release v1.7.9

Bumps [crazy-max/ghaction-chocolatey](https://github.com/crazy-max/ghaction-chocolatey) from 2 to 3.
- [Release notes](https://github.com/crazy-max/ghaction-chocolatey/releases)
- [Commits](https://github.com/crazy-max/ghaction-chocolatey/compare/v2...v3)

---
updated-dependencies:
- dependency-name: crazy-max/ghaction-chocolatey
  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@v3
+        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@v3
+        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 +1,41 @@
 ## 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
+- bump webgpu mode [ai00_server v0.4.2](https://github.com/Ai00-X/ai00_server) (huge performance improvement)
+- upgrade to rwkv 0.8.26 (state-tuned model support)
+- update defaultConfigs and manifest.json
+- chores
+
+## Breaking Changes
+
+- change the default value of `presystem` to false
+
+For the convenience of using the future state-tuned models, the default value of `presystem` has been set to false. This
+means that the RWKV-Runner service will no longer automatically insert recommended RWKV pre-prompts for you:
+
+```
+User: hi
+
+Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
+```
+
+If you are using the API service and conducting a rigorous RWKV conversation, please manually send the above messages to
+the `/chat/completions` API's `messages` array, or manually send `presystem: true` to have the server automatically
+insert pre-prompts.
+
+If you are using the RWKV-Runner client for chatting, you can enable `Insert default system prompt at the beginning` in
+the preset editor.
+
+Of course, in reality, even if you do not perform the above, there is usually no significant negative impact.
+
+If you are using the new RWKV state-tuned models, you do not need to perform the above.
+
+The new RWKV state-tuned models can be downloaded here, they are very interesting:
+
+- https://huggingface.co/BlinkDL/rwkv-6-state-instruct-aligned
+- https://huggingface.co/BlinkDL/temp-latest-training-models
+
+If you are interested in state-tuning, please refer
+to: https://github.com/BlinkDL/RWKV-LM#state-tuning-tuning-the-initial-state-zero-inference-overhead
 
 ## 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
@@ -89,7 +94,8 @@ English | [简体中文](README_ZH.md) | [日本語](README_JA.md)
 - Built-in model conversion tool.
 - Built-in download management and remote model inspection.
 - Built-in one-click LoRA Finetune. (Windows Only)
-- Can also be used as an OpenAI ChatGPT and GPT-Playground client. (Fill in the API URL and API Key in Settings page)
+- Can also be used as an OpenAI ChatGPT, GPT-Playground, Ollama and more clients. (Fill in the API URL and API Key in
+  Settings page)
 - Multilingual localization.
 - Theme switching.
 - Automatic updates.
@@ -231,10 +237,12 @@ 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
@@ -84,8 +89,8 @@
 - 内蔵モデル変換ツール
 - ダウンロード管理とリモートモデル検査機能内蔵
 - 内蔵のLoRA微調整機能を搭載しています (Windowsのみ)
-- このプログラムは、OpenAI ChatGPTとGPT Playgroundのクライアントとしても使用できます（設定ページで `API URL` と `API Key`
-  を入力してください）
+- このプログラムは、OpenAI ChatGPT、GPT Playground、Ollama などのクライアントとしても使用できます（設定ページで `API URL`
+  と `API Key` を入力してください）
 - 多言語ローカライズ
 - テーマ切り替え
 - 自動アップデート
@@ -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
@@ -78,7 +83,7 @@ API兼容的接口，这意味着一切ChatGPT客户端都是RWKV客户端。
 - 内置模型转换工具
 - 内置下载管理和远程模型检视
 - 内置一键LoRA微调 (仅限Windows)
-- 也可用作 OpenAI ChatGPT 和 GPT Playground 客户端 (在设置内填写API URL和API Key)
+- 也可用作 OpenAI ChatGPT, GPT Playground, Ollama 等服务的客户端 (在设置内填写API URL和API Key)
 - 多语言本地化
 - 主题切换
 - 自动更新
@@ -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

@@ -1,15 +1,22 @@
 package backend_golang
 
 import (
+	"archive/zip"
 	"bufio"
+	"bytes"
 	"context"
 	"errors"
 	"io"
+	"log"
+	"net"
 	"net/http"
+	"net/http/httputil"
+	"net/url"
 	"os"
 	"os/exec"
 	"path/filepath"
 	"runtime"
+	"strings"
 	"syscall"
 	"time"
 
@@ -23,6 +30,8 @@ type App struct {
 	ctx           context.Context
 	HasConfigData bool
 	ConfigData    map[string]any
+	Dev           bool
+	proxyPort     int
 	exDir         string
 	cmdPrefix     string
 }
@@ -32,6 +41,63 @@ func NewApp() *App {
 	return &App{}
 }
 
+func (a *App) newFetchProxy() {
+	go func() {
+		handler := func(w http.ResponseWriter, r *http.Request) {
+			if r.Method == "OPTIONS" {
+				w.Header().Set("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
+				w.Header().Set("Access-Control-Allow-Headers", "*")
+				w.Header().Set("Access-Control-Allow-Origin", "*")
+				return
+			}
+			proxy := &httputil.ReverseProxy{
+				ModifyResponse: func(res *http.Response) error {
+					res.Header.Set("Access-Control-Allow-Origin", "*")
+					return nil
+				},
+				Director: func(req *http.Request) {
+					realTarget := req.Header.Get("Real-Target")
+					if realTarget != "" {
+						realTarget, err := url.PathUnescape(realTarget)
+						if err != nil {
+							log.Printf("Error decoding target URL: %v\n", err)
+							return
+						}
+						target, err := url.Parse(realTarget)
+						if err != nil {
+							log.Printf("Error parsing target URL: %v\n", err)
+							return
+						}
+						req.Header.Set("Accept", "*/*")
+						req.Header.Del("Origin")
+						req.Header.Del("Referer")
+						req.Header.Del("Real-Target")
+						req.Header.Del("Sec-Fetch-Dest")
+						req.Header.Del("Sec-Fetch-Mode")
+						req.Header.Del("Sec-Fetch-Site")
+						req.URL.Scheme = target.Scheme
+						req.URL.Host = target.Host
+						req.URL.Path = target.Path
+						req.URL.RawQuery = url.PathEscape(target.RawQuery)
+						log.Println("Proxying to", realTarget)
+					} else {
+						log.Println("Real-Target header is missing")
+					}
+				},
+			}
+			proxy.ServeHTTP(w, r)
+		}
+		http.HandleFunc("/", handler)
+		listener, err := net.Listen("tcp", "127.0.0.1:0")
+		if err != nil {
+			return
+		}
+		a.proxyPort = listener.Addr().(*net.TCPAddr).Port
+
+		http.Serve(listener, nil)
+	}()
+}
+
 // startup is called when the app starts. The context is saved
 // so we can call the runtime methods
 func (a *App) OnStartup(ctx context.Context) {
@@ -39,10 +105,20 @@ 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 +126,9 @@ 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()
 		}
@@ -62,6 +138,7 @@ func (a *App) OnStartup(ctx context.Context) {
 	a.midiLoop()
 	a.watchFs()
 	a.monitorHardware()
+	a.newFetchProxy()
 }
 
 func (a *App) OnBeforeClose(ctx context.Context) bool {
@@ -149,6 +226,7 @@ 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 +246,35 @@ 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 {
@@ -202,3 +302,7 @@ func (a *App) RestartApp() error {
 func (a *App) GetPlatform() string {
 	return runtime.GOOS
 }
+
+func (a *App) GetProxyPort() int {
+	return a.proxyPort
+}

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
 }
@@ -178,14 +227,14 @@ func (a *App) InstallPyDep(python string, cnMirror bool) (string, error) {
 
 	if runtime.GOOS == "windows" {
 		ChangeFileLine("./py310/python310._pth", 3, "Lib\\site-packages")
-		installScript := python + " ./backend-python/get-pip.py -i https://pypi.tuna.tsinghua.edu.cn/simple --no-warn-script-location\n" +
+		installScript := python + " ./backend-python/get-pip.py -i https://mirrors.aliyun.com/pypi/simple --no-warn-script-location\n" +
 			python + " -m pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cu117 --no-warn-script-location\n" +
-			python + " -m pip install -r ./backend-python/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple --no-warn-script-location\n" +
+			python + " -m pip install -r ./backend-python/requirements.txt -i https://mirrors.aliyun.com/pypi/simple --no-warn-script-location\n" +
 			"exit"
 		if !cnMirror {
-			installScript = strings.Replace(installScript, " -i https://pypi.tuna.tsinghua.edu.cn/simple", "", -1)
+			installScript = strings.Replace(installScript, " -i https://mirrors.aliyun.com/pypi/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
 		}
@@ -193,7 +242,7 @@ func (a *App) InstallPyDep(python string, cnMirror bool) (string, error) {
 	}
 
 	if cnMirror {
-		return Cmd(python, "-m", "pip", "install", "-r", "./backend-python/requirements_without_cyac.txt", "-i", "https://pypi.tuna.tsinghua.edu.cn/simple")
+		return Cmd(python, "-m", "pip", "install", "-r", "./backend-python/requirements_without_cyac.txt", "-i", "https://mirrors.aliyun.com/pypi/simple")
 	} else {
 		return Cmd(python, "-m", "pip", "install", "-r", "./backend-python/requirements_without_cyac.txt")
 	}

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__":
@@ -100,6 +102,7 @@ if __name__ == "__main__":
                 "time_mix_w2",
                 "time_decay_w1",
                 "time_decay_w2",
+                "lora.0",
             ],
         )
         print(f"Saved to {args.output}")

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==0.8.26
 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==0.8.26
 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

@@ -53,8 +53,11 @@ class ChatCompletionBody(ModelConfigBody):
     assistant_name: Union[str, None] = Field(
         None, description="Internal assistant name", min_length=1
     )
+    system_name: Union[str, None] = Field(
+        None, description="Internal system name", min_length=1
+    )
     presystem: bool = Field(
-        True, description="Whether to insert default system prompt at the beginning"
+        False, description="Whether to insert default system prompt at the beginning"
     )
 
     model_config = {
@@ -68,12 +71,13 @@ class ChatCompletionBody(ModelConfigBody):
                 "stop": None,
                 "user_name": None,
                 "assistant_name": None,
+                "system_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 +98,10 @@ 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 +148,7 @@ 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 +160,27 @@ 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 +202,25 @@ 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",
+                                }
+                            )
                         ],
                     }
                 )
@@ -225,38 +236,29 @@ 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",
+                            }
+                        )
                     ],
                 }
 
 
-@router.post("/v1/chat/completions", tags=["Completions"])
-@router.post("/chat/completions", tags=["Completions"])
-async def chat_completions(body: ChatCompletionBody, request: Request):
-    model: TextRWKV = global_var.get(global_var.Model)
-    if model is None:
-        raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
-
-    if body.messages is None or body.messages == []:
-        raise HTTPException(status.HTTP_400_BAD_REQUEST, "messages not found")
-
-    interface = model.interface
-    user = model.user if body.user_name is None else body.user_name
-    bot = model.bot if body.assistant_name is None else body.assistant_name
-
+def chat_template_old(
+    model: TextRWKV, body: ChatCompletionBody, interface: str, user: str, bot: str
+):
     is_raven = model.rwkv_type == RWKVType.Raven
 
     completion_text: str = ""
@@ -325,6 +327,53 @@ The following is a coherent verbose detailed conversation between a girl named {
         completion_text += append_message + "\n\n"
     completion_text += f"{bot}{interface}"
 
+    return completion_text
+
+
+def chat_template(
+    model: TextRWKV, body: ChatCompletionBody, interface: str, user: str, bot: str
+):
+    completion_text: str = ""
+    if body.presystem:
+        completion_text = (
+            f"{user}{interface} hi\n\n{bot}{interface} Hi. "
+            + "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
+        )
+
+    system = "System" if body.system_name is None else body.system_name
+    for message in body.messages:
+        append_message: str = ""
+        if message.role == Role.User:
+            append_message = f"{user}{interface} " + message.content
+        elif message.role == Role.Assistant:
+            append_message = f"{bot}{interface} " + message.content
+        elif message.role == Role.System:
+            append_message = f"{system}{interface} " + message.content
+        completion_text += append_message + "\n\n"
+    completion_text += f"{bot}{interface}"
+
+    return completion_text
+
+
+@router.post("/v1/chat/completions", tags=["Completions"])
+@router.post("/chat/completions", tags=["Completions"])
+async def chat_completions(body: ChatCompletionBody, request: Request):
+    model: TextRWKV = global_var.get(global_var.Model)
+    if model is None:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
+
+    if body.messages is None or body.messages == []:
+        raise HTTPException(status.HTTP_400_BAD_REQUEST, "messages not found")
+
+    interface = model.interface
+    user = model.user if body.user_name is None else body.user_name
+    bot = model.bot if body.assistant_name is None else body.assistant_name
+
+    if model.version < 5:
+        completion_text = chat_template_old(model, body, interface, user, bot)
+    else:
+        completion_text = chat_template(model, body, interface, user, bot)
+
     user_code = model.pipeline.decode([model.pipeline.encode(user)[0]])
     bot_code = model.pipeline.decode([model.pipeline.encode(bot)[0]])
     if type(body.stop) == str:
@@ -333,9 +382,9 @@ The following is a coherent verbose detailed conversation between a girl named {
         body.stop.append(f"\n\n{user_code}")
         body.stop.append(f"\n\n{bot_code}")
     elif body.stop is None:
-        body.stop = default_stop
-    if not body.presystem:
-        body.stop.append("\n\n")
+        body.stop = default_stop + [f"\n\n{user_code}", f"\n\n{bot_code}"]
+    # if not body.presystem:
+    #     body.stop.append("\n\n")
 
     if body.stream:
         return EventSourceResponse(

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/cpp/model.py

@@ -9,6 +9,9 @@ class RWKV:
         self.model = rwkv_cpp_model.RWKVModel(self.library, model_path)
         self.w = {}  # fake weight
         self.w["emb.weight"] = [0] * self.model.n_vocab
+        self.version = (
+            self.model.arch_version_major + self.model.arch_version_minor / 10
+        )
 
     def forward(self, tokens: List[int], state: Union[Any, None] = None):
         return self.model.eval_sequence_in_chunks(tokens, state, use_numpy=True)

backend-python/rwkv_pip/cpp/rwkv_cpp_model.py

@@ -52,9 +52,14 @@ class RWKVModel:
         if 'gpu_layers_count' in kwargs:
             gpu_layer_count = kwargs['gpu_layers_count']
 
-        assert os.path.isfile(model_path), f'{model_path} is not a file'
-        assert thread_count > 0, 'Thread count must be > 0'
-        assert gpu_layer_count >= 0, 'GPU layer count must be >= 0'
+        if not os.path.isfile(model_path):
+            raise ValueError(f'{model_path} is not a file')
+        
+        if not (thread_count > 0):
+            raise ValueError('Thread count must be > 0')  
+
+        if not (gpu_layer_count >= 0):
+            raise ValueError('GPU layer count must be >= 0')
 
         self._library: rwkv_cpp_shared_library.RWKVSharedLibrary = shared_library
 
@@ -84,10 +89,19 @@ class RWKVModel:
             Count of layers to offload onto the GPU, must be >= 0.
         """
 
-        assert layer_count >= 0, 'Layer count must be >= 0'
+        if not (layer_count >= 0):
+            raise ValueError('Layer count must be >= 0')
 
         return self._library.rwkv_gpu_offload_layers(self._ctx, layer_count)
 
+    @property
+    def arch_version_major(self) -> int:
+        return self._library.rwkv_get_arch_version_major(self._ctx)
+
+    @property
+    def arch_version_minor(self) -> int:
+        return self._library.rwkv_get_arch_version_minor(self._ctx)
+
     @property
     def n_vocab(self) -> int:
         return self._library.rwkv_get_n_vocab(self._ctx)
@@ -133,7 +147,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -207,7 +222,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -281,7 +297,8 @@ class RWKVModel:
             Logits vector of shape (n_vocab); state for the next step.
         """
 
-        assert self._valid, 'Model was freed'
+        if not self._valid:
+            raise ValueError('Model was freed')
 
         use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
 
@@ -320,7 +337,8 @@ class RWKVModel:
         The object must not be used anymore after calling this method.
         """
 
-        assert self._valid, 'Already freed'
+        if not self._valid:
+            raise ValueError('Already freed')
 
         self._valid = False
 
@@ -344,16 +362,25 @@ class RWKVModel:
     def _validate_tensor(self, tensor: NumpyArrayOrPyTorchTensor, name: str, size: int) -> None:
         if self._is_pytorch_tensor(tensor):
             tensor: torch.Tensor = tensor
-            assert tensor.device == torch.device('cpu'), f'{name} is not on CPU'
-            assert tensor.dtype == torch.float32, f'{name} is not of type float32'
-            assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
-            assert tensor.is_contiguous(), f'{name} is not contiguous'
+            
+            if tensor.device != torch.device('cpu'):
+                raise ValueError(f'{name} is not on CPU')
+            if tensor.dtype != torch.float32:
+                raise ValueError(f'{name} is not of type float32')
+            if tensor.shape != (size,):
+                raise ValueError(f'{name} has invalid shape {tensor.shape}, expected ({size})')
+            if not tensor.is_contiguous():
+                raise ValueError(f'{name} is not contiguous')
         else:
             import numpy as np
             tensor: np.ndarray = tensor
-            assert tensor.dtype == np.float32, f'{name} is not of type float32'
-            assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
-            assert tensor.data.contiguous, f'{name} is not contiguous'
+
+            if tensor.dtype != np.float32:
+                raise ValueError(f'{name} is not of type float32')
+            if tensor.shape != (size,):
+                raise ValueError(f'{name} has invalid shape {tensor.shape}, expected ({size})')
+            if not tensor.data.contiguous:
+                raise ValueError(f'{name} is not contiguous')
 
     def _get_data_ptr(self, tensor: NumpyArrayOrPyTorchTensor):
         if self._is_pytorch_tensor(tensor):

backend-python/rwkv_pip/cpp/rwkv_cpp_shared_library.py

@@ -6,21 +6,22 @@ import platform
 from typing import Optional, List, Tuple, Callable
 
 QUANTIZED_FORMAT_NAMES: Tuple[str, str, str, str, str] = (
-    'Q4_0',
-    'Q4_1',
-    'Q5_0',
-    'Q5_1',
-    'Q8_0'
+    "Q4_0",
+    "Q4_1",
+    "Q5_0",
+    "Q5_1",
+    "Q8_0",
 )
 
 P_FLOAT = ctypes.POINTER(ctypes.c_float)
 P_INT = ctypes.POINTER(ctypes.c_int32)
 
-class RWKVContext:
 
+class RWKVContext:
     def __init__(self, ptr: ctypes.pointer) -> None:
         self.ptr: ctypes.pointer = ptr
 
+
 class RWKVSharedLibrary:
     """
     Python wrapper around rwkv.cpp shared library.
@@ -39,7 +40,7 @@ class RWKVSharedLibrary:
         #  When Python is greater than 3.8, we need to reprocess the custom dll
         #  according to the documentation to prevent loading failure errors.
         #  https://docs.python.org/3/whatsnew/3.8.html#ctypes
-        if platform.system().lower() == 'windows':
+        if platform.system().lower() == "windows":
             self.library = ctypes.CDLL(shared_library_path, winmode=0)
         else:
             self.library = ctypes.cdll.LoadLibrary(shared_library_path)
@@ -47,39 +48,48 @@ class RWKVSharedLibrary:
         self.library.rwkv_init_from_file.argtypes = [ctypes.c_char_p, ctypes.c_uint32]
         self.library.rwkv_init_from_file.restype = ctypes.c_void_p
 
-        self.library.rwkv_gpu_offload_layers.argtypes = [ctypes.c_void_p, ctypes.c_uint32]
+        self.library.rwkv_gpu_offload_layers.argtypes = [
+            ctypes.c_void_p,
+            ctypes.c_uint32,
+        ]
         self.library.rwkv_gpu_offload_layers.restype = ctypes.c_bool
 
         self.library.rwkv_eval.argtypes = [
-            ctypes.c_void_p, # ctx
-            ctypes.c_int32, # token
-            P_FLOAT, # state_in
-            P_FLOAT, # state_out
-            P_FLOAT  # logits_out
+            ctypes.c_void_p,  # ctx
+            ctypes.c_int32,  # token
+            P_FLOAT,  # state_in
+            P_FLOAT,  # state_out
+            P_FLOAT,  # logits_out
         ]
         self.library.rwkv_eval.restype = ctypes.c_bool
 
         self.library.rwkv_eval_sequence.argtypes = [
-            ctypes.c_void_p, # ctx
-            P_INT, # tokens
-            ctypes.c_size_t, # token count
-            P_FLOAT, # state_in
-            P_FLOAT, # state_out
-            P_FLOAT  # logits_out
+            ctypes.c_void_p,  # ctx
+            P_INT,  # tokens
+            ctypes.c_size_t,  # token count
+            P_FLOAT,  # state_in
+            P_FLOAT,  # state_out
+            P_FLOAT,  # logits_out
         ]
         self.library.rwkv_eval_sequence.restype = ctypes.c_bool
 
         self.library.rwkv_eval_sequence_in_chunks.argtypes = [
-            ctypes.c_void_p, # ctx
-            P_INT, # tokens
-            ctypes.c_size_t, # token count
-            ctypes.c_size_t, # chunk size
-            P_FLOAT, # state_in
-            P_FLOAT, # state_out
-            P_FLOAT  # logits_out
+            ctypes.c_void_p,  # ctx
+            P_INT,  # tokens
+            ctypes.c_size_t,  # token count
+            ctypes.c_size_t,  # chunk size
+            P_FLOAT,  # state_in
+            P_FLOAT,  # state_out
+            P_FLOAT,  # logits_out
         ]
         self.library.rwkv_eval_sequence_in_chunks.restype = ctypes.c_bool
 
+        self.library.rwkv_get_arch_version_major.argtypes = [ctypes.c_void_p]
+        self.library.rwkv_get_arch_version_major.restype = ctypes.c_uint32
+
+        self.library.rwkv_get_arch_version_minor.argtypes = [ctypes.c_void_p]
+        self.library.rwkv_get_arch_version_minor.restype = ctypes.c_uint32
+
         self.library.rwkv_get_n_vocab.argtypes = [ctypes.c_void_p]
         self.library.rwkv_get_n_vocab.restype = ctypes.c_size_t
 
@@ -101,7 +111,11 @@ class RWKVSharedLibrary:
         self.library.rwkv_free.argtypes = [ctypes.c_void_p]
         self.library.rwkv_free.restype = None
 
-        self.library.rwkv_quantize_model_file.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
+        self.library.rwkv_quantize_model_file.argtypes = [
+            ctypes.c_char_p,
+            ctypes.c_char_p,
+            ctypes.c_char_p,
+        ]
         self.library.rwkv_quantize_model_file.restype = ctypes.c_bool
 
         self.library.rwkv_get_system_info_string.argtypes = []
@@ -109,7 +123,9 @@ class RWKVSharedLibrary:
 
         self.nullptr = ctypes.cast(0, ctypes.c_void_p)
 
-    def rwkv_init_from_file(self, model_file_path: str, thread_count: int) -> RWKVContext:
+    def rwkv_init_from_file(
+        self, model_file_path: str, thread_count: int
+    ) -> RWKVContext:
         """
         Loads the model from a file and prepares it for inference.
         Throws an exception in case of any error. Error messages would be printed to stderr.
@@ -122,9 +138,12 @@ class RWKVSharedLibrary:
             Count of threads to use, must be positive.
         """
 
-        ptr = self.library.rwkv_init_from_file(model_file_path.encode('utf-8'), ctypes.c_uint32(thread_count))
+        ptr = self.library.rwkv_init_from_file(
+            model_file_path.encode("utf-8"), ctypes.c_uint32(thread_count)
+        )
 
-        assert ptr is not None, 'rwkv_init_from_file failed, check stderr'
+        if ptr is None:
+            raise ValueError("rwkv_init_from_file failed, check stderr")
 
         return RWKVContext(ptr)
 
@@ -145,17 +164,20 @@ class RWKVSharedLibrary:
             Count of layers to offload onto the GPU, must be >= 0.
         """
 
-        assert layer_count >= 0, 'Layer count must be >= 0'
+        if not (layer_count >= 0):
+            raise ValueError("Layer count must be >= 0")
 
-        return self.library.rwkv_gpu_offload_layers(ctx.ptr, ctypes.c_uint32(layer_count))
+        return self.library.rwkv_gpu_offload_layers(
+            ctx.ptr, ctypes.c_uint32(layer_count)
+        )
 
     def rwkv_eval(
-            self,
-            ctx: RWKVContext,
-            token: int,
-            state_in_address: Optional[int],
-            state_out_address: int,
-            logits_out_address: int
+        self,
+        ctx: RWKVContext,
+        token: int,
+        state_in_address: Optional[int],
+        state_out_address: int,
+        logits_out_address: int,
     ) -> None:
         """
         Evaluates the model for a single token.
@@ -176,21 +198,22 @@ class RWKVSharedLibrary:
             Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
         """
 
-        assert self.library.rwkv_eval(
+        if not self.library.rwkv_eval(
             ctx.ptr,
             ctypes.c_int32(token),
             ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
             ctypes.cast(state_out_address, P_FLOAT),
-            ctypes.cast(logits_out_address, P_FLOAT)
-        ), 'rwkv_eval failed, check stderr'
+            ctypes.cast(logits_out_address, P_FLOAT),
+        ):
+            raise ValueError("rwkv_eval failed, check stderr")
 
     def rwkv_eval_sequence(
-            self,
-            ctx: RWKVContext,
-            tokens: List[int],
-            state_in_address: Optional[int],
-            state_out_address: int,
-            logits_out_address: int
+        self,
+        ctx: RWKVContext,
+        tokens: List[int],
+        state_in_address: Optional[int],
+        state_out_address: int,
+        logits_out_address: int,
     ) -> None:
         """
         Evaluates the model for a sequence of tokens.
@@ -223,23 +246,24 @@ class RWKVSharedLibrary:
             Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
         """
 
-        assert self.library.rwkv_eval_sequence(
+        if not self.library.rwkv_eval_sequence(
             ctx.ptr,
             ctypes.cast((ctypes.c_int32 * len(tokens))(*tokens), P_INT),
             ctypes.c_size_t(len(tokens)),
             ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
             ctypes.cast(state_out_address, P_FLOAT),
-            ctypes.cast(logits_out_address, P_FLOAT)
-        ), 'rwkv_eval_sequence failed, check stderr'
+            ctypes.cast(logits_out_address, P_FLOAT),
+        ):
+            raise ValueError("rwkv_eval_sequence failed, check stderr")
 
     def rwkv_eval_sequence_in_chunks(
-            self,
-            ctx: RWKVContext,
-            tokens: List[int],
-            chunk_size: int,
-            state_in_address: Optional[int],
-            state_out_address: int,
-            logits_out_address: int
+        self,
+        ctx: RWKVContext,
+        tokens: List[int],
+        chunk_size: int,
+        state_in_address: Optional[int],
+        state_out_address: int,
+        logits_out_address: int,
     ) -> None:
         """
         Evaluates the model for a sequence of tokens using `rwkv_eval_sequence`, splitting a potentially long sequence into fixed-length chunks.
@@ -269,15 +293,40 @@ class RWKVSharedLibrary:
             Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
         """
 
-        assert self.library.rwkv_eval_sequence_in_chunks(
+        if not self.library.rwkv_eval_sequence_in_chunks(
             ctx.ptr,
             ctypes.cast((ctypes.c_int32 * len(tokens))(*tokens), P_INT),
             ctypes.c_size_t(len(tokens)),
             ctypes.c_size_t(chunk_size),
             ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
             ctypes.cast(state_out_address, P_FLOAT),
-            ctypes.cast(logits_out_address, P_FLOAT)
-        ), 'rwkv_eval_sequence_in_chunks failed, check stderr'
+            ctypes.cast(logits_out_address, P_FLOAT),
+        ):
+            raise ValueError("rwkv_eval_sequence_in_chunks failed, check stderr")
+
+    def rwkv_get_arch_version_major(self, ctx: RWKVContext) -> int:
+        """
+        Returns the major version used by the given model.
+
+        Parameters
+        ----------
+        ctx : RWKVContext
+            RWKV context obtained from rwkv_init_from_file.
+        """
+
+        return self.library.rwkv_get_arch_version_major(ctx.ptr)
+
+    def rwkv_get_arch_version_minor(self, ctx: RWKVContext) -> int:
+        """
+        Returns the minor version used by the given model.
+
+        Parameters
+        ----------
+        ctx : RWKVContext
+            RWKV context obtained from rwkv_init_from_file.
+        """
+
+        return self.library.rwkv_get_arch_version_minor(ctx.ptr)
 
     def rwkv_get_n_vocab(self, ctx: RWKVContext) -> int:
         """
@@ -358,7 +407,9 @@ class RWKVSharedLibrary:
 
         ctx.ptr = self.nullptr
 
-    def rwkv_quantize_model_file(self, model_file_path_in: str, model_file_path_out: str, format_name: str) -> None:
+    def rwkv_quantize_model_file(
+        self, model_file_path_in: str, model_file_path_out: str, format_name: str
+    ) -> None:
         """
         Quantizes FP32 or FP16 model to one of INT4 formats.
         Throws an exception in case of any error. Error messages would be printed to stderr.
@@ -373,20 +424,25 @@ class RWKVSharedLibrary:
             One of QUANTIZED_FORMAT_NAMES.
         """
 
-        assert format_name in QUANTIZED_FORMAT_NAMES, f'Unknown format name {format_name}, use one of {QUANTIZED_FORMAT_NAMES}'
+        if format_name not in QUANTIZED_FORMAT_NAMES:
+            raise ValueError(
+                f"Unknown format name {format_name}, use one of {QUANTIZED_FORMAT_NAMES}"
+            )
 
-        assert self.library.rwkv_quantize_model_file(
-            model_file_path_in.encode('utf-8'),
-            model_file_path_out.encode('utf-8'),
-            format_name.encode('utf-8')
-        ), 'rwkv_quantize_model_file failed, check stderr'
+        if not self.library.rwkv_quantize_model_file(
+            model_file_path_in.encode("utf-8"),
+            model_file_path_out.encode("utf-8"),
+            format_name.encode("utf-8"),
+        ):
+            raise ValueError("rwkv_quantize_model_file failed, check stderr")
 
     def rwkv_get_system_info_string(self) -> str:
         """
         Returns system information string.
         """
 
-        return self.library.rwkv_get_system_info_string().decode('utf-8')
+        return self.library.rwkv_get_system_info_string().decode("utf-8")
+
 
 def load_rwkv_shared_library() -> RWKVSharedLibrary:
     """
@@ -396,27 +452,27 @@ def load_rwkv_shared_library() -> RWKVSharedLibrary:
 
     file_name: str
 
-    if 'win32' in sys.platform or 'cygwin' in sys.platform:
-        file_name = 'rwkv.dll'
-    elif 'darwin' in sys.platform:
-        file_name = 'librwkv.dylib'
+    if "win32" in sys.platform or "cygwin" in sys.platform:
+        file_name = "rwkv.dll"
+    elif "darwin" in sys.platform:
+        file_name = "librwkv.dylib"
     else:
-        file_name = 'librwkv.so'
+        file_name = "librwkv.so"
 
     # Possible sub-paths to the library relative to the repo dir.
     child_paths: List[Callable[[pathlib.Path], pathlib.Path]] = [
         # No lookup for Debug config here.
         # I assume that if a user wants to debug the library,
         # they will be able to find the library and set the exact path explicitly.
-        lambda p: p / 'backend-python' / 'rwkv_pip' / 'cpp' / file_name,
-        lambda p: p / 'bin' / 'Release' / file_name,
-        lambda p: p / 'bin' / file_name,
+        lambda p: p / "backend-python" / "rwkv_pip" / "cpp" / file_name,
+        lambda p: p / "bin" / "Release" / file_name,
+        lambda p: p / "bin" / file_name,
         # Some people prefer to build in the "build" subdirectory.
-        lambda p: p / 'build' / 'bin' / 'Release' / file_name,
-        lambda p: p / 'build' / 'bin' / file_name,
-        lambda p: p / 'build' / file_name,
+        lambda p: p / "build" / "bin" / "Release" / file_name,
+        lambda p: p / "build" / "bin" / file_name,
+        lambda p: p / "build" / file_name,
         # Fallback.
-        lambda p: p / file_name
+        lambda p: p / file_name,
     ]
 
     working_dir: pathlib.Path = pathlib.Path(os.path.abspath(os.getcwd()))
@@ -430,7 +486,7 @@ def load_rwkv_shared_library() -> RWKVSharedLibrary:
         # .
         working_dir,
         # Repo dir relative to this Python file.
-        pathlib.Path(os.path.abspath(__file__)).parent.parent.parent
+        pathlib.Path(os.path.abspath(__file__)).parent.parent.parent,
     ]
 
     for parent_path in parent_paths:
@@ -440,5 +496,7 @@ def load_rwkv_shared_library() -> RWKVSharedLibrary:
             if os.path.isfile(full_path):
                 return RWKVSharedLibrary(str(full_path))
 
-    assert False, (f'Failed to find {file_name} automatically; '
-                   f'you need to find the library and create RWKVSharedLibrary specifying the path to it')
+    raise ValueError(
+        f"Failed to find {file_name} automatically; "
+        f"you need to find the library and create RWKVSharedLibrary specifying the path to it"
+    )

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
@@ -488,14 +488,19 @@ class RWKV(MyModule):
             print_need_newline = False
 
             REAL_TIME_FIRST = False
+            args.time_state = False
             for x in list(w.keys()):
                 if ".time_faaaa" in x:
                     REAL_TIME_FIRST = True
+                if ".time_state" in x:
+                    args.time_state = True
             if REAL_TIME_FIRST:
                 w = {
-                    k.replace(".time_faaaa", ".time_first")
-                    if ".time_faaaa" in k
-                    else k: v
+                    (
+                        k.replace(".time_faaaa", ".time_first")
+                        if ".time_faaaa" in k
+                        else k
+                    ): v
                     for k, v in w.items()
                 }
                 self.w = w
@@ -552,7 +557,12 @@ 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:
@@ -626,10 +636,12 @@ class RWKV(MyModule):
                         torch.cuda.empty_cache()
 
                 shape = [i for i in w[x].shape if i != 1]
-                if len(shape) > 1:
-                    shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
+                if len(shape) > 2:
+                    shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)} {str(shape[2]).rjust(5)}"
+                elif len(shape) > 1:
+                    shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}      "
                 else:
-                    shape = f" {str(shape[0]).rjust(5)}      "
+                    shape = f" {str(shape[0]).rjust(5)}            "
                 if layer_id == 0 or layer_id >= args.n_layer - 1:
                     if print_need_newline:
                         prxxx("\n", end="")
@@ -2103,16 +2115,25 @@ class RWKV(MyModule):
                         state[i * 3 + 0] = torch.zeros(
                             args.n_embd, dtype=atype, requires_grad=False, device=dev
                         ).contiguous()
-                        state[i * 3 + 1] = torch.zeros(
-                            (
-                                args.n_head,
-                                args.n_att // args.n_head,
-                                args.n_att // args.n_head,
-                            ),
-                            dtype=torch.float,
-                            requires_grad=False,
-                            device=dev,
-                        ).contiguous()
+                        if args.time_state:
+                            state[i * 3 + 1] = (
+                                w[f"blocks.{i}.att.time_state"]
+                                .transpose(1, 2)
+                                .to(dtype=torch.float, device=dev)
+                                .requires_grad_(False)
+                                .contiguous()
+                            )
+                        else:
+                            state[i * 3 + 1] = torch.zeros(
+                                (
+                                    args.n_head,
+                                    args.n_att // args.n_head,
+                                    args.n_att // args.n_head,
+                                ),
+                                dtype=torch.float,
+                                requires_grad=False,
+                                device=dev,
+                            ).contiguous()
                         state[i * 3 + 2] = torch.zeros(
                             args.n_embd, dtype=atype, requires_grad=False, device=dev
                         ).contiguous()

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 +13,41 @@ 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)
+        self.version = float(self.version.replace("v", ""))
+
+        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()}"
 
 
@@ -28,7 +23,10 @@ class RWKVType(Enum):
 
 class AbstractRWKV(ABC):
     def __init__(self, model, pipeline):
+        self.EOS_ID = 0
+
         self.name = "rwkv"
+        self.version = 4
         self.model = model
         self.pipeline = pipeline
         self.model_state = None
@@ -42,6 +40,8 @@ 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 +239,9 @@ 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 +274,18 @@ 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 +316,25 @@ 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 +377,24 @@ 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 +402,24 @@ 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 +477,7 @@ 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 +519,47 @@ 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 +568,41 @@ 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
@@ -550,34 +641,59 @@ 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
+    rwkv.version = model.version
 
     return rwkv
 
 
 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,
+        description="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.",
+    )
 
     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 +713,12 @@ 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 +728,7 @@ 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())
@@ -51,7 +52,14 @@ for x in keys:
     if "time_maa" in x:
         version = max(6, version)
 
+params = f"--vocab_size {vocab_size} --n_layer {n_layer} --n_embd {n_embd}"
+
 if version <= expected_max_version:
-    print(f"--n_layer {n_layer} --n_embd {n_embd}", end="")
+    if version == 6:
+        params += ' --my_testing "x060"'
+    print(
+        f"v{int(version)}/train.py {params}",
+        end="",
+    )
 else:
     raise Exception(f"RWKV{version} is not supported")

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

@@ -1,7 +1,7 @@
 echo $@
 
 if [[ ${cnMirror} == 1 ]]; then
-  export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple"
+  export PIP_INDEX_URL="https://mirrors.aliyun.com/pypi/simple"
   if grep -q "mirrors.aliyun.com" /etc/apt/sources.list; then
     echo "apt cnMirror already set"
   else
@@ -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 6.0)
 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/lora/v6/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/v6/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/v6/cuda/wkv6_cuda.cu

@@ -0,0 +1,242 @@
+#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;
+    _u += h*_N_;
+
+    __shared__ float r[_N_], k[_N_], u[_N_], w[_N_];
+    float state[_N_] = {0};
+
+    __syncthreads();
+    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();
+        w[i] = exp(_w[t]);
+        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_111(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, const F *__restrict__ const _gy,
+    F *__restrict__ const _gr, F *__restrict__ const _gk, F *__restrict__ const _gv, F *__restrict__ const _gu)
+{
+    const int b = blockIdx.x / H;
+    const int h = blockIdx.x % H;
+    const int i = threadIdx.x;
+    _u += h*_N_;
+
+    __shared__ float u_[_N_];
+    __shared__ float r[_N_], k[_N_], v[_N_], w_[_N_], gy[_N_];
+    __syncthreads();
+    u_[i] = float(_u[i]);
+    __syncthreads();
+
+    const float u = u_[i];
+
+    float state[_N_] = {0}, scccc[_N_] = {0}, sdddd[_N_] = {0};
+
+    const int t_0 = b*T*C + h*_N_ + i;
+    const int t_T_1 = t_0 + (T-1)*C;
+    const int t_T = t_0 + T*C;
+
+    float gu = 0;
+    for (int t = t_0; t < t_T; t += C)
+    {
+        __syncthreads();
+        v[i] = float(_v[t]);
+        gy[i] = float(_gy[t]);
+        __syncthreads();
+
+        const float k = float(_k[t]);
+        const float w = exp(_w[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 = t_T_1; t >= t_0; t -= C)
+    {
+        __syncthreads();
+        v[i] = float(_v[t]);
+        gy[i] = float(_gy[t]);
+        __syncthreads();
+
+        const float rr = float(_r[t]);
+        const float w = exp(_w[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 = t_T_1; t >= t_0; t -= C)
+    {
+        __syncthreads();
+        r[i] = float(_r[t]);
+        k[i] = float(_k[t]);
+        w_[i] = exp(_w[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);
+    }
+}
+
+template <typename F>
+__global__ void kernel_backward_222(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, const F *__restrict__ const _gy,
+    F *__restrict__ const _gw)
+{
+    const int b = blockIdx.x / H;
+    const int h = blockIdx.x % H;
+    const int i = threadIdx.x;
+
+    __shared__ float v[_N_], gy[_N_];
+    float saaaa[_N_] = {0}, sbbbb[_T_-2] = {0}, scccc[_N_] = {0};
+
+    const int t_0 = b*T*C + h*_N_ + i;
+    const int t_1 = t_0 + C;
+    const int t_2 = t_0 + 2*C;
+    const int t_T_1 = t_0 + (T-1)*C;
+
+    for (int t = t_T_1; t > t_1; t -= C)
+    {
+        __syncthreads();
+        gy[i] = float(_gy[t]);
+        v[i] = float(_v[t-2*C]);
+        __syncthreads();
+
+        const float r = float(_r[t]);
+        const float w = exp(_w[t-C]);
+        float sum = 0.0f;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = saaaa[j];
+            float x = r * gy[j];
+            s = (s + x) * w;
+            sum += s * v[j];
+        }
+        sbbbb[(t-t_2)/C] = sum * float(_k[t-2*C]);
+    }
+
+    float sss = sbbbb[0];
+    _gw[t_0] = 0;
+    _gw[t_1] = F(sss * _w[t_1]);
+
+    for (int t = t_2; t < t_T_1; t += C)
+    {
+        __syncthreads();
+        gy[i] = float(_gy[t]);
+        v[i] = float(_v[t-2*C]);
+        __syncthreads();
+
+        const float w = exp(_w[t-C]);
+        const float k = float(_k[t-2*C]);
+        float sum = 0.0f;
+
+        #pragma unroll
+        for (int j = 0; j < _N_; j++)
+        {
+            float& s = scccc[j];
+            float x = k * v[j];
+            s = (s + x) * w;
+            sum += s * gy[j];
+        }
+        sss += sbbbb[(t-t_1)/C] - (sum * float(_r[t]));
+        _gw[t] = F(sss * _w[t]);
+    }
+    _gw[t_T_1] = 0;
+}
+
+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, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu)
+{
+    assert(H*_N_ == C);
+    assert(_N_%4 == 0);
+    kernel_backward_111<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, u, gy, gr, gk, gv, gu);
+    kernel_backward_222<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, u, gy, gw);
+}

finetune/lora/v6/cuda/wkv6_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, 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 &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>(), 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, "wkv6 forward");
+    m.def("backward", &backward, "wkv6 backward");
+}
+
+TORCH_LIBRARY(wkv6, m) {
+    m.def("forward", forward);
+    m.def("backward", backward);
+}

finetune/lora/v6/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/v6/src/dataset.py

@@ -0,0 +1,242 @@
+########################################################################################################
+# 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)
+                    # dix = data.pad(idx=idx, 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/v6/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/v6/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/v6/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_CTXLEN"] = str(args.ctx_len)
+    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)
+
+    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, 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.json

@@ -9,16 +9,18 @@
     "preview": "vite preview"
   },
   "dependencies": {
-    "@fluentui/react-components": "^9.20.0",
+    "@fluentui/react-components": "^9.47.2",
     "@fluentui/react-icons": "^2.0.201",
     "@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",
     "html-midi-player": "^1.5.0",
     "i18next": "^22.4.15",
+    "katex": "^0.16.9",
     "lodash-es": "^4.17.21",
     "mobx": "^6.9.0",
     "mobx-react-lite": "^3.4.3",
@@ -34,13 +36,16 @@
     "react-router-dom": "^6.11.1",
     "react-toastify": "^9.1.3",
     "rehype-highlight": "^6.0.0",
+    "rehype-katex": "^6.0.3",
     "rehype-raw": "^6.1.1",
     "remark-breaks": "^3.0.3",
     "remark-gfm": "^3.0.1",
+    "remark-math": "^5.1.1",
     "usehooks-ts": "^2.9.1",
     "uuid": "^9.0.0"
   },
   "devDependencies": {
+    "@tailwindcss/typography": "^0.5.10",
     "@types/file-saver": "^2.0.7",
     "@types/lodash-es": "^4.17.12",
     "@types/react": "^18.2.6",

frontend/src/App.tsx

@@ -83,7 +83,7 @@ const App: FC = observer(() => {
             ))}
           </TabList>
         </div>
-        <div className="h-full w-full p-2 box-border overflow-y-hidden">
+        <div className="h-full w-full py-2 pr-2 box-border overflow-y-hidden">
           <Routes>
             {pages.map(({ path, element }, index) => (
               <Route key={`${path}-${index}`} path={path} element={<LazyImportComponent lazyChildren={element} />} />

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

@@ -4,7 +4,7 @@
   "About": "关于",
   "Settings": "设置",
   "Go to chat page": "前往聊天页",
-  "Manage your configs": "管理你的配置",
+  "Manage your configs, adjust the starting model and parameters": "管理你的配置, 调整启动的模型和参数",
   "Manage models": "管理模型",
   "Run": "运行",
   "Offline": "离线",
@@ -96,7 +96,7 @@
   "Python dependencies are incomplete, would you like to install them?": "Python依赖缺失, 是否安装?",
   "Install": "安装",
   "This is the latest version": "已是最新版",
-  "Use Tsinghua Pip Mirrors": "使用清华大学Pip镜像源",
+  "Use Alibaba Cloud Pip Mirrors": "使用阿里云Pip镜像源",
   "Model Config Exception": "模型配置异常",
   "Use Gitee Updates Source": "使用Gitee更新源",
   "Use Custom CUDA kernel to Accelerate": "使用自定义CUDA算子加速",
@@ -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 +328,31 @@
   "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",
+  "Create a new user or AI message content. You can prepare a chat record with AI here, and fill in the responses you want to get from AI in the tone of AI. When you use this preset, the chat record will be processed, and at this point, AI will better understand what you want it to do or what role to play.": "新建一个 用户 或 AI 的发言内容. 你可以在这里准备好一段你与 AI 的聊天记录, 并用 AI 的口吻正确填写你想得到的 AI 的回复, 这样你在使用这个预设时, 这些聊天记录也会被处理, 并且此时 AI 能更好地理解你希望它做什么 / 扮演什么样的角色",
+  "The name used internally by the model when processing user message, changing this value helps improve the role-playing effect.": "模型内部处理用户发言时使用的名称, 更改此值有助于改善角色扮演效果",
+  "The name used internally by the model when processing AI message, changing this value helps improve the role-playing effect.": "模型内部处理AI发言时使用的名称, 更改此值有助于改善角色扮演效果",
+  "Inside the model, there is a default prompt to improve the model's handling of common issues, but it may degrade the role-playing effect. You can disable this option to achieve a better role-playing effect.": "模型内部有一个默认提示来改善模型处理常规问题的效果, 但它可能会让角色扮演的效果变差, 你可以关闭此选项来获得更好的角色扮演效果",
+  "Exit without saving": "退出而不保存",
+  "Content has been changed, are you sure you want to exit without saving?": "内容已经被修改, 你确定要退出而不保存吗?",
+  "Don't forget to correctly fill in your Ollama API Chat Model Name.": "不要忘记正确填写你的Ollama API 聊天模型名"
 }

frontend/src/components/DialogButton.tsx

@@ -16,20 +16,28 @@ import { LazyImportComponent } from './LazyImportComponent';
 const MarkdownRender = React.lazy(() => import('./MarkdownRender'));
 
 export const DialogButton: FC<{
-  text?: string | null
+  text?: string | null,
   icon?: ReactElement,
   tooltip?: string | null,
   className?: string,
   title: string,
-  contentText: string,
+  content?: string | ReactElement | null
   markdown?: boolean,
   onConfirm?: () => void,
   size?: 'small' | 'medium' | 'large',
   shape?: 'rounded' | 'circular' | 'square',
   appearance?: 'secondary' | 'primary' | 'outline' | 'subtle' | 'transparent',
+  cancelButton?: boolean,
+  confirmButton?: boolean,
+  cancelButtonText?: string,
+  confirmButtonText?: string,
 }> = ({
-  text, icon, tooltip, className, title, contentText, markdown,
-  onConfirm, size, shape, appearance
+  text, icon, tooltip, className, title, content, markdown,
+  onConfirm, size, shape, appearance,
+  cancelButton = true,
+  confirmButton = true,
+  cancelButtonText = 'Cancel',
+  confirmButtonText = 'Confirm'
 }) => {
   const { t } = useTranslation();
 
@@ -41,26 +49,31 @@ export const DialogButton: FC<{
         <Button className={className} icon={icon} size={size} shape={shape} appearance={appearance}>{text}</Button>
       }
     </DialogTrigger>
-    <DialogSurface>
+    <DialogSurface style={{ transform: 'unset' }}>
       <DialogBody>
         <DialogTitle>{title}</DialogTitle>
         <DialogContent>
           {
             markdown ?
               <LazyImportComponent lazyChildren={MarkdownRender}>
-                {contentText}
+                {content}
               </LazyImportComponent> :
-              contentText
+              content
           }
         </DialogContent>
         <DialogActions>
-          <DialogTrigger disableButtonEnhancement>
-            <Button appearance="secondary">{t('Cancel')}</Button>
-          </DialogTrigger>
-          <DialogTrigger disableButtonEnhancement>
-            <Button appearance="primary" onClick={onConfirm}>{t('Confirm')}
-            </Button>
-          </DialogTrigger>
+          {cancelButton && (
+            <DialogTrigger disableButtonEnhancement>
+              <Button appearance="secondary">{t(cancelButtonText)}</Button>
+            </DialogTrigger>
+          )}
+          {confirmButton && (
+            <DialogTrigger disableButtonEnhancement>
+              <Button appearance="primary" onClick={onConfirm}>
+                {t(confirmButtonText)}
+              </Button>
+            </DialogTrigger>
+          )}
         </DialogActions>
       </DialogBody>
     </DialogSurface>

frontend/src/components/MarkdownRender.tsx

@@ -1,6 +1,9 @@
+import 'katex/dist/katex.min.css';
 import ReactMarkdown from 'react-markdown';
 import rehypeRaw from 'rehype-raw';
 import rehypeHighlight from 'rehype-highlight';
+import rehypeKatex from 'rehype-katex';
+import remarkMath from 'remark-math';
 import remarkGfm from 'remark-gfm';
 import remarkBreaks from 'remark-breaks';
 import { FC } from 'react';
@@ -21,27 +24,94 @@ 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>
+    <div dir="auto" className="prose markdown-body" style={{ maxWidth: '100%' }}>
+      {props.disabled ?
+        <div style={{ whiteSpace: 'pre-wrap' }} className={props.className}>
+          {props.children}
+        </div> :
+        <ReactMarkdown className={props.className}
+          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={[remarkMath, remarkGfm, remarkBreaks]}
+          rehypePlugins={[
+            rehypeKatex,
+            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/ResetConfigsButton.tsx

@@ -9,7 +9,7 @@ import { defaultModelConfigs, defaultModelConfigsMac } from '../pages/defaultCon
 export const ResetConfigsButton: FC<{ afterConfirm?: () => void }> = ({ afterConfirm }) => {
   const { t } = useTranslation();
   return <DialogButton icon={<ArrowReset20Regular />} tooltip={t('Reset All Configs')} title={t('Reset All Configs')}
-    contentText={t('Are you sure you want to reset all configs? This will obtain the latest preset configs, but will override your custom configs and cannot be undone.')}
+    content={t('Are you sure you want to reset all configs? This will obtain the latest preset configs, but will override your custom configs and cannot be undone.')}
     onConfirm={() => {
       commonStore.setModelConfigs(commonStore.platform !== 'darwin' ? defaultModelConfigs : defaultModelConfigsMac, false);
       commonStore.setCurrentConfigIndex(0, true);

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/ToolTipButton.tsx

@@ -26,10 +26,12 @@ export const ToolTipButton: FC<{
   onClick,
   showDelay = 0
 }) => {
-  return (
-    <Tooltip content={desc} showDelay={showDelay} hideDelay={0} relationship="label">
+  return (desc ?
+      <Tooltip content={desc} showDelay={showDelay} hideDelay={0} relationship="label">
+        <Button style={style} className={className} disabled={disabled} icon={icon} onClick={onClick} size={size}
+          shape={shape} appearance={appearance}>{text}</Button>
+      </Tooltip> :
       <Button style={style} className={className} disabled={disabled} icon={icon} onClick={onClick} size={size}
         shape={shape} appearance={appearance}>{text}</Button>
-    </Tooltip>
   );
 };

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/AudiotrackButton.tsx

@@ -28,7 +28,7 @@ export const AudiotrackButton: FC<{
         {t('Open MIDI Input Audio Tracks')}
       </Button>
     </DialogTrigger>
-    <DialogSurface style={{ paddingTop: 0, maxWidth: '90vw', width: 'fit-content' }}>
+    <DialogSurface style={{ paddingTop: 0, maxWidth: '90vw', width: 'fit-content', transform: 'unset' }}>
       <DialogBody>
         <DialogContent className="overflow-hidden">
           <CustomToastContainer />

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,
+  getReqUrl,
+  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
@@ -471,19 +471,16 @@ 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', {
+                  const {
+                    url,
+                    headers
+                  } = await getReqUrl(commonStore.getCurrentModelConfig().apiParameters.apiPort, '/midi-to-text');
+                  fetch(url, {
                     method: 'POST',
+                    headers,
                     body: bodyForm
                   }).then(async r => {
                       if (r.status === 200) {
@@ -502,7 +499,7 @@ const AudiotrackEditor: FC<{ setPrompt: (prompt: string) => void }> = observer((
                         commonStore.setTracks(tracks);
                         refreshTracksTotalTime();
                       } else {
-                        toast(r.statusText + '\n' + (await r.text()), {
+                        toast('Failed to fetch - ' + r.status + ' - ' + r.statusText + ' - ' + (await r.text()), {
                           type: 'error'
                         });
                       }
@@ -510,8 +507,6 @@ 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 +38,23 @@ 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,
+  getReqUrl,
+  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, systemName, 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';
+import { AvatarProps } from '@fluentui/react-avatar';
 
 let chatSseControllers: {
   [id: string]: AbortController
@@ -73,6 +93,18 @@ const MoreUtilsButton: FC<{
   </Menu>;
 });
 
+const HiddenAvatar: FC<AvatarProps> = ({ children, hidden, ...props }) => {
+  if (hidden) {
+    return <div>{children}</div>;
+  } else {
+    return (
+      <Avatar {...props}>
+        {children}
+      </Avatar>
+    );
+  }
+};
+
 const ChatMessageItem: FC<{
   uuid: string,
   onSubmit: (message: string | null, answerId: string | null,
@@ -110,7 +142,9 @@ const ChatMessageItem: FC<{
   return <div
     className={classnames(
       'flex gap-2 mb-2 overflow-hidden',
-      messageItem.side === 'left' ? 'flex-row' : 'flex-row-reverse'
+      messageItem.side === 'left' && 'flex-row',
+      messageItem.side === 'right' && 'flex-row-reverse',
+      messageItem.side === 'center' && 'flex-row justify-center'
     )}
     onMouseEnter={() => {
       const utils = document.getElementById('utils-' + uuid);
@@ -121,22 +155,26 @@ const ChatMessageItem: FC<{
       if (utils) utils.classList.add('invisible');
     }}
   >
-    <Avatar
+    <HiddenAvatar
       color={messageItem.color}
       name={messageItem.sender}
       image={avatarImg ? { src: avatarImg } : undefined}
+      hidden={messageItem.side === 'center'}
     />
     <div
       className={classnames(
-        'flex p-2 rounded-lg overflow-hidden',
+        'flex rounded-lg overflow-hidden',
         editing ? 'grow' : '',
-        messageItem.side === 'left' ? 'bg-gray-200' : 'bg-blue-500',
-        messageItem.side === 'left' ? 'text-gray-600' : 'text-white'
+        messageItem.side === 'center' ? 'p-1' : 'p-2',
+        commonStore.settings.darkMode ? 'bg-neutral-800 border-neutral-600 border-[1px]' : (messageItem.side === 'right' ? 'bg-blue-500' : 'bg-gray-200'),
+        commonStore.settings.darkMode ? 'text-white' : (messageItem.side === 'right' ? 'text-white' : 'text-gray-600'),
+        messageItem.side === 'center' && 'text-opacity-60 bg-opacity-30 border-opacity-30'
       )}
     >
       {!editing ?
         <div className="flex flex-col">
-          <MarkdownRender>{messageItem.content}</MarkdownRender>
+          <MarkdownRender className={classnames(messageItem.side === 'center' && 'text-xs')}
+            disabled={!commonStore.chatParams.markdown || messageItem.side === 'center'}>{messageItem.content}</MarkdownRender>
           {uuid in commonStore.attachments &&
             <div className="flex grow">
               <div className="grow" />
@@ -212,7 +250,7 @@ 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 +266,7 @@ 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 +277,7 @@ 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 +306,82 @@ 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={() => {
@@ -288,14 +394,18 @@ const SidePanel: FC = observer(() => {
             return;
           const messageItem = commonStore.conversation[uuid];
           if (messageItem.type !== MessageType.Error) {
-            savedContent += `${messageItem.sender === userName ? user : bot}: ${messageItem.content}\n\n`;
+            if (messageItem.sender === userName) {
+              savedContent += `${user}: ${messageItem.content}\n\n`;
+            } else if (messageItem.sender === botName) {
+              savedContent += `${bot}: ${messageItem.content}\n\n`;
+            }
           }
         });
 
         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 });
@@ -331,7 +441,7 @@ const ChatPanel: FC = observer(() => {
         [welcomeUuid]: {
           sender: botName,
           type: MessageType.Normal,
-          color: 'colorful',
+          color: 'neutral',
           avatarImg: logo,
           time: new Date().toISOString(),
           content: commonStore.platform === 'web' ? t('Hello, what can I do for you?') : t('Hello! I\'m RWKV, an open-source and commercially usable large language model.'),
@@ -365,7 +475,7 @@ const ChatPanel: FC = observer(() => {
   // if answerId is not null, override the answer with new response;
   // if startUuid is null, start generating api body messages from first message;
   // if endUuid is null, generate api body messages until last message;
-  const onSubmit = useCallback((message: string | null = null, answerId: string | null = null,
+  const onSubmit = useCallback(async (message: string | null = null, answerId: string | null = null,
     startUuid: string | null = null, endUuid: string | null = null, includeEndUuid: boolean = false) => {
     if (message) {
       const newId = uuid();
@@ -412,6 +522,8 @@ const ChatPanel: FC = observer(() => {
         messages.push({ role: 'user', content: messageItem.content });
       } else if (messageItem.done && messageItem.type === MessageType.Normal && messageItem.sender === botName) {
         messages.push({ role: 'assistant', content: messageItem.content });
+      } else if (messageItem.done && messageItem.type === MessageType.Normal && messageItem.sender === systemName) {
+        messages.push({ role: 'system', content: messageItem.content });
       }
     });
 
@@ -422,7 +534,7 @@ const ChatPanel: FC = observer(() => {
     commonStore.conversation[answerId] = {
       sender: botName,
       type: MessageType.Normal,
-      color: 'colorful',
+      color: 'neutral',
       avatarImg: logo,
       time: new Date().toISOString(),
       content: '',
@@ -433,38 +545,48 @@ const ChatPanel: FC = observer(() => {
     commonStore.setConversationOrder(commonStore.conversationOrder);
     setTimeout(scrollToBottom);
     let answer = '';
+    let finished = false;
+    const finish = () => {
+      finished = true;
+      if (answerId! in chatSseControllers)
+        delete chatSseControllers[answerId!];
+      commonStore.conversation[answerId!].done = true;
+      commonStore.conversation[answerId!].content = commonStore.conversation[answerId!].content.trim();
+      commonStore.setConversation(commonStore.conversation);
+      commonStore.setConversationOrder([...commonStore.conversationOrder]);
+    };
     const chatSseController = new AbortController();
     chatSseControllers[answerId] = chatSseController;
+    const { url, headers } = await getReqUrl(port, '/v1/chat/completions', true);
     fetchEventSource( // https://api.openai.com/v1/chat/completions || http://127.0.0.1:${port}/v1/chat/completions
-      getServerRoot(port, true) + '/v1/chat/completions',
+      url,
       {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
-          Authorization: `Bearer ${commonStore.settings.apiKey}`
+          Authorization: `Bearer ${commonStore.settings.apiKey}`,
+          ...headers
         },
         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
+          presystem: commonStore.activePreset?.presystem || undefined
         }),
         signal: chatSseController?.signal,
         onmessage(e) {
+          if (finished) return;
           scrollToBottom();
           if (e.data.trim() === '[DONE]') {
-            if (answerId! in chatSseControllers)
-              delete chatSseControllers[answerId!];
-            commonStore.conversation[answerId!].done = true;
-            commonStore.conversation[answerId!].content = commonStore.conversation[answerId!].content.trim();
-            commonStore.setConversation(commonStore.conversation);
-            commonStore.setConversationOrder([...commonStore.conversationOrder]);
+            finish();
             return;
           }
           let data;
@@ -481,11 +603,21 @@ const ChatPanel: FC = observer(() => {
             commonStore.conversation[answerId!].content = answer;
             commonStore.setConversation(commonStore.conversation);
             commonStore.setConversationOrder([...commonStore.conversationOrder]);
+
+            if (data.choices[0]?.finish_reason) {
+              finish();
+              return;
+            }
           }
         },
         async onopen(response) {
           if (response.status !== 200) {
-            commonStore.conversation[answerId!].content += '\n[ERROR]\n```\n' + response.statusText + '\n' + (await response.text()) + '\n```';
+            let errText = await response.text();
+            try {
+              errText = JSON.stringify(JSON.parse(errText), null, 2);
+            } catch (e) {
+            }
+            commonStore.conversation[answerId!].content += '\n[ERROR]\n```\n' + response.status + ' - ' + response.statusText + '\n' + errText + '\n```';
             commonStore.setConversation(commonStore.conversation);
             commonStore.setConversationOrder([...commonStore.conversationOrder]);
             setTimeout(scrollToBottom);
@@ -519,7 +651,7 @@ 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} />
           )}
@@ -528,7 +660,7 @@ const ChatPanel: FC = observer(() => {
           <DialogButton tooltip={t('Clear')}
             icon={<Delete28Regular />}
             size={mq ? 'large' : 'small'} shape="circular" appearance="subtle" title={t('Clear')}
-            contentText={t('Are you sure you want to clear the conversation? It cannot be undone.')}
+            content={t('Are you sure you want to clear the conversation? It cannot be undone.')}
             onConfirm={() => {
               if (generating) {
                 for (const id in chatSseControllers) {
@@ -536,7 +668,7 @@ const ChatPanel: FC = observer(() => {
                 }
                 chatSseControllers = {};
               }
-              setActivePreset(commonStore.activePreset);
+              setActivePreset(commonStore.activePreset, commonStore.activePresetIndex);
             }} />
           <div className="relative flex grow">
             <Textarea
@@ -599,8 +731,10 @@ const ChatPanel: FC = observer(() => {
                         const urlPath = `/file-to-text?file_name=${attachmentName}`;
                         const bodyForm = new FormData();
                         bodyForm.append('file_data', blob, attachmentName);
-                        fetch(getServerRoot(port) + urlPath, {
+                        const { url, headers } = await getReqUrl(port, urlPath);
+                        fetch(url, {
                           method: 'POST',
+                          headers,
                           body: bodyForm
                         }).then(async r => {
                             if (r.status === 200) {
@@ -623,7 +757,7 @@ const ChatPanel: FC = observer(() => {
                                   autoClose: 1000
                                 });
                             } else {
-                              toast(r.statusText + '\n' + (await r.text()), {
+                              toast('Failed to fetch - ' + r.status + ' - ' + r.statusText + ' - ' + (await r.text()), {
                                 type: 'error'
                               });
                             }

frontend/src/pages/Completion.tsx

@@ -14,7 +14,7 @@ import { ToolTipButton } from '../components/ToolTipButton';
 import { ArrowSync20Regular } from '@fluentui/react-icons';
 import { defaultPresets } from './defaultConfigs';
 import { CompletionParams, CompletionPreset } from '../types/completion';
-import { getServerRoot } from '../utils';
+import { getReqUrl } from '../utils';
 
 let completionSseController: AbortController | null = null;
 
@@ -68,7 +68,7 @@ const CompletionPanel: FC = observer(() => {
     });
   };
 
-  const onSubmit = (prompt: string) => {
+  const onSubmit = async (prompt: string) => {
     commonStore.setCompletionSubmittedPrompt(prompt);
 
     if (commonStore.status.status === ModelStatus.Offline && !commonStore.settings.apiUrl && commonStore.platform !== 'web') {
@@ -80,14 +80,21 @@ const CompletionPanel: FC = observer(() => {
     prompt += params.injectStart.replaceAll('\\n', '\n');
 
     let answer = '';
+    let finished = false;
+    const finish = () => {
+      finished = true;
+      commonStore.setCompletionGenerating(false);
+    };
     completionSseController = new AbortController();
+    const { url, headers } = await getReqUrl(port, '/v1/completions', true);
     fetchEventSource( // https://api.openai.com/v1/completions || http://127.0.0.1:${port}/v1/completions
-      getServerRoot(port, true) + '/v1/completions',
+      url,
       {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
-          Authorization: `Bearer ${commonStore.settings.apiKey}`
+          Authorization: `Bearer ${commonStore.settings.apiKey}`,
+          ...headers
         },
         body: JSON.stringify({
           prompt,
@@ -102,9 +109,10 @@ const CompletionPanel: FC = observer(() => {
         }),
         signal: completionSseController?.signal,
         onmessage(e) {
+          if (finished) return;
           scrollToBottom();
           if (e.data.trim() === '[DONE]') {
-            commonStore.setCompletionGenerating(false);
+            finish();
             return;
           }
           let data;
@@ -119,11 +127,16 @@ const CompletionPanel: FC = observer(() => {
           if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
             answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
             setPrompt(prompt + answer.replace(/\s+$/, '') + params.injectEnd.replaceAll('\\n', '\n'));
+
+            if (data.choices[0]?.finish_reason) {
+              finish();
+              return;
+            }
           }
         },
         async onopen(response) {
           if (response.status !== 200) {
-            toast(response.statusText + '\n' + (await response.text()), {
+            toast(response.status + ' - ' + response.statusText + ' - ' + (await response.text()), {
               type: 'error'
             });
           }
@@ -188,7 +201,7 @@ 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 +212,7 @@ 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
@@ -275,7 +288,7 @@ const CompletionPanel: FC = observer(() => {
             onSubmit(commonStore.completionSubmittedPrompt);
           }} />
           <DialogButton className="grow" text={t('Reset')} title={t('Reset')}
-            contentText={t('Are you sure you want to reset this page? It cannot be undone.')}
+            content={t('Are you sure you want to reset this page? It cannot be undone.')}
             onConfirm={() => {
               setPreset(defaultPresets.find((preset) => preset.name === name)!);
             }} />

frontend/src/pages/Composition.tsx

@@ -15,17 +15,18 @@ 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
 } from '../../wailsjs/go/backend_golang/App';
-import { getServerRoot, getSoundFont, toastWithButton } from '../utils';
+import { getReqUrl, getSoundFont, toastWithButton } from '../utils';
 import { CompositionParams } from '../types/composition';
 import { useMediaQuery } from 'usehooks-ts';
 import { AudiotrackButton } from './AudiotrackManager/AudiotrackButton';
@@ -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);
 
@@ -132,11 +135,20 @@ const CompositionPanel: FC = observer(() => {
     }
   }, [commonStore.midiPorts]);
 
-  const generateNs = (autoPlay: boolean) => {
-    fetch(getServerRoot(port) + '/text-to-midi', {
+  const generateNs = async (autoPlay: boolean) => {
+    if (commonStore.getCurrentModelConfig().modelParameters.modelName.toLowerCase().includes('abc')) {
+      import('abcjs').then(ABCJS => {
+        ABCJS.renderAbc('abc-paper', commonStore.compositionParams.prompt, { responsive: 'resize' });
+      });
+      return;
+    }
+
+    const { url, headers } = await getReqUrl(port, '/text-to-midi');
+    fetch(url, {
       method: 'POST',
       headers: {
-        'Content-Type': 'application/json'
+        'Content-Type': 'application/json',
+        ...headers
       },
       body: JSON.stringify({
         'text': commonStore.compositionParams.prompt.replaceAll(/<pad>|<start>|<end>/g, '').replaceAll('  ', ' ').trim()
@@ -165,7 +177,7 @@ const CompositionPanel: FC = observer(() => {
     });
   };
 
-  const onSubmit = (prompt: string) => {
+  const onSubmit = async (prompt: string) => {
     commonStore.setCompositionSubmittedPrompt(prompt);
 
     if (commonStore.status.status === ModelStatus.Offline && !commonStore.settings.apiUrl && commonStore.platform !== 'web') {
@@ -175,14 +187,22 @@ const CompositionPanel: FC = observer(() => {
     }
 
     let answer = '';
+    let finished = false;
+    const finish = () => {
+      finished = true;
+      commonStore.setCompositionGenerating(false);
+      generateNs(commonStore.compositionParams.autoPlay);
+    };
     compositionSseController = new AbortController();
+    const { url, headers } = await getReqUrl(port, '/v1/completions', true);
     fetchEventSource( // https://api.openai.com/v1/completions || http://127.0.0.1:${port}/v1/completions
-      getServerRoot(port, true) + '/v1/completions',
+      url,
       {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
-          Authorization: `Bearer ${commonStore.settings.apiKey}`
+          Authorization: `Bearer ${commonStore.settings.apiKey}`,
+          ...headers
         },
         body: JSON.stringify({
           prompt,
@@ -194,10 +214,10 @@ const CompositionPanel: FC = observer(() => {
         }),
         signal: compositionSseController?.signal,
         onmessage(e) {
+          if (finished) return;
           scrollToBottom();
           if (e.data.trim() === '[DONE]') {
-            commonStore.setCompositionGenerating(false);
-            generateNs(commonStore.compositionParams.autoPlay);
+            finish();
             return;
           }
           let data;
@@ -212,11 +232,16 @@ const CompositionPanel: FC = observer(() => {
           if (data.choices && Array.isArray(data.choices) && data.choices.length > 0) {
             answer += data.choices[0]?.text || data.choices[0]?.delta?.content || '';
             setPrompt(prompt + answer.replace(/\s+$/, ''));
+
+            if (data.choices[0]?.finish_reason) {
+              finish();
+              return;
+            }
           }
         },
         async onopen(response) {
           if (response.status !== 200) {
-            toast(response.statusText + '\n' + (await response.text()), {
+            toast(response.status + ' - ' + response.statusText + ' - ' + (await response.text()), {
               type: 'error'
             });
           }
@@ -265,7 +290,7 @@ 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 +301,7 @@ 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
@@ -368,13 +393,15 @@ const CompositionPanel: FC = observer(() => {
               onSubmit(commonStore.compositionSubmittedPrompt);
             }} />
             <DialogButton className="grow" text={t('Reset')} title={t('Reset')}
-              contentText={t('Are you sure you want to reset this page? It cannot be undone.')}
+              content={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 +421,38 @@ 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/Home.tsx

@@ -37,7 +37,7 @@ const clientNavCards: NavCard[] = [
   },
   {
     label: 'Configs',
-    desc: 'Manage your configs',
+    desc: 'Manage your configs, adjust the starting model and parameters',
     path: '/configs',
     icon: <DocumentSettings20Regular />
   },

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/PresetsManager/MessagesEditor.tsx

@@ -3,7 +3,7 @@ import { DragDropContext, Draggable, Droppable, DropResult } from 'react-beautif
 import commonStore from '../../stores/commonStore';
 import { observer } from 'mobx-react-lite';
 import { v4 as uuid } from 'uuid';
-import { Button, Card, Dropdown, Option, Textarea } from '@fluentui/react-components';
+import { Card, Dropdown, Option, Textarea } from '@fluentui/react-components';
 import { useTranslation } from 'react-i18next';
 import { ToolTipButton } from '../../components/ToolTipButton';
 import { Delete20Regular, ReOrderDotsVertical20Regular } from '@fluentui/react-icons';
@@ -84,7 +84,10 @@ const MessagesEditor: FC = observer(() => {
 
   return (
     <div className="grid grid-cols-1 gap-2 overflow-hidden">
-      <Button style={{ width: '100%' }} onClick={createNewItem}>{t('New')}</Button>
+      <ToolTipButton text={t('New')}
+        desc={t('Create a new user or AI message content. You can prepare a chat record with AI here, and fill in the responses you want to get from AI in the tone of AI. When you use this preset, the chat record will be processed, and at this point, AI will better understand what you want it to do or what role to play.')}
+        style={{ width: '100%' }}
+        onClick={createNewItem} />
       <div className="overflow-x-hidden overflow-y-auto p-2">
         <DragDropContext onDragEnd={onDragEnd}>
           <Droppable droppableId="droppable">
@@ -123,7 +126,7 @@ const MessagesEditor: FC = observer(() => {
                             }}>
                             <Option value="user">{t('user')!}</Option>
                             <Option value="assistant">{t('assistant')!}</Option>
-                            {/* TODO <Option value="system">{t('system')!}</Option>*/}
+                            <Option value="system">{t('system')!}</Option>
                           </Dropdown>
                           <Textarea resize="vertical" className="grow" value={item.content}
                             style={{ minWidth: 0, borderRadius: 0 }}

frontend/src/pages/PresetsManager/PresetsButton.tsx

@@ -4,6 +4,7 @@ import React, { FC, lazy, PropsWithChildren, ReactElement, useState } from 'reac
 import {
   Button,
   Dialog,
+  DialogActions,
   DialogBody,
   DialogContent,
   DialogSurface,
@@ -51,7 +52,7 @@ const defaultPreset: Preset = {
   stop: '',
   injectStart: '',
   injectEnd: '',
-  presystem: true,
+  presystem: false,
   userName: '',
   assistantName: ''
 };
@@ -60,10 +61,15 @@ const MessagesEditor = lazy(() => import('./MessagesEditor'));
 
 const PresetCardFrame: FC<PropsWithChildren & {
   onClick?: React.MouseEventHandler<HTMLButtonElement>
+  highlight?: boolean
 }> = (props) => {
   return <Button
     className="flex flex-col gap-1 w-32 h-56 break-all"
-    style={{ minWidth: 0, borderRadius: '0.75rem', justifyContent: 'unset' }}
+    style={{
+      minWidth: 0,
+      borderRadius: '0.75rem',
+      justifyContent: 'unset', ...(props.highlight ? { borderColor: '#115ea3', borderWidth: '2px' } : {})
+    }}
     onClick={props.onClick}
   >
     {props.children}
@@ -71,31 +77,28 @@ const PresetCardFrame: FC<PropsWithChildren & {
 };
 
 const PresetCard: FC<{
-  avatarImg: string,
-  name: string,
-  desc: string,
-  tag: string,
   editable: boolean,
+  preset: Preset,
   presetIndex: number,
-  onClick?: () => void
 }> = observer(({
-  avatarImg, name, desc, tag, editable, presetIndex, onClick
+  editable, preset, presetIndex
 }) => {
   const { t } = useTranslation();
 
-  return <PresetCardFrame onClick={(e) => {
-    if (onClick && e.currentTarget.contains(e.target as Node))
-      onClick();
-  }}>
-    <img src={absPathAsset(avatarImg)} className="rounded-xl select-none ml-auto mr-auto h-28" />
-    <Text size={400}>{name}</Text>
+  return <PresetCardFrame highlight={commonStore.activePresetIndex === presetIndex}
+    onClick={(e) => {
+      if (e.currentTarget.contains(e.target as Node))
+        setActivePreset(presetIndex === -1 ? defaultPreset : preset, presetIndex);
+    }}>
+    <img src={absPathAsset(preset.avatarImg)} className="rounded-xl select-none ml-auto mr-auto h-28" />
+    <Text size={400}>{preset.name}</Text>
     <Text size={200} style={{
       overflow: 'hidden', textOverflow: 'ellipsis',
       display: '-webkit-box', WebkitLineClamp: 3, WebkitBoxOrient: 'vertical'
-    }}>{desc}</Text>
+    }}>{preset.desc}</Text>
     <div className="grow" />
     <div className="flex justify-between w-full items-end">
-      <div className="text-xs font-thin text-gray-500">{t(tag)}</div>
+      <div className="text-xs font-thin text-gray-500">{t(preset.tag)}</div>
       {editable ?
         <ChatPresetEditor presetIndex={presetIndex} triggerButton={
           <ToolTipButton size="small" appearance="transparent" desc={t('Edit')} icon={<Edit20Regular />}
@@ -115,9 +118,11 @@ const ChatPresetEditor: FC<{
   presetIndex: number
 }> = observer(({ triggerButton, presetIndex }) => {
   const { t } = useTranslation();
+  const [open, setOpen] = React.useState(false);
+  const [showExitConfirm, setShowExitConfirm] = React.useState(false);
   const [editingMessages, setEditingMessages] = useState(false);
 
-  if (!commonStore.editingPreset)
+  if (open && !commonStore.editingPreset)
     commonStore.setEditingPreset({ ...defaultPreset });
   const editingPreset = commonStore.editingPreset!;
 
@@ -168,26 +173,46 @@ const ChatPresetEditor: FC<{
   };
 
   const savePreset = () => {
+    setOpen(false);
+    setShowExitConfirm(false);
     if (presetIndex === -1) {
       commonStore.setPresets([...commonStore.presets, { ...editingPreset }]);
-      setEditingPreset(defaultPreset);
     } else {
       commonStore.presets[presetIndex] = editingPreset;
       commonStore.setPresets(commonStore.presets);
     }
+    commonStore.setEditingPreset(null);
   };
 
   const activatePreset = () => {
     savePreset();
-    setActivePreset(editingPreset);
+    setActivePreset(editingPreset, presetIndex === -1 ? commonStore.presets.length - 1 : presetIndex);
   };
 
   const deletePreset = () => {
+    if (commonStore.activePresetIndex === presetIndex) {
+      setActivePreset(defaultPreset, -1);
+    }
     commonStore.presets.splice(presetIndex, 1);
     commonStore.setPresets(commonStore.presets);
+    setOpen(false);
+    setShowExitConfirm(false);
+    commonStore.setEditingPreset(null);
   };
 
-  return <Dialog>
+  return <Dialog open={open} onOpenChange={(e, data) => {
+    if (data.open) {
+      setOpen(true);
+    } else if (!commonStore.editingPreset ||
+      (presetIndex === -1 && JSON.stringify(editingPreset) === JSON.stringify(defaultPreset)) ||
+      (presetIndex !== -1 && JSON.stringify(editingPreset) === JSON.stringify(commonStore.presets[presetIndex]))) {
+      setOpen(false);
+      setShowExitConfirm(false);
+      commonStore.setEditingPreset(null);
+    } else {
+      setShowExitConfirm(true);
+    }
+  }}>
     <DialogTrigger disableButtonEnhancement>
       {triggerButton}
     </DialogTrigger>
@@ -196,17 +221,35 @@ const ChatPresetEditor: FC<{
       maxWidth: '80vw',
       maxHeight: '80vh',
       width: '500px',
-      height: '100%'
+      height: '100%',
+      transform: 'unset' // override the style for the new version of @fluentui/react-components to avoid conflicts with react-beautiful-dnd
     }}>
       <DialogBody style={{ height: '100%', overflow: 'hidden' }}>
-        <DialogContent className="flex flex-col gap-1 overflow-hidden">
+        {editingPreset && <DialogContent className="flex flex-col gap-1 overflow-hidden">
           <CustomToastContainer />
+          <Dialog open={showExitConfirm}>
+            <DialogSurface style={{ transform: 'unset' }}>
+              <DialogBody>
+                <DialogContent>
+                  {t('Content has been changed, are you sure you want to exit without saving?')}
+                </DialogContent>
+                <DialogActions>
+                  <Button appearance="secondary" onClick={() => {
+                    setShowExitConfirm(false);
+                  }}>{t('Cancel')}</Button>
+                  <Button appearance="primary" onClick={() => {
+                    setOpen(false);
+                    setShowExitConfirm(false);
+                    commonStore.setEditingPreset(null);
+                  }}>{t('Exit without saving')}</Button>
+                </DialogActions>
+              </DialogBody>
+            </DialogSurface>
+          </Dialog>
           <div className="flex justify-between">{
             presetIndex === -1
               ? <div />
-              : <DialogTrigger disableButtonEnhancement>
-                <Button appearance="subtle" icon={<Delete20Regular />} onClick={deletePreset} />
-              </DialogTrigger>
+              : <Button appearance="subtle" icon={<Delete20Regular />} onClick={deletePreset} />
           }
             <DialogTrigger disableButtonEnhancement>
               <Button appearance="subtle" icon={<Dismiss20Regular />} />
@@ -230,8 +273,9 @@ const ChatPresetEditor: FC<{
             editingMessages ?
               <div className="flex flex-col gap-1">
                 <Labeled flex spaceBetween label={t('Insert default system prompt at the beginning')}
+                  desc={t('Inside the model, there is a default prompt to improve the model\'s handling of common issues, but it may degrade the role-playing effect. You can disable this option to achieve a better role-playing effect.')}
                   content={
-                    <Switch checked={editingPreset.presystem === undefined ? true : editingPreset.presystem}
+                    <Switch checked={editingPreset.presystem === undefined ? false : editingPreset.presystem}
                       onChange={(e, data) => {
                         setEditingPreset({
                           presystem: data.checked
@@ -239,6 +283,7 @@ const ChatPresetEditor: FC<{
                       }} />
                   } />
                 <Labeled flex breakline label={t('User Name')}
+                  desc={t('The name used internally by the model when processing user message, changing this value helps improve the role-playing effect.')}
                   content={
                     <Input placeholder="User" value={editingPreset.userName} onChange={(e, data) => {
                       setEditingPreset({
@@ -247,6 +292,7 @@ const ChatPresetEditor: FC<{
                     }} />
                   } />
                 <Labeled flex breakline label={t('Assistant Name')}
+                  desc={t('The name used internally by the model when processing AI message, changing this value helps improve the role-playing effect.')}
                   content={
                     <Input placeholder="Assistant" value={editingPreset.assistantName} onChange={(e, data) => {
                       setEditingPreset({
@@ -306,14 +352,10 @@ const ChatPresetEditor: FC<{
             <Button onClick={copyPreset}>{t('Copy')}</Button>
           </div>
           <div className="flex justify-between">
-            <DialogTrigger disableButtonEnhancement>
-              <Button appearance="primary" onClick={savePreset}>{t('Save')}</Button>
-            </DialogTrigger>
-            <DialogTrigger disableButtonEnhancement>
-              <Button appearance="primary" onClick={activatePreset}>{t('Activate')}</Button>
-            </DialogTrigger>
+            <Button appearance="primary" onClick={savePreset}>{t('Save')}</Button>
+            <Button appearance="primary" onClick={activatePreset}>{t('Activate')}</Button>
           </div>
-        </DialogContent>
+        </DialogContent>}
       </DialogBody>
     </DialogSurface>
   </Dialog>;
@@ -336,20 +378,16 @@ const ChatPresets: FC = observer(() => {
     {/*  </div>*/}
     {/*</PresetCardFrame>*/}
     <PresetCard
+      preset={defaultPreset}
       presetIndex={-1}
       editable={false}
-      onClick={() => {
-        setActivePreset(defaultPreset);
-      }} avatarImg={defaultPreset.avatarImg} name={defaultPreset.name} desc={defaultPreset.desc} tag={defaultPreset.tag}
     />
     {commonStore.presets.map((preset, index) => {
       return <PresetCard
+        key={index}
+        preset={preset}
         presetIndex={index}
         editable={true}
-        onClick={() => {
-          setActivePreset(preset);
-        }}
-        key={index} avatarImg={preset.avatarImg} name={preset.name} desc={preset.desc} tag={preset.tag}
       />;
     })}
   </div>;
@@ -418,7 +456,7 @@ export const PresetsButton: FC<{
       <ToolTipButton desc={t('Presets')} size={size} shape={shape} appearance={appearance}
         icon={<Accessibility28Regular />} />
     </DialogTrigger>
-    <DialogSurface style={{ paddingTop: 0, maxWidth: '90vw', width: 'fit-content' }}>
+    <DialogSurface style={{ paddingTop: 0, maxWidth: '90vw', width: 'fit-content', transform: 'unset' }}>
       <DialogBody>
         <DialogContent>
           <CustomToastContainer />

frontend/src/pages/Settings.tsx

@@ -17,6 +17,7 @@ import { useTranslation } from 'react-i18next';
 import { checkUpdate, toastWithButton } from '../utils';
 import { RestartApp } from '../../wailsjs/go/backend_golang/App';
 import { Language, Languages } from '../types/settings';
+import { toast } from 'react-toastify';
 
 export const GeneralSettings: FC = observer(() => {
   const { t } = useTranslation();
@@ -113,11 +114,15 @@ export const AdvancedGeneralSettings: FC = observer(() => {
                   commonStore.setSettings({
                     apiCompletionModelName: 'rwkv'
                   });
+              } else if (data.optionText === 'Ollama') {
+                toast(t('Don\'t forget to correctly fill in your Ollama API Chat Model Name.'),
+                  { type: 'info' });
               }
             }}>
             <Option value="">{t('Localhost')!}</Option>
             <Option value="https://rwkv.ai-creator.net/chntuned">RWKV</Option>
             <Option value="https://api.openai.com">OpenAI</Option>
+            <Option value="http://localhost:11434">Ollama</Option>
           </Dropdown>
         </div>
       } />
@@ -246,7 +251,7 @@ const Settings: FC = observer(() => {
                 }
                 {
                   commonStore.settings.language === 'zh' && commonStore.platform !== 'linux' &&
-                  <Labeled label={t('Use Tsinghua Pip Mirrors')} flex spaceBetween content={
+                  <Labeled label={t('Use Alibaba Cloud Pip Mirrors')} flex spaceBetween content={
                     <Switch checked={commonStore.settings.cnMirror}
                       onChange={(e, data) => {
                         commonStore.setSettings({
@@ -272,18 +277,16 @@ 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} ` +
@@ -341,7 +342,7 @@ const LoraFinetune: FC = observer(() => {
       };
 
       if (msg === 'wsl is not enabled') {
-        enableWsl(false);
+        enableWsl(true);
       } else if (msg.includes('wsl.state: The system cannot find the file')) {
         enableWsl(true);
       } else {
@@ -390,7 +391,7 @@ const LoraFinetune: FC = observer(() => {
                     setDataParams({ dataPath: data.value });
                   }} />
                 <DialogButton text={t('Help')} title={t('Help')} markdown
-                  contentText={t('The data path should be a directory or a file in jsonl format (more formats will be supported in the future).\n\n' +
+                  content={t('The data path should be a directory or a file in jsonl format (more formats will be supported in the future).\n\n' +
                     'When you provide a directory path, all the txt files within that directory will be automatically converted into training data. ' +
                     'This is commonly used for large-scale training in writing, code generation, or knowledge bases.\n\n' +
                     'The jsonl format file can be referenced at https://github.com/josStorer/RWKV-Runner/blob/master/finetune/data/sample.jsonl.\n' +
@@ -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' });
                     }