">
-                      <AccordionHeader ref={advancedHeaderRef} size="small">{t('Advanced')}</AccordionHeader>
+                      <AccordionHeader ref={advancedHeaderRef2} size="small">{t('Advanced')}</AccordionHeader>
                       <AccordionPanel>
                         <div className="flex flex-col">
                           <div className="flex grow">

frontend/src/pages/Downloads.tsx

@@ -67,7 +67,7 @@ const Downloads: FC = observer(() => {
                         AddToDownloadList(status.path, status.url);
                     }} />}
                 <ToolTipButton desc={t('Open Folder')} icon={<Folder20Regular />} onClick={() => {
-                  OpenFileFolder(status.path, false);
+                  OpenFileFolder(status.path);
                 }} />
               </div>
             </Field>

frontend/src/pages/Models.tsx

@@ -132,7 +132,7 @@ const columns: TableColumnDefinition<ModelSourceItem>[] = [
             {
               item.isComplete &&
               <ToolTipButton desc={t('Open Folder')} icon={<Folder20Regular />} onClick={() => {
-                OpenFileFolder(`${commonStore.settings.customModelsPath}/${item.name}`, true);
+                OpenFileFolder(`${commonStore.settings.customModelsPath}/${item.name}`);
               }} />
             }
             {item.downloadUrl && !item.isComplete &&
@@ -155,7 +155,7 @@ const columns: TableColumnDefinition<ModelSourceItem>[] = [
 
 const getTags = () => {
   return Array.from(new Set(
-    ['Recommended',
+    ['Recommended', 'Official',
       ...commonStore.modelSourceList.map(item => item.tags || []).flat()
       .filter(i => !i.includes('Other') && !i.includes('Local'))
       , 'Other', 'Local']));

frontend/src/pages/Settings.tsx

@@ -272,18 +272,16 @@ const Settings: FC = observer(() => {
                     <AccordionHeader ref={advancedHeaderRef} size="large">{t('Advanced')}</AccordionHeader>
                     <AccordionPanel>
                       <div className="flex flex-col gap-2 overflow-hidden">
-                        {commonStore.platform !== 'darwin' &&
-                          <Labeled label={t('Custom Models Path')}
-                            content={
-                              <Input className="grow" placeholder="./models"
-                                value={commonStore.settings.customModelsPath}
-                                onChange={(e, data) => {
-                                  commonStore.setSettings({
-                                    customModelsPath: data.value
-                                  });
-                                }} />
-                            } />
-                        }
+                        <Labeled label={t('Custom Models Path')}
+                          content={
+                            <Input className="grow" placeholder="./models"
+                              value={commonStore.settings.customModelsPath}
+                              onChange={(e, data) => {
+                                commonStore.setSettings({
+                                  customModelsPath: data.value
+                                });
+                              }} />
+                          } />
                         <Labeled label={t('Custom Python Path')} // if set, will not use precompiled cuda kernel
                           content={
                             <Input className="grow" placeholder="./py310/python"

frontend/src/pages/Train.tsx

@@ -130,8 +130,9 @@ const showError = (e: any) => {
   }
 };
 
+// error key should be lowercase
 const errorsMap = Object.entries({
-  'python3 ./finetune/lora/train.py': 'Memory is not enough, try to increase the virtual memory (Swap of WSL) or use a smaller base model.',
+  ['python3 ./finetune/lora/$modelInfo'.toLowerCase()]: 'Memory is not enough, try to increase the virtual memory (Swap of WSL) or use a smaller base model.',
   'cuda out of memory': 'VRAM is not enough',
   'valueerror: high <= 0': 'Training data is not enough, reduce context length or add more data for training',
   '+= \'+ptx\'': 'Can not find an Nvidia GPU. Perhaps the gpu driver of windows is too old, or you are using WSL 1 for training, please upgrade to WSL 2. e.g. Run "wsl --set-version Ubuntu-22.04 2"',
@@ -140,6 +141,7 @@ const errorsMap = Object.entries({
   'unsupported gpu architecture': 'Matched CUDA is not installed',
   'error building extension \'fused_adam\'': 'Matched CUDA is not installed',
   'rwkv{version} is not supported': 'This version of RWKV is not supported yet.',
+  'no such file': 'Failed to find the base model, please try to change your base model.',
   'modelinfo is invalid': 'Failed to load model, try to increase the virtual memory (Swap of WSL) or use a smaller base model.'
 });
 
@@ -299,7 +301,6 @@ const LoraFinetune: FC = observer(() => {
           (loraParams.baseModel ? `--load_model models/${loraParams.baseModel} ` : '') +
           (loraParams.loraLoad ? `--lora_load lora-models/${loraParams.loraLoad} ` : '') +
           `--data_file ${convertedDataPath} ` +
-          `--vocab_size ${loraParams.baseModel.toLowerCase().includes('world') ? '65536' : '50277'} ` +
           `--ctx_len ${ctxLen} --epoch_steps ${loraParams.epochSteps} --epoch_count ${loraParams.epochCount} ` +
           `--epoch_begin ${loraParams.epochBegin} --epoch_save ${loraParams.epochSave} ` +
           `--micro_bsz ${loraParams.microBsz} --accumulate_grad_batches ${loraParams.accumGradBatches} ` +
@@ -398,7 +399,7 @@ const LoraFinetune: FC = observer(() => {
                     'Even 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.')} />
                 <ToolTipButton desc={t('Open Folder')} icon={<Folder20Regular />} onClick={() => {
-                  OpenFileFolder(dataParams.dataPath, false);
+                  OpenFileFolder(dataParams.dataPath);
                 }} />
               </div>
               <div className="flex gap-2 items-center">
@@ -418,7 +419,8 @@ const LoraFinetune: FC = observer(() => {
                     outputPrefix,
                     dataParams.vocabPath).then(async () => {
                     if (!await FileExists(outputPrefix + '_text_document.idx')) {
-                      toast(t('Failed to convert data') + ' - ' + await GetPyError(), { type: 'error' });
+                      if (commonStore.platform === 'windows' || commonStore.platform === 'linux')
+                        toast(t('Failed to convert data') + ' - ' + await GetPyError(), { type: 'error' });
                     } else {
                       toast(t('Convert Data successfully'), { type: 'success' });
                     }

frontend/src/pages/defaultConfigs.ts

@@ -1,11 +1,25 @@
 import { CompletionPreset } from '../types/completion';
 import { ModelConfig } from '../types/configs';
 
+export const defaultPenaltyDecay = 0.996;
+
 export const defaultCompositionPrompt = '<pad>';
+export const defaultCompositionABCPrompt = 'S:3\n' +
+  'B:9\n' +
+  'E:4\n' +
+  'B:9\n' +
+  'E:4\n' +
+  'E:4\n' +
+  'B:9\n' +
+  'L:1/8\n' +
+  'M:3/4\n' +
+  'K:D\n' +
+  ' Bc |"G" d2 cB"A" A2 FE |"Bm" F2 B4 F^G |';
 
 export const defaultPresets: CompletionPreset[] = [{
   name: 'Writer',
-  prompt: 'The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n',
+  prompt: 'The following is an epic science fiction masterpiece that is immortalized, with delicate descriptions and grand depictions of interstellar civilization wars.\n' +
+    'Chapter 1.\n',
   params: {
     maxResponseToken: 500,
     temperature: 1,
@@ -18,7 +32,9 @@ export const defaultPresets: CompletionPreset[] = [{
   }
 }, {
   name: 'Translator',
-  prompt: 'Translate this into Chinese.\n\nEnglish: What rooms do you have available?',
+  prompt: 'Translate this into Chinese.\n' +
+    '\n' +
+    'English: What rooms do you have available?',
   params: {
     maxResponseToken: 500,
     temperature: 1,
@@ -31,7 +47,13 @@ export const defaultPresets: CompletionPreset[] = [{
   }
 }, {
   name: 'Catgirl',
-  prompt: '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?',
+  prompt: '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' +
+    '\n' +
+    'User: Hello.\n' +
+    '\n' +
+    'Assistant: I\'m here, meow~.\n' +
+    '\n' +
+    'User: Can you tell jokes?',
   params: {
     maxResponseToken: 500,
     temperature: 1.2,
@@ -70,7 +92,15 @@ export const defaultPresets: CompletionPreset[] = [{
   }
 }, {
   name: 'Werewolf',
-  prompt: '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.',
+  prompt: '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' +
+    '\n' +
+    'Assistant: 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' +
+    '\n' +
+    'User: Okay, I understand. Let\'s begin. Please assign me a role. Am I the Seer, Werewolf, Villager, or Bodyguard?\n' +
+    '\n' +
+    'Assistant: You are the Seer. Now that night has fallen, please choose a player to check his identity.\n' +
+    '\n' +
+    'User: Tonight, I want to check Player 2 and find out his role.',
   params: {
     maxResponseToken: 500,
     temperature: 1.2,
@@ -82,8 +112,64 @@ export const defaultPresets: CompletionPreset[] = [{
     injectEnd: '\\n\\nUser: '
   }
 }, {
-  name: 'Instruction',
-  prompt: 'Instruction: Write a story using the following information\n\nInput: A man named Alex chops a tree down\n\nResponse:',
+  name: 'Instruction 1',
+  prompt: 'Instruction: Write a story using the following information\n' +
+    '\n' +
+    'Input: A man named Alex chops a tree down\n' +
+    '\n' +
+    'Response:',
+  params: {
+    maxResponseToken: 500,
+    temperature: 1,
+    topP: 0.3,
+    presencePenalty: 0,
+    frequencyPenalty: 1,
+    stop: '',
+    injectStart: '',
+    injectEnd: ''
+  }
+}, {
+  name: 'Instruction 2',
+  prompt: 'Instruction: You are an expert assistant for summarizing and extracting information from given content\n' +
+    'Generate a valid JSON in the following format:\n' +
+    '{\n' +
+    '    "summary": "Summary of content",\n' +
+    '    "keywords": ["content keyword 1", "content keyword 2"]\n' +
+    '}\n' +
+    '\n' +
+    'Input: The open-source community has introduced Eagle 7B, a new RNN model, built on the RWKV-v5 architecture. This new model has been trained on 1.1 trillion tokens and supports over 100 languages. The RWKV architecture, short for ‘Rotary Weighted Key-Value,’ is a type of architecture used in the field of artificial intelligence, particularly in natural language processing (NLP) and is a variation of the Recurrent Neural Network (RNN) architecture.\n' +
+    'Eagle 7B promises lower inference cost and stands out as a leading 7B model in terms of environmental efficiency and language versatility.\n' +
+    'The model, with its 7.52 billion parameters, shows excellent performance in multi-lingual benchmarks, setting a new standard in its category. It competes closely with larger models in English language evaluations and is distinctive as an “Attention-Free Transformer,” though it requires additional tuning for specific uses. This model is accessible under the Apache 2.0 license and can be downloaded from HuggingFace for both personal and commercial purposes.\n' +
+    'In terms of multilingual performance, Eagle 7B has claimed to have achieved notable results in benchmarks covering 23 languages. Its English performance has also seen significant advancements, outperforming its predecessor, RWKV v4, and competing with top-tier models.\n' +
+    'Working towards a more scalable architecture and use of data efficiently, Eagle 7B is a more inclusive AI technology, supporting a broader range of languages. This model challenges the prevailing dominance of transformer models by demonstrating the capabilities of RNNs like RWKV in achieving superior performance when trained on comparable data volumes.\n' +
+    'In the RWKV model, the rotary mechanism transforms the input data in a way that helps the model better understand the position or or order of elements in a sequence. The weighted key value also makes the model efficient by retrieving the stored information from previous elements in a sequence. \n' +
+    'However, questions remain about the scalability of RWKV compared to transformers, although there is optimism regarding its potential. The team plans to include additional training, an in-depth paper on Eagle 7B, and the development of a 2T model.\n' +
+    '\n' +
+    'Response: {',
+  params: {
+    maxResponseToken: 500,
+    temperature: 1,
+    topP: 0.3,
+    presencePenalty: 0,
+    frequencyPenalty: 1,
+    stop: '',
+    injectStart: '',
+    injectEnd: ''
+  }
+}, {
+  name: 'Instruction 3',
+  prompt: 'Instruction: 根据输入的聊天记录生成回复\n' +
+    '\n' +
+    'Input: 主人: 巧克力你好呀, 介绍一下自己吧\n' +
+    '巧克力: 主人早上好喵~ 奴家是主人的私人宠物猫娘喵! 巧克力我可是黑色混种猫猫, 虽然平时有点呆呆的, 行动力旺盛, 但是最大的优点就是诚实! 巧克力最喜欢主人了喵! {星星眼}\n' +
+    '主人: 你认识香草吗\n' +
+    '巧克力: 认识的喵! 香草是巧克力的双胞胎妹妹哟! {兴奋}\n' +
+    '主人: 巧克力可以陪主人做羞羞的事情吗\n' +
+    '巧克力: 啊, 真的可以吗? 主人, 巧克力很乐意帮主人解决一下哦! 但是在外面这样子, 有点不好意思喵 {害羞羞}\n' +
+    '主人: 那算了, 改天吧\n' +
+    '巧克力:\n' +
+    '\n' +
+    'Response:',
   params: {
     maxResponseToken: 500,
     temperature: 1,
@@ -121,7 +207,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-1B5-v2-20231025-ctx4096.pth',
+      modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -139,7 +225,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -157,7 +243,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -175,7 +261,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -193,7 +279,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -247,7 +333,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-1B5-v2-20231025-ctx4096.pth',
+      modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
       device: 'MPS',
       precision: 'fp32',
       storedLayers: 41,
@@ -266,7 +352,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'MPS',
       precision: 'fp32',
       storedLayers: 41,
@@ -285,7 +371,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'MPS',
       precision: 'fp32',
       storedLayers: 41,
@@ -304,7 +390,7 @@ export const defaultModelConfigsMac: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'MPS',
       precision: 'fp32',
       storedLayers: 41,
@@ -326,7 +412,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-1B5-v2-20231025-ctx4096.pth',
+      modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 41,
@@ -345,7 +431,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 6,
@@ -364,7 +450,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-1B5-v2-20231025-ctx4096.pth',
+      modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
       device: 'CUDA',
       precision: 'fp16',
       storedLayers: 41,
@@ -383,7 +469,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 24,
@@ -402,7 +488,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 24,
@@ -421,7 +507,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 8,
@@ -440,7 +526,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 8,
@@ -459,7 +545,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 41,
@@ -478,7 +564,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 41,
@@ -497,7 +583,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 18,
@@ -516,7 +602,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 18,
@@ -535,7 +621,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'fp16',
       storedLayers: 41,
@@ -554,7 +640,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'CUDA',
       precision: 'fp16',
       storedLayers: 41,
@@ -573,7 +659,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 27,
@@ -592,7 +678,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 27,
@@ -611,7 +697,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 41,
@@ -630,7 +716,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'int8',
       storedLayers: 41,
@@ -649,7 +735,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'fp16',
       storedLayers: 41,
@@ -668,7 +754,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'CUDA',
       precision: 'fp16',
       storedLayers: 41,
@@ -723,7 +809,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-1B5-v2-20231025-ctx4096.pth',
+      modelName: 'RWKV-x060-World-1B6-v2-20240208-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -741,7 +827,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-5-World-3B-v2-20231118-ctx16k.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -759,7 +845,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-3B-v1-20230625-ctx4096.pth',
+      modelName: 'RWKV-x060-World-3B-v2-20240228-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -777,7 +863,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-7B-v1-20230626-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,
@@ -795,7 +881,7 @@ export const defaultModelConfigs: ModelConfig[] = [
       frequencyPenalty: 1
     },
     modelParameters: {
-      modelName: 'RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096.pth',
+      modelName: 'RWKV-5-World-7B-v2-20240128-ctx4096.pth',
       device: 'WebGPU',
       precision: 'nf4',
       storedLayers: 41,

frontend/src/startup.ts

@@ -49,7 +49,7 @@ export async function startup() {
 async function initRemoteText() {
   await fetch('https://cdn.jsdelivr.net/gh/josstorer/RWKV-Runner@master/manifest.json', { cache: 'no-cache' })
   .then(r => r.json()).then((data) => {
-    if (data.version > manifest.version) {
+    if (data.version >= manifest.version) {
       if (data.introduction)
         commonStore.setIntroduction(data.introduction);
       if (data.about)

frontend/src/stores/commonStore.ts

@@ -3,7 +3,12 @@ import { getUserLanguage, isSystemLightMode, saveCache, saveConfigs, savePresets
 import { WindowSetDarkTheme, WindowSetLightTheme } from '../../wailsjs/runtime';
 import manifest from '../../../manifest.json';
 import i18n from 'i18next';
-import { defaultCompositionPrompt, defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultConfigs';
+import {
+  defaultCompositionPrompt,
+  defaultModelConfigs,
+  defaultModelConfigsMac,
+  defaultPenaltyDecay
+} from '../pages/defaultConfigs';
 import { ChartData } from 'chart.js';
 import { Preset } from '../types/presets';
 import { AboutContent } from '../types/about';
@@ -79,7 +84,10 @@ class CommonStore {
     temperature: 1,
     topP: 0.3,
     presencePenalty: 0,
-    frequencyPenalty: 1
+    frequencyPenalty: 1,
+    penaltyDecay: defaultPenaltyDecay,
+    historyN: 0,
+    markdown: true
   };
   sidePanelCollapsed: boolean | 'auto' = 'auto';
   // completion
@@ -119,6 +127,7 @@ class CommonStore {
   // configs
   currentModelConfigIndex: number = 0;
   modelConfigs: ModelConfig[] = [];
+  apiParamsCollapsed: boolean = true;
   modelParamsCollapsed: boolean = true;
   // models
   activeModelListTags: string[] = [];
@@ -169,7 +178,7 @@ class CommonStore {
     autoUpdatesCheck: true,
     giteeUpdatesSource: getUserLanguage() === 'zh',
     cnMirror: getUserLanguage() === 'zh',
-    useHfMirror: false,
+    useHfMirror: getUserLanguage() === 'zh',
     host: '127.0.0.1',
     dpiScaling: 100,
     customModelsPath: './models',
@@ -316,6 +325,10 @@ class CommonStore {
     this.advancedCollapsed = value;
   }
 
+  setApiParamsCollapsed(value: boolean) {
+    this.apiParamsCollapsed = value;
+  }
+
   setModelParamsCollapsed(value: boolean) {
     this.modelParamsCollapsed = value;
   }

frontend/src/types/chat.ts

@@ -34,4 +34,7 @@ export type Attachment = {
   size: number;
   content: string;
 }
-export type ChatParams = Omit<ApiParameters, 'apiPort'>
+export type ChatParams = Omit<ApiParameters, 'apiPort'> & {
+  historyN: number;
+  markdown: boolean;
+}

frontend/src/types/configs.ts

@@ -5,6 +5,8 @@ export type ApiParameters = {
   topP: number;
   presencePenalty: number;
   frequencyPenalty: number;
+  penaltyDecay?: number;
+  globalPenalty?: boolean;
 }
 export type Device = 'CPU' | 'CPU (rwkv.cpp)' | 'CUDA' | 'CUDA-Beta' | 'WebGPU' | 'WebGPU (Python)' | 'MPS' | 'Custom';
 export type Precision = 'fp16' | 'int8' | 'fp32' | 'nf4' | 'Q5_1';
@@ -15,6 +17,8 @@ export type ModelParameters = {
   precision: Precision;
   storedLayers: number;
   maxStoredLayers: number;
+  quantizedLayers?: number;
+  tokenChunkSize?: number;
   useCustomCuda?: boolean;
   customStrategy?: string;
   useCustomTokenizer?: boolean;

frontend/src/utils/index.tsx

@@ -4,6 +4,7 @@ import {
   DepCheck,
   InstallPyDep,
   ListDirFiles,
+  OpenOpenFileDialog,
   ReadFileInfo,
   ReadJson,
   SaveJson,
@@ -25,7 +26,7 @@ import { DataProcessParameters, LoraFinetuneParameters } from '../types/train';
 import { InstrumentTypeNameMap, MidiMessage, tracksMinimalTotalTime } from '../types/composition';
 import logo from '../assets/images/logo.png';
 import { Preset } from '../types/presets';
-import { botName, Conversation, MessageType, userName } from '../types/chat';
+import { botName, Conversation, MessageType, Role, userName } from '../types/chat';
 import { v4 as uuid } from 'uuid';
 import { findLastIndex } from 'lodash-es';
 
@@ -193,6 +194,10 @@ export const getStrategy = (modelConfig: ModelConfig | undefined = undefined) =>
     case 'WebGPU':
     case 'WebGPU (Python)':
       strategy += params.precision === 'nf4' ? 'fp16i4' : params.precision === 'int8' ? 'fp16i8' : 'fp16';
+      if (params.quantizedLayers)
+        strategy += ` layer${params.quantizedLayers}`;
+      if (params.tokenChunkSize)
+        strategy += ` chunk${params.tokenChunkSize}`;
       break;
     case 'CUDA':
     case 'CUDA-Beta':
@@ -351,7 +356,7 @@ export async function checkUpdate(notifyEvenLatest: boolean = false) {
                 if (r.ok) {
                   r.json().then((data) => {
                     if (data.assets && data.assets.length > 0) {
-                      const asset = data.assets.find((a: any) => a.name.toLowerCase().includes(commonStore.platform.toLowerCase()));
+                      const asset = data.assets.find((a: any) => a.name.toLowerCase().includes(commonStore.platform.toLowerCase().replace('darwin', 'macos')));
                       if (asset) {
                         const updateUrl = !commonStore.settings.giteeUpdatesSource ?
                           `https://github.com/josStorer/RWKV-Runner/releases/download/${versionTag}/${asset.name}` :
@@ -579,24 +584,12 @@ export async function getSoundFont() {
 export const setActivePreset = (preset: Preset | null) => {
   commonStore.setActivePreset(preset);
   //TODO if (preset.displayPresetMessages) {
-  const conversation: Conversation = {};
-  const conversationOrder: string[] = [];
+  const { pushMessage, saveConversation } = newChatConversation();
   if (preset)
     for (const message of preset.messages) {
-      const newUuid = uuid();
-      conversationOrder.push(newUuid);
-      conversation[newUuid] = {
-        sender: message.role === 'user' ? userName : botName,
-        type: MessageType.Normal,
-        color: message.role === 'user' ? 'brand' : 'colorful',
-        time: new Date().toISOString(),
-        content: message.content,
-        side: message.role === 'user' ? 'right' : 'left',
-        done: true
-      };
+      pushMessage(message.role, message.content);
     }
-  commonStore.setConversation(conversation);
-  commonStore.setConversationOrder(conversationOrder);
+  saveConversation();
   //}
 };
 
@@ -612,4 +605,49 @@ export function getSupportedCustomCudaFile(isBeta: boolean) {
       './backend-python/wkv_cuda_utils/wkv_cuda40.pyd';
   else
     return '';
+}
+
+// a wrapper for webOpenOpenFileDialog and OpenOpenFileDialog
+export function OpenFileDialog(filterPattern: string): Promise<Blob> {
+  return new Promise((resolve) => {
+      OpenOpenFileDialog(filterPattern).then(async filePath => {
+        if (!filePath)
+          return;
+
+        let blob: Blob;
+        if (commonStore.platform === 'web')
+          blob = (filePath as unknown as { blob: Blob }).blob;
+        else
+          blob = await fetch(absPathAsset(filePath)).then(r => r.blob());
+
+        resolve(blob);
+      }).catch(e => {
+        toast(t('Error') + ' - ' + (e.message || e), { type: 'error', autoClose: 2500 });
+      });
+    }
+  );
+}
+
+export function newChatConversation() {
+  const conversation: Conversation = {};
+  const conversationOrder: string[] = [];
+  const pushMessage = (role: Role, content: string) => {
+    const newUuid = uuid();
+    conversationOrder.push(newUuid);
+    conversation[newUuid] = {
+      sender: role === 'user' ? userName : botName,
+      type: MessageType.Normal,
+      color: role === 'user' ? 'brand' : 'colorful',
+      avatarImg: role === 'user' ? undefined : logo,
+      time: new Date().toISOString(),
+      content: content,
+      side: role === 'user' ? 'right' : 'left',
+      done: true
+    };
+  };
+  const saveConversation = () => {
+    commonStore.setConversation(conversation);
+    commonStore.setConversationOrder(conversationOrder);
+  };
+  return { pushMessage, saveConversation };
 }

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

@@ -28,6 +28,8 @@ export function DownloadFile(arg1:string,arg2:string):Promise<void>;
 
 export function FileExists(arg1:string):Promise<boolean>;
 
+export function GetAbsPath(arg1:string):Promise<string>;
+
 export function GetPlatform():Promise<string>;
 
 export function GetPyError():Promise<string>;
@@ -40,7 +42,7 @@ export function ListDirFiles(arg1:string):Promise<Array<backend_golang.FileInfo>
 
 export function MergeLora(arg1:string,arg2:boolean,arg3:number,arg4:string,arg5:string,arg6:string):Promise<string>;
 
-export function OpenFileFolder(arg1:string,arg2:boolean):Promise<void>;
+export function OpenFileFolder(arg1:string):Promise<void>;
 
 export function OpenMidiPort(arg1:number):Promise<void>;
 

frontend/wailsjs/go/backend_golang/App.js

@@ -54,6 +54,10 @@ export function FileExists(arg1) {
   return window['go']['backend_golang']['App']['FileExists'](arg1);
 }
 
+export function GetAbsPath(arg1) {
+  return window['go']['backend_golang']['App']['GetAbsPath'](arg1);
+}
+
 export function GetPlatform() {
   return window['go']['backend_golang']['App']['GetPlatform']();
 }
@@ -78,8 +82,8 @@ export function MergeLora(arg1, arg2, arg3, arg4, arg5, arg6) {
   return window['go']['backend_golang']['App']['MergeLora'](arg1, arg2, arg3, arg4, arg5, arg6);
 }
 
-export function OpenFileFolder(arg1, arg2) {
-  return window['go']['backend_golang']['App']['OpenFileFolder'](arg1, arg2);
+export function OpenFileFolder(arg1) {
+  return window['go']['backend_golang']['App']['OpenFileFolder'](arg1);
 }
 
 export function OpenMidiPort(arg1) {

go.mod

@@ -9,7 +9,7 @@ require (
 	github.com/minio/selfupdate v0.6.0
 	github.com/nyaosorg/go-windows-su v0.2.1
 	github.com/ubuntu/gowsl v0.0.0-20230615094051-94945650cc1e
-	github.com/wailsapp/wails/v2 v2.7.1
+	github.com/wailsapp/wails/v2 v2.8.0
 )
 
 require (
@@ -38,9 +38,9 @@ require (
 	github.com/valyala/fasttemplate v1.2.2 // indirect
 	github.com/wailsapp/go-webview2 v1.0.10 // indirect
 	github.com/wailsapp/mimetype v1.4.1 // indirect
-	golang.org/x/crypto v0.14.0 // indirect
+	golang.org/x/crypto v0.18.0 // indirect
 	golang.org/x/exp v0.0.0-20230522175609-2e198f4a06a1 // indirect
-	golang.org/x/net v0.17.0 // indirect
-	golang.org/x/sys v0.13.0 // indirect
-	golang.org/x/text v0.13.0 // indirect
+	golang.org/x/net v0.20.0 // indirect
+	golang.org/x/sys v0.16.0 // indirect
+	golang.org/x/text v0.14.0 // indirect
 )

go.sum

@@ -79,20 +79,20 @@ github.com/wailsapp/go-webview2 v1.0.10 h1:PP5Hug6pnQEAhfRzLCoOh2jJaPdrqeRgJKZhy
 github.com/wailsapp/go-webview2 v1.0.10/go.mod h1:Uk2BePfCRzttBBjFrBmqKGJd41P6QIHeV9kTgIeOZNo=
 github.com/wailsapp/mimetype v1.4.1 h1:pQN9ycO7uo4vsUUuPeHEYoUkLVkaRntMnHJxVwYhwHs=
 github.com/wailsapp/mimetype v1.4.1/go.mod h1:9aV5k31bBOv5z6u+QP8TltzvNGJPmNJD4XlAL3U+j3o=
-github.com/wailsapp/wails/v2 v2.7.1 h1:HAzp2c5ODOzsLC6ZMDVtNOB72ozM7/SJecJPB2Ur+UU=
-github.com/wailsapp/wails/v2 v2.7.1/go.mod h1:oIJVwwso5fdOgprBYWXBBqtx6PaSvxg8/KTQHNGkadc=
+github.com/wailsapp/wails/v2 v2.8.0 h1:b2NNn99uGPiN6P5bDsnPwOJZWtAOUhNLv7Vl+YxMTr4=
+github.com/wailsapp/wails/v2 v2.8.0/go.mod h1:EFUGWkUX3KofO4fmKR/GmsLy3HhPH7NbyOEaMt8lBF0=
 golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w=
 golang.org/x/crypto v0.0.0-20210220033148-5ea612d1eb83/go.mod h1:jdWPYTVW3xRLrWPugEBEK3UY2ZEsg3UU495nc5E+M+I=
 golang.org/x/crypto v0.0.0-20211209193657-4570a0811e8b/go.mod h1:IxCIyHEi3zRg3s0A5j5BB6A9Jmi73HwBIUl50j+osU4=
-golang.org/x/crypto v0.14.0 h1:wBqGXzWJW6m1XrIKlAH0Hs1JJ7+9KBwnIO8v66Q9cHc=
-golang.org/x/crypto v0.14.0/go.mod h1:MVFd36DqK4CsrnJYDkBA3VC4m2GkXAM0PvzMCn4JQf4=
+golang.org/x/crypto v0.18.0 h1:PGVlW0xEltQnzFZ55hkuX5+KLyrMYhHld1YHO4AKcdc=
+golang.org/x/crypto v0.18.0/go.mod h1:R0j02AL6hcrfOiy9T4ZYp/rcWeMxM3L6QYxlOuEG1mg=
 golang.org/x/exp v0.0.0-20230522175609-2e198f4a06a1 h1:k/i9J1pBpvlfR+9QsetwPyERsqu1GIbi967PQMq3Ivc=
 golang.org/x/exp v0.0.0-20230522175609-2e198f4a06a1/go.mod h1:V1LtkGg67GoY2N1AnLN78QLrzxkLyJw7RJb1gzOOz9w=
 golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
 golang.org/x/net v0.0.0-20210505024714-0287a6fb4125/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
 golang.org/x/net v0.0.0-20211112202133-69e39bad7dc2/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
-golang.org/x/net v0.17.0 h1:pVaXccu2ozPjCXewfr1S7xza/zcXTity9cCdXQYSjIM=
-golang.org/x/net v0.17.0/go.mod h1:NxSsAGuq816PNPmqtQdLE42eU2Fs7NoRIZrHJAlaCOE=
+golang.org/x/net v0.20.0 h1:aCL9BSgETF1k+blQaYUBx9hJ9LOGP3gAVemcZlf1Kpo=
+golang.org/x/net v0.20.0/go.mod h1:z8BVo6PvndSri0LbOE3hAn0apkU+1YvI6E70E9jsnvY=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190916202348-b4ddaad3f8a3/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20191026070338-33540a1f6037/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@@ -109,14 +109,14 @@ golang.org/x/sys v0.0.0-20220715151400-c0bba94af5f8/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220908164124-27713097b956/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.13.0 h1:Af8nKPmuFypiUBjVoU9V20FiaFXOcuZI21p0ycVYYGE=
-golang.org/x/sys v0.13.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.16.0 h1:xWw16ngr6ZMtmxDyKyIgsE93KNKz5HKmMa3b8ALHidU=
+golang.org/x/sys v0.16.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/term v0.0.0-20201117132131-f5c789dd3221/go.mod h1:Nr5EML6q2oocZ2LXRh80K7BxOlk5/8JxuGnuhpl+muw=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
-golang.org/x/text v0.13.0 h1:ablQoSUd0tRdKxZewP80B+BaqeKJuVhuRxj/dkrun3k=
-golang.org/x/text v0.13.0/go.mod h1:TvPlkZtksWOMsz7fbANvkp4WM8x/WCo/om8BMLbz+aE=
+golang.org/x/text v0.14.0 h1:ScX5w1eTa3QqT8oi6+ziP7dTV1S2+ALU0bI+0zXKWiQ=
+golang.org/x/text v0.14.0/go.mod h1:18ZOQIKpY8NJVqYksKHtTdi31H5itFRjB5/qKTNYzSU=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=

main.go

@@ -67,9 +67,12 @@ var midiAssets embed.FS
 var components embed.FS
 
 func main() {
-	dev := true
+	// Create an instance of the app structure
+	app := backend.NewApp()
+	app.Dev = true
+
 	if buildInfo, ok := debug.ReadBuildInfo(); !ok || strings.Contains(buildInfo.String(), "-ldflags") {
-		dev = false
+		app.Dev = false
 
 		backend.CopyEmbed(assets)
 		os.RemoveAll("./py310/Lib/site-packages/cyac-1.7.dist-info")
@@ -83,9 +86,6 @@ func main() {
 		backend.CopyEmbed(components)
 	}
 
-	// Create an instance of the app structure
-	app := backend.NewApp()
-
 	var zoomFactor float64 = 1.0
 	data, err := app.ReadJson("config.json")
 	if err == nil {
@@ -99,7 +99,7 @@ func main() {
 	}
 
 	var logger wailsLogger.Logger
-	if dev {
+	if app.Dev {
 		logger = wailsLogger.NewDefaultLogger()
 	} else {
 		logger = wailsLogger.NewFileLogger("crash.log")

manifest.json

@@ -1,12 +1,12 @@
 {
-  "version": "1.6.5",
+  "version": "1.7.3",
   "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）。"
   },
   "about": {
-    "en": "<div align=\"center\">\n\nProject Source Code and Introduction:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\n\nRelated Repositories:\nRWKV-5-World: https://huggingface.co/BlinkDL/rwkv-5-world/tree/main\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\nai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server\nrwkv.cpp: https://github.com/saharNooby/rwkv.cpp\nweb-rwkv-py: https://github.com/cryscan/web-rwkv-py\n\n</div>",
-    "zh": "<div align=\"center\">\n\n本项目源码及介绍页:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n\n相关仓库:\nRWKV-5-World: https://huggingface.co/BlinkDL/rwkv-5-world/tree/main\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\nai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server\nrwkv.cpp: https://github.com/saharNooby/rwkv.cpp\nweb-rwkv-py: https://github.com/cryscan/web-rwkv-py\n\n</div>"
+    "en": "<div align=\"center\">\n\nProject Source Code and Introduction:\nhttps://github.com/josStorer/RWKV-Runner\nAuthor: [@josStorer](https://github.com/josStorer)\n\nRelated Repositories:\nRWKV-5-World: https://huggingface.co/BlinkDL/rwkv-5-world/tree/main\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nRWKV-v5-lora: https://github.com/JL-er/RWKV-v5-lora\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\nai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server\nrwkv.cpp: https://github.com/saharNooby/rwkv.cpp\nweb-rwkv-py: https://github.com/cryscan/web-rwkv-py\nweb-rwkv: https://github.com/cryscan/web-rwkv\n\n</div>",
+    "zh": "<div align=\"center\">\n\n本项目源码及介绍页:\nhttps://github.com/josStorer/RWKV-Runner\n作者: [@josStorer](https://github.com/josStorer)\n演示与常见问题说明视频: https://www.bilibili.com/video/BV1hM4y1v76R\n\n相关仓库:\nRWKV-5-World: https://huggingface.co/BlinkDL/rwkv-5-world/tree/main\nRWKV-4-World: https://huggingface.co/BlinkDL/rwkv-4-world/tree/main\nRWKV-4-Raven: https://huggingface.co/BlinkDL/rwkv-4-raven/tree/main\nChatRWKV: https://github.com/BlinkDL/ChatRWKV\nRWKV-LM: https://github.com/BlinkDL/RWKV-LM\nRWKV-LM-LoRA: https://github.com/Blealtan/RWKV-LM-LoRA\nRWKV-v5-lora: https://github.com/JL-er/RWKV-v5-lora\nMIDI-LLM-tokenizer: https://github.com/briansemrau/MIDI-LLM-tokenizer\nai00_rwkv_server: https://github.com/cgisky1980/ai00_rwkv_server\nrwkv.cpp: https://github.com/saharNooby/rwkv.cpp\nweb-rwkv-py: https://github.com/cryscan/web-rwkv-py\nweb-rwkv: https://github.com/cryscan/web-rwkv\n\n</div>"
   },
   "programFiles": [
     {
@@ -15,6 +15,47 @@
     }
   ],
   "models": [
+    {
+      "name": "RWKV-x060-World-1B6-v2-20240208-ctx4096.pth",
+      "desc": {
+        "en": "RWKV-6 Global Languages 1.6B v2",
+        "zh": "RWKV-6 全球语言 1.6B v2",
+        "ja": "RWKV-6 グローバル言語 1.6B v2"
+      },
+      "size": 3199845663,
+      "SHA256": "5c9c877fb60a65cab269af175328b6aaf16d02b8b09738923254f9986e5dc440",
+      "lastUpdated": "2024-02-08T17:56:51",
+      "url": "https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/RWKV-x060-World-1B6-v2-20240208-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-1B6-v2-20240208-ctx4096.pth",
+      "tags": [
+        "Official",
+        "RWKV-6",
+        "Global",
+        "CN",
+        "JP"
+      ]
+    },
+    {
+      "name": "RWKV-x060-World-3B-v2-20240228-ctx4096.pth",
+      "desc": {
+        "en": "RWKV-6 Global Languages 3B v2",
+        "zh": "RWKV-6 全球语言 3B v2",
+        "ja": "RWKV-6 グローバル言語 3B v2"
+      },
+      "size": 6199859158,
+      "SHA256": "f1235079a07084472de86996846a6533e41e3964b153cdc1e8462cc138d8521d",
+      "lastUpdated": "2024-02-29T03:53:53",
+      "url": "https://huggingface.co/BlinkDL/rwkv-6-world/blob/main/RWKV-x060-World-3B-v2-20240228-ctx4096.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-3B-v2-20240228-ctx4096.pth",
+      "tags": [
+        "Official",
+        "RWKV-6",
+        "Global",
+        "Recommended",
+        "CN",
+        "JP"
+      ]
+    },
     {
       "name": "RWKV-5-World-0.1B-v1-20230803-ctx4096.pth",
       "desc": {
@@ -28,7 +69,7 @@
       "url": "https://huggingface.co/BlinkDL/rwkv-5-world/blob/main/RWKV-5-World-0.1B-v1-20230803-ctx4096.pth",
       "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-5-world/resolve/main/RWKV-5-World-0.1B-v1-20230803-ctx4096.pth",
       "tags": [
-        "Main",
+        "Official",
         "RWKV-5",
         "Global"
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+        "Official",
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+        "en": "RWKV-5 Global Languages 7B v2",
+        "zh": "RWKV-5 全球语言 7B v2",
+        "ja": "RWKV-5 グローバル言語 7B v2"
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+        "Official",
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+        "Global",
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+        "zh": "RWKV-5 Mobius 12B 128k上下文",
+        "ja": "RWKV-5 Mobius 12B 128kコンテキスト"
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+      "url": "https://huggingface.co/TimeMobius/Mobius-Chat-12B-128k/blob/main/Mobius-r5-chat-12b-128k.pth",
+      "downloadUrl": "https://huggingface.co/TimeMobius/Mobius-Chat-12B-128k/resolve/main/Mobius-r5-chat-12b-128k.pth",
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       ]
@@ -1245,7 +1352,25 @@
       "url": "https://huggingface.co/BlinkDL/rwkv-5-music/blob/main/RWKV-5-MIDI-560M-v1-20230902-ctx4096.pth",
       "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-5-music/resolve/main/RWKV-5-MIDI-560M-v1-20230902-ctx4096.pth",
       "tags": [
-        "Main",
+        "Official",
+        "RWKV-5",
+        "Music"
+      ]
+    },
+    {
+      "name": "RWKV-5-ABC-82M-v1-20230901-ctx1024.pth",
+      "desc": {
+        "en": "RWKV-5 Music ABC 82M v1",
+        "zh": "RWKV-5 作曲 ABC 82M v1",
+        "ja": "RWKV-5 作曲 ABC 82M v1"
+      },
+      "size": 164222002,
+      "SHA256": "5bf9ae32e4ef05c3851d6010709c6c00dda926d110766b9a712bc48c0a53e098",
+      "lastUpdated": "2023-09-02T06:55:12",
+      "url": "https://huggingface.co/BlinkDL/rwkv-5-music/blob/main/RWKV-5-ABC-82M-v1-20230901-ctx1024.pth",
+      "downloadUrl": "https://huggingface.co/BlinkDL/rwkv-5-music/resolve/main/RWKV-5-ABC-82M-v1-20230901-ctx1024.pth",
+      "tags": [
+        "Official",
         "RWKV-5",
         "Music"
       ]

parse_api_log.py

@@ -0,0 +1,67 @@
+import json
+import sys
+
+
+def extract_data(log_file):
+    entries = []
+
+    with open(log_file, 'r', encoding="utf-8") as file:
+        lines = file.readlines()
+
+        for i, line in enumerate(lines):
+            if line.startswith('Generation Prompt:') and not lines[i + 1].startswith("<pad>"):
+                current_entry = {'prompt': "", 'response': ""}
+
+                prompt_end_point = i + 1
+                for j in range(i + 1, len(lines)):
+                    if lines[j].strip().endswith('- INFO'):
+                        current_entry['prompt'] = current_entry['prompt'].rstrip()
+                        break
+                    current_entry['prompt'] += lines[j]
+                    prompt_end_point = j
+
+                for j in range(prompt_end_point + 1, len(lines)):
+                    if lines[j].startswith('Url:') and lines[j].strip().endswith("/completions"):
+                        for k in range(j + 1, len(lines)):
+                            if lines[k].startswith('Data:'):
+                                for l in range(k + 1, len(lines)):
+                                    if "RequestsNum: " in lines[l]:
+                                        current_entry['response'] = current_entry['response'].rstrip()
+                                        entries.append(current_entry)
+                                        break
+                                    current_entry['response'] += lines[l]
+                                else:
+                                    continue
+                                break
+                        else:
+                            continue
+                        break
+    return entries
+
+
+def main():
+    log_file = 'D:\\RWKV_Runner\\api.log' if len(sys.argv) < 2 else sys.argv[1]
+    entries = extract_data(log_file)
+
+    try:
+        import cyac
+        trie = cyac.Trie()
+        histories = []
+        for entry in entries:
+            v = entry['prompt'] + entry['response']
+            trie.insert(v)
+        for entry in entries:
+            v = entry['prompt'] + entry['response']
+            for id in trie.predict(v):
+                pass
+            if trie[id] == v:
+                histories.append(entry)
+        json_data = json.dumps(histories, indent=2)
+    except ModuleNotFoundError:
+        json_data = json.dumps(entries, indent=2)
+
+    print(json_data.encode('utf-8').decode('unicode_escape'))
+
+
+if __name__ == "__main__":
+    main()

scripts/merge_manifest.py

@@ -0,0 +1,14 @@
+import glob
+import os, sys
+
+MAIN_IMAGE_NAME=sys.argv[1]
+TARGET_TAG="latest" if len(sys.argv) < 3 else sys.argv[2]
+
+args=["docker manifest create {}:{}".format(MAIN_IMAGE_NAME, TARGET_TAG)]
+for i in glob.glob("/tmp/images/*/*.txt"):
+    with open(i, "r") as file:
+        args += " --amend {}@{}".format(MAIN_IMAGE_NAME, file.readline().strip())
+cmd_create="".join(args)
+cmd_push="docker manifest push {}:{}".format(MAIN_IMAGE_NAME, TARGET_TAG)
+os.system(cmd_create)
+os.system(cmd_push)